Miracle-dz/UniFAR-FTUs huggingface dataset:
Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest

Tree beta-diversity denotes the variation in species composition at stand level, it is a key indicator of forest degradation, and is conjointly required with alpha-diversity for management decision making but has seldom been considered. Our aim was to map it in a continuous way with remote sensing technologies over a tropical landscape with different disturbance histories. We extracted a floristic gradient of dissimilarity through a non-metric multidimensional scaling ordination based on the ecological importance value of each species, which showed sensitivity to different land use history through significant differences in the gradient scores between the disturbances. After finding strong correlations between the floristic gradient and the rapidEye multispectral textures and LiDAR-derived variables, it was linearly regressed against them; variable selection was performed by fitting mixed-effect models. The redEdge band mean, the Canopy Height Model, and the infrared band variance explained 68% of its spatial variability, each coefficient with a relative importance of 49%, 32.5%, and 18.5% respectively. Our results confirmed the synergic use of LiDAR and multispectral sensors to map tree beta-diversity at stand level. This approach can be used, combined with ground data, to detect effects (either negative or positive) of management practices or natural disturbances on tree species composition.
Cost-effectiveness of introducing the pneumococcal conjugate vaccine for children under 5 years in the Islamic Republic of Iran.

Background Pneumococcal disease caused by Streptococcus pneumoniae results in considerable mortality and morbidity. Pneumococcal conjugate vaccines (PCV), such as PCV-13, can prevent invasive pneumococcal disease and avoid disability and death. The cost of introducing PCV-13 in childhood immunization schedules should be assessed against the cost of pneumococcal diseases for each community. Aims This study aimed to evaluate the cost-effectiveness of introducing PCV-13 in the national immunization programme for children under 5 years in the Islamic Republic of Iran. Methods The TRIVAC decision support model was used to estimate total costs of introducing PCV-13 and the disability- adjusted life years (DALYs) averted. The main pneumococcal diseases were considered-pneumonia, meningitis, acute otitis media, and non-pneumonia, non-meningitis infections-in terms of hospital admissions, outpatient visits and deaths. Local data were used to estimate costs. Results Pneumococcal disease is estimated to affect 18 713 211 children under 5 years (519 412 pneumonia, 18 148 116 acute otitis media, 6884 meningitis, and 38 799 non-pneumonia, non-meningitis) in 10 years (2014-2023) without use of the vaccine. Introduction of PCV-13 would prevent 4 900 084 cases of pneumococcal disease (190 849 pneumonia, 4 692 450 acute otitis media, 2529 meningitis, and 14 256 non-pneumonia, non-meningitis). Pneumococcal infection would cause 287 950 hospital admissions and 29 399 deaths; vaccination could avert 105 802 hospital admissions and 9997 deaths. The incremental cost-effectiveness was estimated to be US$ 1890 and US$ 1538 per averted DALY for the government and society respectively. Conclusion According to WHO-recommended thresholds for interpreting cost-effectiveness, introduction of PCV-13 for children under 5 years in the Islamic Republic of Iran would be cost-effective.
Efficient Online Portfolio Selection with Heuristic AI Algorithm

Online portfolio selection is one of the most important problems in several research communities, including finance, engineering, statistics, artificial intelligence, and machine learning, etc. The primary aim of online portfolio selection is to determine portfolio weights in every investment period (i.e., daily, weekly, monthly etc.) to maximize the investor’s final wealth after the end of investment period (e.g., 1 year or longer). In this paper, we present an efficient online portfolio selection strategy that employs a heuristic artificial intelligence (AI) algorithm to maximise the total wealth based on historical stock prices. Based on empirical studies conducted on recent historical datasets for the period 2000 to 2017 on four different stock markets (i.e., NYSE, S&P500, DJIA, and TSX), the proposed algorithm has been shown to outperform both Anticor and OLMAR —the two most prominent portfolio selection strategies in contemporary literature. The algorithm achieved 34.22 of the total wealth while Anticor and OLMAR returned 1.08, and 4.52 respectively, on the NYSE market. On the S&P market, the algorithm returned 15.39 of the total wealth while the Anticor and OLMAR returned 6.20 and 2.2,respectively. On the TSX market, the algorithm returned 2.43 of the total wealth while Anticor and Olmar returned 0.96 and 0.41, respectively.
SemEval-2019 Task 2: Unsupervised Lexical Frame Induction

This paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019. Given a set of prespecified syntactic forms in context, the task requires that verbs and their arguments be clustered to resemble semantic frame structures. Results are useful in identifying polysemous words, i.e., those whose frame structures are not easily distinguished, as well as discerning semantic relations of the arguments. Evaluation of unsupervised frame induction methods fell into two tracks: Task A) Verb Clustering based on FrameNet 1.7; and B) Argument Clustering, with B.1) based on FrameNet’s core frame elements, and B.2) on VerbNet 3.2 semantic roles. The shared task attracted nine teams, of whom three reported promising results. This paper describes the task and its data, reports on methods and resources that these systems used, and offers a comparison to human annotation.
Social Processes in the Experience and Regulation of Emotions

Social Processes in the Experience and Regulation of Emotions Jocelyn Shu The quality of our lives can be characterized, in part, by the emotions we experience. Feeling a preponderance of negative emotions is characteristic of a range of psychological and affective disorders. As such, the ability to regulate emotions has been recognized as critical for maintaining mental health. While definitions of emotions abound, they have been primarily conceptualized as intrapersonal responses to one’s environment. Yet, while our social interactions are an inseparable aspect of our emotional lives, relatively little emphasis has been placed in prior research on the social bases of emotional experiences. This dissertation presents three bodies of research that investigate the role of social processes in experiencing and regulating negative emotions. In the first body of research, I present four studies that investigate how empathy, the ability to experience another person’s emotions, is involved in experiencing anxiety. In the second body of research, I transition to investigating the social bases of emotion regulation. Here, I present two multi-phase studies that investigate how social emotion regulation may be best implemented to help others experiencing different kinds of negative emotions. The third body of research investigates the neural bases of social emotion regulation. The results of these studies highlight how social processes are an inherent part of emotional experiences and emotion regulation.
Regional responses to refugees: Insights from Asia

There are many reasons why thinking about refugees in terms of regions makes sense. Overwhelmingly, the social, economic and political dynamics that result from refugee displacement are contained within, and carried by, regions. By far, the majority of refugees who have fled their country remain within the region in which they were born; and 85 percent of refugees remain in the developing world (United Nations High Commissioner for Refugees (UNHCR), 2018b: 2). Responding to refugee movement on a regional level might also be politically advantageous.
Design of 1-D Stable Variable Fractional Delay IIR Filters Using Finite Impulse Response Fitting

In this paper, a novel finite impulse response fitting-based approach for the design of 1-D stable variable fractional delay IIR filters is proposed. The design algorithm is decomposed into three design stages: Firstly, a set of fixed delay FIR filters is designed by using weighted least-squares method. Then, the related set of fixed delay stable IIR filters is determined via finite impulse response fitting. Finally, the design result is obtained by fitting each of the fixed delay IIR filter coefficients as 1-D polynomials. It is shown that the proposed method always guarantees the filter obtained to be stable and has very low computation complexity comparable with that of designing fixed FIR filters. Two illustrative design examples are given to justify the effectiveness and advantages over other existing approaches for designing variable fractional delay IIR filters
Investigation into the performance of turbulence models for fluid flow and heat transfer phenomena in electronic applications

Heat is extracted away from an electronic package by convection, conduction, and/or radiation. The amount of heat extracted by forced convection using air is highly dependent on the characteristics of the airflow around the package which includes its velocity and direction. Turbulence in the air is also important and is required to be modeled accurately in thermal design codes that use computational fluid dynamics (CFD). During air cooling the flow can be classified as laminar, transitional, or turbulent. In electronics systems, the flow around the packages is usually in the transition region, which lies between laminar and turbulent flow. This requires a low-Reynolds number numerical model to fully capture the impact of turbulence on the fluid flow calculations. This paper provides comparisons between a number of turbulence models with experimental data. These models included the distance from the nearest wall and the local velocity (LVEL), Wolfshtein, Norris and Reynolds, k-/spl epsiv/, k-/spl omega/, shear-stress transport (SST), and k/spl epsiv//kl models. Results show that in terms of the fluid flow calculations most of the models capture the difficult wake recirculation region behind the package reasonably well, although for packages whose heights cause a high degree of recirculation behind the package the SST model appears to struggle. The paper also demonstrates the sensitivity of the models to changes in the mesh density; this study is aimed specifically at thermal design engineers as mesh independent simulations are rarely conducted in an industrial environment.
Social Justice, Sport and Racism: A Position Statement

ABSTRACT Despite common claims that society is “post-racial” and that sport is apolitical, issues of racial injustice continue to permeate sport and recreational institutions. Drawing insight from critical race theory, we articulate a vision of social justice oriented toward illuminating and addressing issues of racial injustice in sport and recreation. In particular, we highlight pressing matters of racial injustice in sport at the elite, competitive levels, as well as community and recreational settings. We also discuss contemporary examples and strategies for resisting racial injustice, noting the challenges and difficulties associated with various approaches. In doing so, we hope to provide direction for scholars and activists in fighting for social justice with respect to racism and racial inequality in sport.
Towards path-based semantic dissimilarity estimation for scene representation using bottleneck analysis

