Single-cell Hi-C (scHi-C) provides unprecedented insight into 3D genome organization, but its sparse and noisy data pose challenges in accurately detecting A/B compartments, which are crucial for understanding chromatin structure and gene regulation. We presented scDIAGRAM, a data-driven method for annotating A/B compartments in single cells using direct statistical modeling and graph community detection. Unlike existing approaches, scDIAGRAM infers chromatin compartments directly from individual scHi-C matrix without imputation or external reference features, and subsequently assigns A/B labels using conventional genomic annotations. Accuracy and robustness of scDIAGRAM were illustrated through simulated scHi-C datasets and a human cell line. We applied scDIAGRAM to real scHi-C datasets from the mouse brain cortex, mouse embryonic development, and human acute myeloid leukemia, demonstrating its ability to capture compartmental shifts associated with transcriptional variation. This robust framework offers new insights into the functional roles of chromatin compartments at single-cell resolution across various biological contexts.

The chlorophyll (Chl) is closely related to vegetation respiration and photosynthesis, and its long time series data with high spatial resolution is significant for dynamics monitoring for vegetation growth status and environmental management. However, extant methodologies for long-term Chl content retrieval are encumbered with data lacunae and imbalance in high spatial resolution and continuous time series cover-age. To address these limitations, we have developed a novel Chl assimilation inversion model with Kalman filtering method (KFCAM) to synthesize high spatiotemporal resolution Chl content data in Wuhan. The KFCAM is used to I) obtain Chl time-varying rates from low-resolution (Landsat-8) images and high-resolution Chl information from Sentinel-2/SPOT5 images, simultaneously II) calculating the state error and Kalman gain; then III) this model integrates the high-resolution information into the time series by state updating model, and finally IV) synthesis continuous time series images for Chl with 10 m resolution. The results demonstrate that I) the synthetic Chl image with KFCAM exhibits high accuracy with a R2 of 0.906±0.047 and a RMSE of 1.801±0.369, and II) the RMSE values reveal a 12% and 33% reduction compare to the Spatio-Temporal Gap-Filling (STGF) and temporal adaptive reflectance fusion model (STARFM) respectively. III) Furthermore, in comparative experiments that considered different assimilation time intervals and changes in land cover, KFCAM outperformed two reference models in terms of stability and anti-interference capability. It provides a novel means for large scale and global Chl detection.

Cooperative multi-agent reinforcement learning (MARL) is extensively used for solving complex cooperative tasks, and value decomposition methods are a prevalent approach for this domain. However, these methods have not been successful in addressing both homogeneous and heterogeneous tasks simultaneously which is a crucial aspect for the practical application of cooperative agents. On one hand, value decomposition methods demonstrate superior performance in homogeneous tasks. Nevertheless, they tend to produce agents with similar policies, which is unsuitable for heterogeneous tasks. On the other hand, solutions based on personalized observation or assigned roles are well-suited for heterogeneous tasks. However, they often lead to a trade-off situation where the agent's performance in homogeneous scenarios is negatively affected due to the aggregation of distinct policies. An alternative approach is to adopt sequential execution policies, which offer a flexible form for learning both types of tasks. However, learning sequential execution policies poses challenges in terms of credit assignment, and the limited information about subsequently executed agents can lead to sub-optimal solutions, which is known as the relative over-generalization problem. To tackle these issues, this paper proposes Greedy Sequential Execution (GSE) as a solution to learn the optimal policy that covers both scenarios. In the proposed GSE framework, we introduce an individual utility function into the framework of value decomposition to consider the complex interactions between agents. This function is capable of representing both the homogeneous and heterogeneous optimal policies. Furthermore, we utilize greedy marginal contribution calculated by the utility function as the credit value of the sequential execution policy to address the credit assignment and relative over-generalization problem. We evaluated GSE in both homogeneous and heterogeneous scenarios. The results demonstrate that GSE achieves significant improvement in performance across multiple domains, especially in scenarios involving both homogeneous and heterogeneous tasks.

Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior coordination. Many works propose to abstract teammate instances into high-level representation of types and then pre-train the best response for each type. However, most of them do not consider the distribution of teammate instances within a type. This could expose the agent to the hidden risk of type confounding. In the worst case, the best response for an abstract teammate type could be the worst response for all specific instances of that type. This work addresses the issue from the lens of causal inference. We first theoretically demonstrate that this phenomenon is due to the spurious correlation brought by uncontrolled teammate distribution. Then, we propose our solution, CTCAT, which disentangles such correlation through an instance-wise teammate feedback rectification. This operation reweights the interaction of teammate instances within a shared type to reduce the influence of type confounding. The effect of CTCAT is evaluated in multiple domains, including classic ad hoc teamwork tasks and real-world scenarios. Results show that CTCAT is robust to the influence of type confounding, a practical issue that directly hazards the robustness of our trained agents but was unnoticed in previous works.

Offline Reinforcement Learning (RL) has demonstrated promising results in various applications by learning policies from previously collected datasets, reducing the need for online exploration and interactions. However, real-world scenarios usually involve partial observability, which brings crucial challenges of the deployment of offline RL methods: i) the policy trained on data with full observability is not robust against the masked observations during execution, and ii) the information of which parts of observations are masked is usually unknown during training. In order to address these challenges, we present Offline RL with DiscrEte pRoxy representations (ORDER), a probabilistic framework which leverages novel state representations to improve the robustness against diverse masked observabilities. Specifically, we propose a discrete representation of the states and use a proxy representation to recover the states from masked partial observable trajectories. The training of ORDER can be compactly described as the following three steps. i) Learning the discrete state representations on data with full observations, ii) Training the decision module based on the discrete representations, and iii) Training the proxy discrete representations on the data with various partial observations, aligning with the discrete representations. We conduct extensive experiments to evaluate ORDER, showcasing its effectiveness in offline RL for diverse partially observable scenarios and highlighting the significance of discrete proxy representations in generalization performance. ORDER is a flexible framework to employ any offline RL algorithms and we hope that ORDER can pave the way for the deployment of RL policy against various partial observabilities in the real world.

Human brain can effectively integrate visual and auditory information. Dynamic Vision Sensor (DVS) and Dynamic Audio Sensor (DAS) are event-based sensors imitating the mechanism of human retina and cochlea. Since the sensors record the visual and auditory input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on unimodality, however, audiovisual multimodal SNNs are still limited. In this paper, we propose an end-to-end event-based multimodal spiking neural network. The network consists of visual and auditory unimodal subnetworks and a novel attention-based cross-modal subnetwork for fusion. The attention mechanism measures the significance of each modality and allocates the weights to two modalities. We evaluate our proposed multimodal network on an event-based audiovisual joint dataset (MNIST-DVS and N-TIDIGITS datasets). Experimental results show the performance improvement of this multimodal network and the effectiveness of our proposed attention mechanism.

Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of storage and computation. This hinders their deployment on scenarios where resources are limited. In this work, we propose TinyLight, the first DRL-based ATSC model that is designed for devices with extremely limited resources. TinyLight first constructs a super-graph to associate a rich set of candidate features with a group of light-weighted network blocks. Then, to diminish the model's resource consumption, we ablate edges in the super-graph automatically with a novel entropy-minimized objective function. This enables TinyLight to work on a standalone microcontroller with merely 2KB RAM and 32KB ROM. We evaluate TinyLight on multiple road networks with real-world traffic demands. Experiments show that even with extremely limited resources, TinyLight still achieves competitive performance. The source code and appendix of this work can be found at https://bit.ly/38hH8t8.

Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on the task of object recognition. However, events from the event-based camera are triggered by dynamic changes, which makes it an ideal choice to capture actions in the visual scene. Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. Motion features are extracted and utilized from events to local and finally to global perception for action recognition. To the best of the authors’ knowledge, it is the first attempt of SNN to apply motion information to event-based action recognition. We evaluate our proposed SNN on three event-based action recognition datasets, including our newly published DailyAction-DVS dataset comprising 12 actions collected under diverse recording conditions. Extensive experimental results show the effectiveness of motion information and our proposed SNN architecture for event-based action recognition.

