nils-herrmann/clean_scirepeval huggingface dataset:
Deep Neural Models for Medical Concept Normalization in User-Generated Texts

In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS). This is a challenging task since medical terminology is very different when coming from health care professionals or from the general public in the form of social media texts. We approach it as a sequence learning problem with powerful neural networks such as recurrent neural networks and contextualized word representation models trained to obtain semantic representations of social media expressions. Our experimental evaluation over three different benchmarks shows that neural architectures leverage the semantic meaning of the entity mention and significantly outperform existing state of the art models.
Parameter estimation for biochemical reaction networks using Wasserstein distances

We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances. We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters. We then use Bayesian optimization to find parameters minimizing this distance based on the trained Gaussian process. The effectiveness of our method is demonstrated on the three-stage model of gene expression and a genetic feedback loop for which moment-based methods are known to perform poorly. Our method is applicable to any simulator model of stochastic reaction networks, including Brownian dynamics.
Merger of galactic cores made of ultralight bosonic dark matter

We study binary mergers of ultralight bosonic dark matter cores by solving the Gross-Pitaevskii-Poisson system of equations. The analysis centers on the dynamics of the relaxation process and the behavior of the configuration resulting from the merger, including the Gravitational Cooling with its corresponding emission of mass and angular momentum. The oscillations of density and size of the final configuration are characterized, indicating that for the equal mass case the dependency of the amplitude and frequency of these oscillations on the impact parameter of the pre-merger configuration is linear. The amplitude of these oscillations changes by a factor of two or more indicating the final configuration does not approach a clear stationary state even though it oscillates around a virialized state. For the unequal mass case, global quantities also indicate the final configuration oscillates around a virialized state, although the density does not show a dominant oscillation mode. The evolution of the angular momentum prior and post merger is analyzed in all cases.
Asynchronous snapshots of actor systems for latency-sensitive applications

The actor model is popular for many types of server applications. Efficient snapshotting of applications is crucial in the deployment of pre-initialized applications or moving running applications to different machines, e.g for debugging purposes. A key issue is that snapshotting blocks all other operations. In modern latency-sensitive applications, stopping the application to persist its state needs to be avoided, because users may not tolerate the increased request latency. In order to minimize the impact of snapshotting on request latency, our approach persists the application’s state asynchronously by capturing partial heaps, completing snapshots step by step. Additionally, our solution is transparent and supports arbitrary object graphs. We prototyped our snapshotting approach on top of the Truffle/Graal platform and evaluated it with the Savina benchmarks and the Acme Air microservice application. When performing a snapshot every thousand Acme Air requests, the number of slow requests ( 0.007% of all requests) with latency above 100ms increases by 5.43%. Our Savina microbenchmark results detail how different utilization patterns impact snapshotting cost. To the best of our knowledge, this is the first system that enables asynchronous snapshotting of actor applications, i.e. without stop-the-world synchronization, and thereby minimizes the impact on latency. We thus believe it enables new deployment and debugging options for actor systems.
Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach*

Many road accidents occur due to distracted drivers. Today, driver monitoring is essential even for the latest autonomous vehicles to alert distracted drivers in order to take over control of the vehicle in case of emergency. In this paper, a spatio-temporal approach is applied to classify drivers’ distraction level and movement decisions using convolutional neural networks (CNNs). We approach this problem as action recognition to benefit from temporal information in addition to spatial information. Our approach relies on features extracted from sparsely selected frames of an action using a pre-trained BN-Inception network. Experiments show that our approach outperforms the state-of-the art results on the Distracted Driver Dataset (96.31%), with an accuracy of 99.10% for 10-class classification while providing real-time performance. We also analyzed the impact of fusion using RGB and optical flow modalities with a very recent data level fusion strategy. The results on the Distracted Driver and Brain4Cars datasets show that fusion of these modalities further increases the accuracy.
ELG: An Event Logic Graph

