nils-herrmann/clean_scirepeval huggingface dataset:
Deep Kernel Learning for Clustering

We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data. Our neural network produces sample embeddings that are motivated by--and are at least as expressive as--spectral clustering. Our training objective, based on the Hilbert Schmidt Information Criterion, can be optimized via gradient adaptations on the Stiefel manifold, leading to significant acceleration over spectral methods relying on eigendecompositions. Finally, our trained embedding can be directly applied to out-of-sample data. We show experimentally that our approach outperforms several state-of-the-art deep clustering methods, as well as traditional approaches such as $k$-means and spectral clustering over a broad array of real-life and synthetic datasets.
Real-Time Elderly Healthcare Monitoring Expert System Using Wireless Sensor Network

Elderly chronic diseases are the main cause of death in the world, accounting 60% of all death. Because elderly with chronic diseases at the early stages has no observed symptoms, and then symptoms starts to appear, it is critical to observe the symptoms as early as possible to avoid any complication. This paper presents an expert system for an Elderly Health Care (EHC) at elderly home tailored for the specific needs of Elderly. The proposed EHC aims to develop an integrated and multidisciplinary method to employ communication technologies and information for covering real health needs of elderly people, mainly of people at high risk due to social and geographic isolation in addition to specific chronic diseases. The proposed EHC provides personalized intervention plans covering chronic diseases such as (body temperature (BT), blood pressure (BP), and Heart beat rate (HR)). The processes and architecture of the proposed EHC are based on the server side and three main clients, one for the elderly and another two for the nurse and the physician’s whom take care of them. The proposed EHC model is discussed for proving the usefulness and effectiveness of the expert system.
Efficient Implementation of Cluster Expansion Models in Surface Kinetic Monte Carlo Simulations with Lateral Interactions: Subtraction Schemes, Supersites, and the Supercluster Contraction

While lateral interaction models for reactions at surfaces have steadily gained popularity and grown in terms of complexity, their use in chemical kinetics has been impeded by the low performance of current kinetic Monte Carlo (KMC) algorithms. The origins of the additional computational cost in KMC simulations with lateral interactions are traced back to the more elaborate cluster expansion Hamiltonian, the more extensive rate updating, and to the impracticality of rate‐catalog‐based algorithms for interacting adsorbate systems. Favoring instead site‐based algorithms, we propose three ways to reduce the cost of KMC simulations: (1) representing the lattice energy by a smaller Supercluster Hamiltonian without loss of accuracy, (2) employing the subtraction schemes for updating key quantities in the simulation that undergo only small, local changes during a reaction event, and (3) applying efficient search algorithms from a set of established methods (supersite approach). The cost of the resulting algorithm is fixed with respect to the number of lattice sites for practical lattice sizes and scales with the square of the range of lateral interactions. The overall added cost of including a complex lateral interaction model amounts to less than a factor 3. Practical issues in implementation due to finite numerical accuracy are discussed in detail, and further suggestions for treating long‐range lateral interactions are made. We conclude that, while KMC simulations with complex lateral interaction models are challenging, these challenges can be overcome by modifying the established variable step‐size method by employing the supercluster, subtraction, and supersite algorithms (SSS‐VSSM).
One-time learning in a biologically-inspired Salience-affected Artificial Neural Network (SANN)

Standard artificial neural networks (ANNs), loosely based on the structure of columns in the cortex, model key cognitive aspects of brain function such as learning and classification, but do not model the affective (emotional) aspects of brain function. However these are a key feature of the brain (the associated ‘ascending systems’ have been hard-wired into the brain by evolutionary processes). These emotions are associated with memories when neuromodulators such as dopamine and noradrenaline affect entire patterns of synaptically activated neurons. Here we present a bio-inspired ANN architecture which we call a Salience-Affected Neural Network (SANN), which, at the same time as local network processing of task-specific information, includes non-local salience (significance) effects responding to an input salience signal. During pattern recognition, inputs similar to the salience-affected inputs in the training data will produce reverse salience signals corresponding to those experienced when the memories were laid down. In addition, training with salience affects the weights of connections between nodes, and improves the overall accuracy of a classification of images similar to the salience-tagged input after just a single iteration of training. Note that we are not aiming to present an accurate model of the biological salience system; rather we present an artificial neural network inspired by those biological systems in the human brain, that has unique strengths.
BERT-based Ranking for Biomedical Entity Normalization

Developing high-performance entity normalization algorithms that can alleviate the term variation problem is of great interest to the biomedical community. Although deep learning-based methods have been successfully applied to biomedical entity normalization, they often depend on traditional context-independent word embeddings. Bidirectional Encoder Representations from Transformers (BERT), BERT for Biomedical Text Mining (BioBERT) and BERT for Clinical Text Mining (ClinicalBERT) were recently introduced to pre-train contextualized word representation models using bidirectional Transformers, advancing the state-of-the-art for many natural language processing tasks. In this study, we proposed an entity normalization architecture by fine-tuning the pre-trained BERT / BioBERT / ClinicalBERT models and conducted extensive experiments to evaluate the effectiveness of the pre-trained models for biomedical entity normalization using three different types of datasets. Our experimental results show that the best fine-tuned models consistently outperformed previous methods and advanced the state-of-the-art for biomedical entity normalization, with up to 1.17% increase in accuracy.
On the Adversarial Robustness of Neural Networks without Weight Transport

Neural networks trained with backpropagation, the standard algorithm of deep learning which uses weight transport, are easily fooled by existing gradient-based adversarial attacks. This class of attacks are based on certain small perturbations of the inputs to make networks misclassify them. We show that less biologically implausible deep neural networks trained with feedback alignment, which do not use weight transport, can be harder to fool, providing actual robustness. Tested on MNIST, deep neural networks trained without weight transport (1) have an adversarial accuracy of 98% compared to 0.03% for neural networks trained with backpropagation and (2) generate non-transferable adversarial examples. However, this gap decreases on CIFAR-10 but is still significant particularly for small perturbation magnitude less than 1/2.
That which we call private

The guarantees of security and privacy defenses are often strengthened by relaxing the assumptions made about attackers or the context in which defenses are deployed. Such relaxations can be a highly worthwhile topic of exploration---even though they typically entail assuming a weaker, less powerful adversary---because there may indeed be great variability in both attackers' powers and their context. 
However, no weakening or contextual discounting of attackers' power is assumed for what some have called "relaxed definitions" in the analysis of differential-privacy guarantees. Instead, the definitions so named are the basis of refinements and more advanced analyses of the worst-case implications of attackers---without any change assumed in attackers' powers. 
Because they more precisely bound the worst-case privacy loss, these improved analyses can greatly strengthen the differential-privacy upper-bound guarantees---sometimes lowering the differential-privacy epsilon by orders-of-magnitude. As such, to the casual eye, these analyses may appear to imply a reduced privacy loss. This is a false perception: the privacy loss of any concrete mechanism cannot change with the choice of a worst-case-loss upper-bound analysis technique. Practitioners must be careful not to equate real-world privacy with differential-privacy epsilon values, at least not without full consideration of the context.
LSTM-based Flow Prediction

In this paper, a method of prediction on continuous time series variables from the production or flow -- an LSTM algorithm based on multivariate tuning -- is proposed. The algorithm improves the traditional LSTM algorithm and converts the time series data into supervised learning sequences regarding industrial data's features. The main innovation of this paper consists in introducing the concepts of periodic measurement and time window in the industrial prediction problem, especially considering industrial data with time series characteristics. Experiments using real-world datasets show that the prediction accuracy is improved, 54.05% higher than that of traditional LSTM algorithm.
Synthetic Elastography Using B-Mode Ultrasound Through a Deep Fully Convolutional Neural Network

Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently developed, advanced technique assesses the speed of a laterally traveling shear wave after an acoustic radiation force “push” to estimate local Young’s moduli in an operator-independent fashion. In this work, we show how synthetic SWE (sSWE) images can be generated based on conventional B-mode imaging through deep learning. Using side-by-side-view B-mode/SWE images collected in 50 patients with prostate cancer, we show that sSWE images with a pixel-wise mean absolute error of 4.5 ± 0.96 kPa with regard to the original SWE can be generated. Visualization of high-level feature levels through ${t}$ -distributed stochastic neighbor embedding reveals substantial overlap between data from two different scanners. Qualitatively, we examined the use of the sSWE methodology for B-mode images obtained with a scanner without SWE functionality. We also examined the use of this type of network in elasticity imaging in the thyroid. Limitations of the technique reside in the fact that networks have to be retrained for different organs, and that the method requires standardization of the imaging settings and procedure. Future research will be aimed at the development of sSWE as an elasticity-related tissue typing strategy that is solely based on B-mode ultrasound acquisition, and the examination of its clinical utility.
An Empirical Guide to the Behavior and Use of Scalable Persistent Memory

After nearly a decade of anticipation, scalable nonvolatile memory DIMMs are finally commercially available with the release of Intel's 3D XPoint DIMM. This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. Researchers have not idly waited for real nonvolatile DIMMs (NVDIMMs) to arrive. Over the past decade, they have written a slew of papers proposing new programming models, file systems, libraries, and applications built to exploit the performance and flexibility that NVDIMMs promised to deliver. Those papers drew conclusions and made design decisions without detailed knowledge of how real NVDIMMs would behave or how industry would integrate them into computer architectures. Now that 3D XPoint NVDIMMs are actually here, we can provide detailed performance numbers, concrete guidance for programmers on these systems, reevaluate prior art for performance, and reoptimize persistent memory software for the real 3D XPoint DIMM. In this paper, we explore the performance properties and characteristics of Intel's new 3D XPoint DIMM at the micro and macro level. First, we investigate the basic characteristics of the device, taking special note of the particular ways in which its performance is peculiar relative to traditional DRAM or other past methods used to emulate NVM. From these observations, we recommend a set of best practices to maximize the performance of the device. With our improved understanding, we then explore the performance of prior art in application-level software for persistent memory, taking note of where their performance was influenced by our guidelines.
Optically targeted search for gravitational waves emitted by core-collapse supernovae during the first and second observing runs of advanced LIGO and advanced Virgo

We present the results from a search for gravitational-wave transients associated with core-collapse supernovae observed within a source distance of approximately 20 Mpc during the first and second observing runs of Advanced LIGO and Advanced Virgo. No significant gravitational-wave candidate was detected. We report the detection efficiencies as a function of the distance for waveforms derived from multidimensional numerical simulations and phenomenological extreme emission models. For neutrino-driven explosions the distance at which we reach 50% detection efficiency is approaching 5 kpc, and for magnetorotationally-driven explosions is up to 54 kpc. However, waveforms for extreme emission models are detectable up to 28 Mpc. For the first time, the gravitational-wave data enabled us to exclude part of the parameter spaces of two extreme emission models with confidence up to 83%, limited by coincident data coverage. Besides, using ad hoc harmonic signals windowed with Gaussian envelopes we constrained the gravitational-wave energy emitted during core-collapse at the levels of $4.27\times 10^{-4}\,M_\odot c^2$ and $1.28\times 10^{-1}\,M_\odot c^2$ for emissions at 235 Hz and 1304 Hz respectively. These constraints are two orders of magnitude more stringent than previously derived in the corresponding analysis using initial LIGO, initial Virgo and GEO 600 data.
Bulk properties of the system formed in 
Au+Au
 collisions at 
sNN=14.5
 GeV at the BNL STAR detector

We report systematic measurements of bulk properties of the system created in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 14.5 GeV recorded by the STAR detector at the Relativistic Heavy Ion Collider (RHIC).The transverse momentum spectra of $\pi^{\pm}$, $K^{\pm}$ and $p(\bar{p})$ are studied at mid-rapidity ($|y| < 0.1$) for nine centrality intervals. The centrality, transverse momentum ($p_T$),and pseudorapidity ($\eta$) dependence of inclusive charged particle elliptic flow ($v_2$), and rapidity-odd charged particles directed flow ($v_{1}$) results near mid-rapidity are also presented. These measurements are compared with the published results from Au+Au collisions at other energies, and from Pb+Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 2.76 TeV. The results at $\sqrt{s_{\mathrm{NN}}}$ = 14.5 GeV show similar behavior as established at other energies and fit well in the energy dependence trend. These results are important as the 14.5 GeV energy fills the gap in $\mu_B$, which is of the order of 100 MeV,between $\sqrt{s_{\mathrm{NN}}}$ =11.5 and 19.6 GeV. Comparisons of the data with UrQMD and AMPT models show poor agreement in general.
On the planar edge-length ratio of planar graphs

The edge-length ratio of a straight-line drawing of a graph is the ratio between the lengths of the longest and of the shortest edge in the drawing. The planar edge-length ratio of a planar graph is the minimum edge-length ratio of any planar straight-line drawing of the graph. 
In this paper, we study the planar edge-length ratio of planar graphs. We prove that there exist $n$-vertex planar graphs whose planar edge-length ratio is in $\Omega(n)$; this bound is tight. We also prove upper bounds on the planar edge-length ratio of several families of planar graphs, including series-parallel graphs and bipartite planar graphs.
The Reliability of the Low-latency Estimation of Binary Neutron Star Chirp Mass

The LIGO and Virgo Collaborations currently conduct searches for gravitational waves from compact binary coalescences in real time. For promising candidate events, a sky map and distance estimation are released in low latency to facilitate their electromagnetic follow-up. Currently, no information is released about the masses of the compact objects. Recently, Margalit & Metzger suggested that knowledge of the chirp mass of the detected binary neutron stars could be useful to prioritize the electromagnetic follow-up effort, and urged the LIGO-Virgo collaboration to release chirp mass information in low latency. One might worry that low-latency searches for compact binaries make simplifying assumptions that could introduce biases in the mass parameters: neutron stars are treated as point particles with dimensionless spins below 0.05 perfectly aligned with the orbital angular momentum. Furthermore, the template bank used to search for them has a finite resolution. In this paper we show that none of these limitations can introduce chirp mass biases larger than ∼10−3 M⊙. Even the total mass is usually accurately estimated, with biases smaller than 6%. The mass ratio and effective inspiral spins, on the other hand, can suffer from more severe biases.
Design Considerations for a Ground-based Search for Transiting Planets around L and T Dwarfs

We present design considerations for a ground-based survey for transiting exoplanets around L and T dwarfs, spectral classes that have yet to be thoroughly probed for planets. We simulate photometry for L and T targets with a variety of red-optical and near-infrared (NIR) detectors, and compare the scatter in the photometry to anticipated transit depths. Based on these results, we recommend the use of a low-dark-current detector with H-band NIR photometric capabilities. We then investigate the potential for performing a survey for Earth-sized planets for a variety of telescope sizes. We simulate planetary systems around a set of spectroscopically confirmed L and T dwarfs using measured M dwarf planet occurrence rates from Kepler, and simulate their observation in surveys ranging in duration from 120 to 600 nights, randomly discarding 30% of nights to simulate weather losses. We find that an efficient survey design uses a 2 m class telescope with a NIR instrument and 360–480 observing nights, observing multiple L and T targets each night with a dithering strategy. Surveys conducted in such a manner have over an 80% chance of detecting at least one planet, and detect around 2 planets, on average. The number of expected detections depends on the true planet occurrence rate, however, which may in fact be higher for L and T dwarfs than for M dwarfs.
Generating Information Extraction Patterns from Overlapping and Variable Length Annotations using Sequence Alignment

Sequence alignments are used to capture patterns composed of elements representing multiple conceptual levels through the alignment of sequences that contain overlapping and variable length annotations. The alignments also determine the proper context window of words and phrases that most directly impact the meaning of a given target within a sentence, eliminating the need to predefine a fixed context window of words surrounding the targets. We evaluated the system using the CoNLL-2003 named entity recognition (NER) task.
Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords.
Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ∼2800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ∼2.5 per cent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).