nils-herrmann/clean_scirepeval huggingface dataset:
Ultra-light Dark Matter Is Incompatible with the Milky Way’s Dwarf Satellites

The density profiles of dwarf galaxies are a highly varied set. If the dark matter is an ultra-light particle such as axions, then simulations predict a distinctive and unique profile. If the axion mass is large enough to fit the ultra-faint dwarf (UFD) satellites ( eV), then the models do not fit the density profile of Fornax and Sculptor and are ruled out by more than confidence. If the axion mass is in the mass range that can fit mass profiles of Fornax and Sculptor dwarf spheroidals, then its extended profile implies enormous masses ( ) for the UFDs. These large masses for the UFDS are ruled out by more than confidence by dynamical friction arguments. The tension would increase further considering star formation histories and stellar masses of the UFDs. Unless future ultra-light dark matter (ULDM) simulations with baryonic feedback show a significant change in the density structure of the halos, the current data is incompatible with the ULDM scenario. Relaxing the slope constraint from classical dwarf galaxies would lead to excluding ULDM with mass less than .
Covariant Hamiltonian for gravity coupled top-forms

We review the covariant canonical formalism initiated by D'Adda, Nelson and Regge in 1985, and extend it to include a definition of form-Poisson brackets (FPB) for geometric theories coupled to $p$-forms, gauging free differential algebras. The form-Legendre transformation and the form-Hamilton equations are derived from a $d$-form Lagrangian with $p$-form dynamical fields $\phi$. Momenta are defined as derivatives of the Lagrangian with respect to the "velocities" $d\phi$ and no preferred time direction is used. Action invariance under infinitesimal form-canonical transformations can be studied in this framework, and a generalized Noether theorem is derived, both for global and local symmetries. We apply the formalism to vielbein gravity in $d=3$ and $d=4$. In the $d=3$ theory we can define form-Dirac brackets, and use an algorithmic procedure to construct the canonical generators for local Lorentz rotations and diffeomorphisms. In $d=4$ the canonical analysis is carried out using FPB, since the definition of form-Dirac brackets is problematic. Lorentz generators are constructed, while diffeomorphisms are generated by the Lie derivative. A "doubly covariant" hamiltonian formalism is presented, allowing to maintain manifest Lorentz covariance at every stage of the Legendre transformation. The idea is to take curvatures as "velocities" in the definition of momenta.
Statistical Methods for the Search of Sterile Neutrinos

The statistical methods applied to search for short-baseline neutrino oscillations induced by a sterile neutrino with mass at the eV scale are reviewed and compared. The comparison is performed under limit setting and signal discovery scenarios, considering both when an oscillation would enhance the neutrino interaction rate in the detector and when it would reduce it. The sensitivity of the experiments and the confidence regions extracted for specific data sets are found to change significantly according to the test statistic used for the hypothesis testing. The most general test statistic does not make assumptions on the value of the parameters of interest for the analysis, while the others restrict the allowed parameter space. The restriction of the parameter space could lead to miss a signal or, vice versa, to claim a discovery when this is not statistically significant. Similar issues are found also when the validity of Wilks' theorem is assumed. A standardized analysis approach based on the most general kind of hypothesis test is proposed.
Testability of leptogenesis with three RH-neutrinos below the electroweak scale

The Standard Model extended with right-handed neutrinos whose masses are below the electroweak scale provides a simultaneous solution for the origin of neutrino masses and of the baryon asymmetry of the Universe, that can be tested in current experiments. If three right-handed neutrinos participate to the processes, their parameter space of solutions extends to very large mixing angles, saturating the current experimental constraints. Solutions with right-handed neutrino masses at the GeV scale can be probed in the decay of $B$ mesons at the LHC. For this channel the collision of isotopes of intermediate mass such as Ar provides a better sensitivity per unit of running time compared to collisions with protons.
The Impact of an Inter-rater Bias on Neural Network Training

