nils-herrmann/clean_scirepeval huggingface dataset:
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction

Deep reinforcement learning has made significant progress in the field of continuous control, such as physical control and autonomous driving. However, it is challenging for a reinforcement model to learn a policy for each task sequentially due to catastrophic forgetting. Specifically, the model would forget knowledge it learned in the past when trained on a new task. We consider this challenge from two perspectives: i) acquiring task-specific skills is difficult since task information and rewards are not highly related; ii) learning knowledge from previous experience is difficult in continuous control domains. In this paper, we introduce an end-to-end framework namely Continual Diversity Adversarial Network (CDAN). We first develop an unsupervised diversity exploration method to learn task-specific skills using an unsupervised objective. Then, we propose an adversarial self-correction mechanism to learn knowledge by exploiting past experience. The two learning procedures are presumably reciprocal. To evaluate the proposed method, we propose a new continuous reinforcement learning environment named Continual Ant Maze (CAM) and a new metric termed Normalized Shorten Distance (NSD). The experimental results confirm the effectiveness of diversity exploration and self-correction. It is worthwhile noting that our final result outperforms baseline by 18.35% in terms of NSD, and 0.61 according to the average reward.
Mitigating bias in algorithmic hiring: evaluating claims and practices

There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their practices, focusing particularly on efforts to detect and mitigate bias. Our analysis considers both technical and legal perspectives. Technically, we consider the various choices vendors make regarding data collection and prediction targets, and explore the risks and trade-offs that these choices pose. We also discuss how algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law.
Disentangled Skill Embeddings for Reinforcement Learning

We propose a novel framework for multi-task reinforcement learning (MTRL). Using a variational inference formulation, we learn policies that generalize across both changing dynamics and goals. The resulting policies are parametrized by shared parameters that allow for transfer between different dynamics and goal conditions, and by task-specific latent-space embeddings that allow for specialization to particular tasks. We show how the latent-spaces enable generalization to unseen dynamics and goals conditions. Additionally, policies equipped with such embeddings serve as a space of skills (or options) for hierarchical reinforcement learning. Since we can change task dynamics and goals independently, we name our framework Disentangled Skill Embeddings (DSE).
A sheaf-theoretic approach to tropical homology

We introduce a sheaf-theoretic approach to tropical homology, especially for tropical homology with potentially non-compact supports. Our setup is suited to study the functorial properties of tropical homology, and we show that it behaves analogously to classical Borel-Moore homology in the sense that there are proper push-forwards, cross products, and cup products with tropical cohomology classes, and that it satisfies identities like the projection formula and the Kunneth theorem. Our framework allows for a natural definition of the tropical cycle class map, which we show to be a natural transformation. Finally, we prove Poincare-Verdier duality over the integers on tropical manifolds.
Quantum chromodynamics axion in a hot and magnetized medium

It is analyzed the effects of a hot and magnetized medium on the axion mass, self-coupling and topological susceptibility when in presence of an anisotropic external magnetic field along the $z$-direction, within the Nambu--Jona-Lasinio effective model for quantum chromodynamics. The effects of both Magnetic Catalysis and Inverse Magnetic Catalysis are explicitly taken into account through appropriate matching of parameters with those from lattice Monte-Carlo numerical simulations. It is also analyzed the dependence of the results with respect to different model parametrizations in the context of the Nambu--Jona-Lasinio model.
Visualizing Representational Dynamics with Multidimensional Scaling Alignment

Representational similarity analysis (RSA) has been shown to be an effective framework to characterize brain-activity profiles and deep neural network activations as representational geometry by computing the pairwise distances of the response patterns as a representational dissimilarity matrix (RDM). However, how to properly analyze and visualize the representational geometry as dynamics over the time course from stimulus onset to offset is not well understood. In this work, we formulated the pipeline to understand representational dynamics with RDM movies and Procrustes-aligned Multidimensional Scaling (pMDS), and applied it to neural recording of monkey IT cortex. Our results suggest that the the multidimensional scaling alignment can genuinely capture the dynamics of the category-specific representation spaces with multiple visualization possibilities, and that object categorization may be hierarchical, multi-staged, and oscillatory (or recurrent).
A Super-Earth and Sub-Neptune Transiting the Late-type M Dwarf LP 791-18

