Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based contexts, to learn entity embeddings, but they are limited in capturing the multi-context features. Moreover, most approaches directly utilize the embedding similarity to determine entity alignment without considering the global interaction among entities and relations. In this work, we propose an Informed Multi-context Entity Alignment (IMEA) model to address these issues. In particular, we introduce Transformer to flexibly capture the relation, path, and neighborhood contexts, and design holistic reasoning to estimate alignment probabilities based on both embedding similarity and the relation/entity functionality. The alignment evidence obtained from holistic reasoning is further injected back into the Transformer via the proposed soft label editing to inform embedding learning. Experimental results on several benchmark datasets demonstrate the superiority of our IMEA model compared with existing state-of-the-art entity alignment methods.

It is well known the sample covariance has a consistent bias in the spectrum, for example spectrum of Wishart matrix follows the Marchenko-Pastur law. We in this work introduce an iterative algorithm 'Concent' that actively eliminate this bias and recover the true spectrum for small and moderate dimensions.

Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate objective functions. Classical BO methods assume that the objective function is a black box. However, internal information about objective function computation is often available. For example, when optimizing a manufacturing line's throughput with simulation, we observe the number of parts waiting at each workstation, in addition to the overall throughput. Recent BO methods leverage such internal information to dramatically improve performance. We call these "grey-box" BO methods because they treat objective computation as partially observable and even modifiable, blending the black-box approach with so-called "white-box" first-principles knowledge of objective function computation. This tutorial describes these methods, focusing on BO of composite objective functions, where one can observe and selectively evaluate individual constituents that feed into the overall objective; and multi-fidelity BO, where one can evaluate cheaper approximations of the objective function by varying parameters of the evaluation oracle.

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, MINT, BRICS, G7, Western / Eastern Europe or Southeast Asia. Also, we can serve you with customize research services as HTF MI holds a database repository that includes public organizations and Millions of Privately held companies with expertise across various Industry domains. About Author: HTF Market Intelligence consulting is uniquely positioned empower and inspire with research and consulting services to empower businesses with growth strategies, by offering services with extraordinary depth and breadth of thought leadership, research, tools, events and experience that assist in decision making. Contact US: Craig Francis (PR & Marketing Manager) HTF Market Intelligence Consulting Private Limited Unit No. 429, Parsonage Road Edison, NJ New Jersey USA – 08837 Phone: 1 (206) 317 1218 [email protected]

A new spy-in-the-sky camera which identified 15,000 cases of drivers using mobile phones could also catch motorists eating, drinking, or not wearing a seatbelt, its makers say. Smart cameras linked to a new, automated system using artificial intelligence (AI) are being trialled on an undisclosed motorway - ahead of a blanket ban on holding a mobile device while driving which comes into force in early 2022. The cameras instantly analyse high-definition photos taken through the windscreen of passing cars, and Jenoptik, the enforcement technology firm testing the cameras in the UK, believes they will be crucial in providing evidence to prosecute offenders. The pilot scheme has been running since spring and it is hoped a wider rollout across the country will be possible next year. But Acusensus, the Australian firm who designed the cameras, admits that they can be used to catch motorists doing anything from eating, drinking, smoking, adjusting the radio or using navigation devices in a holder.

A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios. In these settings, error signals from multiple contexts can interfere with one another, ultimately leading to a phenomenon known as catastrophic forgetting. In this article we investigate biologically inspired architectures as solutions to these problems. Specifically, we show that the biophysical properties of dendrites and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Our key contributions are as follows. First, we propose a novel artificial neural network architecture that incorporates active dendrites and sparse representations into the standard deep learning framework. Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training. Analysis on both benchmarks demonstrates the emergence of overlapping but distinct and sparse subnetworks, allowing the system to fluidly learn multiple tasks with minimal forgetting. Our neural implementation marks the first time a single architecture has achieved competitive results on both multi-task and continual learning settings. Our research sheds light on how biological properties of neurons can inform deep learning systems to address dynamic scenarios that are typically impossible for traditional ANNs to solve.

In this paper, we address the problem of finding a correspondence, or matching, between the functions of two programs in binary form, which is one of the most common task in binary diffing. We introduce a new formulation of this problem as a particular instance of a graph edit problem over the call graphs of the programs. In this formulation, the quality of a mapping is evaluated simultaneously with respect to both function content and call graph similarities. We show that this formulation is equivalent to a network alignment problem. We propose a solving strategy for this problem based on max-product belief propagation. Finally, we implement a prototype of our method, called QBinDiff, and propose an extensive evaluation which shows that our approach outperforms state of the art diffing tools.

'Exercise can improve hippocampal function, however, the amount of exercise and mechanisms mediating improvement remain largely unknown.' The active place avoidance (APA) task is a dry-arena task used to assess spatial navigation and memory in rodents. In this task, a subject is put on a rotating circular arena and avoids an invisible sector that is stable in relation to the room. Rotation of the arena means that the subject's avoidance must be active, otherwise the subject will be moved in the to-be-avoided sector by the rotation of the arena and a slight electric shock will be administered.

They are launching their first therapeutic device – iSyncWave, which integrates both EEG brain mapping and LED therapy at 2022 CES Las Vegas. An open-to-public CES Show Day press conference will take place on Jan 6th at the CES provided conference room. As the company slogan states, "Overcome mental pandemic in just 10 minutes, "press and visitors can expect a complete analysis report of EEG (brainwave) and HRV (Heart rate variability) operated by AI deep learning algorithms in just 10 minutes to assess the condition of brain and dysfunctionality including the early diagnostic insights which could play a major role in discovering a neuro-related diseases in early stages. Recently with the addition of software upgrades, and an FCC approval allowed the iSyncWave to be exported to the EU, Asia, and Australia already use for research and diagnostics. "We will provide life-changing mental healthcare experience to introduce neuro-mental illness biomarker and the patent EEG/HRV technology," says CEO Dr. Seung Wan Kang.

This year at Future Tense Fiction we've spent a lot of time thinking about how, in many ways, 2021 has felt a lot like 2020. But at the same time, so much has changed--how we work and think, how we commute, how we interact with animals, technology, and our fellow humans. This year we published 11 stories (we took December off!) that touch upon relationships, transportation, right to repair and supply chain shortages, communication, information overload and scarcity, and much, much more. We broadly explored themes like learning futures, with Simon Brown's "Speaker" (where humans learn to communicate with other species and struggle to overcome the assumption of human excellence), Leigh Alexander's "The Void" (about the struggle with information scarcity in an information-overloaded world) and Shiv Ramdas' "The Trolley Solution" (about a university attempting to automate how it teaches its students), as well as ideas of mobility--a theme we're continuing into 2022, so stay tuned--with Linda Nagata's "Ride" (about a neighborhood that's embraced an algorithm to run all of its traffic and transit patterns). We began publishing fiction back in 2016 and made it monthly as of January 2018.