nils-herrmann/clean_scirepeval huggingface dataset:
Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform

Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans. However, there has not been much research in developing recommendation systems to help borrowers make wise decisions. On P2PL platforms, borrowers can either apply for bidding loans, where the interest rate is determined by lenders bidding on a loan or traditional loans where the P2PL platform determines the interest rate. Different borrower grades — determining the credit worthiness of borrowers get different interest rates via these two mechanisms. Hence, it is essential to determine which type of loans borrowers should apply for. In this paper, we build a recommendation system that recommends to any new borrower the type of loan they should apply for. Using our recommendation system, any borrower can achieve lowered interest rates with a higher likelihood of getting funded.
Subexponential-Time Algorithms for Sparse PCA

We study the computational cost of recovering a unit-norm sparse principal component $x \in \mathbb{R}^n$ planted in a random matrix, in either the Wigner or Wishart spiked model (observing either $W + \lambda xx^\top$ with $W$ drawn from the Gaussian orthogonal ensemble, or $N$ independent samples from $\mathcal{N}(0, I_n + \beta xx^\top)$, respectively). Prior work has shown that when the signal-to-noise ratio ($\lambda$ or $\beta\sqrt{N/n}$, respectively) is a small constant and the fraction of nonzero entries in the planted vector is $\|x\|_0 / n = \rho$, it is possible to recover $x$ in polynomial time if $\rho \lesssim 1/\sqrt{n}$. While it is possible to recover $x$ in exponential time under the weaker condition $\rho \ll 1$, it is believed that polynomial-time recovery is impossible unless $\rho \lesssim 1/\sqrt{n}$. We investigate the precise amount of time required for recovery in the "possible but hard" regime $1/\sqrt{n} \ll \rho \ll 1$ by exploring the power of subexponential-time algorithms, i.e., algorithms running in time $\exp(n^\delta)$ for some constant $\delta \in (0,1)$. For any $1/\sqrt{n} \ll \rho \ll 1$, we give a recovery algorithm with runtime roughly $\exp(\rho^2 n)$, demonstrating a smooth tradeoff between sparsity and runtime. Our family of algorithms interpolates smoothly between two existing algorithms: the polynomial-time diagonal thresholding algorithm and the $\exp(\rho n)$-time exhaustive search algorithm. Furthermore, by analyzing the low-degree likelihood ratio, we give rigorous evidence suggesting that the tradeoff achieved by our algorithms is optimal.
Vocal Interactivity in Crowds, Flocks and Swarms: Implications for Voice User Interfaces

Recent years have seen an explosion in the availability of Voice User Interfaces. However, user surveys suggest that there are issues with respect to usability, and it has been hypothesised that contemporary voice-enabled systems are missing crucial behaviours relating to user engagement and vocal interactivity. However, it is well established that such ostensive behaviours are ubiquitous in the animal kingdom, and that vocalisation provides a means through which interaction may be coordinated and managed between individuals and within groups. Hence, this paper reports results from a study aimed at identifying generic mechanisms that might underpin coordinated collective vocal behaviour with a particular focus on closed-loop negative-feedback control as a powerful regulatory process. A computer-based real-time simulation of vocal interactivity is described which has provided a number of insights, including the enumeration of a number of key control variables that may be worthy of further investigation.
Flocking in complex environments—Attention trade-offs in collective information processing

The ability of biological and artificial collectives to outperform solitary individuals in a wide variety of tasks depends crucially on the efficient processing of social and environmental information at the level of the collective. Here, we model collective behavior in complex environments with many potentially distracting cues. Counter-intuitively, large-scale coordination in such environments can be maximized by strongly limiting the cognitive capacity of individuals, where due to self-organized dynamics the collective self-isolates from disrupting information. We observe a fundamental trade-off between coordination and collective responsiveness to environmental cues. Our results offer important insights into possible evolutionary trade-offs in collective behavior in biology and suggests novel principles for design of artificial swarms exploiting attentional bottlenecks.
RoBERTa: A Robustly Optimized BERT Pretraining Approach

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.
Investing for Discovery in Astronomy

