They form a canonical representation , making testing of functional properties such as satisfiability and equivalence straightforward . A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures . Using OBDDs , a wide variety of problems can be solved through symbolic analysis . Finding an optimal solution to this problem is NP-complete . Due to the high cost of graph algorithms , it is nearly impossible to come up with close to optimal solutions that do not have very high cost ( higher order polynomial ) . Therefore , our goal is to find a heuristic that gives good performance , and that has relatively low cost . Graphs are a fundamental data representation that has been used extensively in various domains . In graph-based applications , a systematic exploration of the graph such as a breadth-first search ( BFS ) often serves as a key component in the processing of their massive data sets . Vector space model is the dominant text representation largely due to its simplicity . Graphs are becoming an alternative text representation that have the ability to capture important information in text such as term order , term co-occurrence and term relationships that are not considered by the vector space model . Substantially better text classification performance has been demonstrated for term weighting schemes which use a graph representation . We present a novel graph embedding algorithm that strikes a balance between local manifold structures and global discriminative information . A novel graph structure is designed to depict explicitly the local manifold structures of traffic signs with various appearances and to intuitively model between-class discriminative information . Through this graph structure , our algorithm effectively learns a compact and discriminative subspace . To this end , we first abstract the video by temporal superpixels . Second , we propose a novel graph structure exploiting the saliency cues to assign the edge weights . The salient segments are then extracted by applying a spectral foreground detection method , quantum cuts , on this graph . Requirements for simulation environments that facilitate multiresolution multistage model specification are introduced . A multimodel specification formalism based on graph of models is suggested along with design precepts to enable flexible dynamic model updating . The notion of multisimulation is introduced to enable exploratory simulation using various types of multimodels . Our main technical contribution is to establish a low cost for this overhead for the class of multithreaded block-resilient HBP ( Hierarchical Balanced Parallel ) computations . Using this and other techniques , we develop block-resilient HBP algorithms with low false sharing costs for several fundamental problems including scans , matrix multiplication , FFT , sorting , and hybrid block-resilient HBP algorithms for list ranking and graph connected components . Most of these algorithms are derived from known multicore algorithms , but are further refined to achieve a low false sharing overhead . In this paper , the geometric meaning of the transitive closure of a fuzzy relation in corresponding fuzzy graph is first given . An optimal algorithm , which is based on the computation of graph connected components , for fuzzy classification problem is proposed . For any given n samples , the worst case time complexity T(n ) of the algorithm satisfies that O(n)≤T(n)≤O(n 2 ) . This paper proposes a fast training method for graph classification based on a boosting algorithm and its application to sentimental analysis with input texts represented by graphs . Graph format is very suitable for representing texts structured with Natural Language Processing techniques such as morphological analysis , Named Entity Recognition , and parsing . A number of classification methods which represent texts as graphs have been proposed so far . The comparison unit can be a word , phrase , node , concept or sub-graph depending on the type of approach . Experimentation with different approaches , Text-Text , Graph-Graph , Text-Cmap ( Concept map ) has been tried , to determine a feasible solution for automating the descriptive answer evaluation process . A comparative analysis of all the three approaches has been done . According to them , the auther considers the directed graph and its invarse graph are suitable for the representation of module library structure . In this paper ; the calling graph and called graph of a module are used to describe the calling and called relationship about the modules . Finally , the basic data structure , main functions and advantages of MDLC -- a tool for managing module library are introduced . Even with the increased use of technology to connect distance/time and position/time graphs , the preservice elementary and middle school teachers still struggled with understanding what the graphs represented . Guidelines will be provided that focus on how to design lessons to address the graph misconceptions including : opportunities to interpret misunderstandings , opportunities to create a story and match the story with the graph using technology and a specific and direct connection to movement , slope and y-intercept . 1 ) . For a simple graphGE with edge set E , take independent random points xk , k?V(GE ) , on Sd , and letDE be the minimum value of the spherical distance between xi , xj for { i , j } ? E. This conventional pipeline , however , has been greatly reshaped by convolutional neural networks ( ConvNets ) , a main driver behind an explosive rise in performance across many computer vision tasks . Recent pose estimation systems have universally adopted ConvNets as their main building block , largely replacing hand - crafted features and graphical models ; this strategy has yielded drastic improvements on standard benchmarks . We continue along this trajectory and introduce a novel “ stacked hourglass ” network design for predicting human pose . Moreover , the dataset contains a large number of examples that do not involve any reentrancies , as shown in Table [ reference ] , so that the overall scores may not be representative of the ability of models to capture reentrancies . It is expected that the benefit of the graph models will be more evident for those examples containing more reentrancies . To test this hypothesis , we evaluate the various scenarios as a function of the number of reentrancies in each example , using the Meteor score as a metric . Patchy - san uses WL as one possible labeling procedure to compute receptive fields . Deep graph kernels and graph invariant kernels compare graphs based on the existence or count of small substructures such as shortest paths , graphlets , subtrees , and other graph invariants . In contrast , Patchy - san learns substructures from graph data and is not limited to a predefined set of motifs . Moreover , while all graph kernels have a training complexity at least quadratic in the number of graphs , which is prohibitive for large - scale problems , Patchy - san scales linearly with the number of graphs . Graph neural networks ( GNNs ) are a recurrent neural network architecture defined on graphs . GNNs apply recurrent neural networks for walks on the graph structure , propagating node representations until a fixed point is reached . PROTEINS is a graph collection where nodes are secondary structure elements and edges indicate neighborhood in the amino - acid sequence or in 3D space . Graphs are classified as enzyme or non - enzyme . D & D is a data set of protein structures classified into enzymes and non - enzymes . In order to solve the tasks that require more than a single step it must compress all the relevant relations into a fixed size vector , then perform the remaining relational reasoning in the last forward layers . Relational networks , interaction networks and our proposed model can all be seen as an instance of Graph Neural Networks scarselli2009graph , gilmer2017neural . Graph neural networks with message passing computations go back to . This suggests the use of a spectral formulation , based on a dual graph representing the shape , yielding bases for a function space built over the mesh . With this graph representation we face multiple challenges in building a convolutional neural architecture . One is how to share coefficients and conduct multiscale analysis in different parts of the graph for a single shape . The work of Kiefel et al . [ reference ] is based on the pictorial structures model but differs in the underlying graph representation . Hierarchical models [ reference ][ reference ] represent the relationships between parts at different scales and sizes in a hierarchical tree structure . is the matrix of activations in the layer ; . In the following , we show that the form of this propagation rule can be motivated via a first - order approximation of localized spectral filters on graphs hammond2011wavelets , defferrard2016convolutional . subsection : Spectral Graph Convolutions [ reference ] ) . Therefore , we propose a graph - based method