Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices

Exemplar clustering attempts to find a subset of data-points that summarizes the entire data-set in the sense of minimizing the sum of distances from each point to its closest exemplar. It has many important applications in machine learning including document and video summarization, data compression, scalability of kernel methods and Gaussian processes, active learning and feature selection. A key challenge in the adoption of exemplar clustering to large-scale applications has been the availability of accurate and scalable algorithms. We propose an approach that combines structured similarity matrix representations with submodular greedy maximization that can dramatically increase the scalability of exemplar clustering and still enjoys good approximation guarantees. Exploiting structured similarity matrices within the context of submodular greedy algorithms is by no means trivial, as naive approaches still require computing all the entries of the matrix. We propose a randomized approach based on sampling sign-patterns of columns of the similarity matrix and establish accuracy guarantees. We demonstrate significant computational speed-ups while still achieving highly accurate solutions, and solve problems with up-to millions of data-points in around a minute or less on a single commodity computer.
Antagonist antibodies directed against calcitonin gene-related peptide and methods using same

The invention features methods for preventing or treating CGRP associated disorders such as vasomotor symptoms, including headaches (e.g., migraine, cluster headache, and tension headache) and hot flushes, by administering an anti-CGRP antagonist antibody. Antagonist antibody G1 and antibodies derived from G1 directed to CGRP are also described.
The analgesic efficacy of topical capsaicin is enhanced by glyceryl trinitrate in painful osteoarthritis: a randomized, double blind, placebo controlled study

The aim of this study was to assess if the pain of osteoarthritis is reduced by topical capsaicin and to determine whether addition of glyceryl trinitrate has an effect on analgesic efficacy and tolerability of capsaicin. A randomized, double blind, placebo controlled study was carried out on 200 adult patients attending a Pain Clinic with osteoarthritis pain. Patients applied one of four creams topically over the affected joint over a 6 week period. Creams contained either placebo (vehicle), 0.025% capsaicin, 1.33% glyceryl trinitrate or 0.025% capsaicin + 1.33% glyceryl trinitrate. Analgesic efficacy, tolerability of cream and analgesic consumption were assessed. One hundred and sixty‐seven of 200 patients completed the study. Baseline visual analogue scores (0–10 scale) for pain were 6.40. There was a significant reduction in pain scores in the glyceryl trinitrate group (mean decrease 0.59, p< 0.05, 95% confidence limits 0.04–1.14), 0.025% capsaicin group (mean decrease 0.5, p< 0.05, 95% confidence limits 0.05–1.05) and the glyceryl trinitrate capsaicin group (mean decrease 1.1, p<0.05, 95% confidence limits 0.22–1.98). Baseline discomfort of application scores were similar for all but the capsaicin groups (they were significantly higher (by 2.1 units, p< 0.001)). The odds ratio in favour of continuing treatment was 2.1 (95% confidence limits 1.0–4.4) for glyceryl trinitrate and 2.4 (95% confidence limits 1.2–5.1) for capsaicin and 5.0 (95% confidence limits 3.8–6.4) for capsaicin GTN combination. The study showed that topical capsaicin and glyceryl trinitrate have an analgesic effect in painful osteoarthritis. When used together this effect is increased with the combination being more tolerable than capsaicin alone. Analgesic consumption is decreased by capsaicin, glyceryl trinitrate and to a greater extent by both combined.
Computer Use and Attitudes Towards Computerised Therapy Amongst Young People and Parents Attending Child and Adolescent Mental Health Services.

BACKGROUND There has been little research examining attitudes towards computerised therapy in young people. METHOD A self-report survey of 37 young people and 31 parents using Child and Adolescent Mental Health Services (CAMHS). RESULTS Young people reported high levels of computer usage and online help-seeking behaviour. Young people were cautious about computerised therapy whereas parents were more positive, identifying more benefits than concerns. CONCLUSIONS Mental health professionals need to be aware of young people's online help-seeking behaviour in order to correct inaccuracies and maximise uptake of computerised therapy.
A simple optimally unbiased MA filter for timekeeping

In this paper, we address the design of an optimally unbiased moving average (MA) filter for time error online estimating and synchronization in Global Positioning System (GPS)-based timekeeping. We proceed from the slowly changing nature of a time error generated by a local clock, assuming its function to be linear for N points in the average. Employing the inherent property of a simple MA to produce the lowest noise for the given averaging interval and using linear regression as a proper stochastic approximation to evaluate the bias, we find the optimal weighting coefficients. The designed optimally unbiased MA filter and its improved modification produce two times more noise than a simple MA when N /spl Gt/ 1. Both the numerical simulation and the filtering of the real GPS-based time error processes are provided and discussed in detail.
Gravitational outlier detection for wireless sensor networks