How should we invest our available resources to best sustain astronomy's track record of discovery, established over the past few decades? Two strong hints come from (1) our history of astronomical discoveries and (2) literature citation patterns that reveal how discovery and development activities in science are strong functions of team size. These argue that progress in astronomy hinges on support for a diversity of research efforts in terms of team size, research tools and platforms, and investment strategies that encourage risk taking. 
These ideas also encourage us to examine the implications of the trend toward "big team science" and "survey science" in astronomy over the past few decades, and to reconsider the common assumption that progress in astronomy always means "trading up" to bigger apertures and facilities. Instead, the considerations above argue that we need a balanced set of investments in small- to large-scale initiatives and team sizes both large and small. Large teams tend to develop existing ideas, whereas small teams are more likely to fuel the future with disruptive discoveries. While large facilities are the "value" investments that are guaranteed to produce discoveries, smaller facilities are the "growth stocks" that are likely to deliver the biggest science bang per buck, sometimes with outsize returns. One way to foster the risk taking that fuels discovery is to increase observing opportunity, i.e., create more observing nights and facilitate the exploration of science-ready data.
Action Guidance with MCTS for Deep Reinforcement Learning

Deep reinforcement learning has achieved great successes in recent years, however, one main challenge is the sample inefficiency. In this paper, we focus on how to use action guidance by means of a non-expert demonstrator to improve sample efficiency in a domain with sparse, delayed, and possibly deceptive rewards: the recently-proposed multi-agent benchmark of Pommerman. We propose a new framework where even a non-expert simulated demonstrator, e.g., planning algorithms such as Monte Carlo tree search with a small number rollouts, can be integrated within asynchronous distributed deep reinforcement learning methods. Compared to a vanilla deep RL algorithm, our proposed methods both learn faster and converge to better policies on a two-player mini version of the Pommerman game.
Accurate and Robust Pulmonary Nodule Detection by 3D Feature Pyramid Network with Self-supervised Feature Learning

Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limits the automatic diagnosis in routine clinical practice. Moreover, the CT scans collected from multiple manufacturers may affect the robustness of Computer-aided diagnosis (CAD) due to the differences in intensity scales and machine noises. In this paper, we propose a novel self-supervised learning assisted pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the resolution of nodules, as well as a parallel top-down path to transit the high-level semantic features to complement low-level general features. Furthermore, a High Sensitivity and Specificity (HS2) network is introduced to eliminate the false positive nodule candidates by tracking the appearance changes in continuous CT slices of each nodule candidate on Location History Images (LHI). In addition, in order to improve the performance consistency of the proposed framework across data captured by different CT scanners without using additional annotations, an effective self-supervised learning schema is applied to learn spatiotemporal features of CT scans from large-scale unlabeled data. The performance and robustness of our method are evaluated on several publicly available datasets with significant performance improvements. The proposed framework is able to accurately detect pulmonary nodules with high sensitivity and specificity and achieves 90.6% sensitivity with 1/8 false positive per scan which outperforms the state-of-the-art results 15.8% on LUNA16 dataset.
Low-Rank Matrix Completion: A Contemporary Survey

As a paradigm to recover unknown entries of a matrix from partial observations, low-rank matrix completion (LRMC) has generated a great deal of interest. Over the years, there have been lots of works on this topic, but it might not be easy to grasp the essential knowledge from these studies. This is mainly because many of these works are highly theoretical or a proposal of new LRMC technique. In this paper, we give a contemporary survey on LRMC. In order to provide a better view, insight, and understanding of potentials and limitations of the LRMC, we present early scattered results in a structured and accessible way. Specifically, we classify the state-of-the-art LRMC techniques into two main categories and then explain each category in detail. We next discuss the issues to be considered when one considers using the LRMC techniques. These include intrinsic properties required for the matrix recovery and how to exploit a special structure in the LRMC design. We also discuss the convolutional neural network (CNN)-based LRMC algorithms exploiting the graph structure of a low-rank matrix. Furthermore, we present the recovery performance and the computational complexity of state-of-the-art LRMC techniques. Our hope is that this paper will serve as a useful guide for practitioners and non-experts to catch the gist of the LRMC.
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and also shown potential to significantly speed up MR reconstruction with reduced measurements. This article gives an overview of deep learning-based image reconstruction methods for MRI. Three types of deep learning-based approaches are reviewed, the data-driven, model-driven and integrated approaches. The main structure of each network in three approaches is explained and the analysis of common parts of reviewed networks and differences in-between are highlighted. Based on the review, a number of signal processing issues are discussed for maximizing the potential of deep reconstruction for fast MRI. the discussion may facilitate further development of "optimal" network and performance analysis from a theoretical point of view.
Large Scale Continuous-Time Mean-Variance Portfolio Allocation via Reinforcement Learning

