Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System

AbstractIn recent studies, the authors have successfully demonstrated the ability of an ensemble Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid EnKF, which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the WSR-88D and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and bia.
Draft -april 24 2005 -draft

Introduction The idea that values may be embodied in technical systems and devices (artifacts) has taken root in a variety of disciplinary approaches to the study of technology, society, and humanity. pragmatic turn from this largely descriptive posture sets forth values as a design aspiration, exhorting designers and producers to include values in the set of criteria by which the excellence of technologies is judged. For those who commit to the goal of creating systems embodied with values, the ideal world is one whose technologies further not only instrumental values such as functional efficiency, safety, reliability, and ease of use, but also the substantive values to which societies and their peoples It is one thing to subscribe, generally, to these ideals, even to make a pragmatic commitment to them, but putting them into practice, which can be considered a form of political or moral activism, in the design of technical systems is not straightforward. Experienced designers will recall the not too distant past when interface, usability, and even safety were overlooked features of software system design. While these have edged into the mainstream of research and practice we are still at the shaky beginnings of thinking systematically about the practice of designing with values in mind. Even conscientious designers, by which we mean designers who support the principle of integrating values into systems and devices, will not find it easy to apply standard design methodologies, honed for the purpose of meeting functional requirements, to the unfamiliar turf of values. 2 At least two sets of factors contribute to the difficulty of taking values into consideration during design. One which strikes us as epistemological in origin stems from the need to incorporate diverse and frequently far-flung areas of knowledge and know-how into the design process that are not normally conceived as an element of the Values at Play DRAFT 2 design toolkit. Design teams that commit to taking values seriously might even need to innovate in these unfamiliar terrains. The other set of factors stem from the scarcity of explicit guidelines, or methodologies, for reliably embodying values in software systems, in particular, in other technologies, more generally. We refer to these as practical. Accordingly, this paper divided into two parts. In Part One, we describe the epistemological challenges facing conscientious designers. In Part Two, we address the practical challenges by offering our own methodological framework for systematically bringing values, into the …
Evaluation of Causal Distributed Shared Memory for Data-race-free Programs

Distributed Shared Memory (DSM) is becoming an accepted abstraction for programming distributed systems. Although DSM could simplify the programming of distributed applications, maintaining a consistent view of shared memory operations across processors in a distributed system can be expensive. The causal consistency model of DSM can allow more eecient implementations of DSM because it requires that only causally ordered memory operations be viewed in the same order at diierent processors. Also, weakly ordered systems have been proposed which advocate the use of synchronization information to reduce the frequency of communication between processors. We have implemented a system that exploits both the weaker consistency of causal memory and the synchronization information used in weakly ordered systems. Consistency is ensured by locally invalidating data that is suspected to be causally overwritten and this is only done when certain synchronization operations complete at a processor. Data-race-free programs can be developed in this system assuming that the system provided sequentially consistent memory. By implementing applications that have a variety of data sharing patterns, we show that performance close to message passing implementations of the applications can be achieved in the causal DSM system. The improved performance is due to a signiicant reduction in communication costs compared to a strongly consistent memory system. These results show that causal memory can meet the consistency and performance requirements of many distributed applications.
Obstetric Facility Quality and Newborn Mortality in Malawi: A Cross-Sectional Study

Background Ending preventable newborn deaths is a global health priority, but efforts to improve coverage of maternal and newborn care have not yielded expected gains in infant survival in many settings. One possible explanation is poor quality of clinical care. We assess facility quality and estimate the association of facility quality with neonatal mortality in Malawi. Methods and Findings Data on facility infrastructure as well as processes of routine and basic emergency obstetric care for all facilities in the country were obtained from 2013 Malawi Service Provision Assessment. Birth location and mortality for children born in the preceding two years were obtained from the 2013–2014 Millennium Development Goals Endline Survey. Facilities were classified as higher quality if they ranked in the top 25% of delivery facilities based on an index of 25 predefined quality indicators. To address risk selection (sicker mothers choosing or being referred to higher-quality facilities), we employed instrumental variable (IV) analysis to estimate the association of facility quality of care with neonatal mortality. We used the difference between distance to the nearest facility and distance to a higher-quality delivery facility as the instrument. Four hundred sixty-seven of the 540 delivery facilities in Malawi, including 134 rated as higher quality, were linked to births in the population survey. The difference between higher- and lower-quality facilities was most pronounced in indicators of basic emergency obstetric care procedures. Higher-quality facilities were located a median distance of 3.3 km further from women than the nearest delivery facility and were more likely to be in urban areas. Among the 6,686 neonates analyzed, the overall neonatal mortality rate was 17 per 1,000 live births. Delivery in a higher-quality facility (top 25%) was associated with a 2.3 percentage point lower newborn mortality (95% confidence interval [CI] -0.046, 0.000, p-value 0.047). These results imply a newborn mortality rate of 28 per 1,000 births at low-quality facilities and of 5 per 1,000 births at the top 25% of facilities, accounting for maternal and newborn characteristics. This estimate applies to newborns whose mothers would switch from a lower-quality to a higher-quality facility if one were more accessible. Although we did not find an indication of unmeasured associations between the instrument and outcome, this remains a potential limitation of IV analysis. Conclusions Poor quality of delivery facilities is associated with higher risk of newborn mortality in Malawi. A shift in focus from increasing utilization of delivery facilities to improving their quality is needed if global targets for further reductions in newborn mortality are to be achieved.
An improved unsharp masking method for borehole image enhancement

