nils-herrmann/clean_scirepeval huggingface dataset:
An assets-liabilities dynamical model of banking system and systemic risk governance

We consider the problem of governing systemic risk in an assets-liabilities dynamical model of banking system. In the model considered each bank is represented by its assets and its liabilities.The capital reserves of a bank are the difference between assets and liabilities of the bank. A bank is solvent when its capital reserves are greater or equal to zero otherwise the bank is failed.The banking system dynamics is defined by an initial value problem for a system of stochastic differential equations whose independent variable is time and whose dependent variables are the assets and the liabilities of the banks.The banking system model presented generalizes those discussed in [4],[3] and describes a homogeneous population of banks. The main features of the model are a cooperation mechanism among banks and the possibility of the (direct) intervention of the monetary authority in the banking system dynamics. We call systemic risk or systemic event in a bounded time interval the fact that in that time interval at least a given fraction of the banks fails. The probability of systemic risk in a bounded time interval is evaluated using statistical simulation. The systemic risk governance pursues the goal of keeping the probability of systemic risk in a bounded time interval between two given thresholds.The monetary authority is responsible for the systemic risk governance.The governance consists in the choice of the assets and of the liabilities of a kind of "ideal bank" as functions of time and in the choice of the rules that regulate the cooperation mechanism among banks.These rules are obtained solving an optimal control problem for the pseudo mean field approximation of the banking system model. The governance induces the banks of the system to behave like the "ideal bank". Shocks acting on the assets or on the liabilities of the banks are simulated.
Cosmic voids in modified gravity scenarios

Modified gravity (MG) theories aim to reproduce the observed acceleration of the Universe by reducing the dark sector while simultaneously recovering General Relativity (GR) within dense environments. Void studies appear to be a suitable scenario to search for imprints of alternative gravity models on cosmological scales. Voids cover an interesting range of density scales where screening mechanisms fade out, which reaches from a density contrast δ ≈ −1 close to their centers to δ ≈ 0 close to their boundaries. We present an analysis of the level of distinction between GR and two modified gravity theories, the Hu–Sawicki f(R) and the symmetron theory. This study relies on the abundance, linear bias, and density profile of voids detected in N-body cosmological simulations. We define voids as connected regions made up of the union of spheres with a mean density given by ρ̅v = 0.2 ρ̅m, but disconnected from any other voids. We find that the height of void walls is considerably affected by the gravitational theory, such that it increases for stronger gravity modifications. Finally, we show that at the level of dark matter N-body simulations, our constraints allow us to distinguish between GR and MG models with |fR0| > 10−6 and zSSB >  1. Differences of best-fit values for MG parameters that are derived independently from multiple void probes may indicate an incorrect MG model. This serves as an important consistency check.
Tensor Non-Gaussianity Search: Current Status and Future Prospects

Primordial gravitational waves (GWs) are said to be a smoking gun of cosmic inflation, while, even if they are detected, the specification of their origins will still be required for establishing the true inflationary model. Testing non-Gaussianity in the tensor-mode anisotropies of the cosmic microwave background (CMB) is one of the most powerful ways to identify sources of GW signals. In this paper, we review studies of the search for tensor non-Gaussianities employing the CMB bispectrum and forecast future developments. No significant signal has so far been found from temperature and $E$-mode polarization data, while orders-of-magnitude improvement in detection limit can be achieved by adding the information of $B$-mode polarization. There is already an established methodology for bispectrum estimation, which encourages a follow-up investigation with next-decadal CMB $B$-mode surveys.
Food for thought: Ethical considerations of user trust in computer vision

In computer vision research, especially when novel applications of tools are developed, ethical implications around user perceptions of trust in the underlying technology should be considered and supported. Here, we describe an example of the incorporation of such considerations within the long-term care sector for tracking resident food and fluid intake. We highlight our recent user study conducted to develop a Goldilocks quality horizontal prototype designed to support trust cues in which perceived trust in our horizontal prototype was higher than the existing system in place. We discuss the importance and need for user engagement as part of ongoing computer vision-driven technology development and describe several important factors related to trust that are relevant to developing decision-making tools.
Anisotropies of different mass compositions of cosmic rays