Accuracy of sensed data and reliable delivery are the key concerns in addition to several other network‐related issues in wireless sensor networks (WSNs). Early detection of outliers reduces subsequent unwanted transmissions, thus preserving network resources. Recent techniques on outlier detection in WSNs are computationally expensive and based on message exchange. Message exchange‐based techniques incur communication overhead and are less preferred in WSNs. On the other hand, machine learning‐based outlier detection techniques are computationally expensive for resource constraint sensor nodes. The novelty of this paper is that it proposes a simple, non message exchange based, in‐network, real‐time outlier detection algorithm based on Newton's law of gravity. The mechanism is evaluated for its accuracy in detecting outliers, computational cost, and its influence on the network traffic and delay. The outlier detection mechanism resulted in almost 100% detection accuracy. Because the mechanism involves no message exchanges, there is a significant reduction in network traffic, energy consumption and end‐to‐end delay. An extension of the proposed algorithm for transient data sets is proposed, and analytic evaluation justifies that the mechanism is reactive to time series data. Copyright © 2016 John Wiley & Sons, Ltd.
The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning

Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called ''iterated learning,'' in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people's predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this method to discriminate between two models of human judgments: a simple Bayesian model (Griffiths & Tenenbaum, 2006) and a recently proposed alternative model that assumes people store only a few instances of each type of event in memory (MinK; Mozer, Pashler, & Homaei, 2008). Although testing these models using standard experimental procedures is difficult due to differences in the number of free parameters and the need to make assumptions about the knowledge of individual learners, we show that the two models make very different predictions about the outcome of iterated learning. The results of an experiment using this methodology provide a rich picture of how much people know about the distributions of everyday quantities, and they are inconsistent with the predictions of the MinK model. The results suggest that accurate predictions about everyday events reflect relatively sophisticated knowledge on the part of individuals.
Correlation Between Mutation Rate and Genome Size in Riboviruses: Mutation Rate of Bacteriophage Qβ

Genome sizes and mutation rates covary across all domains of life. In unicellular organisms and DNA viruses, they show an inverse relationship known as Drake’s rule. However, it is still unclear whether a similar relationship exists between genome sizes and mutation rates in RNA genomes. Coronaviruses, the RNA viruses with the largest genomes (∼30 kb), encode a proofreading 3′ exonuclease that allows them to increase replication fidelity. However, it is unknown whether, conversely, the RNA viruses with the smallest genomes tend to show particularly high mutation rates. To test this, we measured the mutation rate of bacteriophage Qβ, a 4.2-kb levivirus. Amber reversion-based Luria–Delbrück fluctuation tests combined with mutant sequencing gave an estimate of 1.4 × 10−4 substitutions per nucleotide per round of copying, the highest mutation rate reported for any virus using this method. This estimate was confirmed using a direct plaque sequencing approach and after reanalysis of previously published estimates for this phage. Comparison with other riboviruses (all RNA viruses except retroviruses) provided statistical support for a negative correlation between mutation rates and genome sizes. We suggest that the mutation rates of RNA viruses might be optimized for maximal adaptability and that the value of this optimum may in turn depend inversely on genome size.
Efficacy of infliximab in extrapulmonary sarcoidosis: results from a randomised trial

The aim of the present study was to investigate the efficacy of infliximab for the treatment of extrapulmonary sarcoidosis. A prospective, randomised, double-blind, placebo-controlled trial was conducted, with infliximab at 3 and 5 mg·kg−1 body weight administered over 24 weeks. Extrapulmonary organ severity was determined by a novel severity tool (extrapulmonary physician organ severity tool; ePOST) with an adjustment for the number of organs involved (ePOSTadj). In total, 138 patients enrolled in the trial of infliximab versus placebo for the treatment of chronic corticosteroid-dependent pulmonary sarcoidosis. The baseline severity of extrapulmonary organ involvement, as measured by ePOST, was similar across treatment groups. After 24 weeks of drug-therapy study, the change from baseline to week 24 in ePOST was greater for the combined infliximab group compared with the placebo group. After adjustment for the number of extrapulmonary organs involved, the improvement in ePOSTadj observed in the combined infliximab group was also greater than that observed in placebo-treated patients, after 24 weeks of therapy. The improvements in ePOST and ePOSTadj were not maintained during a subsequent 24-week washout period. Infliximab may be beneficial compared with placebo in the treatment of extrapulmonary sarcoidosis in patients already receiving corticosteroids, as assessed by the severity tool described in the present study.
Unsupervised Learning Using Generative Adversarial Networks on micro-Doppler spectrograms

