Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. Thus, designing efficient filtering algorithms on table constraints has attracted significant research efforts. In turn, there have been great improvements in efficiency over time with the evolution and development of AC and GAC algorithms. In this paper, we survey the existing filtering algorithms for table constraint focusing on historically important ideas and recent successful techniques shown to be effective.
Technology commercialization front-end framework: Metallurgical industry

The development and commercialization of technologies within the metallurgical industry often spans a time-frame of decades. This paper proposes a managerial framework that aims at increasing the success rate and speed of technology commercialization within this industry, focusing on the front end of the process. A case study research strategy was applied, with an in-depth evaluation of two cases. The cases involved technologies that were developed by a metallurgical research and development organization. For each of the cases, secondary data was collected, followed by interviews. The data was evaluated, compared, and consolidated into a framework. For the technology development stages of concept development, and for research and development, important factors were identified that contribute to the successful development of technologies with the objective of commercialization. During concept development, the most important factor was determined to be the identification of the need or opportunity within the market, whereas the most important factors during research and development were determined to be the involvement of a project champion, a suitable team, and a potential implementer. The central driver of technology commercialization was identified to be the market, along with the fulfilment of a need or opportunity within the market.
The Phytopathological Significance of Mycelial Individualism

Phytopathologists have long been aware of the considerable intraspecific variation that can exist in natural populations of phytopathogenic mycelial fungi. However, the significance of this variation has largely been perceived in terms of fungal-plant rather than interfungal relationships. Corresponding­ ly, questions about variation in the pathogenicity and virulence of different fungal lineages have received more attention than questions about the way in which these lineages have the potential to interact antagonistically or syn­ ergistically with one another (55). The purpose of this article is to explore the many ways in which mycelial interactions may, nonetheless, be relevant to our understanding of the etiology and control of plant disease. An important step in understanding mycelial interactions is to have a clear perspective of the units of selection in natural fungal populations. Historical­ ly, this issue has been clouded both by the indeterminate nature of fungal mycelia, which have the potential to continue extension indefinitely, and by the concept that hyphal anastomosis could allow extensive cooperation be­ tween colonies of diverse genetic origin (22, 57, 58). The resultant view that members of the same species could pool their genetic resources in a versatile
Smart Water Leakage and Theft Detection using IoT

Water is one of the most important resources that is required for life sustainability on earth. If we talk about humans, the whole body comprises up to 60% of water of the entire body mass. Earth surface is covered with 70% of water and out of that only 2.5% of water is freshwater. Again, out of that 2.5%, only 0.3% of it is available on earth. Hence, it is very necessary to utilize water intelligently. However, due to water leakage and theft a lot of water gets wasted. According to The Hindu report form 2010, In Delhi, capital of India, around 40% water was either stolen or leaked out due to which a huge sum of people didn’t have access to water. Another report from NITI Ayog suggested that around 2,00,000 people died every year due to inability to access safe drinking water. So, work here presents a design of an IoT based system that would detect water leakage in real-time which would help concerned authorities to take appropriate action so that water loss is minimized.
Analysis and Study of Several Game Algorithms for Public Safety

Security issues have always been the focus of attention in countries around the world. In the past few years, game theory has gradually formed some algorithms in the field of resource allocation and scheduling in the security field and has been successfully applied in related aspects. This paper elaborates the research status of resource allocation algorithm in security game model, then the algorithm and model are analyzed in detail. Finally, the problems and application value are evaluated objectively in order to provide reference for similar research. At present, ARMOR patrol scheduling system based on DOBSS algorithm has been successfully applied in this field. There is also an IRIS air police dispatch system based on ASPEN extended algorithm. And TRUST urban rail safety transportation system with compact representation of mixed strategies. Even HBGS algorithm can be closely combined with various related algorithms in the future
Depth-of-Field Rendering Using Progressive Lens Sampling in Direct Volume Rendering

Direct volume rendering is a widely used technique for extracting information from three-dimensional scalar fields acquired by measurement or numerical simulation. However, the translucency of direct volume rendering to express the internal structure of the volume often makes it difficult to recognize the depth of complex structures. In this paper, we propose a new method for applying depth-of-field effects to volume ray-casting to improve the depth perception. A thin lens camera model is used to simulate rays passing through different parts of lens. The proposed method is implemented in the GPU pipeline with no preprocessing, so any acceleration techniques of volume ray-casting can be applied without restrictions. We also propose a multi-pass rendering framework using progressive lens sampling. This new technique uses a different number of lens samples per pixel, depending on the size of the circle of confusion at the point where each ray intersects the volume data. In the experiments with various data, we demonstrated that higher quality images with better depth perception were generated up to 9x faster than the existing depth-of-field method in direct volume rendering.
Ozone symptoms in leaves of woody plants in open‐top chambers: ultrastructural and physiological characteristics

