nils-herrmann/clean_scirepeval huggingface dataset:
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation

Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution have been limited to naive or inefficient architectures. In this paper, we introduce spatio-temporal sub-pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real-time speed. Specifically, we discuss the use of early fusion, slow fusion and 3D convolutions for the joint processing of multiple consecutive video frames. We also propose a novel joint motion compensation and video super-resolution algorithm that is orders of magnitude more efficient than competing methods, relying on a fast multi-resolution spatial transformer module that is end-to-end trainable. These contributions provide both higher accuracy and temporally more consistent videos, which we confirm qualitatively and quantitatively. Relative to single-frame models, spatio-temporal networks can either reduce the computational cost by 30% whilst maintaining the same quality or provide a 0.2dB gain for a similar computational cost. Results on publicly available datasets demonstrate that the proposed algorithms surpass current state-of-the-art performance in both accuracy and efficiency.
NIST Big Data Interoperability Framework : Volume 1 , Definitions

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. The growth of data is outpacing scientific and technological advances in data analytics. Opportunities exist with Big Data to address the volume, velocity and variety of data through new scalable architectures. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework (NBDIF) series of volumes. This volume, Volume 1, contains a definition of Big Data and related terms necessary to lay the groundwork for discussions surrounding Big Data.
Socially guided intrinsic motivation for robot learning of motor skills

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.
Second Generation Biofuels: High-Efficiency Microalgae for Biodiesel Production

The use of fossil fuels is now widely accepted as unsustainable due to depleting resources and the accumulation of greenhouse gases in the environment that have already exceeded the “dangerously high” threshold of 450 ppm CO2-e. To achieve environmental and economic sustainability, fuel production processes are required that are not only renewable, but also capable of sequestering atmospheric CO2. Currently, nearly all renewable energy sources (e.g. hydroelectric, solar, wind, tidal, geothermal) target the electricity market, while fuels make up a much larger share of the global energy demand (∼66%). Biofuels are therefore rapidly being developed. Second generation microalgal systems have the advantage that they can produce a wide range of feedstocks for the production of biodiesel, bioethanol, biomethane and biohydrogen. Biodiesel is currently produced from oil synthesized by conventional fuel crops that harvest the sun’s energy and store it as chemical energy. This presents a route for renewable and carbon-neutral fuel production. However, current supplies from oil crops and animal fats account for only approximately 0.3% of the current demand for transport fuels. Increasing biofuel production on arable land could have severe consequences for global food supply. In contrast, producing biodiesel from algae is widely regarded as one of the most efficient ways of generating biofuels and also appears to represent the only current renewable source of oil that could meet the global demand for transport fuels. The main advantages of second generation microalgal systems are that they: (1) Have a higher photon conversion efficiency (as evidenced by increased biomass yields per hectare): (2) Can be harvested batch-wise nearly all-year-round, providing a reliable and continuous supply of oil: (3) Can utilize salt and waste water streams, thereby greatly reducing freshwater use: (4) Can couple CO2-neutral fuel production with CO2 sequestration: (5) Produce non-toxic and highly biodegradable biofuels. Current limitations exist mainly in the harvesting process and in the supply of CO2 for high efficiency production. This review provides a brief overview of second generation biodiesel production systems using microalgae.
The biochemistry of apoptosis

Apoptosis — the regulated destruction of a cell — is a complicated process. The decision to die cannot be taken lightly, and the activity of many genes influence a cell's likelihood of activating its self-destruction programme. Once the decision is taken, proper execution of the apoptotic programme requires the coordinated activation and execution of multiple subprogrammes. Here I review the basic components of the death machinery, describe how they interact to regulate apoptosis in a coordinated manner, and discuss the main pathways that are used to activate cell death.
A Language Extension for Expressing Constraints on Data Access

Controlled sharing of information is needed and desirable for many applications and is supported in operating systems by access control mechanisms. This paper shows how to extend programming languages to provide controlled sharing. The extension permits expression of access constraints on shared data. Access constraints can apply both to simple objects, and to objects that are components of larger objects, such as bank account records in a bank's data base. The constraints are stated declaratively, and can be enforced by static checking similar to type checking. The approach can be used to extend any strongly-typed language, but is particularly suitable for extending languages that support the notion of abstract data types.
Understanding Graph Sampling Algorithms for Social Network Analysis

