Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-driven Animation of Noninterpenetrating Mesh Ensembles

Simulating realistic, physically based, deformable motions, such as those of ﬂowers, can be time consuming, especially for meshes undergoing complex contact behaviors with noninterpenetration constraints. Although such simulations are well-understood in graphics, their wide-spread use in interactive applications remains limited by computing costs. We introduce the Ensemble Motion Graph (EMG) algorithm for data-driven animation of dynamic, deformable, mesh ensembles, that is particularly well-suited to stochastic, tree-structured scenes. EMGs permit motion transitions to occur asynchronously within the scene, thereby decreasing transition errors, and diffusing them in space and time. Unfortunately, asynchronous transitions also introduce highly undesirable, nonphysical interpenetrations between close-proxity and/or interacting mesh groups. To address this fundamental problem of data-driven mesh animation, mesh groups transition at times determined by the solution to a nonlinear in-teger programming problem involving geometric noninterpenetra-tion constraints. Our main contribution is an efﬁcient, randomized algorithm to solve these integer programs efﬁciently by exploiting the functional structure of the objective and collision constraints. We present several complex examples of interpenetration-free, data-driven animation of contact-intensive, physically based motion datasets at interactive rates.
Sequence Prediction Model for Aspect-Level Sentiment Classification

Aspect-level sentiment classiﬁcation aims to distinguish the sentiment polarity of each aspect in a given sentence. It is more complex than text-level sentiment classiﬁcation in that it is a ﬁne-grained task. Existing methods, which formulate this task as predicting the sentiment polarity of a provided (sentence, aspect) pair, tend to ignore the relationship between the sentiment polarity of aspects. In this paper, we propose a sequence prediction model with a sentiment polarity fusion module which sequentially predicts the sentiment polarity of each aspect within sentence. Besides, we use the temporal attention mechanism to keep track of what has been focused on, which discourages repeated attention to the context words with strong sentiment polarity when predicting the sentiment polarity of different aspects. Experimental results on ﬁve benchmarking collections illustrate that our proposed model 3 outperforms a range of base-line models by a substantial margin, and further demonstrate that the relationship between the sentiment polarity of aspects is helpful to solve the aspect-level sentiment classiﬁcation.
Object Allocation and Positive Graph Externalities

The worth of an entity does not only come from its intrinsic value. The other entities in the neighborhood also inﬂuence this quantity. We introduce and study a model where some heterogeneous objects have to be placed on a network so that the elements with high value may exert a positive externality on neighboring elements whose value is lower. We aim at maximizing this positive inﬂuence called graph externality . By exploiting a connection with the minimum dominating set problem, we prove that the problem is NP -hard when the maximum degree is 3, but polynomial time solv-able when the maximum degree is 2. We also present exact and approximation algorithms for special cases. In particular, if only two valuations exist, then a natural greedy strategy, which works well for maximum coverage problems, leads to a constant approximation al-gorithm. With extensive numerical experiments we ﬁnally show that a greedy algorithm performs very well for general valuations.
Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

On the purpose of detecting communities, many algorithms have been proposed for the disjointed community sets. The major challenge of detecting communities from the real-world problems is to determine the overlapped communities. The overlapped vertices belong to some communities, so it is difficult to be detected using the modularity maximization approach. The major problem is that the overlapping structure barely be found by maximizing the fuzzy modularity function. In this paper, we firstly introduce a node weight allocation problem to formulate the overlapping property in the community detection. We propose an extension of modularity, which is a better measure for overlapping communities based on reweighting nodes, to design the proposed algorithm. We use the genetic algorithm for solving the node weight allocation problem and detecting the overlapping communities. To fit the properties of various instances, we introduce three refinement strategies to increase the solution quality. In the experiments, the proposed method is applied on both synthetic and real networks, and the results show that the proposed solution can detect the nontrivial valuable overlapping nodes which might be ignored by other algorithms.
JND-GAN: Human-Vision-Systems Inspired Generative Adversarial Networks for Image-to-Image Translation

Image-to-image translation aims to learn the mapping between two visual domains. At the beginning of designing the existing image-to-image translation method, it was not considered whether the generated image is realistic or not. In this work, we present a novel approach to address the problem of generating ﬁ-delity in the area of image-to-image translation. In particular, humans judge whether an image is realistic or not with unique human vision’s feeling rather than paying attention to the real-world semantics. Inspired by this, we propose an effective network loss to capture the pixel-level representations and human vision system information for verisimilar image-to-image translation. To enforce both structural and translation-model consistency during adaptation, we propose a novel Just-Noticeable-Difference loss based on a visual recognition task. The Just-Noticeable-Difference loss not only guides the over-all representation to be discriminative, but also enforces our cycle loss before and after mapping between domains. Qualitative results show that our model can generate realistic images on a wide range of tasks without paired training data. For quantitative comparisons, we measure realism with user study and diversity with a perceptual distance metric. We apply the proposed model to domain adaptation and show competitive performance when compared to the state-of-the-art on many datasets.
Thermodynamics in Ecology—An Introductory Review