The evolution and development of events have their own basic principles, which make events happen sequentially. Therefore, the discovery of such evolutionary patterns among events are of great value for event prediction, decision-making and scenario design of dialog systems. However, conventional knowledge graph mainly focuses on the entities and their relations, which neglects the real world events. In this paper, we present a novel type of knowledge base - Event Logic Graph (ELG), which can reveal evolutionary patterns and development logics of real world events. Specifically, ELG is a directed cyclic graph, whose nodes are events, and edges stand for the sequential, causal, conditional or hypernym-hyponym (is-a) relations between events. We constructed two domain ELG: financial domain ELG, which consists of more than 1.5 million of event nodes and more than 1.8 million of directed edges, and travel domain ELG, which consists of about 30 thousand of event nodes and more than 234 thousand of directed edges. Experimental results show that ELG is effective for the task of script event prediction.
Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs Using Deep Convolutional Neural Networks

Knee osteoarthritis (OA) is the most common musculoskeletal disease in the world. In primary healthcare, knee OA is diagnosed using clinical examination and radiographic assessment. Osteoarthritis Research Society International (OARSI) atlas of OA radiographic features allows performing independent assessment of knee osteophytes, joint space narrowing and other knee features. This provides a fine-grained OA severity assessment of the knee, compared to the gold standard and most commonly used Kellgren–Lawrence (KL) composite score. In this study, we developed an automatic method to predict KL and OARSI grades from knee radiographs. Our method is based on Deep Learning and leverages an ensemble of residual networks with 50 layers. We used transfer learning from ImageNet with a fine-tuning on the Osteoarthritis Initiative (OAI) dataset. An independent testing of our model was performed on the Multicenter Osteoarthritis Study (MOST) dataset. Our method yielded Cohen’s kappa coefficients of 0.82 for KL-grade and 0.79, 0.84, 0.94, 0.83, 0.84 and 0.90 for femoral osteophytes, tibial osteophytes and joint space narrowing for lateral and medial compartments, respectively. Furthermore, our method yielded area under the ROC curve of 0.98 and average precision of 0.98 for detecting the presence of radiographic OA, which is better than the current state-of-the-art.
Credit Assignment as a Proxy for Transfer in Reinforcement Learning

The ability to transfer representations to novel environments and tasks is a sensible requirement for general learning agents. Despite the apparent promises, transfer in Reinforcement Learning is still an open and under-exploited research area. In this paper, we suggest that credit assignment, regarded as a supervised learning task, could be used to accomplish transfer. Our contribution is twofold: we introduce a new credit assignment mechanism based on self-attention, and show that the learned credit can be transferred to in-domain and out-of-domain scenarios.
A Scenario for Ultradiffuse Satellite Galaxies with Low Velocity Dispersions: The Case of [KKS 2000]04

A scenario for achieving a low velocity dispersion for the galaxy [KKS 2000]04 (aka NGC 1052-DF2) and similar galaxies is presented. A progenitor halo corresponding to a z = 0 halo of mass and a low concentration parameter (but consistent with cosmological simulations) infalls onto a Milky Way–size host at early times. Substantial removal of cold gas from the inner regions by supernova feedback and ram pressure, assisted by tidal stripping of the dark matter in the outer regions, leads to a substantial reduction of the velocity dispersion of stars within one effective radius. In this framework, the observed stellar content of [KKS 2000]04 is associated with a progenitor mass close to that inferred from the global stellar-to-halo-mass ratio. As far as the implications of kinematics are concerned, even if at a ∼20 Mpc distance, it is argued that [KKS 2000]04 is no more peculiar than numerous early type galaxies with seemingly little total dark-matter content.
Convolutional Reservoir Computing for World Models

Recently, reinforcement learning models have achieved great success, completing complex tasks such as mastering Go and other games with higher scores than human players. Many of these models collect considerable data on the tasks and improve accuracy by extracting visual and time-series features using convolutional neural networks (CNNs) and recurrent neural networks, respectively. However, these networks have very high computational costs because they need to be trained by repeatedly using a large volume of past playing data. In this study, we propose a novel practical approach called reinforcement learning with convolutional reservoir computing (RCRC) model. The RCRC model has several desirable features: 1. it can extract visual and time-series features very fast because it uses random fixed-weight CNN and the reservoir computing model; 2. it does not require the training data to be stored because it extracts features without training and decides action with evolution strategy. Furthermore, the model achieves state of the art score in the popular reinforcement learning task. Incredibly, we find the random weight-fixed simple networks like only one dense layer network can also reach high score in the RL task.
Embedding Climate Change Engagement in Astronomy Education and Research

