Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-destructive estimation of Oecophylla smaragdina colony biomass

References [1] Sanders, D. & van Veen, F.J.F. 2011. Ecosystem engineering and predation: the multi-trophic impact of two ant species. Journal of Animal Ecology 80:569-576. [2] Folgarait, P. 1998. Ant biodiversity and its relationship to ecosystem functioning: a review. Biodiversity and Conservation 7:1221-1244. [3] Holldobler, B. & Wilson, E.O. 1990. The Ants. Belknap Press of Harvard University Press, Cambridge, USA. [4] Fittkau, E.J. & Klinge, H. 1973. On Biomass and Trophic Structure of the Central Amazonian Rain Forest Ecosystem. Biotropica 5:2-14. Introduction
Effects of soil warming during spring on photosynthetic recovery in boreal Norway spruce stands

The effect of soil thawing and soil temperature on postwinter recovery of photosynthetic capacity was studied, during late spring and early summer, in Norway spruce stands in northern Sweden. Soil temperature was manipulated by means of buried heating cables. The warming treatment was applied to stands with low (natural) and high (fertilized) availability of nutrients. Soil thawing, expressed as water availability, was followed by means of sapflow in stems, and shoot water potentials. The recovery of photosynthetic capacity was assessed by measuring the rate of light‐saturated photosynthesis (Amax), and maximum photochemical efficiency of photosystem II in detached shoots, and chlorophyll a fluorescence. Accumulation of starch reserves in the needles was followed as an independent indicator of photosynthetic performance in situ. Snowmelt and soil thawing occurred more than one month earlier in heated than in unheated plots. This was expressed both as sapflow and as differences in shoot water potential between treatments. During May, the rates of Amax were significantly higher on heated than on control plots. The effect of soil warming on Amax was, however, not reflected in chlorophyll fluorescence or needle starch content. The time course of the recovery of photosynthetic capacity was mainly controlled by mean air temperature and by the frequency of severe night frosts, and to a lesser extent by earlier soil thawing and higher soil temperatures.
Does Psa affect kiwifruit susceptibility to leafrollers

Analysis of kiwifruit industry pest monitoring and spray diary data showed increases in the incidence of leafroller larvae on fruit during crop monitoring in the 2011/12 season in regions with the virulent strain of the disease Pseudomonas syringae pv. actinidiae (Psa-V). Four factors were considered as explanations: Psa-V infection and its effects on plant defences, leafroller sprays, Psa-V sprays and leafroller sampling. The most plausible cause of the increased leafroller incidence is Psa-V infection. A putative mechanism is hormonal 'cross-talk' resulting in a trade-off between pest and pathogen resistance. The susceptibility of vines to leafrollers in Psa-V-infected regions appears to persist for several weeks longer after fruit set than in uninfected vines. The paper discusses the need to develop a better understanding of the interactions of biotic and abiotic stressors on plant defence responses to pests and diseases and to incorporate these into a plant-defence- centred approach to Integrated Pest Management.
Physical and Functional Responses to Experimental Marsh Surface Elevation Manipulation in Coos Bay's South Slough

Dike material was used as fill to construct high, mid, and low intertidal elevations in a subsided marsh located in the South Slough National Estuarine Research Reserve, Oregon. Marsh surface elevation change (including fill consolidation and compression of the original marsh soils), vertical accretion, tidal channel development, emergent vegetation colonization, and fish use were monitored over 3 years. Significant marsh surface elevation loss was detected at all elevations, with fill consolidation accounting for 70% of the loss at the highest elevation. Vertical accretion averaged 0.19 cm/yr in the sparsely vegetated Kunz Marsh compared with 0.70 cm/yr at the densely vegetated reference sites. Tidal channel development was influenced as much by marsh surface gradient as by marsh surface elevation. Vegetation colonization was directly correlated with elevation, whereas density and species richness of fish was inversely correlated with elevation. Manipulating the marsh surface to mid‐marsh elevations favors rapid vegetation colonization and facilitates vertical accretion‐dominated tidal channel development. Low marsh elevations result in initially slower developing vegetation colonization and channel development but are more beneficial to fish during the early stages of marsh recovery. High marsh elevations appear to sacrifice tidal channel development and associated fish access for rapid vegetation colonization.
Strategic Human Resource Management and Organizational Performance in the Nigerian Oil and Gas Industry: The Mediating Role of Organizational Climate

The purpose of this paper is to evaluate the effects of strategic human resources management (SHRM) practices on the primary measures as expressed by the Organisational Climate and the secondary measures as expressed by the organizational performance, and to find out the effect of the Organisational Climate on the organizational performance of Nigerian oil and gas companies. To achieve these objectives, an empirical study was conducted via the administration of 450 selfadministered copies of questionnaire to a randomly selected senior and management staff often (10) Oil and Gas Companies, that have implemented SHRM in Nigeria. Organizational climate was measured through a set of questionnaire that we developed based on the eight organizational climate dimension proposed by Litwin and stringer (1968). Strategic human resources management (SHRM) practices was measured using six dimensions (strategic HRM alignment, selection system, training and development system, performance appraisal system, compensation system, and career planning system) proposed by Abdulkadir (2012). Organizational performance was measured by the following variables: profitability, growth, sustainable competitive advantage and customer service dimensions. Using the framework from Abdulkadir (2012), data collected was subjected to regression and correlation analysis. The three-step regression procedure was used to corroborate the seven hypotheses. Findings based on the survey revealed that strategic human resources management (SHRM) practices positively affected both performance measures in the Nigerian oil and gas companies. Specifically, apart from career planning system, in descending order, the results suggest that, Strategic HRM alignment, training and development, Compensation System, Selection System, and Performance appraisal system and are the key SHRM practices that influenced both organizational climate and performance in the Nigerian oil and gas industry. Results also suggest that the relationship between SHRM practices and organizational performance in the Nigerian oil and gas industry is moderately influenced by organizational climate. The model provides predictive implications on improved organizational climate and organizational performance, given the activities of some factors manifesting strategic 57 Awolusi Olawumi Dele et al.: Strategic Human Resource Management and Organizational Performance in the Nigerian Oil and Gas Industry: The Mediating Role of Organizational Climate human resources management (SHRM) practices. Hence, to improve both organizational climate and organizational performance, oil and gas companies could control their strategic human resources management (SHRM) practices.
End-to-End 3D Point Cloud Instance Segmentation Without Detection

3D instance segmentation plays a predominant role in environment perception of robotics and augmented reality. Many deep learning based methods have been presented recently for this task. These methods rely on either a detection branch to propose objects or a grouping step to assemble same-instance points. However, detection based methods do not ensure a consistent instance label for each point, while the grouping step requires parameter-tuning and is computationally expensive. In this paper, we introduce a novel framework to enable end-to-end instance segmentation without detection and a separate step of grouping. The core idea is to convert instance segmentation to a candidate assignment problem. At first, a set of instance candidates is sampled. Then we propose an assignment module for candidate assignment and a suppression module to eliminate redundant candidates. A mapping between instance labels and instance candidates is further sought to construct an instance grouping loss for the network training. Experimental results demonstrate that our method is more effective and efficient than previous approaches.
X-CLEaVER

Learning-to-Rank (LtR) solutions are commonly used in large-scale information retrieval systems such as Web search engines, which have to return highly relevant documents in response to user query within fractions of seconds. The most effective LtR algorithms adopt a gradient boosting approach to build additive ensembles of weighted regression trees. Since the required ranking effectiveness is achieved with very large ensembles, the impact on response time and query throughput of these solutions is not negligible. In this article, we propose X-CLEaVER, an iterative meta-algorithm able to build more efficient and effective ranking ensembles. X-CLEaVER interleaves the iterations of a given gradient boosting learning algorithm with pruning and re-weighting phases. First, redundant trees are removed from the given ensemble, then the weights of the remaining trees are fine-tuned by optimizing the desired ranking quality metric. We propose and analyze several pruning strategies and we assess their benefits showing that interleaving pruning and re-weighting phases during learning is more effective than applying a single post-learning optimization step. Experiments conducted using two publicly available LtR datasets show that X-CLEaVER can be successfully exploited on top of several LtR algorithms as it is effective in optimizing the effectiveness of the learnt ensembles, thus obtaining more compact forests that hence are much more efficient at scoring time.
TESA: Tensor Element Self-Attention via Matricization

Representation learning is a fundamental part of modern computer vision, where abstract representations of data are encoded as tensors optimized to solve problems like image segmentation and inpainting. Recently, self-attention in the form of Non-Local Block has emerged as a powerful technique to enrich features, by capturing complex interdependencies in feature tensors. However, standard self-attention approaches leverage only spatial relationships, drawing similarities between vectors and overlooking correlations between channels. In this paper, we introduce a new method, called Tensor Element Self-Attention (TESA) that generalizes such work to capture interdependencies along all dimensions of the tensor using matricization. An order R tensor produces R results, one for each dimension. The results are then fused to produce an enriched output which encapsulates similarity among tensor elements. Additionally, we analyze self-attention mathematically, providing new perspectives on how it adjusts the singular values of the input feature tensor. With these new insights, we present experimental results demonstrating how TESA can benefit diverse problems including classification and instance segmentation. By simply adding a TESA module to existing networks, we substantially improve competitive baselines and set new state-of-the-art results for image inpainting on Celeb and low light raw-to-rgb image translation on SID.
Impact of temperature and precipitation on rice productivity in rice-wheat cropping system of Punjab province.

