Miracle-dz/UniFAR-FTUs huggingface dataset:
TOR signaling in plants: conservation and innovation

ABSTRACT Target of rapamycin (TOR) is an evolutionarily conserved protein kinase that plays a central role in both plants and animals, despite their distinct developmental programs and survival strategies. Indeed, TOR integrates nutrient, energy, hormone, growth factor and environmental inputs to control proliferation, growth and metabolism in diverse multicellular organisms. Here, we compare the molecular composition, upstream regulators and downstream signaling relays of TOR complexes in plants and animals. We also explore and discuss the pivotal functions of TOR signaling in basic cellular processes, such as translation, cell division and stem/progenitor cell regulation during plant development. Summary: This Primer gives an overview of the role of TOR signaling networks in plant growth and development, highlighting similarities and differences between the function of TOR complexes in plants and animals.
Learning Out-of-Vocabulary Words in Intelligent Personal Agents

Semantic parsers play a vital role in intelligent agents to convert natural language instructions to an actionable logical form representation. However, after deployment, these parsers suffer from poor accuracy on encountering out-of-vocabulary (OOV) words, or significant accuracy drop on previously supported instructions after retraining. Achieving both goals simultaneously is non-trivial. In this paper, we propose novel neural networks based parsers to learn OOV words; one incorporating a new hybrid paraphrase generation model, and an enhanced sequence-to-sequence model. Extensive experiments on both benchmark and custom datasets show our new parsers achieve significant accuracy gain on OOV words and phrases, and in the meanwhile learn OOV words while maintaining accuracy on previously supported instructions.
Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models

In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Boolean Satisfiability (FD-SAT-Plan) as well as Binary Linear Programming (FD-BLP-Plan). Experimentally, we show the effectiveness of learning complex transition models with BNNs, and test the runtime efficiency of both encodings on the learned factored planning problem. After this initial investigation, we present an incremental constraint generation algorithm based on generalized landmark constraints to improve the planning accuracy of our encodings. Finally, we show how to extend the best performing encoding (FD-BLP-Plan+) beyond goals to handle factored planning problems with rewards.
The dilemma of knowledge use in political decision-making: National Guidelines in a Swedish priority-setting context

Abstract There is a growing recognition of the importance of evidence to support allocative policy decisions in health care. This study is based on interviews with politicians in four regional health authorities in Sweden. Drawing on theories of strategic use of knowledge, the article analyses how politicians perceive and make use of expert knowledge represented by the National Guidelines, embracing both a scientific and a political rationale. As health care is an organisation with a dual basis for legitimacy – at the same time a political and an action organisation – it affects knowledge use. We investigate how the context of health care priority setting influences the conditions for knowledge use among regional politicians. Our findings illustrate the dilemma of political decision-makers and how they prefer to use expert knowledge. The politicians use this policy instrument in a legitimising fashion, as it will fit into the current political debate on more equal care. As an instrument for resource allocation the politicians noted that ‘facts’ per se could not provide them with a sufficient basis for legitimising their governing of health care. The dualistic organisational context makes knowledge important as a political weapon in negotiations with the medical profession.
Machine Learning and Constraint Programming for Relational-To-Ontology Schema Mapping

The problem of integrating heterogeneous data sources into an ontology is highly relevant in the database field. Several techniques exist to approach the problem, but side constraints on the data cannot be easily implemented and thus the results may be inconsistent. In this paper we improve previous work by Taheriyan et al. [2016a] using Machine Learning (ML) to take into account inconsistencies in the data (unmatchable attributes) and encode the problem as a variation of the Steiner Tree, for which we use work by De Uña et al. [2016] in Constraint Programming (CP). Combining ML and CP achieves state-of-the-art precision, recall and speed, and provides a more flexible framework for variations of the problem.
Generative Warfare Nets: Ensemble via Adversaries and Collaborators

Generative Adversarial Nets are a powerful method for training generative models of complex data, where a Generator and a Discriminator confront with each other and get optimized in a two-player minmax manner. In this paper, we propose the Generative Warfare Nets (GWN) that involve multiple generators and multiple discriminators from two sides to exploit the advantages of Ensemble Learning. We maintain the authorities for the generators and the discriminators to enhance inter-side interactions, and utilize the mechanisms of imitation and innovation to model intra-side interactions among the generators, where they can not only learn from but also compete with each other. Extensive experiments on three natural image datasets show that GWN can achieve state-of-the-art Inception scores and produce diverse high-quality synthetic results.
Nocturnal activity in wild chimpanzees (Pan troglodytes): Evidence for flexible sleeping patterns and insights into human evolution.

