Miracle-dz/UniFAR-FTUs huggingface dataset:
MRI and cerebrospinal fluid biomarkers for predicting progression to Alzheimer's disease in patients with mild cognitive impairment: a diagnostic accuracy study

Objectives To assess the incremental value of MRI and cerebrospinal fluid (CSF) analysis after a short memory test for predicting progression to Alzheimer's disease from a pragmatic clinical perspective. Design Diagnostic accuracy study in a multicentre prospective cohort study. Setting Alzheimer Disease Neuroimaging Initiative participants with complete data on neuropsychological assessment, MRI of the brain and CSF analysis. Participants Patients with mild cognitive impairment (MCI; n=181) were included. Mean follow-up was 38.9 months (range 5.5–75.9). Main outcome measures Diagnostic accuracy of individual instruments and incremental value of entorhinal cortex volume on MRI and p-τ/Aβ ration in CSF after administration of Rey's Auditory Verbal Learning Memory Test are calculated and expressed as the ‘Net Reclassification Improvement’ (NRI), which is the change in the percentage of individuals that are correctly diagnosed as Alzheimer or non-Alzheimer case. Results Tested in isolation, a short memory test, MRI and CSF all substantially contribute to the differentiation of those MCI patients who remain stable during follow-up from those who progress to develop Alzheimer's disease. The memory test, MRI and CSF improved the diagnostic classification by 21% (95% CI 15.1 to 26.9), 22.1% (95% CI 16.1 to 28.1) and 18.8% (95% CI 13.1 to 24.5), respectively. After administration of a short memory test, however, the NRI of MRI is +1.1% (95% CI 0.1 to 3.9) and of CSF is −2.2% (95% CI −5.6 to −0.6). Conclusions After administration of a brief test of memory, MRI or CSF do not substantially affect diagnostic accuracy for predicting progression to Alzheimer's disease in patients with MCI. The NRI is an intuitive and easy to interpret measure for evaluation of potential added value of new diagnostic instruments in daily clinical practice.
Evaluation of PET/MRI for Tumor Volume Delineation for Head and Neck Cancer

Introduction Computed tomography (CT), combined positron emitted tomography and CT (PET/CT), and magnetic resonance imaging (MRI) are commonly used in head and neck radiation planning. Hybrid PET/MRI has garnered attention for potential added value in cancer staging and treatment planning. Herein, we compare PET/MRI vs. planning CT for head and neck cancer gross tumor volume (GTV) delineation. Material and methods We prospectively enrolled patients with head and neck cancer treated with definitive chemoradiation to 60–70 Gy using IMRT. We performed pretreatment contrast-enhanced planning CT and gadolinium-enhanced PET/MRI. Primary and nodal volumes were delineated on planning CT (GTV-CT) prospectively before treatment and PET/MRI (GTV-PET/MRI) retrospectively after treatment. GTV-PET/MRI was compared to GTV-CT using separate rigid registrations for each tumor volume. The Dice similarity coefficient (DSC) metric evaluating spatial overlap and modified Hausdorff distance (mHD) evaluating mean orthogonal distance difference were calculated. Minimum dose to 95% of GTVs (D95) was compared. Results Eleven patients were evaluable (10 oropharynx, 1 larynx). Nine patients had evaluable primary tumor GTVs and seven patients had evaluable nodal GTVs. Mean primary GTV-CT and GTV-PET/MRI size were 13.2 and 14.3 cc, with mean intersection 8.7 cc, DSC 0.63, and mHD 1.6 mm. D95 was 65.3 Gy for primary GTV-CT vs. 65.2 Gy for primary GTV-PET/MRI. Mean nodal GTV-CT and GTV-PET/MRI size were 19.0 and 23.0 cc, with mean intersection 14.4 cc, DSC 0.69, and mHD 2.3 mm. D95 was 62.3 Gy for both nodal GTV-CT and GTV-PET/MRI. Conclusion In this series of patients with head and neck (primarily oropharynx) cancer, PET/MRI and CT-GTVs had similar volumes (though there were individual cases with larger differences) with overall small discrepancies in spatial overlap, small mean orthogonal distance differences, and similar radiation doses.
Scene Category Context for 3 D Object Detection with RGBD cameras

