nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial Learning for Improved Onsets and Frames Music Transcription

Automatic music transcription is considered to be one of the hardest problems in music information retrieval, yet recent deep learning approaches have achieved substantial improvements on transcription performance. These approaches commonly employ supervised learning models that predict various time-frequency representations, by minimizing element-wise losses such as the cross entropy function. However, applying the loss in this manner assumes conditional independence of each label given the input, and thus cannot accurately express inter-label dependencies. To address this issue, we introduce an adversarial training scheme that operates directly on the time-frequency representations and makes the output distribution closer to the ground-truth. Through adversarial learning, we achieve a consistent improvement in both frame-level and note-level metrics over Onsets and Frames, a state-of-the-art music transcription model. Our results show that adversarial learning can significantly reduce the error rate while increasing the confidence of the model estimations. Our approach is generic and applicable to any transcription model based on multi-label predictions, which are very common in music signal analysis.
Max-Plus Matching Pursuit for Deterministic Markov Decision Processes

We consider deterministic Markov decision processes (MDPs) and apply max-plus algebra tools to approximate the value iteration algorithm by a smaller-dimensional iteration based on a representation on dictionaries of value functions. The setup naturally leads to novel theoretical results which are simply formulated due to the max-plus algebra structure. For example, when considering a fixed (non adaptive) finite basis, the computational complexity of approximating the optimal value function is not directly related to the number of states, but to notions of covering numbers of the state space. In order to break the curse of dimensionality in factored state-spaces, we consider adaptive basis that can adapt to particular problems leading to an algorithm similar to matching pursuit from signal processing. They currently come with no theoretical guarantees but work empirically well on simple deterministic MDPs derived from low-dimensional continuous control problems. We focus primarily on deterministic MDPs but note that the framework can be applied to all MDPs by considering measure-based formulations.
Reanalysis of the Pulsar Scenario to Explain the Cosmic Positron Excess Considering the Recent Developments

The slow diffusion of cosmic rays could be common around pulsars as indicated by the recent observations of HAWC, which can significantly change the pulsar interpretation of the well-known positron excess. Meanwhile, the latest measurement by AMS-02 shows a clear high-energy cutoff in the positron spectrum. Here, we check all the identified pulsars under the two-zone diffusion model to explain the new AMS-02 data. We find that the candidates must be nearby and middle-aged. Geminga, which was generally believed to be a very likely candidate, has recently been disfavored by Fermi-LAT observations of the GeV γ-ray flux. Following recent studies which indicate that PSR B1055−52 is much closer to the Earth than previously assumed, we propose for the first time that PSR B1055−52 is the most promising source of the positron excess. PSR B1055−52 can well reproduce both the intensity and the high-energy cutoff of the AMS-02 positron spectrum.
Designing Game of Theorems

"Theorem proving is similar to the game of Go. So, we can probably improve our provers using deep learning, like DeepMind built the super-human computer Go program, AlphaGo." Such optimism has been observed among participants of AITP2017. But is theorem proving really similar to Go? In this paper, we first identify the similarities and differences between them and then propose a system in which various provers keep competing against each other and changing themselves until they prove conjectures provided by users.
Privacy, Altruism, and Experience: Estimating the Perceived Value of Internet Data for Medical Uses

People increasingly turn to the Internet when they have a medical condition. The data they create during this process is a valuable source for medical research and future health services. When used for these purposes, it is imperative to balance use with user privacy. One way to understand how to harmonize these requirements is to match the perceived value that users assign to their data with the value of the services derived from them. Here we describe novel experiments where methods from Mechanism Design, Crowdsourcing and Data Science were used together to elicit truthful valuations from users for their Internet data and for services derived from these data, specifically for medical screening. In these experiments, 880 people from around the world were asked to participate in an auction to provide their data for uses differing in their contribution to the participant and to society, and in the disease they addressed. Some users were offered monetary compensation for their participation, while others were asked to pay to participate. Our findings show that 99% of people were willing to contribute their data in exchange for monetary compensation and an analysis of their data, while 53% were willing to pay to have their data analyzed. The average perceived value users assigned to their data was estimated at US$49. Their value for services which offer personal benefit to them was US$22, while the value of this service offered to the general public was US$20. Our findings show that it is possible to place a monetary value on health-related uses of highly personal data. Our methodology can be extended to other areas where sensitive data may be exchanged for services to individuals and to society.
Hindi Question Generation Using Dependency Structures

Hindi question answering systems suffer from a lack of data. To address the same, this paper presents an approach towards automatic question generation. We present a rule-based system for question generation in Hindi by formalizing question transformation methods based on karaka-dependency theory. We use a Hindi dependency parser to mark the karaka roles and use IndoWordNet a Hindi ontology to detect the semantic category of the karaka role heads to generate the interrogatives. We analyze how one sentence can have multiple generations from the same karaka role's rule. The generations are manually annotated by multiple annotators on a semantic and syntactic scale for evaluation. Further, we constrain our generation with the help of various semantic and syntactic filters so as to improve the generation quality. Using these methods, we are able to generate diverse questions, significantly more than number of sentences fed to the system.
Search for diboson resonances in hadronic final states in 139 fb$^{-1}$ of $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

Narrow resonances decaying into WW , W Z or Z Z boson pairs are searched for in 139 fb−1 of proton–proton collision data at a centre-of-mass energy of √ s = 13 TeV recorded with the ATLAS detector at the Large Hadron Collider from 2015 to 2018. The diboson system is reconstructed using pairs of high transverse momentum, large-radius jets. These jets are built from a combination of calorimeterand tracker-inputs compatible with the hadronic decay of a boosted W or Z boson, using jet mass and substructure properties. The search is performed for diboson resonances with masses greater than 1.3 TeV. No significant deviations from the background expectations are observed. Exclusion limits at the 95% confidence level are set on the production cross-section times branching ratio into dibosons for resonances in a range of theories beyond the Standard Model, with the highest excluded mass of a new gauge boson at 3.8 TeV in the context of mass-degenerate resonances that couple predominantly to gauge bosons.
Optimal Measurement Network of Pairwise Differences

When both the difference between two quantities and their individual values can be measured or computational predicted, multiple quantities can be determined from the measurements or predictions of select individual quantities and select pairwise differences. These measurements and predictions form a network connecting the quantities through their differences. Here, I analyze the optimization of such networks, where the trace (A-optimal), the largest eigenvalue (E-optimal), or the determinant (D-optimal) of the covariance matrix associated with the estimated quantities are minimized with respect to the allocation of the measurement (or computational) cost to different measurements (or predictions). My statistical analysis of the performance of such optimal measurement networks-based on large sets of simulated data-suggests that they substantially accelerate the determination of the quantities, and that they may be useful in applications such as the computational prediction of binding free energies of candidate drug molecules.
Kam method for Cryo-EM particle reconstruction

The Cryo-EM 3D particle reconstruction is essential for identifying protein and uncover the biological mechanism of the macro-molecules. In this paper, we use Kam method for reconstruction. Kam method is \textit{ab-initio}, and it assumes that the projection angles of the particle are uniformly distributed. Based on the data covariance matrix, we compute the radial frequency component of the matrix. The particle density function can be obtained by the radial frequency component and the angular frequency basis function. In order to uniquely and accurately identify the radial frequency component, an initial guess of the structure is applied. Experiment shows that Kam method works for low resolution particle reconstruction. Further improvement can be made by including projection angle distribution in covariance matrix, and applying the fast algorithm to enhance computation speed.
On structural and dynamical factors determining the integrated basin instability of power-grid nodes.

In electric power systems delivering alternating current, it is essential to maintain its synchrony of the phase with the rated frequency. The synchronization stability that quantifies how well the power-grid system recovers its synchrony against perturbation depends on various factors. As an intrinsic factor that we can design and control, the transmission capacity of the power grid affects the synchronization stability. Therefore, the transition pattern of the synchronization stability with the different levels of transmission capacity against external perturbation provides the stereoscopic perspective to understand the synchronization behavior of power grids. In this study, we extensively investigate the factors affecting the synchronization stability transition by using the concept of basin stability as a function of the transmission capacity. For a systematic approach, we introduce the integrated basin instability, which literally adds up the instability values as the transmission capacity increases. We first take simple 5-node motifs as a case study of building blocks of power grids, and a more realistic IEEE 24-bus model to highlight the complexity of decisive factors. We find that both structural properties such as gate keepers in network topology and dynamical properties such as large power input/output at nodes cause synchronization instability. The results suggest that evenly distributed power generation and avoidance of bottlenecks can improve the overall synchronization stability of power-grid systems.
On the use of multiple compartment epidemiological models to describe the dynamics of influenza in Europe

We develop a multiple compartment Susceptible-Infected-Recovered (SIR) model to analyze the spread of several infectious diseases through different geographic areas. Additionally, we propose a data-quality sensitive optimization framework for fitting this model to observed data. 
We fit the model to the temporal profile of the number of people infected by one of six influenza strains in Europe over $7$ influenza seasons. In addition to describing the temporal and spatial spread of influenza, the model provides an estimate of the inter-country and intra-country infection and recovery rates of each strain and in each season. We find that disease parameters remain relatively stable, with a correlation greater than $0.5$ over seasons and stains. Clustering of influenza strains by the inferred disease parameters is consistent with genome sub-types. Surprisingly, our analysis suggests that inter-country human mobility plays a negligible role in the spread of influenza in Europe. Finally, we show that the model allows the estimation of disease load in countries with poor or none existent data from the disease load in adjacent countries. 
Our findings reveal information on the spreading mechanism of influenza and on disease parameters. These can be used to assist in disease surveillance and in control of influenza as well as of other infectious pathogens in a heterogenic environment.
Investment Ranking Challenge: Identifying the best performing stocks based on their semi-annual returns

In the IEEE Investment ranking challenge 2018, participants were asked to build a model which would identify the best performing stocks based on their returns over a forward six months window. Anonymized financial predictors and semi-annual returns were provided for a group of anonymized stocks from 1996 to 2017, which were divided into 42 non-overlapping six months period. The second half of 2017 was used as an out-of-sample test of the model's performance. Metrics used were Spearman's Rank Correlation Coefficient and Normalized Discounted Cumulative Gain (NDCG) of the top 20% of a model's predicted rankings. The top six participants were invited to describe their approach. The solutions used were varied and were based on selecting a subset of data to train, combination of deep and shallow neural networks, different boosting algorithms, different models with different sets of features, linear support vector machine, combination of convoltional neural network (CNN) and Long short term memory (LSTM).
Exploring Model-based Planning with Policy Networks

Model-based reinforcement learning (MBRL) with model-predictive control or online planning has shown great potential for locomotion control tasks in terms of both sample efficiency and asymptotic performance. Despite their initial successes, the existing planning methods search from candidate sequences randomly generated in the action space, which is inefficient in complex high-dimensional environments. In this paper, we propose a novel MBRL algorithm, model-based policy planning (POPLIN), that combines policy networks with online planning. More specifically, we formulate action planning at each time-step as an optimization problem using neural networks. We experiment with both optimization w.r.t. the action sequences initialized from the policy network, and also online optimization directly w.r.t. the parameters of the policy network. We show that POPLIN obtains state-of-the-art performance in the MuJoCo benchmarking environments, being about 3x more sample efficient than the state-of-the-art algorithms, such as PETS, TD3 and SAC. To explain the effectiveness of our algorithm, we show that the optimization surface in parameter space is smoother than in action space. Further more, we found the distilled policy network can be effectively applied without the expansive model predictive control during test time for some environments such as Cheetah. Code is released in this https URL.
X-ray extinction from interstellar dust

We present a study on the prospects of observing carbon, sulfur, and other lower abundance elements (namely Al, Ca, Ti, and Ni) present in the interstellar medium using future X-ray instruments. We focus in particular on the detection and characterization of interstellar dust along the lines of sight.
Methods. We compared the simulated data with different sets of dust aggregates, either obtained from past literature or measured by us using the SOLEIL-LUCIA synchrotron beamline. Extinction by interstellar grains induces modulations of a given photolelectric edge, which can be in principle traced back to the chemistry of the absorbing grains. We simulated data of instruments with characteristics of resolution and sensitivity of the current Athena, XRISM, and Arcus concepts.
Results. In the relatively near future, the depletion and abundances of the elements under study will be determined with confidence. In the case of carbon and sulfur, the characterization of the chemistry of the absorbing dust will be also determined, depending on the dominant compound. For aluminum and calcium, despite the large depletion in the interstellar medium and the prominent dust absorption, in many cases the edge feature may not be changing significantly with the change of chemistry in the Al- or Ca-bearing compounds. The exinction signature of large grains may be detected and modeled, allowing a test on different grain size distributions for these elements. The low cosmic abundance of Ti and Ni will not allow us a detailed study of the edge features.
Modeling AGI Safety Frameworks with Causal Influence Diagrams

Proposals for safe AGI systems are typically made at the level of frameworks, specifying how the components of the proposed system should be trained and interact with each other. In this paper, we model and compare the most promising AGI safety frameworks using causal influence diagrams. The diagrams show the optimization objective and causal assumptions of the framework. The unified representation permits easy comparison of frameworks and their assumptions. We hope that the diagrams will serve as an accessible and visual introduction to the main AGI safety frameworks.
Performance Evaluation Methodology for Long-Term Visual Object Tracking

