nils-herrmann/clean_scirepeval huggingface dataset:
SHORTEST PATHS FOR THE REEDS-SHEPP CAR : A WORKED OUT EXAMPLE OF THE USE OF GEOMETRIC TECHNIQUES IN NONLINEAR OPTIMAL CONTROL

We illustrate the use of the techniques of modern geometric optimal control theory by studying the shortest paths for a model of a car that can move forwards and backwards. This problem was discussed in recent work by Reeds and Shepp who showed, by special methods, (a) that shortest path motion could always be achieved by means of trajectories of a special kind, namely, concatenations of at most five pieces, each of which is either a straight line or a circle, and (b) that these concatenations can be classified into 48 three-parameter families. We show how these results fit in a much more general framework, and can be discovered and proved by applying in a systematic way the techniques of Optimal Control Theory. It turns out that the “classical” optimal control tools developed in the 1960’s, such as the Pontryagin Maximum Principle and theorems on the existence of optimal trajectories, are helpful to go part of the way and get some information on the shortest paths, but do not suffice to get the full result. On the other hand, when these classical techniques are combined with the use of a more recently developed body of theory, namely, geometric methods based on the Lie algebraic analysis of trajectories, then one can recover the full power of the Reeds-Shepp results, and in fact slightly improve upon them by lowering their 48 to a 46.
Does the Tyndall effect describe the blue hue periodically observed in subdermal hyaluronic acid gel placement?

The blue hue of skin overlying injected hyaluronic acid (HA) fillers in certain cases has been hypothesized in the literature as related to the Tyndall effect. This investigation aims to understand the relevant optical concepts and to discuss the plausibility of this assertion.



Theoretic and physical aspects of relevant optical theories including the Tyndall effect, the Raleigh criterion and the Mie Solution are discussed, with simple examples. The physical properties of the system (both HA and subcutaneous tissue) are explored. Alternate concepts of dermal hue generation are discussed.



The Tyndall effect (and Rayleigh criterion) describe optical phenomenon that occur as light passes through colloidal solutions containing uniform spherical particles of sizes less than the length of a wavelength of visible light. HA fillers are complex, large, non-spherical, cross-linked hydrogels, and thus are not well characterized by these theories.Skin is a complex optical surface in which shorter wavelengths of light are selectively filtered at superficial depths. Light passing through to subdermal HA would have low blue light amplitude, minimizing what light could be preferentially scattered. Further, should blue hues be 'generated' subdermally, the same skin filters work in reverse, making the blue light poorly detectable by an external observer.



The Tyndall effect is unlikely to cause dermal hue changes in HA filler instillation. Optical and perceptual processes explaining superficial vein coloration may better describe subdermal HA hue changes. Vein coloration is thought to be related to three processes: the reflective properties of the skin, the absorptive properties of blood and the perceptive properties of an observer's eyes. Subdermal HA may simulate these phenomena by a number of undetermined, yet plausible mechanisms.
Cyber security of smart grids modeled through epidemic models in cellular automata

Due to their distributed management, smart grids can be vulnerable to malicious attacks that undermine their cyber security. An adversary can take control of few nodes in the network and spread digital attacks like an infection, whose diffusion is facilitated by the lack of centralized supervision within the smart grid. In this paper, we propose to investigate these phenomena by means of epidemic models applied to cellular automata. We show that the common key parameters of epidemic models, such as the basic reproductive ratio, are also useful in this context to understand the extent of the grid portion that can be compromised. At the same time, the lack of mobility of individuals limits the spreading of the infection. In particular, we evaluate the role of the grid connectivity degree in both containing the epidemics and avoiding its spreading on the entire network, and also increasing the number of nodes that do not get any contact with the cyber attacks.
Top-down facilitation of visual recognition.

