Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.

We propose a multi-agent variant of the classical multi-armed bandit problem, in which there are N agents and K arms, and pulling an arm generates a (possibly different) stochastic reward to each agent. Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm", as each agent may perceive a different arm as best for her. Instead, we seek to learn a fair distribution over arms. Drawing on a long line of research in economics and computer science, we use the Nash social welfare as our notion of fairness. We design multi-agent variants of three classic multi-armed bandit algorithms, and show that they achieve sublinear regret, now measured in terms of the Nash social welfare.

We introduce T-Basis, a novel concept for a compact representation of a set of tensors, each of an arbitrary shape, which is often seen in Neural Networks. Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks. Owing its name to the T-shape of nodes in diagram notation of Tensor Rings, T-Basis is simply a list of equally shaped three-dimensional tensors, used to represent Tensor Ring nodes. Such representation allows us to parameterize the tensor set with a small number of parameters (coefficients of the T-Basis tensors), scaling logarithmically with each tensor's size in the set and linearly with the dimensionality of T-Basis. We evaluate the proposed approach on the task of neural network compression and demonstrate that it reaches high compression rates at acceptable performance drops. Finally, we analyze memory and operation requirements of the compressed networks and conclude that T-Basis networks are equally well suited for training and inference in resource-constrained environments and usage on the edge devices.

Factorizing tensors has recently become an important optimization module in a number of machine learning pipelines, especially in latent variable models. We show how to do this efficiently in the streaming setting. Given a set of $n$ vectors, each in $\mathbb{R}^d$, we present algorithms to select a sublinear number of these vectors as coreset, while guaranteeing that the CP decomposition of the $p$-moment tensor of the coreset approximates the corresponding decomposition of the $p$-moment tensor computed from the full data. We introduce two novel algorithmic techniques: online filtering and kernelization. Using these two, we present six algorithms that achieve different tradeoffs of coreset size, update time and working space, beating or matching various state of the art algorithms. In the case of matrices ($2$-ordered tensor), our online row sampling algorithm guarantees $(1 \pm \epsilon)$ relative error spectral approximation. We show applications of our algorithms in learning single topic modeling.

Ahead of Ubisoft's Forward gaming event, the company offered us some remote demos of two of its AAA releases this year. While my colleague had no issues playing Watch Dogs Legion, my substandard internet connection meant my session with Assassin’s Creed Valhalla was taxing. After losing its way with back-to-back-to-back releases in the early-to-mid ‘10s, 2017’s Egypt-based Origins was a return to form for the Assassin’s Creed series, followed a year later by the similarly good Odyssey, which mapped mainland Greece and its many Aegean islands.

Healthcare is on the brink of revolution, owing to the rapid industrialisation of medicine. We are not talking about'global challenges', but far from meeting them. Healthcare is very splintered across the geography that it would be impractical to implement "technological advances" without giving due cognisance to socio-cultural issues. I find it surprising, because investors' "money (either in the form of grants or venture capital) can be better used by working on modest goals, thereby scaling them. Academia is highly risked averse in several ways; grant committees find it easier for a group think and there's no funding of a breakout idea.

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.

To be an international trade or development hub, the WHO should take seriously this opportunity. We should not treat other countries as an obstacle to globalisation. A globalisation that requires the development of high-quality, effective and sustainable healthcare would also need to address the real needs of the non-human animals. It must be seen in that the current outbreak in Wuhan's Zhuhai Province in China is one of the most complex challenges to social control of the animal population in Wuhan, the number of infected animals at the time are estimated to be about 200,000, and the number of non-human animals is estimated to be about 400,000. All these numbers are based on the assumption that the animal rights of non-human animals are not strictly based on the rights and behaviour of non-human animals but only on the rights and behaviour of them.

In a move that caused a ripple effect across the Middle East, Iranian General Qassem Soleimani was killed in a US drone strike near Baghdad's international airport on January 3. On that day, the Pentagon announced the attack was carried out "at the direction of the president". In a new report examining the legality of armed drones and the Soleimani killing in particular, Agnes Callamard, UN special rapporteur on extrajudicial and arbitrary killings, said the US raid that killed Soleimani was "unlawful". Callamard presented her report at the Human Rights Council in Geneva on Thursday. The United States, which is not a member after quitting the council in 2018, rejected the report saying it gave "a pass to terrorists". In Callamard's view, the consequences of targeted killings by armed drones have been neglected by states.

In mathematics, a type of variation called direct variation describes'a simple relationship between two variables -- such that we observe an increase of one variable when the other increases, or a decrease of the same when the other reduces'. This law works in business as well, wherein the population of the workforce determines the productivity of the company. However, recent development in artificial intelligence defies this law. In 2020, a business could have only a team of essential workers with a couple of well-utilized computers and do more than a larger company which has the help of its thousands of workers but without digital intervention or its innovative implementation whatsoever. The usefulness of computer technology in business has been paced recently in such a way that most businesses will not need to hire more workers in the future.