This study was carried out in the summer of 2001 at the Lattecaldo open-top chamber research facility (Canton Ticino, southern Switzerland). The aim of this research study was to examine the behaviour of Fraxinus excelsior, Prunes avium and Viburnum lantana seedlings grown in charcoal-filtered (CF) (∼50% of the ambient O 3 ) and non-filtered (NF) (∼92% of the ambient O 3 ) air open-top chambers. Investigations included the assessment of visible foliar symptom development, anatomical and ultrastructural analysis of symptoms, measurements of direct chlorophyll a fluorescence (fluorescence findings were processed by means of the JIP test) and leaf gas exchange measurements. The three species displayed different foliar symptoms. In F. excelsior, symptoms consisted of punctiform stipples with necrotic cells (hypersensitive response, HR), whereas, in P. avium and V. lantana, reddening developed, revealing the accumulation of anthocyanins. In V. lantana, symptoms appeared earlier than in the other species; in F. excelsior, symptoms developed more rapidly and led to premature leaf abscission. In F. excelsior, at least at the beginning, the onset of symptoms was combined with an enhanced photosynthetic efficiency (compensation mechanism), whereas, in P. avium and V. lantana, this efficiency progressively decreased. The fluorescence parameters most closely connected to ozone stress were as follows: a reduction in performance index (PI ABS ) and active reaction centres (RC/CS 0 ), and an increase in the variable fluorescence relative to 30ms (V I ) and of the dissipation processes. Dissipation is a form of defence mechanism against oxidative stress and is related to the role of the deactivated reaction centres (the silent centres) as well as anthocyanins. Symptom development correlated in all three species with the reduction in reaction centres. Symptomatic leaves had a lower net photosynthetic rate (P n ). Net photosynthesis correlated with the reduction in V 1 , which suggests an accumulation of reduced plastoquinone, produced in the luminous phase of photosynthesis, which was not capable of reaching the dark phase reactions.
TCMINet: Face Parsing for Traditional Chinese Medicine Inspection via a Hybrid Neural Network With Context Aggregation

Facial medical analysis, including the inspection of the face and inner facial components, has always been a primary part of the diagnostic method in Traditional Chinese Medicine (TCM). The existing literature merely focus on detecting or segmenting single face organs such as tongue, eyes, or lips. In this paper, we make the first attempt to deal with multiple organs simultaneously and develop an end-to-end hybrid network with context aggregation (named TCMINet) to achieve face parsing for Traditional Chinese Medicine Inspection (TCMI). Additionally, we construct a new dataset named TCMID to overcome the lackness of accurate annotated data. In order to verify the generalization ability of TCMINet, we manually relabel images in two popular face parsing datasets referred to as LFW-PL★ and HELEN★ for test. The extensive ablation evaluations and experimental comparisons demonstrate that the proposed TCMINet outperforms state-of-the-art methods under various evaluation metrics. It runs at 267ms per face ( $512\times 512$ image) on Nvidia Titan Xp GPU, being possible to be integrated into engineering solutions.
Research on Medical Image Classification Based on Machine Learning

In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. Because the deep neural network needs a large number of labeled samples to complete the training, and the cost of medical image labeling is very high, the training samples needed to train the deep neural network are insufficient. In this paper, a semi supervised learning based image classification method is proposed, which uses a small amount of labeled pathological image data to train the network model, and then integrates the features extracted by the network to classify the image. The results show that the classification effect of the neural network is better than convolution neural network and other traditional image classification models. To some extent, it can reduce the dependence of neural network on a large number of training samples, and effectively reduce the over fitting phenomenon of the network. Through the analysis of the overall classification accuracy and kappa coefficient of different classification methods under different sample numbers, it is found that the overall classification accuracy and kappa coefficient are increasing with the increasing number of training samples. Especially in the case of a small number of training samples, compared with other deep neural networks and traditional classification methods, the classification accuracy of the counter neural network is about 10% higher than that of other neural networks and traditional classification methods, and the advantages are more obvious.
Methodology for Maximizing Information Transmission of Haptic Devices: A Survey