Being able to keep the graph scale small while capturing the properties of the original social graph, graph sampling provides an efficient, yet inexpensive solution for social network analysis. The challenge is how to create a small, but representative sample out of the massive social graph with millions or even billions of nodes. Several sampling algorithms have been proposed in previous studies, but there lacks fair evaluation and comparison among them. In this paper, we analyze the state-of art graph sampling algorithms and evaluate their performance on some widely recognized graph properties on directed graphs using large-scale social network datasets. We evaluate not only the commonly used node degree distribution, but also clustering coefficient, which quantifies how well connected are the neighbors of a node in a graph. Through the comparison we have found that none of the algorithms is able to obtain satisfied sampling results in both of these properties, and the performance of each algorithm differs much in different kinds of datasets.
A machine learning framework for sport result prediction

Machine learning (ML) is one of the intelligent methodologies that have shown promising results in the domains of classification and prediction. One of the expanding areas necessitating good predictive accuracy is sport prediction, due to the large monetary amounts involved in betting. In addition, club managers and owners are striving for classification models so that they can understand and formulate strategies needed to win matches. These models are based on numerous factors involved in the games, such as the results of historical matches, player performance indicators, and opposition information. This paper provides a critical analysis of the literature in ML, focusing on the application of Artificial Neural Network (ANN) to sport results prediction. In doing so, we identify the learning methodologies utilised, data sources, appropriate means of model evaluation, and specific challenges of predicting sport results. This then leads us to propose a novel sport prediction framework through which ML can be used as a learning strategy. Our research will hopefully be informative and of use to those performing future research in this application area.
Inferring user interests in the Twitter social network

We propose a novel mechanism to infer topics of interest of individual users in the Twitter social network. We observe that in Twitter, a user generally follows experts on various topics of her interest in order to acquire information on those topics. We use a methodology based on social annotations (proposed earlier by us) to first deduce the topical expertise of popular Twitter users, and then transitively infer the interests of the users who follow them. This methodology is a sharp departure from the traditional techniques of inferring interests of a user from the tweets that she posts or receives. We show that the topics of interest inferred by the proposed methodology are far superior than the topics extracted by state-of-the-art techniques such as using topic models (Labeled LDA) on tweets. Based upon the proposed methodology, we build a system Who Likes What, which can infer the interests of millions of Twitter users. To our knowledge, this is the first system that can infer interests for Twitter users at such scale. Hence, this system would be particularly beneficial in developing personalized recommender services over the Twitter platform.
On the design of LDA models for aspect-based opinion mining

Aspect-based opinion mining, which aims to extract aspects and their corresponding ratings from customers reviews, provides very useful information for customers to make purchase decisions. In the past few years several probabilistic graphical models have been proposed to address this problem, most of them based on Latent Dirichlet Allocation (LDA). While these models have a lot in common, there are some characteristics that distinguish them from each other. These fundamental differences correspond to major decisions that have been made in the design of the LDA models. While research papers typically claim that a new model outperforms the existing ones, there is normally no "one-size-fits-all" model. In this paper, we present a set of design guidelines for aspect-based opinion mining by discussing a series of increasingly sophisticated LDA models. We argue that these models represent the essence of the major published methods and allow us to distinguish the impact of various design decisions. We conduct extensive experiments on a very large real life dataset from Epinions.com (500K reviews) and compare the performance of different models in terms of the likelihood of the held-out test set and in terms of the accuracy of aspect identification and rating prediction.
Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology.

The goal of this project was to determine whether screening different groups of elderly individuals in a general or specialty practice would be beneficial in detecting dementia.



Epidemiologic studies of aging and dementia have demonstrated that the use of research criteria for the classification of dementia has yielded three groups of subjects: those who are demented, those who are not demented, and a third group of individuals who cannot be classified as normal or demented but who are cognitively (usually memory) impaired.