The spectral hardenings of cosmic ray nuclei above ∼ 200 GV followed by softenings around 10 TV, the knee of the all-particle spectrum around PeV energies, as well as the pattern change of the amplitude and phase of the large-scale anisotropies around 100 TeV indicate the complexities of the origin and transportation of Galactic cosmic rays. It has been shown that nearby source(s) are most likely to be the cause of such spectral features of both the spectra and the anisotropies. In this work, we study the anisotropy features of different mass composition (or mass groups) of cosmic rays in this nearby source model. We show that even if the spectral features from the nearby source component is less distinctive compared with the background component from e.g., the population of distant sources, the anisotropy features are more remarkable to be identified. Measurements of the anisotropies of each mass composition (group) of cosmic rays by the space experiments such as DAMPE and HERD and the ground-based experiments such as LHAASO in the near future are expected to be able to critically test this scenario.
Are Disentangled Representations Helpful for Abstract Visual Reasoning?

A disentangled representation encodes information about the salient factors of variation in the data independently. Although it is often argued that this representational format is useful in learning to solve many real-world down-stream tasks, there is little empirical evidence that supports this claim. In this paper, we conduct a large-scale study that investigates whether disentangled representations are more suitable for abstract reasoning tasks. Using two new tasks similar to Raven's Progressive Matrices, we evaluate the usefulness of the representations learned by 360 state-of-the-art unsupervised disentanglement models. Based on these representations, we train 3600 abstract reasoning models and observe that disentangled representations do in fact lead to better down-stream performance. In particular, they enable quicker learning using fewer samples.
Data-driven modeling of electron recoil nucleation in PICO 
C3F8
 bubble chambers

The PICO Collaboration wishes to thank SNOLAB and 
its staff for support through underground space, logistical 
and technical services. SNOLAB operations are supported 
by the Canada Foundation for Innovation and the Province 
of Ontario Ministry of Research and Innovation, with 
underground access provided by Vale at the Creighton 
mine site. We wish to acknowledge the support of the 
Natural Sciences and Engineering Research Council of 
Canada (NSERC) and the Canada Foundation for 
Innovation (CFI) for funding. We acknowledge the support 
from National Science Foundation (NSF) (Grants 
No. 0919526,  1242637, No. 1205987, 
and No. 1806722). We acknowledge that this work is 
supported by the U.S. Department of Energy (DOE) Office 
of Science, Office of High Energy Physics (under Award 
No. DE-SC-0012161), by DGAPA-UNAM (PAPIIT 
No. IA100118) and Consejo Nacional de Ciencia y 
Tecnologia (CONACyT, M?exico, Grants No. 252167 and 
No. A1-S-8960), by the Department of Atomic Energy 
(DAE), Government of India, under the Centre for AstroParticle Physics II project (CAPP-II) at the Saha 
Institute of Nuclear Physics (SINP), European Regional 
Development Fund?Project ?Engineering Applications of 
Microworld Physics?  This work is partially 
supported by the Kavli Institute for Cosmological Physics at 
the University of Chicago through NSF Grant No. 1125897, 
and an endowment from the Kavli Foundation and its 
founder Fred Kavli. We also wish to acknowledge the 
support from Fermi National Accelerator Laboratory under 
Contract No. DE-AC02-07CH11359, and Pacific Northwest 
National Laboratory, which is operated by Battelle for the 
U.S. Department of Energy under Contract No. DE-AC05- 
76RL01830. We also thank Compute Canada [75] and the 
Center for Advanced Computing, ACENET, Calcul Qu?ebec, 
Compute Ontario, and WestGrid for computational support.
Stabilizing GANs with Octave Convolutions

In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (e.g. Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.
Extreme value theory of evolving phenomena in complex dynamical systems: Firing cascades in a model of a neural network.

We extend the scope of the dynamical theory of extreme values to include phenomena that do not happen instantaneously but evolve over a finite, albeit unknown at the onset, time interval. We consider complex dynamical systems composed of many individual subsystems linked by a network of interactions. As a specific example of the general theory, a model of a neural network, previously introduced by other authors to describe the electrical activity of the cerebral cortex, is analyzed in detail. On the basis of this analysis, we propose a novel definition of a neuronal cascade, a physiological phenomenon of primary importance. We derive extreme value laws for the statistics of these cascades, both from the point of view of exceedances (that satisfy critical scaling theory in a certain regime) and of block maxima.
Anti-efficient encoding in emergent communication

