Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate-adjusted provenancing: a strategy for climate-resilient ecological restoration

Investments in ecological restoration are estimated at $US 2 trillion per annum worldwide and are increasing rapidly ( Cunningham, 2008 ; Williams et al., 2014 ). These investments are occurring in an environment of accelerated climate change that is projected to continue into the next century, yet they currently take little account of such change. This has significant implications for the long-term success of restoration plantings across millions of hectares, with germplasm used in current restoration efforts potentially poorly-adapted to future climates. New approaches that optimize the climate-resilience of these restoration efforts are thus essential ( Breed et al., 2013 ; Williams et al., 2014 ; Havens et al., 2015 ). A promising, but as yet untapped, opportunity for enhancing the climate-resilience of restoration investments rests in the exploitation of natural genetic variability of plant species. The capacity of plants to adapt to environmental change through plasticity, selection, or gene flow is only beginning to be explored ( Nicotra et al., 2010 ; Hoffmann and Sgro, 2011 ; Aitken and Whitlock, 2013 ; Alberto et al., 2013 ). Informed strategies for sourcing germplasm that capitalize on inherent genetic diversity and adaptive capacity offer significant promise for improving the success of extensive plantings to restore landscapes that are eroded, salinized, desertified, highly fragmented or degraded through introduced competitors, herbivores, or diseases. Here we describe a new strategy for sourcing germplasm for ecological restoration to promote adaptation in a changing climate. We argue that a "climate-adjusted" provenancing strategy (Figure 1A ) should combine genetic diversity and adaptability, targeting projected climate change directions whilst allowing for uncertainty in such projections as well as unforeseen selective agents. We introduce climate-adjusted provenancing in the context of historical approaches to provenancing, and highlight emerging research to test this strategy.
Creativity at the Knowledge Frontier: The Impact of Specialization in Fast- and Slow-paced Domains*

Using the impact of the Soviet Union’s collapse on the performance of theoretical mathematicians as a natural experiment, we attempt to resolve the controversy in prior research on whether specialists or generalists have superior creative performance. While many have highlighted generalists’ advantage due to access to a wider set of knowledge components, others have underlined the benefits that specialists can derive from their deep expertise. We argue that this disagreement might be partly driven by the fact that the pace of change in a knowledge domain shapes the relative return from being a specialist or a generalist. We show that generalist scientists performed best when the pace of change was slower and their ability to draw from diverse knowledge domains was an advantage in the field, but specialists gained advantage when the pace of change increased and their deeper expertise allowed them to use new knowledge created at the knowledge frontier. We discuss and test the roles of cognitive mechanisms and of competition for scarce resources. Specifically, we show that specialists became more desirable collaborators when the pace of change was faster, but when the pace of change was slower, generalists were more sought after as collaborators. Overall, our results highlight trade-offs associated with specialization for creative performance.
Searching for effects in big data: Why p-values are not advised and what to use instead

P-values of null hypothesis significance testing have long been the standard and decisive measure of deductive statistics. However, for decades, top statistical methodologists have argued that focusing on p-values is not conducive to science, and that these tests are regularly misunderstood. The standard replacement or at least complement proposed for p-values by those critics are confidence intervals and statistical effects sizes. Regrettably, analyzing and comparing huge data sets (from data mining or simulation based data farming) with two measures is awkward. As a single-value measure of first interpretation for the scanning of Big Data this article proposes statistically secured effect sizes either based on exact, mathematically sophisticated confidence intervals for effect sizes or simplified approximations. It is further argued that simplified secured effect sizes are among the most instructive single measures of statistical interpretation completely perspicuous for the layman.
BLIND SEPARATION OF NON-STATIONARY CONVOLUTIVELY MIXED SIGNALS IN THE TIME DOMAIN

—This paper proposes a new algorithm for solving the Blind Signal Separation (BSS) problem for convolutive mixing completely in the time domain. The closed form expressions used for ﬁrst and second order optimization techniques derived in [1] are extended to accommodate the more practical convolutive mixing scenario. Traditionally convolutive BSS problems are solved in the frequency domain [2], [3], [4] but this requires additional solving of the inherent frequency permutation problem. We demonstrate the performance of the algorithm using two optimization methods with a convolutive synthetic mixing system and real speech data.
Stratified Bacterial Diversity along Physico-chemical Gradients in High-Altitude Modern Stromatolites

