Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?

Network-level traffic condition forecasting has been intensively studied for decades. Although prediction accuracy has been continuously improved with emerging deep learning models and ever-expanding traffic data, traffic forecasting still faces many challenges in practice. These challenges include the robustness of data-driven models, the inherent unpredictability of traffic dynamics, and whether further improvement of traffic forecasting requires more sensor data. In this paper, we focus on this latter question and particularly on data from loop detectors. To answer this, we propose an uncertainty-aware traffic forecasting framework to explore how many samples of loop data are truly effective for training forecasting models. Firstly, the model design combines traffic flow theory with graph neural networks, ensuring the robustness of prediction and uncertainty quantification. Secondly, evidential learning is employed to quantify different sources of uncertainty in a single pass. The estimated uncertainty is used to "distil" the essence of the dataset that sufficiently covers the information content. Results from a case study of a highway network around Amsterdam show that, from 2018 to 2021, more than 80\% of the data during daytime can be removed. The remaining 20\% samples have equal prediction power for training models. This result suggests that indeed large traffic datasets can be subdivided into significantly smaller but equally informative datasets. From these findings, we conclude that the proposed methodology proves valuable in evaluating large traffic datasets' true information content. Further extensions, such as extracting smaller, spatially non-redundant datasets, are possible with this method.

Generative Structural Design Integrating BIM and Diffusion Model

Intelligent structural design using AI can effectively reduce time overhead and increase efficiency. It has potential to become the new design paradigm in the future to assist and even replace engineers, and so it has become a research hotspot in the academic community. However, current methods have some limitations to be addressed, whether in terms of application scope, visual quality of generated results, or evaluation metrics of results. This study proposes a comprehensive solution. Firstly, we introduce building information modeling (BIM) into intelligent structural design and establishes a structural design pipeline integrating BIM and generative AI, which is a powerful supplement to the previous frameworks that only considered CAD drawings. In order to improve the perceptual quality and details of generations, this study makes 3 contributions. Firstly, in terms of generation framework, inspired by the process of human drawing, a novel 2-stage generation framework is proposed to replace the traditional end-to-end framework to reduce the generation difficulty for AI models. Secondly, in terms of generative AI tools adopted, diffusion models (DMs) are introduced to replace widely used generative adversarial network (GAN)-based models, and a novel physics-based conditional diffusion model (PCDM) is proposed to consider different design prerequisites. Thirdly, in terms of neural networks, an attention block (AB) consisting of a self-attention block (SAB) and a parallel cross-attention block (PCAB) is designed to facilitate cross-domain data fusion. The quantitative and qualitative results demonstrate the powerful generation and representation capabilities of PCDM. Necessary ablation studies are conducted to examine the validity of the methods. This study also shows that DMs have the potential to replace GANs and become the new benchmark for generative problems in civil engineering.

Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection

Recently, pretrained language models have shown state-of-the-art performance on the vulnerability detection task. These models are pretrained on a large corpus of source code, then fine-tuned on a smaller supervised vulnerability dataset. Due to the different training objectives and the performance of the models, it is interesting to consider whether the models have learned the semantics of code relevant to vulnerability detection, namely bug semantics, and if so, how the alignment to bug semantics relates to model performance. In this paper, we analyze the models using three distinct methods: interpretability tools, attention analysis, and interaction matrix analysis. We compare the models' influential feature sets with the bug semantic features which define the causes of bugs, including buggy paths and Potentially Vulnerable Statements (PVS). We find that (1) better-performing models also aligned better with PVS, (2) the models failed to align strongly to PVS, and (3) the models failed to align at all to buggy paths. Based on our analysis, we developed two annotation methods which highlight the bug semantics inside the model's inputs. We evaluated our approach on four distinct transformer models and four vulnerability datasets and found that our annotations improved the models' performance in the majority of settings - 11 out of 16, with up to 9.57 points improvement in F1 score compared to conventional fine-tuning. We further found that with our annotations, the models aligned up to 232% better to potentially vulnerable statements. Our findings indicate that it is helpful to provide the model with information of the bug semantics, that the model can attend to it, and motivate future work in learning more complex path-based bug semantics. Our code and data are available at https://figshare.com/s/4a16a528d6874aad51a0.

Relation Extraction from News Articles (RENA): A Tool for Epidemic Surveillance

Relation Extraction from News Articles (RENA) is a browser-based tool designed to extract key entities and their semantic relationships in English language news articles related to infectious diseases. Constructed using the React framework, this system presents users with an elegant and user-friendly interface. It enables users to input a news article and select from a choice of two models to generate a comprehensive list of relations within the provided text. As a result, RENA allows real-time parsing of news articles to extract key information for epidemic surveillance, contributing to EPIWATCH, an open-source intelligence-based epidemic warning system.

LISNeRF Mapping: LiDAR-based Implicit Mapping via Semantic Neural Fields for Large-Scale 3D Scenes

Large-scale semantic mapping is crucial for outdoor autonomous agents to fulfill high-level tasks such as planning and navigation. This paper proposes a novel method for large-scale 3D semantic reconstruction through implicit representations from LiDAR measurements alone. We firstly leverages an octree-based and hierarchical structure to store implicit features, then these implicit features are decoded to semantic information and signed distance value through shallow Multilayer Perceptrons (MLPs). We adopt off-the-shelf algorithms to predict the semantic labels and instance IDs of point cloud. Then we jointly optimize the implicit features and MLPs parameters with self-supervision paradigm for point cloud geometry and pseudo-supervision pradigm for semantic and panoptic labels. Subsequently, Marching Cubes algorithm is exploited to subdivide and visualize the scenes in the inferring stage. For scenarios with memory constraints, a map stitching strategy is also developed to merge sub-maps into a complete map. As far as we know, our method is the first work to reconstruct semantic implicit scenes from LiDAR-only input. Experiments on three real-world datasets, SemanticKITTI, SemanticPOSS and nuScenes, demonstrate the effectiveness and efficiency of our framework compared to current state-of-the-art 3D mapping methods.

