nils-herrmann/clean_scirepeval huggingface dataset:
Automatic basis selection techniques for RBF networks

This paper proposes a generic criterion that defines the optimum number of basis functions for radial basis function (RBF) neural networks. The generalization performance of an RBF network relates to its prediction capability on independent test data. This performance gives a measure of the quality of the chosen model. An RBF network with an overly restricted basis gives poor predictions on new data, since the model has too little flexibility (yielding high bias and low variance). By contrast, an RBF network with too many basis functions also gives poor generalization performance since it is too flexible and fits too much of the noise on the training data (yielding low bias but high variance). Bias and variance are complementary quantities, and it is necessary to assign the number of basis function optimally in order to achieve the best compromise between them. In this paper we use Stein's unbiased risk estimator to derive an analytical criterion for assigning the appropriate number of basis functions. Two cases of known and unknown noise have been considered and the efficacy of this criterion in both situations is illustrated experimentally. The paper also shows an empirical comparison between this method and two well known classical methods, cross validation and the Bayesian information criterion, BIC.
A Comparison of Methods for Clustering 16S rRNA Sequences into OTUs

Recent studies of 16S rRNA sequences through next-generation sequencing have revolutionized our understanding of the microbial community composition and structure. One common approach in using these data to explore the genetic diversity in a microbial community is to cluster the 16S rRNA sequences into Operational Taxonomic Units (OTUs) based on sequence similarities. The inferred OTUs can then be used to estimate species, diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into OTUs, relatively little guidance is available on their relative performance and the choice of key parameters for each method. In this study, we conducted a comprehensive evaluation of ten existing OTU inference methods. We found that the appropriate dissimilarity value for defining distinct OTUs is not only related with a specific method but also related with the sample complexity. For data sets with low complexity, all the algorithms need a higher dissimilarity threshold to define OTUs. Some methods, such as, CROP and SLP, are more robust to the specific choice of the threshold than other methods, especially for shorter reads. For high-complexity data sets, hierarchical cluster methods need a more strict dissimilarity threshold to define OTUs because the commonly used dissimilarity threshold of 3% often leads to an under-estimation of the number of OTUs. In general, hierarchical clustering methods perform better at lower dissimilarity thresholds. Our results show that sequence abundance plays an important role in OTU inference. We conclude that care is needed to choose both a threshold for dissimilarity and abundance for OTU inference.
On the role of color in the perception of motion in animated visualizations

Although luminance contrast plays a predominant role in motion perception, significant additional effects are introduced by chromatic contrasts. In this paper, relevant results from psychophysical and physiological research are described to clarify the role of color in motion detection. Interpreting these psychophysical experiments, we propose guidelines for the design of animated visualizations, and a calibration procedure that improves the reliability of visual motion representation. The guidelines are applied to examples from texture-based flow visualization, as well as graph and tree visualization.
A 16-bit 10Gsps current steering RF DAC in 65nm CMOS achieving 65dBc ACLR multi-carrier performance at 4.5GHz Fout

This paper presents an RF DAC fabricated in a 65nm 1p7m CMOS process. The DAC is capable of >10Gsps operation dissipating ~800mW. At 3Gsps the SFDR > 70dBc beyond 1GHz and the IM3 performance is <; -80dBc within the same range. Signal processing is incorporated into the DAC providing interpolation and modulation through the full Nyquist band. Features and performance enable a wide application range including cable infrastructure, wireless communications, instrumentation, defense & aerospace. These performance capabilities are enabled by a modified DAC switch output structure and a novel current source calibration scheme1.
Sequence-Discriminative Training of Neural Networks

In this chapter we explore sequence-discriminative training techniques for neural-network–hidden-Markov-model (NN-HMM) hybrid speech recognition systems. We first review different sequence-discriminative training criteria for NNHMM hybrid systems, including maximum mutual information (MMI), boosted, minimum phone error, and state-level minimum Bayes risk (sMBR). We then focus on the sMBR criterion, and demonstrate a few heuristics, such as denominator language model order and frame-smoothing, that may improve the recognition performance. We further propose a two-forward-pass procedure to speed up sequencediscriminative training when memory is the main constraint. Experiments were conducted on the AMI meeting corpus.
Material classification and semantic segmentation of railway track images with deep convolutional neural networks

The condition of railway tracks needs to be periodically monitored to ensure passenger safety. Cameras mounted on a moving vehicle such as a hi-rail vehicle or a geometry inspection car can generate large volumes of high resolution images. Extracting accurate information from those images has been challenging due to background clutter in railroad environments. In this paper, we describe a novel approach to visual track inspection using material classification and semantic segmentation with Deep Convolutional Neural Networks (DCNN). We show that DCNNs trained end-to-end for material classification are more accurate than shallow learning machines with hand-engineered features and are more robust to noise. Our approach results in a material classification accuracy of 93.35% using 10 classes of materials. This allows for the detection of crumbling and chipped tie conditions at detection rates of 86.06% and 92.11%, respectively, at a false positive rate of 10 FP/mile on the 85-mile Northeast Corridor (NEC) 2012-2013 concrete tie dataset.
Human error analysis of commercial aviation accidents: application of the Human Factors Analysis and Classification system (HFACS).

The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the U.S. military as a tool for investigating and analyzing the human causes of aviation accidents. Based on Reason's (1990) model of latent and active failures, HFACS addresses human error at all levels of the system, including the condition of aircrew and organizational factors. The purpose of the present study was to assess the utility of the HFACS framework as an error analysis and classification tool outside the military.



The HFACS framework was used to analyze human error data associated with aircrew-related commercial aviation accidents that occurred between January 1990 and December 1996 using database records maintained by the NTSB and the FAA.



Investigators were able to reliably accommodate all the human causal factors associated with the commercial aviation accidents examined in this study using the HFACS system. In addition, the classification of data using HFACS highlighted several critical safety issues in need of intervention research.



These results demonstrate that the HFACS framework can be a viable tool for use within the civil aviation arena. However, additional research is needed to examine its applicability to areas outside the flight deck, such as aircraft maintenance and air traffic control domains.
Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies

Besides attracting a billion dollar economy, Bitcoin revolutionized the field of digital currencies and influenced many adjacent areas. This also induced significant scientific interest. In this survey, we unroll and structure the manyfold results and research directions. We start by introducing the Bitcoin protocol and its building blocks. From there we continue to explore the design space by discussing existing contributions and results. In the process, we deduce the fundamental structures and insights at the core of the Bitcoin protocol and its applications. As we show and discuss, many key ideas are likewise applicable in various other fields, so that their impact reaches far beyond Bitcoin itself.
Camera Tracking for Augmented Reality Media

This paper presents a camera tracking system for the spatial stabilization of Augmented Reality (AR) media. Our approach integrates both artificial landmarks (fiducials) and natural features for camera tracking. Artificial landmarks are used for system initialization and computation of initial camera pose. Robust and extendible tracking is achieved by dynamically calibrating the 3D positions of a priori uncalibrated natural features. Analysis and experimental results demonstrate the effectiveness of this approach for presenting stabilized AR media in long camera-motion video sequences.
Cache-oblivious priority queue and graph algorithm applications

(MATH) In this paper we develop an optimal cache-oblivious priority queue data structure, supporting insertion, deletion, and deletemin operations in <i>O</i>(<sup>1</sup> \over <sub>B</sub> log<i><sup>M/B</sup></i><i><sup>N</sup></i> \over <i><sub>B</sub></i>) amortized memory transfers, where <i>M</i> and <i>B</i> are the memory and block transfer sizes of any two consecutive levels of a multilevel memory hierarchy. In a cache-oblivious data structure, <i>M</i> and <i>B</i> are not used in the description of the structure. The bounds match the bounds of several previously developed external-memory (cache-aware) priority queue data structures, which all rely crucially on knowledge about <i>M</i> and <i>B</i>. Priority queues are a critical component in many of the best known external- memory graph algorithms, and using our cache-oblivious priority queue we develop several cache- oblivious graph algorithms.
A hierarchical Dirichlet language model

We discuss a hierarchical probabilistic model whose predictions are similar to those of the popular language modelling procedure known as 'smoothing'. A number of interesting differences from smoothing emerge. The insights gained from a probabilistic view of this problem point towards new directions for language modelling. The ideas of this paper are also applicable to other problems such as the modelling of triphomes in speech, and DNA and protein sequences in molecular biology. The new algorithm is compared with smoothing on a two million word corpus. The methods prove to be about equally accurate, with the hierarchical model using fewer computational resources.
Compile-time composition of run-time data and iteration reorderings

Many important applications, such as those using sparse data structures, have memory reference patterns that are unknown at compile-time. Prior work has developed run-time reorderings of data and computation that enhance locality in such applications.This paper presents a compile-time framework that allows the explicit composition of run-time data and iteration-reordering transformations. Our framework builds on the iteration-reordering framework of Kelly and Pugh to represent the effects of a given composition. To motivate our extension, we show that new compositions of run-time reordering transformations can result in better performance on three benchmarks.We show how to express a number of run-time data and iteration-reordering transformations that focus on improving data locality. We also describe the space of possible run-time reordering transformations and how existing transformations fit within it. Since sparse tiling techniques are included in our framework, they become more generally applicable, both to a larger class of applications, and in their composition with other reordering transformations. Finally, within the presented framework data need be remapped only once at runtime for a given composition thus exhibiting one example of overhead reductions the framework can express.
Growers versus showers: a meaningful (or useful) distinction?