The problem of inter-rater variability is often discussed in the context of manual labeling of medical images. It is assumed to be bypassed by automatic model-based approaches for image segmentation which are considered `objective', providing single, deterministic solutions. However, the emergence of data-driven approaches such as Deep Neural Networks (DNNs) and their application to supervised semantic segmentation - brought this issue of raters' disagreement back to the front-stage. In this paper, we highlight the issue of inter-rater bias as opposed to random inter-observer variability and demonstrate its influence on DNN training, leading to different segmentation results for the same input images. In fact, lower Dice scores are calculated if training and test segmentations are of different raters. Moreover, we demonstrate that inter-rater bias in the training examples is amplified when considering the segmentation predictions for the test data. We support our findings by showing that a classifier-DNN trained to distinguish between raters based on their manual annotations performs better when the automatic segmentation predictions rather than the raters' annotations were tested. 
For this study, we used the ISBI 2015 Multiple Sclerosis (MS) challenge dataset, which includes annotations by two raters with different levels of expertise. The results obtained allow us to underline a worrisome clinical implication of a DNN bias induced by an inter-rater bias during training. Specially, we show that the differences in MS-lesion load estimates increase when the volume calculations are done based on the DNNs' segmentation predictions instead of the manual annotations used for training.
VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view

This paper introduces VolMap, a real-time approach for the semantic segmentation of a 3D LiDAR surrounding view system in autonomous vehicles. We designed an optimized deep convolution neural network that can accurately segment the point cloud produced by a 360\degree{} LiDAR setup, where the input consists of a volumetric bird-eye view with LiDAR height layers used as input channels. We further investigated the usage of multi-LiDAR setup and its effect on the performance of the semantic segmentation task. Our evaluations are carried out on a large scale 3D object detection benchmark containing a LiDAR cocoon setup, along with KITTI dataset, where the per-point segmentation labels are derived from 3D bounding boxes. We show that VolMap achieved an excellent balance between high accuracy and real-time running on CPU.
Instant automatic diagnosis of diabetic retinopathy

The purpose of this study is to evaluate the performance of the OphtAI system for the automatic detection of referable diabetic retinopathy (DR) and the automatic assessment of DR severity using color fundus photography. OphtAI relies on ensembles of convolutional neural networks trained to recognize eye laterality, detect referable DR and assess DR severity. The system can either process single images or full examination records. To document the automatic diagnoses, accurate heatmaps are generated. The system was developed and validated using a dataset of 763,848 images from 164,660 screening procedures from the OPHDIAT screening program. For comparison purposes, it was also evaluated in the public Messidor-2 dataset. Referable DR can be detected with an area under the ROC curve of AUC = 0.989 in the Messidor-2 dataset, using the University of Iowa's reference standard (95% CI: 0.984-0.994). This is significantly better than the only AI system authorized by the FDA, evaluated in the exact same conditions (AUC = 0.980). OphtAI can also detect vision-threatening DR with an AUC of 0.997 (95% CI: 0.996-0.998) and proliferative DR with an AUC of 0.997 (95% CI: 0.995-0.999). The system runs in 0.3 seconds using a graphics processing unit and less than 2 seconds without. OphtAI is safer, faster and more comprehensive than the only AI system authorized by the FDA so far. Instant DR diagnosis is now possible, which is expected to streamline DR screening and to give easy access to DR screening to more diabetic patients.
Uncertainty Based Detection and Relabeling of Noisy Image Labels

Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy labels based on the different distributions of the predictive uncertainties from a DNN over the clean and noisy data. Additionally, the behavior of the uncertainty over the course of training helps to identify the network weights which best can be used to relabel the noisy labels. Data with noisy labels can therefore be cleaned in an iterative process. Our proposed method can be easily implemented, and shows promising performance on the task of noisy label detection on CIFAR-10 and CIFAR-100.
Deep Learning-Based Classification Of the Defective Pistachios Via Deep Autoencoder Neural Networks

Pistachio nut is mainly consumed as raw, salted or roasted because of its high nutritional properties and favorable taste. Pistachio nuts with shell and kernel defects, besides not being acceptable for a consumer, are also prone to insects damage, mold decay, and aflatoxin contamination. In this research, a deep learning-based imaging algorithm was developed to improve the sorting of nuts with shell and kernel defects that indicate the risk of aflatoxin contamination, such as dark stains, oily stains, adhering hull, fungal decay and Aspergillus molds. This paper presents an unsupervised learning method to classify defective and unpleasant pistachios based on deep Auto-encoder neural networks. The testing of the designed neural network on a validation dataset showed that nuts having dark stain, oily stain or adhering hull with an accuracy of 80.3% can be distinguished from normal nuts. Due to the limited memory available in the HPC of university, the results are reasonable and justifiable.
Style Generator Inversion for Image Enhancement and Animation