A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures outperform existing ones in interpretation potential and in better distinguishing between different tracking behaviors. We show that these measures generalize the short-term performance measures, thus linking the two tracking problems. Furthermore, the new measures are highly robust to temporal annotation sparsity and allow annotation of sequences hundreds of times longer than in the current datasets without increasing manual annotation labor. A new challenging dataset of carefully selected sequences with many target disappearances is proposed. A new tracking taxonomy is proposed to position trackers on the short-term/long-term spectrum. The benchmark contains an extensive evaluation of the largest number of long-term tackers and comparison to state-of-the-art short-term trackers. We analyze the influence of tracking architecture implementations to long-term performance and explore various re-detection strategies as well as influence of visual model update strategies to long-term tracking drift. The methodology is integrated in the VOT toolkit to automate experimental analysis and benchmarking and to facilitate future development of long-term trackers.
Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks

Neural networks traditionally produce a scalar value for an activated neuron. Capsules, on the other hand, produce a vector of values, which has been shown to correspond to a single, composite feature wherein the values of the components of the vectors indicate properties of the feature such as transformation or contrast. We present a new way of parameterizing and training capsules that we refer to as homogeneous vector capsules (HVCs). We demonstrate, experimentally, that altering a convolutional neural network (CNN) to use HVCs can achieve superior classification accuracy without increasing the number of parameters or operations in its architecture as compared to a CNN using a single final fully connected layer. Additionally, the introduction of HVCs enables the use of adaptive gradient descent, reducing the dependence a model’s achievable accuracy has on the finely tuned hyperparameters of a non-adaptive optimizer. We demonstrate our method and results using two neural network architectures. For the CNN architecture referred to as Inception v3, replacing the fully connected layers with HVCs increased the test accuracy by an average of 1.32% across all experiments conducted. For a simple monolithic CNN, we show HVCs improve test accuracy by an average of 19.16%.
New perspectives on the BOSS small-scale lensing discrepancy for the Planck ΛCDM cosmology

We investigate the abundance, small-scale clustering, and galaxy–galaxy lensing signal of galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS). To this end, we present new measurements of the redshift and stellar mass dependence of the lensing properties of the galaxy sample. We analyse to what extent models assuming the Planck18 cosmology fit to the number density and clustering can accurately predict the small-scale lensing signal. In qualitative agreement with previous BOSS studies at redshift z ∼ 0.5 and with results from the Sloan Digital Sky Survey, we find that the expected signal at small scales ($0.1 \lt r_{\rm p}\lt 3 \, h^{-1}\, {\rm {Mpc}}$) is higher by $\sim 25{{\ \rm per\ cent}}$ than what is measured. Here, we show that this result is persistent over the redshift range 0.1 < z < 0.7 and for galaxies of different stellar masses. If interpreted as evidence for cosmological parameters different from the Planck cosmic microwave background (CMB) findings, our results imply $S_8 = \sigma _8 \sqrt{\Omega _{\rm m}/ 0.3} = 0.744 \pm 0.015$, whereas S8 = 0.832 ± 0.013 for Planck18. However, in addition to being in tension with CMB results, such a change in cosmology alone does not accurately predict the lensing amplitude at larger scales. Instead, other often neglected systematics like baryonic feedback or assembly bias are likely contributing to the small-scale lensing discrepancy. We show that either effect alone, though, is unlikely to completely resolve the tension. Ultimately, a combination of the two effects in combination with a moderate change in cosmological parameters might be needed.
Dynamical Uniform Bounds for Fibers and a Gap Conjecture

We prove a uniform version of the Dynamical Mordell–Lang Conjecture for étale maps; also, we obtain a gap result for the growth rate of heights of points in an orbit along an arbitrary endomorphism of a quasiprojective variety defined over a number field. More precisely, for our 1st result, we assume $X$ is a quasi-projective variety defined over a field $K$ of characteristic $0$, endowed with the action of an étale endomorphism $\Phi $, and $f\colon X\longrightarrow Y$ is a morphism with $Y$ a quasi-projective variety defined over $K$. Then for any $x\in X(K)$, if for each $y\in Y(K)$, the set $S_{x,y}:=\{n\in{\mathbb{N}}\colon f(\Phi ^n(x))=y\}$ is finite, then there exists a positive integer $N_x$ such that $\sharp S_{x,y}\le N_x$ for each $y\in Y(K)$. For our 2nd result, we let $K$ be a number field, $f:X\dashrightarrow{\mathbb{P}}^1$ is a rational map, and $\Phi $ is an arbitrary endomorphism of $X$. If ${\mathcal{O}}_{\Phi }(x)$ denotes the forward orbit of $x$ under the action of $\Phi $, then either $f({\mathcal{O}}_{\Phi }(x))$ is finite, or $\limsup _{n\to \infty } h(f(\Phi ^n(x)))/\log (n)>0$, where $h(\cdot )$ represents the usual logarithmic Weil height for algebraic points.