Modeling Global and Local Interactions for Online Conversation Recommendation

In this paper, we investigated the influence of local and global interaction on the efficiency of continuous-time quantum spatial search. To do so, we analyzed numerically the chimera graph, which is defined as 2D grid with each node replaced by complete bipartite graph. Our investigation provides a numerical evidence that with a large number of local interactions the quantum spatial search is optimal, contrary to the case with limited number of such interactions. The result suggests that relatively large number of local interactions with the marked vertex is necessary for optimal search, which in turn would imply that poorly connected vertices are hard to be found.

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs (MMKGs), whose entities can be associated with relational triples and related images. Most previous studies treat the graph structure as a special modality, and fuse different modality information with separate uni-modal encoders, neglecting valuable relational associations in modalities. Other studies refine each uni-modal information with graph structures, but may introduce unnecessary relations in specific modalities. To this end, we propose a novel local-to-global interaction network for MMEA, termed as LoginMEA. Particularly, we first fuse local multi-modal interactions to generate holistic entity semantics and then refine them with global relational interactions of entity neighbors. In this design, the uni-modal information is fused adaptively, and can be refined with relations accordingly. To enrich local interactions of multi-modal entity information, we devise modality weights and low-rank interactive fusion, allowing diverse impacts and element-level feature interactions among modalities. To capture global interactions of graph structures, we adopt relation reflection graph attention networks, which fully capture relational associations between entities. Extensive experiments demonstrate superior results of our method over 5 cross-KG or bilingual benchmark datasets, indicating the effectiveness of capturing local and global interactions.

This thesis deals with the design and analysis of distributed systems in which homogeneous, autonomous agents collaborate to achieve a common goal. The class of problems studied includes consensus algorithms in which all agents eventually come to an agreement about a specific action. The thesis proposes a framework, called local-global, for analyzing these systems. A local interaction is an interaction among subsets of agents, while a global interaction is one among all agents in the system. Global interactions, in practice, are rare, yet they are the basis by which correctness of a system is measured. For example, if the problem is to compute the average of a measurement made separately by each agent, and all the agents in the system could exchange values in a single action, then the solution is straightforward: each agent gets the values of all others and computes the average independently. However, if the system consists of a large number of agents with unreliable communication, this scenario is highly unlikely. Thus, the design challenge is to ensure that sequences of local interactions lead, or converge, to the same state as a global interaction. The local-global framework addresses this challenge by describing each local interaction as if were a global one, encompassing all agents within the system. This thesis outlines the concept in detail, using it to design algorithms, prove their correctness, and ultimately develop executable implementations that are reliable. To this end, the tools of formal methods are employed: algorithms are modeled, and mechanically checked, within the PVS theorem prover; programs are also verified using the Spin model checker; and interface specification languages are used to ensure local-global properties are still maintained within Java and C# implementations. The thesis presents example applications of the framework and discusses a class of problems to which the framework can be applied.

We propose a general strategy named ‘divide, conquer and combine’ for multimodal fusion. Instead of directly fusing features at holistic level, we conduct fusion hierarchically so that both local and global interactions are considered for a comprehensive interpretation of multimodal embeddings. In the ‘divide’ and ‘conquer’ stages, we conduct local fusion by exploring the interaction of a portion of the aligned feature vectors across various modalities lying within a sliding window, which ensures that each part of multimodal embeddings are explored sufficiently. On its basis, global fusion is conducted in the ‘combine’ stage to explore the interconnection across local interactions, via an Attentive Bi-directional Skip-connected LSTM that directly connects distant local interactions and integrates two levels of attention mechanism. In this way, local interactions can exchange information sufficiently and thus obtain an overall view of multimodal information. Our method achieves state-of-the-art performance on multimodal affective computing with higher efficiency.

This work contains a proposition of an artificial modular neural network (MNN) in which every module network exchanges input/output information with others simultaneously. It further studies the basic dynamical characteristics of this network through both computer simulations and analytical considerations. A notable feature of this model is that it has generic representation with regard to the number of composed modules, network topologies, and classes of introduced interactions. The information processing of the MNN is described as the minimization of a total-energy function that consists of partial-energy functions for modules and their interactions, and the activity and weight dynamics are derived from the total-energy function under the Lyapunov stability condition. This concept was realized by Cross-Coupled Hopfield Nets (CCHN) that one of the authors proposed. In this paper, in order to investigate the basic dynamical properties of CCHN, we offer a representative model called Cross-Coupled Hopfield Nets with Local And Global Interactions (CCHN-LAGI) to which two distinct classes of interactions – local and global interactions – are introduced. Through a conventional test for associative memories, it is confirmed that our energy-function-based approach gives us proper dynamics of CCHN-LAGI even if the networks have different modularity. We also discuss the contribution of a single interaction and the joint contribution of the two distinct interactions through the eigenvalue analysis of connection matrices.

In any biological population, the individuals with low reproductive success appear recursively at every generation. This phenomenon is prominent in rearing animals and insects, such as aquaculture. We consider the variation in the fitness of male and assume that all individuals live on a lattice. Recently, lattice models have been applied to sexual populations. In these cases, simulations have been carried out by two methods: local and global interactions. In the former, birth process occurs between neighboring sites, whereas in the latter it occurs between any pair of lattice sites. In the present paper, we deal with the population dynamics of a single species that contains two or three types of males and one female. Our attention is paid on the conditions of both “stability” and “sustainability”. Here the stability means that the system reaches a surviving equilibrium in population dynamics. In contrast, the sustainability denotes that the population size at the equilibrium is sufficiently high. Simulation for two-male system exhibits the Allee effect: the population goes extinct, unless both densities of male and female are substantially high. For the stability, the high-fitness male is more important than the subordinate male. However, for the sustainability, our results shows that the low-fitness male plays more important role than high-fitness male. Especially if the low-fitness male is not reproductive, the population tends to go extinct. This tendency is conspicuous, when we consider the local interaction. Moreover, simulations are carried out for three-male system. It is found that the sustainability is influenced not by the fitness variation in male but the average of male fitness. Our results suggest that fitness reductions in individuals can become very critical for maintaining artificially breeding populations.

Background: Protein secondary structure prediction is an important task in bioinformatics and structural biology. Protein’s structure is the basis for its corresponding function. Experimental methods for determining the tertiary structure of proteins are both costly and time-consuming. Since the tertiary structure of proteins is further formed by secondary structure, leveraging computational approaches for efficient prediction of protein secondary structure is important. Both local and global interactions between amino acids affect the prediction results. Objective: We propose a module aimed at processing sequence profile features for deep feature extraction and constructing a lightweight network to extract fused features. Methods: To enhance the network’s ability to capture both local and global interactions, we propose an efficient method InConTPSS, which integrates convolution operation with different receptive fields and temporal convolutional networks in the inception architecture. Concurrently, InConTPSS takes into account the issue of distribution imbalance across various states of secondary structures and improves the predictive performance of scarce categories. Results: Experimental results on six benchmark datasets (including CASP12, CASP13, CASP14, CB513, TEST2016, and TEST2018) demonstrate our method achieves state-of-the-art performance with a simpler model on both 3-state and 8-state secondary structure prediction. result: Experimental results on benchmark datasets (including CASP12, CASP13, CASP14, CB513, TEST2016 and TEST2018) demonstrate our method achieves state-of-the-art performance with a simpler model on both 3-state and 8-state secondary structure prediction. Conclusion: Through the combination of the convolutional layer and temporal convolutional network, the inception network structure can effectively process the fused features and improve the prediction results. InConTPSS achieves the most advanced performance in protein secondary structure prediction, and the reasonable use of label-distribution-aware margin loss in our method can effectively improve the prediction accuracy of scarce secondary structures.

Global and local interaction matching model for knowledge-grounded response selection in retrieval-based chatbots

Chameleon sequences (ChSeqs) refer to sequence strings of identical amino acids that can adopt different conformations in protein structures. Researchers have detected and studied ChSeqs to understand the interplay between local and global interactions in protein structure formation. The different secondary structures adopted by one ChSeq challenge sequence-based secondary structure predictors. With increasing numbers of available Protein Data Bank structures, we here identify a large set of ChSeqs ranging from 6 to 10 residues in length. The homologous ChSeqs discovered highlight the structural plasticity involved in biological function. When compared with previous studies, the set of unrelated ChSeqs found represents an about 20-fold increase in the number of detected sequences, as well as an increase in the longest ChSeq length from 8 to 10 residues. We applied secondary structure predictors on our ChSeqs and found that methods based on a sequence profile outperformed methods based on a single sequence. For the unrelated ChSeqs, the evolutionary information provided by the sequence profile typically allows successful prediction of the prevailing secondary structure adopted in each protein family. Our dataset will facilitate future studies of ChSeqs, as well as interpretations of the interplay between local and nonlocal interactions. A user-friendly web interface for this ChSeq database is available at prodata.swmed.edu/chseq.

