Miracle-dz/UniFAR-FTUs huggingface dataset:
Global compilation of interferometric synthetic aperture radar earthquake source models: 1. Comparisons with seismic catalogs

[1] While many earthquakes have now been studied using interferometric synthetic aperture radar (InSAR) data, a full assessment of the quality and additional value of InSAR source parameters compared to seismological techniques is still lacking. We compile a catalog of source models obtained using InSAR and estimate the corresponding centroid moment tensor (CMT) parameters; we refer to this compilation as the ICMT archive. We compare source parameters from over 70 InSAR studies of 57 global earthquakes with those in the Global CMT (GCMT), International Seismological Centre (ISC) and Engdahl‐Hilst‐Buland (EHB) seismic catalogs. We find an overall good agreement between fault strike, dip and rake values in the GCMT and ICMT archives. Likewise, the differences in seismic moment between these two archives are relatively small, and we do not find support for previously suggested trends of InSAR leading to larger moments than seismic data. However, epicentral locations show substantial discrepancies, which are larger for the GCMT (median differences of ∼21 km) than for the EHB and ISC catalogs (median differences of ∼10 km). Since InSAR data have a high spatial resolution, and thus should map epicentral locations accurately, this allows us to obtain a first independent estimate of epicentral location errors in the seismic catalogs. Earthquake depths from InSAR are systematically shallower than those in the EHB catalog, with a median of differences of ∼5 km. While this trend may be partly due to unmodeled crustal complexity, it is also compatible with the observation that the rupture of crustal earthquakes tends to propagate upward in the seismogenic layer.
The Volumetric Barrier for Semidefinite Programming

We consider the volumetric barrier for semidefinite programming, or "generalized" volumetric barrier, as introduced by Nesterov and Nemirovskii. We extend several fundamental properties of the volumetric barrier for a polyhedral set to the semidefinite case. Our analysis facilitates a simplified proof of self-concordance for the semidefinite volumetric barrier, as well as for the combined volumetric-logarithmic barrier for semidefinite programming. For both of these barriers we obtain self-concordance parameters equal to those previously shown to hold in the polyhedral case.
Molecular alterations between the primary breast cancer and the subsequent locoregional/metastatic tumor.

BACKGROUND Metastatic breast cancers have historically been presumed to have the same predictive biomarkers as the initial primary tumor. We compared the expression of these biomarkers in a large paired tissue microarray (TMA) series of primary and subsequent relapsed tumors. METHODS Using the British Columbia Cancer Agency Breast Cancer Outcomes Unit database, patients with biopsy-proven relapses were identified and linked to a large TMA series of primary breast cancers from 1986-1992. Charts were reviewed, and tissue blocks of the metastatic cancer were collected to create a separate TMA. Immunohistochemical assessment with the same antibodies and conditions was performed for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER)-2 on both the primary and relapsed tumors. RESULTS One hundred sixty cases were received that had tumor adequate for analyses. Of these, 71.9% had no changes in either the ER or PR status or HER-2 status. Of the 45 (28.1%; 95% confidence interval [CI], 21.2%-35.1%) tumors that did have changes in receptor status, 7.5% were in-breast recurrences or new breast primaries, 4.4% had changes in PR status only and were therefore deemed clinically irrelevant, and 19.4% (95% CI, 13.3%-25.5%) had changes in either the ER or HER-2 status from regional or distant relapses. Five percent of tumors had a receptor status change going from ER(+) or PR(+) to ER(-) or PR(-); 9.4% went from ER(-) or PR(-) to ER(+) or PR(+). With regard to HER-2 status, 3.8% of tumors went from positive to negative and 1.3% went from negative to positive. For all discordant cases, biopsies of the relapsed lesion were obtained prior to initiation of first-line treatment for metastatic disease. In the primary tumors that were ER(+), time to relapse was significantly shorter in the discordant relapsed cases than in the concordant ones (p = .0002). Changes in loss or gain of either biomarker were seen across the discordant cases. CONCLUSIONS A significant proportion of relapsed tumors had changes in either ER or HER-2 status, which would dramatically alter treatment recommendations and clinical behavior. This study suggests that biopsies of relapsed and metastatic breast cancers should be performed routinely in clinical practice.
A new flexible controller for a humanoid robot that considers visual and force information interaction