In the problem of 3D object detection for indoor scene understanding, contextual relationships such as scene category can provide informative clues that improve object detection performance. In this paper, we extend our previous 3D object detection framework [7] to leverage positive context clues, by integrating scene appearance information with the appearance and geometry features from our previous work. As in the previous work, we test the effectiveness of our approach on the same SUN-RGBD dataset [8]. The experimental results demonstrate that through effective integration of positive context clues such as scene appearance, our approach achieves a significant improvement over the previous work’s performance [7].
Cognitive Biases in Governing: Technology Preferences in Election Administration

Cognitive biases are heuristics that shape individual preferences and decisions in a way at odds with means-end rationality. The effects of cognitive biases on governing are underexplored. We study how election administrators’ cognitive biases shape their preferences for e-voting technology using data from a national survey of local election officials. The Technology Acceptance Model, which employs a rational, means-end perspective, suggests that the perceived benefits of e-voting machines explain their popularity. But our findings indicate that cognitive biases also play a role, even after controlling for perceived benefits and costs of the technology. The findings point to a novel cognitive bias that is of particular interest to research on e-government: officials who have a general faith in technology are attracted to more innovative alternatives. We also find that local election officials who prefer e-voting machines do so in part because they overvalue the technology they already possess, and because they are overly confident in their own judgment.
Two-Dimensional Multi-Domain Hybrid Spectral-WENO Methods for Conservation Laws

Abstract : The multi-domain hybrid Spectral-WENO(Weighted Essentially Non-Oscillatory) method (Hybrid) is introduced for the numerical solution of two dimensional nonlinear hyperbolic systems in a Cartesian physical domain which is partitioned into a grid of rectangular subdomains. The main idea of the Hybrid scheme is to conjugate the spectral and WENO methods for solving problems with shock or high gradients such that the scheme adapts its solver spatially and temporally depending on the smoothness of the solution in a given subdomain. Built as a multi-domain method, an adaptive algorithm is used to keep the solutions parts exhibiting high gradients and discontinuities always inside WENO subdomains while the smooth parts of the solution are kept inside a spectral one, avoiding oscillations related to the well-known Gibbs phenomenon and increasing the numerical efficiency of the overall scheme. A higher order version of the multiresolution analysis proposed by Harten is used to determine the smoothness of the solution in each subdomain. We also discuss interface conditions for the two dimensional problem and the switching procedure between WENO and spectral subdomains. The Hybrid method is applied to the two-dimensional Shock-Vortex Interaction and the Richtmyer-Meshkov Instability (RMI) problems.
Lee's Typology of Love Styles: A Confirmatory Factor Analysis of the Hendrick-Hendrick Measure with Implications for Counseling.

Love is among the most fundamental aspects of the experience of being human, but measures of perceptions of the experience of love have only recently been explored. One of the most popular measures was developed by C. Hendrick and S. Hendrick. Most previous studies of the measure have used exploratory factor analysis and orthogonal factor rotation. In the present study, confirmatory factor analytic methods were conducted using the LISREL program. Results with 185 undergraduate and graduate students (69.2 percent female, and 83.2 percent non-minority) support a view that a more appropriate model might be a "G," or General factor theory, and a model in which love styles are correlated. Two tables present tests of model fit and items sorted by factor loading. Appendixes contain the variance-covariance matrix, LISREL maximum likelihood estimates and matrices, and modification indices for the factor matrix. (Contains 33 references.) (SLD) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** loveaera.wp1 3/15/93 U.S DEPARTMENT OF EDUCATIOH OMce ot Educasona, Research and mprovernent EDUC TIONAL RESOURCES INFORMATION CENTER tERICt trus document has been reproduced as recewed Iron, th2 Person Ot organaabon Ongtnatmg MroOr Changes have been made to trnbrove reproducbon Quahly Romts of v.ew or opmonsstated,ntrusdoCument c10 oOt necessanly represent offical OERI posit.on Or MAC,/ "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY AC 08c 726/1-1/15.600 TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC). " LEE'S TYPOLOGY OF LOVE STYLES: A CONFIRMATORY FACTOR ANALYSIS OF THE HENDRICK-HENDRICK MEASURE WITH IMPLICATIONS FOR COUNSELING Bruce Thompson Texas AM
A space-time Tx scheme for two-cell MISO-BC with delayed CSIT