In ad-hoc teamwork, an agent is required to cooperate with unknown teammates without prior coordination. To swiftly adapt to an unknown teammate, most works adopt a type-based approach, which pre-trains the agent with a set of pre-prepared teammate types, then associates the unknown teammate with a particular type. Typically, these types are collected manually. This hampers previous works by both the availability and diversity of types they manage to obtain. To eliminate these limitations, this work addresses to achieve ad-hoc teamwork in a type-free approach. Specifically, we propose the model of Entropy-regularized Deep Recurrent Q-Network (EDRQN) to generate teammates automatically, meanwhile utilize them to pre-train our agent. These teammates are obtained from scratch and are designed to perform the task with various behaviors, therefore their availability and diversity are both ensured. We evaluate our model on several benchmark domains of ad-hoc teamwork. The result shows that even if our model has no access to any pre-prepared teammate types, it still achieves significant performance.

Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras. Since AER cameras record the visual input as asynchronous discrete events, they are inherently suitable to coordinate with the spiking neural network (SNN), which is biologically plausible and energy-efficient on neuromorphic hardware. However, using SNN to perform the AER object classification is still challenging, due to the lack of effective learning algorithms for this new representation. To tackle this issue, we propose an AER object classification model using a novel segmented probability-maximization (SPA) learning algorithm. Technically, 1) the SPA learning algorithm iteratively maximizes the probability of the classes that samples belong to, in order to improve the reliability of neuron responses and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced in SPA to locate informative time points segment by segment, based on which information within the whole event stream can be fully utilized by the learning. Extensive experimental results show that, compared to state-of-the-art methods, not only our model is more effective, but also it requires less information to reach a certain level of accuracy.

This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network (SNN) using spike-timing-dependent plasticity (STDP) for object recognition with MuST. MuST extracts the features contained in both the spatial and temporal information of AER event flow, and forms an informative and compact feature spike representation. We show not only how MuST exploits spikes to convey information more effectively, but also how it benefits the recognition using SNN. The recognition process is performed in an unsupervised manner, which does not need to specify the desired status of every single neuron of SNN, and thus can be flexibly applied in real-world recognition tasks. The experiments are performed on five AER datasets including a new one named GESTURE-DVS. Extensive experimental results show the effectiveness and advantages of the proposed approach.

Neurons communicate nonlinearly through spike activities. Generalized linear models (GLMs) describe spike activities with a cascade of a linear combination across inputs, a static nonlinear function, and an inhomogeneous Bernoulli or Poisson process, or Cox process if a self-history term is considered. This structure considers the output nonlinearity in spike generation but excludes the nonlinear interaction among input neurons. Recent studies extend GLMs by modeling the interaction among input neurons with a quadratic function, which considers the interaction between every pair of input spikes. However, quadratic effects may not fully capture the nonlinear nature of input interaction. We therefore propose a staged point-process model to describe the nonlinear interaction among inputs using a few hidden units, which follows the idea of artificial neural networks. The output firing probability conditioned on inputs is formed as a cascade of two linear-nonlinear (a linear combination plus a static nonlinear function) stages and an inhomogeneous Bernoulli process. Parameters of this model are estimated by maximizing the log likelihood on output spike trains. Unlike the iterative reweighted least squares algorithm used in GLMs, where the performance is guaranteed by the concave condition, we propose a modified Levenberg-Marquardt (L-M) algorithm, which directly calculates the Hessian matrix of the log likelihood, for the nonlinear optimization in our model. The proposed model is tested on both synthetic data and real spike train data recorded from the dorsal premotor cortex and primary motor cortex of a monkey performing a center-out task. Performances are evaluated by discrete-time rescaled Kolmogorov-Smirnov tests, where our model statistically outperforms a GLM and its quadratic extension, with a higher goodness-of-fit in the prediction results. In addition, the staged point-process model describes nonlinear interaction among input neurons with fewer parameters than quadratic models, and the modified L-M algorithm also demonstrates fast convergence.

Depth of Field (DoF) is an indispensable feature of photo realistic rendering and photography retouching. In this paper, we propose an image-based rendering technique which can simulate the depth-of-field effect. The proposed technique can render the depth-of-field effect automatically without any interactions. Compared to the ordinary depth-of-field rendering technique, our algorithm is less time-consuming and needs no additional depth maps to assist the depth-of-field rendering. In our proposed algorithm, the saliency detection technique is employed to simulate the depth information. The flash-based technique is also introduced to promote the final depth-of-field rendering visual effect.