nils-herrmann/clean_scirepeval huggingface dataset:
Morphological evolution and galactic sizes in the L-Galaxies SA model

In this work we update the L-Galaxies semi-analytic model (SAM) to better follow the physical processes responsible for the growth of bulges via disc instabilities (leading to pseudo-bulges) and mergers (leading to classical bulges). We address the former by considering the contribution of both stellar and gaseous discs in the stability of the galaxy, and we update the latter by including dissipation of energy in gas-rich mergers. Furthermore, we introduce angular momentum losses during cooling and find that an accurate match to the observed correlation between stellar disc scale length and mass at z ∼ 0.0 requires that the gas loses 20% of its initial specific angular momentum to the corresponding dark matter halo during the formation of the cold gas disc. We reproduce the observed trends between the stellar mass and specific angular momentum for both disc- and bulge-dominated galaxies, with the former rotating faster than the latter of the same mass. We conclude that a two-component instability recipe provides a morphologically diverse galaxy sample which matches the observed fractional breakdown of galaxies into different morphological types. This recipe also enables us to obtain an excellent fit to the morphology-mass relation and stellar mass function of different galactic types. Finally, we find that energy dissipation during mergers reduces the merger remnant sizes and allows us to match the observed mass-size relation for bulge-dominated systems.
Numerical black hole initial data and shadows in dynamical Chern–Simons gravity

We present a scheme for generating first-order metric perturbation initial data for an arbitrary background and source. We then apply this scheme to derive metric perturbations in order-reduced dynamical Chern–Simons gravity (dCS). In particular, we solve for metric perturbations on a black hole background that are sourced by a first-order dCS scalar field. This gives us the leading-order metric perturbation to the spacetime in dCS gravity. We then use these solutions to compute black hole shadows in the linearly perturbed spacetime by evolving null geodesics. We present a novel scheme to decompose the shape of the shadow into multipoles parametrized by the spin of the background black hole and the perturbation parameter . We find that we can differentiate the presence of a pure Kerr spacetime from a spacetime with a dCS perturbation using the shadow, allowing in part for a null-hypothesis test of general relativity. We then consider these results in the context of the event horizon telescope.
Feature learning for fault detection in high-dimensional condition monitoring signals

Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. This article proposes an integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training. The approach is based on stacked extreme learning machines (namely hierarchical extreme learning machines) and comprises an autoencoder, performing unsupervised feature learning, stacked with a one-class classifier monitoring the distance of the test data to the training healthy class, thereby assessing the health of the system. This study provides a comprehensive evaluation of hierarchical extreme learning machines fault detection capability compared to other machine learning approaches, such as stand-alone one-class classifiers (extreme learning machines and support vector machines); these same one-class classifiers combined with traditional dimensionality reduction methods (principal component analysis) and a deep belief network. The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault. The proposed algorithm for fault detection, combining feature learning with the one-class classifier, demonstrates a better performance, particularly in cases where condition monitoring data contain several non-informative signals.
Mixture of Expert/Imitator Networks: Scalable Semi-supervised Learning Framework

The current success of deep neural networks (DNNs) in an increasingly broad range of tasks involving artificial intelligence strongly depends on the quality and quantity of labeled training data. In general, the scarcity of labeled data, which is often observed in many natural language processing tasks, is one of the most important issues to be addressed. Semisupervised learning (SSL) is a promising approach to overcoming this issue by incorporating a large amount of unlabeled data. In this paper, we propose a novel scalable method of SSL for text classification tasks. The unique property of our method, Mixture of Expert/Imitator Networks, is that imitator networks learn to “imitate” the estimated label distribution of the expert network over the unlabeled data, which potentially contributes a set of features for the classification. Our experiments demonstrate that the proposed method consistently improves the performance of several types of baseline DNNs. We also demonstrate that our method has the more data, better performance property with promising scalability to the amount of unlabeled data.
Balancing Authority and Diversity in Influential Node Mining