Planets occur most frequently around cool dwarfs, but only a handful of specific examples are known to orbit the latest-type M stars. Using TESS photometry, we report the discovery of two planets transiting the low-mass star called LP 791-18 (identified by TESS as TOI 736). This star has spectral type M6V, effective temperature 2960 K, and radius 0.17 R⊙, making it the third-coolest star known to host planets. The two planets straddle the radius gap seen for smaller exoplanets; they include a 1.1R⊕ planet on a 0.95 day orbit and a 2.3R⊕ planet on a 5 day orbit. Because the host star is small the decrease in light during these planets’ transits is fairly large (0.4% and 1.7%). This has allowed us to detect both planets’ transits from ground-based photometry, refining their radii and orbital ephemerides. In the future, radial velocity observations and transmission spectroscopy can both probe these planets’ bulk interior and atmospheric compositions, and additional photometric monitoring would be sensitive to even smaller transiting planets.
Thermal Relic Targets with Exponentially Small Couplings.

If dark matter was produced in the early Universe by the decoupling of its annihilations into known particles, there is a sharp experimental target for the size of its coupling. We show that if dark matter was produced by inelastic scattering against a lighter particle from the thermal bath, then its coupling can be exponentially smaller than the coupling required for its production from annihilations. As an application, we demonstrate that dark matter produced by inelastic scattering against electrons provides new thermal relic targets for direct detection and fixed target experiments.
Searching for muonic forces with the ATLAS detector

The LHC copiously produces muons via different processes, and the muon sample will be large at the high-luminosity LHC (HL-LHC). In this work we propose to leverage this large muon sample and utilize the HL-LHC as a muon fixed-target experiment, with the ATLAS calorimeter as the target. We consider a novel analysis for the ATLAS detector, which takes advantage of the two independent muon momentum measurements by the inner detector and the muon system. We show that a comparison of the two measurements, before and after the calorimeters, can probe new force carriers that are coupled to muons and escape detection. The proposed analysis, based on muon samples from $W$ and $Z$ decays only, has a comparable reach to other proposals. In particular, it can explore the part of parameter-space that could explain the muon $g-2$ anomaly.
The dust-to-gas and dust-to-metal ratio in galaxies from z = 0 to 6

We present predictions for the evolution of the galaxy dust-to-gas ratio (DGR) and dust-to-metal ratio (DTM) from z = 0 → 6, using a model for the production, growth, and destruction of dust grains implemented into the simba cosmological hydrodynamic galaxy formation simulation. In our model, dust forms in stellar ejecta, grows by the accretion of metals, and is destroyed by thermal sputtering and supernovae. Our simulation reproduces the observed dust mass function at z = 0, but modestly underpredicts the mass function by ∼×3 at z ∼ 1–2. The z = 0 DGR versus metallicity relationship shows a tight positive correlation for star-forming galaxies, while it is uncorrelated for quenched systems. There is little evolution in the DGR–metallicity relationship between z = 0 and 6. We use machine learning techniques to search for the galaxy physical properties that best correlate with the DGR and DTM. We find that the DGR is primarily correlated with the gas-phase metallicity, though correlations with the depletion time-scale, stellar mass, and gas fraction are non-negligible. We provide a crude fitting relationship for DGR and DTM versus the gas-phase metallicity, along with a public code package that estimates the DGR and DTM given a set of galaxy physical properties.
Perspectives of photon physics at future colliders

We review current results on physics with photons at the LHC and discuss realistic perspectives of photon physics at future colliders. In particular, we focus on Standard Model (SM) measurements with photons at the upcoming high-luminosity and a possible high-energy LHC as well as jet measurements at an Electron-Ion Collider (EIC) to be constructed either at BNL or at JLAB and their potential to constrain nuclear parton densities. We also discuss future searches for physics beyond the SM with photons in the high-luminosity phase of the LHC.
SurfCon: Synonym Discovery on Privacy-Aware Clinical Data

Unstructured clinical texts contain rich health-related information. To better utilize the knowledge buried in clinical texts, discovering synonyms for a medical query term has become an important task. Recent automatic synonym discovery methods leveraging raw text information have been developed. However, to preserve patient privacy and security, it is usually quite difficult to get access to large-scale raw clinical texts. In this paper, we study a new setting named synonym discovery on privacy-aware clinical data (i.e., medical terms extracted from the clinical texts and their aggregated co-occurrence counts, without raw clinical texts). To solve the problem, we propose a new framework SurfCon that leverages two important types of information in the privacy-aware clinical data, i.e., the surface form information, and the global context information for synonym discovery. In particular, the surface form module enables us to detect synonyms that look similar while the global context module plays a complementary role to discover synonyms that are semantically similar but in different surface forms, and both allow us to deal with the OOV query issue (i.e., when the query is not found in the given data). We conduct extensive experiments and case studies on publicly available privacy-aware clinical data, and show that SurfCon can outperform strong baseline methods by large margins under various settings.
Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations

Recent years have seen fast development in synthesizing realistic human faces using AI technologies. Such fake faces can be weaponized to cause negative personal and social impact. In this work, we develop technologies to defend individuals from becoming victims of recent AI synthesized fake videos by sabotaging would-be training data. This is achieved by disrupting deep neural network (DNN) based face detection method with specially designed imperceptible adversarial perturbations to reduce the quality of the detected faces. We describe attacking schemes under white-box, gray-box and black-box settings, each with decreasing information about the DNN based face detectors. We empirically show the effectiveness of our methods in disrupting state-of-the-art DNN based face detectors on several datasets.
Generating Counterfactual and Contrastive Explanations using SHAP

With the advent of GDPR, the domain of explainable AI and model interpretability has gained added impetus. Methods to extract and communicate visibility into decision-making models have become legal requirement. Two specific types of explanations, contrastive and counterfactual have been identified as suitable for human understanding. In this paper, we propose a model agnostic method and its systemic implementation to generate these explanations using shapely additive explanations (SHAP). We discuss a generative pipeline to create contrastive explanations and use it to further to generate counterfactual datapoints. This pipeline is tested and discussed on the IRIS, Wine Quality & Mobile Features dataset. Analysis of the results obtained follows.
Adversarial Examples to Fool Iris Recognition Systems

Adversarial examples have recently proven to be able to fool deep learning methods by adding carefully crafted small perturbation to the input space image. In this paper, we study the possibility of generating adversarial examples for code-based iris recognition systems. Since generating adversarial examples requires back-propagation of the adversarial loss, conventional filter bank-based iris-code generation frameworks cannot be employed in such a setup. Therefore, to compensate for this shortcoming, we propose to train a deep auto-encoder surrogate network to mimic the conventional iris code generation procedure. This trained surrogate network is then deployed to generate the adversarial examples using the iterative gradient sign method algorithm . We consider non-targeted and targeted attacks through three attack scenarios. Considering these attacks, we study the possibility of fooling an iris recognition system in white-box and black-box frameworks.
A Cyclically-Trained Adversarial Network for Invariant Representation Learning

Recent studies show that deep neural networks are vulnerable to adversarial examples which can be generated via certain types of transformations. Being robust to a desired family of adversarial attacks is then equivalent to being invariant to a family of transformations. Learning invariant representations then naturally emerges as an important goal to achieve which we explore in this paper within specific application contexts. Specifically, we propose a cyclically-trained adversarial network to learn a mapping from image space to latent representation space and back such that the latent representation is invariant to a specified factor of variation (e.g., identity). The learned mapping assures that the synthesized image is not only realistic, but has the same values for unspecified factors (e.g., pose and illumination) as the original image and a desired value of the specified factor. Unlike disentangled representation learning, which requires two latent spaces, one for specified and another for unspecified factors, invariant representation learning needs only one such space. We encourage invariance to a specified factor by applying adversarial training using a variational autoencoder in the image space as opposed to the latent space. We strengthen this invariance by introducing a cyclic training process (forward and backward cycle). We also propose a new method to evaluate conditional generative networks. It compares how well different factors of variation can be predicted from the synthesized, as opposed to real, images. In quantitative terms, our approach attains state-of-the-art performance in experiments spanning three datasets with factors such as identity, pose, illumination or style. Our method produces sharp, high-quality synthetic images with little visible arte-facts compared to previous approaches.
Reinforcement Learning with Convex Constraints

In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints. For instance, the designer may want to limit the use of unsafe actions, increase the diversity of trajectories to enable exploration, or approximate expert trajectories when rewards are sparse. In this paper, we propose an algorithmic scheme that can handle a wide class of constraints in RL tasks: specifically, any constraints that require expected values of some vector measurements (such as the use of an action) to lie in a convex set. This captures previously studied constraints (such as safety and proximity to an expert), but also enables new classes of constraints (such as diversity). Our approach comes with rigorous theoretical guarantees and only relies on the ability to approximately solve standard RL tasks. As a result, it can be easily adapted to work with any model-free or model-based RL. In our experiments, we show that it matches previous algorithms that enforce safety via constraints, but can also enforce new properties that these algorithms do not incorporate, such as diversity.
SeGMA: Semi-Supervised Gaussian Mixture Autoencoder

We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and is implemented in a typical Wasserstein autoencoder framework. We choose a mixture of Gaussians as a target distribution in latent space, which provides a natural splitting of data into clusters. To connect Gaussian components with correct classes, we use a small amount of labeled data and a Gaussian classifier induced by the target distribution. SeGMA is optimized efficiently due to the use of the Cramer–Wold distance as a maximum mean discrepancy penalty, which yields a closed-form expression for a mixture of spherical Gaussian components and, thus, obviates the need of sampling. While SeGMA preserves all properties of its semi-supervised predecessors and achieves at least as good generative performance on standard benchmark data sets, it presents additional features: 1) interpolation between any pair of points in the latent space produces realistically looking samples; 2) combining the interpolation property with disentangling of class and style information, SeGMA is able to perform continuous style transfer from one class to another; and 3) it is possible to change the intensity of class characteristics in a data point by moving the latent representation of the data point away from specific Gaussian components.
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-Rays

Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals. There has been a surge of work on automatic interpretation of chest X-rays using deep learning approaches after the availability of large open source chest X-ray dataset from NIH. However, the labels are not sufficiently rich and descriptive for training classification tools. Further, it does not adequately address the findings seen in Chest X-rays taken in anterior-posterior (AP) view which also depict the placement of devices such as central vascular lines and tubes. In this paper, we present a new chest X-ray benchmark database of 73 rich sentence-level descriptors of findings seen in AP chest X-rays. We describe our method of obtaining these findings through a semi-automated ground truth generation process from crowdsourcing of clinician annotations. We also present results of building classifiers for these findings that show that such higher granularity labels can also be learned through the framework of deep learning classifiers.
The fate of dense scalar stars

Long-lived pseudo-solitonic objects, known as oscillons/oscillatons, which we collectively call real scalar stars, are ubiquitous in early Universe cosmology of scalar field theories. Typical examples are axions stars and moduli stars. Using numerical simulations in full general relativity to include the effects of gravity, we study the fate of real scalar stars and find that depending on the scalar potential they are either meta-stable or collapse to black holes. In particular we find that for KKLT potentials the configurations are meta-stable despite the asymmetry of the potential, consistently with the results from lattice simulations that do not include gravitational effects. For α-attractor potentials collapse to black holes is possible in a region of the parameter space where scalar stars would instead seem to be meta-stable or even disperse without including gravity. Each case gives rise to different cosmological implications which may affect the stochastic spectrum of gravitational waves.
Uncertainty in the Predictive Capability of Detectors that Process Waveforms from Explosions.

Explosions near ground generate multiple geophysical waveforms in the radiation-dominated range of their signature fields. Multi-phenomological explosion monitoring (MultiPEM) at these ranges requires the predictive capability to forecast trigger rates of digital detectors that process such waveform data, and thereby accurately anticipate the probability that hypothetical explosions can be identified in operations. To confront this challenge, we derive and compare the predicted and observed performance of three digital detectors that process radio, acoustic and seismic waveform data that record a small, aboveground explosion. We measure this comparison with the peak range in magnitude (magnitude discrepancy) over which different performance curves report the same probability of detection, within an interval of moderate detection probability, and thereby quantify solutions to three topical monitoring questions. In particular, our solutions (1) demonstrate how empirically parameterized detectors that operate in a variable noisy environments provide fair-to-very good forecasting capability to detect small explosions, (2) show that the observed performance of a particular waveform detector can better forecast performance curves constructed from different observations, when compared to theoretical performance curves, and (3) provide an upper bound on detection uncertainty, in terms of a physical source attribute (magnitude)
Discovery of the first Ca-bearing molecule in space: CaNC.

We report on the detection of calcium isocyanide, CaNC, in the carbon-rich evolved star IRC+10216. We derived a column density for this species of (2±0.5)×1011 cm-2. Based on the observed line profiles and the modelling of its emission through the envelope, the molecule has to be produced in the intermediate and outer layers of the circumstellar envelope where other metal-isocyanides have previously been found in this source. The abundance ratio of CaNC relative to MgNC and FeCN is ≃1/60 and ≃1, respectively. We searched for the species CaF, CaCl, CaC, CaCCH, and CaCH3 for which accurate frequency predictions are available. Only upper limits have been obtained for these molecules.