to find cluster centers . More specifically , we build a graph of top ranking proposals according to the spatial similarity for each positive object class . Unsupervised learning methods offer one promising avenue for acquiring a concise representation of the state with a good similarity metric . This can be accomplished using dimensionality reduction , clustering , or graph - based techniques . In our work , we draw on recent developments in representation learning with neural networks , as discussed in the following section . The focus of this paper is on feature representation for dependency parsing , using recent techniques from the neural - networks ( ‘ ‘ deep learning ’ ’ ) literature . Modern approaches to dependency parsing can be broadly categorized into graph - based and transition - based parsers . Graph - based parsers treat parsing as a search - based structured prediction problem in which the goal is learning a scoring function over dependency trees such that the correct tree is scored above all other trees . document : Graph - Structured Representations for Visual Question Answering This paper proposes to improve visual question answering ( VQA ) with structured representations of both scene contents and questions . One challenge raised by GraphSAGE is how to smartly combine neighbour features based on graph structure . GAT handles this problem through attention , however the challenge with GAT is its scalability over large and dense graphs . In this work , we proposed a new architecture to address these issues that is more efficient and is capable of incorporating different edge type information . The GAT is more effective on the decrease of the individual performance variation than the single-gate TFT in low-voltage operation . Moreover , the OFF-current of the GAT is almost the same as that of the single-gate TFT with the LDD . The GAT is an attractive candidate to achieve high-density , low-voltageoperation , and low-power SRAMs . All the participants carried out several tasks with the GAT App and completed System Usability Scale ( SUS ) questionnaires afterwards . The data revealed an average SUS score of 84.06 , which indicates that the gas engineers enjoyed using the GAT App and will recommend it to their colleagues . However , there is a need for more user testing for result generalisation . This header is generated by a client-side proxy named GAP , which runs on mobile devices with Windows CE operating system ( Pocket PC , Smartphone , among others ) . The transcoding header is interpreted by a server-side proxy named GAT MT , that converts Web documents to WML , XHTML-MP , PDF , plain text , PostScript , XML and HTML adapted for mobile devices , making use of XSL technologies ( XSLT and XSL-FO ) This header is generated by a client-side Proxy named GAP , which runs on mobile devices with Windows CE operating system ( Pocket PC , Smartphone , among others ) . The transcoding header is interpreted by a server-side Proxy , named GAT MT , that converts Web documents to WML , XHTML-MP , PDF , plain text , PostScript , XML and HTML adapted for mobile devices . To do this , the mechanism uses XSL technologies ( XSLT and XSL-FO ) to define Stylesheets according to a specific device . It has the domestic independent intellectual property rights . The GAT high-accurate gyro station 's main body adopts the space technology . The GAT instrument adopts the magnetic tape technolgy . For more efficient text entry , we present the concept of gaze-assisted typing ( GAT ) , which uses both a touchpad and eye tracking . We initially examined GAT with a minimal eye input load , and demonstrated that the GAT technology was 51 % faster than a two-step touch input typing method ( i.e.,M-SwipeBoard : 5.85 words per minute ( wpm ) and GAT : 8.87 wpm ) . We also compared GAT methods with varying numbers of touch gestures . The results showed that a GAT requiring five different touch gestures was the most preferred , although all GAT techniques were equally efficient . Conventional wavelet transform ( WT ) filters have less effect on de-noising and correction of a north-seeking gyroscope sensor exposed to vibration , since the optimal wavelet decomposed level for de-noising is difficult to determine . To solve this problem , this paper proposes an optimized WT filter which is suited to the magnetic levitation gyroscope ( GAT ) . The proposed method was tested on an equivalent mock-up network of the tunnels associated with the Hong Kong‒Zhuhai‒Macau Bridge . These confirmed the accuracy of GAT as well as its lower computational cost . The numerical results obtained showed that the GAT outperformed the incomplete GAT ( IGAT ) , which is solely based on PEM and Cornish-Fisher expansion , thanks to the ability of AA to bound the outputs used in the LGSA . We provide an overview of the ways in which Triana interacts with services ( e.g. , Web and P2P services ) and then how we interact with core Grid components , such as resource managers and data management systems through the extensive use of the GridLab GAT interface . We describe in detail the GAT philosophy and implementation and then show how the various GAT primitives can be represented in an intuitive fashion within a Triana workflow . This approach , which we refer to as the Visual GAT , differs substantially from other approaches because we do not tie our implementation to any specific underlying Grid middleware technologies ; rather , we base our implementation on application level requirements and model such primitives from a user ’s perspective by hiding as much complexity as possible without undermining the core capabilities required . Ninety multi-engine instrument rated pilots were assigned to no motion , sustained linear scaled down analog motion , and washout motion in a GAT II simulator for determining the effects of degree of motion upon the predictive validity of flight simulators . Five instrument and five contact maneuvers were flown in the simulator followed by flight in a Piper Aztec aircraft . No eigendecompositions or similar costly matrix operations are required . The time complexity of a single GAT attention head computing F features may be expressed as O ( |V |F F + |E|F ) , where F is the number of input features , and |V | and |E| are the numbers of nodes and edges in the graph , respectively . • As mentioned in Section 1 , GAT can be reformulated as a particular instance of MoNet [ reference ] . More specifically , setting the pseudo - coordinate function to be u ( x , y ) GraphSAGE * corresponds to the best GraphSAGE result we were able to obtain by just modifying its architecture . Const - GAT corresponds to a model with the same architecture as GAT , but with a constant attention mechanism ( assigning same importance to each neighbor ; GCN - like inductive operator ) . section : Inductive the best result we were able to achieve with GraphSAGE by just modifying its architecture ( this was with a three - layer GraphSAGE - LSTM with [ reference ][ reference ][ reference ] features computed in each layer and 128 features used for aggregating neighborhoods ) . Finally , we report the 10 - run result of our constant attention GAT model ( as Const - GAT ) , to fairly evaluate the benefits of the attention mechanism against a GCN - like aggregation scheme ( with the same architecture ) . Our results successfully demonstrate state - of - the - art performance being achieved or matched across all four datasets - in concordance with our expectations , as per the discussion in Section 2.2 . A maximal information , that can not be extended to a more precise one , corresponds to a pure state ; otherwise we will have a mixed state . Now the Schrödinger equation determines a unique linear history Ss(t 0 ) , 2 , i 2 , s(t1)T for our p , where each i represents the observer ’s information about p at time t i . Whenever the initial state is pure , all the other states of the sequence will be pure . In the early 1930s , Erwin Schrödinger , motivated by his quest for a more classical formulation of quantum mechanics , posed a large deviation problem for a cloud of independent Brownian particles . He showed that the solution to the problem could be obtained through a system of two linear equations with nonlinear coupling at the boundary ( Schrödinger system ) . Existence and uniqueness for such a system , which represents a sort of bottleneck for the problem , was first established by Fortet in 1938/1940 under rather general assumptions by proving convergence of an ingenious but complex approximation method . ABSTRACT In this work , we study the dynamic behaviour for a heat equation with exponential polynomial kernel memory to be a controller for a Schrödinger system . By introducing some new variables , the time-variant system is transformed into a time-invariant one . Considering the enormous advances in nanometer-scale CMOS technology that now allows one to reliably fabricate billions of switching devices on a single silicon die , electrostatically controlled quantum dots ( implemented as