The motive of borehole image enhancement is to highlight the details of geological objects. In this paper, an improved unsharp masking algorithm is presented to enhance the contrast of borehole images. This method introduces the local histogram equalization into the nonlinear unsharp masking scheme. The unsharp masking approache is simple and effective for contrast enhancement of high-frequency components, but it suffers from two main drawbacks: (1) The resence of the linear highpass filter makes the system extremely sensitive to the noises. (2) It enhances high-contrast areas much more than areas that do not exhibit high image dynamics. The nonlinear unsharp masking algorithm can adaptively enhance local details for different contrast areas. The local histogram equalization provides high-frequency components of images, which is insensitive to the noises. The proposed method is compared with other techniques in the paper and the experimental result demonstrates that the proposed approach can enhance the fine details of borehole image effectively. It not only reduces the noise influence of the unsharp masking technique, but also enhances the local details of geological objects in borehole images.
Biophysical Characterisation of Neuroglobin of the Icefish, a Natural Knockout for Hemoglobin and Myoglobin. Comparison with Human Neuroglobin

The Antarctic icefish Chaenocephalus aceratus lacks the globins common to most vertebrates, hemoglobin and myoglobin, but has retained neuroglobin in the brain. This conserved globin has been cloned, over-expressed and purified. To highlight similarities and differences, the structural features of the neuroglobin of this colourless-blooded fish were compared with those of the well characterised human neuroglobin as well as with the neuroglobin from the retina of the red blooded, hemoglobin and myoglobin-containing, closely related Antarctic notothenioid Dissostichus mawsoni. A detailed structural and functional analysis of the two Antarctic fish neuroglobins was carried out by UV-visible and Resonance Raman spectroscopies, molecular dynamics simulations and laser-flash photolysis. Similar to the human protein, Antarctic fish neuroglobins can reversibly bind oxygen and CO in the Fe2+ form, and show six-coordination by distal His in the absence of exogenous ligands. A very large and structured internal cavity, with discrete docking sites, was identified in the modelled three-dimensional structures of the Antarctic neuroglobins. Estimate of the free-energy barriers from laser-flash photolysis and Implicit Ligand Sampling showed that the cavities are accessible from the solvent in both proteins. Comparison of structural and functional properties suggests that the two Antarctic fish neuroglobins most likely preserved and possibly improved the function recently proposed for human neuroglobin in ligand multichemistry. Despite subtle differences, the adaptation of Antarctic fish neuroglobins does not seem to parallel the dramatic adaptation of the oxygen carrying globins, hemoglobin and myoglobin, in the same organisms.
Application of crosswavelet transform and Wavelet Coherence for classification of ECG patterns

This paper presents a method for classification of ECG patterns using Cross Wavelet Transform (XWT) and Wavelet Coherence (WC) techniques. The cross-correlation is the measure of similarity between two waveforms. The application of the Continuous Wavelet Transform to two time series and the cross examination of the two decomposition reveals localized similarities in time and scale. A pathologically varying pattern in QT zone of inferior lead III, shows the presence of Inferior Myocardial Infarction (IMI). In this work classification of normal and IMI is studied. The Cross Wavelet Transform and Wavelet Coherence is used for the cross examination of a single normal and abnormal (IMI) beats. A normal beat template is selected as the absolute normal pattern and the coherence between various other normal and abnormal is computed. A parameter pa, equal to the summation of the coherence values over the QT zone distinguishes normal and abnormal clusters. From this cluster a threshold value is determined, which is used for classification of the subjects. All data for the purpose of analysis is considered from PTB diagnostic ECG database. The classification accuracy and sensitivity is obtained as 90% respectively.
2. Goals 3. Our Approach