One of the main motivations for training high quality image generative models is their potential use as tools for image manipulation. Recently, generative adversarial networks (GANs) have been able to generate images of remarkable quality. Unfortunately, adversarially-trained unconditional generator networks have not been successful as image priors. One of the main requirements for a network to act as a generative image prior, is being able to generate every possible image from the target distribution. Adversarial learning often experiences mode-collapse, which manifests in generators that cannot generate some modes of the target distribution. Another requirement often not satisfied is invertibility i.e. having an efficient way of finding a valid input latent code given a required output image. In this work, we show that differently from earlier GANs, the very recently proposed style-generators are quite easy to invert. We use this important observation to propose style generators as general purpose image priors. We show that style generators outperform other GANs as well as Deep Image Prior as priors for image enhancement tasks. The latent space spanned by style-generators satisfies linear identity-pose relations. The latent space linearity, combined with invertibility, allows us to animate still facial images without supervision. Extensive experiments are performed to support the main contributions of this paper.
Explicit Disentanglement of Appearance and Perspective in Generative Models

Disentangled representation learning finds compact, independent and easy-to-interpret factors of the data. Learning such has been shown to require an inductive bias, which we explicitly encode in a generative model of images. Specifically, we propose a model with two latent spaces: one that represents spatial transformations of the input data, and another that represents the transformed data. We find that the latter naturally captures the intrinsic appearance of the data. To realize the generative model, we propose a Variationally Inferred Transformational Autoencoder (VITAE) that incorporates a spatial transformer into a variational autoencoder. We show how to perform inference in the model efficiently by carefully designing the encoders and restricting the transformation class to be diffeomorphic. Empirically, our model separates the visual style from digit type on MNIST, separates shape and pose in images of human bodies and facial features from facial shape on CelebA.
From Data Quality to Model Quality: An Exploratory Study on Deep Learning

In the field of deep learning, people strive to construct high-quality deep neural networks (DNNs) to improve the accuracy of predicting. As well known, the quality of training data have great impacts on the quality of DNN models, since all the DNN models are obtained by training using these training data. However, there is not any reported systematic study on how the quality of training data affects the quality of DNN model. To study the relationships between data quality and model quality, we mainly consider four aspects of data quality including Skewed Classes, Sample Complexity, Label Quality, and Noisy Data in this paper. We design experiments on MNIST and Cifar-10, and attempt to find out the influences of four aspects on the quality of DNN models. Pearson correlation coefficient and Spearman correlation coefficient are utilized to evaluate such influences. Experimental results show that all the four aspects of data quality have significant impacts on the quality of DNN models. It means that the decrease of data quality in these four aspects will reduce the accuracy of the DNN models.
Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars

Autonomous terrestrial vehicles must be capable of perceiving traffic lights and recognizing their current states to share the streets with human drivers. Most of the time, human drivers can easily identify the relevant traffic lights. To deal with this issue, a common solution for autonomous cars is to integrate recognition with prior maps. However, additional solution is required for the detection and recognition of the traffic light. Deep learning techniques have showed great performance and power of generalization including traffic related problems. Motivated by the advances in deep learning, some recent works leveraged some state-of-the-art deep detectors to locate (and further recognize) traffic lights from 2D camera images. However, none of them combine the power of the deep learning-based detectors with prior maps to recognize the state of the relevant traffic lights. Based on that, this work proposes to integrate the power of deep learning-based detection with the prior maps used by our car platform IARA (acronym for Intelligent Autonomous Robotic Automobile) to recognize the relevant traffic lights of predefined routes. The process is divided in two phases: an offline phase for map construction and traffic lights annotation; and an online phase for traffic light recognition and identification of the relevant ones. The proposed system was evaluated on five test cases (routes) in the city of Vitória, each case being composed of a video sequence and a prior map with the relevant traffic lights for the route. Results showed that the proposed technique is able to correctly identify the relevant traffic light along the trajectory.
Classifying logistic vehicles in cities using Deep learning

Rapid growth in delivery and freight transportation is increasing in urban areas; as a result the use of delivery trucks and light commercial vehicles is evolving. Major cities can use traffic counting as a tool to monitor the presence of delivery vehicles in order to implement intelligent city planning measures. Classical methods for counting vehicles use mechanical, electromagnetic or pneumatic sensors, but these devices are costly, difficult to implement and only detect the presence of vehicles without giving information about their category, model or trajectory. This paper proposes a Deep Learning tool for classifying vehicles in a given image while considering different categories of logistic vehicles, namely: light-duty, medium-duty and heavy-duty vehicles. The proposed approach yields two main contributions: first we developed an architecture to create an annotated and balanced database of logistic vehicles, reducing manual annotation efforts. Second, we built a classifier that accurately classifies the logistic vehicles passing through a given road. The results of this work are: first, a database of 72 000 images for 4 vehicles classes; and second two retrained convolutional neural networks (InceptionV3 and MobileNetV2) capable of classifying vehicles with accuracies over 90%.
On Physical Adversarial Patches for Object Detection