Diversity is a crucial criterion in many ranking and mining tasks. In this paper, we study how to incorporate node diversity into influence maximization (IM). We consider diversity as a reverse measure of the average similarity between selected nodes, which can be specified using node embedding or community detection results. Our goal is to identify a set of nodes which are simultaneously influential and diverse. Three most commonly used utilities in economics (i.e., Perfect Substitutes, Perfect Complements, and Cobb-Douglas) are proposed to jointly model influence spread and diversity as two factors. We formulate diversified IM as an optimization problem of these utilities, for which we present two approximation algorithms based on non-monotonic submodular maximization and traditional IM respectively. Experimental results show that our diversified IM framework outperforms other natural heuristics, such as embedding and diversified ranking, both in utility maximization and result diversification.
On the relationship between Dropout and Equiangular Tight Frames

Dropout is a popular regularization technique in neural networks. Yet, the reason for its success is still not fully understood. This paper provides a new interpretation of Dropout from a frame theory perspective. By drawing a connection to recent developments in analog channel coding, we suggest that for a certain family of autoencoders with a linear encoder, the minimizer of an optimization with dropout regularization on the encoder is an equiangular tight frame (ETF). Since this optimization is non-convex, we add another regularization that promotes such structures by minimizing the cross-correlation between filters in the network. We demonstrate its applicability in convolutional and fully connected layers in both feed-forward and recurrent networks. All these results suggest that there is indeed a relationship between dropout and ETF structure of the regularized linear operations.
Deep Imitative Models for Flexible Inference, Planning, and Control

Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. Yet, reward functions that evoke desirable behavior are often difficult to specify. In this paper, we propose Imitative Models to combine the benefits of IL and goal-directed planning. Imitative Models are probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals. We derive families of flexible goal objectives, including constrained goal regions, unconstrained goal sets, and energy-based goals. We show that our method can use these objectives to successfully direct behavior. Our method substantially outperforms six IL approaches and a planning-based approach in a dynamic simulated autonomous driving task, and is efficiently learned from expert demonstrations without online data collection. We also show our approach is robust to poorly specified goals, such as goals on the wrong side of the road.
Figuring Out Gas & Galaxies in Enzo (FOGGIE). I. Resolving Simulated Circumgalactic Absorption at 2 ≤ z ≤ 2.5

We present simulations from the new “Figuring Out Gas & Galaxies in Enzo” (FOGGIE) project. In contrast to most extant simulations of galaxy formation, which concentrate computational resources on galactic disks and spheroids with fluid and particle elements of fixed mass, the FOGGIE simulations focus on extreme spatial and mass resolution in the circumgalactic medium (CGM) surrounding galaxies. Using the Enzo code and a new refinement scheme, FOGGIE reaches spatial resolutions of 381 comoving h−1 pc and resolves extremely low masses (≲1–100 ) out to 100 comoving h−1 kpc from the central halo. At these resolutions, cloud and filament-like structures giving rise to simulated absorption are smaller, and better resolved, than the same structures simulated with standard density-dependent refinement. Most of the simulated absorption arises in identifiable and well-resolved structures with masses ≲104 , well below the mass resolution of typical zoom simulations. However, integrated quantities such as mass surface density and ionic covering fractions change at only the ≲30% level as resolution is varied. These relatively small changes in projected quantities—even when the sizes and distribution of absorbing clouds change dramatically—indicate that commonly used observables provide only weak constraints on the physical structure of the underlying gas. Comparing the simulated absorption features to the KODIAQ (Keck Observatory Database of Ionized Absorption toward Quasars) survey of z ∼ 2–3.5 Lyman limit systems, we show that high-resolution FOGGIE runs better resolve the internal kinematic structure of detected absorption and better match the observed distribution of absorber properties. These results indicate that circumgalactic medium resolution is key in properly testing simulations of galaxy evolution with circumgalactic observations.
Deep Transfer Reinforcement Learning for Text Summarization

Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explored as in areas such as image analysis. In this paper, we study the problem of transfer learning for text summarization and discuss why existing state-of-the-art models fail to generalize well on other (unseen) datasets. We propose a reinforcement learning framework based on a self-critic policy gradient approach which achieves good generalization and state-of-the-art results on a variety of datasets. Through an extensive set of experiments, we also show the ability of our proposed framework to fine-tune the text summarization model using only a few training samples. To the best of our knowledge, this is the first work that studies transfer learning in text summarization and provides a generic solution that works well on unseen data.
FlowQA: Grasping Flow in History for Conversational Machine Comprehension

Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
Assessing and Remedying Coverage for a Given Dataset