Pattern of temperature and precipitation are changing due to global warming, resulting inhaving impact on crop productivity.The objective of this study was to estimate the impact of climat ic variableson rice productivity in the rice - wheat cropping system of the Punjab. Aggregatedtime series data were used for rice cropCobb Douglas type production function wasemployedwith rice yield as dependent variable and climatic factorsas independent variables. Results showed thatan increase intemperature by 1.5 o C and 3 o C would enhance rice yield by 2.09% and 4.33%, respectively compared to the base year regression estimates. However, an increase in precipitation by 5% and 15% duringSeptember-Octobercould adversely affect rice productivity by 5.71% and 15.26%,respectively. However, its decrease ispositively related with rice yield. Evolving and disseminating rice varieties having adaptation to climate change should be the focus of future research and development. Improved farm management practices,creating awareness among farmersabout climate change and strengthening extension department are some measuresto be taken for adaptation to climate change in the rice region .
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension

While neural networks with attention mechanisms have achieved superior performance on many natural language processing tasks, it remains unclear to which extent learned attention resembles human visual attention. In this paper, we propose a new method that leverages eye-tracking data to investigate the relationship between human visual attention and neural attention in machine reading comprehension. To this end, we introduce a novel 23 participant eye tracking dataset - MQA-RC, in which participants read movie plots and answered pre-defined questions. We compare state of the art networks based on long short-term memory (LSTM), convolutional neural models (CNN) and XLNet Transformer architectures. We find that higher similarity to human attention and performance significantly correlates to the LSTM and CNN models. However, we show this relationship does not hold true for the XLNet models – despite the fact that the XLNet performs best on this challenging task. Our results suggest that different architectures seem to learn rather different neural attention strategies and similarity of neural to human attention does not guarantee best performance.
Spectral analysis of soils from Mogi-Guaçú (SP) Region

With the development of remote sensing techniques, the understanding of soil spectral response with the purpose to assist soil mapping and estimate soil attributes, become necessary. For this, the objective of this work was to analyze the spectral response of eight soil profiles from Mogi-Guacu (SP) region obtained in laboratory and correlate them with soil texture and their chemical and mineralogical characteristics. The soils were sampled in each horizon and radiometric data was obtained by a spectroradiometer in laboratory. It was possible to discriminate the spectral response of each soil and relate them with texture, organic matter and iron oxides content. The analysis of reflected electromagnetic energy permitted to evaluate the soils mineralogical compounds like iron oxides, kaolinite, gibbsite and 2:1 clay minerals. These results indicate that this technique can be used as an auxiliary method in soil studies and mapping.
AMEP_A_179303 693..701

Antonia J Clarke Annette Burgess Christie van Diggele Craig Mellis 1Faculty of Medicine and Health, University of Sydney School of Medicine, Central Clinical School, University of Sydney, Sydney, Australia; 2Faculty of Medicine and Health, University of Sydney School of Medicine, Education Office, University of Sydney, Sydney, Australia; 3Faculty of Medicine and Health, University of Sydney School of Medicine, Sydney Health Education Research Network, University of Sydney, Sydney, Australia Abstract: Reverse mentoring is a relatively contemporary concept that relies on the reversal of the traditional roles of mentor and mentee and the abolition of the mentorship model as an apprenticeship or hierarchy. Typically, a younger specialist takes on the role of mentor and an older, more experienced specialist the role of mentee. Reverse mentoring is founded in learning and social theories of mentorship and has been practically applied in information technology, business and education fields. However, there is a role for reverse mentoring in medical education and the health sciences, particularly with the inclusion of new technologies in a changing health landscape, and the emphasis on interdisciplinary teamwork and improved workplace culture. Further investigation and analysis of reverse mentoring is warranted, with a particular focus on the implementation of the reverse mentor model in the field of medical education and the health sciences. To assist those considering implementation of mentorship programs in their workplace, this article provides an overview of recent literature, with suggested applications of “reverse mentoring” in the medical education context.
PropagationNet: Propagate Points to Curve to Learn Structure Information

