Miracle-dz/UniFAR-FTUs huggingface dataset:
Registration of multimodal and temporal images of the retina using a combined feature-based and statistics-based method

Registration of retinal images is important for doctors to diagnose because it merges information of several images. This paper presents an automated multimodal and temporal retinal image registration method which is especially robust to infrared radiation (IR) fundus images, which may suffer terrible imaging artifacts, such as extremely low contrast, overexposed edges, or invisibility of blood vessels. The method we propose combines the feature-based registration method and the statistics-based registration method by using the OD detection method based on Line operator to detect the OD for the fundamental registration and use the maximal mutual information method for fine registration and improve them by down-sampling, getting Region of Interest (ROI), bilateral filter and so on to get a better effect. Experiments over optical coherence tomography (OCT) and IR dataset show that our proposed method outperforms other methods by obtaining an accuracy of 82% and an average time consuming of 5.1 second.
Comparison of Sequential Monte Carlo Filtering with Kalman Filtering for Nonlinear State Estimation

In this paper different filtering techniques for nonlinear state estimation are explored and compared. We distinguish between approaches that approximate the nonlinear function (extended Kalman filter) and other approaches approximating the distribution of measurements and state (unscented Kalman filter and sequential Monte Carlo filter). The paper is showing both, the algorithms and simulated examples where a vehicle moves along a nonlinear trajectory such as a circle arc or a clothoid. It is shown, that the estimation of the system state can be improved if the nonlinearities were taken into account.
Deep Age Distribution Learning for Apparent Age Estimation

Apparent age estimation has attracted more and more researchers since its potential applications in the real world. Apparent age estimation differs from chronological age estimation that in apparent age estimation each facial image is labelled by multiple individuals, the mean age is the ground truth age and the uncertainty is introduced by the standard deviation. In this paper, we propose a novel method called Deep Age Distribution Learning(DADL) to deal with such situation. According to the given mean age and the standard deviation, we generate a Gaussian age distribution for each facial image as the training target instead of the single age. DADL first detects the facial region in image and aligns the facial image. Then, it uses deep Convolutional Neural Network(CNN) pre-trained based on the VGGFace and fine-tuned on the age dataset to extract the predicted age distribution. Finally it uses ensemble method to get the result. Our DADL method got a good performance in ChaLearn Looking at People 2016-Track 1: Age Estimation and ranked the 2nd place among 105 registered participants.
DYNAMICAL CENTERS AND NONCIRCULAR MOTIONS IN THINGS GALAXIES: IMPLICATIONS FOR DARK MATTER HALOS

We present harmonic decompositions of the velocity fields of 19 galaxies from The H i Nearby Galaxy Survey (THINGS) which quantify the magnitude of the noncircular motions in these galaxies and yield observational estimates of the elongations of the dark matter halo potentials. Additionally, we present accurate dynamical center positions for these galaxies. We show that the positions of the kinematic and photometric centers of the large majority of the galaxies in our sample are in good agreement. The median absolute amplitude of the noncircular motions, averaged over our sample, is 6.7 km s−1, with ∼90% of the galaxies having median noncircular motions of less than ∼9 km s−1. As a fraction of the total rotation velocity, this translates into 4.5% on average. The mean elongation of the gravitational potential, after a statistical correction for an unknown viewing angle, is 0.017 ± 0.020, which is consistent with a round potential. Our derived noncircular motions and elongations are smaller than what is needed to bring cold dark matter (CDM) simulations in agreement with the observations. In particular, the amplitudes of the noncircular motions are not high enough to hide the steep central mass–density profiles predicted by CDM simulations. We show that the amplitudes of the noncircular motions decrease toward lower luminosities and later Hubble types.
Arrestin Competition Influences the Kinetics and Variability of the Single-Photon Responses of Mammalian Rod Photoreceptors