Recent advances in self-supervised foundation models for computational cancer histopathology have shown great promise for predicting clinical phenotypes from routine hematoxylin and eosin (H&E)-stained images. However, application of these models in the context of discovery, for example for identification of novel biomarkers or drug targets, remains challenging as this typically requires linking whole slide imaging (WSI) data to molecular data, which has historically been scarce. Recently, spatial omics (SO) has emerged as a powerful technology for high-throughput profiling of RNAs and proteins directly within cancer tissues. This presents new and exciting opportunities to improve histopathology models for making molecular predictions about the tumor microenvironment. In this work, we develop machine learning models that can generate transcriptomic profiles at two different spatial scales: (1) cellular-level resolution, i.e. SO, and (2) slide-level resolution, i.e. bulk RNA-sequencing. We present a preprocessing and benchmarking framework to enable rigorous comparison of models on an in-house SO dataset, as well as a public bulk RNA-seq dataset (TCGA-COAD/READ), of colorectal cancer. We fine-tune recently published digital pathology foundation models for these tasks, and find that they outperform both general pathology-naïve image foundation models, as well as specialized digital pathology deep learning models. We also find that existing models cannot be simultaneously trained using supervision from multiple spatial resolutions. To address this, we propose a graph neural network (GNN) modeling approach that can model both local interactions between cells within tumor niches, as well as global interactions across tumor regions. We show that our GNN model outperforms existing models for both cell-level and slide-level tasks. This work presents a novel multi-scale framework for predicting transcriptome profiles from WSIs. Citation Format: Tianyi Zhang, Malay Singh, Dwivedi Naman, Timothy Antoni, Ju Can, Firas Elbayoumi, Iain Bee Huat Tan, Tiantian He, Hwee Kuan Lee, Shyam Prabhakar, Grace Hui Ting Yeo. Multi-Scale Modeling for Prediction of Spatial Single-Cell and Bulk Transcriptomic Profiles from Whole-Slide Images of Colorectal Cancer [abstract]. In: Proceedings of Frontiers in Cancer Science 2024; 2024 Nov 13-15; Singapore. Philadelphia (PA): AACR; Cancer Res 2025;85(15_Suppl):Abstract nr P24.

A method of synchronization of several identical chaotic systems using a new type of control was proposed. It is based on predicting the trajectories of each of the nonlinear systems and correcting them by the deviation of the prediction from the desired value. Three types of control with global interaction between the systems, local interaction between the systems, and one leading oscillator affecting the rest of them were proposed. After synchronization, all systems make identical chaotic motions. This method features low level of the control action; in principle, control may be made arbitrarily small. Numerical modeling bore out efficiency of this method for any number of oscillators.

Modelling the dynamics of multi-agent reinforcement learning has long been an important research topic. Most of the previous works focus on agents learning under global interactions. In this paper, we investigate learning in a population of agents with local interactions, such that agents learn their policies concurrently by playing with some other agents locally, without the knowledge of the whole population. We derive the stochastic differential equations (SDEs) to describe the Q-values dynamics of each individual agent under the stochastic environment. Applying the Fokker-Planck equation, the time evolution of the probability distribution (PDF) of the population Q-values is worked out. We validate our model through comparisons with agent-based simulations on typical symmetric games with various settings, and the results verify that the model can precisely capture the behaviour of the multi-agent system.

To address the issues of neglecting the spatiotemporal correlations among process variables, low‐level features are vulnerable to noise interference, and the gradual loss of key information layer by layer during deep network training in traditional stacked autoencoder‐based soft‐sensor models, this paper proposes a hierarchical complementary enhanced autoencoder integrating spatio‐temporal interaction feature (ST‐HCEAE) model. Firstly, a spatio‐temporal interaction network (STIN) is constructed to extract the global and local spatio‐temporal interaction relationships. Temporal features are captured by using the temporal attention mechanism. The maximum information coefficient (MIC) is adopted to obtain the global spatial interaction relationship across time steps. The gated graph attention (GGAT) mechanism is proposed to obtain the local spatial interaction relationship at each time step, and the spatio‐temporal interaction features are obtained through adaptive fusion. Secondly, the hierarchical complementary enhancement (HCE) module is introduced. By adopting the dynamic fusion strategy of upsampling hierarchical complementary enhancement (HCE‐up) and downsampling hierarchical complementary enhancement (HCE‐down), and leveraging bidirectional information compensation between adjacent hidden layers, the problems of noise interference in the feature extraction process and key information deficiency in deep network training are effectively addressed. Afterwards, multi‐layer features are fused through the gating mechanism to establish a regression prediction model. Finally, two industrial cases, namely the debutanizer tower and sulfur recovery, were adopted for experimental verification. The experimental results show that, compared with the other five existing modeling methods, the proposed ST‐HCEAE method has higher prediction accuracy.

Probabilistic smooth attention for deep multiple instance learning in medical imaging

Rational expectations equilibria of economies with local interactions