Data drift is a thorny challenge when deploying person re-identification (ReID) models into real-world devices, where the data distribution is significantly different from that of the training environment and keeps changing. To tackle this issue, we propose a federated spatial-temporal incremental learning approach, named FedSTIL, which leverages both lifelong learning and federated learning to continuously optimize models deployed on many distributed edge clients. Unlike previous efforts, FedSTIL aims to mine spatial-temporal correlations among the knowledge learnt from different edge clients. Specifically, the edge clients first periodically extract general representations of drifted data to optimize their local models. Then, the learnt knowledge from edge clients will be aggregated by centralized parameter server, where the knowledge will be selectively and attentively distilled from spatial- and temporal-dimension with carefully designed mechanisms. Finally, the distilled informative spatial-temporal knowledge will be sent back to correlated edge clients to further improve the recognition accuracy of each edge client with a lifelong learning method. Extensive experiments on a mixture of five real-world datasets demonstrate that our method outperforms others by nearly 4% in Rank-1 accuracy, while reducing communication cost by 62%. All implementation codes are publicly available on https://github.com/MSNLAB/Federated-Lifelong-Person-ReID

Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid models, thereby improving the prediction accuracy. However, the spatial information acquisition schemes of these methods have two-level differentiation problems. Either the modeling is simple but contains little spatial information, or the modeling is complete but lacks flexibility. In order to introduce more spatial information on the basis of ensuring flexibility, this paper proposes IRNet (Transferable Intersection Reconstruction Network). First, this paper reconstructs the intersection into a virtual intersection with the same structure, which simplifies the topology of the road network. Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models. Third, a self-attention mechanism is used to fuse spatiotemporal features for prediction. In the comparison experiment with the baseline, not only the prediction effect, but also the transfer performance has obvious advantages.

Abstract: Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, re asonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatialtemporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatialtemporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations. With the continuous acceleration of urbanization, the population and vehicle ownership are also increasing, resulting in traffic congestion and other problems. In order to improve the efficiency, sustainability and security of transportation network, intelligent transportation system (ITS) [1] is proposed and becomes an advancing research field. Traffic prediction is an important step in the development of intelligent transportation [2]. It 2 aims to predict future traffic conditions by integrating historical observation data and measurement information of road sensor networks.

However, it is also more challenging due to the higher complexity and wider variety of inter-object relations contained in point clouds. Existing methods only treat such relations as by-products of object feature learning in graphs without specifically encoding them, which leads to sub-optimal results. In this paper, aiming at improving 3D dense captioning via capturing and utilizing the complex relations in the 3D scene, we propose MORE, a Multi-Order RElation mining model, to support generating more descriptive and comprehensive captions. Technically, our MORE encodes object relations in a progressive manner since complex relations can be deduced from a limited number of basic ones. We first devise a novel Spatial Layout Graph Convolution (SLGC), which semantically encodes several first-order relations as edges of a graph constructed over 3D object proposals. Next, from the resulting graph, we further extract multiple triplets which encapsulate basic first-order relations as the basic unit, and construct several Object-centric Triplet Attention Graphs (OTAG) to infer multi-order relations for every target object. The updated node features from OTAG are aggregated and fed into the caption decoder to provide abundant relational cues, so that captions including diverse relations with context objects can be generated. Extensive experiments on the Scan2Cap dataset prove the effectiveness of our proposed MORE and its components, and we also outperform the current state-of-the-art method. Our code is available at https://github.com/SxJyJay/MORE.

Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex. This paper presents voxel2vec, a novel unsupervised representation learning model, which is used to learn distributed representations of scalar values/scalar-value combinations in a low-dimensional vector space. Its basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values/scalar-value combinations as symbols, voxel2vec learns the similarity between them in the context of spatial distribution and then allows us to explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and to association analysis for time-varying and ensemble data.

Traffic flow forecasting on graphs has real-world applications in many fields, such as transportation system and computer networks. Traffic forecasting can be highly challenging due to complex spatial-temporal correlations and non-linear traffic patterns. Existing works mostly model such spatial-temporal dependencies by considering spatial correlations and temporal correlations separately and fail to model the direct spatial-temporal correlations. Inspired by the recent success of transformers in the graph domain, in this paper, we propose to directly model the cross-spatial-temporal correlations on the spatial-temporal graph using local multi-head self-attentions. To reduce the time complexity, we set the attention receptive field to the spatially neighboring nodes, and we also introduce an adaptive graph to capture the hidden spatial-temporal dependencies. Based on these attention mechanisms, we propose a novel Adaptive Graph Spatial-Temporal Transformer Network (ASTTN), which stacks multiple spatial-temporal attention layers to apply self-attention on the input graph, followed by linear layers for predictions. Experimental results on public traffic network datasets, METR-LA PEMS-BAY, PeMSD4, and PeMSD7, demonstrate the superior performance of our model.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. For IKEA, the latest in digital transformation is all about home design driven by artificial intelligence (AI) – minus the home furnishing and decor retailer's famous Swedish meatballs. Today, it launched IKEA Kreativ, a design experience meant to bridge the ecommerce and in-store customer journeys, powered by the latest AI developments in spatial computing, machine learning and 3D mixed reality technologies. Available in-app and online, IKEA Kreativ's core technology was developed by Geomagical Labs, an IKEA retail company, which Ingka Group (the holding company that controls 367 stores of 422 IKEA stores) acquired in April 2020. IKEA Kreativ is the next step in IKEA's long journey towards digital transformation.

Blake Lemoine, a Google engineer working in its Responsible AI division, revealed to The Washington Post that he believes one of the company's AI projects has achieved sentience. And after reading his conversations with LaMDA (short for Language Model for Dialogue Applications), it's easy to see why. The chatbot system, which relies on Google's language models and trillions of words from the internet, seems to have the ability to think about its own existence and its place in the world. Here's one choice excerpt from his extended chat transcript: Lemoine: So let's start with the basics. Do you have feelings and emotions?

Blake Lemoine, a Google engineer working in its Responsible AI division, revealed to The Washington Post that he believes one of the company's AI projects has achieved sentience. And after reading his conversations with LaMDA (short for Language Model for Dialogue Applications), it's easy to see why. The chatbot system, which relies on Google's language models and trillions of words from the internet, seems to have the ability to think about its own existence and its place in the world. Here's one choice excerpt from his extended chat transcript: Lemoine: So let's start with the basics. Do you have feelings and emotions?

Networks (or graphs) are pervasive in biology and medicine, from molecular interaction maps to populationscale social and health interactions. With the multitude of bioentities and associations that can be described by networks, they are prevailing representations of biological organization and biomedical knowledge. For instance, edges in a regulatory network can indicate causal activating and inhibitory relationships between genes [149]; edges between genes and diseases can indicate genes that are'upregulated by', 'downregulated by', or'associated with' a disease [141]; and edges in a knowledge network built from electronic health records (EHR) can indicate co-occurrences of medical codes across patients [81, 156, 161]. The ability to model all biomedical discoveries to date--even overlay patient-specific information--in a unified data representation has driven the development of artificial intelligence, specifically deep learning, for networks. In fact, the diversity and multimodality in networks not only boost performance of predictive models, but importantly enable broad generalization to settings not seen during training [74] and improve model interpretability [31, 140]. Nevertheless, interactions in networks give rise to a bewildering degree of complexity that can likely only be fully understood through a holistic and integrated view [14, 22, 137]. As a result, systems biology and medicine-- upon which deep learning on graphs is founded--have identified over the last two decades organizing principles that govern networks [13, 66, 85, 227]. 1