Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast quantum mechanical algorithm for database search Lov

Imagine a phone directory containing N names arranged in completely random order. In order to find someone's phone number with a probability of , any classical algorithm (whether deterministic or probabilis-tic) will need to look at a minimum of names. Quantum mechanical systems can be in a superposition of states and simultaneously examine multiple names. By properly adjusting the phases of various operations, successful computations reinforce each other while others interfere randomly. As a result, the desired phone number can be obtained in only steps. The algorithm is within a small constant factor of the fastest possible quantum mechanical algorithm.
A full‐particle Martian upper thermosphere‐exosphere model using the DSMC method

A one‐dimensional full‐particle model of the Martian upper thermosphere‐exosphere has been developed, where the Direct Simulation Monte Carlo (DSMC) method is applied to both thermal and nonthermal components. Our full‐particle model can self‐consistently solve the transition from collisional to collisionless domains in the upper thermosphere, so that the energy deposition from nonthermal energetic components to thermal components in the transition region is properly described. For the solar EUV condition during the Viking 1 measurement (1 EUV case), computed density profiles are in good agreement with those observed by Viking 1 and with the conventional model. For a solar EUV flux 6 times the Viking 1 condition (6 EUV case), the computed heating efficiency is essentially the same as the 1 EUV case but slightly increases by about 10% below the exobase, and temperature deviates from the conventional model in and above the transition region. This result suggests that the conventional heating efficiency of 0.18 is a good approximation for low (1 EUV case) to moderately strong (6 EUV case) solar EUV conditions but would be inappropriate for an extremely strong solar EUV (up to ~100 times stronger flux) environment. We also find that applying different models of the CO2‐O collisional energy transfer rate results in a difference in the calculated exobase temperature by 150 K for the 6 EUV case.
Prevalence of psychiatric disorders in Taiwan defined by the Chinese Diagnostic Interview Schedule

ABSTRACT The Taiwan Psychiatric Epidemiological Project, conducted from 1982 to 1986, used the multistage random sampling method with 5005, 3004 and 2995 subjects selected respectively from metropolitan Taipei (MT), 2 small towns (ST) and 6 rural villages (RV). The case identification tool was the Chinese modified Diagnostic Interview Schedule (DIS‐CM). This study presents the lifetime and one‐year prevalence of 27 and of 17 specific psychiatric disorders respectively. The lifetime prevalence of any disorder defined by the DIS‐CM—excluding tobacco dependence—was 16.3%, 28.0% and 21.5% in the MT, ST and RV samples respectively. The differences in lifetime prevalence between the sexes and between the 3 sampling areas were significant for 15 and 8 disorders respectively. The ST sample seemed to have the most disorders, with the highest prevalence among 3 sampling areas. The mean ratio of one‐year to lifetime prevalence was 0.67. The differences in prevalence rates between the 3 sampling areas and between the international studies are discussed from methodological, social and cultural points of view.
Current perspectives

Besides hereditary degenerative disorders, two main classes of pathologic processes are responsible for the alteration of the sensorineural structures of the inner ear resulting in the impairment of auditory and vestibular functions. One is related to the mechanical exhaustion of the organs by functional overstimulation of the sensorineural receptors; others are related to some intoxication of the sensory cells as a consequence of metabolic disturbances affecting the inner ear, such as with aging, vascular disorders, Meniere's and related diseases, and some xenobiotic molecules, whose primary intention is to cure another disease. Indeed, genetics can also influence the individual susceptibility to these agents; however, their specific pathologic mechanisms would remain the same, As pointed out very clearly by Hawkins, 1 whatever the noxious agent-particularly aging, noise, or d rugsthe damages to the sensory epithelia may look similar, and still the primary mechanisms differ significantly and most often cooperate in aggravating the lesions. Mechanical overstimulation of the end organ is a major cause of cochlear impairment, with all kinds of acoustic trauma, from long-lasting exposure to noise as in occupational deafness to short-term loss in hazardous exposure to very loud, explosive sounds. Similar functional hazards for the vestibular system are rather uncommon. Mechanical vestibular overstimulations occur during life-threatening head or whole-body injuries. Specific effects of such traumas on the vestibular receptors have little been considered. Here aging and systemic disorders consti-
Augmentic Compositional Models for Knowledge Base Completion Using Gradient Representations