OBJECTIVES We investigated occurrences and patterns of terrestrial nocturnal activity in wild chimpanzees (Pan troglodytes) and modelled the influence of various ecological predictors on nocturnal activity. METHODS Data were extracted from terrestrial camera-trap footage and ecological surveys from 22 chimpanzee study sites participating in the Pan African Programme: The Cultured Chimpanzee. We described videos demonstrating nocturnal activity, and we tested the effects of the percentage of forest, abundance of predators (lions, leopards and hyenas), abundance of large mammals (buffalos and elephants), average daily temperature, rainfall, human activity, and percent illumination on the probability of nocturnal activity. RESULTS We found terrestrial nocturnal activity to occur at 18 of the 22 study sites, at an overall average proportion of 1.80% of total chimpanzee activity, and to occur during all hours of the night, but more frequently during twilight hours. We found a higher probability of nocturnal activity with lower levels of human activity, higher average daily temperature, and at sites with a larger percentage of forest. We found no effect of the abundance of predators and large mammals, rainfall, or moon illumination. DISCUSSION Chimpanzee terrestrial nocturnal activity appears widespread yet infrequent, which suggests a consolidated sleeping pattern. Nocturnal activity may be driven by the stress of high daily temperatures and may be enabled at low levels of human activity. Human activity may exert a relatively greater influence on chimpanzee nocturnal behavior than predator presence. We suggest that chimpanzee nocturnal activity is flexible, enabling them to respond to changing environmental factors.
Progressive Generative Hashing for Image Retrieval

Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of semantic condition for the similar image generation, and simultaneously feeds the original image and its codes into the generative adversarial networks (GANs). The real images together with the synthetic ones can further help train a discriminative hashing network based on a triplet loss. By iteratively inputting the learnt codes into the hash conditioned GANs, we can progressively enable the hashing network to discover the semantic relations. Extensive experiments on the widely-used image datasets demonstrate that PGH can significantly outperforms state-of-the-art unsupervised hashing methods.
Adaptive Graph Guided Embedding for Multi-label Annotation

Multi-label annotation is challenging since a large amount of well-labeled training data are required to achieve promising performance. However, providing such data is expensive while unlabeled data are widely available. To this end, we propose a novel Adaptive Graph Guided Embedding (AG2E) approach for multi-label annotation in a semi-supervised fashion, which utilizes limited labeled data associating with large-scale unlabeled data to facilitate learning performance. Specifically, a multi-label propagation scheme and an effective embedding are jointly learned to seek a latent space where unlabeled instances tend to be well assigned multiple labels. Furthermore, a locality structure regularizer is designed to preserve the intrinsic structure and enhance the multi-label annotation. We evaluate our model in both conventional multi-label learning and zero-shot learning scenario. Experimental results demonstrate that our approach outperforms other compared state-of-the-art methods.
A Bayesian Latent Variable Model of User Preferences with Item Context

Personalized recommendation has proven to be very promising in modeling the preference of users over items. However, most existing work in this context focuses primarily on modeling user-item interactions, which tend to be very sparse. We propose to further leverage the item-item relationships that may reflect various aspects of items that guide users' choices. Intuitively, items that occur within the same "context" (e.g., browsed in the same session, purchased in the same basket) are likely related in some latent aspect. Therefore, accounting for the item's context would complement the sparse user-item interactions by extending a user's preference to other items of similar aspects. To realize this intuition, we develop Collaborative Context Poisson Factorization (C2PF), a new Bayesian latent variable model that seamlessly integrates contextual relationships among items into a personalized recommendation approach. We further derive a scalable variational inference algorithm to fit C2PF to preference data. Empirical results on real-world datasets show evident performance improvements over strong factorization models.
Hierarchical Active Learning with Group Proportion Feedback

