Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrological Variability and Uncertainty of Lower Missouri River Basin Under Changing Climate

The lower Missouri River Basin has experienced increasing streamflow and flooding events, with higher risk of extreme hydrologic impacts under changing climate. The newly available North American Regional Climate Change Assessment Program (NARCCAP) climate projections were used as atmospheric forcing for Soil and Water Assessment Tool (SWAT) model which runs with varying potential evapotranspiration (PET) methods to assess the hydrological change and uncertainty of 2040‐2069 over 1968‐1997. The NARCCAP temperature and precipitation predictions were refined using a bias correction method. The results show that, following the seasonal variability of precipitation, various water fluxes would increase in most seasons except the summer. Expected precipitation tends to increase in intensity with little change in frequency, triggering faster surface water concentration to form floods. The greatest streamflow increase would occur from November to February, increasing by around 10% on average. An increase of 3% occurs in the other months except for July and August in which river discharge decreases by around 2%. The climate predictions contribute more uncertainty annually, but PET algorithms gain more influence in winter or when other weather factors such as wind play a relatively more important role on evapotranspiration flux. This study predicts an even wetter environment compared to the historically very wet period, with the possibility of more flooding.
A new maximally supersymmetric background of IIB superstring theory

We present a maximally supersymmetric IIB string background. The geometry is that of a conformally flat lorentzian symmetric space G/K with solvable G, with a homogeneous five-form flux. We give the explicit supergravity solution, compute the isometries, the 32 Killing spinors, and the symmetry superalgebra, and then discuss T-duality and the relation to M-theory.
Sequential estimation of time-varying multipath channel for MIMO-OFDM systems

In this paper, we introduce a pilot-aided multipath channel estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Typical estimation algorithms assume the number of multipath components and delays to be known and constant, while their amplitudes may vary in time. In this work, we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The estimation problem arising from this assumption is solved using random set theory (RST), which is a probability theory of finite sets. Due to the lack of a closed form of the optimal filter, a Rao-Blackwellized particle filter (RBPF) implementation of the channel estimator is derived. Simulation results demonstrate the estimator effectiveness.
Using gamification in an online community

Gamification has recently evolved as an approach to engage and encourage active participation of members in online communities. For an online community to start and proceed on to a sustainable operation, it is important that members are encouraged to contribute positively and frequently. This paper outlines the design and implementation of a gamification model for online communities and its instantiation for a specific online community we have developed in collaboration with a government agency to support welfare recipients transitioning to work. Besides enhancing community members' engagement, badges based gamification has offered us a way to monitor and analyse the online community, including categorising community members by the type of their contributions in the community. We present here some initial results from our online community.
FPGA Design for Algebraic Tori-Based Public-Key Cryptography

Algebraic torus-based cryptosystems are an alternative for Public-Key Cryptography (PKC). It maintains the security of a larger group while the actual computations are performed in a subgroup. Compared with RSA for the same security level, it allows faster exponentiation and much shorter bandwidth for the transmitted data. In this work we implement a torus-based cryptosystem, the so-called CEILIDH, on a multicore platform with an FPGA. This platform consists of a Xilinx MicroBlaze core and a multicore coprocessor. The platform supports CEILIDH, RSA and ECC over prime fields. The results show that one 170-bit torus T6 exponentiation requires 20 ms, which is 5 times faster than 1024-bit RSA implementation on the same platform.
Decode-and-Forward vehicular relaying for 2×2 MIMO LTE-advanced downlink

MIMO is a key technology in improving the performance of wireless communications. Recently the concept of cooperative communication has emerged as a solution to exploit the potential gains of MIMO on a distributed scale. We propose to use cooperative-MIMO links in LTE-A networks where vehicles act as relaying terminals to a designated vehicle using Decode-and-Forward relaying. To maintain orthogonality of signals, a modified Alamouti-based Space-Time Block Coding (STBC) technique is proposed. Our approach allows exploitation of the multiplexing capability and spatial diversity of typical MIMO schemes in distributed way. We further contribute by deriving error rate and diversity gain as a benchmark to assess our analysis and future research studies. Our findings indicate that significant diversity gains and reduced error rates are achievable. As well, a noticeable reduction in the required transmitting power are observed compared to traditional single antenna deployment.
An examination of auditory processing and affective prosody in relatives of patients with auditory hallucinations

