nils-herrmann/clean_scirepeval huggingface dataset:
Reconstructing non-standard cosmologies with dark matter

Once dark matter has been discovered and its particle physics properties have been determined, a crucial question rises concerning how it was produced in the early Universe. If its thermally averaged annihilation cross section is in the ballpark of few× 10−26 cm3/s, the WIMP mechanism in the standard cosmological scenario (i.e. radiation dominated Universe) will be highly favored. If this is not the case one can either consider an alternative production mechanism, or a non-standard cosmology. Here we study the dark matter production in scenarios with a non-standard expansion history. Additionally, we reconstruct the possible non-standard cosmologies that could make the WIMP mechanism viable.
Synthesis of Computable Regular Functions of Infinite Words

Regular functions from infinite words to infinite words can be equivalently
specified by MSO-transducers, streaming $\omega$-string transducers as well as
deterministic two-way transducers with look-ahead. In their one-way
restriction, the latter transducers define the class of rational functions.
Even though regular functions are robustly characterised by several
finite-state devices, even the subclass of rational functions may contain
functions which are not computable (by a Turing machine with infinite input).
This paper proposes a decision procedure for the following synthesis problem:
given a regular function $f$ (equivalently specified by one of the
aforementioned transducer model), is $f$ computable and if it is, synthesize a
Turing machine computing it.
 For regular functions, we show that computability is equivalent to
continuity, and therefore the problem boils down to deciding continuity. We
establish a generic characterisation of continuity for functions preserving
regular languages under inverse image (such as regular functions). We exploit
this characterisation to show the decidability of continuity (and hence
computability) of rational and regular functions. For rational functions, we
show that this can be done in $\mathsf{NLogSpace}$ (it was already known to be
in $\mathsf{PTime}$ by Prieur). In a similar fashion, we also effectively
characterise uniform continuity of regular functions, and relate it to the
notion of uniform computability, which offers stronger efficiency guarantees.
End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans

We present a novel, end-to-end approach to align CAD models to an 3D scan of a scene, enabling transformation of a noisy, incomplete 3D scan to a compact, CAD reconstruction with clean, complete object geometry. Our main contribution lies in formulating a differentiable Procrustes alignment that is paired with a symmetry-aware dense object correspondence prediction. To simultaneously align CAD models to all the objects of a scanned scene, our approach detects object locations, then predicts symmetry-aware dense object correspondences between scan and CAD geometry in a unified object space, as well as a nearest neighbor CAD model, both of which are then used to inform a differentiable Procrustes alignment. Our approach operates in a fully-convolutional fashion, enabling alignment of CAD models to the objects of a scan in a single forward pass. This enables our method to outperform state-of-the-art approaches by 19.04% for CAD model alignment to scans, with approximately 250x faster runtime than previous data-driven approaches.
Dark matter capture in celestial objects: improved treatment of multiple scattering and updated constraints from white dwarfs

We revisit dark matter (DM) capture in celestial objects, including the impact of multiple scattering, and obtain updated constraints on the DM-proton cross section using observations of white dwarfs. Considering a general form for the energy loss distribution in each scattering, we derive an exact formula for the capture probability through multiple scatterings. We estimate the maximum number of scatterings that can take place, in contrast to the number required to bring a dark matter particle to rest. We employ these results to compute a “dark” luminosity LDM, arising solely from the thermalized annihilation products of the captured dark matter. Demanding that LDM not exceed the luminosity of the white dwarfs in the M4 globular cluster, we set a bound on the DM-proton cross section: σp ≲ 10−44 cm2, almost independent of the dark matter mass between 100 GeV and 1 PeV and mildly weakening beyond. This is a stronger constraint than those obtained by direct detection experiments in both large mass (M ≳ 5 TeV) and small mass (M ≲ 10 GeV) regimes. For dark matter lighter than 350 MeV, which is beyond the sensitivity of present direct detection experiments, this is the strongest available constraint.
possis: predicting spectra, light curves, and polarization for multidimensional models of supernovae and kilonovae