How to predict the evolution of ecosystems is one of the numerous questions asked of ecologists by managers and politicians. To answer this we will need to give a scientific definition to concepts like sustainability, integrity, resilience and ecosystem health. This is not an easy task, as modern ecosystem theory exemplifies. Ecosystems show a high degree of complexity, based upon a high number of compartments, interactions and regulations. The last two decades have offered proposals for interpretation of ecosystems within a framework of thermodynamics. The entrance point of such an understanding of ecosystems was delivered more than 50 years ago through Schrödinger’s and Prigogine’s interpretations of living systems as “negentropy feeders” and “dissipative structures”, respectively. Combining these views from the far from equilibrium thermodynamics to traditional classical thermodynamics, and ecology is obviously not going to happen without problems. There seems little reason to doubt that far from equilibrium systems, such as organisms or ecosystems, also have to obey fundamental physical principles such as mass conservation, first and second law of thermodynamics. Both have been applied in ecology since the 1950s and lately the concepts of exergy and entropy have been introduced. Exergy has recently been proposed, from several directions, as a useful indicator of the state, structure and function of the ecosystem. The proposals take two main directions, one concerned with the exergy stored in the ecosystem, the other with the exergy degraded and entropy formation. The implementation of exergy in ecology has often been explained as a translation of the Darwinian principle of “survival of the fittest” into thermodynamics. The fittest ecosystem, being the one able to use and store fluxes of energy and materials in the most efficient manner. The major problem in the transfer to ecology is that thermodynamic properties can only be calculated and not measured. Most of the supportive evidence comes from aquatic ecosystems. Results show that natural and culturally induced changes in the ecosystems, are accompanied by a variations in exergy. In brief, ecological succession is followed by an increase of exergy. This paper aims to describe the state-of-the-art in implementation of thermodynamics into ecology. This includes a brief outline of the history and the derivation of the thermodynamic functions used today. Examples of applications and results achieved up to now are given, and the importance to management laid out. Some suggestions for essential future research agendas of issues that needs resolution are given.
Possible Controllability of Control Argumentation Frameworks

The recent Control Argumentation Framework (CAF) is a generalization of Dung's Argumentation Framework which handles argumentation dynamics under uncertainty; especially it can be used to model the behavior of an agent which can anticipate future changes in the environment. Here we provide new insights on this model by defining the notion of possible controllability of a CAF. We study the complexity of this new form of reasoning for the four classical semantics, and we provide a logical encoding for reasoning with this framework.
Unsupervised Adversarial Learning of Anomaly Detection in the Wild

Unsupervised learning of anomaly detection in high-dimensional data, such as images, is a challenging problem recently subject to intense research. Through careful modelling of the data distribution of normal samples, it is possible to detect deviant samples, so called anomalies. Generative Adversarial Networks (GANs) can model the highly complex, high-dimensional data distribution of normal image samples, and have shown to be a suitable approach to the problem. Previously published GAN-based anomaly detection methods often assume that anomaly-free data is available for training. However, this assumption is not valid in most real-life scenarios, a.k. a. in the wild. In this work, we evaluate the effects of anomaly contaminations in the training data on state-of-the-art GAN-based anomaly detection methods. As expected, detection performance deteriorates. To address this performance drop, we propose to add an additional encoder network already at training time and show that joint generator-encoder training stratiﬁes the latent space, mitigating the problem with contaminated data. We show experimentally that the norm of a query image in this stratiﬁed latent space becomes a highly signiﬁcant cue to discriminate anomalies from normal data. The proposed method achieves state-of-the-art performance on CIFAR-10 as well as on a large, previously untested dataset with cell images.
Tutor-Instructing Global Pruning for Accelerating Convolutional Neural Networks