W hen running software applications and services, we rely on the underlying execution platform: the hardware and the lower levels of the software stack. The execution platform is susceptible to a wide range of threats, ranging from accidental bugs, faults, and leaks to maliciously induced Trojan horses. The problem is aggravated by growing system complexity and by increasingly pertinent outsourcing and supply chain consideration. Traditional mechanisms, which painstakingly validate all system components, are expensive and limited in applicability. What if the platform assurance problem is just too hard? Do we have any hope of securely running software when we cannot trust the underlying hardware, hypervisor, kernel, libraries, and compilers? This article will discuss a potential approach for doing just so: conducting trustworthy computation on untrusted execution platforms. The approach, proof-carrying data (PCD), circumnavi-gates the threat of faults and leakage by reasoning solely about properties of a computa-tion's output data, regardless of the process that produced it. In PCD, the system designer prescribes the desired properties of the computation's outputs. These properties are then enforced using cryptographic proofs attached to all data owing through the system and veriied at the system perimeter as well as internal nodes. Integrity of data, information ow control, and fault isolation are three examples of security properties of which attainment, in the general case and under minimal assumptions, is a major open problem. Even when particular solutions for speciic cases are known, they tend to rely on platform trust assumptions (for example, the kernel is trusted, the central processing unit is trusted), and even then they cannot cross trust boundaries between mutually untrusting parties. For example, in cloud computing, clients are typically interested in both integrity [1] and conndentiality [2] when they delegate their own computations to the untrusted workers. Minimal trust assumptions and very strong cer-tiication guarantees are sometimes almost a basic requirement. For example, within the information technology supply chain, faults can be devastating to security [3] and hard to detect; moreover, hardware and soware components are oen produced in faraway lands from parts of uncertain origin where it is hard to carry out quality assurance in case trust is not available [4]. is all implies risks to the users and organizations [5, 6, 7, 8]. In order to address the aforementioned problems, we propose the following goal: G. A compiler that, given a protocol for a distributed computation and a security property (in …
Combining Automatic Tube Current Modulation with Adaptive Statistical Iterative Reconstruction for Low-Dose Chest CT Screening

Objective To reduce radiation dose while maintaining image quality in low-dose chest computed tomography (CT) by combining adaptive statistical iterative reconstruction (ASIR) and automatic tube current modulation (ATCM). Methods Patients undergoing cancer screening (n = 200) were subjected to 64-slice multidetector chest CT scanning with ASIR and ATCM. Patients were divided into groups 1, 2, 3, and 4 (n = 50 each), with a noise index (NI) of 15, 20, 30, and 40, respectively. Each image set was reconstructed with 4 ASIR levels (0% ASIR, 30% ASIR, 50% ASIR, and 80% ASIR) in each group. Two radiologists assessed subjective image noise, image artifacts, and visibility of the anatomical structures. Objective image noise and signal-to-noise ratio (SNR) were measured, and effective dose (ED) was recorded. Results Increased NI was associated with increased subjective and objective image noise results (P<0.001), and SNR decreased with increasing NI (P<0.001). These values improved with increased ASIR levels (P<0.001). Images from all 4 groups were clinically diagnosable. Images with NI = 30 and 50% ASIR had average subjective image noise scores and nearly average anatomical structure visibility scores, with a mean objective image noise of 23.42 HU. The EDs for groups 1, 2, 3 and 4 were 2.79±1.17, 1.69±0.59, 0.74±0.29, and 0.37±0.22 mSv, respectively. Compared to group 1 (NI = 15), the ED reductions were 39.43%, 73.48%, and 86.74% for groups 2, 3, and 4, respectively. Conclusions Using NI = 30 with 50% ASIR in the chest CT protocol, we obtained average or above-average image quality but a reduced ED.
Optimal Data Distributions in Machine Learning