At an altitude of 3,570 m, the volcanic lake Socompa in the Argentinean Andes is presently the highest site where actively forming stromatolite-like structures have been reported. Interestingly, pigment and microsensor analyses performed through the different layers of the stromatolites (50 mm-deep) showed steep vertical gradients of light and oxygen, hydrogen sulfide and pH in the porewater. Given the relatively good characterization of these physico-chemical gradients, the aim of this follow-up work was to specifically address how the bacterial diversity stratified along the top six layers of the stromatolites which seems the most metabolically important and diversified zone of the whole microbial community. We herein discussed how, in only 7 mm, a drastic succession of metabolic adaptations occurred: i.e., microbial communities shift from a UV-high/oxic world to an IR-low/anoxic/high H2S environment which force stratification and metabolic specialization of the bacterial community, thus, modulating the chemical faces of the Socompa stromatolites. The oxic zone was dominated by Deinococcus sp. at top surface (0.3 mm), followed by a second layer of Coleofasciculus sp. (0.3 to ∼2 mm). Sequences from anoxygenic phototrophic Alphaproteobacteria, along with an increasing diversity of phyla including Bacteroidetes, Spirochaetes were found at middle layers 3 and 4. Deeper layers (5–7 mm) were mostly occupied by sulfate reducers of Deltaproteobacteria, Bacteroidetes and Firmicutes, next to a high diversity and equitable community of rare, unclassified and candidate phyla. This analysis showed how microbial communities stratified in a physicochemical vertical profile and according to the light source. It also gives an insight of which bacterial metabolic capabilities might operate and produce a microbial cooperative strategy to thrive in one of the most extreme environments on Earth.
Performance Analysis of a User Selection Protocol in Cooperative Networks with Power Splitting Protocol-Based Energy Harvesting Over Nakagami-m/Rayleigh Channels

In this work, the system performance analysis of cooperative networks with power splitting protocol-based energy harvesting (EH) over Nakagami-m/Rayleigh channels is proposed. The exact-form expressions of the outage probability (OP) and ergodic capacity (EC) is demonstrated and derived. Using the proposed probabilistic models for wireless channels, we derive OP and EC as a research result. Finally, we conduct Monte Carlo simulations to verify a system performance analysis of the proposed system. The research results demonstrate the effectiveness of EH in the network over Nakagami-m/Rayleigh channels.
Where am I? Characterizing and improving the localization performance of off-the-shelf mobile devices through cooperation

We are increasingly reliant on cellular data services for many types of day-to-day activities, from hailing a cab, to searching for nearby restaurants. Geo-location has become a ubiquitous feature that underpins the functionality of such applications. Network operators can also benefit from accurate mobile terminal localization in order to quickly detect and identify location-related network performance issues, such as coverage holes and congestion, based on mobile measurements. Current implementations of mobile localization on the wildly-popular Android platform depend on either the Global Positioning System (GPS), Android's Network Location Provider (NLP), or a combination of both. In this paper, we extensively study the performance of such systems, in terms of its localization accuracy. We show through real-world measurements that the performance of GPS+NLP is heavily dependent on the mobility of the user, and its gains on localization performance is minimal, and often even detrimental, especially for network round-trip delays up to 1s. Building upon these findings, we evaluate the efficacy of using Tattle, a cooperative local measurement-exchange system, and propose Delay-Adjusted U-CURE, a clustering algorithm that greatly improves the localization performance of both GPS-only, and GPS+NLP techniques, without keeping expensive system states, nor requiring any location anchors nor additional instrumentation, nor any external knowledge that is not available programmatically to application designers. Our results are promising, demonstrating that median location accuracy improvements of over 30% is achievable with just 3 co-located devices, and close to 60% with just 6 co-located devices. These findings can be used by operators to better manage their networks, or by application designers to improve their location-based services.
Neural networks for the visual analysis of regional pollution

This study presents the application of different neural models to a real-life problem of studying the environmental conditions of Castilla y León region (Spain). The goal of this work is to visually and intuitively analyze the level of air pollution in four points of this Spanish region between years 2007 and 2014. The analyzed data were provided by four data acquisition stations from the regional control network of air quality. The main pollutants measured by these stations are analyzed in order to study how the geographical location of these stations and the different seasons of the year are decisive in the behavior of air pollution. Different models for supervised and unsupervised dimensionality reduction have been applied, and subsequent interesting conclusions are obtained.
Ultra High Throughput Architectures for SHA-1 Hash Algorithm on FPGA

This paper first presents a new architecture of SHA-1, which achieved the theoretical upper bound on throughput in the iterative architecture. And then based on the general proposed architecture, this paper implemented other two different kinds of pipelined architectures which are based on the iterative tech- nique and the loop unrolling technique respectively. The latter with 40-stage pipeline reached a throughput up to 76.195Gbps on an Altera Stratix II GX EP2SGX90FF FPGA. At least to the au- thors' knowledge, this is the fastest published FPGA-based design at the time of writing. At last the proposed designs are compared with other published SHA-1 designs, the designs in this paper have obvious advantages both in speed and areas. Based on the analysis of the SHA-1 algorithm and the pre- vious publications, this paper optimized the critical path of SHA-1 first, whose delay was equal to the iteration round of SHA-1 proposed in (8). And then based on the general pro- posed architecture, this paper presented other two different kinds of pipelined architectures which were based on the itera- tive technique and the loop unrolling technique. Compared with other publications, the presented designs in this paper have obvious advantages both in speed and logic requirements. The paper is organized as follows: A description of SHA-1 is presented in Section 2. The optimized architecture of SHA-1 is introduced and analyzed in section 3. The loop unrolling and pipelining techniques are analyzed in section 4. Two different kinds of pipelined architectures are implemented on FPGA in section 5 and the results of the proposed designs are described and compared to other published implementations in Section 6.
To secure amplify-and-forward relay systems via joint cooperative beamforming and jamming