quantum wells ) appear to be promising candidates for a massive implementation of quantum bits ( qubits ) and quantum logic circuits in order to facilitate high-volume production of quantum computers . In this paper , the case of finite two-well and multiple-well potentials arising from semiconductor charged-coupled structures are treated in a rigorous way by Schrödinger formalism . The modeling methodologies presented to allow one to describe the dynamics of quantum states in non-ideal geometries , account for some mechanisms of qubit decoherence and model electrostatic interaction between electrons that lead to entanglement . We discuss the construction and operation of this qubit , its Bloch sphere , and relation with maximally localized Wannier functions which define its position-based nature . We then demonstrate how to build a tight-binding model of single and multiple interacting qubits from first principles of the Schrödinger formalism . We provide all required formulae to calculate the maximally localized functions and the entries of the Hamiltonian matrix in the presence of interaction between qubits . With particular attention to the measurement problem , we review and investigate two unorthodox formulations . First , there is the model advanced by GRWP , a stochastic modification of the standard Schrödinger dynamics admitting statevector reduction as a real physical process . Second , there is the ontological interpretation of Bohm , a causal reformulation of the usual theory admitting no collapse of the statevector . The energy barriers and important anharmonic interactions are identified and general conclusions drawn . The calcu lational methods used to solve the Schrödinger equa tion include pseudopotentials , fast Fourier transforms , efficient matrix diagonalization , and supercells with many atoms . We start from a recent quantum-mechanical characterization of the structure of a network relating the graph Laplacian to a density operator and resulting in a characterization of the network 's entropy . Then we adopt a Schrödinger picture of the dynamics of the network , resulting in an estimation of a hidden time-varying Hamiltonian from the data , from which we derive a measure of Energy exchange . From these variables , using the thermodynamic identity , we obtain temperature under the assumption of constant volume of the system . As a result , a new signal decomposition of multiscale resolution is determined with the ability to observe new information on scale resolution that may be useful for image compression and extension . The second representation extends the Schrödinger representation to include scaling . The hybrid multiscale-Heisenberg representation determines a four-parameter linear group . It exhibited significant antioxidant property . To predict the anticancer activity of the compound , molecular docking studies were done using Schrödinger software . Molecular docking studies for DPDHN was performed on the active site of protein kinase CK2 ( PDB ID : 2OXX , 1M2R and 1M2P ) and to explore the estrogen receptor binding ability , the target protein with PDB ID : 3ERT and 2YLY were selected . The Schrödinger software suite contains a broad array of computational chemistry and molecular modeling tools that can be used to study the interaction of peptides with proteins . These include molecular docking using Glide and Piper , relative binding free energy predictions with FEP+ , conformational searches using MacroModel and Desmond , and structural refinement using Prime and PrimeX. In this review we provide a comprehensive overview of these tools and describe their potential application in the identification and optimization of peptide ligands for proteins . To address this issue , we present a novel multi-grained machine reading comprehension framework that focuses on modeling documents at their hierarchical nature , which are different levels of granularity : documents , paragraphs , sentences , and tokens . We utilize graph attention networks to obtain different levels of representations so that they can be learned simultaneously . The long and short answers can be extracted from paragraph-level representation and token-level representation , respectively . We frame cerebral cortex parcellation as a mesh segmentation task , and address it by taking advantage of recent advances in generalizing convolutions to the graph domain . In particular , we propose to assess graph convolutional networks and graph attention networks , which , in contrast to previous mesh parcellation models , exploit the underlying structure of the data to make predictions . We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-ofthe-art and baselines , highlighting the potential and applicability of these methods to tackle neuroimaging challenges , paving the road towards a better characterization of brain diseases . We then propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space . We further show how the proposed method can be extended to Graph Attention Network ( GAT ) . Experimental results demonstrate better performance of our proposed framework in several real-world applications . The node in graph represents the feature vector from a particular branch for a given attribute , and the edge can be defined by either the prior knowledge or the similarity between two nodes in the embedding with a fully data-driven manner . We analyze that the attention mechanism actually takes effect in the latter case , and utilize the Graph Attention Layer ( GAL ) for exploring on the most relevant attribute feature and refining the task-dependant feature by considering other attributes . Experiments show that by mining the correlations among attributes , our method can improve the recognition accuracy on CelebA and LFWA dataset . Existing methods tend to ignore the relationship among labels . In this paper , a graph attention network-based model is proposed to capture the attentive dependency structure among the labels . The graph attention network uses a feature matrix and a correlation matrix to capture and explore the crucial dependencies between the labels and generate classifiers for the task . Traditionally , the problem of scheduling agents to complete tasks with temporal and spatial constraints has been approached either with exact algorithms , which are computationally intractable for large-scale , dynamic coordination , or approximate methods that require domain experts to craft heuristics for each application . We seek to overcome the limitations of these conventional methods by developing a novel graph attention network formulation to automatically learn features of scheduling problems to allow their deployment . To learn effective policies for combinatorial optimization problems via machine learning , we combine imitation learning on smaller problems with deep Q-learning on larger problems , in a non-parametric framework , to allow for fast , near-optimal scheduling of robot teams . Therefore , a natural research question is how we can jointly model the spatial and temporal context information and learn a unique network representation , while being able to provide interpretable inference over the observed data . To address this question , we propose a generic graph attention neural mechanism named STANE , which guides the context sampling process to focus on the crucial part of the data . Moreover , to interpret the network embedding results , STANE enables the end users to investigate the graph context distributions along three dimensions ( i.e. , nodes , training window length , and time ) . We propose a recommender system for online communities based on a dynamic-graph-attention neural network . We model dynamic user behaviors with a recurrent neural network , and context-dependent social influence with a graph-attention neural network , which dynamically infers the influencers based on users ' current interests . The whole model can be efficiently fit on large-scale data . Among existing works , they do n't take full advantage of local structural information and the relationship between points is difficult to be represented . In this paper , we propose graph attention neural network ( GANN ) to enhance the structural representation of point clouds by relational degree between points . We construct graph structure on point cloud and use the attention mechanism to compute the relationship of different nodes in a neighborhood , and then embedding them into the aggregation operation designed . GRAPH ATTENTION NETWORKS section : ABSTRACT We present graph attention networks ( GATs ) , novel neural network architectures that operate on graph - structured data , leveraging masked self - attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations . By stacking layers in which nodes are able to attend over their neighborhoods ' features , we enable ( implicitly ) specifying different weights to different nodes in a neighborhood , without requiring any kind of costly matrix operation ( such as inversion ) or depending on knowing the graph structure