nils-herrmann/clean_scirepeval huggingface dataset:
Initial conditions of the universe: Decaying tensor modes

Many models of the early universe predict that there should be primordial tensor perturbations. These leave an imprint into the temperature and polarisation anisotropies of the cosmic microwave background (CMB). The differential equation describing the primordial tensor perturbations is a second order differential equation and thus has two solutions. Canonically, the decaying solution of this equation in radiation domination is dropped as it diverges at early times and on superhorizon scales while it is then suppressed at late times. Furthermore, if there is an inflationary phase prior to the radiation domination phase, the amplitude of the decaying mode will also be highly suppressed as it enters the radiation phase, thus its effect will be negligible. In this study we remain agnostic to the early universe models describing pre-radiation domination physics and allow this mode to be present and see what effect it has on the CMB anisotropies. We find that the decaying mode, if normalised at the same time on subhorizon scales as the growing mode leaves an imprint on the CMB anisotropies that is identical to the growing mode. Contrary to expectation, on large scales both modes are poorly constrained for a scale invariant spectrum, and the apparent divergence of the decaying mode does not lead to a divergent physical observable. Quantitatively, the decaying mode can be more constrained both from temperature and polarisation anisotropies. We use a model independent, non-parametric, approach to constrain both of these primordial tensor perturbations using the temperature and polarisation anisotropies. We find that both modes are best constrained at the reionisation and recombination bumps and crucially, at the reionisation bump the decaying mode can be distinguished from the growing mode.
Exploiting Multi-CNN Features in CNN-RNN Based Dimensional Emotion Recognition on the OMG in-the-Wild Dataset

This article presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset. Our approach includes first pre-training with the relevant and large in size, Aff-Wild and Aff-Wild2 emotion databases. Low-, mid- and high-level features are extracted from the trained CNN component and are exploited by RNN subnets in a multi-task framework. Their outputs constitute an intermediate level prediction; final estimates are obtained as the mean or median values of these predictions. Fusion of the networks is also examined for boosting the obtained performance, at Decision-, or at Model-level; in the latter case a RNN was used for the fusion. Our approach, although using only the visual modality, outperformed state-of-the-art methods that utilized audio and visual modalities. Some of our developments have been submitted to the OMG-Emotion Challenge, ranking second among the technologies which used only visual information for valence estimation; ranking third overall. Through extensive experimentation, we further show that arousal estimation is greatly improved when low-level features are combined with high-level ones.
The Bouncer Problem: Challenges to Remote Explainability

The concept of explainability is envisioned to satisfy society's demands for transparency on machine learning decisions. The concept is simple: like humans, algorithms should explain the rationale behind their decisions so that their fairness can be assessed. While this approach is promising in a local context (e.g. to explain a model during debugging at training time), we argue that this reasoning cannot simply be transposed in a remote context, where a trained model by a service provider is only accessible through its API. This is problematic as it constitutes precisely the target use-case requiring transparency from a societal perspective. Through an analogy with a club bouncer (which may provide untruthful explanations upon customer reject), we show that providing explanations cannot prevent a remote service from lying about the true reasons leading to its decisions. More precisely, we prove the impossibility of remote explainability for single explanations, by constructing an attack on explanations that hides discriminatory features to the querying user. We provide an example implementation of this attack. We then show that the probability that an observer spots the attack, using several explanations for attempting to find incoherences, is low in practical settings. This undermines the very concept of remote explainability in general.
Optimal convergence trading with unobservable pricing errors

We study a dynamic portfolio optimization problem related to convergence trading, which is an investment strategy that exploits temporary mispricing by simultaneously buying relatively underpriced assets and selling short relatively overpriced ones with the expectation that their prices converge in the future. We build on the model of Liu and Timmermann (Rev Financ Stud 26(4):1048–1086, 2013) and extend it by incorporating unobservable Markov-modulated pricing errors into the price dynamics of two co-integrated assets. We characterize the optimal portfolio strategies in full and partial information settings under the assumption of unrestricted and beta-neutral strategies. By using the innovations approach, we provide the filtering equation which is essential for solving the optimization problem under partial information. Finally, in order to illustrate the model capabilities, we provide an example with a two-state Markov chain.
Dark matter scattering cross section in Yang-Mills theory

We calculate for the first time the scattering cross section between lightest glueballs in SU(2) pure Yang-Mills theory, which are good candidates of dark matter. In the first step, we evaluate the interglueball potential on lattice using the time-dependent formalism of the HAL QCD method, with one lattice spacing. The statistical accuracy is improved by employing the cluster-decomposition error reduction technique and by using all space-time symmetries. We then derive the scattering phase shift and the scattering cross section at low energy, which is compared with the observational constraint on the dark matter self-scattering. We determine the lower bound on the scale parameter of the SU(2) Yang-Mills theory, as $\Lambda$ > 60 MeV.
CLEVRER: CoLlision Events for Video REpresentation and Reasoning