Yafi et al. have conducted a study that will be of great interest to the lay community and also of import to practicing urologists who routinely encounter patients with concerns about the appearance of their phallus . In one study 14% of men expressed dissatisfaction with their genitals with flaccid penile length being the most common source of dissatisfaction . The concerns of such men tend to be the perception that their penis is small, or at least smaller than the penises of other men.
FCNN: Fourier Convolutional Neural Networks

The Fourier domain is used in computer vision and machine learning as image analysis tasks in the Fourier domain are analogous to spatial domain methods but are achieved using different operations. Convolutional Neural Networks (CNNs) use machine learning to achieve state-of-the-art results with respect to many computer vision tasks. One of the main limiting aspects of CNNs is the computational cost of updating a large number of convolution parameters. Further, in the spatial domain, larger images take exponentially longer than smaller image to train on CNNs due to the operations involved in convolution methods. Consequently, CNNs are often not a viable solution for large image computer vision tasks. In this paper a Fourier Convolution Neural Network (FCNN) is proposed whereby training is conducted entirely within the Fourier domain. The advantage offered is that there is a significant speed up in training time without loss of effectiveness. Using the proposed approach larger images can therefore be processed within viable computation time. The FCNN is fully described and evaluated. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset. The results demonstrate that convolution in the Fourier domain gives a significant speed up without adversely affecting accuracy. For simplicity the proposed FCNN concept is presented in the context of a basic CNN architecture, however, the FCNN concept has the potential to improve the speed of any neural network system involving convolution.
No Unbiased Estimator of the Variance of K-Fold Cross-Validation

Most machine learning researchers perform quantitative experiments to estimate generalization error and compare algorithm performances. In order to draw statistically convincing conclusions, it is important to estimate the uncertainty of such estimates. This paper studies the estimation of uncertainty around the K-fold cross-validation estimator. The main theorem shows that there exists no universal unbiased estimator of the variance of K-fold cross-validation. An analysis based on the eigendecomposition of the covariance matrix of errors helps to better understand the nature of the problem and shows that naive estimators may grossly underestimate variance, as con£rmed by numerical experiments.
Ambulance redeployment: An approximate dynamic programming approach

Emergency medical service (EMS) providers are charged with the task of managing ambulances so that the time required to respond to emergency calls is minimized. One approach that may assist in reducing response times is ambulance redeployment, i.e., repositioning idle ambulances in real time. We formulate a simulation model of EMS operations to evaluate the performance of a given allocation policy and use this model in an approximate dynamic programming (ADP) context to compute high-quality redeployment policies. We find that the resulting ADP policies perform much better than sub-optimal static policies and marginally better than near-optimal static policies. Representative computational results for Edmonton, Alberta are included.
The self-fulfilling prophecy in close relationships: rejection sensitivity and rejection by romantic partners.

The authors hypothesized a self-fulfilling prophecy wherein rejection expectancies lead people to behave in ways that elicit rejection from their dating partners. The hypothesis was tested in 2 studies of conflict in couples: (a) a longitudinal field study where couples provided daily-diary reports and (b) a lab study involving behavioral observations. Results from the field study showed that high rejection-sensitive (HRS) people's relationships were more likely to break up than those of low rejection-sensitive (LRS) people. Conflict processes that contribute to relationship erosion were revealed for HRS women but not for HRS men. Following naturally occurring relationship conflicts, HRS women's partners were more rejecting than were LRS women's partners. The lab study showed that HRS women's negative behavior during conflictual discussions helped explain their partners' more rejecting postconflict responses.
A fast learning algorithm for image segmentation with max-pooling convolutional networks

We present a fast algorithm for training MaxPooling Convolutional Networks to segment images. This type of network yields record-breaking performance in a variety of tasks, but is normally trained on a computationally expensive patch-by-patch basis. Our new method processes each training image in a single pass, which is vastly more efficient. We validate the approach in different scenarios and report a 1500-fold speed-up. In an application to automated steel defect detection and segmentation, we obtain excellent performance with short training times.
Adjustable Autonomy : From Theory to Implementation