Learning of classification models in practice often relies on nontrivial human annotation effort in which humans assign class labels to data instances. As this process can be very time consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. In this work we solve this problem by exploring a new approach that actively learns classification models from groups, which are subpopulations of instances, and human feedback on the groups. Each group is labeled with a number in [0,1] interval representing a human estimate of the proportion of instances with one of the class labels in this subpopulation. To form the groups to be annotated, we develop a hierarchical active learning framework that divides the whole population into smaller subpopulations, which allows us to gradually learn more refined models from the subpopulations and their class proportion labels. Our extensive experiments on numerous datasets show that our method is competitive and outperforms existing approaches for reducing the human annotation cost.
Temporal Belief Memory: Imputing Missing Data during RNN Training

We propose a bio-inspired approach named Temporal Belief Memory (TBM) for handling missing data with recurrent neural networks (RNNs). When modeling irregularly observed temporal sequences, conventional RNNs generally ignore the real-time intervals between consecutive observations. TBM is a missing value imputation method that considers the time continuity and captures latent missing patterns based on irregular real time intervals of the inputs. We evaluate our TBM approach with real-world electronic health records (EHRs) consisting of 52,919 visits and 4,224,567 events on a task of early prediction of septic shock. We compare TBM against multiple baselines including both domain experts' rules and the state-of-the-art missing data handling approach using both RNN and long-short term memory. The experimental results show that TBM outperforms all the competitive baseline approaches for the septic shock early prediction task.
The Trilateral Evolutionary Game of Agri-Food Quality in Farmer-Supermarket Direct Purchase: A Simulation Approach

The prevalence of opportunistic behaviors in agri-food production and circulation results in frequent quality accidents in emerging economies. Numerous researches have discussed effective countermeasures to this problem, but few of them focus on the effectiveness and stability of quality assurance systems. Owing to the bounded rationality and information asymmetry, the dynamic quality game among producers, marketers, and consumers has significant characteristics of complexity. This paper aims at discussing the farmer-supermarket direct purchase’s contributions to ensure the agri-food quality and analyzing the effectiveness, stability, and key factors of this new industrial organization. Based on the evolutionary game theory, we establish the trilateral-game payoff matrix, build up the replicator dynamic equations, and discuss possible evolutionary stable states. The simulation results show that the evolutionary system converges to desired stability faster, when the high-quality agri-food’s market premium increases and the penalty for violating quality standards increases. Furthermore, when farmers share more high-quality agri-food’s market premiums and marketers compensate more for violating the quality standards than before, the evolutionary system also converges to desired stability faster. Therefore, the quality information tracing technology, farmers and marketers’ fair distribution of profits and risks, and consumers’ capabilities to safeguard their legal rights are the three key factors to maintain the effectiveness and stability of quality assurance systems.
Redundancy-resistant Generative Hashing for Image Retrieval

By optimizing probability distributions over discrete latent codes, Stochastic Generative Hashing (SGH) bypasses the critical and intractable binary constraints on hash codes. While encouraging results were reported, SGH still suffers from the deficient usage of latent codes, i.e., there often exist many uninformative latent dimensions in the code space, a disadvantage inherited from its auto-encoding variational framework. Motivated by the fact that code redundancy usually is severer when more complex decoder network is used, in this paper, we propose a constrained deep generative architecture to simplify the decoder for data reconstruction. Specifically, our new framework forces the latent hashing codes to not only reconstruct data through the generative network but also retain minimal squared L2 difference to the last real-valued network hidden layer. Furthermore, during posterior inference, we propose to regularize the standard auto-encoding objective with an additional term that explicitly accounts for the negative redundancy degree of latent code dimensions. We interpret such modifications as Bayesian posterior regularization and design an adversarial strategy to optimize the generative, the variational, and the redundancy-resistanting parameters. Empirical results show that our new method can significantly boost the quality of learned codes and achieve state-of-the-art performance for image retrieval.
An unexpected world population variation of MCT1 polymorphism 1470T > A involved in lactate transport

