nils-herrmann/clean_scirepeval huggingface dataset:
Stationarity of the detrended time series of S&P500

Our study presents the analysis of stock market data of S&P500 before and after been detrended. The analysis is based on two types of returns, simple return and log-return respectively. Both of them are non-stationary time series. This means that their statistical distribution change over time. Consequently a detrended process is made to neutralize the non-stationary effects. The detrended process is obtained by decomposing the financial time series into a deterministic trend and random fluctuations. We present an alternative method on detrending time series based on the classical moving average (MA) models, where Kurtosis is used to determine the windows size. Then, the dentrending fluctuation analysis (DFA) is use to show that the detrended part is stationary. This is done by considering the autocorrelation of detrended price return and the power spectrum analysis of detrended price.
Truncated lognormal distributions and scaling in the size of naturally defined population clusters.

Using population data of high spatial resolution for a region in the south of Europe, we define cities by aggregating individuals to form connected clusters. The resulting cluster-population distributions show a smooth decreasing behavior covering six orders of magnitude. We perform a detailed study of the distributions, using state-of-the-art statistical tools. By means of scaling analysis we rule out the existence of a power-law regime in the low-population range. The logarithmic-coefficient-of-variation test allows us to establish that the power-law tail for high population, characteristic of Zipf's law, has a rather limited range of applicability. Instead, lognormal fits describe the population distributions in a range covering from a few dozen individuals to more than 1×10^{6} (which corresponds to the population of the largest cluster).
Resolvent estimates, wave decay, and resonance-free regions for star-shaped waveguides

Using coordinates $(x,y)\in \mathbb R\times \mathbb R^{d-1}$, we introduce the notion that an unbounded domain in $\mathbb R^d$ is star shaped with respect to $x=\pm \infty$. For such domains, we prove estimates on the resolvent of the Dirichlet Laplacian near the continuous spectrum. When the domain has infinite cylindrical ends, this has consequences for wave decay and resonance-free regions. Our results also cover examples beyond the star-shaped case, including scattering by a strictly convex obstacle inside a straight planar waveguide.
TE-ETH: Lower Bounds for QBFs of Bounded Treewidth

The problem of deciding the validity (QSAT) of quantified Boolean formulas (QBF) is a vivid research area in both theory and practice. In the field of parameterized algorithmics, the well-studied graph measure treewidth turned out to be a successful parameter. A well-known result by Chen in parameterized complexity is that QSAT when parameterized by the treewidth of the primal graph of the input formula together with the quantifier depth of the formula is fixed-parameter tractable. More precisely, the runtime of such an algorithm is polynomial in the formula size and exponential in the treewidth, where the exponential function in the treewidth is a tower, whose height is the quantifier depth. A natural question is whether one can significantly improve these results and decrease the tower while assuming the Exponential Time Hypothesis (ETH). In the last years, there has been a growing interest in the quest of establishing lower bounds under ETH, showing mostly problem-specific lower bounds up to the third level of the polynomial hierarchy. Still, an important question is to settle this as general as possible and to cover the whole polynomial hierarchy. In this work, we show lower bounds based on the ETH for arbitrary QBFs parameterized by treewidth (and quantifier depth). More formally, we establish lower bounds for QSAT and treewidth, namely, that under ETH there cannot be an algorithm that solves QSAT of quantifier depth i in runtime significantly better than i-fold exponential in the treewidth and polynomial in the input size. In doing so, we provide a versatile reduction technique to compress treewidth that encodes the essence of dynamic programming on arbitrary tree decompositions. Further, we describe a general methodology for a more fine-grained analysis of problems parameterized by treewidth that are at higher levels of the polynomial hierarchy.
QuaRL: Quantization for Sustainable Reinforcement Learning

Deep reinforcement learning continues to show tremendous potential in achieving tasklevel autonomy, however, its computational and energy demands remain prohibitively high. In this paper, we tackle this problem by applying quantization to reinforcement learning. To that end, we introduce a novel Reinforcement Learning (RL) training paradigm, , to speed up actor-learner distributed RL training. ActorQ leverages 8-bit quantized actors to speed up data collection without affecting learning convergence. Our quantized distributed RL training system, , demonstrates end-to-end speedups of > 1.5 × 2.5 ×, and faster convergence over full precision training on a range of tasks (Deepmind Control Suite) and different RL algorithms (D4PG, DQN). Furthermore, we compare the carbon emissions (Kgs of CO2) of  versus standard reinforcement learning on various tasks. Across various settings, we show that ActorQ enables more environmentally friendly reinforcement learning by achieving 2.8× less carbon emission and energy compared to training RLagents in full-precision. Finally, we demonstrate empirically that aggressively quantized RL-policies (up to 4/5 bits) enable significant speedups on quantization-friendly (supports native quantization) resource-constrained edge devices, without degrading accuracy. We believe that this is the first of many future works on enabling computationally /energy efficient and sustainable reinforcement learning. The source code for QuaRL is available here: https://github.com/harvard-edge/QuaRL.
Stabilizing Deep Reinforcement Learning with Conservative Updates