We present possis, a time-dependent three-dimensional Monte Carlo code for modelling radiation transport in supernovae and kilonovae. The code incorporates wavelength- and time-dependent opacities, and predicts viewing-angle dependent spectra, light curves, and polarization for both idealized and hydrodynamical explosion models. We apply the code to a kilonova model with two distinct ejecta components, one including lanthanide elements with relatively high opacities and the other devoid of lanthanides and characterized by lower opacities. We find that a model with total ejecta mass $M_\mathrm{ej}=0.04\, \mathrm{M}_\odot$ and half-opening angle of the lanthanide-rich component Φ = 30° provides a good match to GW 170817/AT 2017gfo for orientations near the polar axis (i.e. for a system viewed close to face-on). We then show how crucial is the use of self-consistent multidimensional models in place of combining one-dimensional models to infer important parameters, such as the ejecta masses. We finally explore the impact of Mej and Φ on the synthetic observables and highlight how the relatively fast computation times of possis make it well-suited to perform parameter-space studies and extract key properties of supernovae and kilonovae. Spectra calculated with possis in this and future studies will be made publicly available.
Ultralight Thomas-Fermi dark matter

Author(s): Pal, K; Sales, LV; Wudka,  We investigate the viability of a simple dark matter (DM) model consisting of a single fermion in the context of galactic dynamics. We use a consistent approach that does not presume a particular DM density profile but instead requires that the DM+baryon system is in hydrostatic equilibrium. Using a phenomenological baryon density profile, the model then predicts the DM distribution with a core like behavior close to the galactic center. The presence of supermassive black holes (SMBHs) in the center of large galaxies arises naturally in this framework. Using data from a set of large elliptical and spiral galaxies, and from a small set of dwarf galaxies, we find that the model can explain most of the bulk galactic properties, as well as some of the features observed in the rotation curves, provided the DM mass is in the $\mathcal{O}$(50 eV) range. More precise tests of the model require better modeling of the baryon profile and better control on the uncertainties in the data.
Abelian tropical covers

The goal of this article is to classify unramified covers of a fixed tropical base curve $\Gamma$ with an action of a finite abelian group G that preserves and acts transitively on the fibers of the cover. We introduce the notion of dilated cohomology groups for a tropical curve $\Gamma$, which generalize simplicial cohomology groups of $\Gamma$ with coefficients in G by allowing nontrivial stabilizers at vertices and edges. We show that G-covers of $\Gamma$ with a given collection of stabilizers are in natural bijection with the elements of the corresponding first dilated cohomology group of $\Gamma$.
Alzheimer's Disease Brain MRI Classification: Challenges and Insights

In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover that when we split that data into training and testing sets at the subject level, we are not able to obtain similar performance, bringing the validity of many of the previous studies into question. Furthermore, we point out that previous works use different subsets of the ADNI data, making comparison across similar works tricky. In this study, we present the results of three splitting methods, discuss the motivations behind their validity, and report our results using all of the available subjects.
BowNet: Dilated Convolution Neural Network for Ultrasound Tongue Contour Extraction

Ultrasound imaging is safe, relatively affordable, and capable of real-time performance. One application of this technology is to visualize and to characterize human tongue shape and motion during a real-time speech to study healthy or impaired speech production. Due to the noisy nature of ultrasound images with low-contrast characteristic, it might require expertise for non-expert users to recognize organ shape such as tongue surface (dorsum). To alleviate this difficulty for quantitative analysis of tongue shape and motion, tongue surface can be extracted, tracked, and visualized instead of the whole tongue region. Delineating the tongue surface from each frame is a cumbersome, subjective, and error-prone task. Furthermore, the rapidity and complexity of tongue gestures have made it a challenging task, and manual segmentation is not a feasible solution for real-time applications. Employing the power of state-of-the-art deep neural network models and training techniques, it is feasible to implement new fully-automatic, accurate, and robust segmentation methods with the capability of real-time performance, applicable for tracking of the tongue contours during the speech. This paper presents two novel deep neural network models named BowNet and wBowNet benefits from the ability of global prediction of decoding-encoding models, with integrated multi-scale contextual information, and capability of full-resolution (local) extraction of dilated convolutions. Experimental results using several ultrasound tongue image datasets revealed that the combination of both localization and globalization searching could improve prediction result significantly. Assessment of BowNet models using both qualitatively and quantitatively studies showed them outstanding achievements in terms of accuracy and robustness in comparison with similar techniques.
Identifying Visible Actions in Lifestyle Vlogs

We consider the task of identifying human actions visible in online videos. We focus on the widely spread genre of lifestyle vlogs, which consist of videos of people performing actions while verbally describing them. Our goal is to identify if actions mentioned in the speech description of a video are visually present. We construct a dataset with crowdsourced manual annotations of visible actions, and introduce a multimodal algorithm that leverages information derived from visual and linguistic clues to automatically infer which actions are visible in a video.
Interpreting OWL Complex Classes in AutomationML based on Bidirectional Translation