ABSTRACT A common missense mutation (1470T > A) in gene SLC16A1 responsible for an amino acid substitution in protein MCT1 has been associated with differential lactate transport and hence, differences in physical performance and muscle injuries in relation to physical exercise. This study describes, for the first time, the worldwide variation of MCT1 variant 1470T > A at an intra- and inter-continental level. Two thousand five hundred and four individual genotypes of 26 populations clustered in 5 population groups have been analysed with data downloaded from the public database 1000 Genomes Phase 3 Browser. Several parameters of population differentiation and structure have been explored as well as selection signatures in the whole gene. Allele T, the common variant, shows extremely high values in Sub-Saharan African groups (frequencies 86–91%) as compared with the remaining world regions (69–49%). TT genotype also predominates in African groups, showing significant differences with the rest of world populations. In view of the evidence that the TT genotype is associated with clinical implications and a better predisposition to sprint/power performances, we suggest that the high presence of the TT genotype in African populations should be taken into account in future association studies at both medical and sports fields.
Coarse-to-fine Image Co-segmentation with Intra and Inter Rank Constraints

Image co-segmentation is the problem of automatically discovering the common objects co-occurring in a set of relevant images and segmenting them as foreground simultaneously. Although a bunch of approaches have been proposed to address this problem, many of them still suffer from certain limitations, e.g., supervised feature learning and complex models, which hinder their capability in the real-world scenarios. To alleviate these limitations, we propose a novel coarse-to-fine co-segmentation (CFC) framework, which utilizes the coarse foreground and background proposals to learn a robust similarity measure of the features in an unsupervised way, and then devises a simple objective function based on the definition of image co-segmentation. Specifically, we first generate superpixels for all the images and extract their features. Instead of using existing distance metrics, we utilize object proposal methods to generate coarse foreground and background to learn a similarity measure of superpixels to construct a robust feature similarity graph. Then we design an intuitive objective function to learn a segmentation similarity graph which should be consistent with feature similarity graph and also be able to co-segment the superpixels in the images into either foreground and background. This objective function can be further reformulated as a graph learning problem with intra and inter rank constraints. Experiments on two commonly used image datasets (iCoseg and MSRC) demonstrate that CFC outperforms other state-of-the-art methods. Notably, this performance is achieved by using only HSV feature.
A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter

The availability of massive social media data has enabled the prediction of people’s future behavioral trends at an unprecedented large scale. Information cascades study on Twitter has been an integral part of behavior analysis. A number of methods based on the transactional features (such as keyword frequency) and the semantic features (such as sentiment) have been proposed to predict the future cascading trends. However, an in-depth understanding of the pros and cons of semantic and transactional models is lacking. This paper conducts a comparative study of both approaches in predicting information diffusion with three mechanisms: retweet cascade, url cascade, and hashtag cascade. Experiments on Twitter data show that the semantic model outperforms the transactional model, if the exterior pattern is less directly observable (i.e. hashtag cascade). When it becomes more directly observable (i.e. retweet and url cascades), the semantic method yet delivers approximate accuracy (i.e. url cascade) or even worse accuracy (i.e. retweet cascade). Further, we demonstrate that the transactional and semantic models are not independent, and the performance gets greatly enhanced when combining both.
Sequential Recommender System based on Hierarchical Attention Networks

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or item-item interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones.
DEL: Deep Embedding Learning for Efficient Image Segmentation

Image segmentation has been explored for many years and still remains a crucial vision problem. Some efficient or accurate segmentation algorithms have been widely used in many vision applications. However, it is difficult to design a both efficient and accurate image segmenter. In this paper, we propose a novel method called DEL (deep embedding learning) which can efficiently transform superpixels into image segmentation. Starting with the SLIC superpixels, we train a fully convolutional network to learn the feature embedding space for each superpixel. The learned feature embedding corresponds to a similarity measure that measures the similarity between two adjacent superpixels. With the deep similarities, we can directly merge the superpixels into large segments. The evaluation results on BSDS500 and PASCAL Context demonstrate that our approach achieves a good trade-off between efficiency and effectiveness. Specifically, our DEL algorithm can achieve comparable segments when compared with MCG but is much faster than it, i.e. 11.4fps vs. 0.07fps.
Image Captioning with Visual-Semantic LSTM

In this paper, a novel image captioning approach is proposed to describe the content of images. Inspired by the visual processing of our cognitive sys-tem, we propose a visual-semantic LSTM model to locate the attention objects with their low-level features in the visual cell, and then successively extract high-level semantic features in the semantic cell. In addition, a state perturbation term is introduced to the word sampling strategy in the REIN-FORCE based method to explore proper vocabularies in the training process. Experimental results on MS COCO and Flickr30K validate the effectiveness of our approach when compared to the state-of-the-art methods.