America is facing a health care crisis primarily due to its aging population. Physician shortages have come to the forefront recently, as many hospitals are overwhelmed due to the COVID-19 pandemic. In truth, our looming physician shortage is a generation in the making, as baby boomer doctors retire in droves. This is all occurring as lifespans are increasing--hence, there are fewer doctors to treat more patients. Exacerbating the problem is that medical schools are not churning out medical students fast enough due to capacity constraints, and it takes 12 to 15 years to train a doctor. Today, more than half of active physicians are older than 55, and by the year 2032, the Association of American Medical Colleges projects a shortfall of 122,000 doctors in the United States.

Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting Internet of Everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and providing high-quality service. As a rapid evolving paradigm, the AI-empowered mobile communications demand large amounts of data acquired from real network environment for systematic test and verification. Hence, we build the world's first true-data testbed for 5G/B5G intelligent network (TTIN), which comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. In the TTIN, true network data acquisition, storage, standardization, and analysis are available, which enable system-level online verification of B5G/6G-orientated key technologies and support data-driven network optimization through the closed-loop control mechanism. This paper elaborates on the system architecture and module design of TTIN. Detailed technical specifications and some of the established use cases are also showcased.

This paper defines a new visual reasoning paradigm by introducing an important factor, i.e., transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, like one image. We argue that this kind of state driven visual reasoning approach has limitations in reflecting whether the machine has the ability to infer the dynamics between different states, which has been shown as important as state-level reasoning for human cognition in Piaget's theory. To tackle this problem, we propose a novel transformation driven visual reasoning task. Given both the initial and final states, the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet (object, attribute, value) or a sequence of triplets, respectively. Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i.e., Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event and View. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and real data need to be investigated in this direction. Code is available at: https://github.com/hughplay/TVR.

The need for "intelligence" in such automation systems stems from the fact that most robotic operations in industry are currently limited to rote and repetitive tasks performed within structured environments. This leaves an entire swath of more complex tasks with high degrees of uncertainty and dynamic environments [7] difficult or even impossible to automate. Examples include maintenance and material handling for producing the desired product in manufacturing systems [8], robot surgeries and pharmacy automation in healthcare systems [9], safe working environments in disaster management for deep-sea operation, and nuclear energy [10], fruit picking, crop sensing, and selective weeding in agriculture systems [11]. A fundamental question concerning the notion of intelligent automation in this context then becomes: How can we enable adaptable industrial automation systems that can analyze and act upon their perceived environment rather than merely executing a set of predefined programs? Adaptability is among the key characteristics of industrial automation systems in response to unpredictable changes or disruptions in the process [12].

Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in financial risk. There have emerged many studies that implement machine learning and deep learning techniques which are based on vector space to deal with corporate credit rating. Recently, considering the relations among enterprises such as loan guarantee network, some graph-based models are applied in this field with the advent of graph neural networks. But these existing models build networks between corporations without taking the internal feature interactions into account. In this paper, to overcome such problems, we propose a novel model, Corporate Credit Rating via Graph Neural Networks, CCR-GNN for brevity. We firstly construct individual graphs for each corporation based on self-outer product and then use GNN to model the feature interaction explicitly, which includes both local and global information. Extensive experiments conducted on the Chinese public-listed corporate rating dataset, prove that CCR-GNN outperforms the state-of-the-art methods consistently.

Humans are remarkable for continuously learning throughout the entirety of their lives, from acquiring physical reasoning and language skills at a young age [64, 43], to the ability to reason about the detailed complexities inherent in everyday adult life. One key quality of this learning is that it happens at multiple scales, both in terms of time and abstraction, in a process termed meta-learning or learning to learn. The fundamental principle of meta-learning is that learning proceeds faster with more experience, via the acquisition of inductive biases or knowledge that allows for more efficient learning in the future [66, 59, 57]. These favorable properties of meta-learning have recently gained it considerable renewed interest within the deep learning/artificial intelligence community. Despite their tremendous successes in recent years [46, 61], deep learning systems still require many orders of magnitude of data than humans [40, 12]. Although early work demonstrated the feasibility for neural networks to discover their own learning rules [10, 58], it was only recently that the field has experienced a resurgence of new research in meta-learning using deep neural networks. This has demonstrated the wide-ranging potential of neural networks to meta-learn all aspects of the learning process. Deep neural networks are typically trained via backpropagation, which adjusts the weights of the neural network so that given a set of input data, the network outputs match some desired target outputs (e.g., classification labels).