Reliable signal transduction via G-protein-coupled receptors requires proper receptor inactivation. For example, signals originating from single rhodopsin molecules vary little from one to the next, requiring reproducible inactivation of rhodopsin by phosphorylation and arrestin binding. We determined how reduced concentrations of rhodopsin kinase (GRK1) and/or arrestin1 influenced the kinetics and variability of the single-photon responses of mouse rod photoreceptors. These experiments revealed that arrestin, in addition to its role in quenching the activity of rhodopsin, can tune the kinetics of rhodopsin phosphorylation by competing with GRK1. This competition influenced the variability of the active lifetime of rhodopsin. Biasing the competition in favor of GRK1 revealed that rhodopsin remained active through much of the single-photon response under the conditions of our experiments. This long-lasting rhodopsin activity can explain the characteristic time course of single-photon response variability. Indeed, explaining the late time-to-peak of the variance required an active lifetime of rhodopsin approximately twice that of the G-protein transducin. Competition between arrestins and kinases may be a general means of influencing signals mediated by G-protein-coupled receptors, particularly when activation of a few receptors produces signals of functional importance.
REAL-TIME GENERALIZATION OF GEODATA IN THE WEB

The growing demand for dissemination of spatial data in the Web gives at the same time new challenges also for the problem of generalization. The Extensible Markup Language (XML) technology is widely regarded as a basis for general data processing in the Web. The Geographic Information (GI) community has also started adopting XML techniques for GI processing on the Web platform. Once XML-based GI resources and services start proliferating in the Web, means are needed to transform the original data into an appropriate form for the given end user task and device. In this paper, the Extensible Stylesheet Language Transformation (XSLT) is introduced as a tool to perform real -time generalization of XML-encoded spatial data. Certain generalization effects can be achieved by filtering the source data according to pre-defined rules, by selecting a proper geometry from a set of alternative geometries, and by setting the style properties appropriately. The XSLT extension mechanism enables generalization methods based on sophisticated coordinate manipulations, e.g. line smoothing, to be performed. The paper concludes by presenting a three -tier pilot system, developed at the Finnish Geodetic Institute (FGI), consisting of a Smallworld database server, a Java Servlet middle layer and a Web browser-based visualization layer.
Particle Filter Rejuvenation and Latent Dirichlet Allocation

Previous research has established several methods of online learning for latent Dirichlet allocation (LDA). However, streaming learning for LDA— allowing only one pass over the data and constant storage complexity—is not as well explored. We use reservoir sampling to reduce the storage complexity of a previously-studied online algorithm, namely the particle filter, to constant. We then show that a simpler particle filter implementation performs just as well, and that the quality of the initialization dominates other factors of performance.
Comparative evaluation of entity resolution approaches with FEVER

We present FEVER, a new evaluation platform for entity resolution approaches. The modular structure of the FEVER framework supports the incorporation or reconstruction of many previously proposed approaches for entity resolution. A distinctive feature of FEVER is that it not only evaluates traditional measures such as precision and recall but also the effort for configuring (e.g., parameter tuning, training) a good entity resolution approach. FEVER thus strives for a fair comparative evaluation of different approaches by considering both the effectiveness and configuration effort.
Valuing Credit Derivatives Using an Implied Copula Approach

