Artificial intelligence is becoming increasingly significant in facility management. In particular, predictive maintenance is a significant application of AI in this domain. Predictive maintenance is the process of using data to predict when equipment will fail and needs to be repaired or replaced. This is significant because it can help prevent equipment failures, which can lead to disruptions in service. With the pressure on organizations to do more with less, Facilities Management (FM) must challenge itself to be a strategic business enabler.

The attendees learned what JAX is and its fundamental yet unique features, which make it efficient to use when executing deep learning workloads. After that, they started training their first JAX-powered deep learning model. TFUG Taipei hosted Python JAX Image classification and helped people learn JAX and how to use it in Colab. They shared knowledge about the difference between JAX and Numpy, the advantages of JAX, and how to use it in Colab. Introduction to JAX by ML GDE João Araújo (Brazil) shared the basics of JAX in Deep Learning Indaba 2022.

How to improve the accuracy of emotion recognition has become a primary issue. In recent years, with the continuous development of artificial intelligence technology, human-computer interaction has become the focus of research in the field of information science. As one of the critical technologies to realize human-computer interaction, emotion recognition has gradually received a lot of attention from researchers. At present, most of the research works on emotion recognition are based on single-modal, such as facial expressions [1-3], body movements [4-5] and speech text [6-7]. However, emotion recognition based on unimodal often has limitations and, in most cases, could only reflect a portion of human emotional expression. Multimodal emotion recognition can link individual unimodal channels and use the feature complementarity between channels to combine multiple information to determine the emotional state. Studies have shown that the multimodal emotion recognition approach has better performance than unimodal emotion judgment in most cases [8]. The difficulty of multimodal recognition is not only to control the internal information of individual modality (Intra-modality), but also to complement the interactive features between individual modalities (Inter-modality). It has been extensively studied by scholars, such as Tensor Fusion Network (TFN) proposed by Zadeh et al [9], Polynomial Tensor Pooling (PTP) proposed by Hou et al [10], and Memory Fusion Network (MFN) presented by Zadeh et al [11].

Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue that such coarse-grained sentence embeddings can not fully model the mentions/entities, especially when the attention scores towards mentions/entities are relatively low. In this work, we propose GER, a \textbf{G}raph enhanced \textbf{E}ntity \textbf{R}etrieval framework, to capture more fine-grained information as complementary to sentence embeddings. We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph. Hence, we can learn the fine-grained information about mention/entity by aggregating information from these knowledge units. To avoid the graph information bottleneck for the central mention/entity node, we construct a hierarchical graph and design a novel Hierarchical Graph Attention Network~(HGAN). Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models. The code has been available at https://github.com/wutaiqiang/GER-WSDM2023.

Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes. This paper describes our contribution to the Registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends the iRegNet. In particular, incorporating an unsupervised learning-based paradigm as well as several minor modifications to the network pipeline, allows the enhanced iRegNet method to achieve respectable results. Experimental findings show that the enhanced self-supervised model is able to improve the initial mean median registration absolute error (MAE) from 8.20 (7.62) mm to the lowest value of 3.51 (3.50) for the training set while achieving an MAE of 2.93 (1.63) mm for the validation set. Additional qualitative validation of this study was conducted through overlaying pre-post MRI pairs before and after the de-formable registration. The proposed method scored 5th place during the testing phase of the MICCAI BraTS-Reg 2022 challenge. The docker image to reproduce our BraTS-Reg submission results will be publicly available.

The increasing availability of geometric data has motivated the need for information processing over non-Euclidean domains modeled as manifolds. The building block for information processing architectures with desirable theoretical properties such as invariance and stability is convolutional filtering. Manifold convolutional filters are defined from the manifold diffusion sequence, constructed by successive applications of the Laplace-Beltrami operator to manifold signals. However, the continuous manifold model can only be accessed by sampling discrete points and building an approximate graph model from the sampled manifold. Effective linear information processing on the manifold requires quantifying the error incurred when approximating manifold convolutions with graph convolutions. In this paper, we derive a non-asymptotic error bound for this approximation, showing that convolutional filtering on the sampled manifold converges to continuous manifold filtering. Our findings are further demonstrated empirically on a problem of navigation control.

Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (InfoMax) to learn node representations applied in downstream tasks. To explore better generalization from GCL to downstream tasks, previous methods heuristically define data augmentation or pretext tasks. However, the generalization ability of GCL and its theoretical principle are still less reported. In this paper, we first propose a metric named GCL-GE for GCL generalization ability. Considering the intractability of the metric due to the agnostic downstream task, we theoretically prove a mutual information upper bound for it from an information-theoretic perspective. Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks. We empirically validate our theoretical findings on a number of representative benchmarks, and experimental results demonstrate that our model achieves state-of-the-art performance.

The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals. Our analysis shows that a significant net health benefit may be attained by adaptive NPIs formed by partial social distancing measures, coupled with moderate levels of the society's willingness to pay for health gains (health losses averted). We demonstrate that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.

The Israeli medical imaging vendor Nanox says it has a vision for the future of healthcare to address health disparities and lack of access to care. It envisions a new business model and plans to leverage a package of new technologies, including cold-cathode X-ray technology to help reduce costs, coupled with a new and inexpensive imaging system that combines teleradiology with artificial intelligence (AI). The business model is to enable any clinic or hospital in the developing world or rural areas to access its technology and no upfront costs using a pay-per-exam fee. The exams will be read by remote teleradiologists, including subspecialists, and AI will help augment clinical staff and radiologists to offer additional health screenings for all patients scanned. After a few years of talk, the vendor now appears on the edge of making this a reality.

Markov Chain Monte Carlo (MCMC) methods are the workhorse of Bayesian computation when closed formulas for estimators or probability distributions are not available. For this reason they have been central to the development and success of high-dimensional Bayesian statistics in the last decades, where one attempts to generate samples from some posterior distribution Π( |data) arising from a prior Π on D-dimensional Euclidean space and the observed data vector. MCMC methods tend to perform well in a large variety of problems, are very flexible and user-friendly, and enjoy many theoretical guarantees. Under mild assumptions, they are known to converge to their stationary'target' distributions as a consequence of the ergodic theorem, albeit perhaps at a slow speed, requiring a large number of iterations to provide numerically accurate algorithms. When the target distribution is log-concave, MCMC algorithms are known to mix rapidly, even in high dimensions.