This White Paper is a call to action for astronomers to respond to climate change with a large structural transition within our profession. Many astronomers are deeply concerned about climate change and act upon it in their personal and professional lives, and many organizations within astronomy have incorporated incremental changes. We need a collective impact model to better network and grow our efforts so that we can achieve results that are on the scale appropriate to address climate change at the necessary level indicated by scientific research; e.g., becoming carbon neutral by 2050. We need to implement strategies within two primary drivers of our field: (1) Education and Outreach, and (2) Research Practices and Infrastructure. (1) In the classroom and through public talks, astronomers reach a large audience. Astronomy is closely connected to the science of climate change, and it is arguably the most important topic we include in our curriculum. Due to misinformation and disinformation, climate change communication is different than for other areas of science. We therefore need to expand our communication and implement effective strategies, for which there is now a considerable body of research. (2) On a per-person basis astronomers have an outsized carbon impact. There are numerous ways we can reduce our footprint; e.g., in the design and operation of telescope facilities and in the optimization and reduction of travel. Fortunately, many of these solutions are win-win scenarios, e.g., increasing the online presence of conferences will reduce the carbon footprint while increasing participation, especially for astronomers working with fewer financial resources. Astronomers have an obligation to act on climate change in every way possible, and we need to do it now. In this White Paper, we outline a plan for collective impact using a Networked Improvement Community (NIC) approach.
Brownian bridge with random length and pinning point for modelling of financial information

Developed countries are increasingly relying on gas storage to ensure security of supply. In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal. This enables to catch some empirical facts on the behavior of gas storage markets: when the Brownian bridge process is away from the boundaries x and y, the holder of the gas storage contract can be relatively sure that the best decision is to do nothing. However, when the bridge information process absorbs y, the holder of the contract should take the decision of withdrawing gas on the other hand, they should take the decision to inject gas when the process absorbs x. In this sense the Brownian bridge information process leaks information concerning the time at which the holder of a storage contract can choose to inject gas, do nothing, or withdraw gas. The issue of storage valuation is not limited to gas markets, storages also plays a significant, balancing role in, for example, oil markets, soft commodity markets and even electricity. The principle of our approach is applicable to those markets as well. In this paper we define and study the Brownian bridge with random length and pinning point. Its main objectives is to see if the properties of Brownian bridges with deterministic length and pinning point remain valid in case its length and pinning point are random. Amongst other we prove that the bridge fails to be Markovian for pinning points having a law, which is absolutely continuous with respect to the Lebesgue measure.
Total variation based community detection using a nonlinear optimization approach

Maximizing the modularity of a network is a successful tool to identify an important community of nodes. However, this combinatorial optimization problem is known to be NP-complete. Inspired by recent nonlinear modularity eigenvector approaches, we introduce the modularity total variation $TV_Q$ and show that its box-constrained global maximum coincides with the maximum of the original discrete modularity function. Thus we describe a new nonlinear optimization approach to solve the equivalent problem leading to a community detection strategy based on $TV_Q$. The proposed approach relies on the use of a fast first-order method that embeds a tailored active-set strategy. We report extensive numerical comparisons with standard matrix-based approaches and the Generalized RatioDCA approach for nonlinear modularity eigenvectors, showing that our new method compares favourably with state-of-the-art alternatives.
Self-supervised Training of Proposal-based Segmentation via Background Prediction

While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this in scenarios where annotating data is prohibitively expensive, we introduce a self-supervised approach to object detection and segmentation, able to work with monocular images captured with a moving camera. At the heart of our approach lies the observation that segmentation and background reconstruction are linked tasks, and the idea that, because we observe a structured scene, background regions can be re-synthesized from their surroundings, whereas regions depicting the object cannot. We therefore encode this intuition as a self-supervised loss function that we exploit to train a proposal-based segmentation network. To account for the discrete nature of object proposals, we develop a Monte Carlo-based training strategy that allows us to explore the large space of object proposals. Our experiments demonstrate that our approach yields accurate detections and segmentations in images that visually depart from those of standard benchmarks, outperforming existing self-supervised methods and approaching weakly supervised ones that exploit large annotated datasets.
Federated PCA with Adaptive Rank Estimation