upfront . section : GAT ARCHITECTURE In this section , we will present the building block layer used to construct arbitrary graph attention networks ( through stacking this layer ) , and directly outline its theoretical and practical benefits and limitations compared to prior work in the domain of neural graph processing . section : GRAPH ATTENTIONAL LAYER We will start by describing a single graph attentional layer , as the sole layer utilized throughout all of the GAT architectures used in our experiments . The particular attentional setup utilized by us closely follows the work of [ reference ]- but the framework is agnostic to the particular choice of attention mechanism . In these models , LSTM ( Long Short - Term Memory networks ) , its variants GRU ( Gated Recurrent Units ) and Bi - LSTM , are usually utilized to encode the sentences since they were capable of learning long - term dependencies inside sentences . For example , Conneau et al . proposed a generic NLI training scheme and compared several sentence encoding architectures : LSTM or GRU , Bi - LSTM with mean / max pooling , self - attention network and hierarchical convolutional networks . The experimental results demonstrated that the Bi - LSTM with max pooling achieved the best performance . / ChenZLWJI17 , DBLP : conf / repeval / NieB17 , self - attention network DBLP : journals / corr / abs - 1709 - 04696 , DBLP : journals / corr / abs - 1801 - 10296 , and more complicated neural networks DBLP : conf / acl / BowmanGRGMP16 , DBLP : conf / eacl / YuM17a , DBLP : conf / eacl / YuM17 , DBLP : journals / corr / ChoiYL17 . The main contributions of our work are three - fold . First , we propose a stacked attention network for image QA tasks . Second , we perform comprehensive evaluations on four image QA benchmarks , demonstrating that the proposed multiple - layer SAN outperforms previous state - of - the - art approaches by a substantial margin . Some other works perform image attention multiple times in a stacked manner . In , the authors propose a stacked attention network , which runs multiple hops to infer the answer progressively . To capture fine - grained information from the question , Xu propose a multi - hop image attention scheme . We find that SchNet shows the best performance for OTs of different sizes ( from bithiophene to sexithiophene ) , achieving average prediction errors in the range of 20 - 80meV compared to the results from ( time-dependent ) density functional theory . We show that SchNet also consistently outperforms shallow feed-forward neural networks , especially in difficult cases with large molecules or limited training data . We further show that SchNet could predict the transition dipole moment accurately , a task previously known to be difficult for feed-forward neural networks , and we ascribe the relatively large errors in transition dipole prediction seen for some OT configurations to the charge-transfer character of their excited states . This allows for an easy implementation and evaluation of new models . For now , SchNetPack includes implementations of ( weighted ) atom-centered symmetry functions and the deep tensor neural network SchNet , as well as ready-to-use scripts that allow one to train these models on molecule and material datasets . Based on the PyTorch deep learning framework , SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations , as well as parallelize the model across multiple GPUs . With the rise of deep neural networks for quantum chemistry applications , there is a pressing need for architectures that , beyond delivering accurate predictions of chemical properties , are readily interpretable by researchers . Here , we describe interpretation techniques for atomistic neural networks on the example of Behler-Parrinello networks as well as the end-to-end model SchNet . Both models obtain predictions of chemical properties by aggregating atom-wise contributions . In this chapter , we present neural network architectures that are able to learn efficient representations of molecules and materials . In particular , the continuous-filter convolutional network SchNet accurately predicts chemical properties across compositional and configurational space on a variety of datasets . Beyond that , we analyze the obtained representations to find evidence that their spatial and chemical properties agree with chemical intuition . In this work , we extend the SchNet architecture by using weighted skip connections to assemble the final representation . This enables us to study the relative importance of each interaction block for property prediction . Here , we examine the applicability of two existing machine learning methodologies to the prediction of excitation energies from time-dependent density functional theory . To this end , we systematically study the performance of various 2- and 3-body descriptors as well as the deep neural network SchNet to predict extensive as well as intensive properties such as the transition energies from the ground state to the first and second excited state . As perhaps expected current state-of-the-art machine learning techniques are more suited to predict extensive as opposed to intensive quantities . Machine learning , in general , and deep learning , in particular , are ideally suitable for representing quantum-mechanical interactions , enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space . Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers . We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials , where our model learns chemically plausible embeddings of atom types across the periodic table .
They form a canonical representation , making testing of functional properties such as satisfiability and equivalence straightforward . A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures . Using OBDDs , a wide variety of problems can be solved through symbolic analysis .
Finding an optimal solution to this problem is NP-complete . Due to the high cost of graph algorithms , it is nearly impossible to come up with close to optimal solutions that do not have very high cost ( higher order polynomial ) . Therefore , our goal is to find a heuristic that gives good performance , and that has relatively low cost .
Graphs are a fundamental data representation that has been used extensively in various domains . In graph-based applications , a systematic exploration of the graph such as a breadth-first search ( BFS ) often serves as a key component in the processing of their massive data sets .
Vector space model is the dominant text representation largely due to its simplicity . Graphs are becoming an alternative text representation that have the ability to capture important information in text such as term order , term co-occurrence and term relationships that are not considered by the vector space model . Substantially better text classification performance has been demonstrated for term weighting schemes which use a graph representation .
We present a novel graph embedding algorithm that strikes a balance between local manifold structures and global discriminative information . A novel graph structure is designed to depict explicitly the local manifold structures of traffic signs with various appearances and to intuitively model between-class discriminative information . Through this graph structure , our algorithm effectively learns a compact and discriminative subspace .
To this end , we first abstract the video by temporal superpixels . Second , we propose a novel graph structure exploiting the saliency cues to assign the edge weights . The salient segments are then extracted by applying a spectral foreground detection method , quantum cuts , on this graph .
Requirements for simulation environments that facilitate multiresolution multistage model specification are introduced . A multimodel specification formalism based on graph of models is suggested along with design precepts to enable flexible dynamic model updating . The notion of multisimulation is introduced to enable exploratory simulation using various types of multimodels .
Our main technical contribution is to establish a low cost for this overhead for the class of multithreaded block-resilient HBP ( Hierarchical Balanced Parallel ) computations . Using this and other techniques , we develop block-resilient HBP algorithms with low false sharing costs for several fundamental problems including scans , matrix multiplication , FFT , sorting , and hybrid block-resilient HBP algorithms for list ranking and graph connected components . Most of these algorithms are derived from known multicore algorithms , but are further refined to achieve a low false sharing overhead .
In this paper , the geometric meaning of the transitive closure of a fuzzy relation in corresponding fuzzy graph is first given . An optimal algorithm , which is based on the computation of graph connected components , for fuzzy classification problem is proposed . For any given n samples , the worst case time complexity T(n ) of the algorithm satisfies that O(n)≤T(n)≤O(n 2 ) .