Perfect channel state information at the transmitter (CSIT) is required to completely characterize the optimal degrees of freedom (DoF) of an interfering network by zero-forcing. However, it is not always realistic to know the CSIT perfectly when the channel is varying rapidly with time. In that case, a delayed CSIT is a more reasonable assumption. In this paper, we study the DoF of interfering multiple input single output (MISO) broadcast channel (BC) when the delayed CSIT is known perfectly. We propose a space-time transmission scheme based on interference alignment for two-cell interfering MISO-BC with M transmit antennas and K users in each cell. Considering the various cases, we suggest that the achievable per cell DoF converges to K/K+1 in the case when M = K and the outdated CSIT is known perfectly. The DoF per cell approaches 1 as the number of users in each cell approach to infinity.
Linear Local Distance coding for classification of HEp-2 staining patterns

Indirect Immunofluorescence (IIF) on Human Epithelial-2 (HEp-2) cells is the recommended methodology for detecting some specific autoimmune diseases by searching for antinuclear antibodies (ANAs) within a patient's serum. Due to the limitations of IIF such as subjective evaluation, automated Computer-Aided Diagnosis (CAD) system is required for diagnostic purposes. In particular, staining patterns classification of HEp-2 cells is a challenging task. In this paper, we adopt a feature extraction-coding-pooling framework which has shown impressive performance in image classification tasks, because it can obtain discriminative and effective image representation. However, the information loss is inevitable in the coding process. Therefore, we propose a Linear Local Distance (LLD) coding method to capture more discriminative information. LLD transforms original local feature to local distance vector by searching for local nearest few neighbors of local feature in the class-specific manifolds. The obtained local distance vector is further encoded and pooled together to get salient image representation. We demonstrate the effectiveness of LLD method on a public HEp-2 cells dataset containing six major staining patterns. Experimental results show that our approach has a superior performance to the state-of-the-art coding methods for staining patterns classification of HEp-2 cells.
Fast connection recovery against region failures with landmark-based source routing

Communication networks are vulnerable to the natural disasters or malicious attacks, which may cause a large area of corruptions and long term disconnection of network services. Such significant outage has become a serious problem today with regard to the stringent reliability requirement of the mission critical applications. In this paper, we focus on fast and effective failure recovery in face of region failures. Rather than rely on the time consuming failure localization, we introduce and evaluate a direct restoration mechanism upon detecting the path failure, which use the geographically distributed intermediate nodes, named “landmarks”, to reestablish the connection through a (near-)shortest detour path. The experimental analysis on randomly generated and realistic network topologies have shown that this approach can achieve high reliability and low stretch in case of region failures, and thus significantly reduce the impact of region failures on network based mission critical services.
Rangesum histograms

