nils-herrmann/clean_scirepeval huggingface dataset:
NuSTAR tests of sterile-neutrino dark matter: New Galactic bulge observations and combined impact

We analyze two dedicated NuSTAR observations with exposure ${\sim}190$ ks located ${\sim}10^\circ$ from the Galactic plane, one above and the other below, to search for x-ray lines from the radiative decay of sterile-neutrino dark matter. These fields were chosen to minimize astrophysical x-ray backgrounds while remaining near the densest region of the dark matter halo. We find no evidence of anomalous x-ray lines in the energy range 5--20 keV, corresponding to sterile neutrino masses 10--40 keV. Interpreted in the context of sterile neutrinos produced via neutrino mixing, these observations provide the leading constraints in the mass range 10--12 keV, improving upon previous constraints in this range by a factor ${\sim}2$. We also compare our results to Monte Carlo simulations, showing that the fluctuations in our derived limit are not dominated by systematic effects. An updated model of the instrumental background, which is currently under development, will improve NuSTAR's sensitivity to anomalous x-ray lines, particularly for energies 3--5 keV.
A Center in Your Neighborhood: Fairness in Facility Location

When selecting locations for a set of facilities, standard clustering algorithms may place unfair burden on some individuals and neighborhoods. We formulate a fairness concept that takes local population densities into account. In particular, given $k$ facilities to locate and a population of size $n$, we define the "neighborhood radius" of an individual $i$ as the minimum radius of a ball centered at $i$ that contains at least $n/k$ individuals. Our objective is to ensure that each individual has a facility within at most a small constant factor of her neighborhood radius. We present several theoretical results: 
We show that optimizing this factor is NP-hard; we give an approximation algorithm that guarantees a factor of at most 2 in all metric spaces; and we prove matching lower bounds in some metric spaces. We apply a variant of this algorithm to real-world address data, showing that it is quite different from standard clustering algorithms and outperforms them on our objective function and balances the load between facilities more evenly.
The effect of fluctuating fuzzy axion haloes on stellar dynamics: a stochastic model

Fuzzy dark matter of ultralight axions has gained attention, largely in light of the galactic scale problems associated with cold dark matter. But the large de Broglie wavelength, believed to possibly alleviate these problems, also leads to fluctuations that place constraints on ultralight axions. We adapt and extend a method, previously devised to describe the effect of gaseous fluctuations on cold dark matter cusps, in order to determine the imprints of ultralight axion haloes on the motion of classical test particles. We first evaluate the effect of fluctuations in a statistically homogeneous medium of classical particles, then in a similar system of ultralight axions. In the first case, one recovers the classical two body relaxation time (and diffusion coefficients) from white noise density fluctuations. In the second situation, the fluctuations are not born of discreteness noise but from the finite de Broglie wavelength; correlation therefore exists over this scale, while white noise is retained on larger scales, elucidating the correspondence with classical relaxation. The resulting density power spectra and correlation functions are compared with those inferred from numerical simulations, and the relaxation time arising from the associated potential fluctuations is evaluated. We then apply our results to estimate the heating of discs embedded in axion dark haloes. We find that this implies an axion mass $m \gtrsim 2 \times 10^{-22} \, {\rm eV}$. We finally apply our model to the case of the central cluster of Eridanus II, confirming that far stronger constraints on m may in principle be obtained, and discussing the limitations associated with the assumptions leading to these.
Robust Regression via Deep Negative Correlation Learning

Nonlinear regression has been extensively employed in many computer vision problems (e.g., crowd counting, age estimation, affective computing). Under the umbrella of deep learning, two common solutions exist i) transforming nonlinear regression to a robust loss function which is jointly optimizable with the deep convolutional network, and ii) utilizing ensemble of deep networks. Although some improved performance is achieved, the former may be lacking due to the intrinsic limitation of choosing a single hypothesis and the latter usually suffers from much larger computational complexity. To cope with those issues, we propose to regress via an efficient "divide and conquer" manner. The core of our approach is the generalization of negative correlation learning that has been shown, both theoretically and empirically, to work well for non-deep regression problems. Without extra parameters, the proposed method controls the bias-variance-covariance trade-off systematically and usually yields a deep regression ensemble where each base model is both "accurate" and "diversified". Moreover, we show that each sub-problem in the proposed method has less Rademacher Complexity and thus is easier to optimize. Extensive experiments on several diverse and challenging tasks including crowd counting, personality analysis, age estimation, and image super-resolution demonstrate the superiority over challenging baselines as well as the versatility of the proposed method.
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis

Different convolutional neural network (CNN) models have been tested for their application in histological image analyses. However, these models are prone to overfitting due to their large parameter capacity, requiring more data or valuable computational resources for model training. Given these limitations, we introduced a novel architecture (termed PlexusNet). We utilized 310 Hematoxylin and Eosin stained (HE it helped reduce the slide number to examine by 43.8% without consequences, although its parameter capacity was 200 times smaller than ResNet18. We found that the partitioning of the development set influences the model calibration for all models. However, with PlexusNet architecture, we could achieve comparable well-calibrated models trained on different partitions. In conclusion, PlexusNet represents a novel model architecture for histological image analysis that achieves classification performance comparable to other models while providing orders-of-magnitude parameter reduction.
Camera Pose Correction in SLAM Based on Bias Values of Map Points

Accurate camera pose estimation result is essential for visual SLAM (VSLAM). This paper presents a novel pose correction method to improve the accuracy of the VSLAM system. Firstly, the relationship between the camera pose estimation error and bias values of map points is derived based on the optimized function in VSLAM. Secondly, the bias value of the map point is calculated by a statistical method. Finally, the camera pose estimation error is compensated according to the first derived relationship. After the pose correction, procedures of the original system, such as the bundle adjustment (BA) optimization, can be executed as before. Compared with existing methods, our algorithm is compact and effective and can be easily generalized to different VSLAM systems. Additionally, the robustness to system noise of our method is better than feature selection methods, due to all original system information is preserved in our algorithm while only a subset is employed in the latter. Experimental results on benchmark datasets show that our approach leads to considerable improvements over state-of-the-art algorithms for absolute pose estimation.
KL property of exponent 1/2 of 𝓁2, 0-norm and DC regularized factorizations for low-rank matrix recovery

This paper is concerned with the factorization form of the rank regularized loss minimization problem. To cater for the scenario in which only a coarse estimation is available for the rank of the true matrix, an $\ell_{2,0}$-norm regularized term is added to the factored loss function to reduce the rank adaptively; and account for the ambiguities in the factorization, a balanced term is then introduced. For the least squares loss, under a restricted condition number assumption on the sampling operator, we establish the KL property of exponent $1/2$ of the nonsmooth factored composite function and its equivalent DC reformulations in the set of their global minimizers. We also confirm the theoretical findings by applying a proximal linearized alternating minimization method to the regularized factorizations.
DAST Model: Deciding About Semantic Complexity of a Text

Measuring text complexity is an essential task in several fields and applications (such as NLP, semantic web, smart education, etc.). The semantic layer of text is more tacit than its syntactic structure and, as a result, calculation of semantic complexity is more difficult than syntactic complexity. While there are famous and powerful academic and commercial syntactic complexity measures, the problem of measuring semantic complexity is still a challenging one. In this paper, we introduce the DAST model, which stands for Deciding About Semantic Complexity of a Text. DAST proposes an intuitionistic approach to semantics that lets us have a well-defined model for the semantics of a text and its complexity: semantic is considered as a lattice of intuitions and, as a result, semantic complexity is defined as the result of a calculation on this lattice. A set theoretic formal definition of semantic complexity, as a 6-tuple formal system, is provided. By using this formal system, a method for measuring semantic complexity is presented. The evaluation of the proposed approach is done by a set of three human-judgment experiments. The results show that DAST model is capable of deciding about semantic complexity of text. Furthermore, the analysis of the results leads us to introduce a Markovian model for the process of common-sense, multiple-steps and semantic-complexity reasoning in people. The results of Experiments demonstrate that our method outperforms the random baseline with improvement in better precision and competes with other methods by less error percentage.
Analyzing collective motion with machine learning and topology

We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D’Orsogna et al.,  (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based on topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters.
Kinesthetic Learning - Haptic User Interfaces for Gyroscopic Precession Simulation

Some forces in nature are difficult to comprehend due to their non-intuitive and abstract nature. Forces driving gyroscopic precession are invisible, yet their effect is very important in a variety of applications, from space navigation to motion tracking. Current technological advancements in haptic interfaces, enables development of revolutionary user interfaces, combining multiple modalities: tactile, visual and auditory. Tactile augmented user interfaces have been deployed in a variety of areas, from surgical training to elementary education. This research provides an overview of haptic user interfaces in higher education, and presents the development and assessment of a haptic-user interface that supports the learner's understanding of gyroscopic precession forces. The visual-haptic simulator proposed, is one module from a series of simulators targeted at complex concept representation, using multi-modal user interfaces. Various higher education domains, from classical physics to mechanical engineering, will benefit from the mainstream adoption of multi-modal interfaces for hands-on training and content delivery. Experimental results are promising, and underline the valuable impact that haptic user interfaces have on enabling abstract concepts understanding, through kinesthetic learning and hands-on practice.
A Future Percent-level Measurement of the Hubble Expansion at Redshift 0.8 with Advanced LIGO

Simultaneous measurements of distance and redshift can be used to constrain the expansion history of the universe and associated cosmological parameters. Merging binary black hole (BBH) systems are standard sirens—their gravitational waveform provides direct information about the luminosity distance to the source. There is, however, a perfect degeneracy between the source masses and redshift; some nongravitational information is necessary to break the degeneracy and determine the redshift of the source. Here we suggest that the pair instability supernova (PISN) process, thought to be the source of the observed upper limit on the black hole mass in merging BBH systems at , imprints a mass scale in the population of BBH mergers and permits a measurement of the redshift–luminosity–distance relation with these sources. We simulate five years of BBH detections in the Advanced LIGO and Virgo detectors with a realistic BBH merger rate, mass distribution with smooth PISN cutoff, and measurement uncertainty. We show that after one year of operation at design sensitivity the BBH population can constrain H(z) to at a pivot redshift . After five years the constraint improves to . If the PISN cutoff is sharp, the uncertainty is smaller by about a factor of two. This measurement relies only on general relativity and the presence of a mass scale that is approximately fixed or calibrated across cosmic time; it is independent of any distance ladder. Observations by future “third-generation” gravitational wave detectors, which can see BBH mergers throughout the universe, would permit subpercent cosmographical measurements to z ≳ 4 within one month of observation.
Web-enabled Intelligent System for Continuous Sensor Data Processing and Visualization

A large number of sensors deployed in recent years in various setups and their data is readily available in dedicated databases or in the cloud. Of particular interest is real-time data processing and 3D visualization in web-based user interfaces that facilitate spatial information understanding and sharing, hence helping the decision making process for all the parties involved. In this research, we provide a prototype system for near real-time, continuous X3D-based visualization of processed sensor data for two significant applications: thermal monitoring for residential/commercial buildings and nitrogen cycle monitoring in water beds for aquaponics systems. As sensors are sparsely placed, in each application, where they collect data for large periods (of up to one year), we employ a Finite Differences Method and a Neural Networks model to approximate data distribution in the entire volume.
BERT for Coreference Resolution: Baselines and Analysis

We apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication.
Fairness warnings and fair-MAML: learning fairly with minimal data

Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The first is a model-agnostic algorithm that provides interpretable boundary conditions for when a fairly trained model may not behave fairly on similar but slightly different tasks within a given domain. The second is a fair meta-learning approach to train models that can be quickly fine-tuned to specific tasks from only a few number of sample instances while balancing fairness and accuracy. We demonstrate experimentally the individual utility of each model using relevant baselines and provide the first experiment to our knowledge of K-shot fairness, i.e. training a fair model on a new task with only K data points. Then, we illustrate the usefulness of both algorithms as a combined method for training models from a few data points on new tasks while using Fairness Warnings as interpretable boundary conditions under which the newly trained model may not be fair.
Optimal best arm selection for general distributions

Given a finite set of unknown distributions $\textit{or arms}$ that can be sampled from, we consider the problem of identifying the one with the largest mean using a delta-correct algorithm (an adaptive, sequential algorithm that restricts the probability of error to a specified delta) that has minimum sample complexity. Lower bounds for delta-correct algorithms are well known. Further, delta-correct algorithms that match the lower bound asymptotically as delta reduces to zero have also been developed in literature when the arm distributions are restricted to a single parameter exponential family. In this paper, we first observe a negative result that some restrictions are essential as otherwise under a delta-correct algorithm, distributions with unbounded support would require an infinite number of samples in expectation. We then propose a delta-correct algorithm that matches the lower bound as delta reduces to zero under a mild restriction that a known bound on the expectation of a non-negative, increasing convex function (for example, the squared moment) of underlying random variables, exists. We also propose batch processing and identify optimal batch sizes to substantially speed up the proposed algorithm. This best arm selection problem is a well studied classic problem in the simulation community. It has many learning applications including in recommendation systems and in product selection.
Effect of overlap on spreading dynamics on multiplex networks