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the optimality of a multivariate Gaussian feedback policy, with time-decaying variance, in trading off exploration and exploitation. Based on a provable policy improvement theorem, we devise a scalable and data-efficient RL algorithm and conduct large scale empirical tests using data from the S&P 500 stocks. We found that our method consistently achieves over 10% annualized returns and it outperforms econometric methods and the deep RL method by large margins, for both long and medium terms of investment with monthly and daily trading.
Market and Long Term Accounting Operational Performance

Following the value relevance literature, this study verifies whether the marketplace differentiates companies of high, medium, and low long-term operational performance, measured by accounting information on profitability, sales variation and indebtedness. The data comprises the Corporate Financial Statements disclosed during the period from 1996 to 2009 and stock prices of companies listed on the Sao Paulo Stock Exchange and Commodities and Futures Exchange - BM&FBOVESPA. The final sample is composed of 142 non-financial companies. Five year mobile windows were used, which resulted in ten five-year periods. After checking each company indices, the accounting variables were unified in an Index Performance Summary to synthesize the final performance for each five-year period, which allowed segregation in operational performance levels. Multiple regressions were performed using panel data techniques, fixed effects model and dummies variables, and then hypothesis tests were made. Regarding the explanatory power of each individual variable, the results show that not all behaviors are according to the research hypothesis and that the Brazilian stock market differentiates companies of high and low long-term operational performance. This distinction is not fully perceived between companies of high and medium operational performance.
Fragmentation in Population III Galaxies Formed through Ionizing Radiation

Population III stars forming in minihalos tend to be relatively inefficient, with each minihalo hosting one or a small number of stars which are more massive than local stars, but still challenging to observe directly at high redshift. Here we explore a possible mechanism for the generation of larger clusters of such stars: a nearby ionizing source that ionizes a late forming halo, delaying its collapse until the halo is sufficiently large enough that the core can self-shield and suffer runaway collapse. We use simulations with a simple but accurate model for the radiative ionizing flux and confirm the basic predictions of previous work: higher ionizing fluxes can delay the collapse to lower redshifts and higher masses, up to an order of magnitude above the atomic cooling limit. In a limited number of runs we also examine the fragmentation of the cores at even higher resolution, using both simple estimates and sink particles to show that the number of fragments is generally small, at most a handful, and that the mass accretion rate on the fragments is of order 10−3 M⊙ yr−1. This rate is sufficiently high enough that the descent on the main sequence (and hence the suppression of accretion) is delayed until the stellar masses are of order 100–1000 M⊙, but not high enough to produce direct collapse black holes of mass ∼105 M⊙. The resulting clusters are larger than those produced in minihalos, but are still likely to fall short of being easily detectable in James Webb Space Telescope blind fields.
Discovery of a nearby 1700 km s−1 star ejected from the Milky Way by Sgr A*

We present the serendipitous discovery of the fastest main-sequence hyper-velocity star (HVS) by the Southern Stellar Stream Spectroscopic Survey (S5). The star S5-HVS1 is a ∼2.35 M⊙ A-type star located at a distance of ∼9 kpc from the Sun and has a heliocentric radial velocity of 1017 ± 2.7 $\mathrm{\, km\, s^{-1}}$ without any signature of velocity variability. The current 3D velocity of the star in the Galactic frame is 1755 ± 50 $\mathrm{\, km\, s^{-1}}$. When integrated backwards in time, the orbit of the star points unambiguously to the Galactic Centre, implying that S5-HVS1 was kicked away from Sgr A* with a velocity of ∼1800 $\mathrm{\, km\, s^{-1}}$ and travelled for 4.8 Myr to its current location. This is so far the only HVS confidently associated with the Galactic Centre. S5-HVS1 is also the first hyper-velocity star to provide constraints on the geometry and kinematics of the Galaxy, such as the Solar motion Vy,⊙ = 246.1 ± 5.3 $\mathrm{\, km\, s^{-1}}$ or position R0 = 8.12 ± 0.23 kpc. The ejection trajectory and transit time of S5-HVS1 coincide with the orbital plane and age of the annular disc of young stars at the Galactic Centre, and thus may be linked to its formation. With the S5-HVS1 ejection velocity being almost twice the velocity of other hyper-velocity stars previously associated with the Galactic Centre, we question whether they have been generated by the same mechanism or whether the ejection velocity distribution has been constant over time.
Quantifying the thermal Sunyaev–Zel’dovich effect and excess millimetre emission in quasar environments

