In this work, we study how the performance of a given direction changes with its sampling ratio in Multilingual Neural Machine Translation (MNMT). By training over 200 multilingual models with various model sizes, data sizes, and language directions, we find it interesting that the performance of certain translation direction does not always improve with the increase of its weight in the multi-task optimization objective. Accordingly, scalarization method leads to a multitask trade-off front that deviates from the traditional Pareto front when there exists data imbalance in the training corpus, which poses a great challenge to improve the overall performance of all directions. Based on our observations, we propose the Double Power Law to predict the unique performance trade-off front in MNMT, which is robust across various languages, data adequacy, and the number of tasks. Finally, we formulate the sample ratio selection problem in MNMT as an optimization problem based on the Double Power Law. In our experiments, it achieves better performance than temperature searching and gradient manipulation methods with only 1/5 to 1/2 of the total training budget.

In this work, we study how the performance of a given direction changes with its sampling ratio in Multilingual Neural Machine Translation (MNMT). By training over 200 multilingual models with various model sizes, data sizes, and language directions, we find it interesting that the performance of certain translation direction does not always improve with the increase of its weight in the multi-task optimization objective. Accordingly, scalarization method leads to a multitask trade-off front that deviates from the traditional Pareto front when there exists data imbalance in the training corpus, which poses a great challenge to improve the overall performance of all directions. Based on our observations, we propose the Double Power Law to predict the unique performance trade-off front in MNMT, which is robust across various languages, data adequacy, and the number of tasks. Finally, we formulate the sample ratio selection problem in MNMT as an optimization problem based on the Double Power Law. In our experiments, it achieves better performance than temperature searching and gradient manipulation methods with only 1/5 to 1/2 of the total training budget.

We focus on one of the simplest origami building blocks, the degree-four origami vertex [34-36], and demonstrate While traditionally regarded as an art form, origami through a combination of experiments and modeling has recently made a profound impact on engineering how the position of the central vertex and the orientation applications, playing a pivotal role in the design of deployable of the creases significantly impact its crawling ability and reconfigurable structures [1-6], metamaterials and resulting trajectory. Guided by our model, we with programmable responses [7, 8] and mechanical identify designs that can move straight when the folding logic elements [9, 10]. Further, origami principles have angle is actuated within a certain range and turn when provided an opportunity to simplify and accelerate the actuated in another range. This enables the creation fabrication of robots, since they enable their manufacturing of simple machines that can follow complex trajectories from a flat composite through folding [11-16].

In this report, we delve into two critical research inquiries. Firstly, we explore the extent to which Reinforcement Learning (RL) agents exhibit multimodal distributions in the context of stop-and-go traffic scenarios. Secondly, we investigate how RL-controlled Robot Vehicles (RVs) effectively navigate their direction and coordinate with other vehicles in complex traffic environments. Our analysis encompasses an examination of multimodality within queue length, outflow, and platoon size distributions for both Robot and Human-driven Vehicles (HVs). Additionally, we assess the Pearson coefficient correlation, shedding light on relationships between queue length and outflow, considering both identical and differing travel directions. Furthermore, we delve into causal inference models, shedding light on the factors influencing queue length across scenarios involving varying travel directions. Through these investigations, this report contributes valuable insights into the behaviors of mixed traffic (RVs and HVs) in traffic management and coordination.

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The idea of indestructible killer robots may sound like something straight out of the Terminator movie. But they could soon become a reality, as scientists have just witnessed metal healing itself for the first time, without any human intervention. A US-based study has overturned everything we thought we knew about metals by revealing that cracks from wear and tear can actually mend themselves under certain conditions. It's a discovery that has the potential to revolutionise engineering, with the prospect of self-healing engines, planes and even robots now on the horizon. 'This was absolutely stunning to watch first-hand,' said Brad Boyce, a scientist at Sandia National Laboratories who led the study with Texas A&M University.

However, due to the limitation of available resources, it is impractical to store all facts in KGs, which leads to the incompleteness [5], and the algorithms of KGC are required to solve the problem. There are a lot of researches focusing on KGC or link prediction tasks aiming to infer missing facts automatically based on known facts. Pioneering additive models [6-8] take the transformation from head entities to tail entities as a translation problem, while multiplicative models [9-12] try to measure the plausibility of unknown triplets by applying proper semantic similarity-based score function. Benefiting from the development of neural networks, several works concentrate on the deeper nonlinear interactions among entities and relations with innovative model structures [13-18]. Furthermore, some recent studies introduce GCN to take the structure information into consideration by aggregating neighborhood information [19-22], which brings significant improvement. Despite the high-performance of them, they fail to utilize direction information implied in different neighbors while merging them, which is important for making reasonable predictions. As shown in Figure 1, there exists original and inverse edges (relations) in KGs, according to the direction of them, the link prediction tasks can be divided into forward and backward sub-tasks, neighbors can also be grouped into forward and backward neighbors, and sub-tasks in different directions always have diverse preferences for neighbors. For example, while dealing with forward sub-task (Stan Lee, profession,?), it can be solved from backward neighbors including Spider-Man, Iron Man and Captain America; on the other hand, the answer for query (Stan Lee, ethnicity

Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines. EvoCraft is an API for programmatically generating structures in Minecraft, but the initial work in this domain was not capable of evolving flying machines. This paper applies fitness-based evolution and quality diversity search in order to evolve flying machines. Although fitness alone can occasionally produce flying machines, thanks in part to a more sophisticated fitness function than was used previously, the quality diversity algorithm MAP-Elites is capable of discovering flying machines much more reliably, at least when an appropriate behavior characterization is used to guide the search for diverse solutions.

Zebra Medical Vision's Chief Medical Officer, Dr. Orit Wimpfheimer, on the future of radiology and how to juggle a high-flying career with being a mom of nine Dr. Orit Wimpfheimer is a diagnostic radiologist who founded her Israel-based teleradiology company in 2001. She joined Zebra Medical Vision, initially as clinical director, and now as chief medical officer, bringing her experience to direct and promote AI technology. What initially sparked your interest in medicine and subsequently, AI in medicine? I came from a family of doctors. My father, uncle and two brothers were all doctors, so I grew up in a family where medicine was central to many of our conversations around the dinner table.

Today in video game news: Pixelated ghosts who carry artisan chocolates. Game designer Eric Barone, who works under the alias ConcernedApe, announced Thursday that the game was in development, releasing a trailer and several colorful screenshots. Barone describes the game as being about "magical haunted ghost chocolate." Barone describes the game as being about "magical haunted ghost chocolate." Details on the project are scarce, but the gameplay video shows a mysterious castle filled with moody lighting and a chocolate shop attended by little ghost employees shaped like bonbons, carrying actual chocolate bonbons over their heads.