Securing the wireless transmissions in the physical layer is a new perspective of security. In a wireless relay system, cooperative beamforming and jamming are two efficient schemes to improve the physical-layer security in the presence of passive eavesdroppers. However, in most works these two techniques are used in phase II of the cooperative transmission, which leaves the phase I out of protection. In this paper, we propose a joint cooperative beamforming and jamming scheme to enhance the security of a cooperative relay network, where a part of intermediate nodes adopt distributed beamforming while others jam the eavesdropper, simultaneously, such that both phases are secured. Since the instantaneous channel state information (CSI) of the eavesdropper may not be known, we propose a cooperative artificial noise transmission based secrecy strategy, subjected to the individual power constraint of each node. The beamformer weights and power allocation can be obtained by solving a second-order convex cone programming (SOCP) together with a linear programming problem. Simulations show the joint scheme greatly improves the security.
Maintaining a wormhole with a scalar field

It is well known that it takes matter that violates the averaged weak energy condition to hold the throat of a wormhole open. The production of such ``exotic'' matter is usually discussed within the context of quantum field theory. In this paper I show that it is possible to produce the exotic matter required to hold a wormhole open classically. This is accomplished by coupling a scalar field to matter that satisfies the weak energy condition. The energy-momentum tensor of the scalar field and the matter separately satisfy the weak energy condition, but there exists an interaction energy-momentum tensor that does not. It is this interaction energy-momentum tensor that allows the wormhole to be maintained.
Integrating cognitively-oriented and pedagogically-oriented methods in adaptive educational hypermedia

Adaptive educational hypermedia (AEH) is a learning tool that adapts learning content and flow to students' needs and offers students a freedom to explore materials. Adaptation and personalization in AEH are produced from interaction of learner, pedagogic, domain, adaptation, and presentation models. This paper discusses Knowledge Map, a prototype tool for managing and visualizing domain model of AEH and it integrates cognitively-oriented and pedagogically-oriented methods. Domain model organizes knowledge space, which contains a network of concepts or topics, and hyperspace, in which adaptation and personalization are offered to students. There are two methods implemented. The first method is cognitively-oriented, by which concepts learned in a course are organized in a network of concepts. With this first method, student can get a visualization of the network and explore nodes. The main drawback of this method is that students may get lost after doing deep exploration. The second is mixed pedagogically-oriented and cognitively-oriented method that organizes a course in a hierarchic structure of topics and sub-topics and a graph of concepts for each sub-topic. A Greedy algorithm has been implemented to guide students to certain topics. To test the methods' performances, a quantitative experiment has been conducted with three tested parameters: impression, learnability, and helpfulness. The experiments results show that the mixed method visualization is more impressive as this is more helpful for students to understand learning materials than the other method. Furthermore, the mixed method is more learnable, as students find it easier to gain a whole view of learning materials organized by this method rather than those organized by the cognitively-oriented method.
The prevalence of Non-suicidal Self-Injury (NSSI) among high school students in relation to age and sex.

OBJECTIVES The undertaken research aimed at determining the frequency of deliberate self-injurious behaviour (D-SIB) among the students of secondary schools and also the analysis of the frequency of repeated Non-Suicidal Self-Injury (NSSI) occurrences in accordance with DSM-5 criteria in reference to the age and sex in the studied population. METHODS The data was collected via survey method according to the questionnaire prepared in compliance with the criteria DSM-5 and Self-Harm Inventory. The study included randomly selected students: 1193 boys and 1027 girls in Bialystok aged 12 and 19 (average age ± SD:16.8 ± 1.65). Statistical analysis of the data was carried out using the application Statistica 10.0 PL, StatSoft. RESULTS These results indicate that D-SIB and NSSI affect both sexes. In the studied group 8.3 % of students engage in deliberate self-injurious behaviour. The percentage of NSSI was 4.8% (6.3% in the group of boys, 3.2 % among girls; p(Chi2)=0.01). Self-cutting was most common among 15-year-old pupils ((D-SIB:14.75%; NNSI:8.1%). The majority of respondents (82% of girls and 74% of boys) revealed that as a result of self-injury behaviour they experience relief. CONCLUSIONS Conducting further research in the area of NSSI seems to be crucial due to chronicity and prevalence as well as the fact that numerous repeated self-injuries bringing relief or causing positive state of mind might indicate a mechanism similar to an addiction syndrome in adolescence.
Secular trends in HIV knowledge and attitudes among Vietnamese women based on the Multiple Indicator Cluster Surveys, 2000, 2006, and 2011: what do we know and what should we do to protect them?