A <i>rangesum query</i> to an array <b>A</b> is a pair (<i>l, r</i>) of range endpoints, which should be answered by Σ<inf><i>l</i>≤<i>i</i><i>r</i></inf><b>A</b>[<i>i</i>]. To compress <b>A</b>, we consider representing an array <b>A</b> lossily by a histogram, a function that is constant on each of a small number of buckets. We then answer range queries from <b>H</b> instead of from <b>A</b>, <i>i.e.</i>, as Σ<inf><i>l</i>≤<i>i</i><i>r</i></inf><b>H</b>[<i>i</i>]. An optimal rangesum histogram <b>H</b> for this purpose is one whose bucket boundaries and constant heights within buckets are chosen to minimize the expected square error, <i>E<inf>l, r</inf></i>[(Σ<inf><i>l</i>≤<i>i</i><i>r</i></inf><b>A</b>[<i>i</i>]--Σ<inf><i>l</i>≤<i>i</i><i>r</i></inf><b>H</b>[<i>i</i>].)<sup>2</sup>], assuming each rangesum query is equally likely. Rangesum histograms find many applications in database systems.In a degenerate variation, all rangesum queries are over ranges of size one, namely, individual points; histograms optimal for this special case are called <i>pointwise optimal histograms</i>. Pointwise optimal histogram is a classical notion in statistics and approximation theory, but rangesum optimal histogram appears to be novel in these areas. While optimal pointwise histograms can be constructed efficiently by simple dynamic progrmming, no efficient (even approximate) general rangesmn histogram construction algorithms were previously known. In practice, all commercial database systems use heuristically built histograms for pointwise and rangesum queries.We present the first general algorithms for approximate rangesum histograms. Given parameter <i>B</i>, we denote by (α, β)-approximation an algorithm to produce a (α<i>B</i>)-bucket histogram with error at most β times the error of the optimal <i>B</i>-bucket histogram. We give a (2, 1)-approximation with runtime <i>O</i>(<i>N</i><sup>2</sup><i>B</i>), a (2, 1+<i>∊</i>)-approximation with runtime <i>N</i> + (<i>B</i> log(<i>N</i>)/∊)<i><sup>O(1)</sup></i> (1), and a (1, 1 + ∊)-approximation with runtime <i>O</i>(<i>B</i><sup>3</sup><i>N</i><sup>4</sup>/∊<sup>2</sup>). We also consider the problem of dynamic maintenance of rangesum histograms for data updated by additive changes, and we give a (2, 1 + ∊)-approximation that uses space (<i>B</i>log(<i>N</i>)/∊)<sup>O(1)</sup> and time (<i>B</i>log(<i>N</i>)/∊)<sup>O(1)</sup> for update and query operations. The bounds are nearly competitive with some of the best known bounds for constructing pointwise optimal histograms modulo small additional number of buckets used; however, rangesum histograms are substantially harder to construct because of the long range dependence between subproblems.
Inefficient Policies, Inefficient Institutions and Trade

Despite the general belief among economists on the growth-enhancing role of international trade and significant trade opening over the past 25 years, the growth performance of many developing economies, especially of those in Latin America and Africa, has been disappointing. While this poor growth performance has many potential causes, in this paper I argue that part of the reason may be related to the interaction between weak institutions and trade. In particular, I construct a model in which trade opening in societies with weak institutions (in particular autocratic and elite-controlled political systems) may lead to worse economic policies. The reason is that general equilibrium price effects of taxation and expropriation in closed economies also hurt the elites, and this puts a natural barrier against inefficient policies. Trade openness removes this barrier and enables groups with political power to exercise this power in more inefficient ways.
Data modeling of scientific experimentation

Microcomputers running data acquisition, analysis, and communication software have helped scientists automate their laboratories, improve the quality of research, enhance productivity, and facilitate collaboration. Despite the growing use of these and other software tools, reliance on the traditional pen and laboratory notebook for recording and organizing experimental data remains commonplace. This paper presents a data model of scientific experimentation for designing database systems that chronicle the empirical study of natural processes.
PARAMETRIZATION OF THE DARK ENERGY EQUATION OF STATE

A comparative study of various parametrizations of the dark energy equation of state is made. Astrophysical constraints from LSS, CMB and BBN are laid down to test the physical viability and cosmological compatibility of these parametrizations. A critical evaluation of the four-index parametrizations reveals that Hannestad–Mortsell as well as Lee parametrizations are simple and transparent in probing the evolution of the dark energy during the expansion history of the universe and they satisfy the LSS, CMB and BBN constraints on the dark energy density parameter.
Top-down design flow for MOEMS

As MOEMS (Micro-Opto-Electro-Mechanical Systems) transition from laboratory curiosities to production products inserted into telecommunication systems, a top down design flow is necessary to minimize time-to-market. We present a structured- custom approach for MOEMS design based on the concept of using parameterized behavioral models as a means to improve and speed up the design process. This approach enables a user to quickly explore a larger design space on the interaction of the behavioral models with their surrounding system. Once the simulation is completed to the designer's satisfaction, a device layout can be output for FEM verification on critical areas or to generate the masks for fabrication.
On-chip enucleation of oocyte by magnetically driven microtools with ultrasonic vibration

This paper describes an innovative driving method for magnetically driven microtools (MMT). 1.1 μm positioning accuracy of the MMT was obtained by applying a piezoelectric ceramic, which induced nanoscale vibration to the microfluidic chip, and reducing the friction on the MMT. Using this drive method, the enucleation process was conducted by dual-arm MMT. Permanent magnets are used to supply the driving power to the MMT allowing sufficient positioning accuracy to cut oocytes precisely. The nucleus was cut within 10 s, and the removed area was less than 25% of the original oocyte.
Observational signatures of feedback in qso absorption spectra