This paper presents the implementation of a Generative Adversarial Network (GAN) and Adversarial Autoencoder (AAE) trained in an unsupervised manner using micro-Doppler (mD) spectrograms of human gait. Once the GAN network was trained, the domain where micro-Doppler feature learning happens is inspected. This domain is then accessed by building the AAE and different network visualizations are shown. The benefits of unsupervised training are highlighted by investigating the self-learned spectrogram features, revealing the potential of unsupervised adversarial training techniques for mD spectrogram feature learning methods.
Disproportionate sampling for population subgroups in telephone surveys

Population studies often seek to examine phenomena in important population subgroups or to compare results among these and other subgroups. When subgroups of interest comprise a relatively small percentage of the population and acceptable subgroup member lists are not available to serve as sampling frames, it may be prohibitively expensive even by telephone to screen through a sample of the entire population. This paper considers some statistical effects of estimation from a class of two‐stratum telephone sample designs where part of the frame with a higher subgroup concentration is disproportionately sampled compared to the rest of the frame. Using proportionate sampling as a reference, the relative impact of this disproportionate design is determined for nominal and effective sample sizes, where the latter are tied to the effect of variation in sample weights that occurs in disproportionately allocated samples. Findings are illustrated using two recent telephone surveys. Whereas nominal subgroup sample sizes may be improved by disproportionate sampling, we conclude that both the survey designer and analyst should use this type of design cautiously in telephone surveys. Copyright © 2007 John Wiley & Sons, Ltd.
Merging dominant sets and DBSCAN for robust clustering and image segmentation

Dominant sets clustering is a promising clustering approach based on a graph-theoretic concept of a cluster. With the pairwise similarity matrix of data as input, dominant sets clustering determines the number of clusters by itself and possesses some other nice properties. However, the original dominant sets clustering algorithm is sensitive to similarity measures, and appropriate parameters are required to generate satisfactory clustering results. In order to solve this problem, we firstly use histogram equalization to transform the similarity matrix and remove the sensitiveness to similarity parameters. In the second step we extend the clusters by merging dominant sets clustering and DBSCAN. Our algorithm requires no user-defined parameters, and is able to generate clusters of arbitrary shapes and determine the number of clusters automatically. In experiments of data clustering and image segmentation our algorithm performs evidently better than the original dominant sets clustering, and also comparably to other state-of-the-art clustering algorithms.
Bonferroni-Based Size-Correction for Nonstandard Testing Problems

We develop a set of powerful and flexible size-correction procedures for general nonstandard testing environments in which the asymptotic distribution of a test statistic is discontinuous in a nuisance parameter under the null hypothesis. Examples of this form of testing problem are pervasive in econometrics and complicate inference by making the size difficult to control. The test constructions introduced here simultaneously control the asymptotic size of the test uniformly over the nuisance parameter space while leading to tests with desirable power properties. They have the flexibility to allow the user to direct the power of the resultant test toward alternatives of particular interest. We introduce three types of size-corrected critical values that make use of reasoning derived from Bonferroni bounds. The new methods provide complementary alternatives to existing size-correction methods, entailing substantially higher power for many testing problems. The critical value constructions are developed for an expanded class of testing problems, allowing application in problems to which previously available size-corrections only yield tests with negligible power. We detail the construction and performance of the new tests in examples of testing after conservative and consistent model selection in the linear regression model.
Stability analysis of piecewise discrete-time linear systems

Presents a stability analysis method for piecewise discrete-time linear systems based on a piecewise smooth Lyapunov function. It is shown that the stability of the system can be established if a piecewise Lyapunov function can be constructed and, moreover, the function can be obtained by solving a set of linear matrix inequalities (LMIs) that is numerically feasible with commercially available software.
Loss of p53 Accelerates Neointimal Lesions of Vein Bypass Grafts in Mice

The transcription factor p53 is essentially involved in regulation of cell death and proliferation. Recently, we have established a mouse model for vein graft arteriosclerosis by grafting autologous jugular veins or vena cava to carotid arteries. Using this model, we studied the role of p53 in the development of vein graft arteriosclerosis in p53−/− mice. Four weeks after grafting, neointimal hyperplasia of vein grafts in p53−/− mice was increased 2-fold compared with that of wild-type controls. Cell component analysis revealed that neointimal lesions in p53−/− mice consisted mainly of &agr;-actin positive smooth muscle cells (SMCs), whereas the majority of cells in wild-type mice were MAC-1 (CD11b/18)-positive at 4 weeks. Importantly, SMC apoptosis as determined by TUNEL assay was significantly reduced in p53−/− vein grafts. TUNEL positive cells in wild-type vein grafts markedly increased from 0.5% to 6.4% of total cells 4 weeks postoperatively, but remained virtually unchanged in p53−/− grafts (0.8%). Immunofluorescence analysis revealed that increased p53 expression in neointimal SMCs of wild-type, but not p53−/−, mice coincided with oxidative DNA damage in vein grafts. Interestingly, SMCs of p53−/− mice showed increased apoptosis in response to TNF&agr; and decreased apoptosis in response to sodium nitroprusside. Additionally, p53-deficient SMCs showed a higher rate of proliferation and migration and expressed higher levels of matrix metalloproteinases. Thus, p53 deficiency accelerates neointima formation by facilitating SMC proliferation as well as abrogating cell apoptosis.
A Reduced-Complexity Multiband MIMO Receiver with Estimation of Analog Devices Imperfection