This paper proposes a fast training method for graph classification based on a boosting algorithm and its application to sentimental analysis with input texts represented by graphs . Graph format is very suitable for representing texts structured with Natural Language Processing techniques such as morphological analysis , Named Entity Recognition , and parsing . A number of classification methods which represent texts as graphs have been proposed so far .
The comparison unit can be a word , phrase , node , concept or sub-graph depending on the type of approach . Experimentation with different approaches , Text-Text , Graph-Graph , Text-Cmap ( Concept map ) has been tried , to determine a feasible solution for automating the descriptive answer evaluation process . A comparative analysis of all the three approaches has been done .
According to them , the auther considers the directed graph and its invarse graph are suitable for the representation of module library structure . In this paper ; the calling graph and called graph of a module are used to describe the calling and called relationship about the modules . Finally , the basic data structure , main functions and advantages of MDLC -- a tool for managing module library are introduced .
Even with the increased use of technology to connect distance/time and position/time graphs , the preservice elementary and middle school teachers still struggled with understanding what the graphs represented . Guidelines will be provided that focus on how to design lessons to address the graph misconceptions including : opportunities to interpret misunderstandings , opportunities to create a story and match the story with the graph using technology and a specific and direct connection to movement , slope and y-intercept .
1 ) . For a simple graphGE with edge set E , take independent random points xk , k?V(GE ) , on Sd , and letDE be the minimum value of the spherical distance between xi , xj for { i , j } ? E.
This conventional pipeline , however , has been greatly reshaped by convolutional neural networks ( ConvNets ) , a main driver behind an explosive rise in performance across many computer vision tasks . Recent pose estimation systems have universally adopted ConvNets as their main building block , largely replacing hand - crafted features and graphical models ; this strategy has yielded drastic improvements on standard benchmarks . We continue along this trajectory and introduce a novel “ stacked hourglass ” network design for predicting human pose .
Moreover , the dataset contains a large number of examples that do not involve any reentrancies , as shown in Table [ reference ] , so that the overall scores may not be representative of the ability of models to capture reentrancies . It is expected that the benefit of the graph models will be more evident for those examples containing more reentrancies . To test this hypothesis , we evaluate the various scenarios as a function of the number of reentrancies in each example , using the Meteor score as a metric .
Patchy - san uses WL as one possible labeling procedure to compute receptive fields . Deep graph kernels and graph invariant kernels compare graphs based on the existence or count of small substructures such as shortest paths , graphlets , subtrees , and other graph invariants . In contrast , Patchy - san learns substructures from graph data and is not limited to a predefined set of motifs . Moreover , while all graph kernels have a training complexity at least quadratic in the number of graphs , which is prohibitive for large - scale problems , Patchy - san scales linearly with the number of graphs . Graph neural networks ( GNNs ) are a recurrent neural network architecture defined on graphs . GNNs apply recurrent neural networks for walks on the graph structure , propagating node representations until a fixed point is reached .
PROTEINS is a graph collection where nodes are secondary structure elements and edges indicate neighborhood in the amino - acid sequence or in 3D space . Graphs are classified as enzyme or non - enzyme . D & D is a data set of protein structures classified into enzymes and non - enzymes .
In order to solve the tasks that require more than a single step it must compress all the relevant relations into a fixed size vector , then perform the remaining relational reasoning in the last forward layers . Relational networks , interaction networks and our proposed model can all be seen as an instance of Graph Neural Networks scarselli2009graph , gilmer2017neural . Graph neural networks with message passing computations go back to .
This suggests the use of a spectral formulation , based on a dual graph representing the shape , yielding bases for a function space built over the mesh . With this graph representation we face multiple challenges in building a convolutional neural architecture . One is how to share coefficients and conduct multiscale analysis in different parts of the graph for a single shape .
The work of Kiefel et al . [ reference ] is based on the pictorial structures model but differs in the underlying graph representation . Hierarchical models [ reference ][ reference ] represent the relationships between parts at different scales and sizes in a hierarchical tree structure .
is the matrix of activations in the layer ; . In the following , we show that the form of this propagation rule can be motivated via a first - order approximation of localized spectral filters on graphs hammond2011wavelets , defferrard2016convolutional . subsection : Spectral Graph Convolutions
[ reference ] ) . Therefore , we propose a graph - based method to find cluster centers . More specifically , we build a graph of top ranking proposals according to the spatial similarity for each positive object class .
Unsupervised learning methods offer one promising avenue for acquiring a concise representation of the state with a good similarity metric . This can be accomplished using dimensionality reduction , clustering , or graph - based techniques . In our work , we draw on recent developments in representation learning with neural networks , as discussed in the following section .
The focus of this paper is on feature representation for dependency parsing , using recent techniques from the neural - networks ( ‘ ‘ deep learning ’ ’ ) literature . Modern approaches to dependency parsing can be broadly categorized into graph - based and transition - based parsers . Graph - based parsers treat parsing as a search - based structured prediction problem in which the goal is learning a scoring function over dependency trees such that the correct tree is scored above all other trees .
document : Graph - Structured Representations for Visual Question Answering This paper proposes to improve visual question answering ( VQA ) with structured representations of both scene contents and questions .
One challenge raised by GraphSAGE is how to smartly combine neighbour features based on graph structure . GAT handles this problem through attention , however the challenge with GAT is its scalability over large and dense graphs . In this work , we proposed a new architecture to address these issues that is more efficient and is capable of incorporating different edge type information .
The GAT is more effective on the decrease of the individual performance variation than the single-gate TFT in low-voltage operation . Moreover , the OFF-current of the GAT is almost the same as that of the single-gate TFT with the LDD . The GAT is an attractive candidate to achieve high-density , low-voltageoperation , and low-power SRAMs .
All the participants carried out several tasks with the GAT App and completed System Usability Scale ( SUS ) questionnaires afterwards . The data revealed an average SUS score of 84.06 , which indicates that the gas engineers enjoyed using the GAT App and will recommend it to their colleagues . However , there is a need for more user testing for result generalisation .
This header is generated by a client-side proxy named GAP , which runs on mobile devices with Windows CE operating system ( Pocket PC , Smartphone , among others ) . The transcoding header is interpreted by a server-side proxy named GAT MT , that converts Web documents to WML , XHTML-MP , PDF , plain text , PostScript , XML and HTML adapted for mobile devices , making use of XSL technologies ( XSLT and XSL-FO )
This header is generated by a client-side Proxy named GAP , which runs on mobile devices with Windows CE operating system ( Pocket PC , Smartphone , among others ) . The transcoding header is interpreted by a server-side Proxy , named GAT MT , that converts Web documents to WML , XHTML-MP , PDF , plain text , PostScript , XML and HTML adapted for mobile devices . To do this , the mechanism uses XSL technologies ( XSLT and XSL-FO ) to define Stylesheets according to a specific device .
It has the domestic independent intellectual property rights . The GAT high-accurate gyro station 's main body adopts the space technology . The GAT instrument adopts the magnetic tape technolgy .
For more efficient text entry , we present the concept of gaze-assisted typing ( GAT ) , which uses both a touchpad and eye tracking . We initially examined GAT with a minimal eye input load , and demonstrated that the GAT technology was 51 % faster than a two-step touch input typing method ( i.e.,M-SwipeBoard : 5.85 words per minute ( wpm ) and GAT : 8.87 wpm ) . We also compared GAT methods with varying numbers of touch gestures . The results showed that a GAT requiring five different touch gestures was the most preferred , although all GAT techniques were equally efficient .
Conventional wavelet transform ( WT ) filters have less effect on de-noising and correction of a north-seeking gyroscope sensor exposed to vibration , since the optimal wavelet decomposed level for de-noising is difficult to determine . To solve this problem , this paper proposes an optimized WT filter which is suited to the magnetic levitation gyroscope ( GAT ) . The proposed method was tested on an equivalent mock-up network of the tunnels associated with the Hong Kong‒Zhuhai‒Macau Bridge .
These confirmed the accuracy of GAT as well as its lower computational cost . The numerical results obtained showed that the GAT outperformed the incomplete GAT ( IGAT ) , which is solely based on PEM and Cornish-Fisher expansion , thanks to the ability of AA to bound the outputs used in the LGSA .
We provide an overview of the ways in which Triana interacts with services ( e.g. , Web and P2P services ) and then how we interact with core Grid components , such as resource managers and data management systems through the extensive use of the GridLab GAT interface . We describe in detail the GAT philosophy and implementation and then show how the various GAT primitives can be represented in an intuitive fashion within a Triana workflow . This approach , which we refer to as the Visual GAT , differs substantially from other approaches because we do not tie our implementation to any specific underlying Grid middleware technologies ; rather , we base our implementation on application level requirements and model such primitives from a user ’s perspective by hiding as much complexity as possible without undermining the core capabilities required .
Ninety multi-engine instrument rated pilots were assigned to no motion , sustained linear scaled down analog motion , and washout motion in a GAT II simulator for determining the effects of degree of motion upon the predictive validity of flight simulators . Five instrument and five contact maneuvers were flown in the simulator followed by flight in a Piper Aztec aircraft .
No eigendecompositions or similar costly matrix operations are required . The time complexity of a single GAT attention head computing F features may be expressed as O ( |V |F F + |E|F ) , where F is the number of input features , and |V | and |E| are the numbers of nodes and edges in the graph , respectively .
• As mentioned in Section 1 , GAT can be reformulated as a particular instance of MoNet [ reference ] . More specifically , setting the pseudo - coordinate function to be u ( x , y )
GraphSAGE * corresponds to the best GraphSAGE result we were able to obtain by just modifying its architecture . Const - GAT corresponds to a model with the same architecture as GAT , but with a constant attention mechanism ( assigning same importance to each neighbor ; GCN - like inductive operator ) . section : Inductive
the best result we were able to achieve with GraphSAGE by just modifying its architecture ( this was with a three - layer GraphSAGE - LSTM with [ reference ][ reference ][ reference ] features computed in each layer and 128 features used for aggregating neighborhoods ) . Finally , we report the 10 - run result of our constant attention GAT model ( as Const - GAT ) , to fairly evaluate the benefits of the attention mechanism against a GCN - like aggregation scheme ( with the same architecture ) . Our results successfully demonstrate state - of - the - art performance being achieved or matched across all four datasets - in concordance with our expectations , as per the discussion in Section 2.2 .
A maximal information , that can not be extended to a more precise one , corresponds to a pure state ; otherwise we will have a mixed state . Now the Schrödinger equation determines a unique linear history Ss(t 0 ) , 2 , i 2 , s(t1)T for our p , where each i represents the observer ’s information about p at time t i . Whenever the initial state is pure , all the other states of the sequence will be pure .
In the early 1930s , Erwin Schrödinger , motivated by his quest for a more classical formulation of quantum mechanics , posed a large deviation problem for a cloud of independent Brownian particles . He showed that the solution to the problem could be obtained through a system of two linear equations with nonlinear coupling at the boundary ( Schrödinger system ) . Existence and uniqueness for such a system , which represents a sort of bottleneck for the problem , was first established by Fortet in 1938/1940 under rather general assumptions by proving convergence of an ingenious but complex approximation method .
ABSTRACT In this work , we study the dynamic behaviour for a heat equation with exponential polynomial kernel memory to be a controller for a Schrödinger system . By introducing some new variables , the time-variant system is transformed into a time-invariant one .
Considering the enormous advances in nanometer-scale CMOS technology that now allows one to reliably fabricate billions of switching devices on a single silicon die , electrostatically controlled quantum dots ( implemented as quantum wells ) appear to be promising candidates for a massive implementation of quantum bits ( qubits ) and quantum logic circuits in order to facilitate high-volume production of quantum computers . In this paper , the case of finite two-well and multiple-well potentials arising from semiconductor charged-coupled structures are treated in a rigorous way by Schrödinger formalism . The modeling methodologies presented to allow one to describe the dynamics of quantum states in non-ideal geometries , account for some mechanisms of qubit decoherence and model electrostatic interaction between electrons that lead to entanglement .
We discuss the construction and operation of this qubit , its Bloch sphere , and relation with maximally localized Wannier functions which define its position-based nature . We then demonstrate how to build a tight-binding model of single and multiple interacting qubits from first principles of the Schrödinger formalism . We provide all required formulae to calculate the maximally localized functions and the entries of the Hamiltonian matrix in the presence of interaction between qubits .
With particular attention to the measurement problem , we review and investigate two unorthodox formulations . First , there is the model advanced by GRWP , a stochastic modification of the standard Schrödinger dynamics admitting statevector reduction as a real physical process . Second , there is the ontological interpretation of Bohm , a causal reformulation of the usual theory admitting no collapse of the statevector .
The energy barriers and important anharmonic interactions are identified and general conclusions drawn . The calcu lational methods used to solve the Schrödinger equa tion include pseudopotentials , fast Fourier transforms , efficient matrix diagonalization , and supercells with many atoms .
We start from a recent quantum-mechanical characterization of the structure of a network relating the graph Laplacian to a density operator and resulting in a characterization of the network 's entropy . Then we adopt a Schrödinger picture of the dynamics of the network , resulting in an estimation of a hidden time-varying Hamiltonian from the data , from which we derive a measure of Energy exchange . From these variables , using the thermodynamic identity , we obtain temperature under the assumption of constant volume of the system .
As a result , a new signal decomposition of multiscale resolution is determined with the ability to observe new information on scale resolution that may be useful for image compression and extension . The second representation extends the Schrödinger representation to include scaling . The hybrid multiscale-Heisenberg representation determines a four-parameter linear group .
It exhibited significant antioxidant property . To predict the anticancer activity of the compound , molecular docking studies were done using Schrödinger software . Molecular docking studies for DPDHN was performed on the active site of protein kinase CK2 ( PDB ID : 2OXX , 1M2R and 1M2P ) and to explore the estrogen receptor binding ability , the target protein with PDB ID : 3ERT and 2YLY were selected .
The Schrödinger software suite contains a broad array of computational chemistry and molecular modeling tools that can be used to study the interaction of peptides with proteins . These include molecular docking using Glide and Piper , relative binding free energy predictions with FEP+ , conformational searches using MacroModel and Desmond , and structural refinement using Prime and PrimeX. In this review we provide a comprehensive overview of these tools and describe their potential application in the identification and optimization of peptide ligands for proteins .
To address this issue , we present a novel multi-grained machine reading comprehension framework that focuses on modeling documents at their hierarchical nature , which are different levels of granularity : documents , paragraphs , sentences , and tokens . We utilize graph attention networks to obtain different levels of representations so that they can be learned simultaneously . The long and short answers can be extracted from paragraph-level representation and token-level representation , respectively .
We frame cerebral cortex parcellation as a mesh segmentation task , and address it by taking advantage of recent advances in generalizing convolutions to the graph domain . In particular , we propose to assess graph convolutional networks and graph attention networks , which , in contrast to previous mesh parcellation models , exploit the underlying structure of the data to make predictions . We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-ofthe-art and baselines , highlighting the potential and applicability of these methods to tackle neuroimaging challenges , paving the road towards a better characterization of brain diseases .
We then propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space . We further show how the proposed method can be extended to Graph Attention Network ( GAT ) . Experimental results demonstrate better performance of our proposed framework in several real-world applications .
The node in graph represents the feature vector from a particular branch for a given attribute , and the edge can be defined by either the prior knowledge or the similarity between two nodes in the embedding with a fully data-driven manner . We analyze that the attention mechanism actually takes effect in the latter case , and utilize the Graph Attention Layer ( GAL ) for exploring on the most relevant attribute feature and refining the task-dependant feature by considering other attributes . Experiments show that by mining the correlations among attributes , our method can improve the recognition accuracy on CelebA and LFWA dataset .
Existing methods tend to ignore the relationship among labels . In this paper , a graph attention network-based model is proposed to capture the attentive dependency structure among the labels . The graph attention network uses a feature matrix and a correlation matrix to capture and explore the crucial dependencies between the labels and generate classifiers for the task .
Traditionally , the problem of scheduling agents to complete tasks with temporal and spatial constraints has been approached either with exact algorithms , which are computationally intractable for large-scale , dynamic coordination , or approximate methods that require domain experts to craft heuristics for each application . We seek to overcome the limitations of these conventional methods by developing a novel graph attention network formulation to automatically learn features of scheduling problems to allow their deployment . To learn effective policies for combinatorial optimization problems via machine learning , we combine imitation learning on smaller problems with deep Q-learning on larger problems , in a non-parametric framework , to allow for fast , near-optimal scheduling of robot teams .
Therefore , a natural research question is how we can jointly model the spatial and temporal context information and learn a unique network representation , while being able to provide interpretable inference over the observed data . To address this question , we propose a generic graph attention neural mechanism named STANE , which guides the context sampling process to focus on the crucial part of the data . Moreover , to interpret the network embedding results , STANE enables the end users to investigate the graph context distributions along three dimensions ( i.e. , nodes , training window length , and time ) .
We propose a recommender system for online communities based on a dynamic-graph-attention neural network . We model dynamic user behaviors with a recurrent neural network , and context-dependent social influence with a graph-attention neural network , which dynamically infers the influencers based on users ' current interests . The whole model can be efficiently fit on large-scale data .
Among existing works , they do n't take full advantage of local structural information and the relationship between points is difficult to be represented . In this paper , we propose graph attention neural network ( GANN ) to enhance the structural representation of point clouds by relational degree between points . We construct graph structure on point cloud and use the attention mechanism to compute the relationship of different nodes in a neighborhood , and then embedding them into the aggregation operation designed .
GRAPH ATTENTION NETWORKS section : ABSTRACT We present graph attention networks ( GATs ) , novel neural network architectures that operate on graph - structured data , leveraging masked self - attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations . By stacking layers in which nodes are able to attend over their neighborhoods ' features , we enable ( implicitly ) specifying different weights to different nodes in a neighborhood , without requiring any kind of costly matrix operation ( such as inversion ) or depending on knowing the graph structure upfront .
section : GAT ARCHITECTURE In this section , we will present the building block layer used to construct arbitrary graph attention networks ( through stacking this layer ) , and directly outline its theoretical and practical benefits and limitations compared to prior work in the domain of neural graph processing . section : GRAPH ATTENTIONAL LAYER We will start by describing a single graph attentional layer , as the sole layer utilized throughout all of the GAT architectures used in our experiments . The particular attentional setup utilized by us closely follows the work of [ reference ]- but the framework is agnostic to the particular choice of attention mechanism .
In these models , LSTM ( Long Short - Term Memory networks ) , its variants GRU ( Gated Recurrent Units ) and Bi - LSTM , are usually utilized to encode the sentences since they were capable of learning long - term dependencies inside sentences . For example , Conneau et al . proposed a generic NLI training scheme and compared several sentence encoding architectures : LSTM or GRU , Bi - LSTM with mean / max pooling , self - attention network and hierarchical convolutional networks . The experimental results demonstrated that the Bi - LSTM with max pooling achieved the best performance .
/ ChenZLWJI17 , DBLP : conf / repeval / NieB17 , self - attention network DBLP : journals / corr / abs - 1709 - 04696 , DBLP : journals / corr / abs - 1801 - 10296 , and more complicated neural networks DBLP : conf / acl / BowmanGRGMP16 , DBLP : conf / eacl / YuM17a , DBLP : conf / eacl / YuM17 , DBLP : journals / corr / ChoiYL17 .
The main contributions of our work are three - fold . First , we propose a stacked attention network for image QA tasks . Second , we perform comprehensive evaluations on four image QA benchmarks , demonstrating that the proposed multiple - layer SAN outperforms previous state - of - the - art approaches by a substantial margin .
Some other works perform image attention multiple times in a stacked manner . In , the authors propose a stacked attention network , which runs multiple hops to infer the answer progressively . To capture fine - grained information from the question , Xu propose a multi - hop image attention scheme .
We find that SchNet shows the best performance for OTs of different sizes ( from bithiophene to sexithiophene ) , achieving average prediction errors in the range of 20 - 80meV compared to the results from ( time-dependent ) density functional theory . We show that SchNet also consistently outperforms shallow feed-forward neural networks , especially in difficult cases with large molecules or limited training data . We further show that SchNet could predict the transition dipole moment accurately , a task previously known to be difficult for feed-forward neural networks , and we ascribe the relatively large errors in transition dipole prediction seen for some OT configurations to the charge-transfer character of their excited states .
This allows for an easy implementation and evaluation of new models . For now , SchNetPack includes implementations of ( weighted ) atom-centered symmetry functions and the deep tensor neural network SchNet , as well as ready-to-use scripts that allow one to train these models on molecule and material datasets . Based on the PyTorch deep learning framework , SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations , as well as parallelize the model across multiple GPUs .
With the rise of deep neural networks for quantum chemistry applications , there is a pressing need for architectures that , beyond delivering accurate predictions of chemical properties , are readily interpretable by researchers . Here , we describe interpretation techniques for atomistic neural networks on the example of Behler-Parrinello networks as well as the end-to-end model SchNet . Both models obtain predictions of chemical properties by aggregating atom-wise contributions .
In this chapter , we present neural network architectures that are able to learn efficient representations of molecules and materials . In particular , the continuous-filter convolutional network SchNet accurately predicts chemical properties across compositional and configurational space on a variety of datasets . Beyond that , we analyze the obtained representations to find evidence that their spatial and chemical properties agree with chemical intuition .
In this work , we extend the SchNet architecture by using weighted skip connections to assemble the final representation . This enables us to study the relative importance of each interaction block for property prediction .
Here , we examine the applicability of two existing machine learning methodologies to the prediction of excitation energies from time-dependent density functional theory . To this end , we systematically study the performance of various 2- and 3-body descriptors as well as the deep neural network SchNet to predict extensive as well as intensive properties such as the transition energies from the ground state to the first and second excited state . As perhaps expected current state-of-the-art machine learning techniques are more suited to predict extensive as opposed to intensive quantities .
Machine learning , in general , and deep learning , in particular , are ideally suitable for representing quantum-mechanical interactions , enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space . Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers . We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials , where our model learns chemically plausible embeddings of atom types across the periodic table .
Symbolic Boolean manipulation with ordered binary-decision diagrams An efficient heuristic for code partitioning Efficient Parallel Graph Exploration on Multi-Core CPU and GPU Supervised Graph-Based Term Weighting Scheme for Effective Text Classification Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition Spatiotemporal Saliency Estimation by Spectral Foreground Detection Requirements and design principles for multisimulation with multiresolution, multistage multimodels Efficient Resource Oblivious Algorithms for Multicores with False Sharing An Optimal Algorithm for Fuzzy Classification Problem Fast Training of a Graph Boosting for Large-Scale Text Classification A comparative analysis of various approaches for automated assessment of descriptive answers MODULE LIBRARY MANAGEMENT AND MDLC EVIDENCES OF UNDERSTANDINGS AND MISCONCEPTIONS OF GRAPHS AFTER EXPLORING USING TECHNOLOGY The Length of the Shortest Edge of a Graph on a Sphere Stacked Hourglass Networks for Human Pose Estimation Structural Neural Encoders for AMR-to-text Generation Learning Convolutional Neural Networks for Graphs Recurrent Relational Networks SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Convolutional Pose Machines Semi-Supervised Classification with Graph Convolutional Networks PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations Graph-Structured Representations for Visual Question Answering Graph Representation Learning Network via Adaptive Sampling High Performance Gate-all-around TFT(GAT) For High-density, Low-voltage-operation, And Low-power Srams A Virtual Reality Based Gas Assessment Application for Training Gas Engineers Multi-Format Web Content Transcoding for Mobile Devices An On-the-Flight Proxy System for Multi-format Web Content Transcoding for Mobile Devices Technical characteristics of GAT high accuracy magnetic suspension gyro station and application in mine surveying Gaze-Assisted Typing for Smart Glasses Application and Optimization of Wavelet Transform Filter for North-Seeking Gyroscope Sensor Exposed to Vibration Tracing harmonic distortion and voltage unbalance in secondary radial distribution networks with photovoltaic uncertainties by an iterative multiphase harmonic load flow Visual Grid Workflow in Triana Predictive Validity of Flight Simulators as a Function of Simulator Motion Graph Attention Networks A fuzzy dynamic semantics for quantum histories Traversing the Schrödinger Bridge Strait: Robert Fortet’s Marvelous Proof Redux Stabilisation of Schrödinger equation in dynamic boundary feedback with a memory-typed heat equation Modeling of Semiconductor Electrostatic Qubits Realized Through Coupled Quantum Dots CMOS Position-Based Charge Qubits: Theoretical Analysis of Control and Entanglement Decoherence in unorthodox formulations of quantum mechanics Total Energy Calculations for Structural Phase Transformations Quantum thermodynamics of time evolving networks Two ways to incorporate scale in the Heisenberg group with an intertwining operator Single crystal XRD, DFT investigations and molecular docking study of 2- ((1, 5-dimethyl-3-oxo-2-phenyl-2, 3-dihydro-1H-pyrazol-4-yl)amino)naphthalene-1, 4-dione as a potential anti- cancer lead molecule Investigating Protein-Peptide Interactions Using the Schrödinger Computational Suite. Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension Convolutional neural networks for mesh-based parcellation of the cerebral cortex Graph Neural Networks with Composite Kernels Exploring Correlations in Multiple Facial Attributes through Graph Attention Network Multi-Label Text Classification using Attention-based Graph Neural Network Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks Session-Based Social Recommendation via Dynamic Graph Attention Networks Graph Attention Neural Networks for Point Cloud Recognition Graph Attention Networks Attention Boosted Sequential Inference Model Neural Natural Language Inference Models Enhanced with External Knowledge Stacked Attention Networks for Image Question Answering Hierarchical Question-Image Co-Attention for Visual Question Answering Deep Learning for Optoelectronic Properties of Organic Semiconductors. SchNetPack: A Deep Learning Toolbox For Atomistic Systems. Quantum-chemical insights from interpretable atomistic neural networks Learning representations of molecules and materials with atomistic neural networks Analysis of Atomistic Representations Using Weighted Skip-Connections Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning SchNet - A deep learning architecture for molecules and materials.
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PACT ECAI Winter Simulation Conference IPDPS PRICAI ECCV NAACL-HLT ICML NeurIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR IEEE Transactions on Pattern Analysis and Machine Intelligence ArXiv Transactions of the Association for Computational Linguistics 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICICM ENC ICCSA (2) UIST ICLR ICPR ICAART (2) CIKM WSDM ICME ICLR ArXiv ACL 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS
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