In many online machine learning and data science tasks such as data summarisation and feature compression, d-dimensional vectors are usually distributed across a large number of clients in a decentralised network and collected in a streaming fashion. This is increasingly common in modern applications due to the sheer volume of data generated and the clients’ constrained resources. In this setting, some clients are required to compute an update to a centralised target model independently using local data while other clients aggregate these updates with a low-complexity merging algorithm. However, some clients with limited storage might not be able to store all of the data samples if d is large, nor compute procedures requiring at least Ω(d) storage-complexity such as Principal Component Analysis, Subspace Tracking, or general Feature Correlation. In this work, we present a novel federated algorithm for PCA that is able to adaptively estimate the rank r of the dataset and compute its r leading principal components when only O(dr) memory is available. This inherent adaptability implies that r does not have to be supplied as a fixed hyper-parameter which is beneficial when the underlying data distribution is not known in advance, such as in a streaming setting. Numerical simulations show that, while using limited-memory, our algorithm exhibits state-of-the-art performance that closely matches or outperforms traditional non-federated algorithms, and in the absence of communication latency, it exhibits attractive horizontal scalability.
Precipitation Nowcasting with Star-Bridge Networks

Precipitation nowcasting, which aims to precisely predict the short-term rainfall intensity of a local region, is gaining increasing attention in the artificial intelligence community. Existing deep learning-based algorithms use a single network to process various rainfall intensities together, compromising the predictive accuracy. Therefore, this paper proposes a novel recurrent neural network (RNN) based star-bridge network (StarBriNet) for precipitation nowcasting. The novelty of this work lies in the following three aspects. First, the proposed network comprises multiple sub-networks to deal with different rainfall intensities and duration separately, which can significantly improve the model performance. Second, we propose a star-shaped information bridge to enhance the information flow across RNN layers. Third, we introduce a multi-sigmoid loss function to take the precipitation nowcasting criterion into account. Experimental results demonstrate superior performance for precipitation nowcasting over existing algorithms, including the state-of-the-art one, on a natural radar echo dataset.
Computational Thinking in Introductory Physics

Computational Thinking (CT) is still a relatively new term in the lexicon of learning objectives and science standards. There is not yet widespread agreement on the precise definition or implementation of CT, and efforts to assess CT are still maturing, even as more states adopt K-12 computer science standards. In this article we will try to summarize what CT means for a typical introductory (i.e. high school or early college) physics class. This will include a discussion of the ways that instructors may already be incorporating elements of CT in their classes without knowing it. Our intention in writing this article is to provide a helpful, concise and readable introduction to this topic for physics instructors. We also put forward some ideas for what the future of CT in introductory physics may look like.
Amortized Monte Carlo Integration

Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions - a computational pipeline which is inefficient when the target function(s) are known upfront. In this paper, we address this inefficiency by introducing AMCI, a method for amortizing Monte Carlo integration directly. AMCI operates similarly to amortized inference but produces three distinct amortized proposals, each tailored to a different component of the overall expectation calculation. At runtime, samples are produced separately from each amortized proposal, before being combined to an overall estimate of the expectation. We show that while existing approaches are fundamentally limited in the level of accuracy they can achieve, AMCI can theoretically produce arbitrarily small errors for any integrable target function using only a single sample from each proposal at runtime. We further show that it is able to empirically outperform the theoretically optimal self-normalized importance sampler on a number of example problems. Furthermore, AMCI allows not only for amortizing over datasets but also amortizing over target functions.
Nuclear electric dipole moment in the cluster model with a triton: 
Li7
 and 
B11

We calculate the electric dipole moment (EDM) of the nuclei Li7 and B11 in the cluster model with α (He4) and triton (H3) clusters as degrees of freedom. The Li7 and B11 nuclei are treated in the two- and three-body problem, respectively, using the Gaussian expansion method, assuming the one-meson exchange P, CP-odd nuclear forces. We find that Li7 and B11 have larger sensitivity to the CP violation than the deuteron. It is also suggested that the EDMs of Li7 and B11, together with those of Li6, Be9 and the 1/21+ excited state of C13, obey an approximate counting rule accounting for the EDM of the cluster and the α-N polarization. We show their sensitivity on the hadronic level CP violation in terms of the chiral effective-field theory and discuss their role in probing new physics beyond the standard model.