This is a survey article on the information transmission capability of haptic devices and ways of maximizing it. It is intended for readers who are engineering professionals interested in developing novel haptic interfaces for a variety of applications but are not necessarily trained in haptic science or human user research. We posit that the ultimate goal of any interface is the exchange of information between a machine and a user, and as such, the evaluation should involve estimating the information-transmission capability with human users. We conducted a literature survey on studies of haptic devices evaluated with human users using an information theoretic framework. Our goal was to discover and summarize best practices that can lead to high information transmission. The results confirmed findings from our own previous studies, uncovered new ways to effectively increase information transmission, and pointed to the need for broader dissemination of proper experimental methodology. We, therefore, present a concise yet comprehensive tutorial on psychophysical methodology for estimating information transfer (IT) and IT rate with humans, survey results on the typical IT achievable with haptic devices, and guidelines for maximizing information transmission with any human–machine interfaces. Although we focus on haptic systems, the information-theoretic framework, the psychophysical methods, and the guidelines presented in this article are applicable to other sensory modalities and multimodal interface systems also.
Forwarding Behavior Prediction Based on Microblog User Features

In microblog networks, when a user posts a microblog, other users may forward the post, and then the forwarding process will bring about the rapid dissemination and diffusion of information. In this paper, we propose a comprehensive and novel approach to predict user forwarding behavior. Firstly, we build the feature sets that affect the microblog forwarding, such as interest topic,geographic location, user aggregation coefficient,neighborhood overlap and so on. These features are classified into four categories: user characteristics, microblog features, network structure features, and interactive behavior characteristics. Secondly, we establish a feature selection model based on Filtering and Wrapping for predicting the forwarding behavior of users. The model includes three aspects: (1)ANOVA(Analysis of variance): The value of each feature is analyzed by variance analysis. If the feature variance is small, the feature provides less information. (2) $\chi ^{2}$ test and point-two-column correlation analysis: They filter discrete and continuous features, respectively. (3)Wrapper analysis: In order to solve the strong correlations between the features, we use LVW(Las vagas wrapper) algorithm to analyze the above feature sets, and then obtain the optimal feature combination. Finally, we propose the forwarding prediction model based on AdaBoost(Adaptive boosting) algorithm. Experimental results demonstrate that the model has the highest precision and F1 score than Naive Bayes, Logistic Regression, Random Forest and SVM(Support vector machine), and the F1 score reached 0.885. Among different topics, our proposed AdaBoost prediction model has good recall and F1 scores for different topics. In addition, by using different feature sets for comparison experiments, it is found that the optimal features selected in this paper are very effective.
7.4-Gbit/s Visible-Light Communication Utilizing Wavelength-Selective Semipolar Micro-Photodetector

Visible light communication (VLC) has drawn significant attention in recent years. Though high-speed visible-light sources have seen significant advances, commercially available photodetectors have low wavelength selectivity and modulation bandwidth in the near-violet-blue wavelengths, making them a bottleneck in VLC links. Here we show a record 7.4-Gbit/s visible-light communication link using a wavelength-selective, (<inline-formula> <tex-math notation="LaTeX">$\mathbf {20}\overline {21}$ </tex-math></inline-formula>)-oriented, semipolar InGaN/GaN multiple-quantum-well micro-photodetector (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PD) on GaN substrate. This is achieved by leveraging on the unique photodetection properties of semipolar <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PDs, combined with an optimized communication system utilizing bit- and power-loading schemes in orthogonal frequency-division multiplexing (OFDM) modulation over a 2-GHz bandwidth. We used a 405-nm violet laser diode transmitter as the responsivity of the <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PD was highest within the responsivity range of 360 - 420 nm. The investigation fully demonstrated the feasibility and favorable choice of semipolar InGaN/GaN <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PDs for multi-Gbit/s optical wireless communication.
Advertising in the Non-profit Sector: An Analysis of the Reception of Non-commercial Advertising in Focus Group Discussions

This article looks at the use of advertising in the non-profit sector, which is a new phenomenon in Czech society. Using the focus group method, the author aims to examine the reception of this type of advertising among various social groups in Czech society. The analysis is based upon the axiom of the dialogic character of every semiotic act. It notes the conditions under which the offer of dialogue from the side of the advertisers, which is conveyed through the advertising campaign, is accepted or refused by the focus group members. In the analysis the respondents did in fact interpret non-commercial advertisement as having dialogic relevance, given their deliberations on who was saying what to whom and why. Special emphasis is placed in the article on methodological issues. It is argued that focus group discussions represent a specific type of communicative situation and, therefore, it is necessary to consider the specifics of this communicative situation when analysing data and interpreting results. Sociologický časopis/Czech Sociological Review, 2006, Vol. 42, No. 2: 379–402
Food selection experiments with young geese.