Deep learning technique has dramatically boosted the performance of face alignment algorithms. However, due to large variability and lack of samples, the alignment problem in unconstrained situations, e.g. large head poses, exaggerated expression, and uneven illumination, is still largely unsolved. In this paper, we explore the instincts and reasons behind our two proposals, i.e. Propagation Module and Focal Wing Loss, to tackle the problem. Concretely, we present a novel structure-infused face alignment algorithm based on heatmap regression via propagating landmark heatmaps to boundary heatmaps, which provide structure information for further attention map generation. Moreover, we propose a Focal Wing Loss for mining and emphasizing the difficult samples under in-the-wild condition. In addition, we adopt methods like CoordConv and Anti-aliased CNN from other fields that address the shift variance problem of CNN for face alignment. When implementing extensive experiments on different benchmarks, i.e. WFLW, 300W, and COFW, our method outperforms the state-of-the-arts by a significant margin. Our proposed approach achieves 4.05% mean error on WFLW, 2.93% mean error on 300W full-set, and 3.71% mean error on COFW.
INSECT BIODIVERSITY OF THE RUSSIAN FAR EAST

The insect fauna of Russian Far East (RFE) is represented by about 31500 estimated species from 629 recorded families of 31 orders. The largest orders are Hymenoptera (72 families, 9000 estimated species), Diptera (119 families, 8000 estimated species), Coleoptera (114 families, 5500 estimated species), Lepidoptera (81 families, 5000 estimated species). The list of recorded insect families as the estimated number of species for each RFE region are given firstly. The distribution of some families in Russia is restricted by RFE only. Among 93 insect species included in Red Data Book of Russian Federation 43 occur in RFE only. The most rich insect fauna associated with mixed broad-leaved-coniferous forests in the south of RFE. Eastern Palaearcric species are dominated here (more than 50 %). There are two hot insect biodiversity spots in RFE: Primorskii krai and Kuril Islands. Comparison of species number in the RFE with other Holarctic regions is given.
Thylakoid protein phosphorylation, state 1-state 2 transitions, and photosystem stoichiometry adjustment: redox control at multiple levels of gene expression

In photosynthesis in chloroplasts, control of thylakoid protein phosphorylation by redox state of inter-photosystem electron carriers makes distribution of absorbed excitation energy between the two photosystems self-regulating. During operation of this regulatory mechanism, reduction of plastoquinone activates a thylakoid protein kinase which phosphorylates the light-harvesting complex LHC II, causing a change in protein recognition that results in redistribution of energy to photosystem I at the expense of photosystem II, thus tending to oxidise the reduced plastoquinone pool. These events correspond to the transition from light-state 1 to light-state 2
Social Media Text Normalization

Recent years some researchers interested in text normalization over social media, as the informal writing styles found in Twitter and other social media data. These informal texts often cause problems for Natural Language processing applications such as various mining research or translation on social media data. Today Facebook supports English translation of post and status for Myanmar Language. However, Most of the translation is not relevant for Myanmar words meaning. Complex nature of Myanmar language’s syntactic structure, informal writing style, slang words and spelling mistakes are challenge in social media text translation work. This paper proposed text normalization that can be deployed as a preprocessing step for opinion mining, machine translation and various Natural Language Processing (NLP) applications to handle social media text. There are three steps in this work: Firstly, candidate words for normalization are selected from the collected raw dataset. In this case, Out-Of-Vocabulary (OOV) words are extracted for normalization. However, not all OOV words need to be normalized. Therefore, ill-formed words are detected from OOV words list for normalization. Second, slang words dictionary is generated for this work. Third, text similarity methods are applied to ill-formed words for normalization. Evaluation will be done on translation by applying normalization in pre-processing step. For translation, Myanmar-English machine translation [14] is used. The experimental results improve by applying proposed normalization to the translation work especially for social media text.
Identification of newts ( Triturus sp.) and their hybrids using molecular methods

Identification of hybrids is important for the study of introgression between related species, but can be difficult when morphological differences between parental species are small. In the case of the newts Triturus vulgaris and T. helveticus , males are readily identifiable during the breeding season when secondary sexual characters are highly developed, but female dimorphism is always much less. Male hybrids with intermediate characters are therefore more likely to be detected than female ones. RAPD (random amplified polymorphic DNA) analysis of three putative newt hybrids and examples of the two parent species T. vulgaris and T. helveticus confirmed hybrid status in all three cases. The maternal parents of each hybrid were identified by amplification of mitochondrial cytochrome b and ATPase genes, including partial sequencing of the latter. One hybrid had a T. vulgaris mother while the other two had T. helveticus mothers. The PCR-based methods therefore provided a quick and simple approach for identifying both hybrid status and parentage in crosses between these two urodele species. The RAPD technique should also prove useful for the identification of larvae, which are morphologically indistinguishable in T. vulgaris and T. helveticus .