In spite of the attention the study of epidemic dynamics on single-layer networks has received, the epidemic dynamics on multiplex networks is still limited and is facing many challenges. In this work, we consider the susceptible-infected-susceptible-type (SIS) epidemic model on multiplex networks and investigate the effect of overlap among layers on the spreading dynamics. To do so, we assume that the prerequisite of one S-node to be infected is that there is at least one infectious neighbor in each layer. A remarkable result is that the overlap can alter the nature of the phase transition for the onset of epidemic outbreak. Specifically speaking, the system undergoes a usual continuous phase transition when two layers are completely overlapped. Otherwise, a discontinuous phase transition is observed, accompanied by the occurrence of a bistable region in which a disease-free phase and an endemic phase are coexisting. As the degree of the overlap decreases, the bistable region is enlarged. The results are validated by both simulation and mean-field theory.
Generator evaluator-selector net for panoptic image segmentation and splitting unfamiliar objects into parts

In machine learning and other fields, suggesting a good solution to a problem is usually a harder task than evaluating the quality of such a solution. This asymmetry is the basis for a large number of selection oriented methods that use a generator system to guess a set of solutions and an evaluator system to rank and select the best solutions. This work examines the use of this approach to the problem of panoptic image segmentation and class agnostic parts segmentation. The generator/evaluator approach for this case consists of two independent convolutional neural nets: a generator net that suggests variety segments corresponding to objects, stuff and parts regions in the image, and an evaluator net that chooses the best segments to be merged into the segmentation map. The result is a trial and error evolutionary approach in which a generator that guesses segments with low average accuracy, but with wide variability, can still produce good results when coupled with an accurate evaluator. The generator consists of a Pointer net that receives an image and a point in the image, and predicts the region of the segment containing the point. Generating and evaluating each segment separately is essential in this case since it demands exponentially fewer guesses compared to a system that guesses and evaluates the full segmentation map in each try. The classification of the selected segments is done by an independent region-specific classification net. This allows the segmentation to be class agnostic and hence, capable of segmenting unfamiliar categories that were not part of the training set. The method was examined on the COCO Panoptic segmentation benchmark and gave results comparable to those of the basic semantic segmentation and Mask-RCNN methods. In addition, the system was used for the task of splitting objects of unseen classes (that did not appear in the training set) into parts.
On topological classification of normed spaces endowed with the weak topology or the topology of compact convergence

In this paper the weak topology on a normed space is studied from the viewpoint of infinite-dimensional topology. Besides the weak topology on a normed space $X$ (coinciding with the topology of uniform convergence on finite subsets of the dual space $X^*$), we consider the topology $c$ of uniform convergence on compact subsets of $X^*$. It is known that this topology coincides with the weak topology on bounded subsets of $X$, but unlike to the latter has much better topological properties (e.g., is stratifiable). 
We prove that for normed spaces $X,Y$ with separable duals the spaces $(X,weak)$, $(Y,weak)$ are sequentially homeomorphic if and only if $\mathcal W(X)=\mathcal W(Y)$, where $\mathcal W(X)$ is the class of topological spaces homeomorphic to closed bounded subsets of $(X,weak)$. Moreover, if $X,Y$ are Banach spaces which are isomorphic to their hyperplanes and have separale duals, then the spaces $(X,weak)$ and $(Y,weak)$ are sequentially homeomorphic if and only of the spaces $(X,c)$ and $(Y,c)$ are homeomorphic. To prove this result, we show that for a normed space $X$ which is isomorphic to its hyperpane and has separable dual, the space $(X,c)$ (resp. $(X,weak)$) is (sequentially) homeomorphic to the product $B\times\mathbb R^\infty$ of the weak unit ball $B$ of $X$ and the linear space $\mathbb R^\infty$ with countable Hamel basis and the strongest linear topology.
Automatic Text Summarization of Legal Cases: A Hybrid Approach

Manual summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain.Lawyers spend a lot of time preparing legal briefs of their clients’ case files.  Text summarization is a constantly evolving field of Natural Language Processing(NLP),which is a subdiscipline of the Artificial Intelligence Field.. In this paper a hybrid method for automatic text summarization of legal cases using k-means clustering technique and tf-idf(term frequency-inverse document frequency) word vectorizer is proposed. The summary generated by the proposed method is compared using ROGUE evaluation parameters with the case summary as prepared by the lawyer for appeal in court. Further, suggestions for improving the proposed method are also presented.