Despite renewed interest in emergent language simulations with neural networks, little is known about the basic properties of the induced code, and how they compare to human language. One fundamental characteristic of the latter, known as Zipf's Law of Abbreviation (ZLA), is that more frequent words are efficiently associated to shorter strings. We study whether the same pattern emerges when two neural networks, a "speaker" and a "listener", are trained to play a signaling game. Surprisingly, we find that networks develop an \emph{anti-efficient} encoding scheme, in which the most frequent inputs are associated to the longest messages, and messages in general are skewed towards the maximum length threshold. This anti-efficient code appears easier to discriminate for the listener, and, unlike in human communication, the speaker does not impose a contrasting least-effort pressure towards brevity. Indeed, when the cost function includes a penalty for longer messages, the resulting message distribution starts respecting ZLA. Our analysis stresses the importance of studying the basic features of emergent communication in a highly controlled setup, to ensure the latter will not strand too far from human language. Moreover, we present a concrete illustration of how different functional pressures can lead to successful communication codes that lack basic properties of human language, thus highlighting the role such pressures play in the latter.
Archive Assisted Archival Fixity Verification Framework

The number of public and private web archives has increased, and we implicitly trust content delivered by these archives. Fixity is checked to ensure an archived resource has remained unaltered since the time it was captured. Some web archives do not allow users to access fixity information and, more importantly, even if fixity information is available, it is provided by the same archive from which the archived resources are requested. In this research, we propose two approaches, namely Atomic and Block, to establish and check fixity of archived resources. In the Atomic approach, the fixity information of each archived web page is stored in a JSON file (or a manifest), and published in a well-known web location (an Archival Fixity server) before it is disseminated to several on-demand web archives. In the Block approach, we first batch together fixity information of multiple archived pages in a single binary-searchable file (or a block) before it is published and disseminated to archives. In both approaches, the fixity information is not obtained directly from archives. Instead, we compute the fixity information (e.g., hash values) based on the playback of archived resources. One advantage of the Atomic approach is the ability to verify fixity of archived pages even with the absence of the Archival Fixity server. The Block approach requires pushing fewer resources into archives, and it performs fixity verification faster than the Atomic approach. On average, it takes about 1.25X, 4X and 36X longer to disseminate a manifest to perma.cc, archive.org, and webcitation.org, respectively, than archive.is, while it takes 3.5X longer to disseminate a block to archive.org than perma.cc. The Block approach performs 4.46X faster than the Atomic approach on verifying the fixity of archived pages.
Generalization bounds for deep convolutional neural networks

We prove bounds on the generalization error of convolutional networks. The bounds are in terms of the training loss, the number of parameters, the Lipschitz constant of the loss and the distance from the weights to the initial weights. They are independent of the number of pixels in the input, and the height and width of hidden feature maps. We present experiments using CIFAR-10 with varying hyperparameters of a deep convolutional network, comparing our bounds with practical generalization gaps.
Defending Against Neural Fake News

Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that closely mimics the style of real news. 
Modern computer security relies on careful threat modeling: identifying potential threats and vulnerabilities from an adversary's point of view, and exploring potential mitigations to these threats. Likewise, developing robust defenses against neural fake news requires us first to carefully investigate and characterize the risks of these models. We thus present a model for controllable text generation called Grover. Given a headline like `Link Found Between Vaccines and Autism,' Grover can generate the rest of the article; humans find these generations to be more trustworthy than human-written disinformation. 
Developing robust verification techniques against generators like Grover is critical. We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data. Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy, demonstrating the importance of public release of strong generators. We investigate these results further, showing that exposure bias -- and sampling strategies that alleviate its effects -- both leave artifacts that similar discriminators can pick up on. We conclude by discussing ethical issues regarding the technology, and plan to release Grover publicly, helping pave the way for better detection of neural fake news.
Learning latent state representation for speeding up exploration

Exploration is an extremely challenging problem in reinforcement learning, especially in high dimensional state and action spaces and when only sparse rewards are available. Effective representations can indicate which components of the state are task relevant and thus reduce the dimensionality of the space to explore. In this work, we take a representation learning viewpoint on exploration, utilizing prior experience to learn effective latent representations, which can subsequently indicate which regions to explore. Prior experience on separate but related tasks help learn representations of the state which are effective at predicting instantaneous rewards. These learned representations can then be used with an entropy-based exploration method to effectively perform exploration in high dimensional spaces by effectively lowering the dimensionality of the search space. We show the benefits of this representation for meta-exploration in a simulated object pushing environment.
Top-$k$ Combinatorial Bandits with Full-Bandit Feedback

