Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future is still far from resolved. The key to predict future facts is to thoroughly understand the historical facts. A TKG is actually a sequence of KGs corresponding to different timestamps, where all concurrent facts in each KG exhibit structural dependencies and temporally adjacent facts carry informative sequential patterns. To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently. Specifically, for the evolution unit, a relation-aware GCN is leveraged to capture the structural dependencies within the KG at each timestamp. In order to capture the sequential patterns of all facts in parallel, the historical KG sequence is modeled auto-regressively by the gate recurrent components. Moreover, the static properties of entities such as entity types, are also incorporated via a static graph constraint component to obtain better entity representations. Fact prediction at future timestamps can then be realized based on the evolutional entity and relation representations. Extensive experiments demonstrate that the RE-GCN model obtains substantial performance and efficiency improvement for the temporal reasoning tasks on six benchmark datasets. Especially, it achieves up to 11.46\% improvement in MRR for entity prediction with up to 82 times speedup comparing to the state-of-the-art baseline.

In 2018, in late October, a distinctly odd painting appeared at the fine art auction house Christe's. At a distance, the painting looks like a 19th-century portrait of an austere gentleman dressed in black. Contained in a gilt frame, the portly gentleman appears middle-aged; his white-collar insinuates that he is a man of the church. The painting seems unassuming, something expected at an auction house that sells billions of dollars of painting each year. However, upon closer inspection, things get a bit odd.

For my PhD, I'm studying how global problems such as wildfires and aid delivery in remote areas can benefit from innovative technologies such as UAV (unmanned aerial vehicle) swarms. Every year, vast areas of forests are destroyed due to wildfires. Wildfires occur more frequently as climate change induces extreme weather conditions. As a result, wildfires are often larger and more intense. Over the past 5 years, countries around the globe witnessed unprecedented effects of wildfires.

Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering. Despite the success of matched filtering, due to its computational cost, there has been recent interest in developing deep convolutional neural networks (CNNs) for signal detection. Designing these networks remains a challenge as most procedures adopt a trial and error strategy to set the hyperparameter values. We propose a new method for hyperparameter optimization based on genetic algorithms (GAs). We compare six different GA variants and explore different choices for the GA-optimized fitness score. We show that the GA can discover high-quality architectures when the initial hyperparameter seed values are far from a good solution as well as refining already good networks. For example, when starting from the architecture proposed by George and Huerta, the network optimized over the 20-dimensional hyperparameter space has 78% fewer trainable parameters while obtaining an 11% increase in accuracy for our test problem. Using genetic algorithm optimization to refine an existing network should be especially useful if the problem context (e.g. statistical properties of the noise, signal model, etc) changes and one needs to rebuild a network. In all of our experiments, we find the GA discovers significantly less complicated networks as compared to the seed network, suggesting it can be used to prune wasteful network structures. While we have restricted our attention to CNN classifiers, our GA hyperparameter optimization strategy can be applied within other machine learning settings.

Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects. In this paper, we take a causal view to addressing this issue. We propose a simple yet effective method, namely, Sentiment Adjustment Figure 1: The causal graph of ABSA. We build our (SENTA), by applying a backdoor adjustment causal model over three main variables: target feature to disentangle those confounding factors. X, predictions Y and confounding factor C. Experimental results on the Aspect Robustness Our goal is to alleviate confounding factors, which is Test Set (ARTS) dataset demonstrate caused by X C, Y C. that our approach improves the performance while maintaining accuracy in the original test set

We establish a link between multiwinner elections and apportionment problems by showing how approval-based multiwinner election rules can be interpreted as methods of apportionment. We consider several multiwinner rules and observe that they induce apportionment methods that are well-established in the literature on proportional representation. For instance, we show that Proportional Approval Voting induces the D'Hondt method and that Monroe's rule induces the largest reminder method. We also consider properties of apportionment methods and exhibit multiwinner rules that induce apportionment methods satisfying these properties.

This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense. This task aims to detect whether the text is humorous and how humorous it is. There are four subtasks in the competition. In this paper, we mainly present our solution, a multi-task learning model based on adversarial examples, for task 1a and 1b. More specifically, we first vectorize the cleaned dataset and add the perturbation to obtain more robust embedding representations. We then correct the loss via the confidence level. Finally, we perform interactive joint learning on multiple tasks to capture the relationship between whether the text is humorous and how humorous it is. The final result shows the effectiveness of our system.

Evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximation guarantees for solving the problem classes of submodular optimization, which have a wide range of applications, such as maximum coverage, sparse regression, influence maximization, document summarization and sensor placement, just to name a few. Though they have provided some theoretical explanation for the general-purpose nature of EAs, the considered submodular objective functions are defined only over sets or multisets. To complement this line of research, this paper studies the problem class of maximizing monotone submodular functions over sequences, where the objective function depends on the order of items. We prove that for each kind of previously studied monotone submodular objective functions over sequences, i.e., prefix monotone submodular functions, weakly monotone and strongly submodular functions, and DAG monotone submodular functions, a simple multi-objective EA, i.e., GSEMO, can always reach or improve the best known approximation guarantee after running polynomial time in expectation. Note that these best-known approximation guarantees can be obtained only by different greedy-style algorithms before.

The late theoretical physicist Albert Einstein has been brought back to life with a digital human platform that recreated the famous scientist's look and voice. Digital Einstein was developed to'put a friendly and well-known face on digital human technology' face between machines and humans.' Complete with the German accent, the digital copy speaks in a soft, friendly tone and is programmed with the same dry sense of humor as the real Einstein was said to have. Users can participate in daily quizzes and ask the AI-powered character questions about science, his life and work. Digital Einstein was developed to'put a friendly and well-known face on digital human technology' face between machines and humans' Einstein is well-known for his work in physics, specifically for the theory of relativity that changed the understanding of space time, gravity and the universe.

New Zealand's most powerful supercomputer for artificial intelligence applications has been installed at the University of Waikato as part of its commitment positioning New Zealand as a world leader in AI research and development. The NVIDIA DGX A100 is the first computer of its kind in New Zealand and is the world's most advanced system for powering universal AI workloads. The machine has been referred to as the Ferrari of computing because of how fast it can rapidly and efficiently process massive amounts of data, allowing students and researchers at the University to process at lightning-fast speeds, enabling machine learning and artificial intelligence that can solve problems from addressing climate change to managing our biodiversity. Machine learning uses algorithms to explore huge data sets and create models that provide answers or outcomes mirroring human decision making. Models can be trained to recognise things like patterns, facial expressions, and spoken words – or they can find anomalies like credit card fraud.