nils-herrmann/clean_scirepeval huggingface dataset:
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics

A fundamental question in neuroscience is how the brain creates an internal model of the world to guide actions using sequences of ambiguous sensory information. This is naturally formulated as a reinforcement learning problem under partial observations, where an agent must estimate relevant latent variables in the world from its evidence, anticipate possible future states, and choose actions that optimize total expected reward. This problem can be solved by control theory, which allows us to find the optimal actions for a given system dynamics and objective function. However, animals often appear to behave suboptimally. Why? We hypothesize that animals have their own flawed internal model of the world, and choose actions with the highest expected subjective reward according to that flawed model. We describe this behavior as rational but not optimal. The problem of Inverse Rational Control (IRC) aims to identify which internal model would best explain an agent's actions. Our contribution here generalizes past work on Inverse Rational Control which solved this problem for discrete control in partially observable Markov decision processes. Here we accommodate continuous nonlinear dynamics and continuous actions, and impute sensory observations corrupted by unknown noise that is private to the animal. We first build an optimal Bayesian agent that learns an optimal policy generalized over the entire model space of dynamics and subjective rewards using deep reinforcement learning. Crucially, this allows us to compute a likelihood over models for experimentally observable action trajectories acquired from a suboptimal agent. We then find the model parameters that maximize the likelihood using gradient ascent. Our method successfully recovers the true model of rational agents. This approach provides a foundation for interpreting the behavioral and neural dynamics of animal brains during complex tasks.
Semi-supervised Learning using Differentiable Reasoning

We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data. We apply it to the Semantic Image Interpretation (SII) task and show that background knowledge provides significant improvement. We find that there is a strong but interesting imbalance between the contributions of updates from Modus Ponens (MP) and its logical equivalent Modus Tollens (MT) to the learning process, suggesting that our approach is very sensitive to a phenomenon called the Raven Paradox. We propose a solution to overcome this situation.
Improving Generalization in Coreference Resolution via Adversarial Training

In order for coreference resolution systems to be useful in practice, they must be able to generalize to new text. In this work, we demonstrate that the performance of the state-of-the-art system decreases when the names of PER and GPE named entities in the CoNLL dataset are changed to names that do not occur in the training set. We use the technique of adversarial gradient-based training to retrain the state-of-the-art system and demonstrate that the retrained system achieves higher performance on the CoNLL dataset (both with and without the change of named entities) and the GAP dataset.
Online Continual Learning with Maximally Interfered Retrieval

Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for modern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay. We retrieve the samples which are most interfered, i.e. whose prediction will be most negatively impacted by the foreseen parameters update. We show a formulation for this sampling criterion in both the generative replay and the experience replay setting, producing consistent gains in performance and greatly reduced forgetting. We release an implementation of our method at this https URL.
The role of cue enhancement and frequency fine-tuning in hearing impaired phone recognition

A speech-based hearing test is designed to identify the susceptible error-prone phones for individual hearing impaired (HI) ear. Only robust tokens in the experiments' noise levels had been chosen for the test. The noise-robustness of tokens is measured as SNR90s of the token, which is the signal to the speech-weighted noise ratio where an NH listener would recognize the token with an accuracy of 90% on average. Two sets of tokens T1 and T2 having the same consonant-vowels but different talkers with distinct SNR90s had been presented at flat gain at listeners' most comfortable level. We studied the effects of frequency fine-tuning of the primary cue by presenting tokens of the same consonant but different vowels with similar SNR90s. Additionally, we investigated the role of changing the intensity of primary cue in HI phone recognition, by presenting tokens from both sets T1 and T2. On average, 85% of tokens are improved when we replaced the CV with the same CV but with a more robust talker. Additionally, using CVs with similar SNR90, on average, tokens are improved by 28%, 28%, 25%, and 19%, when we replaced the vowel with ɑ, ɛ, ə, and ɪ. The confusion pattern in each case provides insight into how these changes affect the phone recognition in each HI ear. We propose to prescribe hearing aid amplification tailored to individual HI ears, based on the confusion pattern, the response from cue enhancement, and the response from frequency fine-tuning of the cue. A speech-based hearing test is designed to identify the susceptible error-prone phones for individual hearing impaired (HI) ear. Only robust tokens in the experiments' noise levels had been chosen for the test. The noise-robustness of tokens is measured as SNR90s of the token, which is the signal to the speech-weighted noise ratio where an NH listener would recognize the token with an accuracy of 90% on average. Two sets of tokens T1 and T2 having the same consonant-vowels but different talkers with distinct SNR90s had been presented at flat gain at listeners' most comfortable level. We studied the effects of frequency fine-tuning of the primary cue by presenting tokens of the same consonant but different vowels with similar SNR90s. Additionally, we investigated the role of changing the intensity of primary cue in HI phone recognition, by presenting tokens from both sets T1 and T2. On average, 85% of tokens are improved when we replaced the CV with the same CV but with a more robust talker. Additionally...
Identification of Relevant Diffusion MRI Metrics Impacting Cognitive Functions Using a Novel Feature Selection Method

Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which diffusion measures are the most informative with regard to cognitive functions in both the healthy state as well as after injury. In this study, we use diffusion MRI to formulate a predictive model for performance on working memory based on the most relevant MRI features. As exhaustive search is impractical, the key challenge is to identify relevant features over a large feature space with high accuracy within reasonable time-frame. To tackle this challenge, we propose a novel improvement of the best first search approach with crossover operators inspired by genetic algorithm. Compared against other heuristic feature selection algorithms, the proposed method achieves significantly more accurate predictions and yields clinically interpretable selected features (improvement of r2 in 8 of 9 cohorts and up to 0.08).
Fine-grained Information Status Classification Using Discourse Context-Aware Self-Attention

Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily depend on many hand-crafted linguistic features. In this paper, we propose a discourse context-aware self-attention neural network model for fine-grained IS classification. On the ISNotes corpus (Markert et al., 2012), our model with the contextually-encoded word representations (BERT) (Devlin et al., 2018) achieves new state-of-the-art performances on fine-grained IS classification, obtaining a 4.1% absolute overall accuracy improvement compared to Hou et al. (2013a). More importantly, we also show an improvement of 3.9% F1 for bridging anaphora recognition without using any complex hand-crafted semantic features designed for capturing the bridging phenomenon.
Gait recognition via deep learning of the center-of-pressure trajectory

The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure (COP) trajectory is sufficiently unique to identify a person with high certainty. Thirty-six adults walked for 30 min on a treadmill equipped with a force platform that continuously recorded the positions of the COP. The raw two-dimensional signals were sliced into segments of two gait cycles. A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2250 segments with an overall accuracy of 99.9%. A second set of 4500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used to fine tune the pretrained CNNs. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures while walking and that CNNs can learn the distinctive features of these trajectories. By applying a pretrained CNN (transfer learning), a couple of strides seem enough to learn and identify new gaits. However, these promising results should be confirmed in a larger sample under realistic conditions.
Multi-View Fuzzy Clustering with The Alternative Learning between Shared Hidden Space and Partition

As the multi-view data grows in the real world, multi-view clus-tering has become a prominent technique in data mining, pattern recognition, and machine learning. How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge. Aiming at this, a hidden space sharing multi-view fuzzy clustering (HSS-MVFC) method is proposed in the present study. This method is based on the classical fuzzy c-means clustering model, and obtains associ-ated information between different views by introducing shared hidden space. Especially, the shared hidden space and the fuzzy partition can be learned alternatively and contribute to each other. Meanwhile, the proposed method uses maximum entropy strategy to control the weights of different views while learning the shared hidden space. The experimental result shows that the proposed multi-view clustering method has better performance than many related clustering methods.
Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games

We consider the task of learning to play families of text-based computer adventure games, i.e., fully textual environments with a common theme (e.g. cooking) and goal (e.g. prepare a meal from a recipe) but with different specifics; new instances of such games are relatively straightforward for humans to master after a brief exposure to the genre but have been curiously difficult for computer agents to learn. We find that the deep Q-learning strategies that have been successfully leveraged for superhuman performance in single-instance action video games can be applied to learn families of text video games when adopting simple strategies that correlate with human-like learning behavior. Specifically, we build agents that learn to tackle simple scenarios before more complex ones using curriculum learning, that familiarize themselves in an unfamiliar environment by navigating before acting, and that explore uncertain environment more thoroughly using multi-armed bandit decision policies. We demonstrate improved task completion rates over reasonable baselines when evaluating on never-before-seen games of that theme.
An ATCA Survey of H i Absorption in the Magellanic Clouds. I. H i Gas Temperature Measurements in the Small Magellanic Cloud