Data analysis impacts virtually every aspect of our society today. Often, this analysis is performed on an existing dataset, possibly collected through a process that the data scientists had limited control over. The existing data analyzed may not include the complete universe, but it is expected to cover the diversity of items in the universe. Lack of adequate coverage in the dataset can result in undesirable outcomes such as biased decisions and algorithmic racism, as well as creating vulnerabilities such as opening up room for adversarial attacks. In this paper, we assess the coverage of a given dataset over multiple categorical attributes. We first provide efficient techniques for traversing the combinatorial explosion of value combinations to identify any regions of attribute space not adequately covered by the data. Then, we determine the least amount of additional data that must be obtained to resolve this lack of adequate coverage. We confirm the value of our proposal through both theoretical analyses and comprehensive experiments on real data.
Non-computability of human intelligence

We revisit the question (most famously) initiated by Turing: can human intelligence be completely modeled by a Turing machine? 
We show that the answer is \emph{no}, assuming a certain weak soundness hypothesis. More specifically we show that at least some meaningful thought processes of the brain cannot be Turing computable. In particular some physical processes are not Turing computable, which is not entirely expected. There are some similarities of our argument with the well known Lucas-Penrose argument, but we work purely on the level of Turing machines, and do not use G\"odel's incompleteness theorem or any direct analogue. Instead we construct directly and use a weak analogue of a G\"odel statement for a certain system which involves our human, this allows us to side-step some (possible) meta-logical issues with their argument.
Do Embedding Models Perform Well for Knowledge Base Completion

In this work, we put into question the effectiveness of the evaluation methods currently used to measure the performance of latent factor models for the task of knowledge base completion. We argue that by focusing on a small subset of possible facts in the knowledge base, current evaluation practices are better suited for question answering tasks, rather than knowledge base completion, where it is also important to avoid the addition of incorrect facts into the knowledge base. We illustrate our point by showing how models with limited expressiveness achieve state-of-the-art performance, even while adding many incorrect (even nonsensical) facts to a knowledge base. Finally, we show that when using a simple evaluation procedure designed to also penalize the addition of incorrect facts, the general and relative performance of all models looks very different than previously seen. This indicates the need for more powerful latent factor models for the task of knowledge base completion.
Severe Constraints on New Physics Explanations of the MiniBooNE Excess.

The MiniBooNE experiment has recently reported an anomalous 4.5σ excess of electronlike events consistent with ν_{e} appearance from a ν_{μ} beam at short baseline. Given the lack of corresponding ν_{μ} disappearance observations, required in the case of oscillations involving a sterile flavor, there is strong motivation for alternative explanations of this anomaly. We consider the possibility that the observed electronlike signal may actually be initiated by particles produced in the MiniBooNE target, without involving new sources of neutrino production or any neutrino oscillations. We find that the electronlike event energy and angular distributions in the full MiniBooNE dataset, including neutrino, antineutrino, and beam dump modes, severely limit and, in some cases, rule out new physics scenarios as an explanation for the observed neutrino and antineutrino mode excesses. Specifically, scenarios in which the particle produced in the target either decays (visibly or semivisibly) or scatters elastically in the detector are strongly disfavored. Using generic kinematic arguments, we extend the existing MiniBooNE results and interpretations to exhaustively constrain previously unconsidered new physics signatures and emphasize the power of the MiniBooNE beam dump search to further limit models for the excess.
Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction

Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional, long short-term memory (convLSTM) modules. We introduce a novel reduced-gate convolutional LSTM(rgcLSTM) architecture that requires a significantly lower parameter budget than a comparable convLSTM. By using a single multi-function gate, our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time and memory requirements. We tested our reduced gate modules within a predictive coding architecture on the moving MNIST and KITTI datasets. We found that our reduced-gate model has a significant reduction of approximately 40 percent of the total number of training parameters and a 25 percent reduction in elapsed training time in comparison with the standard convolutional LSTM model. The performance accuracy of the new model was also improved. This makes our model more attractive for hardware implementation, especially on small devices. We also explored a space of twenty different gated architectures to get insight into how our rgcLSTM fit into that space.
Exploring Modern GPU Memory System Design Challenges through Accurate Modeling