Neural models of Knowledge Base data have typically employed compositional representations of graph objects: entity and relation embeddings are systematically combined to evaluate the truth of a candidate Knowedge Base entry. Using a model inspired by Harmonic Grammar, we propose to tokenize triplet embeddings by subjecting them to a process of optimization with respect to learned wellformedness conditions on Knowledge Base triplets. The resulting model, known as Gradient Graphs, leads to sizable improvements when implemented as a companion to compositional models. Also, we show that the “supracompositional” triplet token embeddings it produces have interpretable properties that prove helpful in performing inference on the resulting triplet representations.
Frequency estimators with high resistance to interference from image frequency components

This paper presents an improved frequency estimation algorithm based on the interpolated discrete Fourier transform. High-accurate frequency estimation can be achieved by taking the geometric mean of two independent estimates, which are derived from the real parts of the two largest spectral bins and the imaginary parts, respectively. In situations where only a small number of sine wave cycles are observed, the ability of the algorithm to cancel interference from image frequency components results in improvements in accuracy. The residual errors of the proposed algorithm have been theoretically analyzed with maximum side-lobe decaying windows, since the windows have simple and uniform analytical expression of interpolation algorithm. The performance of the proposed algorithm was investigated using both Hanning and three-term maximum side-lobe decaying windows. A comparative analysis of systematic errors and noise sensitivity was performed between the new algorithm and traditional algorithms. Both the root mean squared error and the probability density of the errors were investigated under noisy conditions. Simulation results demonstrated that the new algorithm is not only highly resistant to interference from image components but is also resistant to the effects of random noise. The results presented in the paper are useful for identifying the best choice of algorithm in practical engineering applications.
Assessment of nurses’ Knowledge and Practice Related toCaring of Central Venous Line at Aldamam hospital

Central venous catheters (CVC) are known to be associated with risks of complications such as infections and hemorrhages. Good practices of care and management are essential in limiting these risks. Therefore, the investigators felt a real need to assess the staff nurses’ knowledge and practices regarding CVLCare. The objectives of the study were to assess the knowledge and practice of staff nurses regarding care of CVL. In Aldamamhospital,the research approach adopted for the study was quantitative approach and the research design wasdescriptive survey design. The tool for data collection was a structured questionnaire to assess the knowledge of nurses, an observation checklist for nurses related to CVL care technique pre, during and post procedure, and an observation checklist for nurses related to removal of CVL. 33 staff nurses working in Aldamamhospital. The findings of the study concluded that there was a significant statistical difference in nurses’ knowledge and years of experience, and no significant statistical differences in nurse’s practices before, during, after , and removal of central venous line in relation to years of experience.
Recent Advances in the Development of Novel Pharmacological Agents for the Treatment of Cognitive Impairments in Schizophrenia

Robert W. Buchanan, Robert Freedman, Daniel C. Javitt, Anissa Abi-Dargham, and Jeffrey A. Lieberman Department ofPsychiatry,MarylandPsychiatricResearchCenter, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 2128; Department of Psychiatry, University of Colorado Health Sciences Center; Department of Psychiatry, Nathan Kline Institute for Psychiatric Research, New York University School of Medicine; Department of Psychiatry, New York State Psychiatric Institute; Department of Psychiatry, College of Physicians and Surgeons, Columbia University
Weighted log-normal kernel density estimation

ABSTRACT The log-normal (LN) kernel estimator of a density with support [0, ∞) was discussed by Jin and Kawczak (2003). The contribution of this paper is to suggest a new class of LN kernel estimators using the idea of weighted distribution. The asymptotic properties of the new class of estimators are studied. Also, numerical studies based on both simulated and real data set are presented.
Sensitivity Study of WRF Numerical Modeling for Forecasting Heavy Rainfall in Sri Lanka

This study aimed to determine the predictability of the Weather Research and Forecasting (WRF) model with different model physics options to identify the best set of physics parameters for predicting heavy rainfall events during the southwest and northeast monsoon seasons. Two case studies were used for the evaluation: heavy precipitation during the southwest monsoon associated with the simultaneous onset of the monsoon, and a low pressure system over the southwest Bay of Bengal that produced heavy rain over most of the country, with heavy precipitation associated with the northeast monsoon associated with monsoon flow and easterly disturbances. The modeling results showed large variation in the rainfall estimated by the model using the various model physics schemes, but several corresponding rainfall simulations were produced with spatial distribution aligned with rainfall station data, although the amount was not estimated accurately. Moreover, the WRF model was able to capture the rainfall patterns of these events in Sri Lanka, suggesting that the model has potential for operational use in numerical weather prediction in Sri Lanka.
Two-Tailed p-Values and Coherent Measures of Evidence

ABSTRACT In a test of significance, it is common practice to report the p-value as one way of summarizing the incompatibility between a set of data and a proposed model for the data constructed under a set of assumptions together with a null hypothesis. However, the p-value does have some flaws, one being in general its definition for two-sided tests and a related serious logical one of incoherence, in its interpretation as a statistical measure of evidence for its respective null hypothesis. We shall address these two issues in this article.
Why Do Firms Repurchase Stock

In this article, I investigate the relation between stock repurchases and distribution, investment, capital structure, corporate control, and compensation policies over the 1977-96 period. I allow the significance of each motive to change over time to account for adjustments in the percentage of firms influenced by each motive. I find that, throughout the sample period, firms repurchase stock to take advantage of potential undervaluation and, in many periods, to distribute excess capital. However, firms also repurchase stock during certain periods to alter their leverage ratio, fend off takeovers, and counter the dilution effects of stock options. Copyright 2000 by University of Chicago Press.
Ieee Transactions on Parallel and Distributed Systems 1 Distributed Deployment Schemes for Mobile Wireless Sensor Networks to Ensure Multilevel Coverage

—One of the key research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multilevel (k) coverage of the area of interest I. We consider two subproblems: k-coverage placement problem and distributed dispatch problem. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected, while the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy, if they are not in the current positions, such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors, thereby relaxing the limitations of existing results. For the dispatch problem, we propose a competition-based scheme and a pattern-based scheme. The competition-based scheme allows mobile sensors to bid for their closest locations, while the pattern-based scheme allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness.
Comparison of optimization algorithms for interferometric synthetic aperture radar phase unwrapping based on identical Markov random fields

Abstract. Phase unwrapping (PU) is one of the key processes in measuring the elevation or deformation of the Earth’s surface from its interferometric synthetic aperture radar (InSAR) data. PU problems may be formulated as maximum a posteriori estimation estimations of Markov random field (MRF). The key issue of this formulation is energy minimization. Iterated conditional mode (ICM), graph cuts (GC), loopy belief propagation (LBP), and sequential tree-reweighted message passing (TRW-S) have been proposed for the energy minimization. Unfortunately, they differ in the formulation of the MRF model for PU, which raises the question of how they compare against each other on the same MRF model for PU. We address this by investigating the four optimization algorithms and comparing them on an identical MRF model, which gives researchers some guidance as to which optimization method is best suited for solving the PU problem. Experiments using simulated and real-data illustrate that the GC algorithm is clearly the winner among the four algorithms in all cases. The ICM algorithm, although very rapid, performs much worse than the other three especially in the terrain with violent changes or discontinuities. The two message-passing algorithms—LBP and TRW-S—perform completely differently. The LBP algorithm performs surprisingly poorly on solving phase discontinuities issue, whereas the TRW-S algorithm does quite well (second only to the GC algorithm).
Inverse Shape Design of Compressor and Turbine Stages Using a Commercial CFD Program

An aerodynamic inverse shape design of turbomachinery blading in three-dimensional viscous flow is developed and implemented into a commercial CFD program, namely ANSYS-CFX. The design method is based on specifying one blade parameter, the stacking condition that is a line from hub to tip, and two other flow parameters these can be a- a target pressure distribution over the blade suction surface (or a target pressure loading) and a blade thickness distribution, b- or target pressure distributions on pressure and suction surfaces. This inverse design approach is fully consistent with the viscous flow assumption and is independent of the CFD approach taken. The blade walls are assumed to be moving with a virtual velocity that would asymptotically drive the blade to the shape that would correspond to the specified target pressure distribution. This virtual velocity distribution is computed from the difference between the computed and the target pressure distributions. The wall displacement is computed in a Junction Box Routine and communicated to ANSYS-CFX using CFX Expression Language and User Defined Functions at each design step. In ANSYS-CFX, an element based finite volume formulation is used for space discretization. The Arbitrary Lagrangian-Eulerian formulation of the unsteady Reynolds-Averaged Navier Stokes (URANS) equations is solved in a time accurate fashion with the blade motion being the source of unsteadiness. At each time step, the blade shape is modified and dynamic meshing is used to remesh the fluid flow domain. The implementation is first validated on a transonic rotor blade; the capability, robustness and accuracy of the method in satisfying the design target are then assessed on a single subsonic stator blade row, the rotor blade of an axial compressor stage and, the rotor and stator blades of an axial turbine stage where different choices of the design variables are used. The method is finally implemented to the redesign of a transonic compressor stage, a subsonic axial compressor stage and a turbine stage so as to improve their aerodynamic performance.
Development of a motion correction and partial volume correction algorithm for high resolution imaging in Positron Emission Tomography

