Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential for evolutionary responses to climate change – evidence from tree populations

Evolutionary responses are required for tree populations to be able to track climate change. Results of 250 years of common garden experiments show that most forest trees have evolved local adaptation, as evidenced by the adaptive differentiation of populations in quantitative traits, reflecting environmental conditions of population origins. On the basis of the patterns of quantitative variation for 19 adaptation‐related traits studied in 59 tree species (mostly temperate and boreal species from the Northern hemisphere), we found that genetic differentiation between populations and clinal variation along environmental gradients were very common (respectively, 90% and 78% of cases). Thus, responding to climate change will likely require that the quantitative traits of populations again match their environments. We examine what kind of information is needed for evaluating the potential to respond, and what information is already available. We review the genetic models related to selection responses, and what is known currently about the genetic basis of the traits. We address special problems to be found at the range margins, and highlight the need for more modeling to understand specific issues at southern and northern margins. We need new common garden experiments for less known species. For extensively studied species, new experiments are needed outside the current ranges. Improving genomic information will allow better prediction of responses. Competitive and other interactions within species and interactions between species deserve more consideration. Despite the long generation times, the strong background in quantitative genetics and growing genomic resources make forest trees useful species for climate change research. The greatest adaptive response is expected when populations are large, have high genetic variability, selection is strong, and there is ecological opportunity for establishment of better adapted genotypes.
Remote Non-photorealistic Rendering of 3D Models on Mobile Devices

Nowadays, the pervasiveness of mobile devices has increased steadily in our daily life. We utilize them for the chief purposes of communication, learning and entertainment. And there is a growing interest in providing more applications for these small devices. In the realms like digital entertainment, product concept design and architecture draft, we strongly desire to mobile access to the 3D models conveniently although the limited resources like network bandwidth, graphical resources and computing power in such small devices. To overcome the disadvantages of the devices and balance the workload and performance between the client and server, in this paper we present a real time remote rendering method by transferring only the line primitives to client devices from server side, which will be rendered in the client side locally with various non-photorealistic rendering (NPR) techniques. We propose a general framework that enables the user to interactively go through the 3D models with his personal mobile device anytime and anywhere. Moreover, we can enrich the users' experience since NPR based images are usually eyes-attracting
Context-independent real-time event recognition: application to key-image extraction

The significant increase of video data in various domains requires effective ways to represent video by its semantic content. This paper proposes a system to extract useful events defined by approximate but efficient world models, independently of context. Changes and the behavior of lowlevel features of the scene's objects are continually monitored When certain conditions are met, events related to these conditions are detected. The proposed events are sufficiently broad to assist surveillance and retrieval of video shots. Extensive experimentations on more than 10 indoor and outdoor video shots containing a total of 6371 images including sequences with noise and coding artifacts have demonstrated the reliability and the real-time response (up to 10 frames per second) of the proposed system.
Aliso: a Journal of Systematic and Evolutionary Botany Morphology and Anatomy of Foliar Nectaries and Associated Leaves in Mallotus (euphorbiaceae) Morphology and Anatomy of Foliar Nectaries and Associated Leaves in Mallotus (euphorbiaceae)

The morphology and anatomy of the foliar nectaries and associated leaves offour species of Mallotus (Euphorbiaceae) were studied. Light microscopic observations of paraffin-and plastic-embedded specimens were complemented with scanning electron micrographs. Leaf anatomy of the four species is typical of large mesophytic plants. Aattened foliar nectaries are shown to be composed of specialized epidermal cells. The nonvascularized nectaries consist of narrow columnar cells each with a large nucleus, numerous vacuoles, and dense cytoplasm. Subglandular parenchyma cells have more pronounced nuclei, more vacuoles and denser cytoplasm than do typical laminar parenchyma. Structurally, these nectaries are similar to those found in other taxa of Euphorbiaceae and in other families of flowering plants. Brief field observations confirmed that ants are readily attracted to the nectar and probably function in a mutualistic relationship with the plants. The actual mechanism of nectar secretion was not studied.
The role of depressive and anxiety symptoms in the evaluation of cardiac rehabilitation efficacy after coronary artery bypass grafting surgery.

