Miracle-dz/UniFAR-FTUs huggingface dataset:
An improved instrument for measuring time-resolved lanthanide emission and resonance energy transfer

Time-resolved fluorescence, particularly of millisecond lanthanide emission, is of considerable interest because of potentially increased sensitivity when short-lived autofluorescence is a problem. Lanthanides have also previously been shown to be excellent donors in resonance energy transfer measurements, where a lanthanide donor transfers energy to a conventional fluorophore. An instrument for the detection of micro- to millisecond lanthanide luminescence and delayed fluorescence, amidst a background of large prompt fluorescence, is described. Time-delayed emission spectra can be measured with 16 μs dead time while suppressing prompt fluorescence by 25 000-fold. This is accomplished by using a mechanical chopper placed very close to the entrance slit of a diffraction spectrometer, and a charge coupled device detector at the exit port. Excited-state lifetimes can also be measured with a 22 μs dead time while reducing detector ringing due to prompt fluorescence to an insignificant level. This is accomplis.
Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach

This paper proposes a novel change-detection-driven transfer learning (TL) approach to update land-cover maps by classifying remote-sensing images acquired on the same area at different times (i.e., image time series). The proposed approach requires that a reliable training set is available only for one of the images (i.e., the source domain) in the time series whereas it is not for another image to be classified (i.e., the target domain). Unlike other literature TL methods, no additional assumptions on either the similarity between class distributions or the presence of the same set of land-cover classes in the two domains are required. The proposed method aims at defining a reliable training set for the target domain, taking advantage of the already available knowledge on the source domain. This is done by applying an unsupervised-change-detection method to target and source domains and transferring class labels of detected unchanged training samples from the source to the target domain to initialize the target-domain training set. The training set is then optimized by a properly defined novel active learning (AL) procedure. At the early iterations of AL, priority in labeling is given to samples detected as being changed, whereas in the remaining ones, the most informative samples are selected from changed and unchanged unlabeled samples. Finally, the target image is classified. Experimental results show that transferring the class labels from the source domain to the target domain provides a reliable initial training set and that the priority rule for AL results in a fast convergence to the desired accuracy with respect to Standard AL.
A Prototype to Explore Content and Context on Social Community Sites

The SIOC Ontology can be used to express information from the online community sites in a machine-readable form using RDF. This rich data structure allows us to easily analyse and extract social relations from these community sites. We use SIOC information to analyse the social relations between users through the content that they create. We introduce metrics for social neighbourhood and social reputation, formally expressed as SPARQL queries over SIOC data. Finally, we demonstrate these algorithms in our Social SIOC Explorer prototype.
Dynamically Formed Human-Robot Teams Performing Coordinated Tasks

In this new era of space exploration where human-robot teams are envisioned maintaining a long-term presence on other planets, effective coordination of these teams is paramount. Three critical research challenges that must be solved to realize this vision are the human-robot team challenge, the pickup-team challenge, and the effective humanrobot communication challenge. In this paper, we address these challenges, propose a novel approach towards solving these challenges, and situate our approach in the newly introduced treasure hunt domain.
Effects of low-dose lipopolysaccharide and age on spatial learning in different Morris water maze protocols

Objectives: Animals administered lipopolysaccharide exhibit dose-related sickness behaviors (decreased food intake, weight loss, and cognitive changes). While research has demonstrated that spatial learning is impaired following a lipopolysaccharide immune challenge, the results differ depending on the methodology used to evaluate spatial learning. Additionally, few studies have evaluated the effects of low-dose lipopolysaccharide on spatial learning. Therefore, we assessed spatial learning, food intake, and weight changes in adult and aged rats after a low-dose lipopolysaccharide immune challenge in the Morris water maze using two water temperatures. Methods: Adult (5–6 months) and aged (22 months) male Brown-Norway rats were administered either 50 or 100 μg/kg lipopolysaccharide or saline, and then tested in the Morris water maze for 5 days, rested for 7 days, and later underwent 2 days of retention tests. Probe trials were conducted at the end of initial and retention testing. Results: Low-dose lipopolysaccharide administration did not result in food intake or weight changes. While the aged experimental group took longest to improve directional heading error in both cold and warm water, heading error was greater in cold water. Behavioral testing revealed an apparent age and water temperature effect on swim time. Retention and probe trial results showed that aged experimental animals had the worst performance in cold water. Conclusion: We conclude that while low-dose lipopolysaccharide did not result in typical sickness behaviors (decreased food intake or weight), spatial learning and memory were impaired in the aged experimental group. These results have important implications for the care of elderly individuals experiencing mild to moderate infections.
Performance Analysis of Multiple-Access Channel in the Presence of Multiple Interferers