We present the first results from the Small Magellanic Cloud portion of a new Australia Telescope Compact Array H i absorption survey of both of the Magellanic Clouds, comprising over 800 hr of observations. Our new H i absorption line data allow us to measure the temperature and fraction of cold neutral gas in a low-metallicity environment. We observed 22 separate fields, targeting a total of 55 continuum sources, against 37 of which we detected H i absorption; from this we measure a column-density-weighted mean average spin temperature of = 150 K. Splitting the spectra into individual absorption line features, we estimate the temperatures of different gas components and find an average cold gas temperature of ∼30 K for this sample, lower than the average of ∼40 K in the Milky Way. The H i appears to be evenly distributed throughout the SMC, and we detect absorption in 67% of the lines of sight in our sample, including some outside the main body of the galaxy (NH i cm−2). The optical depth and temperature of the cold neutral atomic gas show no strong trend with location spatially or in velocity. Despite the low-metallicity environment, we find an average cold gas fraction of ∼20%, not dissimilar from that of the Milky Way.
Fuzzy dark matter soliton cores around supermassive black holes

We explore the effect of a supermassive black hole (SMBH) on the density profile of a fuzzy dark matter (FDM) soliton core at the centre of a dark matter (DM) halo. We numerically solve the Schrödinger–Poisson equations, treating the black hole as a gravitational point mass, and demonstrate that this additional perturbing term has a ‘squeezing’ effect on the soliton density profile, decreasing the core radius, and increasing the central density. In the limit of large black hole mass, the solution approaches one akin to the hydrogen atom, with radius inversely proportional to the black hole mass. By applying our analysis to two specific galaxies (M87 and the Milky Way) and pairing it with known observational limits on the amount of centrally concentrated DM, we obtain a constraint on the FDM particle mass, finding that the range 10−22.12 eV ≲ m ≲ 10−22.06 eV should be forbidden (taking into account additional factors concerning the lifetime of the soliton in the vicinity of a black hole). Improved observational mass measurements of the black hole and total enclosed masses will significantly extend the lower bound on the excluded FDM mass region, while self-consistent theoretical modelling of the soliton–black hole system can extend the upper bound.
Predicting Lyα Emission from Galaxies via Empirical Markers of Production and Escape in the KBSS

Lyα emission is widely used to detect and confirm high-redshift galaxies and characterize the evolution of the intergalactic medium (IGM). However, many galaxies do not display Lyα emission in typical spectroscopic observations, and intrinsic Lyα emitters represent a potentially biased set of high-redshift galaxies. In this work, we analyze a set of 703 galaxies at 2 ≲ z ≲ 3 with both Lyα spectroscopy and measurements of other rest-frame ultraviolet and optical properties in order to develop an empirical model for Lyα emission from galaxies and understand how the probability of Lyα emission depends on other observables. We consider several empirical proxies for the efficiency of Lyα photon production, as well as the subsequent escape of these photons through their local interstellar medium. We find that the equivalent width of metal-line absorption and the O3 ratio of rest-frame optical nebular lines are advantageous empirical proxies for Lyα escape and production, respectively. We develop a new quantity, , that combines these two properties into a single predictor of net Lyα emission, which we find describes ∼90% of the observed variance in Lyα equivalent width when accounting for our observational uncertainties. We also construct conditional probability distributions demonstrating that galaxy selection based on measurements of galaxy properties yield samples of galaxies with widely varying probabilities of net Lyα emission. The application of the empirical models and probability distributions described here may be used to infer the selection biases of current galaxy surveys and evaluate the significance of high-redshift Lyα (non)detections in studies of reionization and the IGM.
Ultralight dark matter detection with mechanical quantum sensors

We consider the use of quantum-limited mechanical force sensors to detect ultralight (sub-meV) dark matter (DM) candidates which are weakly coupled to the standard model. We show that mechanical sensors with masses around or below the milligram scale, operating around the standard quantum limit, would enable novel searches for DM with natural frequencies around the kHz scale. This would complement existing strategies based on torsion balances, atom interferometers, and atomic clock systems.
p-brane Newton–Cartan geometry