Credit derivatives are among the most important new financial instruments, but also among the most complicated. Each individual issuer is continuously exposed to default risk, and default intensity looking forward is not constant. It typically has a term structure, as revealed in the CDS market. A portfolio of risky bonds, as in a CDO, aggregates the individual risks, and now the correlations among them also become important. CDO tranches then redistribute and split up this aggregate exposure among a set of new securities. Evaluating the resulting tranche exposures requires a model for the individual default risks and their correlations, but even in the industry-standard Gaussian copula model, the problem is computationally intractable without heroic simplifying assumptions. The plainest vanilla model assumes correlations are equal for all pairs of credits. Then, analogous to the way an implied volatility can be extracted from an option's market price, the implied correlation can be extracted from a CDO tranche price. But, as with implied volatility, the resulting tranche correlations differ widely for different tranches, leading to the use of „base correlation,” a different implied correlation concept. Base correlation is still inconsistent with the model it is derived from, but it is not quite as badly behaved as tranche correlations. In this article, Hull and White offer an alternative approach that considerably reduces the inconsistencies in calibrating a copula to a set of CDO tranche prices. The secret is to make default intensities and recovery rates stochastic, rather than requiring a single value. By imposing the restrictions that the single-name CDS and the CDO tranches must all be priced by the model just as they are in the market, and that the probabilities for the set of possible individual default intensities must sum to 1 and exhibit maximum smoothness, Hull and White are able to imply tranche correlations that are much better behaved than the standard approach. The last part of the article extends their procedure in a number of directions, to nonstandard attachment points, bespoke portfolios, and CDO-squared securities.
Variations in Temperature Sensitivity (Q 10) of CH4 Emission from a Subtropical Estuarine Marsh in Southeast China

Understanding the functional relationship between greenhouse gas fluxes and environmental variables is crucial for predicting the impacts of wetlands on future climate change in response to various perturbations. We examined the relationships between methane (CH4) emission and temperature in two marsh stands dominated by the Phragmites australis and Cyperus malaccensis, respectively, in a subtropical estuarine wetland in southeast China based on three years of measurement data (2007–2009). We found that the Q 10 coefficient of CH4 emission to soil temperature (Q s10) from the two marsh stands varied slightly over the three years (P > 0.05), with a mean value of 3.38 ± 0.46 and 3.89 ± 0.41 for the P. australis and C. malaccensis stands, respectively. On the other hand, the three-year mean Q a10 values (Q 10 coefficients of CH4 emission to air temperature) were 3.39 ± 0.59 and 4.68 ± 1.10 for the P. australis and C. malaccensis stands, respectively, with a significantly higher Q a10 value for the C. malaccensis stand in 2008 (P < 0.05). The seasonal variations of Q 10 (Q s10 and Q a10) differed among years, with generally higher values in the cold months than those in the warm months in 2007 and 2009. We found that the Q s10 values of both stands were negatively correlated with soil conductivity, but did not obtain any conclusive results about the difference in Q 10 of CH4 emission between the two tidal stages (before flooding and after ebbing). There were no significant differences in both Q s10 and Q a10 values of CH4 emission between the P. australis stand and the C. malaccensis stands (P > 0.05). Our results show that the Q 10 values of CH4 emission in this estuarine marsh are highly variable across space and time. Given that the overall CH4 flux is governed by a suite of environmental factors, the Q 10 values derived from field measurements should only be considered as a semi-empirical parameter for simulating CH4 emissions.
A Coordination Perspective on Software System Design

In large software systems the identification and p roper management of interdependencies among the pieces of a system becomes a ce ntral concern. Nevertheless, most traditional programming languages and tools focus on representing components, leaving the description and management of interdependencies among components implicit, or distributed among the c omponents. This paper identifies a number of problems with this approach and proposes a new perspective for designing software, which elevates the representation and management of component interdependencies to a distinct design problem. A core element of the perspective is the development of design handbooks, which catalogue the most common k inds of interconnection relationships encountered in software systems, as well as sets of alternative coordination protocols for managing them. The paper presents SYNTHESIS, a software development environment based o n this perspective. A number of experiments performed using SYNTHESIS as well as s ome directions for future research are discussed.
The Use of an Anesthesia Information Management System for Prediction of Antiemetic Rescue Treatment at the Postanesthesia Care Unit