In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can suppress virtually all the detected objects in the image. That is, we can place the patch anywhere in the image, causing all existing objects in the image to be missed entirely by the detector, even those far away from the patch itself. This in turn opens up new lines of physical attacks against object detection systems, which require no modification of the objects in a scene. A demo of the system can be found at this https URL.
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification

Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels. Despite this, evaluation of insect demography is strongly limited by the difficulty of collecting census data at sufficient scale. We propose a method to gather and leverage observations from bystanders, hikers, and entomology enthusiasts in order to provide researchers with data that could significantly help anticipate and identify environmental threats. Finally, we show that there is indeed interest on both sides for such collaboration.
Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods

The goal of this paper is to classify objects mapped by LiDAR sensor into different classes such as vehicles, pedestrians and bikers. Utilizing a LiDAR-based object detector and Neural Networks-based classifier, a novel real-time object detection is presented essentially with respect to aid self-driving vehicles in recognizing and classifying other objects encountered in the course of driving and proceed accordingly. We discuss our work using machine learning methods to tackle a common high-level problem found in machine learning applications for self-driving cars: the classification of pointcloud data obtained from a 3D LiDAR sensor.
Improving the bus flow in a Bus Rapid Transit system: an approach based on cellular automata simulations

We studied the bus flow in a Bus Rapid Transit (BRT) system using a novel approach based on a cellular automata (CA) that properly accounts for bus interactions. The model quantitatively reproduces the bus queuing behaviour for both fixed and random dwell times, with one bus service. For more bus services, our results show that the bus flow strongly depends not only on how the buses are distributed among the services, but also on how the bus stops are physically arranged at the stations. The latter dependence has hitherto not been considered in common BRT optimization methods. By using a genetic algorithm, we could find the bus distribution that maximizes the bus flow for a given stops arrangement at the stations, and explore its evolution with the bus density. Our results suggest a set of guidelines which could be applied on working BRT systems to improve the bus flow.
Simulating the spatial distribution and kinematics of globular clusters within galaxy clusters in illustris

We study the assembly of globular clusters (GCs) in 9 galaxy clusters using the cosmological simulation Illustris. GCs are tagged to individual galaxies at their infall time. The tidal removal of GCs from their galaxies and the distribution of the GCs within the cluster is later followed self-consistently by the simulation. The method relies on the simple assumption of a single power-law relation between halo mass (Mvir) and mass in GCs (MGC) as found in observations. We find that the GCs specific frequency SN as a function of V-band magnitude naturally reproduces the observed ‘U’-shape due to the combination of the power law MGC–Mvir relation and the non-linear stellar mass (M*)–halo mass relation from the simulation. Additional scatter in the SN values is traced back to galaxies with early infall times due to the evolution of the M*–Mvir relation with redshift. GCs that have been tidally removed from their galaxies form the present-day intracluster component, from which about $\sim \!60{{\ \rm per\ cent}}$ were brought in by galaxies that currently orbit within the cluster potential. The remaining ‘orphan’ GCs are contributed by satellite galaxies with a wide range of stellar masses that are fully tidally disrupted at z = 0. This intracluster component is a good dynamical tracer of the dark matter potential. As a consequence of the accreted nature of most intracluster GCs, their orbits are fairly radial with a predicted orbital anisotropy β ≥ 0.5. However, local tangential motions may appear as a consequence of localized substructure, providing a possible interpretation to the β < 0 values suggested in observations of M87.
Hemelb Acceleration and Visualization for Cerebral Aneurysms

A weakness in the wall of a cerebral artery causing a dilation or ballooning of the blood vessel is known as a cerebral aneurysm. Optimal treatment requires fast and accurate diagnosis of the aneurysm. HemeLB is a fluid dynamics solver for complex geometries developed to provide neurosurgeons with information related to the flow of blood in and around aneurysms. On a cost efficient platform, HemeLB could be employed in hospitals to provide surgeons with the simulation results in real-time. In this work, we developed an improved version of HemeLB for GPU implementation and result visualization. A visualization platform for smooth interaction with end users is also presented. Finally, a comprehensive evaluation of this implementation is reported. The results demonstrate that the proposed implementation achieves a maximum performance of 15,168,964 site updates per second, and is capable of speeding up HemeLB for deployment in hospitals and clinical investigations.