Cognitive radios are of interest because they are capable of high frequency utilization efficiency. Multiband multiple-input multiple-output (MIMO) receivers based on heterodyne reception have been proposed for use in cognitive radios. This paper proposes a reduced-complexity multiband MIMO receiver that can achieve high performance by correcting the imperfection of the Hilbert transformer in the intermediate frequency (IF) band. However, the imperfection of the analog devices in the feedback loop causes poor performance. Therefore, a scheme for correcting the imperfection of the analog devices is proposed.
Changes in reciprocal herkogamy during the tristyly–distyly transition in Oxalis alpina increase efficiency in pollen transfer

Although the spatial separation of sexual organs within a flower (herkogamy) has been interpreted as a mechanism that promotes efficient pollen transfer, there have been few attempts to relate variation in herkogamy to probabilities of pollen flow. Here, we used a heterostylous species with variation in reciprocal herkogamy to test this hypothesis. We measured legitimate and illegitimate pollen flow with fluorescent dyes in four selected populations of Oxalis alpina corresponding to the extremes of a previously reported evolutionary gradient from tristyly to distyly. After the breakdown of tristyly, the observed increment in reciprocal herkogamy between the long and short morphs was associated with a 30% increase in the proportion of dye received from compatible illegitimate pollinations. In all populations, the most likely effective pollen vectors were two Heterosarus bee species. Our results support the adaptive value of reciprocal herkogamy in promoting efficient pollen transfer in heterostylous species.
Human-Robot Interaction using Intention Recognition

Recognition of human intention is an important issue in human-robot interaction research and allows a robot to respond adequately according to human's wish. In this paper, we discuss how robots can infer human intention by learning affordance, a concept used to represent the relation between an agent and its environment. Learning of the robot, to understand human and its interaction with environment, is achieved within the framework of action-perception cycle. The action-perception cycle explains how an intelligent agent learns and enhances its ability continuously by interacting with its surrounding. The proposed intention recognition and recommendation system includes several key functions such as joint attention, object recognition, affordance model, motion understanding module and so on. The experimental results show high successful recognition performance and the plausibility of the proposed system.
On exploiting polarization for energy-harvesting enabled cooperative cognitive radio networking

Radio spectrum underutilization and energy inefficiency become urgent bottleneck problems to the sustainable development of wireless technologies. The research philosophies of wireless communications have been shifted from balancing reliability-efficiency tradeoff in the link level to seeking spectrum-energy efficiency in the network level. Global spectrum-energy efficient designs attract significant attention to improving utilization and efficiency, wherein cognitive radio networks and energy scavenging related techniques are of particular interests. In this article, we first provide a systematic study on cooperative cognitive radio networking (CCRN), followed by the discussions on research issues and challenges in designing spectrum-energy complexity efficient CCRN. As an effort to shed light on addressing spectrum-energy inefficiency at a low complexity, a novel CCRN paradigm exploiting polarization and energy harvesting capabilities is elaborated. Specifically, we present a novel polarization and energy harvesting based framework for achieving size-cost effective CCRN. We introduce polarization signal processing to avoid interference between secondary users and primary users in cooperation. We propose a weighted sum spectrum efficiency based problem formulation and multi-timescale dynamic programming based energy/power allocation policy to evaluate the cooperation performance, subject to energy levels, power, and throughput constraints.
Determining Transition Probabilities

Confusion regarding proper use of the terms rate and risk persists in the literature. This has implications for the proper modeling of prognosis and transition between health states in decision analysis and related techniques. The issue is complicated by the plethora of terms related to rate and risk. Although the suggestion to use the terms force and probability as substitutes for rate and risk has some appeal, the change in terminology by itself is unlikely to solve all the confusion or misuse of terms. This paper clarifies the proper definitions and estimations of rates and risks and suggests critical factors for the decision analyst to re member when using, modeling, or interpreting transition rates and risks. Key words: decision models; rate; risk; force; probability; transitions. (Med Decis Making 1994;14:52-58)