Recent exciting, ambitious applications in agent technology involve agents acting individually or in teams in support of critical activities of individual humans or entire human organizations. Applications range from intelligent homes [13], to "routine" organizational coordination[16], to electronic commerce[4] to long-term space missions[12, 6]. These new applications have brought forth an increasing interest in agents’ adjustable autonomy (AA), i.e., in agents’ dynamically adjusting their own level of autonomy based on the situation[8]. In fact, many of these applications will not be deployed, unless reliable AA reasoning is a central component. At the heart of AA is the question of whether and when agents should make autonomous decisions and when they should transfer decision-making control to other entities (e.g., human users). Unfortunately, previous work in adjustable autonomy has focused on individual agent-human interactions and tile techniques developed fail to scale-up to complex heterogeneous organizations. Indeed, as a first step, we focused on a smallscale, but real-world agent-human organization called Electric Elves, where an individual agent and human worked together within a larger multiagent context. Although’the application limits the interactions among entities, key weaknesses of previous approaches to adjustable autonomy are readily apparent. In particular, previous approaches to transferof-control are seen to be too rigid, employing one-shot transfersof-control that can result in unacceptable coordination failures. Furthermore, the previous approaches ignore potential costs (e.g., from delays) to an agent’s team due to such transfers of control. To remedy such problems, we propose a novel approach to AA, based on the notion of a transfer-of-control strategy. A transfer-of-control strategy consists of a conditional sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from the agent to the user or vice versa) and (ii) actions to change an agent’s pre-specified coordination constraints with team members, aimed at minimizing miscoordination costs. The goal is for high quality individual decisions to be made with minimal disruption to the coordination of the team. We operationalize such strategies via Markov decision processes (MDPs) which select the optimal strategy given an uncertain environment and costs to individuals and teams. We have developed a general reward function and state representation for such an MDP, to facilitate application of the approach to different domains. We present results from a careful evaluation of this approach, including via its use in our real-world, deployed Electric Elves system.
MOS charge pumps for low-voltage operation

New MOS charge pumps utilizing the charge transfer switches (CTS’s) to direct charge flow and generate boosted output voltage are described. Using the internal boosted voltage to backward control the CTS of a previous stage yields charge pumps that are suitable for low-voltage operation. Applying dynamic control to the CTS’s can eliminate the reverse charge sharing phenomenon and further improve the voltage pumping gain. The limitation imposed by the diode-configured output stage can be mitigated by pumping it with a clock of enhanced voltage amplitude. Using the new circuit techniques, a 1.2-V-to-3.5-V charge pump and a 2-V-to-16-V charge pump are demonstrated.
Rediscovering Computational Autopoiesis

This paper summarises some initial empirical results from a new computer model (arti cial chemistry) which exhibits spontaneous emergence and persistence of autopoietic organisation. The model is based on a system originally presented by Varela, Maturana and Uribe . In carrying out this re-implementation it was found that an additional interaction (chain-based bond inhibition), not documented in the original description by Varela et al., is critical to the realisation of the autopoietic phenomena. This required interaction was re-discovered only following careful examination of (unpublished) source code for an early version of the original model. The purpose of the paper is thus twofold: rstly to identify and discuss this previously undocumented, but essential, interaction; and secondly to argue, on the basis of this particular case, for the importance of exploiting the emerging technologies which support publication of completely detailed software models (in addition, of course, to conventional publication of summary experimental results).
LocLess: Do you Really Care Where Your Cloud Files Are?

Physical location of data in cloud storage is a problem that gains a lot of attention not only from the actual cloud providers but also from the end users' who lately raise many concerns regarding the privacy of their data. It is a common practice that cloud service providers create replicate users' data across multiple physical locations. However, moving data in different countries means that basically the access rights are transferred based on the local laws of the corresponding country. In other words, when a cloud service provider stores users' data in a different country then the transferred data is subject to the data protection laws of the country where the servers are located. In this paper, we propose LocLess, a protocol which is based on a symmetric searchable encryption scheme for protecting users' data from unauthorized access even if the data is transferred to different locations. The idea behind LocLess is that "Once data is placed on the cloud in an unencrypted form or encrypted with a key that is known to the cloud service provider, data privacy becomes an illusion". Hence, the proposed solution is solely based on encrypting data with a key that is only known to the data owner.