Miracle-dz/UniFAR-FTUs huggingface dataset:
Near-infrared fluorescence imaging in the largely unexplored window of 900-1,000 nm

Near-infrared (NIR) fluorescence imaging has relied on fluorophores that emit in the 700-900 nm NIR-Ia or 1,000-1,700 nm NIR-II window for generating deep-tissue images. Up until now, there have been few fluorophores developed for the 900-1,000 nm NIR-Ib window. This is largely because NIR-Ib light is thought to be strongly absorbed by water. Methods: Here we found that six heptamethine dyes had distinct emission peaks in both the NIR-Ia and NIR-Ib window. We tested the performance of these contrast agents by introducing them into the leaves of the common house plant Epipremnum aureum with early stage anthracnose leaf infections from Khaya senegalensis, as well as injecting them into the hind feet of nude mice and tails of tumour-bearing mice in vivo. Results: Heptamethine dyes yielded superior images of leaf venation, anthracnose infection locations, sentinel lymph nodes, brain tumours and subcutaneous tumours in the NIR-Ib window. We found that NIR-Ib images had markedly enhanced signal-to-background ratio because autofluorescence, scattering and light absorption by biological tissues and water were weaker at longer wavelengths. Conclusion: NIR-Ib fluorescence imaging was a powerful method for studying sentinel lymph nodes, tumours, leaf veins and early anthracnose infection locations in plant leaves. The findings challenge our current view of NIR fluorescence imaging and may have important implications for biomedical research and image-guided cancer surgery.
A Geospatial Analysis of Bark Beetle-Induced Wildfire Risk Zones in the Okanogan Wenatchee National Forest

Over the past 30 years mountain pine beetle (MPB) outbreaks have become widespread throughout the western US and Canada. MPB attacks leave acres of dead trees that may predispose forest landscapes to large fires. With the use of field work and geospatial technology, these outbreaks can be better mapped and assessed to evaluate forest health. This study is designed to map and classify bark beetle infestation in Washington's Wenatchee National Forest. Field work on seventeen randomly selected sites was conducted using the point-centered quarter method. Recent MPB outbreak areas were classified using National Agriculture Imagery Program (NAIP) imagery. A link between MPB attack and forest fires was then quantified using MODIS fire data. Lastly, a predictive infestation model was constructed using the following geophysical parameters: disturbance indices, Landsat TM5 classification of groundcover as well as vegetation stress using hyperspectral data. Selected imagery from the Hyperion sensor was used to run a minimum distance supervised classification in ENVI, in attempt to detect the early “green stage” of infestation. This study detected MPB spread and assessed the fire risk related to infestation.
Eurosurveillance, Volume 18, Issue 5, 31 January 2013 Surveillance and outbreak reports LOW AND DECREASING VACCINE EFFECTIVENESS AGAINST INFLUENZA A(H3) IN 2011/12 AMONG VACCINATION TARGET GROUPS IN EUROPE: RESULTS FROM THE I-MOVE MULTICENTRE CASE–CONTROL STUDY

Surveillance and outbreak reports LOW AND DECREASING VACCINE EFFECTIVENESS AGAINST INFLUENZA A(H3) IN 2011/12 AMONG VACCINATION TARGET GROUPS IN EUROPE: RESULTS FROM THE I-MOVE MULTICENTRE CASE–CONTROL STUDY E Kissling ( )1, M Valenciano1, A Larrauri2, B Oroszi3, J M Cohen4, B Nunes5, D Pitigoi6,7, C Rizzo8, J Rebolledo9,10, I Paradowska-Stankiewicz11, S Jiménez-Jorge2, J K Horváth3, I Daviaud4, R Guiomar5, G Necula6, A Bella8, J O’Donnell9, M Głuchowska11, B C Ciancio12, A Nicoll12, A Moren1
DICT DIABETES COMPLICATIONS WITH FINANCIAL DATA