Model compression and acceleration has recently received ever-increasing research attention. Among them, ﬁlter pruning shows a promising effectiveness, due to its merits in signiﬁ-cant speedup for inference and support on off-the-shelf computing platforms. Most existing works tend to prune ﬁlters in a layer-wise manner, where networks are pruned and ﬁne-tuned layer by layer. However, these methods require intensive computation for per-layer sensitivity analysis and suffer from accumulation of pruning errors. To address these challenges, we propose a novel pruning method, namely T utor-I nstructing global P runing (TIP), to prune the redundant ﬁlters in a global manner. TIP introduces Information Gain (IG) to estimate the contribution of ﬁlters to the class probability distributions of network output. The motivation of TIP is to formulate ﬁlter pruning as a minimization of the IG with respect to a group of pruned ﬁlters under a constraint on the size of pruned network. To solve this problem, we propose a Taylor-based approximate algorithm, which can efﬁciently obtain the IG of each ﬁlter by backpropagation. We comprehensively evaluate our TIP on CIFAR-10 and ILSVRC-12. On ILSVRC-12, TIP reduces FLOPs for ResNet-50 by 54.13% with only a drop in top-5 accuracy by 0.1%, which signiﬁcantly outperforms the state-of-the-art methods.
Bounds on the Sum-Rate of MIMO Causal Source Coding Systems with Memory under Spatio-Temporal Distortion Constraints

In this paper, we derive lower and upper bounds on the OPTA of a two-user multi-input multi-output (MIMO) causal encoding and causal decoding problem. Each user’s source model is described by a multidimensional Markov source driven by additive i.i. d. noise process subject to three classes of spatio-temporal distortion constraints. To characterize the lower bounds, we use state augmentation techniques and a data processing theorem, which recovers a variant of rate distortion function as an information measure known in the literature as nonanticipatory ϵ-entropy, sequential or nonanticipative RDF. We derive lower bound characterizations for a system driven by an i.i. d. Gaussian noise process, which we solve using the SDP algorithm for all three classes of distortion constraints. We obtain closed form solutions when the system’s noise is possibly non-Gaussian for both users and when only one of the users is described by a source model driven by a Gaussian noise process. To obtain the upper bounds, we use the best linear forward test channel realization that corresponds to the optimal test channel realization when the system is driven by a Gaussian noise process and apply a sequential causal DPCM-based scheme with a feedback loop followed by a scaled ECDQ scheme that leads to upper bounds with certain performance guarantees. Then, we use the linear forward test channel as a benchmark to obtain upper bounds on the OPTA, when the system is driven by an additive i.i. d. non-Gaussian noise process. We support our framework with various simulation studies.
SETNet: A Novel Semi-Supervised Approach for Semantic Parsing

In this work, we study on semi-supervised semantic parsing under a multi-task learning framework to alleviate limited performance caused by limited annotated data. Two novel strategies are proposed to leverage unlabeled natural language utterances. The ﬁrst one takes entity predicate sequences as training targets to enhance representation learning. The second one extends Mean Teacher to seq2seq model and generates more target-side data to improve the generalizability of decoder network. Different from original Mean Teacher, our strategy produces hard targets for the student decoder and update the decoder weights instead of the whole model. Experiments demonstrate that our proposed methods signiﬁcantly outperform the supervised baseline and achieve more impressive improvement than previous methods.
Effects of hexaflumuron on phenoloxidase activity in Spodoptera litura ( Fabricius) ( Lepidoptera: Noctuidae)

[Objective]To investigate the effects of hexaflumuron on phenoloxidase ( PO) activity in the common cutworm,Spodoptera litura ( Fabricius) larvae. [Methods] The activities of phenoloxidase directly reacted in vitro with hexaflumuron and that from hexaflumuron-treated 5th instar larvae of S. litura were assayed with enzyme kinetics methods. [Results]The optimum reaction conditions of PO were pH 6. 5 and best working temperature 30℃ by using catechol as substrate. Hexaflumuron could directly combine with enzyme protein,but it was not chelated with Cu2 + in the active center of PO. The PO activity in vitro increased when hexaflumuron concentrations were below 1 153 mg/L,reached its highest level at 138 mg/L and was inhibited above 1 153 mg/L. After the 5th instar larvae were continuously treated with two sublethal concentrations ( 46 mg/L and 92 mg/L) of hexaflumuron for different time,the PO activity was significantly higher than that of the control at the same treatment time and the activation rates at high concentrations were larger than that at low concentrations. The PO activity in the hemolymph,cuticle and head of the 5th instar larvae increased with treatment time. The activation rate of PO at the same treatment time was the highest in the hemolymph and the lowest in the head. After the 5th instar larvae were continuously treated with hexaflumuron,PO activities in prepupae and pupae were also significantly enhanced. [Conclusions]Hexaflumuron can activate phenoloxidase activity in S. litura.
The German Far-right on YouTube: An Analysis of User Overlap and User Comments

ABSTRACT This study focuses on the formation of far-right online communities on YouTube and whether the rise of three new actors (Pegida, Identitarian movement, AfD) can also be observed with user behavior on YouTube. We map the network of far-right, conspiracy and alternative media channels in the German-language YouTube sphere, how this network evolves over time and identify the topics that users discuss. Our analysis shows that the overall common denominator within the German far-right YouTube sphere is the refugee crisis and the problems associated with it. Furthermore, we show that the community is getting denser and more centralized over time.
DRIS norms and limiting nutrients in banana cultivation in the South of Ecuador

