Statistical language models conventionally implement representation learning based on the contextual distribution of words or other formal units, whereas any information related to the logographic features of written text are often ignored, assuming they should be retrieved relying on the cooccurence statistics. On the other hand, as language models become larger and require more data to learn reliable representations, such assumptions may start to fall back, especially under conditions of data sparsity. Many languages, including Chinese and Vietnamese, use logographic writing systems where surface forms are represented as a visual organization of smaller graphemic units, which often contain many semantic cues. In this paper, we present a novel study which explores the benefits of providing language models with logographic information in learning better semantic representations. We test our hypothesis in the natural language inference (NLI) task by evaluating the benefit of computing multi-modal representations that combine contextual information with glyph information. Our evaluation results in six languages with different typology and writing systems suggest significant benefits of using multi-modal embeddings in languages with logograhic systems, especially for words with less occurence statistics.

In immersive humanoid robot teleoperation, there are three main shortcomings that can alter the transparency of the visual feedback: the lag between the motion of the operator's and robot's head due to network communication delays or slow robot joint motion. This latency could cause a noticeable delay in the visual feedback, which jeopardizes the embodiment quality, can cause dizziness, and affects the interactivity resulting in operator frequent motion pauses for the visual feedback to settle; (ii) the mismatch between the camera's and the headset's field-of-views (FOV), the former having generally a lower FOV; and (iii) a mismatch between human's and robot's range of motions of the neck, the latter being also generally lower. In order to leverage these drawbacks, we developed a decoupled viewpoint control solution for a humanoid platform which allows visual feedback with low-latency and artificially increases the camera's FOV range to match that of the operator's headset. Our novel solution uses SLAM technology to enhance the visual feedback from a reconstructed mesh, complementing the areas that are not covered by the visual feedback from the robot. The visual feedback is presented as a point cloud in real-time to the operator. As a result, the operator is fed with real-time vision from the robot's head orientation by observing the pose of the point cloud. Balancing this kind of awareness and immersion is important in virtual reality based teleoperation, considering the safety and robustness of the control system. An experiment shows the effectiveness of our solution.

Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person.

In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we continuously shrink circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect inspection and analysis by state-of-the-art optical and e-beam inspection tools is generally driven by some rule-based techniques, which in turn often causes to misclassification and thereby necessitating human expert intervention. In this work, we have revisited and extended our previous deep learning-based defect classification and detection method towards improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance. This also enables to extract and calibrate each segmented mask and quantify the pixels that make up each mask, which in turn enables us to count each categorical defect instances as well as to calculate the surface area in terms of pixels. We are aiming at detecting and segmenting different types of inter-class stochastic defect patterns such as bridge, break, and line collapse as well as to differentiate accurately between intra-class multi-categorical defect bridge scenarios (as thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as well as thin resists (High NA applications). Our proposed approach demonstrates its effectiveness both quantitatively and qualitatively.

Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type. However, they still suffer from error propagation, relation redundancy and lack of high-level connections between triples. To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples. By metric-based comparison between query embeddings and token embeddings, we can extract all types of triples in one step, thus eliminating the error propagation problem. In addition, we learn the instance-level representation of relational triples via contrastive learning. In this way, relational triples can not only enclose rich class-level semantics but also access to high-order global connections. Experimental results show that our proposed method achieves the state of the art on five widely used benchmarks.

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation.

Our engineering team is split into organisations which we call Fleets. Each Fleet focuses on a core customer journey (onboarding, security, payments, support, new business, growth, and marketing, etc.). Each of these fleets contains multiple smaller teams called Pods, each of which focuses on a specific aspect of the product. Pods will include a product owner, product designer, back-end engineers, Android, iOS, and Web developers, who each bring a unique perspective to the problem you are all contributing towards. Luno offers a "Remote but Reachable" working approach.

For the last several years, Google has been using artificial intelligence to develop a system that can predict floods. It has also been working on wildfire tracking tools. Ahead of the COP27 climate conference taking place next week, the company announced that it is expanding those tools. First, Google says it will offer flood forecasts for river basins in another 18 countries. Those are Brazil, Colombia, Sri Lanka, Burkina Faso, Cameroon, Chad, Democratic Republic of Congo, Ivory Coast, Ghana, Guinea, Malawi, Nigeria, Sierra Leone, Angola, South Sudan, Namibia, Liberia and South Africa. The company previously offered flood warnings to users in India and Bangaldesh with alerts on Android devices and phones that have the Google Search app installed.

Artificial intelligence has found disparities in the amount of airtime women and men were given on CNN, FOX News and MSNBC - females had a 10 percent less chance of speaking during political discussions because male speakers constantly interrupted them. The discovery was made by researchers at Rochester Institute of Technology who analyzed 625,409 dialogues hosted on the three news cable networks from January 2000 through July 2021. The technology revealed women received an average of 72.8 words per chance to speak compared to 81.4 for male speakers and women were interrupted 39.4 percent of the time during discussions - this is compared to the 35.9 percent of the time for men. The team believes their AI could be used during talk shows, interviews and political debates to identify a serial interrupter in real-time, but the study also reinforces previous research that found men interrupt women more to show their dominance. AI analyzed thousands of dialogues from news segments on the three networks and found woman are given a 10 percent less chance at speaking because men interrupt them.

Fox News national security correspondent Jennifer Griffin provides insight on responding to drone attacks in Ukraine on "America Reports." The Paris-based dissident organization National Council of Resistance of Iran (NCRI) accused the Iranian regime of furnishing Russian strongman Vladimir Putin's army with more than 3,500 drones for his scorched-earth war against Ukraine. According to reports from the social network of the People's Mojahedin Organization of Iran (PMOI/MEK) inside the Islamic Republic, "Iran's UAV [unmanned aerial vehicle] sale contract to Russia includes various offensive drones, including Shahed-129, Mohajer-6 and suicide drones Shahed-136 and Shahed-131." MEK is part of the National Council of Resistance of Iran umbrella organization. The NCRI dossier states, "Tehran has sold more than 3,500 UAVs to Russia. Most of these were made at the factories of the Ministry of Defense, with others produced by the factories of the Iranian Aviation and Space Industries Association (IASIA)."