Continuous technology scaling and the introduction of advanced technology nodes in Integrated Circuit (IC) fabrication is constantly exposing new manufacturability issues. One such issue, stemming from complex interaction between design and process, is the problem of design hotspots. Such hotspots are known to vary from design to design and, ideally, should be predicted early and corrected in the design stage itself, as opposed to relying on the foundry to develop process fixes for every hotspot, which would be intractable. In the past, various efforts have been made to address this issue by using a known database of hotspots as the source of information. The majority of these efforts use either Machine Learning (ML) or Pattern Matching (PM) techniques to identify and predict hotspots in new incoming designs. However, almost all of them suffer from high false-alarm rates, mainly because they are oblivious to the root causes of hotspots. In this work, we seek to address this limitation by using a novel database enhancement approach through synthetic pattern generation based on carefully crafted Design of Experiments (DOEs). Effectiveness of the proposed method against the state-of-the-art is evaluated on a 45nm process using industry-standard tools and designs.

Facial recognition is quickly becoming a disruptive technology with few limits imposed by privacy policy. Academic researchers, however, have found ways to -- at least temporarily -- cause problems for certain classes of facial-recognition algorithms, taking advantages of weaknesses in the training algorithm or the resultant recognition model. Last week, a team of computer-science researchers at the National University of Singapore (NUS) published a technique that locates the areas of an image where changes can best disrupt image-recognition algorithms, but where those changes are least noticeable to humans. The technique is general in that it can be used to develop an attack against other machine-learning (ML) algorithms, but the researchers only developed a specific instance, says Mohan Kankanhalli, a professor in the NUS Department of Computer Science and co-author of a paper on the adversarial attack. "Currently, we need to know the class [of algorithm] and can develop a solution for that," he says.

The next time you search for and tap on an image on Google, you may see some helpful information related to what's on your screen. The company is now more deeply integrating its Knowledge Graph with pictures that it finds online. Say you're paging through photos of famous buildings as in the GIF above, you'll see a new element of the interface that highlights people, places or things related to the current picture. You can then tap on these to find out more information about them. As usual, you'll also see prompts for related searches. If you've ever searched for something and seen a panel to the side of the main interface that displays some facts related to your query, then you've seen the Knowledge Graph in action.

Google today announced it will begin showing quick facts related to photos in Google Images, enabled by AI. Starting this week in the U.S., users who search for images on mobile might see information from Google's Knowledge Graph -- Google's database of billions of facts -- including people, places, or things germane to specific pictures. Google says the new feature, which will start to appear on some photos within Google Images before expanding to more languages and surfaces over time, is intended to provide context around both images and the webpages hosting them. It's estimated that images currently make up 12.4% of search queries on Google, and at least a portion of these are irrelevant or manipulated. In an effort to address this, Google earlier this year began identifying misleading photos in Google Images with a fact-check label, expanding the function beyond its standard non-image searches and video.

Processing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy making. A common strategy to analyze driving behavior analysis is to study the maneuvers being performance by the driver. In this paper, we propose TripMD, a system that extracts the most relevant driving patterns from sensor recordings (such as acceleration) and provides a visualization that allows for an easy investigation. Additionally, we test our system using the UAH-DriveSet dataset, a publicly available naturalistic driving dataset. We show that (1) our system can extract a rich number of driving patterns from a single driver that are meaningful to understand driving behaviors and (2) our system can be used to identify the driving behavior of an unknown driver from a set of drivers whose behavior we know.

This forecast is part of the Options Package, as one of I Know First's algorithmic trading tools. Package Name: Options Recommended Positions: Long Forecast Length: 1 Month (5/31/2020 – 7/1/2020) I Know First Average: 13.93% I Know First's State of the Art Algorithm accurately forecasted 8 out of 10 trades in this Options Package for the 1 Month time period. OSTK was the highest-earning trade with a return of 63.69% in 1 Month. Additional high returns came from DVAX and RH, at 35.78% and 19.72% respectively.

The visual performance of Humans is much better than that of computers, probably because of superior high-level image understanding, contextual knowledge, and massively parallel processing. But human capabilities deteriorate drastically after an extended period of surveillance, also certain working environments are either inaccessible or too hazardous for human beings. So for these reasons, automatic recognition systems are developed for various applications. Driven by advances in computing capability and image processing technology, computer mimicry of human vision has recently gained ground in a number of practical applications. Image recognition refers to technologies that identify places, logos, people, objects, buildings, and several other variables in digital images.

We are seeing more references to machine learning in how Google is ranking pages and other documents in search results. That seems to be a direction that will leave what we know as traditional, or old school signals that are referred to as ranking signals behind. It's still worth considering some of those older ranking signals because they may play a role in how things are ranked. As I was going through a new patent application from Google on ranking image search results, I decided that it was worth including what I used to look at when trying to rank images. Images can rank highly in image search, and they can also help pages that they appear upon rank higher in organic web results, because they can help make a page more relevant for the query terms that page may be optimized for.

Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. Some of its applications include systems for factory automation, face recognition, booth monitoring, and security surveillance. Image recognition is embedded in technologies that enable students with learning disabilities to receive the education they need -- in a form they can perceive. Apps powered by computer vision offer text-to-speech options, which allow students with impaired vision or dyslexia to'read' the content. By employing image recognition, Jetpac caught visual cues in the photos and analyzed them to offer live data to its users.

Self-organization of complex morphological patterns from local interactions is a fascinating phenomenon in many natural and artificial systems. In the artificial world, typical examples of such morphogenetic systems are cellular automata. Yet, their mechanisms are often very hard to grasp and so far scientific discoveries of novel patterns have primarily been relying on manual tuning and ad hoc exploratory search. The problem of automated diversity-driven discovery in these systems was recently introduced [26, 61], highlighting that two key ingredients are autonomous exploration and unsupervised representation learning to describe "relevant" degrees of variations in the patterns. In this paper, we motivate the need for what we call Meta-diversity search, arguing that there is not a unique ground truth interesting diversity as it strongly depends on the final observer and its motives. Using a continuous game-of-life system for experiments, we provide empirical evidences that relying on monolithic architectures for the behavioral embedding design tends to bias the final discoveries (both for hand-defined and unsupervisedly-learned features) which are unlikely to be aligned with the interest of a final end-user. To address these issues, we introduce a novel dynamic and modular architecture that enables unsupervised learning of a hierarchy of diverse representations. Combined with intrinsically motivated goal exploration algorithms, we show that this system forms a discovery assistant that can efficiently adapt its diversity search towards preferences of a user using only a very small amount of user feedback.