Cortical analysis related to visual object recognition is traditionally thought to propagate serially along a bottom-up hierarchy of ventral areas. Recent proposals gradually promote the role of top-down processing in recognition, but how such facilitation is triggered remains a puzzle. We tested a specific model, proposing that low spatial frequencies facilitate visual object recognition by initiating top-down processes projected from orbitofrontal to visual cortex. The present study combined magnetoencephalography, which has superior temporal resolution, functional magnetic resonance imaging, and a behavioral task that yields successful recognition with stimulus repetitions. Object recognition elicited differential activity that developed in the left orbitofrontal cortex 50 ms earlier than it did in recognition-related areas in the temporal cortex. This early orbitofrontal activity was directly modulated by the presence of low spatial frequencies in the image. Taken together, the dynamics we revealed provide strong support for the proposal of how top-down facilitation of object recognition is initiated, and our observations are used to derive predictions for future research.
A Risk Ratio Comparison of $l_0$ and $l_1$ Penalized Regression

There has been an explosion of interest in using l1-regularization in place of l0-regularization for feature selection. We present theoretical results showing that while l1-penalized linear regression never outperforms l0-regularization by more than a constant factor, in some cases using an l1 penalty is infinitely worse than using an l0 penalty. We also show that the “optimal” l1 solutions are often inferior to l0 solutions found using stepwise regression. We also compare algorithms for solving these two problems and show that although solutions can be found efficiently for the l1 problem, the “optimal” l1 solutions are often inferior to l0 solutions found using greedy classic stepwise regression. Furthermore, we show that solutions obtained by solving the convex l1 problem can be improved by selecting the best of the l1 models (for different regularization penalties) by using an l0 criterion. In other words, an approximate solution to the right problem can be better than the exact solution to the wrong problem.
Automatic Detection of Malware-Generated Domains with Recurrent Neural Models

Modern malware families often rely on domain-generation algorithms (DGAs) to determine rendezvous points to their command-and-control server. Traditional defence strategies (such as blacklisting domains or IP addresses) are inadequate against such techniques due to the large and continuously changing list of domains produced by these algorithms. This paper demonstrates that a machine learning approach based on recurrent neural networks is able to detect domain names generated by DGAs with high precision. The neural models are estimated on a large training set of domains generated by various malwares. Experimental results show that this data-driven approach can detect malware-generated domain names with a F1 score of 0.971. To put it differently, the model can automatically detect 93 % of malware-generated domain names for a false positive rate of 1:100.
Summarization Evaluation Methods: Experiments and Analysis

Two methods are used for evaluation of summarization systems: an evaluation of generated summaries against an "ideal" summary and evaluation of how well summaries help a person perform in a task such as informa. tion retrieval. We carried out two large experiments to study the two evaluation methods. Our results show that different parameters of an experiment can (h-amatically affect how well a system scores. For example, summary length was found to affect both types of evaluations. For the "ideal" summary based evaluation, accuracy decreases as summary length increases, while for task based evaluations summary length and accuracy on an information retrieval task appear to correlate randomly. In this paper, we show how this parameter and others can affect evaluation results and describe how parameters can be controlled to produce a sound evaluation.
The Human Genetic History of East Asia: Weaving a Complex Tapestry

East Asia encompasses a wide variety of environments, peoples, cultures and languages. Although this review focuses on East Asia, no geographic region can be considered in isolation in terms of human population history, and migrations to and from East Asia have had a major impact. Here, we review the following topics: the initial colonization of East Asia, the direction of migrations between southeast Asia and northern Asia, the genetic relationships of East Asian hunter-gatherers and the genetic impact of various social practices on East Asian populations. By necessity we focus on insights derived from mitochondrial DNA and/or Y-chromosome data; ongoing and future studies of genome-wide SNP or multi-locus re-sequencing data, combined with the use of simulation, model-based methods to infer demographic parameters, will undoubtedly provide additional insights into the population history of East Asia.
A model-driven ontology approach for manufacturing system interoperability and knowledge sharing