OBJECTIVES The aim of this study was to evaluate the efficacy of early 3-week cardiac rehabilitation (CR) in terms of the reduction of negative psychological symptoms, and to determine which factors predispose patients to worse rehabilitation results in this regard. METHODS The study involved a random group of 50 patients (11 women and 39 men) who had undergone coronary artery bypass grafting, with a mean age of 63.3 (± 7.2) years. The following screening tests were used: Beck Depression Inventory, State-Trait Anxiety Inventory and Acceptance of Illness Scale. The pulse rate during the first session of physical training was recorded (t(1) P), and after training the patients assessed their level of exertion (t(1) E) on the Borg Scale (BS). The same procedure was repeated at the end of the rehabilitation (t(2) P, t(2) E). RESULTS Among the psychological parameters examined at t(1) (at baseline), the strongest relationship with poor acceptance of illness after 3 weeks of rehabilitation was indicated by the level of depression (P < 0.001), with a slightly lower correlation with the state anxiety and the trait anxiety results (P = 0.005 and 0.027, respectively). A relationship was also found between the severity of depression in t(1) and the level of exertion measured by the BS at the end of rehabilitation (P = 0.007). Before rehabilitation, depressed patients exhibited higher levels of both trait and state anxiety (P = 0.009 and 0.018, respectively). After rehabilitation in the depressed subgroup, there was no improvement in the subjective assessment of exertion or reduction of state anxiety. Sex and co-morbidities also had considerable importance in the context of CR efficacy. The women showed more severe depressive symptoms (P = 0.01), a higher personality tendency to anxiety (P = 0.036) and poorer results of rehabilitation (in relation to the level of exertion after physical training and the intensity of state anxiety symptoms). There was no reduction of state anxiety in patients who suffered from at least two co-morbidities. CONCLUSIONS The presence of severe anxiety-depressive symptoms before rehabilitation has an effect on its outcome. Psychiatric symptomatology should be diagnosed as early as possible and patients should receive additional therapeutic support.
Target Seeking Crawlers and their Topical Performance

Topic driven crawlers can complement search engines by targeting relevant portions of the Web. A topic driven crawler must exploit the information available about the topic and its underlying context. In this paper we extend our previous research on the design and evaluation of topic driven crawlers by comparing seven different crawlers on a harder problem, namely, seeking highly relevant target pages. We find that exploration is an important aspect of a crawling strategy. We also study how the performance of crawler strategies depends on a number of topical characteristics based on notions of topic generality, cohesiveness, and authoritativeness. Our results reveal that topic generality is an obstacle for most crawlers, that three crawlers tend to perform better when the target pages are clustered together, and that two of these also display better performance when topic targets are highly authoritative.
Demand analysis and tax reform in Pakistan

Pakistan, like many LDCs, derives most of government revenue from indirect taxation. However, the system of taxes and subsidies has grown up piecemeal over the years. Previous exercises in price reform for Pakistan have been forced to make very restrictive assumptions about consumer preferences, and have typically used demand systems that prejudge what are the desirable directions of price reform. In this paper, the methodology of Deaton (1988, 1991) is extended and applied to the 1984-85 Household Income and Expenditure Survey. A theory of quality variation based on separable preferences is developed, and the implications for welfare and empirical analysis laid out. The prices of oils and fats and of sugar do not vary very much in the survey data, and the symmetry and homogeneity restrictions from the theory play an important part in obtaining sharp estimates of own and cross-price elasticities. The parameter estimates suggest that there are significant cross-price elasticities between the high-calorie foods and the presence of these substitution patterns means that the effects of potential price reforms are quite different from those that would be estimated using the traditional assumptions. Based on demand patterns alone, it would be desirable to raise government revenue by raising the consumer price of rice. However, in Pakistan it is not generally possible to decouple the producer and consumer prices of rice.
Analysis and optimization of MAC with constant size congestion window for WLAN

In the context of radio distributed networks, we present a generalized approach for the Medium Access Control (MAC) with fixed congestion window. Our protocol is quite simple to analyze and can be used in a lot of different situations. We give mathematical evidence showing that our performance is tight, in the sense that no protocol with fixed congestion window can do better. We also place ourselves in the WiFi/WiMAX framework, and show experimental results enlightening collision reduction of 14% to 21% compared the best known methods. We show channel capacity improvement, and fairness considerations.
Intercalation-Induced Stress and Heat Generation within Single Lithium-Ion Battery Cathode Particles