Multi-State Brain Network Discovery

Brain network discovery aims to find nodes and edges from the spatio-temporal signals obtained by neuroimaging data, such as fMRI scans of human brains. Existing methods tend to derive representative or average brain networks, assuming observed signals are generated by only a single brain activity state. However, the human brain usually involves multiple activity states, which jointly determine the brain activities. The brain regions and their connectivity usually exhibit intricate patterns that are difficult to capture with only a single-state network. Recent studies find that brain parcellation and connectivity change according to the brain activity state. We refer to such brain networks as multi-state, and this mixture can help us understand human behavior. Thus, compared to a single-state network, a multi-state network can prevent us from losing crucial information of cognitive brain network. To achieve this, we propose a new model called MNGL (Multi-state Network Graphical Lasso), which successfully models multi-state brain networks by combining CGL (coherent graphical lasso) with GMM (Gaussian Mixture Model). Using both synthetic and real world ADHD 200 fMRI datasets, we demonstrate that MNGL outperforms recent state-of-the-art alternatives by discovering more explanatory and realistic results.

Astrocytes as a mechanism for meta-plasticity and contextually-guided network function

Astrocytes are a highly expressed and highly enigmatic cell-type in the mammalian brain. Traditionally viewed as a mediator of basic physiological sustenance, it is increasingly recognized that astrocytes may play a more direct role in neural computation. A conceptual challenge to this idea is the fact that astrocytic activity takes a very different form than that of neurons, and in particular, occurs at orders-of-magnitude slower time-scales. In the current paper, we engage how such time-scale separation may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. This idea is based on the recent supposition that astrocytes, owing to their sensitivity to a host of physiological covariates, may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. We pose a general model of neural-synaptic-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably versus dynamically homogenous networks and conventional non-network-based bandit algorithms. Our results indicate how the presence of neural-astrocyte interaction in the brain may benefit learning over different time-scale and the conveyance of task relevant contextual information onto circuit dynamics.

Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild

Facial expression recognition (FER) in the wild is a challenging task affected by the image quality and has attracted broad interest in computer vision. There is no research using feature fusion and ensemble strategy for FER simultaneously. Different from previous studies, this paper applies both internal feature fusion for a single model and feature fusion among multiple networks, as well as the ensemble strategy. This paper proposes one novel single model named R18+FAML, as well as one ensemble model named R18+FAML-FGA-T2V to improve the performance of the FER in the wild. Based on the structure of ResNet18 (R18), R18+FAML combines internal Feature fusion and three Attention blocks using Multiple Loss functions (FAML) to improve the diversity of the feature extraction. To improve the performance of R18+FAML, we propose a Feature fusion among networks based on the Genetic Algorithm (FGA), which can fuse the convolution kernels for feature extraction of multiple networks. On the basis of R18+FAML and FGA, we propose one ensemble strategy, i.e., the Top Two Voting (T2V) to support the classification of FER, which can consider more classification information comprehensively. Combining the above strategies, R18+FAML-FGA-T2V can focus on the main expression-aware areas. Extensive experiments demonstrate that our single model R18+FAML and the ensemble model R18+FAML-FGA-T2V achieve the accuracies of $\left( 90.32, 62.17, 65.83 \right)\%$ and $\left( 91.59, 63.27, 66.63 \right)\%$ on three challenging unbalanced FER datasets RAF-DB, AffectNet-8 and AffectNet-7 respectively, both outperforming the state-of-the-art results.

3D Masked Autoencoders for Enhanced Privacy in MRI Scans

MRI scans provide valuable medical information, however they also contain sensitive and personally identifiable information (PII) that needs to be protected. Whereas MRI metadata is easily sanitized, MRI image data is a privacy risk because it contains information to render highly-realistic 3D visualizations of a patient's head, enabling malicious actors to possibly identify the subject by cross-referencing a database. Data anonymization and de-identification is concerned with ensuring the privacy and confidentiality of individuals' personal information. Traditional MRI de-identification methods remove privacy-sensitive parts (e.g. eyes, nose etc.) from a given scan. This comes at the expense of introducing a domain shift that can throw off downstream analyses. Recently, a GAN-based approach was proposed to de-identify a patient's scan by remodeling it (e.g. changing the face) rather than by removing parts. In this work, we propose CP-MAE, a model that de-identifies the face using masked autoencoders and that outperforms all previous approaches in terms of downstream task performance as well as de-identification. With our method we are able to synthesize scans of resolution up to $256^3$ (previously 128 cubic) which constitutes an eight-fold increase in the number of voxels. Using our construction we were able to design a system that exhibits a highly robust training stage, making it easy to fit the network on novel data.

Multitask Online Learning: Listen to the Neighborhood Buzz

We study multitask online learning in a setting where agents can only exchange information with their neighbors on an arbitrary communication network. We introduce $\texttt{MT-CO}_2\texttt{OL}$, a decentralized algorithm for this setting whose regret depends on the interplay between the task similarities and the network structure. Our analysis shows that the regret of $\texttt{MT-CO}_2\texttt{OL}$ is never worse (up to constants) than the bound obtained when agents do not share information. On the other hand, our bounds significantly improve when neighboring agents operate on similar tasks. In addition, we prove that our algorithm can be made differentially private with a negligible impact on the regret when the losses are linear. Finally, we provide experimental support for our theory.