To enhance the safety of a humanoid robot when it is operating a complex environment, a number of methods that combine visual and force information have been presented. These methods are generally divided into two approaches. The first approach is to coordinate the visual controller and force controller in a parallel way, and the second approach is to coordinate them in series. However, these two approaches do not consider the interaction between the visual controller and force controller. Specifically, the first approach does not consider the interaction between the controllers. The second approach only considers the effect of the output of the visual controller on the force controller, while the effect of the force controller on the visual controller is not considered. This study presents a design for a new flexible controller for a humanoid robot that considers the interaction of visual and force information. The advantages of the proposed method are that it simultaneously incorporates the functions of a visual servo controller and a flexible controller as well as its ability to consider the interaction of visual and force information when a humanoid robot is operating.
Kernel canonical correlation analysis for robust cooperative spectrum sensing in cognitive radio networks

Spectrum sensing is a key operation in cognitive radio (CR) systems, where secondary users (SUs) are able to exploit spectrum opportunities by first detecting the presence of primary users. In a CR network composed of several SUs, the detection accuracy can be much improved by cooperative spectrum sensing strategies, which exploit the spatial diversity among SUs. However, cooperative detection strategies, which are typically based on energy sensing, do not perform satisfactorily under impairments such as non‐Gaussian noise or interferences. In this paper, we propose a scheme based on kernel canonical correlation analysis (KCCA), which is able to operate in non‐ideal scenarios and in a totally blind fashion. This technique is performed at the fusion centre (FC) by exploiting the non‐linear correlation among the received signals of each SU. In this manner, statistical tests are extracted, allowing the SUs to make decisions either autonomously at each SU or cooperatively at the FC. The performance of the KCCA‐based detector is evaluated by means of simulations and over‐the‐air experiments using a CR testbed composed of several Universal Software Radio Peripheral nodes. Both the simulations and the measurements show that the KCCA‐based detector is able to obtain a significant gain over a conventional energy detector, whose sensing performance is severely degraded by the presence of external interferers. Copyright © 2014 John Wiley & Sons, Ltd.
Identification and Characterization of the First Fragment Hits for SETDB1 Tudor Domain

SET domain bifurcated protein 1 (SETDB1) is a human histone-lysine methyltransferase, which is amplified in human cancers and was shown to be crucial in the growth of non-small and small cell lung carcinoma. In addition to its catalytic domain, SETDB1 harbors a unique tandem tudor domain which recognizes histone sequences containing both methylated and acetylated lysines, and likely contributes to its localization on chromatin. Using X-ray crystallography and NMR spectroscopy fragment screening approaches, we have identified the first small molecule fragment hits that bind to histone peptide binding groove of the TTD of SETDB1. Herein, we describe the binding modes of these fragments and analogues and the biophysical characterization of key compounds. These confirmed small molecule fragments will inform the development of potent antagonists of SETDB1 interaction with histones.
EKNOT: Event Knowledge from News and Opinions in Twitter

We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion in Twitter, and provides a comprehensive summary of the events from both news media and social media's point of view. EKNOT takes a time period as input and outputs a complete picture of the events within the given time range along with the public opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an entity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. Also, if a user is interested in a particular event, he/she can zoom into this event to investigate its aspects (sub-events) summarized in the same manner. EKNOT is built on real-time crawled news articles and tweets, allowing users to explore the dynamics of major events with minimal delays.
On the performance of Kronecker MIMO channel models: A semi-definite relaxation approach

The Kronecker model Is a popular structure In the design and analysis of multiple-input multiple-output (MIMO) systems. Despite being criticized for unrealistic results in some cases, it mathematically facilitates the analysis of MIMO transmission schemes. In our earlier work [1], we proposed algorithms in order to determine a MIMO channel covariance matrix which is as far as possible from the Kronecker model based on semi-definite relaxation approach. In this paper, we develop this approach for different case studies. In particular, we verify our proposed algorithms for different size of antennas. Further, as a case study for the relevance of the Kronecker assumption, numerical analysis will be carried out to evaluate the performance of optimal pilot design for Kronecker and non-Kronecker MIMO channels.
Approximation Algorithms for Solving Cost Observable Markov Decision Processes