ABSTRA C T Models for the formation of galaxies and clusters of galaxies require strong feedback in order to explain the observed properties of these systems. We investigate whether such feedback has observational consequences for the intergalactic medium, as probed in absorption towards background quasars. A typical quasar sight-line intersects one protocluster per unit redshift, and significant feedback from forming galaxies or active galactic nuclei, heating the protocluster gas, will result in a large clearing of reduced absorption in the Lya forest. Such a gap could be detected at redshift *3 when the mean opacity is high. Feedback from Lyman-break galaxies in protoclusters can be probed by the absorption lines produced in their winds. Strong feedback from galaxies has a major impact on the number and properties of absorption lines with column densities NH i , 10 16 cm 22 : This feedback
Collaborative Filtering for Community Threats

We discuss the threats posed by attacks on computer infrastructure, and particularly attacks from distributed, stealthy sources. We propose that these attacks would best be detected by distributed networks of sensors, and that these distributed networks must protect the privacy, confidentiality and anonymity of their members. We briefly outline a network attack detection technique we are working on that is based on privacy-preserving collaborative filtering, and discuss some
Orthographic Syllable as basic unit for SMT between Related Languages

We explore the use of the orthographic syllable, a variable-length consonant-vowel sequence, as a basic unit of translation between related languages which use abugida or alphabetic scripts. We show that orthographic syllable level translation significantly outperforms models trained over other basic units (word, morpheme and character) when training over small parallel corpora.
Endovascular Repair of Aortic Dissection in Marfan Syndrome: Current Status and Future Perspectives

Over the last decades, improvement of medical and surgical therapy has increased life expectancy in Marfan patients. Consequently, the number of such patients requiring secondary interventions on the descending thoracic aorta due to new or residual dissections, and distal aneurysm formation has substantially enlarged. Surgical and endovascular procedures represent two valuable options of treatment, both associated with advantages and drawbacks. The aim of the present manuscript was to review endovascular outcomes in Marfan syndrome and to assess the potential role of Thoracic Endovascular Aortic Repair (TEVAR) in this subset of patients.
AniDiary: Daily Cartoon-Style Diary Exploits Bayesian Networks

AniDiary (Anywhere Diary) uses Bayesian networks to automatically detect landmark events and summarize a user's daily life in a cartoon-style diary. Our goal was to summarize a given user's daily life with a cartoon-style diary based on information collected from mobile devices such as smart phones. Our system, AniDiary (Anywhere Diary), addresses two main problems of typical diary systems-the huge number of events originating from the real-life log and the awkward presentation of the output. Using modular Bayesian networks, AniDiary can detect and visualize landmarks (relevant or novel events) and transform numerous logs into user-friendly cartoon images. The cartoons provide a good starting point for fine-grained searches of detailed information. For example, users can link each cartoon to rich media (photos or videos) that offer more details, reducing the search space and letting users easily recall the linked details.
Biases in small RNA deep sequencing data

High-throughput RNA sequencing (RNA-seq) is considered a powerful tool for novel gene discovery and fine-tuned transcriptional profiling. The digital nature of RNA-seq is also believed to simplify meta-analysis and to reduce background noise associated with hybridization-based approaches. The development of multiplex sequencing enables efficient and economic parallel analysis of gene expression. In addition, RNA-seq is of particular value when low RNA expression or modest changes between samples are monitored. However, recent data uncovered severe bias in the sequencing of small non-protein coding RNA (small RNA-seq or sRNA-seq), such that the expression levels of some RNAs appeared to be artificially enhanced and others diminished or even undetectable. The use of different adapters and barcodes during ligation as well as complex RNA structures and modifications drastically influence cDNA synthesis efficacies and exemplify sources of bias in deep sequencing. In addition, variable specific RNA G/C-content is associated with unequal polymerase chain reaction amplification efficiencies. Given the central importance of RNA-seq to molecular biology and personalized medicine, we review recent findings that challenge small non-protein coding RNA-seq data and suggest approaches and precautions to overcome or minimize bias.