While long short-term memory (LSTM) models have demonstrated stellar performance with streamflow predictions, there are major risks in applying these models in contiguous regions with no gauges, or predictions in ungauged regions (PUR) problems. However, softer data such as the flow duration curve (FDC) may be already available from nearby stations, or may become available. Here we demonstrate that sparse FDC data can be migrated and assimilated by an LSTM-based network, via an encoder. A stringent region-based holdout test showed a median Kling-Gupta efficiency (KGE) of 0.62 for a US dataset, substantially higher than previous state-of-the-art global-scale ungauged basin tests. The baseline model without FDC was already competitive (median KGE 0.56), but integrating FDCs had substantial value. Because of the inaccurate representation of inputs, the baseline models might sometimes produce catastrophic results. However, model generalizability was further meaningfully improved by compiling an ensemble based on models with different input selections.

The game industry has long been troubled by malicious activities utilizing game bots. The game bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry put their best efforts to detect the game bots among players' characters using the learning-based detections. However, one problem with the detection methodologies is that they do not provide rational explanations about their decisions. To resolve this problem, in this work, we investigate the explainabilities of the game bot detection. We develop the XAI model using a dataset from the Korean MMORPG, AION, which includes game logs of human players and game bots. More than one classification model has been applied to the dataset to be analyzed by applying interpretable models. This provides us explanations about the game bots' behavior, and the truthfulness of the explanations has been evaluated. Besides, interpretability contributes to minimizing false detection, which imposes unfair restrictions on human players.

Skeleton-based human action recognition has attracted much attention with the prevalence of accessible depth sensors. Recently, graph convolutional networks (GCNs) have been widely used for this task due to their powerful capability to model graph data. The topology of the adjacency graph is a key factor for modeling the correlations of the input skeletons. Thus, previous methods mainly focus on the design/learning of the graph topology. But once the topology is learned, only a single-scale feature and one transformation exist in each layer of the networks. Many insights, such as multi-scale information and multiple sets of transformations, that have been proven to be very effective in convolutional neural networks (CNNs), have not been investigated in GCNs. The reason is that, due to the gap between graph-structured skeleton data and conventional image/video data, it is very challenging to embed these insights into GCNs. To overcome this gap, we reinvent the split-transform-merge strategy in GCNs for skeleton sequence processing. Specifically, we design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths. Extensive experiments demonstrate that our network outperforms state-of-the-art methods by a significant margin with only 1/5 of the parameters and 1/10 of the FLOPs.

Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict movements intuitively and to avoid obstacles in a forward-looking way. The task of collision avoidance can be divided into a global and a local level. Regarding the global level, we propose an approach called "Predictive Collision Management Path Planning" (PCMP). At the local level, solutions for collision avoidance are used that prevent an inevitable collision. Therefore, the aim of PCMP is to avoid unnecessary local collision scenarios using predictive collision management. PCMP is a graph-based algorithm with a focus on the time dimension consisting of three parts: (1) movement prediction, (2) integration of movement prediction into a time-dependent graph, and (3) time and risk-dependent path planning. The algorithm combines the search for a shortest path with the question: is the detour worth avoiding a possible collision scenario? We evaluate the evasion behavior in different simulation scenarios and the results show that a risk-sensitive agent can avoid 47.3% of the collision scenarios while making a detour of 1.3%. A risk-averse agent avoids up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's evasive behavior can be controlled actively and risk-dependent using PCMP.