In recent years, advances in deep learning have enabled the application of reinforcement learning algorithms in complex domains. However, they lack the theoretical guarantees which are present in the tabular setting and suffer from many stability and reproducibility problems \citep{henderson2018deep}. In this work, we suggest a simple approach for improving stability and providing probabilistic performance improvement in off-policy actor-critic deep reinforcement learning regimes. Experiments on continuous action spaces, in the MuJoCo control suite, show that our proposed method reduces the variance of the process and improves the overall performance.
Learning Neural Causal Models from Unknown Interventions

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical limitations on the identifiability of underlying structures obtained from observational data alone. Interventional data provides much richer information about the underlying data-generating process. However, the extension and application of methods designed for observational data to include interventions is not straightforward and remains an open problem. In this paper we provide a general framework based on continuous optimization and neural networks to create models for the combination of observational and interventional data. The proposed method is even applicable in the challenging and realistic case that the identity of the intervened upon variable is unknown. We examine the proposed method in the setting of graph recovery both de novo and from a partially-known edge set. We establish strong benchmark results on several structure learning tasks, including structure recovery of both synthetic graphs as well as standard graphs from the Bayesian Network Repository.
Task-Relevant Adversarial Imitation Learning

We show that a critical problem in adversarial imitation from high-dimensional sensory data is the tendency of discriminator networks to distinguish agent and expert behaviour using task-irrelevant features beyond the control of the agent. We analyze this problem in detail and propose a solution as well as several baselines that outperform standard Generative Adversarial Imitation Learning (GAIL). Our proposed solution, Task-Relevant Adversarial Imitation Learning (TRAIL), uses a constrained optimization objective to overcome task-irrelevant features. Comprehensive experiments show that TRAIL can solve challenging manipulation tasks from pixels by imitating human operators, where other agents such as behaviour cloning (BC), standard GAIL, improved GAIL variants including our newly proposed baselines, and Deterministic Policy Gradients from Demonstrations (DPGfD) fail to find solutions, even when the other agents have access to task reward.
A Deterministic Theory of Low Rank Matrix Completion

The problem of completing a large low rank matrix using a subset of revealed entries has received much attention in the last ten years. The main result of this paper gives a necessary and sufficient condition, stated in the language of graph limit theory, for a sequence of matrix completion problems with arbitrary missing patterns to be asymptotically solvable. It is then shown that a small modification of the Candès–Recht nuclear norm minimization algorithm provides the required asymptotic solution whenever the sequence of problems is asymptotically solvable. The theory is fully deterministic, with no assumption of randomness. A number of open questions are listed.
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations

Recent advances in deep learning have shown promising results in many low-level vision tasks. However, solving the single-image-based view synthesis is still an open problem. In particular, the generation of new images at parallel camera views given a single input image is of great interest, as it enables 3D visualization of the 2D input scenery. We propose a novel network architecture to perform stereoscopic view synthesis at arbitrary camera positions along the X-axis, or Deep 3D Pan, with "t-shaped" adaptive kernels equipped with globally and locally adaptive dilations. Our proposed network architecture, the monster-net, is devised with a novel "t-shaped" adaptive kernel with globally and locally adaptive dilation, which can efficiently incorporate global camera shift into and handle local 3D geometries of the target image's pixels for the synthesis of naturally looking 3D panned views when a 2-D input image is given. Extensive experiments were performed on the KITTI, CityScapes and our VICLAB_STEREO indoors dataset to prove the efficacy of our method. Our monster-net significantly outperforms the state-of-the-art method, SOTA, by a large margin in all metrics of RMSE, PSNR, and SSIM. Our proposed monster-net is capable of reconstructing more reliable image structures in synthesized images with coherent geometry. Moreover, the disparity information that can be extracted from the "t-shaped" kernel is much more reliable than that of the SOTA for the unsupervised monocular depth estimation task, confirming the effectiveness of our method.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device study.
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Imbalanced Data

We propose a novel unsupervised generative model, Elastic-InfoGAN, that learns to disentangle object identity from other low-level aspects in class-imbalanced datasets. We first investigate the issues surrounding the assumptions about uniformity made by InfoGAN, and demonstrate its ineffectiveness to properly disentangle object identity in imbalanced data. Our key idea is to make the discovery of the discrete latent factor of variation invariant to identity-preserving transformations in real images, and use that as the signal to learn the latent distribution's parameters. Experiments on both artificial (MNIST) and real-world (YouTube-Faces) datasets demonstrate the effectiveness of our approach in imbalanced data by: (i) better disentanglement of object identity as a latent factor of variation; and (ii) better approximation of class imbalance in the data, as reflected in the learned parameters of the latent distribution.
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods

Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field. Comparing these approaches is a challenging task since they highly rely on the data and the setup that is used for training. We provide a public dataset of computed tomography images and simulated low-dose measurements suitable for training this kind of methods. With the LoDoPaB-CT Dataset we aim to create a benchmark that allows for a fair comparison. It contains over 40,000 scan slices from around 800 patients selected from the LIDC/IDRI Database. In this paper we describe how we processed the original slices and how we simulated the measurements. We also include first baseline results.
Intrusion detection systems using classical machine learning techniques vs integrated unsupervised feature learning and deep neural network

Security analysts and administrators face a lot of challenges to detect and prevent network intrusions in their organizations, and to prevent network breaches, detecting the breach on time is crucial. Challenges arise while detecting unforeseen attacks. This work includes a performance comparison of classical machine learning approaches that require vast feature engineering, vs integrated unsupervised feature learning and deep neural networks on the NSL‐KDD dataset. Various trials of experiments were run to identify suitable hyperparameters and network configurations of machine learning models. The DNN using 15 features extracted using Principal Component analysis (PCA) was the most effective modeling method. The further analysis using the Software Defined Networking features also presented a good accuracy using Deep Neural network.
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health KnowledgeGraph

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits. In this work, we describe methods to evaluate a health knowledge graph for robustness. Moving beyond precision and recall, we analyze for which diseases and for which patients the graph is most accurate. We identify sample size and unmeasured confounders as major sources of error in the health knowledge graph. We introduce a method to leverage non-linear functions in building the causal graph to better understand existing model assumptions. Finally, to assess model generalizability, we extend to a larger set of complete patient visits within a hospital system. We conclude with a discussion on how to robustly extract medical knowledge from EHRs.
An ALMA survey of the SCUBA-2 Cosmology Legacy Survey UKIDSS/UDS field: high-resolution dust continuum morphologies and the link between sub-millimetre galaxies and spheroid formation

We present an analysis of the morphology and profiles of the dust continuum emission in 153 bright sub-millimetre galaxies (SMGs) detected with ALMA at signal-to-noise ratios of >8 in high-resolution 0.18 arcsec (∼1 kpc) 870 $\mu$m maps. We measure sizes, shapes, and light profiles for the rest-frame far-infrared emission from these luminous star-forming systems and derive a median effective radius (Re) of 0.10 ± 0.04 arcsec for our sample with a median flux of S870 = 5.6 ± 0.2 mJy. We find that the apparent axial ratio (b/a) distribution of the SMGs peaks at b/a ∼ 0.63 ± 0.02 and is best described by triaxial morphologies, while their emission profiles are best fitted by a Sérsic model with n ≃ 1.0 ± 0.1, similar to exponential discs. This combination of triaxiality and n ∼ 1 Sérsic index are characteristic of bars and we suggest that the bulk of the 870 $\mu$m dust continuum emission in the central ∼2 kpc of these galaxies arises from bar-like structures. As such we caution against using the orientation of shape of the bright dust continuum emission at $\eqsim$ resolution to assess either the orientation of any disc on the sky or tits inclination. By stacking our 870 $\mu$m maps we recover faint extended dust continuum emission on ∼4 kpc scales which contributes 13 ± 1 per cent of the total 870 $\mu$m emission. The scale of this extended emission is similar to that seen for the molecular gas and rest-frame optical light in these systems, suggesting that it represents an extended dust and gas disc at radii larger than the more active bar component. Including this component in our estimated size of the sources we derive a typical effective radius of ≃0.15 ± 0.05 arcsec or 1.2 ± 0.4 kpc. Our results suggest that kpc-scale bars are ubiquitous features of high star-formation rate systems at $z$ ≫ 1, while these systems also contain fainter and more extended gas and stellar envelopes. We suggest that these features, seen some 10–12 Gyr ago, represent the formation phase of the earliest galactic-scale components: stellar bulges.
OpenVSLAM: A Versatile Visual SLAM Framework

In this paper, we introduce OpenVSLAM, a visual SLAM framework with high usability and extensibility. Visual SLAM systems are essential for AR devices, autonomous control of robots and drones, etc. However, conventional open-source visual SLAM frameworks are not appropriately designed as libraries called from third-party programs. To overcome this situation, we have developed a novel visual SLAM framework. This software is designed to be easily used and extended. It incorporates several useful features and functions for research and development. OpenVSLAM is released at https://github.com/xdspacelab/openvslam under the 2-clause BSD license.
The fates of the circumgalactic medium in the FIRE simulations