This work is aimed to introduce MedAttention, a self-attentive recurrent neural network capable of predicting diabetes complications over health plan operators financial data. We only use financial records due to the unavailability of electronic medical records and exam results in several Brazilian health care institutions. The financial records used encodes medical procedures, hospital rates, materials and medicines. Our results succeed to predict complications over 60 to 360 days after the records processing with an area under ROC curve of 0.81 to 0.76 over time gaps. Also we introduce the possibility to visualize the attention mechanism of MedAttention to understand the patterns found.
The Future of the Deep Space Network: Technology Development for K(sub a)-Band Deep Space Communications

Introduction Projections indicate that in the future the number of NASA's robotic deep space missions is likely to increase significantly. A launch rate of up to 4-6 launches per year is projected with up to 25 simultaneous missions active [ 1 ]. Future highresolution mapping missions to other planetary bodies as well as other experiments are likely to require increased downlink capacity. These future deep space communications requirements will, according to baseline loading analysis, exceed the capacity of NASA's Deep Space Network in its present form.
Dispersion and Property Manipulation of Carbon Nanotubes by Self-Assemibles of Amphiphilic Molecules

Over the past two decades, carbon nanotubes (CNTs) have grown as a novel type of nano‐ material and attracted great attention from scientists in different research fields [1-3]. Based on their unique one-dimensional nanostructure, CNTs exhibit excellent mechanical, optical and electronic properties as well as high chemical stability. As claimed by the Nobel lau‐ reate Richard Smalley [4], CNTs would be cheap, environmentally friendly, and do wonders for humankind. CNTs were discovered along with the research on fullerenes. In 1991, Iijima from Japan, who at that time was observing fullerenes produced by arc discharge method under high-resolution TEM, observed some tubular structures formed by coaxial cylinders of graphite layers [5]. These structures are nowadays well-known multi-walled carbon nanotubes (MWNTs) with diameters ranging from 2 to several hundred nanometers and lengths of microns. Two years later, single-walled carbon nanotubes (SWNTs) which contain only one cylinder of graphite were also observed and MWNTs can be equally regarded as a group of coaxial SWNTs with different diameters [6]. Theoretically, SWNTs can be regarded as the equivalent by rolling up a single layer of graphite (graphene). The way of the rolling will dominate the diameter and chirality of SWNTs and hence their electronic properties. Taking a1 and a2 as the basic vector of a graphite layer as shown in Figure 1, the tubes ob‐ tained by rolling up this layer along a1 are called zigzag tubes, while those along a2 are called armchair tubes. The tubes obtained along the vector a will have chirality (a = na1 + ma2). If n m = 3q (q is an integer), the tubes are metallic and can be conductive. When n m ≠ 3q, the tubes are semiconducting.
Estimating Disaggregate Models using Aggregate Data through Augmentation of Individual Choice

In this article, the authors propose a Bayesian method for estimating disaggregate choice models using aggregate data. Compared with existing methods, the advantage of the proposed method is that it allows for the analysis of microlevel consumer dynamic behavior, such as the impact of purchase history on current brand choice, when only aggregate-level data are available. The essence of this approach is to simulate latent choice data that are consistent with the observed aggregate data. When the augmented choice data are made available in each iteration of the Markov chain Monte Carlo algorithm, the dynamics of consumer buying behavior can be explicitly modeled. The authors first demonstrate the validity of the method with a series of simulations and then apply the method to an actual store-level data set of consumer purchases of refrigerated orange juice. The authors find a significant amount of dynamics in consumer buying behavior. The proposed method is useful for managers to understand better the consumer purchase dynamics and brand price competition when they have access to aggregate data only.
Description of ALCOVE

Figure 1: Architecture of the ALCOVE model (Kruschke, 1992). The gray quadrilateral is a three-dimensional depiction of a two-dimensional plane, representing a psychological stimulus space. Points h1, h2, and h3 represent the location of radial-basis (“exemplar”) units within that space. Point x represents the presented stimulus. The lettered circles are category representations, and the arrows connecting to them are variable-strength connection weights from the radial-basis units. α1 and α2 represent the attention allocated to each of the two dimensions of the space; the arrows beside these α illustrate that dimensional allocation acts to stretch and squash psychological space in ALCOVE. Image author: Andy J. Wills. CC BY 4.0.
MATHEMATICALLY MODELING THE SPREAD OF METHAMPHETAMINE USE by BERNADETTE