In this paper, we consider a Rayleigh fading multiple-access interference channel where a group of users communicate with a receiver in the presence of multiple Gaussian interferers. We derive the closed-form analytical expressions for the ergodic sum-capacity in case of uncorrelated multiple-input multiple-output (MIMO) channel and spatially uncorrelated orthogonal space time block coded (OSTBC) MIMO channel. As the example of special cases, we also show the ergodic sum-capacity for the multiple-input single-output (MISO) and single-input single-output (SISO) channels. Moreover, we derive the analytical expression for the complementary cumulative distribution function (CCDF) of sum-capacity in case of OSTBC MIMO channel. Numerical results are presented and verified via Monte-Carlo simulation.
Requirements that Handle IKIWISI, COTS, and Rapid Change

In the good old days, dealing with software requirements was relatively easy. Software requirements were the first order of business and took place before design, cost estimation, planning, or programming. Of course, it wasn't simple. Certain straightforward criteria required satisfaction: completeness; consistency; traceability; and testability. The recent developments of IKIWISI (I'll know it when I see it), COTS (commercial-off-the-shelf) software, and the increasingly rapid change in information technology have combined to unsettle the foundations of the old airtight requirements approach. These complicating factors are examined.
Scalable Constrained Spectral Clustering via the Randomized Projected Power Method

Constrained spectral clustering is an important area with many applications. However, most previous work has only been applied to relatively small data sets: graphs with thousands of points. This prevents this work from being applied to the large data sets found in application domains such as medical imaging and document data. Recent work on constrained and unconstrained spectral clustering has explored scalability of these methods via data approximations such as the Nystrom method which requires the selection of landmarks. However, compressing a graph may lead to undesirable results and poses the additional problem of how to chose landmarks. Instead in this paper, we propose a fast and scalable numerical algorithmic solution for the constrained clustering problem. We show the convergence and stability of our approach by proving its rate of convergence and demonstrate the effectiveness of our algorithm with empirical results on several real data sets. Our approach achieved comparable accuracy as popular constrained spectral clustering algorithms but taking several hundred times less time.
Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy

The aim of this study is to improve the modeling of physiotherapist behaviors on therapy. In order to contribute to a more consistent therapy of the rehabilitation robots used for lower limb, it was aimed that the rehabilitation applications would be made by considering also patient physical information. At this point, the control algorithm of the therapy by means of impedance control has been extended by evaluation of patient physical information can be grouped as weight and length of patient body in addition to force and position (angle) knowledge. The control algorithm using patient physical information as an input was developed by the method of Artificial Neural Networks (ANN) and the architecture of ANN written as multi-layer perceptron (MLP). Also, back propagation learning method is used to train the ANN. The control algorithm computes the impedance parameters by estimating. The proposed method generated successful results in terms of parameter estimation. The obtained results are sufficient for modeling the movements of physiotherapist.
Bivariate low flow return period analysis in the Mahanadi River basin, India using copula

ABSTRACT Assessments of low flows of major river of India are essential for effective management of drought and irrigation water demand/supply mechanism. In the present work, bivariate low flow frequency analysis of Mahanadi basin, that had significant variations in hydrological behaviour from upstream to downstream, was done for two major low flow characteristics, namely, severity and occurrence . Three Copulas have been tested for their applicability to estimate the joint return period of low flow at 2, 5, 10, 20, 50 years return periods. According to the Akaike information criterion (AIC) value, the Clayton Copula and the Frank Copula were found to be the most suitable for the stations in Basantapur and Kantamal, respectively. Further, the conditional low flow return periods were estimated and were found useful for low flow management and ecological analysis in the Mahanadi basin.
Characterization of Alluvial Gold Bearing Sediments of Betare Oya District-East Cameroon, Implication for Gold Exploration and Recovery