The ability to reason about temporal and causal events from videos lies at the core of human intelligence. Most video reasoning benchmarks, however, focus on pattern recognition from complex visual and language input, instead of on causal structure. We study the complementary problem, exploring the temporal and causal structures behind videos of objects with simple visual appearance. To this end, we introduce the CoLlision Events for Video REpresentation and Reasoning (CLEVRER), a diagnostic video dataset for systematic evaluation of computational models on a wide range of reasoning tasks. Motivated by the theory of human casual judgment, CLEVRER includes four types of questions: descriptive (e.g., "what color"), explanatory ("what is responsible for"), predictive ("what will happen next"), and counterfactual ("what if"). We evaluate various state-of-the-art models for visual reasoning on our benchmark. While these models thrive on the perception-based task (descriptive), they perform poorly on the causal tasks (explanatory, predictive and counterfactual), suggesting that a principled approach for causal reasoning should incorporate the capability of both perceiving complex visual and language inputs, and understanding the underlying dynamics and causal relations. We also study an oracle model that explicitly combines these components via symbolic representations.
A Data Science Approach for Honeypot Detection in Ethereum

Ethereum smart contracts have recently drawn a considerable amount of attention from the media, the financial industry and academia. With the increase in popularity, malicious users found new opportunities to profit by deceiving newcomers. Consequently, attackers started luring other attackers into contracts that seem to have exploitable flaws, but that actually contain a complex hidden trap that in the end benefits the contract creator. In the blockchain community, these contracts are known as honeypots. A recent study presented a tool called HONEYBADGER that uses symbolic execution to detect honeypots by analyzing contract bytecode. In this paper, we present a data science detection approach based foremost on the contract transaction behavior. We create a partition of all the possible cases of fund movements between the contract creator, the contract, the transaction sender and other participants. To this end, we add transaction aggregated features, such as the number of transactions and the corresponding mean value and other contract features, for example compilation information and source code length. We find that all aforementioned categories of features contain useful information for the detection of honeypots. Moreover, our approach allows us to detect new, previously undetected honeypots of already known techniques. We furthermore employ our method to test the detection of unknown honeypot techniques by sequentially removing one technique from the training set. We show that our method is capable of discovering the removed honeypot techniques. Finally, we discovered two new techniques that were previously not known.
Identifying Malicious Players in GWAP-based Disaster Monitoring Crowdsourcing System*

Disaster monitoring is challenging due to the lake of infrastructures in monitoring areas. Based on the theory of Game-With-A-Purpose (GWAP), this paper contributes to a novel large-scale crowdsourcing disaster monitoring system. The system analyzes tagged satellite pictures from anonymous players, and then reports aggregated and evaluated monitoring results to its stakeholders. An algorithm based on directed graph centralities is presented to address the core issues of malicious user detection and disaster level calculation. Our method can be easily applied in other human computation systems. In the end, some issues with possible solutions are discussed for our future work.
Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation

Randomized controlled trials (RCTs) represent the paramount evidence of clinical medicine. Using machines to interpret the massive amount of RCTs has the potential of aiding clinical decision-making. We propose a RCT conclusion generation task from the PubMed 200k RCT sentence classification dataset to examine the effectiveness of sequence-to-sequence models on understanding RCTs. We first build a pointer-generator baseline model for conclusion generation. Then we fine-tune the state-of-the-art GPT-2 language model, which is pre-trained with general domain data, for this new medical domain task. Both automatic and human evaluation show that our GPT-2 fine-tuned models achieve improved quality and correctness in the generated conclusions compared to the baseline pointer-generator model. Further inspection points out the limitations of this current approach and future directions to explore.
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

Many real world tasks require multiple agents to work together. Multi-agent reinforcement learning (RL) methods have been proposed in recent years to solve these tasks, but current methods often fail to efficiently learn policies. We thus investigate the presence of a common weakness in single-agent RL, namely value function overestimation bias, in the multi-agent setting. Based on our findings, we propose an approach that reduces this bias by using double centralized critics. We evaluate it on six mixed cooperative-competitive tasks, showing a significant advantage over current methods. Finally, we investigate the application of multi-agent methods to high-dimensional robotic tasks and show that our approach can be used to learn decentralized policies in this domain.
Norm one Tori and Hasse norm principle