The use of methamphetamine is rising faster than most other hard drugs such as cocaine and heroine. To date, mathematical models have not been used to explore the dynamics of methamphetamine use in a population. We propose five mathematical models that can predict and evaluate methamphetamine use: a compartmental model for rural areas, a compartmental model for urban areas, an optimal control model for rural areas, an optimal control model for urban areas, and a metapopulation model. Both the optimal control and metapopulation models are built by extending the proposed compartmental structures. We separate models for urban and rural regions due to differing community characteristics that effect the manner in which methamphetamine is brought into and distributed throughout populations. Similar to models for the spread of infectious diseases, the interaction between susceptible, using, dealing, and recovered individuals in our illicit drug using population acts as a mechanism for the spread of methamphetamine use in each of our models. Thus, we use many techniques from infectious disease modeling literature in the analysis of our models. We also consider several applications of our models to data on methamphetamine use from Hawaii and Missouri. Our models give several important insights to previously observed yet unexplained characteristics regarding the dynamics of methamphetamine spread and the distribution of its use throughout the United States.
Assessment of the Accuracy of the Multiple-Relaxation-Time Lattice Boltzmann Method for the Simulation of Circulating Flows

The present work investigates the accuracy of the Multiple-relaxation-time Lattice Boltzmann Method (MRT LBM) in the simulation of flows with circulation. The flow in a 2Dlid-driven cavity is simulated using MRT LBM for a wide range of Reynolds numbers (100-1000) to assess its accuracy. The lid-driven cavity flow is selected because it is the standard benchmark problem for the testing of numerical methods. The calculated locations of the primary vortex center in addition to those of the two side vortices (lower-left and lower-right) are compared to the previously published results using different numerical techniques such as finite difference, finite element and single-relaxation-time LBM. The horizontal and vertical velocity profiles are also calculated. The results show that the MRT LBM has a superior accuracy compared to other numerical techniques especially for circulating flows.
The Sandwich Variance Estimator: Efficiency Properties and Coverage Probability of Confidence Intervals

The sandwich estimator, often known as the robust covariance matrix estimator or the empirical covariance matrix estimator, has achieved increasing use with the growing popularity of generalized estimating equations. Its virtue is that it provides consistent estimates of the covariance matrix for parameter estimates even when the fitted parametric model fails to hold, or is not even specified. Surprisingly though, there has been little discussion of the properties of the sandwich method other than consistency. We investigate the sandwich estimator in quasilikelihood models asymptotically, and in the linear case analytically. Under certain circumstances we show that when the quasilikelihood model is correct, the sandwich estimate is often far more variable than the usual parametric variance estimate. The increased variance is a fixed feature of the method, and the price one pays to obtain consistency even when the parametric model fails. We show that the additional variability directly affects the coverage probability of confidence intervals constructed from sandwich variance estimates. In fact the use of sandwich estimates combined with t-distribution quantiles gives confidence intervals with coverage probability falling below the nominal value. We propose a simple adjustment to compensate this defect, where the adjustment explicitly considers the variance of the sandwich estimate.
Types of Biological Networks

The study of biological networks, their analysis and modeling are important tasks in life sciences today. Most biological networks are still far from being complete and they are often difficult to interpret due to the complexity of relationships and the peculiarities of the data. This worksheet describes major types of biological networks and useful public databases that contain biological networks.
Embedded resolution of singularities in rigid analytic geometry

On donne une version analytique rigide du theoreme de Hironaka de Resolution Immergee des Singularites, sur un corps algebriquement clos de caracteristique zero avec une norme complete ultrametrique. Cette resolution est locale dans la topologie de Grothendieck. La demonstration est une application du resultat original de Hironaka et de notre foncteur d'analytisation.
Ambiguity in Privacy Policies and Perceived Privacy Risk

