nils-herrmann/clean_scirepeval huggingface dataset:
Mutual exclusivity loss for semi-supervised deep learning

In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term that explicitly forces the classifier's prediction for multiple classes to be mutually-exclusive and effectively guides the decision boundary to lie on the low density space between the manifolds corresponding to different classes of data. Our proposed approach is general and can be used with any backpropagation-based learning method. We show through different experiments that our method can improve the object recognition performance of ConvNets using unlabeled data.
Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition

Unsupervised text embeddings extraction is crucial for text understanding in machine learning. Word2Vec and its variants have received substantial success in mapping words with similar syntactic or semantic meaning to vectors close to each other. However, extracting context-aware word-sequence embedding remains a challenging task. Training over large corpus is difficult as labels are difficult to get. More importantly, it is challenging for pre-trained models to obtain word-sequence embeddings that are universally good for all downstream tasks or for any new datasets. We propose a two-phased ConvDic+DeconvDec framework to solve the problem by combining a word-sequence dictionary learning model with a word-sequence embedding decode model. We propose a convolutional tensor decomposition mechanism to learn good word-sequence phrase dictionary in the learning phase. It is proved to be more accurate and much more efficient than the popular alternating minimization method. In the decode phase, we introduce a deconvolution framework that is immune to the problem of varying sentence lengths. The word-sequence embeddings we extracted using ConvDic+DeconvDec are universally good for a few downstream tasks we test on. The framework requires neither pre-training nor prior/outside information.
An Application of Network Lasso Optimization For Ride Sharing Prediction

Ride sharing has important implications in terms of environmental, social and individual goals by reducing carbon footprints, fostering social interactions and economizing commuter costs. The ride sharing systems that are commonly available lack adaptive and scalable techniques that can simultaneously learn from the large scale data and predict in real-time dynamic fashion. In this paper, we study such a problem towards a smart city initiative, where a generic ride sharing system is conceived capable of making predictions about ride share opportunities based on the historically recorded data while satisfying real-time ride requests. Underpinning the system is an application of a powerful machine learning convex optimization framework called Network Lasso that uses the Alternate Direction Method of Multipliers (ADMM) optimization for learning and dynamic prediction. We propose an application of a robust and scalable unified optimization framework within the ride sharing case-study. The application of Network Lasso framework is capable of jointly optimizing and clustering different rides based on their spatial and model similarity. The prediction from the framework clusters new ride requests, making accurate price prediction based on the clusters, detecting hidden correlations in the data and allowing fast convergence due to the network topology. We provide an empirical evaluation of the application of ADMM network Lasso on real trip record and simulated data, proving their effectiveness since the mean squared error of the algorithm's prediction is minimized on the test rides.
Tunable Online MUS/MSS Enumeration

In various areas of computer science, the problem of dealing with a set of constraints arises. If the set of constraints is unsatisfiable, one may ask for a minimal description of the reason for this unsatisifi- ability. Minimal unsatisifable subsets (MUSes) and maximal satisifiable subsets (MSSes) are two kinds of such minimal descriptions. The goal of this work is the enumeration of MUSes and MSSes for a given constraint system. As such full enumeration may be intractable in general, we focus on building an online algorithm, which produces MUSes/MSSes in an on-the-fly manner as soon as they are discovered. The problem has been studied before even in its online version. However, our algorithm uses a novel approach that is able to outperform current state-of-the art algorithms for online MUS/MSS enumeration. Moreover, the performance of our algorithm can be adjusted using tunable parameters. We evaluate the algorithm on a set of benchmarks.
SUNWARD-PROPAGATING ALFVÉNIC FLUCTUATIONS OBSERVED IN THE HELIOSPHERE

The mixture/interaction of anti-sunward-propagating Alfvénic fluctuations (AFs) and sunward-propagating Alfvénic fluctuations (SAFs) is believed to result in the decrease of the Alfvénicity of solar wind fluctuations with increasing heliocentric distance. However, SAFs are rarely observed at 1 au and solar wind AFs are found to be generally outward. Using the measurements from Voyager 2 and Wind, we perform a statistical survey of SAFs in the heliosphere inside 6 au. We first report two SAF events observed by Voyager 2. One is in the anti-sunward magnetic sector with a strong positive correlation between the fluctuations of magnetic field and solar wind velocity. The other one is in the sunward magnetic sector with a strong negative magnetic field—velocity correlation. Statistically, the percentage of SAFs increases gradually with heliocentric distance, from about 2.7% at 1.0 au to about 8.7% at 5.5 au. These results provide new clues for understanding the generation mechanism of SAFs.
Community Structure in Industrial SAT Instances

Modern SAT solvers have experienced a remarkable progress on solving industrial instances. It is believed that most of these successful techniques exploit the underlying structure of industrial instances. Recently, there have been some attempts to analyze the structure of industrial SAT instances in terms of complex networks, with the aim of explaining the success of SAT solving techniques, and possibly improving them. 
In this paper, we study the community structure, or modularity, of industrial SAT instances. In a graph with clear community structure, or high modularity, we can find a partition of its nodes into communities such that most edges connect variables of the same community. Representing SAT instances as graphs, we show that most application benchmarks are characterized by a high modularity. On the contrary, random SAT instances are closer to the classical Erdös-Rényi random graph model, where no structure can be observed. We also analyze how this structure evolves by the effects of the execution of a CDCL SAT solver, and observe that new clauses learned by the solver during the search contribute to destroy the original structure of the formula. Motivated by this observation, we finally present an application that exploits the community structure to detect relevant learned clauses, and we show that detecting these clauses results in an improvement on the performance of the SAT solver. Empirically, we observe that this improves the performance of several SAT solvers on industrial SAT formulas, especially on satisfiable instances.
Spatial entanglement entropy in the ground state of the Lieb-Liniger model

We consider the entanglement between two spatial subregions in the Lieb-Liniger model of bosons in one spatial dimension interacting via a contact interaction. Using ground state path integral quantum Monte Carlo we numerically compute the R\'{e}nyi entropy of the reduced density matrix of the subsystem as a measure of entanglement. Our numerical algorithm is based on a replica method previously introduced by the authors, which we extend to efficiently study the entanglement of spatial subsystems of itinerant bosons. We confirm a logarithmic scaling of the R\'{e}nyi entropy with subsystem size that is expected from conformal field theory, and compute the non-universal subleading constant for interaction strengths ranging over two orders of magnitude. In the strongly interacting limit, we find agreement with the known free fermion result.
Tagger: Deep Unsupervised Perceptual Grouping

We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Rather than being trained for any specific segmentation, our framework learns the grouping process in an unsupervised manner or alongside any supervised task. By enriching the representations of a neural network, we enable it to group the representations of different objects in an iterative manner. By allowing the system to amortize the iterative inference of the groupings, we achieve very fast convergence. In contrast to many other recently proposed methods for addressing multi-object scenes, our system does not assume the inputs to be images and can therefore directly handle other modalities. For multi-digit classification of very cluttered images that require texture segmentation, our method offers improved classification performance over convolutional networks despite being fully connected. Furthermore, we observe that our system greatly improves on the semi-supervised result of a baseline Ladder network on our dataset, indicating that segmentation can also improve sample efficiency.
Collinear scattering of photoexcited carriers in graphene

We propose an explicitly solvable model for collinear scattering of photoexcited carriers in intrinsic graphene irradiated by monochromatic light. We find that the collinear scattering rate is directly proportional to the photocarrier energy and derive an analytic expression for the corresponding relaxation time. The result agrees with the recent numerical prediction [Mihnev et al.  (2016)] and is able to describe the photocarrier evolution at low energies, where scattering on optical phonons is strongly suppressed.
A review of undirected and acyclic directed Gaussian Markov model selection and estimation

Markov models lie at the interface between statistical independence in a probability distribution and graph separation properties. We review model selection and estimation in directed and undirected Markov models with Gaussian parametrization, emphasizing the main similarities and differences. These two model types are foundationally similar but not equivalent, as we highlight. We report existing results with a unified notation and terminology, taking into account literature from both the artificial intelligence and statistics research communities, which first developed these models. Finally, we point out the main active research areas and open problems now existing with regard to these traditional, albeit rich, Markov models.
Analyzing the Behavior of Visual Question Answering Models

Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze two models, one each from two major classes of VQA models -- with-attention and without-attention and show the similarities and differences in the behavior of these models. We also analyze the winning entry of the VQA Challenge 2016. 
Our behavior analysis reveals that despite recent progress, today's VQA models are "myopic" (tend to fail on sufficiently novel instances), often "jump to conclusions" (converge on a predicted answer after 'listening' to just half the question), and are "stubborn" (do not change their answers across images).
Detecting New and Arbitrary Relations among Linked Data Entities using Pattern Extraction

Although several RDF knowledge bases are available through the LOD initiative, often these data sources remain isolated, lacking schemata and links to other datasets. While there are numerous works that focus on establishing that two resources are identical and on adding more instances of an already existing relation, the problem of finding new relations between any two given datasets has not been investigated in detail. In this paper, given two entity sets, we present an unsupervised approach to enrich the LOD cloud with new relations between them by exploiting the web corpus. During the first phase we gather prospective relations from the corpus through pattern extraction and paraphrase detection. In the second phase, we perform actual enrichment by extracting instances of these relations. We have empirically evaluated our approach on several dataset pairs and found that the system can indeed be used for enriching the existing datasets with new relations.
Cosmic microwave background and inflation in multi-fractional spacetimes

We use FIRAS and PLANCK 2015 data to place observational bounds on inflationary scenarios in multi-fractional spacetimes with q-derivatives. While a power-law expansion in the geometric time coordinate is subject to the usual constraints from the tensor-to-scalar ratio, model-independent best fits of the black-body and scalar spectra yield upper limits on the free parameters of the multi-fractal measure of the theory. When the measure describing the fractal spacetime geometry is non-oscillating, information on the CMB black-body spectrum places constraints on the theory independent from but weaker than those obtained from the Standard Model, astrophysical gravitational waves and gamma-ray bursts (GRBs). When log oscillations are included and the measure describes a discrete fractal spacetime at microscopic scales, we obtain the first observational constraints on the amplitudes of such oscillations and find, in general, strong constraints on the multi-scale geometry and on the dimension of space. These results complete the scan and reduction of the parameter space of the theory. Black-body bounds are obtained also for the theory with weighted derivatives.
Stability of Spreading Processes with General Transmission and Recovery Times

Although viral spreading processes taking place in networks are commonly analyzed using Markovian models in which both the transmission times and the recovery times follow exponential distributions, empirical studies show that, in most real scenarios, the distribution of these times are far from exponential. To overcome this limitation, we first introduce a generalized spreading model that allows for transmission and recovery times to follow arbitrary distributions within an arbitrary accuracy. In this context, we derive conditions for the generalized spreading process to converge towards the disease-free equilibrium (in other words, to eradicate the viral spread) without relying on mean-field approximations. Based on our results, we illustrate how the particular shape of the transmission/recovery distribution heavily influences the boundary of the stability region of the spread, as well as the decay rate inside this region. Therefore, modeling non-exponential transmission/recovery times observed in realistic spreading processes via exponential distributions can induce significant errors in the stability analysis of the dynamics.
Event Identification in Social Networks

Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, they can be seen as a potentially viable source of information to understand the current emerging topics/events. The ability to model emerging topics is a substantial step to monitor and summarize the information originating from social sources. Applying traditional methods for event detection which are often proposed for processing large, formal and structured documents, are less effective, due to the short length, noisiness and informality of the social posts. Recent event detection techniques address these challenges by exploiting the opportunities behind abundant information available in social networks. This article provides an overview of the state of the art in event detection from social networks.
Complex Systems and a Computational Social Science Perspective on the Labor Market

Labor market institutions are central for modern economies, and their polices can directly affect unemployment rates and economic growth. At the individual level, unemployment often has a detrimental impact on people's well-being and health. At the national level, high employment is one of the central goals of any economic policy, due to its close association with national prosperity. The main goal of this thesis is to highlight the need for frameworks that take into account the complex structure of labor market interactions. In particular, we explore the benefits of leveraging tools from computational social science, network science, and data-driven theories to measure the flow of opportunities and information in the context of the labor market. First, we investigate our key hypothesis, which is that opportunity/information flow through weak ties, and this is a key determinant of the length of unemployment. We then extend the idea of opportunity/information flow to clusters of other economic activities, where we expect the flow within clusters of related activities to be higher than within isolated activities. This captures the intuition that within related activities there are more "capitals" involved and that such activities require similar "capabilities." Therefore, more extensive clusters of economic activities should generate greater growth through exploiting the greater flow of opportunities and information. We quantify the opportunity/information flow using a complexity measure of two economic activities (i.e. jobs and exports).
High-Performance Hybrid Electronic Devices from Layered PtSe2 Films Grown at Low Temperature.

Layered two-dimensional (2D) materials display great potential for a range of applications, particularly in electronics. We report the large-scale synthesis of thin films of platinum diselenide (PtSe2), a thus far scarcely investigated transition metal dichalcogenide. Importantly, the synthesis by thermally assisted conversion is performed at 400 °C, representing a breakthrough for the direct integration of this material with silicon (Si) technology. Besides the thorough characterization of this 2D material, we demonstrate its promise for applications in high-performance gas sensing with extremely short response and recovery times observed due to the 2D nature of the films. Furthermore, we realized vertically stacked heterostructures of PtSe2 on Si which act as both photodiodes and photovoltaic cells. Thus, this study establishes PtSe2 as a potential candidate for next-generation sensors and (opto-)electronic devices, using fabrication protocols compatible with established Si technologies.
Using Sequence Ensembles for Seeding Alignments of MinION Sequencing Data

Oxford Nanopore MinION sequencer is currently the smallest sequencing device available. While being able to produce very long reads (reads of up to 100~kbp were reported), it is prone to high sequencing error rates of up to 30%. Since most of these errors are insertions or deletions, it is very difficult to adapt popular seed-based algorithms designed for aligning data sets with much lower error rates. 
Base calling of MinION reads is typically done using hidden Markov models. In this paper, we propose to represent each sequencing read by an ensemble of sequences sampled from such a probabilistic model. This approach can improve the sensitivity and false positive rate of seeding an alignment compared to using a single representative base call sequence for each read.
Degree-correlation, omniscience, and randomized immunization protocols in finite networks

Many naturally occurring networks have a power-law degree distribution as well as a non-zero degree correlation. Despite this, most studies analyzing the efficiency of immunization strategies in networks have concentrated only on power-law degree distribution and ignored degree correlation. This study looks specifically at the effect degree-correlation has on the efficiency of several immunization strategies in scale-free networks. Generally, we found that positive degree correlation raises the number of immunized individuals needed to stop the spread of the infection. Importantly, we found that in networks with positive degree correlation, immunization strategies that utilize knowledge of initial popularity actually perform worse on average than random immunization strategies.
Dynamics and biases of online attention: the case of aircraft crashes

The Internet not only has changed the dynamics of our collective attention but also through the transactional log of online activities, provides us with the opportunity to study attention dynamics at scale. In this paper, we particularly study attention to aircraft incidents and accidents using Wikipedia transactional data in two different language editions, English and Spanish. We study both the editorial activities on and the viewership of the articles about airline crashes. We analyse how the level of attention is influenced by different parameters such as number of deaths, airline region, and event locale and date. We find evidence that the attention given by Wikipedia editors to pre-Wikipedia aircraft incidents and accidents depends on the region of the airline for both English and Spanish editions. North American airline companies receive more prompt coverage in English Wikipedia. We also observe that the attention given by Wikipedia visitors is influenced by the airline region but only for events with a high number of deaths. Finally we show that the rate and time span of the decay of attention is independent of the number of deaths and a fast decay within about a week seems to be universal. We discuss the implications of these findings in the context of attention bias.
Machine-based Multimodal Pain Assessment Tool for Infants: A Review

Bedside caregivers assess infants' pain at constant intervals by observing specific behavioral and physiological signs of pain. This standard has two main limitations. The first limitation is the intermittent assessment of pain, which might lead to missing pain when the infants are left unattended. Second, it is inconsistent since it depends on the observer's subjective judgment and differs between observers. The intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term consequences. To mitigate these limitations, the current standard can be augmented by an automated system that monitors infants continuously and provides quantitative and consistent assessment of pain. Several automated methods have been introduced to assess infants' pain automatically based on analysis of behavioral or physiological pain indicators. This paper comprehensively reviews the automated approaches (i.e., approaches to feature extraction) for analyzing infants' pain and the current efforts in automatic pain recognition. In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.