Young Canada geese (Branta canadensis) and blue geese (Chen caerulescens) were tested experimentally for food preferences among six speces of grasses and sedges. Fifty-cm2 sods of two plant species were presented to two goslings in a test enclosure. The time intervals that individual birds spent feeding on each sod during a 90-minute test were recorded. Two pairs of goslings of each species were tested against all pair combination of the six plants. Each species show significant preference for some plants over others, and the preferences of the two species were demonstrably different. Faults in experimental design are discussed, and it is shown that results cannot be extended directly to free-ranging wild geese. Five species of geese nest on the coastal plains and larger islands of western and northern Hudson Bay. During the brood rearing period, two or more species may frequently be observed grazing together, apparently from a common food supply. This experiment, part of a broad study of food resources and their use by nesting geese, was designed to discover if young Canada and blue geese developed preferences for a restricted range of the available plant species, and, if so, whether the preferences differed between species. In food preference studies involving seeds, it is standard procedure to present test animals weighed amounts of seeds and either to record the order of disappearance (Michael and Beckwith 1955), or the weight differences after a given period of time (McFarland and George 1966). Because both species of geese were eating aerial parts of green vegetation, neither of these approaches was deemed suitable for the present test. In order to maintain the most natural conditions, geese were presented with fresh intact plants still rooted in sod. Although the six plant species tested were all identified in the feces of free-ranging geese, the principal criterion for choosing these species was their availability in sufficient pure stands to provide sods for the experiment. Description of the test plants appears in Table 1. This study was supported by contracts from the Canadian Wildlife Service, and by grants from the National Research Council of Canada, The Wildlife Management Institute, and the Arctic Institute of North America. The generous support of all these agencies is gratefully acknowledged. We wish also to thank R. Jones, T. T. McNulty, M. Harvey, P. Prevett, and W. Baumann or their assistance during the experiments. Special thanks are due to H. Boyd who reviewed manuscript.
Parallel Finite Element Computation of Time-Varying Ionized Field Around Hybrid AC/DC Lines via Fine-Grained Domain Decomposition

The time-varying hybrid ionized field around HVAC and HVDC transmission lines is a computationally demanding problem due to the coupling of the Poisson’s equation and current continuity equation, as well as the involvement of large matrix in traditional Galerkin finite element method (FEM). In this paper, a fine-grained nodal domain decomposition (NDD) scheme, which enables each sub-domain with only one unknown to be solved independently in a massively parallel fashion, was employed to solve the Poisson’s equation. Meanwhile, an upwind nodal charge conservation (NCC) method is applied to solve the current continuity equation without numerical oscillation at each finite element nodal level. The computation of NDD and NCC can both be vectorized and mapped to massive computational cores and utilize the computing power of graphics processor units (GPUs). The interaction between HVAC and HVDC was solved without the Deutsch’s assumption to guarantee the accuracy, and the wind influence can be considered. With the massively parallel NDD scheme and NCC scheme, both the Poisson’s equation and the current continuity equation were solved at each time-step on GPUs to obtain the transient details of the hybrid ionized field. The performance of the proposed method is tested and compared with commercial software, showing a speedup of 17 times for an 8184-node finite element case with a mean relative error of 0.07%.
Performance Analysis of Mixed Interference Aligned MIMO RF/Unified FSO DF Relaying With Heterodyne Detection and Two IMDD Models

This paper considers a dual-hop decode-and-forward (DF) mixed radio frequency (RF)/free space optical (FSO) relaying scheme. In this scheme, the first hop is a multiple-input multiple-output (MIMO) RF interference channel (IC), while the second hop is an FSO channel in which heterodyne detection (HD) as well as intensity modulation-direct detection (IMDD) are considered in a unified analysis manner. It is assumed that the MIMO RF links follow Rayleigh fading, whilst the FSO link encounters Málaga ( $\mathcal {M}$ ) fading with pointing errors. In this work, the Interference alignment (IA) technique is utilized in the MIMO RF IC hop to eliminate the interference, and hence this leads to enhance the multiplexing gain in this hop, and accordingly the overall system performance is improved. The design of the IA precoding matrix and receiving interference eliminating matrix is performed by using adapting orthogonalized minimum leakage (OR-ML) algorithm. For the considered scheme, exact closed-form expressions are derived for the outage probability and ergodic capacity, in these expressions the best-known FSO channel capacity results are utilized. Moreover, not only the IMDD input-independent AWGN but also the IMDD cost-dependent AWGN channel is analyzed in these investigations. In addition, the system performance is studied asymptotically at the high SNRs region, where the diversity order and coding gain analyses are illustrated. Further, we allocate simulation results that verify the deduced analytical and asymptotic expressions.