Partial observability is a result of noisy or imperfect sensors that are not able to reveal the real state of the world. Problems that suffer from partial observability have been modelled as Partially Observable Markov Decision Processes (POMDPs). They have been studied by researchers in Operations Research and Artificial Intelligence for the past 30 years. Nevertheless, solving for the optimal solution or for close approximations to the optimal solution is known to be NP-hard. Current algorithms are very expensive and do not scale well. Many applications can be modelled as POMDPs: quality control, autonomous robots, weapon allocation, medical diagnosis. Designing approximation algorithms to solve problems that have partial observability is the focus of this research. The model we propose (Cost Observable Markov Decision Processes or COMDPs) associates costs with obtaining information about the current state (Bayer 1998). The COMDP’s actions are of two kinds: world actions and observation actions. World actions change the state of the world, but return no observation information. Observation actions return observation information, but the state of the world does not change while performing them. COMDPs are intended to model situations that arise in diagnosis and active vision where there are many observation actions that do not change the world and relatively few world-changing actions. We are particularly interested in problems where there are many alternative sensing actions (including, especially, no sensing at all) and where, if all observation actions are performed, the entire state of the world is observable (but presumably at very great cost). Hence, COMDPs can also be viewed as a form of fully observable Markov Decision Processes (MDPs) where the agent must pay to receive state information (they are "cost-observable"). Any POMDP can be modeled as a COMDP and vice versa. We want to approximately solve COMDPs and determine what POMDP classes these approximations are good for. There are two fundamental problems that any POMDP approximation algorithm must address: how to act when "lost" and how to avoid getting "lost".
LePrEF: Learn to precompute evidence fusion for efficient query evaluation

State-of-the-art search engine ranking methods combine several distinct sources of relevance evidence to produce a high-quality ranking of results for each query. The fusion of information is currently done at query-processing time, which has a direct effect on the response time of search systems. Previous research also shows that an alternative to improve search efficiency in textual databases is to precompute term impacts at indexing time. In this article, we propose a novel alternative to precompute term impacts, providing a generic framework for combining any distinct set of sources of evidence by using a machine-learning technique. This method retains the advantages of producing high-quality results, but avoids the costs of combining evidence at query-processing time. Our method, called Learn to Precompute Evidence Fusion (LePrEF), uses genetic programming to compute a unified precomputed impact value for each term found in each document prior to query processing, at indexing time. Compared with previous research on precomputing term impacts, our method offers the advantage of providing a generic framework to precompute impact using any set of relevance evidence at any text collection, whereas previous research articles do not. The precomputed impact values are indexed and used later for computing document ranking at query-processing time. By doing so, our method effectively reduces the query processing to simple additions of such impacts. We show that this approach, while leading to results comparable to state-of-the-art ranking methods, also can lead to a significant decrease in computational costs during query processing. © 2012 Wiley Periodicals, Inc.
Methods of Randomized Controlled Clinical Trials in Health Services Research

The randomized controlled clinical trial is an increasingly used method in health services research. Analysis of methodology is needed to accelerate practical implementation of trial results, select trials for meta-analysis, and improve trial quality in health services research. The objectives of this study are to explore the methodology of health services research trials, create and validate a streamlined quality evaluation tool, and identify frequent quality defects and confounding effects on quality. The authors developed a quality questionnaire that contained 20 evaluation criteria for health services research trials. One hundred one trials from the Columbia Registry of Controlled Clinical Trials were evaluated using the new quality tool. The overall agreement between independent reviewers, Cohen's kappa, was 0.94 (±0.01). Of a possible score of 100, the trials received an average score of 54.8 (±12.5). Five evaluation criteria indicated significant quality deficiencies (sample size, description of case selection, data on possible adverse effects, analysis of secondary effect variables, and retrospective analysis). The quality of study characteristics was significantly weaker than the quality of reporting characteristics (P <0.001). The total average scores of Medline-indexed journals were better than the non-Medline-indexed journals (P < 0.001). There was a positive correlation between the overall quality and year of publication (R = 0.21, P < 0.05). The authors conclude that the new quality evaluation tool leads to replicable results and there is an urgent need to improve several study characteristics of clinical trials. In comparison to drug trials, site selection, randomization, and blinding often require different approaches in health services research.
Constrained Texture Mapping using Image Warping

We introduce in this paper a new method for smooth foldover‐free warping of images. It allows users to specify the constraints in two different ways: positional constraints to constrain the position of points in the image and gradient constraints to constrain the orientation and scaling of some parts of the image. We then show how our method is used for texture mapping with hard constraints. We start with an unconstrained planar embedding of the target mesh calculated with conventional methods. In order to obtain a mapping that satisfies the user‐defined constraints, we use our warping method to align the features of the texture image with those of the unconstrained embedding. Compared to previous work, our method generates a smoother texture mapping and offers higher level of control for defining the constraints.
Logical effort based power-delay-product optimization

In circuit design and evaluation, power and delay play a major role in deciding the circuit performance. Power and delay are approximately inversely related to each other and if one parameter is decreased the other gets increased. The proposed method uses power-delay-product (PDP) as performance metric to optimize the delay and power. The mathematical model has been developed to minimize the PDP by sizing the gates in a chain of inverters and NAND-NOR-INV combinational circuit. The results are validated using TSMC 0. 18μm, 1.8V CMOS technology in mentor graphics' ELDO Spice. A significant reduction in power 11% and 27% for 3 and 5 stages respectively has been observed with a small increment in delay of 2% and 11%. Similarly, for NAND-NOR-INV circuit a reduction of approximately 14% in power with a minute change in delay has been observed.
Efficient Indoor Localization via Reinforcement Learning