This paper explores the impact of simulator accuracy on architecture design decisions in the general-purpose graphics processing unit (GPGPU) space. We perform a detailed, quantitative analysis of the most popular publicly available GPU simulator, GPGPU-Sim, against our enhanced version of the simulator, updated to model the memory system of modern GPUs in more detail. Our enhanced GPU model is able to describe the NVIDIA Volta architecture in sufficient detail to reduce error in memory system even counters by as much as 66X. The reduced error in the memory system further reduces execution time error versus real hardware by 2.5X. To demonstrate the accuracy of our enhanced model against a real machine, we perform a counter-by-counter validation against an NVIDIA TITAN V Volta GPU, demonstrating the relative accuracy of the new simulator versus the publicly available model. 
We go on to demonstrate that the simpler model discounts the importance of advanced memory system designs such as out-of-order memory access scheduling, while overstating the impact of more heavily researched areas like L1 cache bypassing. Our results demonstrate that it is important for the academic community to enhance the level of detail in architecture simulators as system complexity continues to grow. As part of this detailed correlation and modeling effort, we developed a new Correlator toolset that includes a consolidation of applications from a variety of popular GPGPU benchmark suites, designed to run in reasonable simulation times. The Correlator also includes a database of hardware profiling results for all these applications on NVIDIA cards ranging from Fermi to Volta and a toolchain that enables users to gather correlation statistics and create detailed counter-by-counter hardware correlation plots with minimal effort.
Explanation of Detailed Spectral Properties of FRBs by Axion Star Model

We have proposed a generation mechanism of non repeating ( repeating ) fast radio bursts: They arise by axion star collisions with neutron stars ( accretion disks of galactic black holes ). The axion star as coherent state of axions with mass $m_a$ generates homogeneous electric field oscillating with frequency $m_a/2\pi$ under strong magnetic fields. The field makes electrons coherently oscillate and emit the coherent dipole radiations ( FRBs ). The radiations stop when thermal fluctuations produced by the oscillation disturb the coherent oscillations. Thus, the duration of the FRBs is determined by the time scale of the thermalization; it is much shorter than $1$ms. Line spectra of the dipole radiations are broadened by the thermal effects. The thermally broaden spectra have a feature that the bandwidths $\delta\nu$ are proportional to their center frequencies $\nu_c$. The presence of various center frequencies ( $1.2$GHz$\sim $$7$GHz ) in repeating FRB 121102 is attributed to Doppler shifts owing to the various relativistic velocities of the accretion disk. On the other hand, non repeating FRBs do not show such variety of the center frequencies. They come from the surfaces of neutron stars. The Doppler shift also makes the durations of the bursts with higher frequencies become shorter. Because the magnetic fields of the neutron stars are supposed to be stronger than those of the accretion disks, the peak flux densities of non repeating FRBs are larger than those of repeating FRB 121102. The strong magnetic fields of the neutron stars also lead to much wide bandwidths of non repeating FRBs, which are over the extent of the receiver frequency range. The spectral features of the recently discovered new repeating FRB 180814.J0422+75 are coincident with our general analyses of the repeating FRB 121102.
Pruning Deep Neural Networks using Partial Least Squares

Modern pattern recognition methods are based on convolutional networks since they are able to learn complex patterns that benefit the classification. However, convolutional networks are computationally expensive and require a considerable amount of memory, which limits their deployment on low-power and resource-constrained systems. To handle these problems, recent approaches have proposed pruning strategies that find and remove unimportant neurons (i.e., filters) in these networks. Despite achieving remarkable results, existing pruning approaches are ineffective since the accuracy of the original network is degraded. In this work, we propose a novel approach to efficiently remove filters from convolutional networks. Our approach estimates the filter importance based on its relationship with the class label on a low-dimensional space. This relationship is computed using Partial Least Squares (PLS) and Variable Importance in Projection (VIP). Our method is able to reduce up to 67% of the floating point operations (FLOPs) without penalizing the network accuracy. With a negligible drop in accuracy, we can reduce up to 90% of FLOPs. Additionally, sometimes the method is even able to improve the accuracy compared to original, unpruned, network. We show that employing PLS+VIP as the criterion for detecting the filters to be removed is better than recent feature selection techniques, which have been employed by state-of-the-art pruning methods. Finally, we show that the proposed method achieves the highest FLOPs reduction and the smallest drop in accuracy when compared to state-of-the-art pruning approaches. Codes are available at: this https URL