nils-herrmann/clean_scirepeval huggingface dataset:
Fast Transient Simulation of High-Speed Channels Using Recurrent Neural Network

Generating eye diagrams by using a circuit simulator can be very computationally intensive, especially in the presence of nonlinearities. It often involves multiple Newton-like iterations at every time step when a SPICE-like circuit simulator handles a nonlinear system in the transient regime. In this paper, we leverage machine learning methods, to be specific, the recurrent neural network (RNN), to generate black-box macromodels and achieve significant reduction of computation time. Through the proposed approach, an RNN model is first trained and then validated on a relatively short sequence generated from a circuit simulator. Once the training completes, the RNN can be used to make predictions on the remaining sequence in order to generate an eye diagram. The training cost can also be amortized when the trained RNN starts making predictions. Besides, the proposed approach requires no complex circuit simulations nor substantial domain knowledge. We use two high-speed link examples to demonstrate that the proposed approach provides adequate accuracy while the computation time can be dramatically reduced. In the high-speed link example with a PAM4 driver, the eye diagram generated by RNN models shows good agreement with that obtained from a commercial circuit simulator. This paper also investigates the impacts of various RNN topologies, training schemes, and tunable parameters on both the accuracy and the generalization capability of an RNN model. It is found out that the long short-term memory (LSTM) network outperforms the vanilla RNN in terms of the accuracy in predicting transient waveforms.
An analysis of a mathematical model describing acid‐mediated tumor invasion

We present a mathematical analysis of a reaction‐diffusion model describing acid‐mediated tumor invasion. The model describes the spatial distribution and temporal evolution of tumor cells, normal cells, and excess lactic acid concentration. The model assumes that tumor‐induced alteration of microenvironmental pH provides a simple but complete mechanism for cancer invasion. We provide results on the existence and uniqueness of a solution considering Neumann boundary condition and comments about the same results with Dirichlet boundary conditions. We also provide numerical simulations to the solutions considering both boundary conditions.
Controllability for a population equation with interior degeneracy.

We deal with a degenerate model in divergence form describing the dynamics of a population depending on time, on age and on space. We assume that the degeneracy occurs in the interior of the spatial domain and we focus on null controllability. To this aim, first we prove Carleman estimates for the associated adjoint problem, then, via cut off functions, we prove the existence of a null control function localized in the interior of the space domain. We consider two cases: either the control region contains the degeneracy point $x_0$, or the control region is the union of two intervals each of them lying on one side of $x_0$. This paper complement some previous results, concluding the study of the subject.
Implementation of a Port-graph Model for Finance

In this paper we examine the process involved in the design and implementation of a port-graph model to be used for the analysis of an agent-based rational negligence model. Rational negligence describes the phenomenon that occurred during the financial crisis of 2008 whereby investors chose to trade asset-backed securities without performing independent evaluations of the underlying assets. This has contributed to motivating the search for more effective and transparent tools in the modelling of the capital markets. 
This paper shall contain the details of a proposal for the use of a visual declarative language, based on strategic port-graph rewriting, as a visual modelling tool to analyse an asset-backed securitisation market.
Deeper & Sparser Exploration

We address the problem of efficient exploration by proposing a new meta algorithm in the context of model-based online planning for Bayesian Reinforcement Learning (BRL). We beat the stateof-the-art, while staying computationally faster, in some cases by two orders of magnitude. This is the first Optimism free BRL algorithm to beat all previous state-of-the-art in tabular RL. The main novelty is the use of a candidate policy generator, to generate long-term options in the belief tree, which allows us to create much sparser and deeper trees. We present results on many standard environments and empirically prove its performance.
Neural Network for NILM Based on Operational State Change Classification

Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand. Recently, with the ongoing increment of energy data, there are many data-driven deep learning architectures being applied to solve the non-intrusive energy disaggregation problem. However, most proposed methods try to estimate the on-off state or the power consumption of appliance, which need not only large amount of parameters, but also hyper-parameter optimization prior to training and even preprocessing of energy data for a specified appliance. In this paper, instead of estimating on-off state or power consumption, we adapt a neural network to estimate the operational state change of appliance. Our proposed solution is more feasible across various appliances and lower complexity comparing to previous methods. The simulated experiments in the low sample rate dataset REDD show the competitive performance of the designed method, with respect to other two benchmark methods, Hidden Markov Model-based and Graph Signal processing-based approaches.
Constraints on millicharged dark matter and axionlike particles from timing of radio waves

We derive constraints on millicharged dark matter and axionlike particles using pulsar timing and fast radio burst observations. For dark matter particles of charge ee, the constraint from time of arrival (TOA) of waves is e/mmilli≲10-8  eV-1, for masses mmilli≳10-6  eV. For axionlike particles, the polarization of the signals from pulsars yields a bound in the axial coupling g/ma≲10-13  GeV-1/(10-22  eV), for ma≲10-19  eV. Both bounds scale as (ρ/ρdm)1/2 for fractions of the total dark matter energy density ρdm. We make a precise study of these bounds using TOA from several pulsars, FRB 121102, and polarization measurements of PSR J0437-4715. Our results rule out a new region of the parameter space for these dark matter models.
Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network

Opinion phrase extraction is one of the key tasks in fine-grained sentiment analysis. While opinion expressions could be generic subjective expressions, aspect specific opinion expressions contain both the aspect as well as the opinion expression within the original sentence context. In this work, we formulate the task as an instance of token-level sequence labeling. When multiple aspects are present in a sentence, detection of opinion phrase boundary becomes difficult and label of each word depend not only upon the surrounding words but also with the concerned aspect. We propose a neural network architecture with bidirectional LSTM (Bi-LSTM) and a novel attention mechanism. Bi-LSTM layer learns the various sequential pattern among the words without requiring any hand-crafted features. The attention mechanism captures the importance of context words on a particular aspect opinion expression when multiple aspects are present in a sentence via location and content based memory. A Conditional Random Field (CRF) model is incorporated in the final layer to explicitly model the dependencies among the output labels. Experimental results on Hotel dataset from this http URL showed that our approach outperformed several state-of-the-art baselines.
A Network-centric Framework for Auditing Recommendation Systems

To improve the experience of consumers, all social media, commerce and entertainment sites deploy Recommendation Systems (RSs) that aim to help users locate interesting content. These RSs are black-boxes – the way a chunk of information is filtered out and served to a user from a large information base is mostly opaque. No one except the parent company generally has access to the entire information required for auditing these systems – neither the details of the algorithm nor the user-item interactions are ever made publicly available for third-party auditors. Hence auditing RSs remains an important challenge, especially with the recent concerns about how RSs are affecting the views of the society at large with new technical jargons like “echo chambers”, “confirmation biases”, “filter bubbles” etc. in place. Many prior works have evaluated different properties of RSs such as diversity, novelty, etc. However, most of these have focused on evaluating static snapshots of RSs. Today, auditors are not only interested in these static evaluations on a snapshot of the system, but also interested in how these systems are affecting the society in course of time. In this work, we propose a novel network-centric framework which is not only able to quantify various static properties of RSs, but also is able to quantify dynamic properties such as how likely RSs are to lead to polarization or segregation of information among their users. We apply the framework to several popular movie RSs to demonstrate its utility.
Graph Classification with Recurrent Variational Neural Networks

We address the problem of graph classification based only on structural information. Most standard methods require either the pairwise comparisons of all graphs in the dataset or the extraction of ad-hoc features to perform classification. Those methods respectively raise scalability issues when the number of samples in the dataset is large, and flexibility issues when discriminative information is characterized by exotic features. Recent advances in neural network architectures offer new possibilities for graph analysis in terms of scalability and feature learning. In this paper, we propose a new sequential approach using recurrent neural networks (RNN). Our model sequentially embeds information allowing to model final class membership probabilities. We also propose a regularization based on variational node prediction ending up with better learning and generalization. We experimentally show that our model reaches state-of-the-art classification results on several common molecular datasets. Finally, we perform a qualitative analysis and give some insights about how the joint node prediction helps the model to better classify graphs.
Area propagator and boosted spin networks in loop quantum gravity

Quantum states of geometry in loop quantum gravity are defined as spin networks, which are graph dressed with representations. A spin network edge carries a half-integer spin, representing basic quanta of area, and the standard framework imposes an area matching constraint along the edge: it carries the same spin at its source and target vertices. In the context of coarse-graining, or equivalently of the definition of spin networks as projective limits of graphs, it appears natural to introduce excitations of curvature along the edges. An edge is then treated similarly to a propagator living on the links of Feynman diagrams in quantum field theory: curvature excitations create little loops -tadpoles- which renormalize it. This relaxes the area matching condition, with different spins at both ends of the edge. We show that this is equivalent to combining the usual holonomy along the edge with a Lorentz boost into group elements living on the spin network edges, underlining the fact that the Ashtekar–Barbero connection carries extrinsic curvature degrees of freedom. This finally leads us to introduce a new notion of area waves in loop quantum gravity.
The Cohomology of the Ordinals I: Basic Theory and Consistency Results

In this paper, the first in a projected two-part series, we describe an organizing framework for the study of infinitary combinatorics. This framework is Cech cohomology. We show in particular that the Cech cohomology groups of the ordinals articulate higher-dimensional generalizations of Todorcevic's walks and coherent sequences techniques, and begin to account for those techniques' `unreasonable effectiveness' on $\omega_1$. This discussion occupies the first half of our paper and is written with a general mathematical audience in mind. 
We turn in the paper's second half to more properly set-theoretic considerations. We describe a number of consistency results on the cohomology groups of the ordinals which certify their status as a graded family of incompactness principles. We show in particular that nontrivial cohomology groups on the ordinals are in some tension with large cardinals, and are maximally extant in Godel's model $\mathrm{L}$. We describe forcings to add, then trivialize, nontrivial $n$-cocycles, and conclude with some comparison of these principles with those benchmark incompactness phenomena, the existence of square sequences and failures of stationary reflection.
Identification of epileptic regions from electroencephalographic data: Feigenbaum graphs

Diagnosing epilepsy is a problem of crucial importance. So analysing EEG data is of much importance to help this diagnosis. Assembling the Feigenbaum graphs for EEG signals. And calculating their average clustering, average degree, and average shortest path length. We manage to characterize two different data sets from each other. Each data set consisted of focal and non-focal activity, from where epileptic regions could be identified. This method yields good results for identifying sets of data from epileptic zones. Suggesting our approach could be used to aid physicians with diagnosing epilepsy from EEG data.
Nonlinear diffusion equations with degenerate fast-decay mobility by coordinate transformation

We prove an existence and uniqueness result for solutions to nonlinear diffusion equations with degenerate mobility posed on a bounded interval for a certain density $u$. In case of \emph{fast-decay} mobilities, namely mobilities functions under a Osgood integrability condition, a suitable coordinate transformation is introduced and a new nonlinear diffusion equation with linear mobility is obtained. We observe that the coordinate transformation induces a mass-preserving scaling on the density and the nonlinearity, described by the original nonlinear mobility, is included in the diffusive process. We show that the rescaled density $\rho$ is the unique weak solution to the nonlinear diffusion equation with linear mobility. Moreover, the results obtained for the density $\rho$ allow us to motivate the aforementioned change of variable and to state the results in terms of the original density $u$ without prescribing any boundary conditions.
Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior

Recent research brought awareness of the issue of bots on social media and the significant risks of mass manipulation of public opinion in the context of political discussion. In this work, we leverage Twitter to study the discourse during the 2018 US midterm elections and analyze social bot activity and interactions with humans. We collected 2.6 million tweets for 42 days around the election day from nearly 1 million users. We use the collected tweets to answer three research questions: (i) Do social bots lean and behave according to a political ideology? (ii) Can we observe different strategies among liberal and conservative bots? (iii) How effective are bot strategies in engaging humans? We show that social bots can be accurately classified according to their political leaning and behave accordingly. Conservative bots share most of the topics of discussion with their human counterparts, while liberal bots show less overlap and a more inflammatory attitude. We studied bot interactions with humans and observed different strategies. Finally, we measured bots embeddedness in the social network and the extent of human engagement with each group of bots. Results show that conservative bots are more deeply embedded in the social network and more effective than liberal bots at exerting influence on humans.
MODELING MICROBIAL ABUNDANCES AND DYSBIOSIS WITH BETA-BINOMIAL REGRESSION.

Using a sample from a population to estimate the proportion of the population with a certain category label is a broadly important problem. In the context of microbiome studies, this problem arises when researchers wish to use a sample from a population of microbes to estimate the population proportion of a particular taxon, known as the taxon's relative abundance. In this paper, we propose a beta-binomial model for this task. Like existing models, our model allows for a taxon's relative abundance to be associated with covariates of interest. However, unlike existing models, our proposal also allows for the overdispersion in the taxon's counts to be associated with covariates of interest. We exploit this model in order to propose tests not only for differential relative abundance, but also for differential variability. The latter is particularly valuable in light of speculation that dysbiosis, the perturbation from a normal microbiome that can occur in certain disease conditions, may manifest as a loss of stability, or increase in variability, of the counts associated with each taxon. We demonstrate the performance of our proposed model using a simulation study and an application to soil microbial data.
FDDB-360: Face Detection in 360-Degree Fisheye Images