In the first part of the thesis we explore three fundamental questions that arise naturally when we conceive a machine learning scenario where the training and test distributions can differ. Contrary to conventional wisdom, we show that in fact mismatched training and test distribution can yield better out-of-sample performance. This optimal performance can be obtained by training with the dual distribution. This optimal training distribution depends on the test distribution set by the problem, but not on the target function that we want to learn. We show how to obtain this distribution in both discrete and continuous input spaces, as well as how to approximate it in a practical scenario. Benefits of using this distribution are exemplified in both synthetic and real data sets. In order to apply the dual distribution in the supervised learning scenario where the training data set is fixed, it is necessary to use weights to make the sample appear as if it came from the dual distribution. We explore the negative effect that weighting a sample can have. The theoretical decomposition of the use of weights regarding its effect on the out-of-sample error is easy to understand but not actionable in practice, as the quantities involved cannot be computed. Hence, we propose the Targeted Weighting algorithm that determines if, for a given set of weights, the out-of-sample performance will improve or not in a practical setting. This is necessary as the setting assumes there are no labeled points distributed according to the test distribution, only unlabeled samples. Finally, we propose a new class of matching algorithms that can be used to match the training set to a desired distribution, such as the dual distribution (or the test distribution). These algorithms can be applied to very large datasets, and we show how they lead to improved performance in a large real dataset such as the Netflix dataset. Their computational complexity is the main reason for their advantage over previous algorithms proposed in the covariate shift literature. In the second part of the thesis we apply Machine Learning to the problem of behavior recognition. We develop a specific behavior classifier to study fly aggression, and we develop a system that allows analyzing behavior in videos of animals, with minimal supervision. The system, which we call CUBA (Caltech Unsupervised Behavior Analysis), allows detecting movemes, actions, and stories from time series describing the position of animals in videos. The method summarizes the data, as well as it provides biologists with a mathematical tool to test new hypotheses. Other benefits of CUBA include
Differential Friendly Neighbors Algorithm for Differential Relationships Based Gene Selection and Classification using Microarray Data

Identifying biologically relevant genes in a given tumor classification problem is as important as accurately classifying the samples. Differential expression may not always achieve this task. Differential friendly neighbors (DiffFNs) algorithm is proposed to select the classification relevant and biologically interesting genes that can discriminate two classes of tumors. DiffFNs achieves it by selecting genes based on their differential relationships from one class to the other. DiffFNs has been applied to select genes that can discriminate patients based on their tumor p53 status and Grade status. The results show that DiffFNs identify the p53 activity associated genes which could be submerged in the differentially expressed genes if we used differential expression analysis alone. This result proves the effectiveness of DiffFNs approach in selecting genes that are biologically relevant to the sample classification.
Versioning Information Goods

One prominent feature of information goods is that they have large fixed costs of production, and small variable costs of reproduction. Cost-based pricing makes little sense in this context; value-based pricing is much more appropriate. Different consumers may have radically different values for a particular information good, so techniques for differential pricing become very important. There are many forms of differential pricing; a recent survey can be found in Varian [1989]. In this paper we will focus on a particular aspect of differential pricing known as quality discrimination or versioning. These terms describe situations in which the producer provides different qualities/versions of a good which sell at different prices. As we will see below, the point of versioning is to get the consumers to sort themselves into different groups according to their willingness to pay. Consumers with high willingness to pay choose one version, while consumers with lower willingnesses to pay choose a different version. The producer chooses the versions so as to induce the consumers to “self select” into appropriate categories.
Enteromorpha compressa Exhibits Potent Antioxidant Activity

The green macroalgae, Enteromorpha compressa (Linnaeus) Nees, Ulva lactuca, and E. linza, were seasonally collected from Abu Qir bay at Alexandria (Mediterranean Sea) This work aimed to investigate the seasonal environmental conditions, controlling the green algal growth, predominance, or disappearance and determining antioxidant activity. The freshly collected selected alga (E. compressa) was subjected to pigment analysis (chlorophyll and carotenoids) essential oil and antioxidant enzyme determination (ascorbate oxidase and catalase). The air-dried ground alga was extracted with ethanol (crude extract) then sequentially fractionated by organic solvents of increasing polarity (petroleum ether, chloroform, ethyl acetate, and water). Antioxidant activity of all extracts was assayed using different methods (total antioxidant, DPPH [2, 2 diphenyl-1-picrylhydrazyl], ABTS [2, 2 azino-bis ethylbenzthiazoline-6-sulfonic acid], and reducing power, and β-carotene linoleic acid bleaching methods). The results indicated that the antioxidant activity was concentration and time dependent. Ethyl acetate fraction demonstrated higher antioxidant activity against DPPH method (82.80%) compared to the synthetic standard butylated hydroxyl toluene (BHT, 88.5%). However, the crude ethanolic extract, pet ether, chloroform fractions recorded lower to moderate antioxidant activities (49.0, 66.0, and 78.0%, resp.). Using chromatographic and spectroscopic analyses, an active compound was separated and identified from the promising ethyl acetate fraction.