Abstract Foliar analysis is an effective method to diagnose the nutritional status of plants. However, the mineral concentration in foliar tissue has traditionally been evaluated by assessing the activity of each element, without considering the interactions between them. To address this, dual interactions were calculated using the Diagnosis and Recommendation Integrated System (DRIS) to identify which crop nutrients are most influential in nutrient imbalances and which are the most limiting nutrients for the nutritional status of banana crops in Ecuador. To achieve this, a regional survey of the nutritional status and its productivity levels was conducted for 188 different sites during the crop season in 2017–2018, involving banana cultivars ‘Vallery’ and ‘Williams’, from the Cavendish subgroup. The DRIS calculation method was combined with Beaufils and Jones functions. From the initial 188 foliar samples, 83 samples (representing 44% of the population) were considered to represent the high-yield reference population, with yields of 38–60 t ha−1. The DRIS method defined the mean nutritional balance index, which was not found to be statistically correlated (p > 0.05) with productivity, revealing that there was no significant association with the nutritional status of the plants. Specific DRIS norms were obtained and indicated that deficiencies in K, N, Ca, and Fe, and excesses in Mn, B, Cl, Zn, S, Cu, and Mg were the most limiting nutrients for banana cultivars in the south of Ecuador.
Efficacy of Customized Foot Orthoses in Children Aged 6 - 7 Years Old with Symptomatic Flexible Flat Foot (SFFF)

Introduction: Pediatric Flat Foot (PFF) is a deviation consisting of loss of the Medial Longitudinal Arch in children. Purpose: Aim of this study is to collect information on the recent literature and to investigate through clinical assessment and parental observations the effectiveness of the use of foot orthotics by children with SFFF. Method: 20 children aged 6 - 7 years old with mobility difficulties were evaluated before and after a six-month treatment with orthotics regarding pain, post-game pain, fatigue during game, balance impairment, gait deviations, falls, clumsiness, activity avoidance and worn soles. Results: Study results indicate that foot orthotics (FOs) appears to have a positive impact on the advance of pain, post game pain, gait deviations and worn insoles. It is implied that orthoses have a great potential as a treatment for SFFF. Thorough comprehension of the literature evidence, as well as composition of supplementary studies of larger pediatric populations is essential in order to reach a consensus on the use of foot orthotics (FOs) by children SFFF.
Detection of clusters of lung cancer incidence in Idaho with ecological adjustment for smoking, radon, fine particulate air pollution, elevation, and socioeconomic position

Background The purpose of this study was to better understand spatial patterns in lung cancer incidence in the U.S. state of Idaho using a combination of multilevel modeling of area‐level risk factors and a cluster detection method. Methods Poisson multilevel mixed models were fitted to model the incidence counts of lung cancer at the geographical level of census tract. Six separate models of increasing complexity were estimated. For each set of model results, discrete Poisson scan statistics were calculated using SaTScan to identify spatial clusters. Results Accounting for population sizes by age group and sex, but not for additional risk factors, SaTScan identified thirteen statistically significant clusters. Using the estimated tract‐level counts from the final model, SaTScan identified two statistically significant clusters of census tracts. Conclusions Most of the differences in census tract‐level lung cancer incidence in Idaho were explained by area‐level measurements of known risk factors.
Learning Classifier Chains Using Matrix Regularization: Application to Multimorbidity Prediction

We study a problem of learning classiﬁer chains in multi-label classiﬁcation with a special focus on feature selection. It turns out that standard classiﬁer chains tend to select too many features, when feature selection method is embedded in base learner, which is due to the fact that selection is performed separately for each of the models in the chain. This can be a serious limitation in domains where the acquisition of feature values is costly or where including too many features (e.g. diagnostic tests) is associated with negative effects. We propose a novel method parCC (parsimonious classiﬁer chains) that controls the total number of features without signiﬁcant deterioration in the quality of the prediction. In the proposed method we jointly learn all models in the chain by combining (cid:96) 2 , 1 regularization to select features shared across the models and (cid:96) 1 regularization to select relevant labels in each model. In theoretical analysis we provide a bound on generalization error for the algorithm using Rademacher complexity. We apply our method to predict multimor-bidity (co-occurrence of multiple diseases in one patient) using various medical diagnostic tests. The experiments carried out on a large clinical database (MIMIC III) show that parCC achieves higher accuracy than related methods when the number of features is limited. We also demonstrate the efﬁcacy of the proposed method on a set of standard benchmark datasets.