The requirements for the interoperability of semantics and knowledge have become increasingly important in Product Lifecycle Management (PLM), in the drive towards knowledge-driven decision support in the manufacturing industry. This article presents a novel concept, based on the Model Driven Architecture (MDA). The concept has been implemented under the Interoperable Manufacturing Knowledge Systems (IMKS) project in order to understand the extent to which manufacturing system interoperability can be supported using radically new methods of knowledge sharing. The concept exploits the capabilities of semantically well-defined core concepts formalised in a Common Logic-based ontology language. The core semantics can be specialised to configure multiple application-specific knowledge bases, as well as product and manufacturing information platforms. Furthermore, the utilisation of the expressive ontology language and the generic nature of core concepts help support the specification of system mechanisms to enable the verification of knowledge across multiple platforms. An experimental demonstration, using a test case based on the design and manufacture of an aerospace part, has been realised. This has led to the identification of several benefits of the approach, its current limitations as well as the areas to be considered for further work.
Conditional BERT Contextual Augmentation

We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. BERT demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model. We retrofit BERT to conditional BERT by introducing a new conditional masked language model1 task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain obvious improvement.
A Foundation for Actor Computation

We present an actor language which is an extension of a simple functional language, and provide an operational semantics for this extension. Actor configurations represent open distributed systems, by which we mean that the specification of an actor system explicitly takes into account the interface with external components. We study the composability of such systems. We define and study various notions of testing equivalence on actor expressions and configurations. The model we develop provides fairness. An important result is that the three forms of equivalence, namely, convex, must, and may equivalences, collapse to two in the presence of fairness. We further develop methods for proving laws of equivalence and provide example proofs to illustrate our methodology.
Visual Question Answering: Datasets, Algorithms, and Future Challenges

Visual Question Answering (VQA) is a recent problem in computer vision and natural language processing that has garnered a large amount of interest from the deep learning, computer vision, and natural language processing communities. In VQA, an algorithm needs to answer text-based questions about images. Since the release of the first VQA dataset in 2014, additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, we discuss the limitations of current datasets with regard to their ability to properly train and assess VQA algorithms. We then exhaustively review existing algorithms for VQA. Finally, we discuss possible future directions for VQA and image understanding research.
Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios

Connectivity is probably the most basic building block of the IoT paradigm. Up to now, the two main approaches to provide data access to things have been based on either multihop mesh networks using short-range communication technologies in the unlicensed spectrum, or long-range legacy cellular technologies, mainly 2G/GSM/GPRS, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-gigahertz frequency bands, used to realize access networks with star topology referred to as low-power WANs (LPWANs). In this article, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, particularly for typical smart city applications.
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.

Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision.
Affordance, conventions, and design

I was quietly lurking in the background of a CHI-Web discussion, when I lost all reason: I just couldn't take it anymore. " I put an affordance there, " a participant would say, " I wonder if the object affords clicking … " Affordances this, affordances that. And no data, just opinion. Yikes! What had I unleashed upon the world? " No! " I screamed, and out came this article. I don't know if it changed anyone's minds, but it brought the CHI-Web discussion to a halt (not what good list managers want to happen). But then, Steven Pemberton asked me to submit it here. Hope it doesn't stop the discussion again. Mind you, this is not the exact piece I dashed off to CHI-Web: it has been polished and refined: the requirements of print are more demanding than those of e-mail discussions.
A unifying framework for detecting outliers and change points from non-stationary time series data

We are concerned with the issues of outlier detection and change point detection from a data stream. In the area of data mining, there have been increased interest in these issues since the former is related to fraud detection, rare event discovery, etc., while the latter is related to event/trend by change detection, activity monitoring, etc. Specifically, it is important to consider the situation where the data source is non-stationary, since the nature of data source may change over time in real applications. Although in most previous work outlier detection and change point detection have not been related explicitly, this paper presents a unifying framework for dealing with both of them on the basis of the theory of on-line learning of non-stationary time series. In this framework a probabilistic model of the data source is incrementally learned using an on-line discounting learning algorithm, which can track the changing data source adaptively by forgetting the effect of past data gradually. Then the score for any given data is calculated to measure its deviation from the learned model, with a higher score indicating a high possibility of being an outlier. Further change points in a data stream are detected by applying this scoring method into a time series of moving averaged losses for prediction using the learned model. Specifically we develop an efficient algorithms for on-line discounting learning of auto-regression models from time series data, and demonstrate the validity of our framework through simulation and experimental applications to stock market data analysis.
Mold Fluid Flow : Practical Investigation of Mold Flux Entrainment in CC Molds Due to Shear Layer Instability