In recent years the widespread use of Global Positioning Systems (GPS) has enhanced our abilities to find our way in outdoor environments, dispensing our over-reliance on visual features. However, GPS technologies do not solve the problem of localization and way finding in indoor environments due to transient phenomena such obstacles and fading. With the development of computer vision and deep learning techniques, vision-based localization and way finding have recently received special attention in the scientific community. In this paper we propose a method which learns to localize with high accuracy while minimizing the total number of processes based on reinforcement learning. We train our model on a dataset at MIT campus and we evaluate its performance by comparing with state-of-the-art techniques, obtaining higher accurate results.
Sample size calculations for randomized controlled trials.

Most informed consent documents for randomized controlled trials implicitly or explicitly promise the prospective participant that the trial has a reasonable chance of answering a medically important question. The medical literature, however, is replete with descriptions of trials that provided equivocal answers to the questions they addressed. Papers describing the results of such studies may clearly imply that the trial required a much larger sample size to adequately address the questions it posed. Hidden in file drawers, undoubtedly, are data from other trials whose results never saw the light of day—some, perhaps, victims of inadequate sample size. Although many inadequate-sized studies are performed in a single institution with patients who happen to be available, some are multicenter trials designed with overly optimistic assumptions about the effectiveness of therapy, too high an estimate of the event rate in the control group, or unrealistic assumptions about follow-up and compliance. In this review, I discuss statistical considerations in the choice of sample size and statistical power for randomized controlled trials. Underlying the discussion is the view that investigators should hesitate before embarking on a trial that is unlikely to detect a biologically reasonable effect of therapy. Such studies waste both time and resources.
Analysis of T-wave amplitude adaptation to heart rate using RR-binning of long-term ECG recordings

The prognosis of patients with coronary artery disease at the early stage of the disease is a challenge of modern cardiology. There is an urgent need to risk stratify these patients. Holter technology is a cheap and cost effective tool to evaluate electrical abnormalities in the heart. We propose to investigate T-amplitude adaptation to heart rate (HR) using RR-binning. We used daytime recordings from healthy subjects and subjects with acute myocardial infarction (AMI) from the Telemetric and Holter ECG Warehouse. The AMI subjects were divided into two groups based on location of their infarction (group A: anterior or anterior lateral, group B: inferior or inferior lateral). Both AMI groups had acute and stable phase recordings. Population-based T-adaptation to HR was observed for healthy subjects (R2 = 0.92) but was less pronounced for AMI subjects: Group A- R2acute = 0.56 and R2stable = 0.00, and Group B- R2acute = 0.57 and R2stable = 0.33.
Robust Low-Rank Matrix Factorization Under General Mixture Noise Distributions

Many computer vision problems can be posed as learning a low-dimensional subspace from high-dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problems using L1-norm and L2-norm losses, which mainly deal with the Laplace and Gaussian noises, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as mixture of exponential power (MoEP) distributions and then proposes a penalized MoEP (PMoEP) model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture distribution is adapted from a series of preliminary superor sub-Gaussian candidates. Moreover, by facilitating the local continuity of noise components, we embed Markov random field into the PMoEP model and then propose the PMoEP-MRF model. A generalized expectation maximization (GEM) algorithm and a variational GEM algorithm are designed to infer all parameters involved in the proposed PMoEP and the PMoEPMRF model, respectively. The superiority of our methods is demonstrated by extensive experiments on synthetic data, face modeling, hyperspectral image denoising, and background subtraction.
Four Tissue Segmentation in ADNI II

While Alzheimer’s disease (AD) is the most common cause for dementia among older individuals 1,2 and the primary goal of ADNI, the lifetime risk for stroke equals and may exceed the risk of AD in some circumstances . In addition, MRI evidence of asymptomatic cerebrovascular disease (CVD) occurs in one-third of older individuals . In community based studies, mixed pathologies account for the most common cause of dementia 5 and the impact of cerebrovascular and AD pathology is additive, particularly when the AD pathological burden is mild . Similarly, MRI community based studies have shown that white matter hyperintensities (WMH) increase with advancing age and associated vascular risk factors 4,7 and are associated with an array of cognitive deficits in cross-sectional studies . Similar cross-sectional differences are also seen with clinically silent brain infarctions noted on MRI . Importantly, community based studies show that both WMH and SBI are associated with cognitive decline and incident MCI and dementia .