Background In Vietnam, women are at risk of HIV infection due to many factors. However, there is limited evidence about what women know and how they behave to protect themselves from HIV. Objective The objective of this study was to investigate the trends in comprehensive HIV/AIDS knowledge, attitude, and associated factors among Vietnamese women from 2000 to 2011. Design Data from three waves of the Vietnam Multiple Indicator Cluster Surveys (years 2000, 2006, and 2011) were used. Logistic regression methods examined factors associated with each of two dependent variables, HIV/AIDS knowledge and attitude toward HIV/AIDS. Results Although there was an increasing trend in basic HIV/AIDS knowledge and positive attitude toward the disease, in Vietnamese women in the general population over the survey years, the prevalence of women with basic HIV/AIDS knowledge and positive attitude toward HIV/AIDS was low. Multivariable models indicated that women who had higher levels of education, lived in urban areas, had higher economic status, and knew about places of HIV-related services were more likely to have good HIV/AIDS knowledge (e.g. in 2011, AOR's=3.01; 1.27; 1.88; 2.03, respectively). Women with higher educational attainment, knew about HIV services, and had better HIV knowledge were more likely to report positive attitude toward HIV/AIDS (e.g. in 2011, AOR's=2.50; 1.72; 2.23, respectively). Conclusions This study recommends that public health programs for the control of HIV, such as behavioral change communication campaigns or social policies for women, should focus not only in improving the quality of existing HIV/AIDS counseling and testing services but also on expanding coverage to increase accessibility to these services for women in rural areas. In addition, efforts to raise the level of knowledge about HIV/AIDS and improve attitude toward the disease should be undertaken simultaneously. The results of this study can help inform HIV control policies and practices in other developing countries.
Simulation on email worms propagation

Email is one of the most convenient and indispensable communication mediums in our life. However, virus and email worms quickly evolved the ability to spread through the Internet by various means such as email, and exploiting vulnerabilities, etc. This paper presents a novel email worm propagation simulation algorithm based on the user's behavior and the network topology. With Enron Email Dataset, we construct a simulation environment for the study of email worm spread, and the results show that the presented algorithm in this paper can describe the propagation of email worms accurately.
Revenue analysis of a family of ranking rules for keyword auctions

Keyword auctions lie at the core of the business models of today's leading search engines. Advertisers bid for placement alongside search results, and are charged for clicks on their ads. Advertisers are typically ranked according to a score that takes into account their bids and potential click-through rates. We consider a family of ranking rules that contains those typically used to model Yahoo! and Google's auction designs as special cases. We find that in general neither of these is necessarily revenue-optimal in equilibrium, and that the choice of ranking rule can be guided by considering the correlation between bidders' values and click-through rates. We propose a simple approach to determine a revenue-optimal ranking rule within our family, taking into account effects on advertiser satisfaction and user experience. We illustrate the approach using Monte-Carlo simulations based on distributions fitted to Yahoo! bid and click-through rate data for a high-volume keyword.
Algebras of higher operads as enriched categories II

One of the open problems in higher category theory is the systematic construction of the higher dimensional analogues of the Gray tensor product. In this paper we continue the work of [7] to adapt the machinery of globular operads [4] to this task. The resulting theory includes the Gray tensor product of 2-categories and the Crans tensor product [12] of Gray categories. Moreover much of the previous work on the globular approach to higher category theory is simplified by our new foundations, and we illustrate this by giving an expedited account of many aspects of Cheng's analysis [11] of Trimble's definition of weak n-category. By way of application we obtain an "Ekmann-Hilton" result for braided monoidal 2-categories, and give the construction of a tensor product of A-infinity algebras.
Parallel genetic algorithm with social interaction for solving constrained global optimization problems

The following paper introduces a parallel approach to a social variant of the Genetic Algorithm, called Parallel Genetic Algorithm with Social Interaction (PSIGA). The algorithm is based on social games involving game theory, and it is implemented using the OpenMP API, which is based on the shared memory programming model for multiple processor architectures. The main contribution of this approach is the parallelization using the Shared Memory of the Social Interaction Genetic Algorithm (SIGA) in order to achieve faster and better optimality than its nonparallel counterpart for global optimization problems with restrictions. For means of performance assessment, the algorithm is tested on four instances of engineering design problems and the obtained results compared with the Genetic Algorithm with Social Interaction (SIGA) implemented in sequential programming model.