nils-herrmann/clean_scirepeval huggingface dataset:
Hermione and the Secretary: how gendered task division in introductory physics labs can disrupt equitable learning

Physics labs provide a unique opportunity for students to grow their physics identity and science identity in general since they provide students with an opportunity to tinker with experiments and analyze data in a low-stakes environment. However, it is important to ensure that all students are benefiting from the labs equally and have a positive growth trajectory. Through interviews and reflexive ethnographic observations, we identify and analyze two common modes of work that may disadvantage female students in introductory physics labs. Students who adopt the Secretary archetype are relegated to recording and analyzing data, and thus may miss out on much of the opportunity to grow their physics and science identities by engaging fully in the experimental work. Meanwhile, students in the Hermione archetype shoulder a disproportionate amount of managerial work, and also may not get an adequate opportunity to engage with different aspects of the experimental work that is essential for helping them develop their physics and science identities. We use a physics identity framework to investigate how students under these modes of work may experience stunted growth in their physics and science identity trajectories in their physics lab course. This stunted growth can then perpetuate and reinforce societal stereotypes and biases about who does physics. Our categorization not only gives a vocabulary to discussions about equity in the physics lab, but may also serve as a useful touchstone for those who seek to center equity in efforts to transform physics instruction.
Computational Flight Control: A Domain-Knowledge-Aided Deep Reinforcement Learning Approach

This papers aims to examine the potential of using the emerging deep reinforcement learning techniques in flight control. Instead of learning from scratch, the autopilot structure is fixed as typical three-loop autopilot and deep reinforcement learning is utilised to learn the autopilot gains. This domain-knowledge-aided approach is proved to significantly improve the learning efficiency. To solve the flight control problem, we then formulate a Markovian decision process with a proper reward function that enable the application of reinforcement learning theory. The state-of-the-art deep deterministic policy gradient algorithm is utilised to learn an action policy that maps the observed states to the autopilot gains. Extensive empirical numerical simulations are performed to validate the proposed computational control algorithm.
Deep Active Lesion Segmentation

Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a fully automated segmentation framework that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities of Active Contour Models (ACMs). Our DALS framework benefits from an improved level-set ACM formulation with a per-pixel-parameterized energy functional and a novel multiscale encoder-decoder CNN that learns an initialization probability map along with parameter maps for the ACM. We evaluate our lesion segmentation model on a new Multiorgan Lesion Segmentation (MLS) dataset that contains images of various organs, including brain, liver, and lung, across different imaging modalities—MR and CT. Our results demonstrate favorable performance compared to competing methods, especially for small training datasets.
Encoder-Agnostic Adaptation for Conditional Language Generation

Large pretrained language models have changed the way researchers approach discriminative natural language understanding tasks, leading to the dominance of approaches that adapt a pretrained model for arbitrary downstream tasks. However it is an open-question how to use similar techniques for language generation. Early results in the encoder-agnostic setting have been mostly negative. In this work we explore methods for adapting a pretrained language model to arbitrary conditional input. We observe that pretrained transformer models are sensitive to large parameter changes during tuning. We therefore propose an adaptation that directly injects arbitrary conditioning into self attention, an approach we call pseudo self attention. Through experiments on four diverse conditional text generation tasks we show that this encoder-agnostic technique outperforms strong baselines, produces coherent generations, and is data efficient.
Consistent Community Detection in Continuous-Time Networks of Relational Events

In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data are in the form of relational events with timestamps, which form a continuous-time network. We propose the Community Hawkes Independent Pairs (CHIP) model for community detection on such timestamped relational event data. We demonstrate that applying spectral clustering to adjacency matrices constructed from relational events generated by the CHIP model provides consistent community detection for a growing number of nodes. In particular, we obtain explicit non-asymptotic upper bounds on the misclustering rates based on the separation conditions required on the parameters of the model for consistent community detection. We also develop consistent and computationally efficient estimators for the parameters of the model. We demonstrate that our proposed CHIP model and estimation procedure scales to large networks with tens of thousands of nodes and provides superior fits compared to existing continuous-time network models on several real networks.
Music Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions

Most work on models for music transcription has focused on describing local sequential dependence of notes in musical scores and failed to capture their global repetitive structure, which can be a useful guide for transcribing music. Focusing on the rhythm, we formulate several classes of Bayesian Markov models of musical scores that describe repetitions indirectly by sparse transition probabilities of notes or note patterns. This enables us to construct piece-specific models for unseen scores with unfixed repetitive structure and to derive tractable inference algorithms. Moreover, to describe approximate repetitions, we explicitly incorporate a process of modifying the repeated notes/note patterns. We apply these models as a prior music language model for rhythm transcription, where piece-specific score models are inferred from performed MIDI data by unsupervised learning, in contrast to the conventional supervised construction of score models. Evaluations using vocal melodies of popular music showed that the Bayesian models improved the transcription accuracy for most of the tested model types, indicating the universal efficacy of the proposed approach.
ChainNet: Learning on Blockchain Graphs with Topological Features

With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether dynamics of the transaction graph impacts price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, topological features computed from the blockchain graph using the tools of persistent homology, are found to exhibit higher utility for predicting Bitcoin price dynamics.
A survey on intrinsic motivation in reinforcement learning

The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a number of scientific and technical challenges still need to be addressed, amongst which we can mention the ability to abstract actions or the difficulty to explore the environment which can be addressed by intrinsic motivation (IM). In this article, we provide a survey on the role of intrinsic motivation in DRL. We categorize the different kinds of intrinsic motivations and detail for each category, its advantages and limitations with respect to the mentioned challenges. Additionnally, we conduct an in-depth investigation of substantial current research questions, that are currently under study or not addressed at all in the considered research area of DRL. We choose to survey these research works, from the perspective of learning how to achieve tasks. We suggest then, that solving current challenges could lead to a larger developmental architecture which may tackle most of the tasks. We describe this developmental architecture on the basis of several building blocks composed of a RL algorithm and an IM module compressing information.
Probing the Survival of Planetary Systems in Globular Clusters with Tidal Disruption Events

Among the growing list of confirmed exoplanets, the number of planets identified in dense star clusters remains sparse. Previous analyses have suggested that this may be due in part to dynamical interactions that can unbind planets from their host stars, limiting the survival of planetary systems in clusters. Thus, alternative detection strategies may be necessary to study planets in clusters that may no longer be bound to a host. Here, we use N-body models to explore the evolution of planetary systems in dense star clusters. Depending on various initial conditions, we show that 10%–50% of primordial planetary systems are broken through dynamical encounters over a cluster’s full lifetime, populating clusters with “free-floating” planets. Furthermore, a large number (30%–80%) of planets are ejected from their host cluster through strong dynamical encounters and/or tidal loss. Additionally, we show that planets naturally mix with stellar-mass black holes (BHs) in the cores of their host cluster. As a consequence, up to a few hundred planets will be tidally disrupted through close passages of BHs. We show that these BH–planet tidal disruption events (TDEs) occur in clusters at a rate of up to 10−5 yr−1 in a Milky-Way-type galaxy. In principle, these BH–planet TDEs may be detected by upcoming transient surveys such as the Large Synoptic Survey Telescope at a rate of a few events per year, although identification of these events may prove challenging. The observed rate of BH–planet TDEs could place new constraints upon the formation and survival of planetary systems and BHs in dense star clusters.
Jet charge modification in finite QCD matter

Jet production and jet substructure modification in heavy-ion collisions have played an essential role in revealing the in-medium evolution of parton showers and the determination of the properties of strongly-interacting matter under extreme conditions. It is imperative to extend these studies to include flavor tagging and to devise observables that are sensitive to the partonic origin of jets. The average jet charge, defined as the momentum-weighted sum of the electric charges of particles inside the jet, is a proxy of the electric charge of the quark or gluon that initiates the jet. We demonstrate how the factorization framework of soft-collinear effective theory can be generalized to evaluate the jet charge in a dense strongly-interacting matter environment, such as the one produced in nuclear reactions at collider energies. Observables that can separate the contribution of in-medium branching from the trivial isospin effects are identified and their connection to established jet quenching effects is elucidated. We present predictions for the transverse momentum dependence of the jet charge distribution in nucleus-nucleus collisions and its modification relative to the proton case.
Directly Deflecting Particle Dark Matter.