Intercalation-induced stress and heat generation inside Li-ion battery cathode (LiMn 2 O 4 ) particles under potentiodynamic control are simulated in this paper. We combined analyses of transport and kinetics in determining resulting stresses, which arise from concentration gradients in cathode particles, and heat generation. Two peaks in boundary reaction flux, and resulting stresses, were determined from the modeling of electrochemical kinetics and diffusion, using intrinsic material properties (resulting in two plateaus in the open-circuit potential) and the applied potential. Resistive heating was identified as the most important heat generation source. To probe the impact of the particle shape (equivalent radius and aspect ratio of an ellipsoidal particle) and the potential sweep rate on stress and heat generation, a surrogate-based analysis was also conducted. The systematic study showed that both intercalation-induced stress and time-averaged resistive heat generation rate increase with particle radius and potential sweep rate. Intercalation-induced stress increases first, then decreases as the aspect ratio of an ellipsoidal particle increases, whereas time-averaged resistive heat generation rate decreases as aspect ratio increases. This surrogate-based analysis suggests that ellipsoidal particles with larger aspect ratios are preferred over spherical particles, in improving battery performance when stress and heat generation are the only factors considered.
Infrared Observations of the Candidate LBV 1806-20 and Nearby Cluster Stars

We report near-infrared photometry, spectroscopy, and speckle imaging of the hot, luminous star we identify as candidate LBV 1806-20. We also present photometry and spectroscopy of three nearby stars, which are members of the same star cluster containing LBV 1806-20 and SGR 1806-20. The spectroscopy and photometry show that LBV 1806-20 is similar in many respects to the luminous "Pistol star," albeit with some important differences. They also provide estimates of the effective temperature and reddening of LBV 1806-20 and confirm distance estimates, leading to a best estimate for the luminosity of this star of greater than 5 × 106 L☉. The nearby cluster stars have spectral types and inferred absolute magnitudes that confirm the distance (and thus luminosity) estimate for LBV 1806-20. If we drop kinematic measurements of the distance (15.1 kpc), we have a lower limit on the distance of greater than 9.5 kpc and on the luminosity of greater than 2 × 106 L☉, based on the cluster stars. If we drop both the kinematic and cluster star indicators for distance, an ammonia absorption feature sets yet another lower limit to the distance of greater than 5.7 kpc, with a corresponding luminosity estimate of greater than 7 × 105 L☉ for the candidate LBV 1806-20. Furthermore, on the absis of very high angular resolution speckle images, we determine that LBV 1806-20 is not a cluster of stars but is rather a single star or binary system. Simple arguments based on the Eddington luminosity lead to an estimate of the total mass of LBV 1806-20 (single or binary) exceeding 190 M☉. We discuss the possible uncertainties in these results and their implications for the star formation history of this cluster.
Performance Comparison of Doppler Scale Estimation Methods for Underwater Acoustic OFDM

Doppler scale estimation is one critical step needed by the resampling operation in acoustic communication receivers. In this paper, we compare different Doppler scale estimation methods using either cyclic-prefixed (CP) or zero-padded (ZP) orthogonalfrequency division-multiplexing (OFDM) waveforms. For a CP-OFDM preamble, a self-correlation method allows for blind Doppler scale estimation based on an embedded repetition structure while a cross-correlation method is available with the knowledge of the waveform. For each received ZP-OFDM block, the existence of null subcarriers allows for blind Doppler scale estimation. In addition, a pilot-aided method and a decision-aided method are applicable based on cross-correlation with templates constructed from symbols on pilot subcarriers only and from symbols on all subcarriers after data decoding, respectively. This paper carries out extensive comparisons among these methods using both simulated and real experimental data. Further, the applicabilities of these methods to distributed multiuser systems are investigated.
Capacitary Measures for Completely Monotone Kernels Via Singular Control

We give a singular control approach to the problem of minimizing an energy functional for measures with given total mass on a compact real interval, when energy is defined in terms of a completely monotone kernel. This problem occurs both in potential theory and when looking for optimal financial order execution strategies under transient price impact. In our setup, measures or order execution strategies are interpreted as singular controls, and the capacitary measure is the unique optimal control. The minimal energy, or equivalently the capacity of the underlying interval, is characterized by means of a nonstandard infinite-dimensional Riccati differential equation, which is analyzed in some detail. We then show that the capacitary measure has two Dirac components at the endpoints of the interval and a continuous Lebesgue density in between. This density can be obtained as the solution of a certain Volterra integral equation of the second kind.
Compact and Efficient Micro-Strip Fed Monopole Antenna for WLAN Application