Research on auditory verbal hallucinations (AVHs) indicates that AVH schizophrenia patients show greater abnormalities on tasks requiring recognition of affective prosody (AP) than non-AVH patients. Detecting AP requires accurate perception of manipulations in pitch, amplitude and duration. Schizophrenia patients with AVHs also experience difficulty detecting these acoustic manipulations; with a number of theorists speculating that difficulties in pitch, amplitude and duration discrimination underlie AP abnormalities. This study examined whether both AP and these aspects of auditory processing are also impaired in first degree relatives of persons with AVHs. It also examined whether pitch, amplitude and duration discrimination were related to AP, and to hallucination proneness. Unaffected relatives of AVH schizophrenia patients (N = 19) and matched healthy controls (N = 33) were compared using tone discrimination tasks, an AP task, and clinical measures. Relatives were slower at identifying emotions on the AP task (p = 0.002), with secondary analysis showing this was especially so for happy (p = 0.014) and neutral (p = 0.001) sentences. There was a significant interaction effect for pitch between tone deviation level and group (p = 0.019), and relatives performed worse than controls on amplitude discrimination and duration discrimination. AP performance for happy and neutral sentences was significantly correlated with amplitude perception. Lastly, AVH proneness in the entire sample was significantly correlated with pitch discrimination (r = 0.44) and pitch perception was shown to predict AVH proneness in the sample (p = 0.005). These results suggest basic impairments in auditory processing are present in relatives of AVH patients; they potentially underlie processing speed in AP tasks, and predict AVH proneness. This indicates auditory processing deficits may be a core feature of AVHs in schizophrenia, and are worthy of further study as a potential endophenotype for AVHs.
Prediction of user location using the radiation model and social check-ins

Location-based social networks serve as a source of data for a wide range of applications, from recommendation of places to visit to modelling of city traffic, and urban planning. One of the basic problems in all these areas is the formulation of a predictive model for the location of a certain user at a certain time. In this paper, we propose a new approach for predicting user location, which uses two components to make the prediction, based on (i) coordinates and times of user check-ins and (ii) social interaction between different users. We improve the performance of a state-of-the art model using the radiation model of spatial choice and a social component based on the frequency of matching check-ins of user's friends. Friendship is defined by the presence of reciprocal following on Twitter. Our empirical results highlight an improvement over the state-of-the-art in terms of accuracy, and suggest practical solutions for spatio-temporal and socially-inspired prediction of user location.
Asynchronous Modified Policy Iteration with Single-sided Updates

We present a new algorithm for solving Markov decision problems that extends the modiied policy iteration algorithm of Puterman and Shin 6] in two important ways: 1) The new algorithm is asynchronous in that it allows the values of states to be updated in arbitrary order, and it does not need to consider all actions in each state while updating the policy. 2) The new algorithm converges under more general initial conditions than those required by modiied policy iteration. Speciically, the set of initial policy{value function pairs for which our algorithm guarantees convergence is a strict superset of the set for which modiied policy iteration converges. This generalization was obtained by making a simple and easily implementable change to the policy evaluation operator used in updating the value function. Both the asyn-chronous nature of our algorithm and its convergence under more general conditions expand the range of problems to which our algorithm can be applied.
Can the Lung Cancer Pie Be Divided into Angiogenic Slices?

There are no validated markers for predicting benefit from angiogenesis inhibitors or classifying tumors with distinct angiogenic phenotypes. In patients with non–small cell lung cancer treated with bevacizumab and erlotinib, Franzini and colleagues find that angiogenesis- and hypoxia-associated gene expression signatures predict tumor response and/or clinical outcome, and may define distinct angiogenic patterns. Clin Cancer Res; 21(23); 5188–90. ©2015 AACR. See related article by Franzini et al., p. 5253
Modeling segmental duration in German text-to-speech synthesis

The paper reports on the construction of a model for segmental duration in German. The model predicts the durations of speech sounds in various textual, prosodic, and segmental contexts. It has been implemented in the German version of the Bell Labs text to speech system (R. Sproat and J. Olive, 1995; B. Mobius et al., 1996). The construction of the duration system was made efficient by the use of an interactive statistical analysis package that incorporates the approach outlined by J.P.H. van Santen (1994). The results an stored in tables in a format that can be directly interpreted by the TTS duration module. Tables are constructed in two phases: inferential statistical analysis of the speech corpus, and parameter estimation. The overall correlation between observed and predicted segmental durations is . 896.
Improving statistical machine translation for a speech-to-speech translation task

In this paper we investigate if statistical machine translation (SMT) is possible when only a small bilingual corpus is available for training the system. Using additional knowledge sources which are not domain-speciﬁc improves the performance of the system considerably. We present results on a speech translation task for German to English. Automatic and human evaluation are used to compare the performance of the SMT system to an interlingua-based translation system.
Research on Segmentation of Vertebral Bodies from Spinal MR Imagesbased on Gauss Weighted and Local Contraction