Field studies (location Au pits, sampling of different layers of sedimentary sequences filling the pits, panning of sediments), litho-stratigraphic investigations, grain size analysis and outlining of depositional conditions revealed a characteristic fining upward sequence in all the sections of 08 Au-pits within the Betare Oya basin. A typical profile is 2.5 to 4.5 m thick and is composed of a 30 to 50 cm organic topsoil layer, underlain by a 1.5 m thick reddish brownish gold free saprolite, which is underlain by 1.5 m sandy section. From litho-stratigraphic investigations and grain size analysis results a quartz dominated gravelly 1.4 m thick layer consisting in boulders (275 mm), cobbles (2-−0.0148x2 + 1.2187x + 2.0344, R2 = 0.6929 (sandy layer); 2) y = 0.0617x2 + 1.0849x + 0.8097, R2 = 0.8694 (gravelly layer); and y > R2 is satisfied in both cases. This implies that Au recovery from these layers could be effective through gravity separation using sieves mounted on classifiers of 7 cm (sandy layer) and 10 cm (gravelly layer) diameter.
Computationally Efficient Methods for Incorporation of Spectral Priors in 3-D Optical Tomography

Use of spectral prior in optical tomography has significantly improved accuracy and quality of images, when applied in two-dimensional (2-D) models. However, the size of the problem increases substantially when applied in 3-D. Two methods are presented here that make 3-D spectral imaging computationally feasible. The data-subset approach uses a smaller subset of variable measurement s to reduce the size of the inverse problem. The basic principle consists of using a dynamic criterion to select optimal subset that capture the major changes in the imaging domain. Additionally, the sensitivity matrix is analyzed and made sparse based on a memory requirements (to 8% of full matrix) and provides less tha 2% percent difference in quantification compared to use of full matrices in the image reconstruction.
Physiologically Inspired Model for the Visual Recognition of Transitive Hand Actions

The visual recognition of actions is an important visual function that is critical for motor learning and social communication. Action-selective neurons have been found in different cortical regions, including the superior temporal sulcus, parietal and premotor cortex. Among those are mirror neurons, which link visual and motor representations of body movements. While numerous theoretical models for the mirror neuron system have been proposed, the computational basis of the visual processing of goal-directed actions remains largely unclear. While most existing models focus on the possible role of motor representations in action recognition, we propose a model showing that many critical properties of action-selective visual neurons can be accounted for by well-established visual mechanisms. Our model accomplishes the recognition of hand actions from real video stimuli, exploiting exclusively mechanisms that can be implemented in a biologically plausible way by cortical neurons. We show that the model provides a unifying quantitatively consistent account of a variety of electrophysiological results from action-selective visual neurons. In addition, it makes a number of predictions, some of which could be confirmed in recent electrophysiological experiments.
A personalized people recommender system using global search approach

The goal of people recommender system is to generate meaningful social suggestion to users. The abundant data are the key factor in fulfilling a recommendation task, but the cost of user data in a real-world system is high. In this paper, we propose a novel approach that integrates a global search result with a personalized people recommendation system. Our approach utilizes the user identity as a query keyword and processes the search results through five different customized parsers. This approach solves the cold-start issue in recommendation systems and leverages the cross-domain information in order to provide a better recommendation result. To test our approach, we embedded it into an existing conference navigator system then deployed the system at two international conferences. The survey results indicate largely positive feedback about the system's effectiveness. Our study results also shed some light on the social interactions that take place at an academic conference.
Disentangling human degradation from environmental constraints: macroecological insights into the structure of coral reef fish and benthic communities

Testing ecological theory at macroecological scales may be useful for disentangling abiotic influences from anthropogenic disturbances, and thus provide insights into fundamental processes that structure ecological communities. In tropical coral reef systems, our understanding of community structure is limited to small-scale studies conducted in moderately degraded regions, while larger regional or ocean scale analyses have typically focused on identifying human drivers of reef degradation. In this thesis, my collaborators and I combined stable isotope specimens, underwater visual censuses, and remote sensing data from 43 Pacific islands and atolls in order to examine the relative roles of natural environmental variation and anthropogenic pressures in structuring coral reef fish and benthic communities. First, at unexploited sites on Kiritimati Atoll (Kiribati), isotope estimates indicated that trophic level increased with body size across species and individuals, while negative abundance ~ body size relationships (size spectra) revealed distinct energetic constraints between energy-competing carnivores and energy-sharing herbivores. After demonstrating size structuring of reef fish communities in the absence of humans, we then examined evidence for size-selective exploitation impacts on coral reefs across the Pacific Ocean. Size spectra 'steepened' as human population density increased and proximity to market center decreased, reflecting decreases in large-bodied fish abundance, biomass, turnover rate, and mean trophic level. Depletion of large fish abundances likely diminishes functions such as bioerosion by grazers and food chain connectivity by top predators, further degrading reef community resilience. Next, we considered the relative strengths of abiotic, biotic and anthropogenic influences in determining reef benthic state across spatial scales. We found that from fine (0.25 km) to coarse (1,024 km) grain scales the phase shift index (a multivariate metric of the relative cover of hard coral and macroalgal) was primarily predicted by local
Large-scale study of city dynamics and urban social behavior using participatory sensing