360° cameras offer the possibility to cover a large area, for example an entire room, without using multiple distributed vision sensors. However, geometric distortions introduced by their lenses make computer vision problems more challenging. In this paper we address face detection in 360° fisheye images. We show how a face detector trained on regular images can be re-trained for this purpose, and we also provide a 360° fisheye-like version of the popular FDDB face detection dataset, which we call FDDB-360.
Evolution of the Stellar Mass Function and Infrared Luminosity Function of Galaxies since z = 1.2

We measured evolution of the K-band luminosity function and stellar mass function (SMF) for red and blue galaxies at z < 1.2 using a sample of 353 594 I < 24 galaxies in 8.26 square degrees of Boötes. We addressed several sources of systematic and random error in measurements of total galaxy light, photometric redshift, and absolute magnitude. We have found that the K-band luminosity density for both red and blue galaxies increased by a factor of 1.2 from z ∼ 1.1 to z ∼ 0.3, while the most luminous red (blue) galaxies decreased in luminosity by 0.19(0.33) mag or ×0.83(0.74). These results are consistent with z < 0.2 studies, while our large sample size and area result in smaller Poisson and cosmic variance uncertainties than most z > 0.4 luminosity and mass function measurements. Using an evolving relation for K-band mass-to-light ratios as a function of (B−V) color, we found a slowly decreasing rate of growth in red galaxy stellar mass density of ×2.3 from z ∼ 1.1 to z ∼ 0.3, indicating a slowly decreasing rate of migration from the blue cloud to the red sequence. Unlike some studies of the SMF, we find that massive red galaxies grow by a factor of ×1.7 from z ∼ 1.1 to z ∼ 0.3, with the rate of growth due to mergers decreasing with time. These results are comparable with measurements of merger rates and clustering, and they are also consistent with the red galaxy stellar mass growth implied by comparing K-band luminosity evolution with the fading of passive stellar population models.
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops

While humor is often thought to be beyond the reach of Natural Language Processing, we show that several aspects of single-word humor correlate with simple linear directions in Word Embeddings. In particular: (a) the word vectors capture multiple aspects discussed in humor theories from various disciplines; (b) each individual's sense of humor can be represented by a vector, which can predict differences in people's senses of humor on new, unrated, words; and (c) upon clustering humor ratings of multiple demographic groups, different humor preferences emerge across the different groups. Humor ratings are taken from the work of Engelthaler and Hills (2017) as well as from an original crowdsourcing study of 120,000 words. Our dataset further includes annotations for the theoretically-motivated humor features we identify.
SiamVGG: Visual Tracking using Deeper Siamese Networks

Recently, we have seen a rapid development of Deep Neural Network (DNN) based visual tracking solutions. Some trackers combine the DNN-based solutions with Discriminative Correlation Filters (DCF) to extract semantic features and successfully deliver the state-of-the-art tracking accuracy. However, these solutions are highly compute-intensive, which require long processing time, resulting unsecured real-time performance. To deliver both high accuracy and reliable real-time performance, we propose a novel tracker called SiamVGG. It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking. 
The architecture of SiamVGG is customized from VGG-16, with the parameters shared by both exemplary images and desired input video frames. 
We demonstrate the proposed SiamVGG on OTB-2013/50/100 and VOT 2015/2016/2017 datasets with the state-of-the-art accuracy while maintaining a decent real-time performance of 50 FPS running on a GTX 1080Ti. Our design can achieve 2% higher Expected Average Overlap (EAO) compared to the ECO and C-COT in VOT2017 Challenge.
Multimodal Conditional Learning with Fast Thinking Policy-like Model and Slow Thinking Planner-like Model

This paper studies the supervised learning of the conditional distribution of a high-dimensional output given an input, where the output and input belong to two different modalities, e.g., the output is an image and the input is a sketch. We solve this problem by learning two models that bear similarities to those in reinforcement learning and optimal control. One model is policy-like. It generates the output directly by a non-linear transformation of the input and a noise vector. This amounts to fast thinking because the conditional generation is accomplished by direct sampling. The other model is planner-like. It learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. This amounts to slow thinking because the sampling process is accomplished by an iterative algorithm such as Langevin dynamics. We propose to learn the two models jointly, where the fast thinking policy-like model serves to initialize the sampling of the slow thinking planner-like model, and the planner-like model refines the initial output by an iterative algorithm. The planner-like model learns from the difference between the refined output and the observed output, while the policy-like model learns from how the planner-like model refines its initial output. We demonstrate the effectiveness of the proposed method on various image generation tasks.