As an intuitive description of complex physical, social, or brain systems, complex networks have fascinated scientists for decades. Recently, to abstract a network's topological and dynamical ...attributes, network representation has been a prevalent technique, which can map a network or substructures (like nodes) into a low-dimensional vector space. Since its mainstream methods are mostly based on machine learning, a black box of an input-output data fitting mechanism, the learned vector's dimension is indeterminable and the elements are not interpreted. Although massive efforts to cope with this issue have included, say, automated machine learning by computer scientists and learning theory by mathematicians, the root causes still remain unresolved. Consequently, enterprises need to spend enormous computing resources to work out a set of model hyperparameters that can bring good performance, and business personnel still finds difficulties in explaining the learned vector's practical meaning. Given that, from a physical perspective, this article proposes two determinable and interpretable node representation methods. To evaluate their effectiveness and generalization, this article proposes Adaptive and Interpretable ProbS (AIProbS), a network-based model that can utilize node representations for link prediction. Experimental results showed that the AIProbS can reach state-of-the-art precision beyond baseline models on some small data whose distribution of training and test sets is usually not unified enough for machine learning methods to perform well. Besides, it can make a good trade-off with machine learning methods on precision, determinacy (or robustness), and interpretability. In practice, this work contributes to industrial companies without enough computing resources but who pursue good results based on small data during their early stage of development and who require high interpretability to better understand and carry out their business.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As ...for alleviating the sparsity and cold start problems encountered by recommender systems, researchers generally resort to employing side information or knowledge in recommendation as a strategy for uncovering hidden (indirect) user-item relations, aiming to enrich observed information (or data) for recommendation. However, in the face of the high complexity and large scale of side information and knowledge, this strategy largely relies for efficient implementation on the scalability of recommendation models. Not until after the prevalence of machine learning did graph embedding techniques be a recent concentration, which can efficiently utilize complex and large-scale data. In light of that, equipping recommender systems with graph embedding techniques has been widely studied these years, appearing to outperform conventional recommendation implemented directly based on graph topological analysis (or resolution). As the focus, this article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs and knowledge graphs, and proposes a general design pipeline of that. In addition, after comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models on simulations, this article manifests that the conventional models can still overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the comparative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and conventional recommendation in different tasks, and puts forward some open questions.
Compared with continuous-time memristor (CM), discrete memristor (DM) has not been received adequate attention. In this paper, a new
n-
dimensional generalized DM model is proposed based on the ...discrete theory. Two 2-D discrete mathematical models satisfying the three fingerprints characteristics of memristors are designed. Applying the mathematical model into the Sine map yields a new hyperchaotic map called discrete memristor-based Sine (DM-S) map. The DM-S map has a line of fixed points, and its dynamical behaviors including nonparametric bifurcation and hyperchaos are explored by phase diagrams, bifurcation diagrams, and Lyapunov exponent spectrums. The
i
–
v
characteristics of the DM and the attractors of the DM-S map are implemented by digital signal processor. In addition, the sequences of map are tested by using SP800-22 NIST software.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Fragile X syndrome (FXS) is the most common inherited form of intellectual disability and the leading monogenic cause of autism. The condition stems from loss of fragile X mental retardation protein ...(FMRP), which regulates a wide range of ion channels via translational control, protein-protein interactions and second messenger pathways. Rapidly increasing evidence demonstrates that loss of FMRP leads to numerous ion channel dysfunctions (that is, channelopathies), which in turn contribute significantly to FXS pathophysiology. Consistent with this, pharmacological or genetic interventions that target dysregulated ion channels effectively restore neuronal excitability, synaptic function and behavioural phenotypes in FXS animal models. Recent studies further support a role for direct and rapid FMRP-channel interactions in regulating ion channel function. This Review lays out the current state of knowledge in the field regarding channelopathies and the pathogenesis of FXS, including promising therapeutic implications.
Full text
Available for:
GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time ...financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.
Protein-protein interactions (PPIs) are essential for most biological processes. However, current PPI networks present high levels of noise, sparseness and incompleteness, which limits our ability to ...understand the cell at the system level from the PPI network. Predicting novel (missing) links in noisy PPI networks is an essential computational method for automatically expanding the human interactome and for identifying biologically legitimate but undetected interactions for experimental determination of PPIs, which is both expensive and time-consuming. Recently, graph convolutional networks (GCN) have shown their effectiveness in modeling graph-structured data, which employ a 1-hop neighborhood aggregation procedure and have emerged as a powerful architecture for node or graph representations. In this paper, we propose a novel node (protein) embedding method by combining GCN and PageRank as the latter can significantly improve the GCN's aggregation scheme, which has difficulty in extending and exploring topological information of networks across higher-order neighborhoods of each node. Building on this novel node embedding model, we develop a higher-order GCN variational auto-encoder (HO-VGAE) architecture, which can learn a joint node representation of higher-order local and global PPI network topology for novel protein interaction prediction. It is worth noting that our method is based exclusively on network topology, with no protein attributes or extra biological features used. Extensive computational validations on PPI prediction task demonstrate our method without leveraging any additional biological information shows competitive performance-outperforms all existing graph embedding-based link prediction methods in both accuracy and robustness.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Deep learning (DL) is an emerging and powerful paradigm that allows large-scale task-driven feature learning from big data. However, typical DL is a fully deterministic model that sheds no light on ...data uncertainty reductions. In this paper, we show how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation. The bulk of the proposed fuzzy system is a hierarchical deep neural network that derives information from both fuzzy and neural representations. Then, the knowledge learnt from these two respective views are fused altogether forming the final data representation to be classified. The effectiveness of the model is verified on three practical tasks of image categorization, high-frequency financial data prediction and brain MRI segmentation that all contain high level of uncertainties in the raw data. The fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.
Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable ...deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.
Display omitted
•AMB can be prepared by simultaneous calcination, magnetization, and activation.•AMB has the large surface area and can be conveniently recycled.•AMB showed the strong adsorption of ...norfloxacin.•AMB had a good adsorption effect on norfloxacin in a wide pH range.
Activated magnetic biochar (AMB) was prepared with corn stalks, reed stalks, and willow branches by simultaneous carbonization, magnetization, and activation, and used for norfloxacin removal in water. The exploration results showed that the zeta potential was positively charged at pH 2–10. These prepared activated magnetic biochars have a large specific surface area (>700m2·g−1) and pore volume (>0.3cm3·g−1). The quasi-second-order kinetic adsorption equation could better describe the adsorption of NOR on AMB. The Langmuir isotherm showed the better fitting results on AMB. The AMB showed the strong adsorption of NOR, and the saturated adsorption capacity of corn activated magnetic biochar was the highest, 7.6249mg·g−1. The adsorption of NOR on AMB was a spontaneous endothermic process. The effect of pH on the adsorption behaviors of NOR on AMB was not obvious, and AMB had a good adsorption effect on NOR in a wide pH range.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Here, Selective C3-formylation of indole was achieved under mild conditions using a metal-organic framework (MOF) catalyst. The confined reaction space within the MOF pores effectively suppressed ...undesired side reactions and promoted the formation of the targeted product by controlling the reaction pathway. Density functional theory (DFT) calculations corroborated the experimental observations.
MOF confinement catalysis can hinder the reaction of orthoformate and indole to form the trimethoxymethane compounds, thereby creating indole C3 formaldehyde compounds.
PDF