The World Wide Web Consortium (W3C) has published several recommendations for building and storing ontologies, including the most recent OWL 2 Web Ontology Language (OWL). These initiatives have been followed by practical implementations that popularize OWL in various domains. For example, OWL has been used for conceptual modeling in industrial engineering, and its reasoning facilities are used to provide a wealth of services, e.g. model diagnosis, automated code generation, and semantic integration. More specifically, recent studies have shown that OWL is well suited for harmonizing information of engineering systems stored as AutomationML (AML) files. However, OWL and its tools can be cumbersome for direct use by domain experts such that an ontology expert is often required in practice. Although much attention has been paid in the literature to overcome this issue by transforming OWL ontologies from/to AML models automatically, dealing with OWL complex classes remains an open research question. In this paper, we introduce the AML concept models for representing OWL complex classes in AutomationML, and present algorithms for the bidirectional translation between OWL complex classes and their corresponding AML concept models. We show that this approach provides an efficient and intuitive interface for domain experts to visualize, modify, and create OWL complex classes in typical ontology engineering tasks.
Analysis of the susceptible-infected-susceptible epidemic dynamics in networks via the non-backtracking matrix

We study the stochastic susceptible-infected-susceptible model of epidemic processes on finite directed and weighted networks with arbitrary structure. We present a new lower bound on the exponential rate at which the probabilities of nodes being infected decay over time. This bound is directly related to the leading eigenvalue of a matrix that depends on the non-backtracking and incidence matrices of the network. The dimension of this matrix is $N+M$, where $N$ and $M$ are the number of nodes and edges, respectively. We show that this new lower bound improves on an existing bound corresponding to the so-called quenched mean-field theory. Although the bound obtained from a recently developed second-order moment-closure technique requires the computation of the leading eigenvalue of an $N^2\times N^2$ matrix, we illustrate in our numerical simulations that the new bound is tighter, while being computationally less expensive for sparse networks. We also present the expression for the corresponding epidemic threshold in terms of the adjacency matrix of the line graph and the non-backtracking matrix of the given network.
Performance Analysis and Characterization of Training Deep Learning Models on NVIDIA TX2

Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of enabling high user experiences. Most of the existing work on machine learning at mobile devices is focused on the inference of deep learning models (particularly convolutional neural network and recurrent neural network), but not training. The performance characterization of training deep learning models on mobile devices is largely unexplored, although understanding the performance characterization is critical for designing and implementing deep learning models on mobile devices. 
In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models. We introduce a benchmark suite and tools to study performance of training deep learning models on mobile devices, from the perspectives of memory consumption, hardware utilization, and power consumption. The tools can correlate performance results with fine-grained operations in deep learning models, providing capabilities to capture performance variance and problems at a fine granularity. We reveal interesting performance problems and opportunities, including under-utilization of heterogeneous hardware, large energy consumption of the memory, and high predictability of workload characterization. Based on the performance analysis, we suggest interesting research directions.
Exploration via Hindsight Goal Generation

Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a function on the state space. However, the sparsity of such reward definition makes traditional reinforcement learning algorithms very inefficient.  Replay (HER), a recent advance, has greatly improved sample efficiency and practical applicability for such problems. It exploits previous replays by constructing imaginary goals in a simple heuristic way, acting like an implicit curriculum to alleviate the challenge of sparse reward signal. In this paper, we introduce Hindsight Goal Generation (HGG), a novel algorithmic framework that generates valuable hindsight goals which are easy for an agent to achieve in the short term and are also potential for guiding the agent to reach the actual goal in the long term. We have extensively evaluated our goal generation algorithm on a number of robotic manipulation tasks and demonstrated substantially improvement over the original HER in terms of sample efficiency.
Towards Amortized Ranking-Critical Training for Collaborative Filtering