Let $k$ be a field and $T$ be an algebraic $k$-torus. In 1969, over a global field $k$, Voskresenskii proved that there exists an exact sequence $0\to A(T)\to H^1(k,{\rm Pic}\,\bar{X})^\vee\to Sha(T)\to 0$ where $A(T)$ is the kernel of the weak approximation of $T$, $Sha(T)$ is the Shafarevich-Tate group of $T$, $X$ is a smooth compactification of $T$, $\bar{X}=X\times_k\bar{k}$, ${\rm Pic}\,\bar{X}$ is the Picard group of $\bar{X}$ and $\vee$ stands for the Pontryagin dual. In 1984, Kunyavskii showed that, among 73 cases of 3-dimensional $k$-tori $T$, there exist exactly 2 cases satisfy $H^1(k,{\rm Pic}\,\bar{X})\neq 0$. On the other hand, in 1963, Ono proved that $Sha(T)=0$ if and only if the Hasse norm principle holds for $K/k$ where $T=R^{(1)}_{K/k}(G_m)$ is the norm one torus of $K/k$. First, we show that, among 710 cases of 4-dimensional algebraic $k$-tori $T$, there exist exactly 2 (resp. 20, 688) cases with $H^1(k,{\rm Pic}\, \bar{X})\simeq(Z/2Z)^{\oplus 2}$ (resp. $H^1(k,{\rm Pic}\, \bar{X})\simeq Z/2Z$, $H^1(k,{\rm Pic}\, \bar{X})=0$). Among 6079 cases of 5-dimensional algebraic $k$-tori $T$, there exist exactly 11 (resp. 263, 5805) cases with $H^1(k,{\rm Pic}\, \bar{X})\simeq(Z/2Z)^{\oplus 2}$ (resp. $H^1(k,{\rm Pic}\, \bar{X})\simeq Z/2Z$, $H^1(k,{\rm Pic}\, \bar{X})=0$). Second, we determine $H^1(k,{\rm Pic}\, \bar{X})$ for norm one tori $T=R^{(1)}_{K/k}(G_m)$ with $[K:k]=n\leq 15$ and $n\neq 12$. We also show that $H^1(k,{\rm Pic}\, \bar{X})=0$ for the $5$ Mathieu groups $M_n\leq S_n$. Third, we give a necessary and sufficient condition for the Hasse norm principle for $K/k$ with $[K:k]=n\leq 15$ and $n\neq 12$. As applications of the results, we get the group $T(k)/R$ of $R$-equivalence classes over a local field $k$ and the Tamagawa number $\tau(T)$ over a number field $k$.
Complex networks based word embeddings

Most of the time, the first step to learn word embeddings is to build a word co-occurrence matrix. As such matrices are equivalent to graphs, complex networks theory can naturally be used to deal with such data. In this paper, we consider applying community detection, a main tool of this field, to the co-occurrence matrix corresponding to a huge corpus. Community structure is used as a way to reduce the dimensionality of the initial space. Using this community structure, we propose a method to extract word embeddings that are comparable to the state-of-the-art approaches.
The Option Pricing Model Based on Time Values: An Application of the Universal Approximation Theory on Unbounded Domains

We propose a time value related decision function to treat a classical option pricing problem raised by Hutchinson-Lo-Poggio. In numerical experiments, the new decision function significantly improves the original model of Hutchinson-Lo-Poggio with faster convergence and better generalization performance. By proving a novel universal approximation theorem, we show that our decision function rather than Hutchinson-Lo-Poggio's can be approximated on the entire domain of definition by neural networks. Thus the experimental results are partially explained by the representation properties of networks.
RIC-NN: A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy

Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein. Statistics of strong explanative power, called "factor", have been proposed to summarize the essence of predictive stock returns. The challenge here is to make a multi-factor investment strategy that is consistent over a reasonably long period based on supervised machine learning. Although machine learning methods are increasingly popular in stock return prediction, an inference of the stock return is highly elusive, and naive use of complex machine learning methods easily overfits the current data and results in poor performance on future data. We propose a principled stock return prediction framework that we call Ranked Information Coefficient Neural Network (RIC-NN) that alleviates the overfitting. RIC-NN addresses the difficulty that arises in nonconvex machine learning: Namely, initialization and the stopping of the training model and the transfer among several different tasks (markets). RIC-NN is a deep learning approach and includes the following three novel ideas: (1) nonlinear multi-factor approach, (2) stopping criteria with ranked information coefficient (rank IC), and (3) deep transfer learning among multiple regions. Experimental comparison with the stocks in the Morgan Stanley Capital International indices shows that RIC-NN outperforms not only off-the-shelf machine learning methods but also the average return of major equity investment funds in the last fourteen years.
MLPerf Training Benchmark

Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can increase the time to solution, training is stochastic and time to solution exhibits high variance, and software and hardware systems are so diverse that fair benchmarking with the same binary, code, and even hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that overcomes these challenges. Our analysis quantitatively evaluates MLPerf's efficacy at driving performance and scalability improvements across two rounds of results from multiple vendors.
The Fourier Evaluation of Tracings and Acidosis in Labor: the FETAL Technique.