In this paper, we probe the hot, post-shock gas component of quasar-driven winds through the thermal Sunyaev–Zel’dovich (tSZ) effect. Combining data sets from the Atacama Cosmology Telescope, the Herschel Space Observatory, and the Very Large Array, we measure average spectral energy distributions of 109 829 optically selected, radio quiet quasars from 1.4 to 3000 GHz in six redshift bins between 0.3 < z < 3.5. We model the emission components in the radio and far-infrared, plus a spectral distortion from the tSZ effect. At z > 1.91, we measure the tSZ effect at 3.8σ significance with an amplitude corresponding to a total thermal energy of 3.1 × 1060 erg. If this energy is due to virialized gas, then our measurement implies quasar host halo masses are ∼6 × 1012 h−1 M⊙. Alternatively, if the host dark matter halo masses are ∼2 × 1012 h−1 M⊙ as some measurements suggest, then we measure a >90 per cent excess in the thermal energy over that expected due to virialization. If the measured SZ effect is primarily due to hot bubbles from quasar-driven winds, we find that $(5^{+1.2}_{-1.3}$) per cent of the quasar bolometric luminosity couples to the intergalactic medium over a fiducial quasar lifetime of 100 Myr. An additional source of tSZ may be correlated structure, and further work is required to separate the contributions. At z ≤ 1.91, we detect emission at 95 and 148 GHz that is in excess of thermal dust and optically thin synchrotron emission. We investigate potential sources of this excess emission, finding that CO line emission and an additional optically thick synchrotron component are the most viable candidates.
Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration.
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty

Multi-fidelity Gaussian process (GP) modeling is a common approach to employ in resource-expensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification where multiple datasets of varying fidelities are encountered. Briefly, in its simplest form, a multi-fidelity GP is trained on two separate sources of datasets each with its own fidelity level, e.g., a software code/simulator for the low-fidelity source and real-world experiments for the high-fidelity source. Adaptive sampling for multi-fidelity Gaussian processes is a challenging task since we not only seek to estimate the next sampling location of the design variable, but also account for the data fidelities. This issue is often addressed by including the cost of the data sources as an another element in the search criterion in conjunction with an uncertainty reduction metric. In this work, we extent the traditional design of experiment framework for multi-fidelity GPs by partitioning the prediction uncertainty based on the fidelity level and the associated cost of execution. In addition, we utilize the concept of a meta-model believer which quantifies the effect of adding an exploratory design point on the GP uncertainty prediction. We demonstrate the framework using academic examples as well as an industrial application of a steady-state thermodynamic operation point of a fluidized bed process.
Environment Probing Interaction Policies

A key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment. A common approach to improve inter-environment transfer is to learn policies that are invariant to the distribution of testing environments. However, we argue that instead of being invariant, the policy should identify the specific nuances of an environment and exploit them to achieve better performance. In this work, we propose the 'Environment-Probing' Interaction (EPI) policy, a policy that probes a new environment to extract an implicit understanding of that environment's behavior. Once this environment-specific information is obtained, it is used as an additional input to a task-specific policy that can now perform environment-conditioned actions to solve a task. To learn these EPI-policies, we present a reward function based on transition predictability. Specifically, a higher reward is given if the trajectory generated by the EPI-policy can be used to better predict transitions. We experimentally show that EPI-conditioned task-specific policies significantly outperform commonly used policy generalization methods on novel testing environments.