A single band compact micro-strip fed monopole antenna for Wireless Local Area Network (WLAN) application is presented. The proposed antenna design consists of a Y-Shaped monopole antenna with the ring slot and a designed rectangular slot which surrounds the monopole antenna. The experimental result shows that the proposed single band antenna has a return loss of -22.147 dB and band width is 1.2 GHz which lies at a frequency range of about 5.0 GHz to 6.2 GHz tuned at a centre frequency of 5.8 GHz, which can cover the WLAN-5.8 GHz frequency band. Then, the related radiation patterns have been observed at 5.8 GHz. The gain and efficiency of a proposed antenna is 4.43167 dBi and 80.698%.
Automated diagnostic system using graph clustering algorithm and fuzzy logic method

The paper presents the method of the automated diagnostic system generation using fuzzy logic supported by the graph clustering algorithm. Algorithm of the learning data processing using self-organization method and fuzzy logic module generation are presented. Application of the method for the diagnostics of the analog system (DC motor) is presented and its advantages and disadvantages are considered. Conclusions and future prospects are included.
Performance analysis of wavelet maxima mapping quantisation of wavelet image lowpass subband

A new wavelet oriented scalar quantisation method called wavelet maxima mapping quantisation (WMMQ) for wavelet transformed image lowpass subband has been proposed. The WMMQ is an adaptive quantisation algorithm that exploits image spatial information to refine the quantisation stepsize for a wavelet transformed image lowpass subband. Empirical analysis and statistical measurements have shown substantial improvement can be obtained in image background luminosity (DC magnitude), image contrast and image visibility.
Research Statement Convex Variational Models and Fast Algorithms for Image Processing and Machine Learning * Xavier Bresson

Research Project My research project is to design convex variational models and fast optimization algorithms to solve efficiently problems arising in image processing, machine learning and other applications such as medical imaging and physics. Main contributions My contributions are in the field of computational science and mathematical modeling. The objective of my research is to develop generic mathematical models and efficient numerical algorithms that can be applied to a wide range of real-world applications. To date, my main contributions are: convex variational models and fast algorithms for image processing ; a unified variational model for image processing; and efficient high-dimensional data clustering for machine learning. Convex variational models and fast algorithms for image processing Many problems arising in image processing such as image segmentation or image registration can be posed as non-convex energy minimization models. However, non-convex energy minimization models are highly sensitive to local minima, which makes the initial condition critical to get correct results. Besides, non-convex energies cannot produce fast optimization techniques, which limits their applications in real-world and real-time applications. I have introduced in [4, 13] a convex variational model and a fast algorithm for image segmentation. We have defined a new class of two-phase active contour models which efficiently combines edge detection (such as Caselles, Kimmel and Sapiro [9]) and region detection (such as Chan and Vese [11]) and are robust and fast. The proposed convex variational model is composed of the total variation (TV) norm and a 1-homogenous term. The TV norm is non-differentiable, which makes challenging the development of a fast optimization scheme. We have used an efficient primal optimization algorithm called split-Bregman to deal with fast TV minimization. We have compared our algorithm with the fast parametric min cut/max flow algorithm (a.k.a. graph-cuts) of Boykov and Kolmogorov [3]. Experiments have shown that our algorithm is not only faster but also more accurate and less memory consuming (256x256 images are processed in less than 0.1 seconds on standard CPU), see Figures 1(a-c). Our algorithm is freely available at
Making educational computer games "educational"

Educational computer games provide an appealing context for engaging children in activities that deliver substantive educational content and customized feedback. However, creators of such games can only take full advantage of the power of the medium if the educational content is integrated effectively into the structure of the game. Drawing upon experiences in the creation of numerous real-life games and applications, this paper describes some of the considerations that must be taken into account in creating effective interactive games. These include: matching particular educational topics or concepts to their most appropriate media; placing educational content at the heart of game play so that children engage in the targeted real-world behavior or thinking as they play the game; and building feedback and hint structures in ways that support and scaffold children into challenging content.
Pattern‐Similarity‐Based Model for Time Series Prediction

This research proposes a pattern/shape‐similarity‐based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance‐based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real‐life scenario, it is tested on four different time series. The first is a benchmark Mackey–Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods.