nils-herrmann/clean_scirepeval huggingface dataset:
Recognition of Russian traffic signs in winter conditions. Solutions of the "Ice Vision" competition winners

With the advancements of various autonomous car projects aiming to achieve SAE Level 5, real-time detection of traffic signs in real-life scenarios has become a highly relevant problem for the industry. Even though a great progress has been achieved in this field, there is still no clear consensus on what the state-of-the-art in this field is. 
Moreover, it is important to develop and test systems in various regions and conditions. This is why the "Ice Vision" competition has focused on the detection of Russian traffic signs in winter conditions. The IceVisionSet dataset used for this competition features real-world collection of lossless frame sequences with traffic sign annotations. The sequences were collected in varying conditions, including: different weather, camera exposure, illumination and moving speeds. 
In this work we describe the competition and present the solutions of the 3 top teams.
Deep Learning the Morphology of Dark Matter Substructure

Strong gravitational lensing is a promising probe of the substructure of dark matter halos. Deep-learning methods have the potential to accurately identify images containing substructure, and differentiate weakly interacting massive particle dark matter from other well motivated models, including vortex substructure of dark matter condensates and superfluids. This is crucial in future efforts to identify the true nature of dark matter. We implement, for the first time, a classification approach to identifying dark matter based on simulated strong lensing images with different substructure. Utilizing convolutional neural networks trained on sets of simulated images, we demonstrate the feasibility of deep neural networks to reliably distinguish among different types of dark matter substructure. With thousands of strong lensing images anticipated with the coming launch of Vera C. Rubin Observatory, we expect that supervised and unsupervised deep-learning models will play a crucial role in determining the nature of dark matter.
Machine learning in healthcare - a system's perspective

A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS. 
Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML.
A Linearly Constrained Nonparametric Framework for Imitation Learning

In recent years, a myriad of advanced results have been reported in the community of imitation learning, ranging from parametric to non-parametric, probabilistic to non-probabilistic and Bayesian to frequentist approaches. Meanwhile, ample applications (e.g., grasping tasks and humanrobot collaborations) further show the applicability of imitation learning in a wide range of domains. While numerous literature is dedicated to the learning of human skills in unconstrained environments, the problem of learning constrained motor skills, however, has not received equal attention. In fact, constrained skills exist widely in robotic systems. For instance, when a robot is demanded to write letters on a board, its end-effector trajectory must comply with the plane constraint from the board. In this paper, we propose linearly constrained kernelized movement primitives (LC-KMP) to tackle the problem of imitation learning with linear constraints. Specifically, we propose to exploit the probabilistic properties of multiple demonstrations, and subsequently incorporate them into a linearly constrained optimization problem, which finally leads to a non-parametric solution. In addition, a connection between our framework and the classical model predictive control is provided. Several examples including simulated writing and locomotion tasks are presented to show the effectiveness of our framework.
Extending and Automating Basic Probability Theory with Propositional Computability Logic

Classical probability theory is formulated using sets. In this paper, we extend classical probability theory with propositional computability logic. Unlike other formalisms, computability logic is built on the notion of events/games, which is central to probability theory. 
The probability theory based on CoL is therefore useful for {\it automating} uncertainty reasoning. We describe some basic properties of this new probability theory. We also discuss a novel isomorphism between the set operations and computability logic operations.
Where are the Keys? -- Learning Object-Centric Navigation Policies on Semantic Maps with Graph Convolutional Networks

Emerging object-based SLAM algorithms can build a graph representation of an environment comprising nodes for robot poses and object landmarks. However, while this map will contain static objects such as furniture or appliances, many moveable objects (e.g. the car keys, the glasses, or a magazine), are not suitable as landmarks and will not be part of the map due to their non-static nature. We show that Graph Convolutional Networks can learn navigation policies to find such unmapped objects by learning to exploit the hidden probabilistic model that governs where these objects appear in the environment. The learned policies can generalise to object classes unseen during training by using word vectors that express semantic similarity as representations for object nodes in the graph. Furthermore, we show that the policies generalise to unseen environments with only minimal loss of performance. We demonstrate that pre-training the policy network with a proxy task can significantly speed up learning, improving sample efficiency. Code for this paper is available at https://github.com/nikosuenderhauf/ graphConvNetsForNavigation.
Searching for Accurate Binary Neural Architectures

Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices. Since the weak expression ability of binary weights and features, their accuracy is usually much lower than that of full-precision (i.e. 32-bit) models. Here we present a new frame work for automatically searching for compact but accurate binary neural networks. In practice, number of channels in each layer will be encoded into the search space and optimized using the evolutionary algorithm. Experiments conducted on benchmark datasets and neural architectures demonstrate that our searched binary networks can achieve the performance of full-precision models with acceptable increments on model sizes and calculations.
How Far Can We Push Deconvolution? A SCUBA-2 Test Case

How far can we use multi-wavelength cross-identifications to deconvolve far-infrared images? In this short research note I explore a test case of CLEAN deconvolutions of simulated confused 850 micron SCUBA-2 data, and explore the possible scientific applications of combining this data with ostensibly deeper TolTEC Large Scale Structure (LSS) survey 1.1mm-2mm data. I show that the SCUBA-2 can be reconstructed to the 1.1mm LMT resolution and achieve an 850 micron deconvolved sensitivity of 0.7 mJy RMS, an improvement of at least ~1:5x over naive point source filtered images. The TolTEC/SCUBA-2 combination can constrain cold (<10K) observed-frame colour temperatures, where TolTEC alone cannot.
Operator content of entanglement spectra after global quenches in the transverse field Ising chain

Jacopo Surace,1, 2 Luca Tagliacozzo,1, 3 and Erik Tonni4 1Department of Physics and SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom 2ICFO-Institut de Cincies Fotniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain 3Departament de Fı́sica Quàntica i Astrofı́sica and Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Martı́ i Franquès 1, 08028 Barcelona, Catalonia, Spain 4SISSA and INFN Sezione di Trieste, via Bonomea 265, 34136 Trieste, Italy
Superradiant searches for dark photons in two stage atomic transitions

We study a new mechanism to discover dark photon fields, by resonantly triggering two photon transitions in cold gas preparations. Using coherently prepared cold parahydrogen, coupling sensitivity for sub-meV mass dark photon fields can be advanced by orders of magnitude, with a modified light-shining-through-wall setup. We calculate the effect of a background dark photon field on the dipole moment and corresponding transition rate of cold parahydrogen pumped into its first vibrational excited state by counter-propagating laser beams. The nonlinear amplification of two photon emission triggered by dark photons in a cold parahydrogen sample is numerically simulated to obtain the expected dark photon coupling sensitivity.
Beyond the exoplanet mass-radius relation

Mass and radius are two fundamental properties for characterising exoplanets, but only for a relatively small fraction of exoplanets are they both available. Mass is often derived from radial velocity measurements, while the radius is almost always measured using the transit method. For a large number of exoplanets, either the radius or the mass is unknown, while the host star has been characterised. Several mass-radius relations that are dependent on the planet’s type have been published that often allow us to predict the radius. The same is true for a bayesian code, which forecasts the radius of an exoplanet given the mass or vice versa.
Aims. Our goal is to derive the radius of exoplanets using only observables extracted from spectra used primarily to determine radial velocities and spectral parameters. Our objective is to obtain a mass-radius relation independent of the planet’s type.
Methods. We worked with a database of confirmed exoplanets with known radii and masses, as well as the planets from our Solar System. Using random forests, a machine learning algorithm, we computed the radius of exoplanets and compared the results to the published radii. In addition, we explored how the radius estimates compare to previously published mass-radius relations.
Results. The estimated radii reproduces the spread in radius found for high mass planets better than previous mass-radius relations. The average radius error is 1.8 R⊕ across the whole range of radii from 1–22 R⊕. We find that a random forest algorithm is able to derive reliable radii, especially for planets between 4 R⊕ and 20 R⊕ for which the error is under 25%. The algorithm has a low bias yet a high variance, which could be reduced by limiting the growth of the forest, or adding more data.
 The random forest algorithm is a promising method for deriving exoplanet properties. We show that the exoplanet’s mass and equilibrium temperature are the relevant properties that constrain the radius, and do so with higher accuracy than the previous methods.
Utilizing Shell Galaxies

Stellar shells are low surface brightness features in the form of open, concentric arcs, formed in close-to-radial collisions of galaxies. They occur in at least 10% of early-type galaxies and a small portion of spirals and their unique kinematics carry valuable information about the host galaxies. We discuss a method using measurements of the number and distribution of shells to estimate the mass distribution of the galaxies and the time since the merger. The method is applied on the shells of NGC 4993 - a galaxy hosting the electromagnetic counterpart of the gravitational wave event GW170817, to estimate the probable time since the galactic merger. We used analytical calculations and particle simulations to show that, in special cases, when kinematic data are available, further constraints on mass distribution and merger time can be derived. Applying the methods to the rapidly growing sample of known shell galaxies will constrain the dark-matter content in the galaxies and reveal detailed information on the recent merger history of the Universe.
Cusp-to-core transition in low-mass dwarf galaxies induced by dynamical heating of cold dark matter by primordial black holes

We performed a series of high-resolution N-body simulations to examine whether dark matter candidates in the form of primordial black holes (PBHs) can solve the cusp–core problem in low-mass dwarf galaxies. If some fraction of the dark matter in low-mass dwarf galaxies consists of PBHs and the rest is cold dark matter, dynamical heating of the cold dark matter by the PBHs induces a cusp-to-core transition in the total dark matter profile. The mechanism works for PBHs in the 25–100 M⊙ mass window, consistent with the Laser Interferometer Gravitational-Wave Observatory (LIGO) detections, but requires a lower limit on the PBH mass fraction of 1 ${{\rm per\ cent}}$ of the total dwarf galaxy dark matter content. The cusp-to-core transition time-scale is between 1 and 8 Gyr. This time-scale is also a constant multiple of the relaxation time between cold dark matter particles and PBHs, which depends on the mass, the mass fraction, and the scale radius of the initial density profile of PBHs. We conclude that dark matter cores occur naturally in haloes composed of cold dark matter and PBHs, without the need to invoke baryonic processes.
The maximum accretion rate of hot gas in dark matter haloes

We revisit the question of ‘hot mode’ versus ‘cold mode’ accretion on to galaxies using steady-state cooling flow solutions and idealized 3D hydrodynamic simulations. We demonstrate that for the hot accretion mode to exist, the cooling time is required to be longer than the free-fall time near the radius where the gas is rotationally supported, Rcirc, i.e. the existence of the hot mode depends on physical conditions at the galaxy scale rather than on physical conditions at the halo scale. When allowing for the depletion of the halo baryon fraction relative to the cosmic mean, the longer cooling times imply that a virialized gaseous halo may form in halo masses below the threshold of $\sim 10^{12}\, {\rm M_{\odot }}$ derived for baryon-complete haloes. We show that for any halo mass there is a maximum accretion rate for which the gas is virialized throughout the halo and can accrete via the hot mode of ${\dot{M}}_{\rm crit}\approx 0.7(v_{\rm c}/100\, \rm km\ s^{-1})^{5.4}(R_{\rm circ}/10\, {\rm kpc})(Z/\, })^{-0.9}\, {\rm M_{\odot }}\, {\rm yr}^{-1}$, where Z and vc are the metallicity and circular velocity measured at Rcirc. For accretion rates $\gtrsim {\dot{M}}_{\rm crit}$ the volume-filling gas phase can in principle be ‘transonic’ – virialized in the outer halo but cool and free-falling near the galaxy. We compare ${\dot{M}}_{\rm crit}$ to the average star formation rate (SFR) in haloes at 0 < z < 10 implied by the stellar-mass–halo-mass relation. For a plausible metallicity evolution with redshift, we find that ${\rm SFR}\lesssim {\dot{M}}_{\rm crit}$ at most masses and redshifts, suggesting that the SFR of galaxies could be primarily sustained by the hot mode in halo masses well below the classic threshold of $\sim 10^{12}\, {\rm M_{\odot }}$.
Embedding globular clusters in dark matter minihaloes solves the cusp–core and timing problems in the Fornax dwarf galaxy

We use a fully GPU N-body code to demonstrate that dark matter (DM) minihaloes, as a new component of globular clusters (GCs), resolve both the timing and cusp–core problems in Fornax if the (five or six) GCs were recently accreted (≤3 Gyr ago) by Fornax. Under these assumptions, infall of these GCs does not occur and no star clusters form in the centre of Fornax in accordance with observations. We find that crossings of GCss that have DM minihaloes near the Fornax centre induce a cusp-to-core transition of the DM halo and hence resolve the cusp–core problem in this dwarf galaxy. The DM core size depends on the frequency of GC crossings. Our simulations clearly demonstrate also that between the passages, the DM halo can regenerate its cusp. Moreover, our models are in good agreement with constraints on the DM masses of GCs as our clusters lose a large fraction of their initial DM minihaloes. These results provide circumstantial evidence for the universal existence of DM haloes in GCs.
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle

The principle of the Information Bottleneck (Tishby et al., 1999) produces a summary of information X optimized to predict some other relevant information Y. In this paper, we propose a novel approach to unsupervised sentence summarization by mapping the Information Bottleneck principle to a conditional language modelling objective: given a sentence, our approach seeks a compressed sentence that can best predict the next sentence. Our iterative algorithm under the Information Bottleneck objective searches gradually shorter subsequences of the given sentence while maximizing the probability of the next sentence conditioned on the summary. Using only pretrained language models with no direct supervision, our approach can efficiently perform extractive sentence summarization over a large corpus. Building on our unsupervised extractive summarization, we also present a new approach to self-supervised abstractive summarization, where a transformer-based language model is trained on the output summaries of our unsupervised method. Empirical results demonstrate that our extractive method outperforms other unsupervised models on multiple automatic metrics. In addition, we find that our self-supervised abstractive model outperforms unsupervised baselines (including our own) by human evaluation along multiple attributes.
Regular Partitions and Their Use in Structural Pattern Recognition

Recent years are characterized by an unprecedented quantity of available network data which are produced at an astonishing rate by an heterogeneous variety of interconnected sensors and devices. This high-throughput generation calls for the development of new effective methods to store, retrieve, understand and process massive network data. In this thesis, we tackle this challenge by introducing a framework to summarize large graphs based on Szemeredi's Regularity Remma (RL), which roughly states that any sufficiently large graph can almost entirely be partitioned into a bounded number of random-like bipartite graphs. The partition resulting from the RL gives rise to a summary, which inherits many of the essential structural properties of the original graph. We first extend an heuristic version of the RL to improve its efficiency and its robustness. We use the proposed algorithm to address graph-based clustering and image segmentation tasks. In the second part of the thesis, we introduce a new heuristic algorithm which is characterized by an improvement of the summary quality both in terms of reconstruction error and of noise filtering. We use the proposed heuristic to address the graph search problem defined under a similarity measure. Finally, we study the linkage among the regularity lemma, the stochastic block model and the minimum description length. This study provide us a principled way to develop a graph decomposition algorithm based on stochastic block model which is fitted using likelihood maximization.
Second look to the Polyakov loop Nambu–Jona-Lasinio model at finite baryonic density

We revisit the Polyakov Loop coupled Nambu-Jona-Lasinio model that maintains the Polyakov loop dynamics in the limit of zero temperature. This is of interest for astrophysical applications in the interior of neutron stars. For this purpose we re-examine the form of the potential for the deconfinement order parameter at finite baryonic densities. Since the modification of this potential at any temperature is formally equivalent to assigning a baryonic charge to gluons, we develop a more general formulation of the present model that cures this spurious effect and is normalized to match the asymptotic behaviour of the QCD equation of state given by $\mathcal{O}(\alpha_s^2)$ and partial $\mathcal{O}(\alpha_s^3\ln^2\alpha_s)$ perturbative results.
Learning Index Selection with Structured Action Spaces

Configuration spaces for computer systems can be challenging for traditional and automatic tuning strategies. Injecting task-specific knowledge into the tuner for a task may allow for more efficient exploration of candidate configurations. We apply this idea to the task of index set selection to accelerate database workloads. Index set selection has been amenable to recent applications of vanilla deep RL, but real deployments remain out of reach. In this paper, we explore how learning index selection can be enhanced with task-specific inductive biases, specifically by encoding these inductive biases in better action structures. Index selection-specific action representations arise when the problem is reformulated in terms of permutation learning and we rely on recent work for learning RL policies on permutations. Through this approach, we build an indexing agent that is able to achieve improved indexing and validate its behavior with task-specific statistics. Early experiments reveal that our agent can find configurations that are up to 40% smaller for the same levels of latency as compared with other approaches and indicate more intuitive indexing behavior.
Student epistemological framing on paper-based assessments

Assessments are usually thought of as ways for instructors to get information from students. In this work, we flip this perspective and explore how assessments communicate information to students. Specifically, we consider how assessments may provide information about what faculty and/or researchers think it means to know and do physics, i.e. their epistemologies. Using data from students completing assessment questions during one-on-one think aloud interviews, we explore how assessment features impact (or did not impact) student engagement with the assessment problems. We analyze video recordings and transcripts to infer the epistemological framings and resources students use while completing introductory-level physics problems. Students' framings tended to be fairly stable, but when shifts did occur - they were triggered by a shift in resource, which can be activated by assessment feature. This work extends existing work on epistemological framing into the realm of assessment and allows us to consider the effects of assessments on our students' understanding of physics teaching and learning.