We analyse the different fates of the circumgalactic medium (CGM) in FIRE-2 cosmological simulations, focusing on the redshifts z = 0.25 and 2 representative of recent surveys. Our analysis includes 21 zoom-in simulations covering the halo mass range $M_{\rm h}(z=0) \sim 10^{10} \!-\! 10^{12} \rm {\,M}_\odot$. We analyse both where the gas ends up after first leaving the CGM (its ‘proximate’ fate) and its location at z = 0 (its ‘ultimate’ fate). Of the CGM at z = 2, about half is found in the ISM or stars of the central galaxy by z = 0 in Mh(z = 2) ∼ 5 × 1011 M⊙ haloes, but most of the CGM in lower mass haloes ends up in the intergalactic medium (IGM). This is so even though most of the CGM in Mh(z = 2) ∼ 5 × 1010 M⊙ haloes first accretes on to the central galaxy before being ejected into the IGM. On the other hand, most of the CGM mass at z = 0.25 remains in the CGM by z = 0 at all halo masses analysed. Of the CGM gas that subsequently accretes on to the central galaxy in the progenitors of Mh(z = 0) ∼ 1012 M⊙ haloes, most of it is cool (T ∼ 104 K) at z = 2 but hot (∼Tvir) at z ∼ 0.25, consistent with the expected transition from cold mode to hot mode accretion. Despite the transition in accretion mode, at both z = 0.25 and $2 \, {\gtrsim} 80{{\ \rm per\ cent}}$ of the cool gas in $M_{\rm h} \gtrsim 10^{11} \rm {M}_\odot$ haloes will accrete on to a galaxy. We find that the metallicity of CGM gas is typically a poor predictor of both its proximate and ultimate fates. This is because there is in general little correlation between the origin of CGM gas and its fate owing to substantial mixing while in the CGM.
Impact of neutrino decays on the supernova neutronization-burst flux

The discovery of non-zero neutrino masses invites one to consider decays of heavier neutrinos into lighter ones. We investigate the impact of two-body decays of neutrinos on the neutronization burst of a core-collapse supernova -- the large burst of $\nu_e$ during the first 25 ms post core-bounce. In the models we consider, the $\nu_e$, produced mainly as a $\nu_3\,(\nu_2)$ in the normal (inverted) mass ordering, are allowed to decay to $\nu_1\,(\nu_3)$ or $\bar{\nu}_1\,(\bar{\nu}_3)$, and an almost massless scalar. These decays can lead to the appearance of a neutronization peak for a normal mass ordering or the disappearance of the same peak for the inverted one, thereby allowing one mass ordering to mimic the other. Simulating supernova-neutrino data at the Deep Underground Neutrino Experiment (DUNE) and the Hyper-Kamiokande (HK) experiment, we compute their sensitivity to the neutrino lifetime. We find that, if the mass ordering is known, and depending on the nature of the Physics responsible for the neutrino decay, DUNE is sensitive to lifetimes $\tau/m \lesssim 10^6$ s/eV for a galactic SN sufficiently close-by (around 10 kpc), while HK is sensitive to lifetimes $\tau/m \lesssim 10^7$ s/eV. These sensitivities are far superior to existing limits from solar-system-bound oscillation experiments. Finally, we demonstrate that using a combination of data from DUNE and HK, one can, in general, distinguish between decaying Dirac neutrinos and decaying Majorana neutrinos.
Nonminimal dark sectors: Mediator-induced decay chains and multijet collider signatures

A preponderance of astrophysical and cosmological evidence indicates that the universe contains not only visible matter but also dark matter. In order to suppress the couplings between the dark and visible sectors, a standard assumption is that these two sectors communicate only through a mediator. In this paper we make a simple but important observation: if the dark sector contains multiple components with similar quantum numbers, then this mediator also generically gives rise to dark-sector decays, with heavier dark components decaying to lighter components. This in turn can even give rise to relatively long dark decay chains, with each step of the decay chain also producing visible matter. The visible byproducts of such mediator-induced decay chains can therefore serve as a unique signature of such scenarios. In order to examine this possibility more concretely, we examine a scenario in which a multi-component dark sector is connected through a mediator to Standard-Model quarks. We then demonstrate that such a scenario gives rise to multi-jet collider signatures, and we examine the properties of such jets at both the parton and detector levels. Within relatively large regions of parameter space, we find that such multi-jet signatures are not excluded by existing monojet and multi-jet searches. Such decay cascades therefore represent a potential discovery route for multi-component dark sectors at current and future colliders.