In natural images, it remains challenging to estimate dissimilarities between image elements for scene representation due to gradual variations of illuminations, textures or clutters. To tackle this problem, we utilise a path-based bottleneck analysis method that captures the semantic information between image elements to measure the dissimilarity. By integrating both the spatial continuity and feature consistency into the understanding of the semantic information, we detect the bottlenecks on the proposed double-S path to define the bottleneck distance, which demonstrates a favourable capability of grouping image elements that follow a similar pattern and separating different ones. In the experiments, the method is proved to be robust to noises and invariant to changing illumination and arbitrary scales in natural images. Tests on some challenging datasets validate the advantage of applying the path-based bottleneck distance in image ranking and salient object detection.
The Downtrodden Backers: The Prevalence and Determinants of Well-Being of the working animals; the Case of owners of Equines in Siraro woreda, west arsi zone

The prime objective of the research was to assess the overall patterns of use, treatment, and well-being issues of working animals in Siraro woreda, west Arsi zone, paying much attention to the behavioral aspects of the equine owners than the animals themselves. Both quantitative and qualitative data were collected using small-scale survey, in-depth interview, and observation methods from research participants systematically selected through judgmental/availability sampling technique. The collected quantitative data were analyzed using improved quantitative data analyzing instrument mainly SPSS version 20 where frequency tables, cross-tabs, and percentages were applied for data presentation. And qualitative data were transcribed, organized and analyzed according the themes identiﬁ ed and presented in a narrative manner. Major factors aﬀ ecting working animals’ welfare, such as in- ability to commonly use harnesses/yokes while loading the animals, absence of separate shelter built for the equines, failure of allocating suﬃ cient resting time after loading, inadequacy of fodder, failure to take diseased/injured animals to the nearby veterinary clinic for treatment, have been found in the study area. The belief that equines such as donkeys never tire whatever amount of hours we let them work has also been found to be the widely held cultural value in the study area, potentially aﬀ ecting the way people handle and care for their working animals. There is a need to develop a well-articulated national policy and legal frameworks that speciﬁ cally deal with the protection and maintenance of equine welfare. In addition, the media should play awareness creation and mobilization role.
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing

The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention: many applications in fact require high-speed operations that suit a hardware implementation. However, numerous elements and complex interconnections are usually required, leading to a large area occupation and copious power consumption. Stochastic computing (SC) has shown promising results for low-power area-efficient hardware implementations, even though existing stochastic algorithms require long streams that cause long latencies. In this paper, we propose an integer form of stochastic computation and introduce some elementary circuits. We then propose an efficient implementation of a DNN based on integral SC. The proposed architecture has been implemented on a Virtex7 field-programmable gate array, resulting in 45% and 62% average reductions in area and latency compared with the best reported architecture in the literature. We also synthesize the circuits in a 65-nm CMOS technology, and we show that the proposed integral stochastic architecture results in up to 21% reduction in energy consumption compared with the binary radix implementation at the same misclassification rate. Due to fault-tolerant nature of stochastic architectures, we also consider a quasi-synchronous implementation that yields 33% reduction in energy consumption with respect to the binary radix implementation without any compromise on performance.
Real-time multi-perspective rendering on graphics hardware

Multi-perspective rendering has a variety of applications; examples include lens refraction, curved mirror re- flection, caustics, as well depiction and visualization. However, multi-perspective rendering is not yet practical on polygonal graphics hardware, which so far has utilized mostly single-perspective (pin-hole or orthographic) projections. In this paper, we present a methodology for real-time multi-perspective rendering on polygonal graphics hardware. Our approach approximates a general multi-perspective projection surface (such as a curved mirror and lens) via a piecewise-linear triangle mesh, upon which each triangle is a simple multi-perspective camera, parameterized by three rays at triangle vertices. We derive analytic formula showing that each triangle projection can be implemented as a pair of vertex and fragment programs on programmable graphics hardware. We demonstrate real-time performance of a variety of applications enabled by our technique, including reflection, refraction, caustics, and visualization.
Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain

Water availability is essential for the appropriate analysis of its sustainable management. We performed a comparative study of six hydrological balance models (Témez, ABCD, GR2M, AWBM, GUO-5p, and Thornthwaite-Mather) in several basins with different climatic conditions within Spain in the 1977–2010 period. We applied six statistical indices to compare the results of the models: the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Nash–Sutcliffe model efficiency coefficient (NSE), coefficient of determination (R2), percent bias (PBIAS), and the relative error between observed and simulated run-off volumes (REV). Furthermore, we applied the FITEVAL software to determine the uncertainty of the model. The results show that when the catchments are more humid the obtained results are better. The GR2M model gave the best fit in peninsular Spain in a UNEP aridity index framework above 1, and NSE values above 0.75 in a 95% confidence interval classify GR2M as very good for humid watersheds. The use of REV is also a key index in the assessment of the margin of error. Flow duration curves show good performance in the probabilities of exceedance lower than 80% in wet watersheds and deviations in low streamflows account for less than 5% of the total streamflow.
Kwestionariusz Zaangażowania w Diałania Znaczące (KZDZ) B. Goldberg, E. S. Brintnell i J. Goldberga. Adaptacja Polska

Badania mialy na celu określenie wlaściwości psychometrycznych polskiej wersji Engagement in Meaningful Activities Scale (EMAS) B. Goldberg, E. S. Brintnell, J. Goldberga, ktora sluzy do pomiaru zaangazowania w dzialania znaczące, rozumiane jako aktywności, ktore sluzą celowi istotnemu z perspektywy osobistej lub kulturowej. Przedstawiono definicje kluczowych pojec, dotychczasowe badania związane z kwestionariuszem oraz wstepne wyniki polskie. Osobami badanymi byli: pracownicy instytucji kultury, stowarzyszen zajmujących sie pomocą innym ludziom, korporacji oraz nauczyciele szkol specjalnych i gimnazjow; lącznie 237 osob. Wyniki wskazują na wysoką rzetelnośc narzedzia ( α = 0,88), na związki miedzy zaangazowaniem w dzialania znaczące a zmiennymi takimi, jak znaczenie pracy ( r = 0,65) i satysfakcja z pracy ( r = 0,65) oraz na roznice w zaangazowaniu w dzialania znaczące u osob wykonujących odmienne zawody.
Don't Thrash: How to Cache Your Hash on Flash

This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter, a compact data structure supporting set insertion and membership queries, has found wide application in databases, storage systems, and networks. Because the Bloom filter performs frequent random reads and writes, it is used almost exclusively in RAM, limiting the size of the sets it can represent. This paper first describes the quotient filter, which supports the basic operations of the Bloom filter, achieving roughly comparable performance in terms of space and time, but with better data locality. Operations on the quotient filter require only a small number of contiguous accesses. The quotient filter has other advantages over the Bloom filter: it supports deletions, it can be dynamically resized, and two quotient filters can be efficiently merged. The paper then gives two data structures, the buffered quotient filter and the cascade filter, which exploit the quotient filter advantages and thus serve as SSD-optimized alternatives to the Bloom filter. The cascade filter has better asymptotic I/O performance than the buffered quotient filter, but the buffered quotient filter outperforms the cascade filter on small to medium data sets. Both data structures significantly outperform recently-proposed SSD-optimized Bloom filter variants, such as the elevator Bloom filter, buffered Bloom filter, and forest-structured Bloom filter. In experiments, the cascade filter and buffered quotient filter performed insertions 8.6--11 times faster than the fastest Bloom filter variant and performed lookups 0.94--2.56 times faster.
On Prediction Using Variable Order Markov Models

This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a "decomposed" CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems.
List Decoding of Polar Codes

We describe a successive-cancellation list decoder for polar codes, which is a generalization of the classic successive-cancellation decoder of Arıkan. In the proposed list decoder, L decoding paths are considered concurrently at each decoding stage, where L is an integer parameter. At the end of the decoding process, the most likely among the L paths is selected as the single codeword at the decoder output. Simulations show that the resulting performance is very close to that of maximum-likelihood decoding, even for moderate values of L. Alternatively, if a genie is allowed to pick the transmitted codeword from the list, the results are comparable with the performance of current state-of-the-art LDPC codes. We show that such a genie can be easily implemented using simple CRC precoding. The specific list-decoding algorithm that achieves this performance doubles the number of decoding paths for each information bit, and then uses a pruning procedure to discard all but the L most likely paths. However, straightforward implementation of this algorithm requires Ω(Ln2) time, which is in stark contrast with the O(n log n) complexity of the original successive-cancellation decoder. In this paper, we utilize the structure of polar codes along with certain algorithmic transformations in order to overcome this problem: we devise an efficient, numerically stable, implementation of the proposed list decoder that takes only O(Ln logn) time and O(Ln) space.