The authors conducted computerized literature searches and generated a set of abstracts based on text and index words selected to reflect the key issues to be addressed. Articles were abstracted to determine whether there were sufficient data to recommend the screening of asymptomatic individuals. Other research studies were evaluated to determine whether there was value in identifying individuals who were memory-impaired beyond what one would expect for age but who were not demented. Finally, screening instruments and evaluation techniques for the identification of cognitive impairment were reviewed.



There were insufficient data to make any recommendations regarding cognitive screening of asymptomatic individuals. Persons with memory impairment who were not demented were characterized in the literature as having mild cognitive impairment. These subjects were at increased risk for developing dementia or AD when compared with similarly aged individuals in the general population.



There were sufficient data to recommend the evaluation and clinical monitoring of persons with mild cognitive impairment due to their increased risk for developing dementia (Guideline). Screening instruments, e.g., Mini-Mental State Examination, were found to be useful to the clinician for assessing the degree of cognitive impairment (Guideline), as were neuropsychologic batteries (Guideline), brief focused cognitive instruments (Option), and certain structured informant interviews (Option). Increasing attention is being paid to persons with mild cognitive impairment for whom treatment options are being evaluated that may alter the rate of progression to dementia.
Continual learning in reinforcement environments

Continual learning is the constant development of complex behaviors with no nal end in mind. It is the process of learning ever more complicated skills by building on those skills already developed. In order for learning at one stage of development to serve as the foundation for later learning, a continual-learning agent should learn hierarchically. CHILD, an agent capable of Continual, Hierarchical, Incremental Learning and Development is proposed, described, tested, and evaluated in this dissertation. CHILD accumulates useful behaviors in reinforcement environments by using the Temporal Transition Hierarchies learning algorithm, also derived in the dissertation. This constructive algorithm generates a hierarchical, higher-order neural network that can be used for predicting context-dependent temporal sequences and can learn sequential-task benchmarks more than two orders of magnitude faster than competing neural-network systems. Consequently, CHILD can quickly solve complicated non-Markovian reinforcement-learning tasks and can then transfer its skills to similar but even more complicated tasks, learning these faster still. This continual-learning approach is made possible by the unique properties of Temporal Transition Hierarchies, which allow existing skills to be amended and augmented in precisely the same way that they were constructed in the rst place.
Particle gibbs with ancestor sampling

Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). We present a new PMCMC algorithm that we refer to as particle Gibbs with ancestor sampling (PGAS). PGAS provides the data analyst with an off-the-shelf class of Markov kernels that can be used to simulate, for instance, the typically high-dimensional and highly autocorrelated state trajectory in a state-space model. The ancestor sampling procedure enables fast mixing of the PGAS kernel even when using seemingly few particles in the underlying SMC sampler. This is important as it can significantly reduce the computational burden that is typically associated with using SMC. PGAS is conceptually similar to the existing PG with backward simulation (PGBS) procedure. Instead of using separate forward and backward sweeps as in PGBS, however, we achieve the same effect in a single forward sweep. This makes PGAS well suited for addressing inference problems not only in state-space models, but also in models with more complex dependencies, such as non-Markovian, Bayesian nonparametric, and general probabilistic graphical models.
HydroSense: infrastructure-mediated single-point sensing of whole-home water activity

Recent work has examined infrastructure-mediated sensing as a practical, low-cost, and unobtrusive approach to sensing human activity in the physical world. This approach is based on the idea that human activities (e.g., running a dishwasher, turning on a reading light, or walking through a doorway) can be sensed by their manifestations in an environment's existing infrastructures (e.g., a home's water, electrical, and HVAC infrastructures). This paper presents HydroSense, a low-cost and easily-installed single-point sensor of pressure within a home's water infrastructure. HydroSense supports both identification of activity at individual water fixtures within a home (e.g., a particular toilet, a kitchen sink, a particular shower) as well as estimation of the amount of water being used at each fixture. We evaluate our approach using data collected in ten homes. Our algorithms successfully identify fixture events with 97.9% aggregate accuracy and can estimate water usage with error rates that are comparable to empirical studies of traditional utility-supplied water meters. Our results both validate our approach and provide a basis for future improvements.
Cerebral blood flow differences between long-term meditators and non-meditators