The ubiquitous availability of computing technology such as smartphones, tablets, and other easily portable devices, and the worldwide adoption of social networking sites make it increasingly possible for one to be connected and continuously contribute to this massively distributed information publishing process. In this scenario, people act as social sensors, voluntarily providing data that capture their daily life experiences, and offering diverse observations on both the physical world (e.g., location) and the online world (e.g., events). This large amount of social data can provide new forms of valuable information that are currently not available on this scale via any traditional data collection methods, and can be used to enhance decision making processes. In this article, we argue that location-based social media systems, such as Instagram and Foursquare, can act as valuable sources of large-scale sensing, providing access to important characteristics of urban social behavior much more quickly than traditional methods. We also discuss different applications and techniques that can exploit the data shared in these systems to enable large-scale and near-real-time analyses and visualization of different aspects of city dynamics.
File Sharing as a Form of Music Consumption

Led by the development and widespread application of digital technology, consumers are able to duplicate as well as distribute digitized music products at almost no cost. Music file sharing has been deemed illegal in many countries and is quite detrimental to record labels around the world. The music industry is looking for a business model that would let it tap into the new mode of file-sharing consumption. Until now music file-sharing behavior has not been systematically analyzed in the literature. This paper presents an analytical framework by looking at music file-sharing behavior from the perspectives of moral judgment, expertise, and social networking, and then presents a conceptual model of sharing through a set of hypotheses. An empirical study based on the model provides in-depth insights into sharing behavior. The paper concludes with a discussion of implications and further research possibilities.
Adaptive Just-In-Time Code Diversification

We present a method to regenerate diversified code dynamically in a Java bytecode JIT compiler, and to update the diversification frequently during the execution of the program. This way, we can significantly reduce the time frame in which attackers can let a program leak useful address space information and subsequently use the leaked information in memory exploits. A proof of concept implementation is evaluated, showing that even though code is recompiled frequently, we can achieved smaller overheads than the previous state of the art, which generated diversity only once during the whole execution of a program.
High-Arctic butterflies become smaller with rising temperatures

The response of body size to increasing temperature constitutes a universal response to climate change that could strongly affect terrestrial ectotherms, but the magnitude and direction of such responses remain unknown in most species. The metabolic cost of increased temperature could reduce body size but long growing seasons could also increase body size as was recently shown in an Arctic spider species. Here, we present the longest known time series on body size variation in two High-Arctic butterfly species: Boloria chariclea and Colias hecla. We measured wing length of nearly 4500 individuals collected annually between 1996 and 2013 from Zackenberg, Greenland and found that wing length significantly decreased at a similar rate in both species in response to warmer summers. Body size is strongly related to dispersal capacity and fecundity and our results suggest that these Arctic species could face severe challenges in response to ongoing rapid climate change.
The influence of interfacial tension reduction on organic liquid migration: numerical and experimental comparisons.

The ability to simulate multiphase flow under conditions of surfactant influenced interfacial tension reduction was studied through comparisons with observed tetrachloroethylene (PCE) migration pathways in a heterogeneous laboratory sand tank system. In the first experiment, PCE migration in the absence of surfactant was observed to be impeded by less permeable lenses. Surfactants were used in a second experiment to produce very low interfacial tensions, resulting in PCE migration paths into and through the less permeable materials. A multiphase flow simulator incorporating traditional parametric models was successfully applied to predict the general PCE migration behaviour observed in the sand tank experiments. Migration pathways and final mass distribution, however, were found to be very sensitive to estimated fluid and soil properties, the capillary retention function selected, and capillary pressure scaling factors.
Demo abstract: Distributed debugging architecture for wireless sensor networks

Limited visibility into the global network state renders testing and debugging sensor network applications a challenging task. Existing debugging methods are often non-intrusive and require modifications of the binary image. Hardware based debugging instrumentation such as JTAG has not been widely used beyond a single node, mainly due to its relatively high cost and lack of software support for distributed debugging. This demonstration presents a novel architecture for distributed debugging of wireless sensor networks using a low-cost extension board to access the on-chip debug module of the node's processor. Connecting several of those debug boards using a backbone network provides distributed control and monitoring of the sensor network in test.