We provide a formal definition of p-brane Newton--Cartan (pNC) geometry and establish some foundational results. Our approach is the same followed in the literature for foundations of Newton--Cartan Gravity. Our results provide control of aspects of pNC geometry that are otherwise unclear when using the usual gauge language of non-relativistic theories of gravity. In particular, we obtain a set of necessary and sufficient conditions that a pNC structure must satisfy in order to admit torsion-free, compatible affine connections, and determine the space formed by the latter. Since pNC structures interpolate between Leibnizian structures for p=0 and Lorentzian structures for p=d-1 (with d the dimension of the spacetime manifold), the present work also constitutes a generalisation of results of Newton--Cartan and (pseudo-) Riemannian geometry.
Domain Adaptive Training BERT for Response Selection

We focus on multi-turn response selection in a retrieval-based dialog system. In this paper, we utilize the powerful pre-trained language model Bi-directional Encoder Representations from Transformer (BERT) for a multi-turn dialog system and propose a highly effective post-training method on domain-specific corpus. Although BERT is easily adopted to various NLP tasks and outperforms previous baselines of each task, it still has limitations if a task corpus is too focused on a certain domain. Post-training on domain-specific corpus (e.g., Ubuntu Corpus) helps the model to train contextualized representations and words that do not appear in general corpus (e.g.,English Wikipedia). Experiment results show that our approach achieves new state-of-the-art on two response selection benchmark datasets (i.e.,Ubuntu Corpus V1, Advising Corpus) performance improvement by 5.9% and 6% on Recall@1.
Dark 
CP
 violation and gauged lepton or baryon number for electroweak baryogenesis

We explore the generation of the baryon asymmetry in an extension of the standard model where the lepton number is promoted to a U(1) gauge symmetry with an associated Z′ gauge boson. This is based on a novel electroweak baryogenesis mechanism first proposed by us in Ref. . Extra fermionic degrees of freedom, including a fermionic dark matter χ, are introduced in the dark sector for anomaly cancellation. The lepton number is spontaneously broken at high scale and the effective theory, containing the standard model, the Z′, the fermionic dark matter, and an additional complex scalar field S, violates CP in the dark sector. The complex scalar field couples to the Higgs portal and is essential in enabling a strong first order phase transition. Dark CP violation is diffused in front of the bubble walls and creates a chiral asymmetry for χ, which in turn creates a chemical potential for the standard model leptons. Weak sphalerons are then in charge of transforming the net lepton charge asymmetry into net baryon number. We explore the model phenomenology related to the leptophilic Z′, the dark matter candidate, the Higgs boson, and the additional scalar, as well as implications for electric dipole moments. We also discuss the case when baryon number U(1)B is promoted to a gauge symmetry, and discuss electroweak baryogenesis and its corresponding phenomenology.
Early black-hole seeds in the first billion years

Supermassive black holes with billion solar masses are in place already within the first Gyr, however, their origin and growth in such a short lapse of time is extremely challenging to understand. Here, we discuss the formation paths of early black-hole seeds, showing the limits of light black-hole seeds from stellar origin and the expected characteristics of heavy/massive black-hole seeds originated by gas direct collapse in peculiar primordial conditions. To draw conclusions on the possible candidates and the role of the ambient medium, we use results from N-body hydrodynamic simulations including atomic and molecular non-equilibrium abundance calculations, cooling, star formation, feedback mechanisms, stellar evolution, metal spreading of several heavy elements from SNII, AGB and SNIa, and multifrequency radiative transfer over 150 frequencies coupled to chemistry and SED emission for popII-I and popIII stellar sources. Standard stellar-origin light black holes are unlikely to be reliable seeds of early supermassive black holes, because, under realistic assumptions, they cannot grow significantly in less than a billion years. Alternatively, massive black-hole seeds might originate from direct collapse of pristine gas in primordial quiescent mini-haloes that are exposed to stellar radiation from nearby star forming regions. The necessary conditions required to form these heavy seeds must be complemented with information on the complex features of local environments and the fine balance between chemistry evolution and radiative transfer.