This paper present a segmentation of vertebral bodies from spinal MR images based on neighborhood informa- tion Gauss weighted and local contraction. First use a cut-off window (5�5) around each pixel and stack the gray values inside the window into a vector, adopt the Gaussian kernel function to incorporate local spatial information, an adaptive local scaling parameter is used to refine the segmentation rather than a fixed scaling parameter to avoid the manually tuned parameter. Finally, the built affinity is introduced into the segmentation process by using a graph-based method to achieve the complete target. Extensively experiments show that the present method can segment the vertebral bodies smoothly and clearly, and it has stronger anti-noise property and higher segmentation precision than the conventional methods. The robust and accurate result of segmentation should serve image registration and the analysis of spinal de- formities. It is a general method for segmenting object that can develop to segment other tissues and organs.
Operational risk and insurance: a ruin probabilistic reserving approach

A new methodology for financial and insurance operational risk capital estimation is proposed. It is based on using the finite time probability of (non-)ruin as an operational risk measure, within a general risk model. It allows for inhomogeneous operational loss frequency (dependent inter-arrival times) and dependent loss severities which may have any joint discrete or continuous distribution. Under the proposed methodology, operational risk capital assessment is viewed not as a one off exercise, performed at some moment of time, but as dynamic reserving, following a certain risk capital accumulation function. The latter describes the accumulation of risk capital with time and may be any nondecreasing, mpositive real function hHtL. Under these reasonably general assumptions, the probability of mnon-ruin is explicitly expressed using closed form expressions, derived by Ignatov and Kaishev (2000, 2004, 2007) and Ignatov, Kaishev and Krachunov (2001) and by setting it to a high enough preassigned mvalue, say 0.99, it is possible to obtain not just a value for the capital charge but a (dynamic) risk capital accumulation strategy, hHtL. In view of its generality, the proposed methodology is capable of accommodating any (heavy tailed) mdistributions, such as the Generalized Pareto Distribution, the Lognormal distribution the g-and-h mdistribution and the GB2 distribution. Applying this methodology on numerical examples, we demonstrate that dependence in the loss severities may have a dramatic effect on the estimated risk capital. In addition, we show also that one and the same high enough survival probability may be achieved by mdifferent risk capital accumulation strategies one of which may possibly be preferable to accumulating capital just linearly, as has been assumed by Embrechts et al. (2004). The proposed methodology takes into account also the effect of insurance on operational losses, in which case it is proposed to take the probability of joint survival of the financial institution and the insurance provider as a joint operational risk measure. The risk capital allocation strategy is then obtained in such a way that the probability of joint survival is equal to a preassigned high enough value, say 99.9 %
A Cluster-Based Noise Detection Algorithm

For a classification problem, noise in real-world data can dramatically lower the predictive accuracy of a learner and increase the time in building model. Researchers have proved that preprocessing noise before learning can bring more advantages. Previous work mostly focus on class noise detection for the difficulties of attribute noise detection. In this paper, we present a cluster-based noise detection algorithm, which synthetically considers attribute and class noise detection. Meanwhile, it has the ability of handling different types of datasets. Our algorithm separately detects class and attributes noise by computing the deviation to the center in the same cluster. we test its effect by adding different types of noise and noise level into datasets from the UCI repository, Our approach shows significant effectiveness in improving the predictive accuracy of classification.
INFLUENCE OF EXTERNAL ACTION ON CHAOTIC DYNAMICS OF VIRTUAL CATHODE OSCILLATIONS

Results of the numerical simulation of the electron beam modulation influence on virtual cathode oscillation are presented. It is shown that system demonstrates wide diversity of nonlinear oscillation including synchronisation by an external action. Physical processes in the beam are considered. We show that chaotic behaviour is the result of the beam stratification on several groups of electrons with different lifetime. Phase relations for different synchronisation regimes are analysed.
Visioning future emergency healthcare collaboration: Perspectives from large and small medical centers

New video technologies are emerging to facilitate collaboration in emergency healthcare. One such technology is 3D telepresence technology for medical consultation (3DMC) that may provide richer visual information to support collaboration between medical professionals to, ideally, enhance patient care in real time.Today only an early prototype of 3DMC exists. To better understand 3DMC’s potential for adoption and use in emergency health-care before large amounts of development resources are invested we conducted a visioning study. That is, we shared our vision of 3DMC with emergency room physicians,nurses,administrators,and information technology (IT) professionals working at large and small medical centers, and asked them to share their perspectives regarding 3DMC’s potential beneﬁts and disadvantages in emergency healthcare and its compatibility and/or lack thereof with their and their organization’s current ways of working. We found that social and technical challenges can be identiﬁed regarding new innovations even before working prototypes are available. The compatibility of 3DMC with current ways of working was conceptualized by participants in terms of processes, relationships, and resources. Both common and unique perceptions regarding 3DMC emerged, illustrating the need for 3DMC, and other collaboration technologies,to support interwo-ven situational awareness across different technological frames.
A new efficient EM-ICP algorithm for non-linear registration of 3D point sets