Software designers and engineers make use of software specifications to design and develop a software system. Software specifications are generally expressed in natural language and are thus subject to its underlying ambiguity. Ambiguity in these specifications could lead to different stakeholders, including the software designers, regulators and users having different interpretations of the behavior and functionality of the system. One example where policy and specification overlap is when the data practices in the privacy polices describe the website’s functionality such as collection of particular types of user data to provide a service. Website companies describe their data practices in their privacy policies and these data practices should not be inconsistent with the website’s specification. Software designers can use these data practices to inform the design of the website, regulators align these data practices with government regulations to check for compliance, and users can use these data practices to better understand what the website does with their information and make informed decisions about using the services provided by the website. In order to summarize their data practices comprehensively and accurately over multiple types of products and under different situations, and to afford flexibility for future practices these website companies resort to using ambiguity in describing their data practices. This ambiguity in data practices thus undermines its utility as an effective way to inform software design choices, or act as a regulatory mechanism, and does not give the users an accurate description of corporate data practices, thus increasing the perceived privacy risk for the user. In this thesis, we propose a theory of ambiguity to understand, identify, and measure ambiguity in data practices described in the privacy policies of website companies. In addition, we also propose an empirically validated framework to measure the associated privacy risk perceived by users due to ambiguity in natural language. This theory and framework could benefit the software designers by helping them better align the functionality of the website with the company data practices described in privacy policies, and the policy writers by providing them linguistic guidelines to help them write unambiguous policies.
Controlled Growth of High‐Quality ZnO‐Based Films and Fabrication of Visible‐Blind and Solar‐Blind Ultra‐Violet Detectors

ZnO is a wide‐bandgap (3.37 eV at room temperature) oxide semiconductor that is attractive for its great potential in short‐wavelength optoelectronic devices, in which high quality films and heterostructures are essential for high performance. In this study, controlled growth of ZnO‐based thin films and heterostructures by molecular beam epitaxy (MBE) is demonstrated on different substrates with emphasis on interface engineering. It is revealed that ultrathin AlN or MgO interfacial layers play a key role in establishing structural and chemical compatibility between ZnO and substrates. Furthermore, a quasi‐homo buffer is introduced prior to growth of a wurtzite MgZnO epilayer to suppress the phase segregation of rock‐salt MgO, achieving wide‐range bandgap tuning from 3.3 to 4.55 eV. Finally, a visible‐blind UV detector exploiting a double heterojunction of n‐ZnO/insulator‐MgO/p‐Si and a solar‐blind UV detector using MgZnO as an active layer are fabricated by using the growth techniques discussed here.
Stereotactic radiosurgery with charged-particle beams: technique and clinical experience

Stereotactic radiosurgery using charged-particle beams has been the subject of biomedical research and clinical development for almost 60 years. Energetic beams of charged particles of proton mass or greater (e.g., nuclei of hydrogen, helium or carbon atoms) manifest unique physical and radiobiological properties that offer advantages for neurosurgical application and for neuroscience research. These beams can be readily collimated to any desired cross-sectional size and shape. At higher kinetic energies, the beams can penetrate entirely through the patient in a similar fashion to high-energy photon beams but without exponential fall-off of dose. At lower kinetic energies, the beams exhibit increased dose-deposition (Bragg ionization peak) at a finite depth in tissue that is determined by the beam’s energy as it enters the patient. These properties enable highly precise, 3-dimensional placement of radiation doses to conform to uniquely shaped target volumes anywhere within the brain. Given the radiosurgical requirements for diagnostic image acquisition and fusion, precise target delineation and treatment planning, and millimeter- or even submillimeter-accurate dose delivery, reliable stereotactic fixation and immobilization techniques have been mandatory for intracranial charged particle radiosurgery. Non-invasive approaches initially used thermoplastic masks with coordinate registration made by reference to bony landmarks, a technique later supplemented by using vacuum-assisted dental fixation and implanted titanium fiducial markers for image guidance. More-invasive stereotaxis has utilized surgically fixed reference frames, including those that can be removed and reconnected days later to sockets that have been implanted in the outer table of the patient’s skull. Since 1954 more than 15,000 neurosurgical patients and 12,000 ocular patients worldwide have been treated with stereotactic charged-particle radiosurgery for various localized and systemic malignant and nonmalignant disorders. Therapeutic efficacy has been demonstrated clearly for the treatment of selected intracranial disorders and for uveal melanomas. Its role in the treatment of subfoveal neovascularization and as boost therapy for primary brain tumors are the subjects of ongoing investigation. Charged-particle radiosurgery is particularly advantageous for the conformal treatment of large and/or irregularly shaped target volumes, and for the treatment of lesions located adjacent to sensitive organs at risk, as well as for children due to their increased sensitivity to intellectual deficits and secondary malignancies from ionizing radiation.
A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters

A model based on Mie theory is described that estimates bulk participate refractive index n¯p from in situ optical measurements alone. Bulk refractive index is described in terms of the backscattering ratio and the hyperbolic slope of the particle size distribution (PSD). The PSD slope ξ is estimated from the hyperbolic slope of the particulate attenuation spectrum γ according to the relationship γ ≈ ξ − 3, verified with Mie theory. Thus the required in situ measurements are the particulate backscattering coefficient, the total particulate scattering coefficient, and the particulate attenuation coefficient. These parameters can be measured with commercially available instrumentation with rapid sampling rates and real-time data return. Application of the model to data from the Gulf of California yielded results that agreed with expectations, e.g., predicted n¯p was 1.04–1.05 in the chlorophyll maximum and 1.14–1.18 near sediments. Below the chlorophyll maximum in case I type waters, predicted n¯p values were between 1.10 and 1.12, suggesting the presence of a significant inorganic mineral component in the background or detrital organic particles with low water content.
Agent-Based Simulation of the Search Behavior in China's Resale Housing Market

In the paradigm of the search theory, we established the search model applicable to the characteristics of China's resale housing market, by modeling the search behavior for buyer and seller, respectively. Setting the parameters based on the Beijing housing market survey in August 2012, we implemented agentbased simulation to study the dynamics of the search behavior measured by search intensity and search time. Sensitivity test was also used to analyze the determinants of the search behavior for trading agents. The simulation results validate the idiosyncratic feature of the agent's search behavior, which is consistent with theoretical analysis. The increase of matching efficiency promotes the agents' search intensities, but the higher unit search cost can reduce the agents' search intensities. The buyer's search behavior is more sensitive to the change in the market tightness ratio. Brokerage service lowers the transaction price and lessens the agents' search intensities. Sensitivity test further reveals that, the matching efficiency and the market tightness ratio play very important role in improving housing market liquidity. The changes in the search cost and the broker commission rate can reduce the agents' search intensities significantly and there are critical turning points at which the abrupt change occurs.
IMPROVED TURNOVER PREDICTION OF SHARES USING HYBRID FEATURE SELECTION

Predicting the total turnover of a company in the most unstable stock market and trade conditions has always proved to be a costly affair causing rise and fall of several trades. Data mining is a well-known sphere of Computer Science that aims at extracting meaningful information from large databases. However, despite the existence of many algorithms for the purpose of predicting future trends, their efficiency is questionable as their predictions suffer from a high error rate. The objective of this paper is to investigate and rate the performance of classifiers based on the features selected by Hybrid Feature Selection. The authorized dataset for predicting the turnover was taken fromwww.bsc. com and included the stock market values of various companies over the past 10 years. The algorithms were investigated using the Weka tool. The Hybrid feature selection (HFS) algorithm, was run on this dataset to extract the important and influential features for classification. With these extracted features, the Total Turnover of the company was predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression. This prediction mechanism was implemented to predict the turnover of a company on an everyday basis and hence could help navigate through dubious stock markets trades. An accuracy rate of was achieved by the above prediction process. Moreover, the importance of the stock market attributes through Incremental Feature Selection (IFS) was established as well.