Top-k Combinatorial Bandits generalize multi-armed bandits, where at each round any subset of $k$ out of $n$ arms may be chosen and the sum of the rewards is gained. We address the full-bandit feedback, in which the agent observes only the sum of rewards, in contrast to the semi-bandit feedback, in which the agent observes also the individual arms' rewards. We present the Combinatorial Successive Accepts and Rejects (CSAR) algorithm, which generalizes SAR (Bubeck et al, 2013) for top-k combinatorial bandits. Our main contribution is an efficient sampling scheme that uses Hadamard matrices in order to estimate accurately the individual arms' expected rewards. We discuss two variants of the algorithm, the first minimizes the sample complexity and the second minimizes the regret. We also prove a lower bound on sample complexity, which is tight for $k=O(1)$. Finally, we run experiments and show that our algorithm outperforms other methods.
Personalizing human activity recognition models using incremental learning

In this study, the aim is to personalize inertial sensor data-based human activity recognition models using incremental learning. At first, the recognition is based on user-independent model. However, when personal streaming data becomes available, the incremental learning-based recognition model can be updated, and therefore personalized, based on the data without user-interruption. The used incremental learning algorithm is Learn++ which is an ensemble method that can use any classifier as a base classifier. In fact, study compares three different base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and classification and regression tree (CART). Experiments are based on publicly open data set and they show that already a small personal training data set can improve the classification accuracy. Improvement using LDA as base classifier is 4.6 percentage units, using QDA 2.0 percentage units, and 2.3 percentage units using CART. However, if the user-independent model used in the first phase of the recognition process is not accurate enough, personalization cannot improve recognition accuracy.
Directly Detecting Signals from Absorption of Fermionic Dark Matter.

We present a new class of direct detection signals; absorption of fermionic dark matter. We enumerate the operators through dimension six which lead to fermionic absorption, study their direct detection prospects, and summarize additional constraints on their suppression scale. Such dark matter is inherently unstable as there is no symmetry which prevents dark matter decays. Nevertheless, we show that fermionic dark matter absorption can be observed in direct detection and neutrino experiments while ensuring consistency with the observed dark matter abundance and required lifetime. For dark matter masses well below the GeV scale, dedicated searches for these signals at current and future experiments can probe orders of magnitude of unexplored parameter space.
New Constraints on Early-type Galaxy Assembly from Spectroscopic Metallicities of Globular Clusters in M87

The observed characteristics of globular cluster (GC) systems, such as metallicity distributions, are commonly used to place constraints on galaxy formation models. However, obtaining reliable metallicity values is particularly difficult because of our limited means to obtain high quality spectroscopy of extragalactic GCs. Often, “color–metallicity relations” are invoked to convert easier-to-obtain photometric measurements into metallicities, but there is no consensus on what form these relations should take. In this paper we make use of multiple photometric data sets and iron metallicity values derived from applying full-spectrum stellar population synthesis models to deep Keck/LRIS spectra of 177 GCs centrally located around M87 to obtain a new color–metallicity relation. Our new relation differs substantially from previous relations in the blue, and we present evidence that the M87 relation differs from that of the Milky Way GCs, suggesting environmental dependence of GC properties. We use our color–metallicity relation to derive a new GC metallicity-host galaxy luminosity relation for red and blue GCs and find a shallower relation for the blue GCs than what previous work has found and that the metal-poor GCs are more enriched than what was previously found. This could indicate that the progenitor satellite galaxies that now make up the stellar halos of early-type galaxies are more massive and formed later than previously thought, or that the properties of metal-poor GCs are less dependent on their present-day host, indicating a common origin.
On the Generalization Gap in Reparameterizable Reinforcement Learning

Understanding generalization in reinforcement learning (RL) is a significant challenge, as many common assumptions of traditional supervised learning theory do not apply. We focus on the special class of reparameterizable RL problems, where the trajectory distribution can be decomposed using the reparametrization trick. For this problem class, estimating the expected return is efficient and the trajectory can be computed deterministically given peripheral random variables, which enables us to study reparametrizable RL using supervised learning and transfer learning theory. Through these relationships, we derive guarantees on the gap between the expected and empirical return for both intrinsic and external errors, based on Rademacher complexity as well as the PAC-Bayes bound. Our bound suggests the generalization capability of reparameterizable RL is related to multiple factors including "smoothness" of the environment transition, reward and agent policy function class. We also empirically verify the relationship between the generalization gap and these factors through simulations.