In this paper, we present a new method for non-linear pairwise registration of point sets. In this method, we consider the points of the first set as the draws of a Gaussian mixture model whose centres are the points of the second set displaced by a deformation. Next we perform {\it maximum a posteriori} estimation of the parameters (which include the unknown transformation) of this model using the expectation-maximisation algorithm. Compared to other methods using the same ''EM-ICP'' paradigm/framework, we propose three key modifications leading to an efficient algorithm allowing for fast registration of large point sets: 1) symmetrisation of the point-to-point correspondences; 2) specification of priors on these correspondences using differential geometry; 3) efficient encoding of deformations using the RKHS theory and the Fourier analysis. The resulting algorithm is efficient and is able to register large data sets. We evaluate the added value of the modifications and compare our method to the state-of-the-art CPD algorithm on synthetic data.
Piecewise SALT Sampling for Estimating Suspended Sediment Yields

A probability sampling method called SALT (Selection At List 'Time) has been developed for collecting and summarizing data on delivery of suspended sediment in rivers. It is based on sampling and estimating yield using a suspended-sediment rating curve for high discharges and simple random sampling for low flows. The method gives unbiased estimates of total yield and variance. The technique has been modified by replacing the rating curve with a user-specified average sampling rate function. This function allows easier specification of field sampling parameters for specified conditions and helps avoid the extremes of data collection. It also improves the distribution of samples if the intent is to estimate suspended sediment yield during storms specified after data collection. This form of SALT sampling is called Piecewise SALT sampling. The Author: ROBERT B. THOMAS is a mathematical statistician assigned to the Station's research unit studying the effects of forest management on hillslope processes, fishery resources, and stream environments, and is stationed in Arcata, California. Acknowledgment: I thank the Simpson Timber Company for its cooperation in collecting data at the test site.
Tableaux Based Decision Procedures for Modal Logics of Confluence and Density

In this paper we present a decision procedures for modal logics with transitive models together with additionnal properties like confluence and density. These procedures are tableaux-based, and we show how to handle them in this well-known framework by generalizing usual tableaux (which are trees) to richer structures: directed acyclic graphs.
Comparative informatics

underserved users struggling to adapt to powerful but misaligned technologies. Understanding when difference matters for design (“the difference that makes a difference,” to use anthropologist Gregory Bateson’s useful phrase) is at the heart of CI. We believe CI should center on diligent, critical, empirical analysis—what we call grounded comparison. To illustrate grounded comparison, we present a study of regional styles in word processing and an ethnographic investigation of online work groups. Both examples indicate the breadth of methodological approaches appropriate to comparative analysis, which also encompass traditional laboratory studies [2].
A Robust Method for Mobile Robot Tracking Object on Behavior-Based Robotics

Introduce a robust and flexible mobile robot control system. Motion description language is brought in behavior-based robotics. The behavior-based robotics does well in controlling mobile robot. Using motion description language, not only the interactions between discrete and continuous dynamics existing in a robotics system can be described, but the complexity of steering a robot can also be measured quantitatively. In this paper, the problem of object-following by nonholonomic wheeled mobile robots is studied using this method, and a Matlab simulative results verify the validity of this method in the end.
Tracklet Association with Online Target-Specific Metric Learning

This paper presents a novel introduction of online target-specific metric learning in track fragment (tracklet) association by network flow optimization for long-term multi-person tracking. Different from other network flow formulation, each node in our network represents a tracklet, and each edge represents the likelihood of neighboring tracklets belonging to the same trajectory as measured by our proposed affinity score. In our method, target-specific similarity metrics are learned, which give rise to the appearance-based models used in the tracklet affinity estimation. Trajectory-based tracklets are refined by using the learned metrics to account for appearance consistency and to identify reliable tracklets. The metrics are then re-learned using reliable tracklets for computing tracklet affinity scores. Long-term trajectories are then obtained through network flow optimization. Occlusions and missed detections are handled by a trajectory completion step. Our method is effective for long-term tracking even when the targets are spatially close or completely occluded by others. We validate our proposed framework on several public datasets and show that it outperforms several state of art methods.
An improved method for feature weighting to document categorization

Based on the significance of feature frequency, an improved weighting method for feature frequency is proposed, which considers the decision-making information in evaluating the contribution of feature frequency to document categorization. Experimental results show that this method could improve the space distribution of the document set. Furthermore, the values of macro accuracy, macro recall rate and macroF1 are all improved significantly.