We used an anesthesia information management system (AIMS) to devise a score for predicting antiemetic rescue treatment as an indicator for postoperative nausea and vomiting (PONV) in the postanesthesia care unit (PACU). Furthermore, we wanted to investigate whether data collected with an AIMS are suitable for comparable clinical investigations. Over a 3-yr period (January 1, 1997, to December 31, 1999), data sets of 27,626 patients who were admitted postoperatively to the PACU were recorded online by using the automated anesthesia record keeping system NarkoData® (IMESO GmbH, Hüttenberg, Germany). Ten patient-related, 5 operative, 15 anesthesia-related, and 4 postoperative variables were studied by using forward stepwise logistic regression. Not only can the probability of having PONV in the PACU be estimated from the 3 previously described patient-related (female gender, odds ratio [OR] = 2.45; smoker, OR = 0.53; and age, OR = 0.995) and one operative variables (duration of surgery, OR = 1.005), but 3 anesthesia-related variables (intraoperative use of opioids, OR = 4.18; use of N2O, OR = 2.24; and IV anesthesia with propofol, OR = 0.40) are predictive. In implementing an equation for risk calculation into the AIMS, the individual risk of PONV can be calculated automatically.
Pseudorandom generators for low degree polynomials

We investigate constructions of pseudorandom generators that fool polynomial tests of degree <i>d</i> in <i>m</i> variables over finite fields F. Our main construction gives a generator with seed length <i>O</i>(<i>d</i><sup>4</sup> log <i>m</i> (1 + log(<i>d</i> ⁄ ε) ⁄ log log <i>m</i>) + log |F|) bits that achieves arbitrarily small bias ε and works whenever |F| is at least polynomial in <i>d</i>, log <i>m</i>, and 1⁄ε. We also present an alternate construction that uses a seed that can be described by <i>O</i>(<i>c</i><sup>2</sup><i>d</i><sup>8</sup><i>m</i><sup>6⁄(<i>c</i>-2)</sup> log(<i>d</i>⁄ε) + log |F|) bits (more precisely, <i>O</i>(<i>c</i><sup>2</sup><i>d</i><sup>8</sup><i>m</i><sup>6⁄(<i>c</i>-2)</sup>) field elements, each chosen from a set of size poly(<i>cd</i>⁄ε), plus two field elements ranging over all of F), works whenever |F| is at least polynomial in <i>c</i>, <i>d</i>, and 1⁄ε, and has the property that every element of the output is a function of at most <i>c</i> field elements in the input. Both generators are computable by small arithmetic circuits. The main tool used in the construction is a reduction that allows us to transform any "dense" hitting set generator for polynomials into a pseudorandom generator.
TensorFlow Acceleration on ARM Hikey Board

There is huge demand for targeting complex and large-scale machine learning applications particularly those based on popular actively-maintained frameworks such as TensorFlow and CAFFE to a variety of platforms with accelerators ranging from high-end desktop GPUs to resource-constrained embedded or mobile GPUs, FPGAs, and DSPs. However, to deliver good performance different platforms may require different algorithms or data structures, yet code should be easily portable and reused as much as possible across different devices. The open SYCL standard addresses this by providing parallel processing through a single-source programming model enabling the same standard C++ code to be used on the CPU and accelerator. This allows high-level C++ abstractions and templates to be used to quickly configure device and host code to cover specific features of the platform. By targeting OpenCL, SYCL enables C++ applications such as TensorFlow to run efficiently on OpenCL devices without having to write OpenCL code.
Energy efficient integrated authentication and access control mechanisms for Internet of Things

Internet of Things is seen as one of the advance paradigm in widespread applications, and also in upcoming generations of ICT. Due to the heterogeneous nature of the Internet of Things, challenges concern for security and with privacy must be considered during the design of IoT. Access control and authentication are two key techniques in this context to ensure secure communication between IoT devices. For this purpose, IoT devices should be secure and lightweight in nature. In this paper, we present the Elliptic Curve Cryptography based Mutual Authentication (EMA) and Capability Based Access Control (CBAC) model to ensure secure authorization along with protocol evaluation and performance analysis. The protocol is evaluated by using the AVISPA tool which is a security protocol verification tool, shows that EMA and CBAC are secure against attacks. Further, the details of performance analysis in terms of running or execution time and energy consumption are discussed and compared with other existing protocols.
Class II Molecule Arthritis-Associated HLA-DRB1*0401 MHC Interaction with the Rheumatoid Citrulline Allows for a High-Affinity Peptide Cutting Edge: The Conversion of Arginine to