We have studied a number of long-term meditators in previous studies. The purpose of this study was to determine if there are differences in baseline brain function of experienced meditators compared to non-meditators. All subjects were recruited as part of an ongoing study of different meditation practices. We evaluated 12 advanced meditators and 14 non-meditators with cerebral blood flow (CBF) SPECT imaging at rest. Images were analyzed with both region of interest and statistical parametric mapping. The CBF of long-term meditators was significantly higher (p<.05) compared to non-meditators in the prefrontal cortex, parietal cortex, thalamus, putamen, caudate, and midbrain. There was also a significant difference in the thalamic laterality with long-term meditators having greater asymmetry. The observed changes associated with long-term meditation appear in structures that underlie the attention network and also those that relate to emotion and autonomic function.
Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. We propose a novel, accurate tightly-coupled visual-inertial odometry pipeline for such cameras that leverages their outstanding properties to estimate the camera ego-motion in challenging conditions, such as high-speed motion or high dynamic range scenes. The method tracks a set of features (extracted on the image plane) through time. To achieve that, we consider events in overlapping spatio-temporal windows and align them using the current camera motion and scene structure, yielding motion-compensated event frames. We then combine these feature tracks in a keyframebased, visual-inertial odometry algorithm based on nonlinear optimization to estimate the camera’s 6-DOF pose, velocity, and IMU biases. The proposed method is evaluated quantitatively on the public Event Camera Dataset [19] and significantly outperforms the state-of-the-art [28], while being computationally much more efficient: our pipeline can run much faster than real-time on a laptop and even on a smartphone processor. Furthermore, we demonstrate qualitatively the accuracy and robustness of our pipeline on a large-scale dataset, and an extremely high-speed dataset recorded by spinning an event camera on a leash at 850 deg/s.
Software-Based Resolver-to-Digital Converter for DSP-Based Drives Using an Improved Angle-Tracking Observer

In this paper, a new resolver-to-digital conversion method is presented. This method is based on synchronous demodulation of resolver's output signals. A modified angle-tracking observer is proposed to extract the rotor angle in high speeds as well as in low speeds. An estimation algorithm, which is based on the sign and absolute values of sine and cosine of the rotor angle, is introduced to find an appropriate initial value for this observer. This method can be incorporated in a DSP-based motor drive system. The proposed method is simple and cost effective when an appropriate microcontroller is selected for motor control. This method has been successfully implemented in a DSP board. Several tests at different speeds and initial positions have been performed to evaluate the proposed method.
Scaling up Dynamic Topic Models

Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods are all batch algorithms that scan the full dataset before each update of the model and make inexact variational approximations with mean-field assumptions. Due to a lack of a more scalable inference algorithm, despite the usefulness, DTMs have not captured large topic dynamics. This paper fills this research void, and presents a fast and parallelizable inference algorithm using Gibbs Sampling with Stochastic Gradient Langevin Dynamics that does not make any unwarranted assumptions. We also present a Metropolis-Hastings based O(1) sampler for topic assignments for each word token. In a distributed environment, our algorithm requires very little communication between workers during sampling (almost embarrassingly parallel) and scales up to large-scale applications. We are able to learn the largest Dynamic Topic Model to our knowledge, and learned the dynamics of 1,000 topics from 2.6 million documents in less than half an hour, and our empirical results show that our algorithm is not only orders of magnitude faster than the baselines but also achieves lower perplexity.
Mechanisms for multi-level marketing

Multi-level marketing is a marketing approach that motivates its participants to promote a certain product among their friends. The popularity of this approach increases due to the accessibility of modern social networks, however, it existed in one form or the other long before the Internet age began (the infamous Pyramid scheme that dates back at least a century is in fact a special case of multi-level marketing). This paper lays foundations for the study of reward mechanisms in multi-level marketing within social networks. We provide a set of desired properties for such mechanisms and show that they are uniquely satisfied by geometric reward mechanisms. The resilience of mechanisms to false-name manipulations is also considered; while geometric reward mechanisms fail against such manipulations, we exhibit other mechanisms which are false-name-proof.