13 Declaration . 15 Copyright . 17 Acknowledgements . 19 Abbreviations . 21 Chapter 1 . 23 Background and Medical Context . 23 1.0 Introduction . 23 1.1 Alzheimer’s Disease . 24 1.2 Imaging biomarkers . 26 1.3 Types of imaging biomarkers . 26 1.3. 1 Structural MRI . 27 1.3. 2 PET . 29 1.3. 3 Comparison of MRI volumetry and PET . 30 1.4 Why differing results for the hippocampus between 2 imaging biomarkers? 31 1.5 Motion Correction . 32
Magnetic Waves Excited by Newborn Interstellar Pickup Ions Measured by the Voyager Spacecraft from 1 to 45 au. III. Observation Times

In our companion papers we analyze 637 intervals of magnetic wave activity observed by the Voyager 1 and 2 spacecraft from its launch in 1977 through the end of 1990. We argue that these waves originate from cyclotron resonance with newborn interstellar pickup He+ and H+. As the list of wave events is long and potentially useful to other researchers, we take this opportunity to list the times of those wave events here, along with their heliocentric location, ellipticity (often referred to as polarization), and whether thermal ion data is available from the PLS instrument. A brief overview of the analysis contained within the companion papers is also provided.
Asymptotically Minimax Predictive Density for Sparse Count Data

Predictive density estimation under the Kullback--Leibler loss in high-dimensional sparse count data models is discussed. In particular, Poisson sequence models under sparsity constraints are discussed. Sparsity in count data implies zero-inflation or quasi zero-inflation, that is, situations where there exists an excess of zeros or near-zero counts. We investigate the exact asymptotic minimax Kullback--Leibler risks in sparse and quasi-sparse Poisson sequence models, providing a class of Bayes predictive densities that attain exact asymptotic minimaxity. For application, we discuss adaptation to an unknown sparsity, and also discuss the performance of the proposed Bayes predictive densities in settings where current observations are missing completely at random. Both simulation studies and applications to real data show the efficiency of the proposed Bayes predictive densities.
Optimal False Discovery Control of Minimax Estimator

In the analysis of high dimensional regression models, there are two important objectives: statistical estimation and variable selection. In literature, most works focus on either optimal estimation, e.g., minimax $L_2$ error, or optimal selection behavior, e.g., minimax Hamming loss. However in this study, we investigate the subtle interplay between the estimation accuracy and selection behavior. Our result shows that an estimator's $L_2$ error rate critically depends on its performance of type I error control. Essentially, the minimax convergence rate of false discovery rate over all rate-minimax estimators is a polynomial of the true sparsity ratio. This result helps us to characterize the false positive control of rate-optimal estimators under different sparsity regimes. More specifically, under near-linear sparsity, the number of yielded false positives always explodes to infinity under worst scenario, but the false discovery rate still converges to 0; under linear sparsity, even the false discovery rate doesn't asymptotically converge to 0. On the other side, in order to asymptotically eliminate all false discoveries, the estimator must be sub-optimal in terms of its convergence rate. This work attempts to offer rigorous analysis on the incompatibility phenomenon between selection consistency and rate-minimaxity observed in the high dimensional regression literature.
BUSEMANN FUNCTIONS AND GIBBS MEASURES IN DIRECTED POLYMER MODELS ON Z2 BY CHRISTOPHER JANJIGIAN*

We consider random walk in a space-time random potential, also known as directed random polymer measures, on the planar square lattice with nearest-neighbor steps and general i.i. d. weights on the vertices. We construct covariant cocycles and use them to prove new results on existence, uniqueness/nonuniqueness, and asymptotic directions of semi-infinite polymer measures (solutions to the Dobrushin–Lanford–Ruelle equations). We also prove nonexistence of covariant or deterministically directed bi-infinite polymer measures. Along the way, we prove almost sure existence of Busemann function limits in directions where the limiting free energy has some regularity.
Robust Estimation of High-Dimensional Vector Autoregressive Models

High dimensional non-Gaussian time series data are increasingly encountered in a wide range of applications. Conventional estimation methods and technical tools are inadequate when it comes to ultra high dimensional and heavy-tailed data. We investigate robust estimation of high dimensional autoregressive models with fat-tailed innovation vectors by solving a regularized regression problem using convex robust loss function. As a significant improvement, the dimension can be allowed to increase exponentially with the sample size to ensure consistency under very mild moment conditions. To develop the consistency theory, we establish a new Bernstein type inequality for the sum of autoregressive models. Numerical results indicate a good performance of robust estimates.