Collaborative filtering is widely used in modern recommender systems. Recent research shows that variational autoencoders (VAEs) yield state-of-the-art performance by integrating flexible representations from deep neural networks into latent variable models, mitigating limitations of traditional linear factor models. VAEs are typically trained by maximizing the likelihood (MLE) of users interacting with ground-truth items. While simple and often effective, MLE-based training does not directly maximize the recommendation-quality metrics one typically cares about, such as top-N ranking. In this paper we investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to directly optimize the non-differentiable quality metrics of interest. Specifically, we train a critic network to approximate ranking-based metrics, and then update the actor network (represented here by a VAE) to directly optimize against the learned metrics. In contrast to traditional learning-to-rank methods that require to re-run the optimization procedure for new lists, our critic-based method amortizes the scoring process with a neural network, and can directly provide the (approximate) ranking scores for new lists. Empirically, we show that the proposed methods outperform several state-of-the-art baselines, including recently-proposed deep learning approaches, on three large-scale real-world datasets. The code to reproduce the experimental results and figure plots is on Github: this https URL
Analyzing the Structure of Attention in a Transformer Language Model

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads.
Transfer Learning for Ultrasound Tongue Contour Extraction with Different Domains

Medical ultrasound technology is widely used in routine clinical applications such as disease diagnosis and treatment as well as other applications like real-time monitoring of human tongue shapes and motions as visual feedback in second language training. Due to the low-contrast characteristic and noisy nature of ultrasound images, it might require expertise for non-expert users to recognize tongue gestures. Manual tongue segmentation is a cumbersome, subjective, and error-prone task. Furthermore, it is not a feasible solution for real-time applications. In the last few years, deep learning methods have been used for delineating and tracking tongue dorsum. Deep convolutional neural networks (DCNNs), which have shown to be successful in medical image analysis tasks, are typically weak for the same task on different domains. In many cases, DCNNs trained on data acquired with one ultrasound device, do not perform well on data of varying ultrasound device or acquisition protocol. Domain adaptation is an alternative solution for this difficulty by transferring the weights from the model trained on a large annotated legacy dataset to a new model for adapting on another different dataset using fine-tuning. In this study, after conducting extensive experiments, we addressed the problem of domain adaptation on small ultrasound datasets for tongue contour extraction. We trained a U-net network comprises of an encoder-decoder path from scratch, and then with several surrogate scenarios, some parts of the trained network were fine-tuned on another dataset as the domain-adapted networks. We repeat scenarios from target to source domains to find a balance point for knowledge transfer from source to target and vice versa. The performance of new fine-tuned networks was evaluated on the same task with images from different domains.
Online Object Representations with Contrastive Learning

We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics. The main contributions of this paper are: 1) a self-supervising objective trained with contrastive learning that can discover and disentangle object attributes from video without using any labels; 2) we leverage object self-supervision for online adaptation: the longer our online model looks at objects in a video, the lower the object identification error, while the offline baseline remains with a large fixed error; 3) to explore the possibilities of a system entirely free of human supervision, we let a robot collect its own data, train on this data with our self-supervise scheme, and then show the robot can point to objects similar to the one presented in front of it, demonstrating generalization of object attributes. An interesting and perhaps surprising finding of this approach is that given a limited set of objects, object correspondences will naturally emerge when using contrastive learning without requiring explicit positive pairs. Videos illustrating online object adaptation and robotic pointing are available at: this https URL.
Shape of shortest paths in random spatial networks.

In the classic model of first-passage percolation, for pairs of vertices separated by a Euclidean distance L, geodesics exhibit deviations from their mean length L that are of order L^{χ}, while the transversal fluctuations, known as wandering, grow as L^{ξ}. We find that when weighting edges directly with their Euclidean span in various spatial network models, we have two distinct classes defined by different exponents ξ=3/5 and χ=1/5, or ξ=7/10 and χ=2/5, depending only on coarse details of the specific connectivity laws used. Also, the travel-time fluctuations are Gaussian, rather than Tracy-Widom, which is rarely seen in first-passage models. The first class contains proximity graphs such as the hard and soft random geometric graph, and the k-nearest neighbor random geometric graphs, where via Monte Carlo simulations we find ξ=0.60±0.01 and χ=0.20±0.01, showing a theoretical minimal wandering. The second class contains graphs based on excluded regions such as β skeletons and the Delaunay triangulation and are characterized by the values ξ=0.70±0.01 and χ=0.40±0.01, with a nearly theoretically maximal wandering exponent. We also show numerically that the so-called Kardar-Parisi-Zhang (KPZ) relation χ=2ξ-1 is satisfied for all these models. These results shed some light on the Euclidean first-passage process but also raise some theoretical questions about the scaling laws and the derivation of the exponent values and also whether a model can be constructed with maximal wandering, or non-Gaussian travel fluctuations, while embedded in space.