Mechanisms for mold flux entrainment in continuous casting molds include vortexing and suction down the SEN due to asymmetric fluid flow, excessive upward flow impingement on the meniscus, surface level fluctuations, argon bubble effects, and shear-layer instability. The latter mechanism, also called Kelvin-Helmholtz instability, is a crucial phenomenon which has been investigated previously only with over-simplified theoretical models and room-temperature physical models using surrogate fluids, both of which behave very differently from steel casters. This paper critically reviews previous literature on entrainment mechanisms. It then presents a realistic numerical model to investigate the Kelvin-Helmholtz entrainment mechanism in steel continuous casters and validates it with a simplified analytical solution of this phenomenon. Finally, the critical meniscus velocity to avoid mold flux entrainment via this mechanism is briefly discussed.
Explanation in Computational Stylometry

Computational stylometry, as in authorship attribution or profiling, has a large potential for applications in diverse areas: literary science, forensics, language psychology, sociolinguistics, even medical diagnosis. Yet, many of the basic research questions of this field are not studied systematically or even at all. In this paper we will go into these problems, and suggest that a reinterpretation of current and historical methods in the framework and methodology of machine learning of natural language processing would be helpful. We also argue for more attention in research for explanation in computational stylometry as opposed to purely quantitative evaluation measures and propose a strategy for data collection and analysis for achieving progress in computational stylometry. We also introduce a fairly new application of computational stylometry in internet security. 1 Meta-knowledge Extraction from Text The form of a text is determined by many factors. Content plays a role (the topic of a text determines in part its vocabulary), text type (genre, register) is important and will determine part of the writing style, but also psychological and sociological aspects of the author of the text will be sources of stylistic language variation. These psychological factors include personality, mental health, and being a native speaker or not; sociological factors include age, gender, education level, and region of language acquisition. Writing style is a combination of consistent decisions in language production at different linguistic levels (lexical choice, syntactic structures, discourse coherence, ...) that is linked to specific authors or author groups such as male authors or teenage authors. It remains to be seen whether this link is consistent over time and whether there are style features that are unconscious and cannot be controlled, as some researchers have argued. The basic research question for computational stylometry seems then to describe and explain the causal relations between psychological and sociological properties of authors on the one hand, and their writing style on the other. These theories can be used to develop systems that generate text in a particular style, or perhaps more usefully, systems that detect the identity of authors (authorship attribution and verification) or some of their psychological or sociological properties (profiling) from text. A limit hypothesis arising from this definition is that style is unique for an individual, like her fingerprint, earprint or genome. This has been called the human stylome hypothesis:
Failure Trends in a Large Disk Drive Population

It is estimated that over 90% of all new information produced in the world is being stored on magnetic media, most of it on hard disk drives. Despite their importance, there is relatively little published work on the failure patterns of disk drives, and the key factors that affect their lifetime. Most available data are either based on extrapolation from accelerated aging experiments or from relatively modest sized field studies. Moreover, larger population studies rarely have the infrastructure in place to collect health signals from components in operation, which is critical information for detailed failure analysis. We present data collected from detailed observations of a large disk drive population in a production Internet services deployment. The population observed is many times larger than that of previous studies. In addition to presenting failure statistics, we analyze the correlation between failures and several parameters generally believed to impact longevity. Our analysis identifies several parameters from the drive’s self monitoring facility (SMART) that correlate highly with failures. Despite this high correlation, we conclude that models based on SMART parameters alone are unlikely to be useful for predicting individual drive failures. Surprisingly, we found that temperature and activity levels were much less correlated with drive failures than previously reported.