Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions and out-of-view movements. Despite the attempts to further incorporate global search, prevailing mechanisms that cooperate local and global search are relatively static, thus are still sub-optimal for improving tracking performance. By further studying the local and global search results, we raise a question: can we allow more dynamics for cooperating both results? In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy. In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame. Guided by different attention, we maintain diversified tracking results for the target to build multi-trajectory tracking history, allowing more candidates to represent the true target trajectory. After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that delivers improved tracking performance. Extensive experimental results show that our proposed tracking strategy achieves compelling performance on various large-scale tracking benchmarks. The project page of this paper can be found at https://sites.google.com/view/mt-track/.

In the literature, many methods have been proposed The extended state observer (ESO) is an inherent component for tuning bandwidth-parameterized observers. In [49] of the robust control framework that relies on the cancellation and [38], the authors presented an analytical tuning method providing of disturbances using their lumped estimate in the feedforward the best performance of the ADRC structure expressed component of the robust control law. The general idea of solely upon the control-error-dependent criteria in a noiseless such control structure was utilized in many specific robust algorithms environment. In [39] and [6], the authors considered also the such as active disturbance rejection control (ADRC) control cost as a factor that needs to be minimized to reduce the [13, 54], disturbance observer based control (DOBC) [5, 23], energy consumption of the robust control process, while in [26] or robust observer based control [16], while its applicability the observation error of the measured signals was taken into has been proven in many fields including power electronics account. Tuning procedures described in [28] and [12] have [20, 25, 46], temperature control [53], motion control [32, 42], utilized prior knowledge about the plant structure and some and robotics [30, 22]. Besides the fact that there is a wide variety known or identified model parameters to obtain assumed control of ESO architectures that deal with disadvantages of a most performance requirements. In [28] and [14], the authors commonly used Luenberger-like extended high-gain observer presented an observer tuning method that is relative to gains (HGO) [52, 8] in terms of the general disturbance observation of the selected ADRC controller. Some methods consider automatic quality [34], transient performance [40], or the robustness to tools designed for tuning the overall ADRC structure, measurement noise [37, 21], the final characteristics of the control including observer gains, to satisfy some predefined criteria determining system performance depend greatly on the appropriate tuning the robustness of the control structure [36].

This is the land of Spotify and many tech-driven firms. Sweden is one of the countries where there's a large presence of multinational IT firms and start-up tech companies with constant work in the field of research and development. With such a focus on R&D, it has several technology parks as well and all of this calls for a huge number of qualified IT professionals and data scientists throughout the country. The major companies in the field of data science in Sweden are vert well aware of the importance for individuals skilled in data science, AI/ML and deep learning. As per the reports, Sweden continually keeps facing a shortage of IT and Data science professionals and this in the past led to a shortage of more than 30 thousand IT and practitioners in 2012 and the numbers are even higher now as the supply to their demands are never met. Rather the inflation keeps increasing each passing minute. As the Swedish customers are also gaining awareness and maturity in the IT and tech products or associated products and services, demands for the individuals skilled in same are very much of crucial importance here. Reports also suggest that by 2035 Sweden is going to have to face a major shortage of individuals skilled in IT and Engineering fields at both junior and senior levels of the company hierarchy.

Here on Earth, we have more detailed maps of Mars than of our own ocean, and that's a problem. A massive force for surviving climate change, the ocean absorbs 90% of the heat caused by emissions and generates 50% of the oxygen we breathe. "We have the ocean to thank for so many aspects of our safety and well-being," says Dawn Wright, oceanographer and chief scientist at geographic information system (GIS) provider Esri, who notes the ocean also provides renewable energy, a major food source, and a transportation corridor for not only ships but submarine internet cables. Now, the same type of smart maps and geospatial technology guiding outer space exploration support the quest to better understand and protect our ocean. "For the first time, our knowledge of the ocean can approach our knowledge of the land," Wright says.

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named ``embedder" is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.

This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions related to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how do the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-Label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.

Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration, also known as \emph{entity alignment} (EA). However, most existing EA methods are inefficient and poor in scalability. A recent summary points out that some of them even require several days to deal with a dataset containing 200,000 nodes (DWY100K). We believe over-complex graph encoder and inefficient negative sampling strategy are the two main reasons. In this paper, we propose a novel KG encoder -- Dual Attention Matching Network (Dual-AMN), which not only models both intra-graph and cross-graph information smartly, but also greatly reduces computational complexity. Furthermore, we propose the Normalized Hard Sample Mining Loss to smoothly select hard negative samples with reduced loss shift. The experimental results on widely used public datasets indicate that our method achieves both high accuracy and high efficiency. On DWY100K, the whole running process of our method could be finished in 1,100 seconds, at least 10* faster than previous work. The performances of our method also outperform previous works across all datasets, where Hits@1 and MRR have been improved from 6% to 13%.

Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent years. They learn node representations from a sequence of evolving graphs but not only the latest network, for preserving both structural and temporal information from the dynamic networks. Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper. Our survey inspects the data model, representation learning technique, evaluation and application of current related works and derives common patterns from them. Specifically, we present two basic data models, namely, discrete model and continuous model for dynamic networks. Correspondingly, we summarize two major categories of dynamic network embedding techniques, namely, structural-first and temporal-first that are adopted by most related works. Then we build a taxonomy that refines the category hierarchy by typical learning models. The popular experimental data sets and applications are also summarized. Lastly, we have a discussion of several distinct research topics in dynamic network embedding.

A robotic fruit packing machine that uses artificial intelligence could be adapted to harvesting in rural and regional Australia.

As a single person wandering through the world, it can be difficult to find someone who loves all the right things: parks, subways, bike lanes, human-scale buildings, high-density housing, debates over the ideal length of a city block. Even on a dating app, you can't always tell from a profile who might be thinking, behind a smile, I hate cars. But if this is exactly the sort of partner--or friend or fling--you're looking for, there is a solution: Join the wildly popular Facebook meme group and leftist community NUMTOTs ("New Urbanist Memes for Transit-Oriented Teens," which isn't really just for teens) and request access to its private spin-off group, NUMTinder. With about 8,000 members living mostly in North America, the United Kingdom, and Australia, NUMTinder is a makeshift dating environment for those who consider liking public transportation to be a core part of their personality, or those for whom a lack of interest in urban planning is a deal breaker. Almost everyone in the group posts at least one selfie with a bike or a subway entrance to demonstrate commitment to the lifestyle, and when a new member introduces herself, it's not uncommon for her to brag about the fact that she doesn't have a driver's license.