We propose a new strategy to directly detect light particle dark matter that has long-ranged interactions with ordinary matter. The approach involves distorting the local flow of dark matter with time-varying fields and measuring these distortions with shielded resonant detectors. We apply this idea to sub-MeV dark matter particles with very small electric charges or coupled to a light vector mediator, including the freeze-in parameter space targeted by low mass direct detection efforts. This approach can probe dark matter masses ranging from 10 MeV to below a meV, extending beyond the capabilities of existing and proposed direct detection experiments.
Reviving millicharged dark matter for 21-cm cosmology

The existence of millicharged dark matter (mDM) can leave a measurable imprint on 21-cm cosmology through mDM-baryon scattering. However, the minimal scenario is severely constrained by existing cosmological bounds on both the fraction of dark matter that can be millicharged and the mass of mDM particles. We point out that introducing a long-range force between a millicharged subcomponent of dark matter and the dominant cold dark matter (CDM) component leads to efficient cooling of baryons in the early universe, while also significantly extending the range of viable mDM masses. Such a scenario can explain the anomalous absorption signal in the sky-averaged 21-cm spectrum observed by EDGES, and leads to a number of testable predictions for the properties of the dark sector. The mDM mass can then lie between 10 MeV and a few hundreds of GeVs, and its scattering cross section with baryons lies within an unconstrained window of parameter space above direct detection limits and below current bounds from colliders. In this allowed region, mDM can make up as little as $10^{-8}$ of the total dark matter energy density. The CDM mass ranges from 10 MeV to a few GeVs, and has an interaction cross section with the Standard Model that is induced by a loop of mDM particles. This cross section is generically within reach of near-future low-threshold direct detection experiments.
Joint Embedding of 3D Scan and CAD Objects

3D scan geometry and CAD models often contain complementary information towards understanding environments, which could be leveraged through establishing a mapping between the two domains. However, this is a challenging task due to strong, lower-level differences between scan and CAD geometry. We propose a novel approach to learn a joint embedding space between scan and CAD geometry, where semantically similar objects from both domains lie close together. To achieve this, we introduce a new 3D CNN-based approach to learn a joint embedding space representing object similarities across these domains. To learn a shared space where scan objects and CAD models can interlace, we propose a stacked hourglass approach to separate foreground and background from a scan object, and transform it to a complete, CAD-like representation to produce a shared embedding space. This embedding space can then be used for CAD model retrieval; to further enable this task, we introduce a new dataset of ranked scan-CAD similarity annotations, enabling new, fine-grained evaluation of CAD model retrieval to cluttered, noisy, partial scans. Our learned joint embedding outperforms current state of the art for CAD model retrieval by 12% in instance retrieval accuracy.
Neutrino Mass Ordering Obscured by Nonstandard Interactions.

One of the major open questions in particle physics is the issue of the neutrino mass ordering (NMO). The current data of the two long-baseline experiments NOνA and T2K, interpreted in the standard three-flavor scenario, provide a ∼2.4σ indication in favor of the normal neutrino mass ordering. We show that such an indication is completely washed out if one assumes the existence of neutral-current nonstandard interactions (NSI) of the flavor changing type involving the e-τ flavors. This implies that the claim for a discovery of the NMO will require a careful consideration of the impact of hypothetical NSI.
Speed of quantum information spreading in chaotic systems

We present a general theory of quantum information propagation in chaotic quantum many-body systems. The generic expectation in such systems is that quantum information does not propagate in localized form; instead, it tends to spread out and scramble into a form that is inaccessible to local measurements. To characterize this spreading, we define an information speed via a quench-type experiment and derive a general formula for it as a function of the entanglement density of the initial state. As the entanglement density varies from zero to one, the information speed varies from the entanglement speed to the butterfly speed. We verify that the formula holds both for a quantum chaotic spin chain and in field theories with an AdS/CFT gravity dual. For the second case, we study in detail the dynamics of entanglement in two-sided Vaidya-AdS-Reissner-Nordstrom black branes. We also show that, with an appropriate decoding process, quantum information can be construed as moving at the information speed, and, in the case of AdS/CFT, we show that a locally detectable signal propagates at the information speed in a spatially local variant of the traversable wormhole setup.
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries

We study adversarial examples in a black-box setting where the adversary only has API access to the target model and each query is expensive. Prior work on black-box adversarial examples follows one of two main strategies: (1) transfer attacks use white-box attacks on local models to find candidate adversarial examples that transfer to the target model, and (2) optimization-based attacks use queries to the target model and apply optimization techniques to search for adversarial examples. We propose hybrid attacks that combine both strategies, using candidate adversarial examples from local models as starting points for optimization-based attacks and using labels learned in optimization-based attacks to tune local models for finding transfer candidates. We empirically demonstrate on the MNIST, CIFAR10, and ImageNet datasets that our hybrid attack strategy reduces cost and improves success rates. We also introduce a seed prioritization strategy which enables attackers to focus their resources on the most promising seeds. Combining hybrid attacks with our seed prioritization strategy enables batch attacks that can reliably find adversarial examples with only a handful of queries.
New loop quantum cosmology modifications from gauge-covariant fluxes

Loop quantum cosmology is a symmetry reduced quantization of cosmological spacetimes based on loop quantum gravity. While it has been successful in resolution of various cosmological singularities and connecting Planck scale physics to phenomenology, its connection with loop quantum gravity has remained elusive. It is therefore important to integrate more and more features of the full theory into this framework and understand the reliability of physical predictions. In particular, if one wishes to connect the effective Hamiltonian in loop quantum cosmology to an expectation value of the scalar constraint operator in suitable coherent states for the full theory, one has to go beyond the standard setting of loop quantum cosmology. One possibility is to introduce gauge-covariant fluxes, which become necessary because the presence of a finite regularization parameter causes functions build out of the standard discretized variables to be in general not gauge invariant. Following the construction of gauge-covariant fluxes pioneered by Thiemann in , we show that the physics of loop quantum cosmology is affected in a non-trivial way. The bounce turns out to be generically asymmetric with a rescaling of the Newton's constant in the pre-bounce branch. Gauge-covariant fluxes result in a higher order quantum difference equation in comparison to loop quantum cosmology. Even the behavior of matter, which behaves innocuously in loop quantum cosmology, is enriched, resulting in an effective non-minimal coupling. These effects are shown to be common to different choices of regularization parameters.
On Regularization Properties of Artificial Datasets for Deep Learning

The paper discusses regularization properties of artificial data for deep learning. Artificial datasets allow to train neural networks in the case of a real data shortage. It is demonstrated that the artificial data generation process, described as injecting noise to high-level features, bears several similarities to existing regularization methods for deep neural networks. One can treat this property of artificial data as a kind of “deep” regularization. It is thus possible to regularize hidden layers of the network by generating the training data in a certain way.
Towards Reducing Biases in Combining Multiple Experts Online

In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person’s fitness for a particular opportunity. In this paper, we aim to accomplish approximate group fairness in an online stochastic decision-making process, where the fairness metric we consider is equalized odds. Our work follows from the classical learning-fromexperts scheme, assuming a finite set of classifiers (human experts, rules, options, etc) that cannot be modified. We run separate instances of the algorithm for each label class as well as sensitive groups, where the probability of choosing each instance is optimized for both fairness and regret. Our theoretical results show that approximately equalized odds can be achieved without sacrificing much regret. We also demonstrate the performance of the algorithm on real data sets commonly used by the fairness community.
The natural selection of words: Finding the features of fitness

We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of 4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to 2000 AD. We present a supervised learning algorithm that is able to predict the future leader of a synset: the word in the synset that will have the highest frequency. The algorithm uses features based on a word’s length, the characters in the word, and the historical frequencies of the word. It can predict change of leadership (including the identity of the new leader) fifty years in the future, with an F-score considerably above random guessing. Analysis of the learned models provides insight into the causes of change in the leader of a synset. The algorithm confirms observations linguists have made, such as the trend to replace the -ise suffix with -ize, the rivalry between the -ity and -ness suffixes, and the struggle between economy (shorter words are easier to remember and to write) and clarity (longer words are more distinctive and less likely to be confused with one another). The results indicate that integration of the Google Books Ngram Corpus with WordNet has significant potential for improving our understanding of how language evolves.