Rheumatoid arthritis (RA) is genetically associated with MHC class II molecules that contain the shared epitope. These MHC molecules may participate in disease patho-genesis by selectively binding arthritogenic peptides for presentation to autoreactive CD4 (cid:1) T cells. The nature of the arthritogenic Ag is not known, but recent work has identified posttranslationally modified proteins containing citrulline (deiminated arginine) as specific targets of the IgG Ab response in RA patients. To understand how citrulline might evoke an autoimmune reaction, we have studied T cell responses to citrulline-containing peptides in HLA-DRB1*0401 transgenic (DR4-IE tg) mice. In this study, we demonstrate that the conversion of arginine to citrulline at the peptide side-chain position interacting with the shared epitope significantly increases peptide-MHC affinity and leads to the activation CD4 (cid:1) T cells in DR4-IE tg mice. These results reveal how DRB1 alleles with the shared epitope could initiate an autoimmune response to citrullinated self-Ags in RA patients. The Journal of Immunology, 2003, 171: 538–541. and *1302 molecules was determined relative to radio-labeled peptide probes as described previously (12). The nanomolar concentra- tion of unlabelled vimentin peptide necessary for 50% inhibition of the labeled peptide to the purified HLA-DRB1 molecules (IC 50 ) was used as an approxi- mation of the affinity of interaction ( K D ). Results are expressed as the inverse of the IC 50 values measured in nanomolar concentration.
Study of a Bias in the Offline Evaluation of a Recommendation Algorithm

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper describes this bias and discuss the relevance of a weighted offline evaluation to reduce this bias for different classes of recommendation algorithms.
Alcohol recovery:the search for the meaning in life

The purpose of this study was to examine in depth, the process which individuals undergo in their effort to overcome alcoholism. Multiple case studies were employed to draw forth the unique perspectives of five recovered alcoholics. The data was collected by means of minimally structured interviews with an emphasis on the subjective description of their experiences. A special focus of the study was the role of meaning of life upon recovery. The five case studies were presented as the co-researchers saw themselves. The cases were compared for commonalities. Sixteen themes were identified in the five cases except for the theme of spiritual awakening which was common for three of the five co-researchers. These themes constitute a longitudinal and progressive pattern of recovery in which (1) the beginning involves the realization that drinking is a problem and the decision to quit drinking; (2) the middle involves the development of significant supportive relationships and the development of new perspectives, values and goals; (3) the end involves self exploration and development of deeper meaning in life. The commonalities were synthesized into a general story of their recovery from alcoholism. The development of sources of meaning in their lives was significant in their recovery. The major sources of meaning were: family, work, contributing to others and the
Multimodal Transitions for Generative Stochastic Networks

Generative Stochastic Networks (GSNs) have been recently introduced as an alternative to traditional probabilistic modeling: instead of parametrizing the data distribution directly, one parametrizes a transition operator for a Markov chain whose stationary distribution is an estimator of the data generating distribution. The result of training is therefore a machine that generates samples through this Markov chain. However, the previously introduced GSN consistency theorems suggest that in order to capture a wide class of distributions, the transition operator in general should be multimodal, something that has not been done before this paper. We introduce for the first time multimodal transition distributions for GSNs, in particular using models in the NADE family (Neural Autoregressive Density Estimator) as output distributions of the transition operator. A NADE model is related to an RBM (and can thus model multimodal distributions) but its likelihood (and likelihood gradient) can be computed easily. The parameters of the NADE are obtained as a learned function of the previous state of the learned Markov chain. Experiments clearly illustrate the advantage of such multimodal transition distributions over unimodal GSNs.