Adequate fetal and neonatal development depend upon the presence of a normal acid-base environment during pregnancy and the smooth transition from intra-uterine to extra-uterine life. Current methods to assess fetal pH and acid-base status are invasive and carry significant maternal and fetal risks. Given these limitations, obstetrical care providers developed the electronic fetal monitoring (EFM) system, a non-invasive tool, which evaluates beat-to-beat fetal heart rate (FHR) patterns in order to predict fetal oxygenation status in real-time. Every year, about 85 percent of the approximately 4 million live births in the United States are evaluated using EFM. Unfortunately, though there is ample physiological evidence that FHR patterns are inextricably linked to fetal acid-base status, the use of EFM has not been shown to reliably predict neonatal pH, nor has it reduced the incidence of adverse perinatal outcomes, including long-term neurological morbidity and cerebral palsy (CP). The poor specificity associated with the current interpretation of the EFM therefore leads to a paradox we have henceforth defined as the Obstetrical Paradox. In this study, we develop and seek to determine whether a novel, non-invasive method known as the FETAL technique (Fourier Evaluation of Tracings and Acidosis in Labor), which applies the Fourier Transform to EFM tracings and determines the spectral frequency distributions of the FHR, improves the assessment of the fetal pH in real time. We hypothesize that the improvement in the sensitivity and specificity of the EFM with the use of the FETAL technique will lead to a significant reduction in the rate of neonatal hypoxic injury and in the rate of caesarean deliveries for suspected fetal distress. The implications of a successful application of the FETAL technique would have paradigm-shifting consequences in the provision of modern obstetrical care.
Method for the semantic indexing of concept hierarchies, uniform representation, use of relational database systems and generic and case-based reasoning

This paper presents a method for semantic indexing and describes its application in the field of knowledge representation. Starting point of the semantic indexing is the knowledge represented by concept hierarchies. The goal is to assign keys to nodes (concepts) that are hierarchically ordered and syntactically and semantically correct. With the indexing algorithm, keys are computed such that concepts are partially unifiable with all more specific concepts and only semantically correct concepts are allowed to be added. The keys represent terminological relationships. Correctness and completeness of the underlying indexing algorithm are proven. The use of classical relational databases for the storage of instances is described. Because of the uniform representation, inference can be done using case-based reasoning and generic problem solving methods.
Dark matter around primordial black hole at the radiation-dominated stage

The accumulation of dark matter particles near the primordial black holes starts at the radiation-dominated cosmological stage and produces the central density spikes. The spikes can be the bright gamma-ray sources due to dark matter annihilation. We present the self-consistent derivation of the equation of motion of particle in the metrics of primordial black hole immersed into cosmological background. By numerical solution of this equation we find the central dark matter density profile. The density growth is suppressed in the central part of the profile compared with previous calculations.
Cosmological discordances. III. More on measure properties, large-scale-structure constraints, the Hubble constant and 
Planck
 data

Consistency between cosmological data sets is essential for ongoing and future cosmological analyses. We first investigate the questions of stability and applicability of some moment-based inconsistency measures to multiple data sets. We show that the recently introduced index of inconsistency (IOI) is numerically stable while it can be applied to multiple data sets. We use an illustrative construction of constraints as well as an example with real data sets (i.e. WMAP versus Planck) to show some limitations of the application of the Karhunen-Loeve decomposition to discordance measures. Second, we perform various consistency analyzes using IOI between multiple current data sets while \textit{working with the entire common parameter spaces}. We find current Large-Scale-Structure (LSS) data sets (Planck CMB lensing, DES lensing-clustering and SDSS RSD) all to be consistent with one another. This is found to be not the case for Planck temperature (TT) versus polarization (TE,EE) data, where moderate inconsistencies are present. Noteworthy, we find a strong inconsistency between joint LSS probes and Planck with IOI=5.27, and a moderate tension between DES and Planck with IOI=3.14. Next, using the IOI metric, we compare the Hubble constant from five independent probes. We confirm previous strong tensions between local measurement (SH0ES) and Planck as well as between H0LiCOW and Planck, but also find new strong tensions between SH0ES measurement and the joint LSS probes with IOI=6.73 (i.e. 3.7-$\sigma$ in 1D) as well as between joint LSS and combined probes SH0ES+H0LiCOW with IOI=8.59 (i.e. 4.1-$\sigma$ in 1D). Whether due to systematic effects in the data sets or problems with the underlying model, sources of these old and new tensions need to be identified and dealt with.