Several BDD variants were designed to exploit special features of Boolean functions to achieve better compression rates.Deciding a priori which variant to use is as hard as constructing the diagrams themselves and the conversion between variants comes in general with a prohibitive cost.This observation leads naturally to a growing interest into when and how one can combine existing variants to benefit from their respective sweet spots.In this paper, we introduce a novel framework, termed \lambdaDD (LDD), that revisits BDD from a purely functional point of view.The framework allows to classify the already existing variants, including the most recent ones like ChainDD and ESRBDD, as implementations of a special class of ordered models.We enumerate, in a principled way, all the models of this class and isolate its most expressive model.This new model, termed \lambdaDD-O-NUCX, is suitable for both dense and sparse Boolean functions, and, unlike ChainDD and ESRBDD, is invariant by negation.The canonicity of \lambdaDD-O-NUCX is formally verified using the Coq proof assistant.We furthermore provide experimental evidence corroborating our theoretical findings: more expressive \lambdaDD models achieve, indeed, better memory compression rates.

Intent and Slot Identification are two important tasks in Spoken Language Understanding (SLU). For a natural language utterance, there is a high correlation between these two tasks. A lot of work has been done on each of these using Recurrent-Neural-Networks (RNN), Convolution Neural Networks (CNN) and Attention based models. Most of the past work used two separate models for intent and slot prediction. Some of them also used sequence-to-sequence type models where slots are predicted after evaluating the utterance-level intent. In this work, we propose a parallel Intent and Slot Prediction technique where separate Bidirectional Gated Recurrent Units (GRU) are used for each task. We posit the usage of MLB (Multimodal Low-rank Bilinear Attention Network) fusion for improvement in performance of intent and slot learning. To the best of our knowledge, this is the first attempt of using such a technique on text based problems. Also, our proposed methods outperform the existing state-of-the-art results for both intent and slot prediction on two benchmark datasets

We present a system that utilizes machine learning for tactic proof search in the Coq Proof Assistant. In a similar vein as the TacticToe project for HOL4, our system predicts appropriate tactics and finds proofs in the form of tactic scripts. To do this, it learns from previous tactic scripts and how they are applied to proof states. The performance of the system is evaluated on the Coq Standard Library. Currently, our predictor can identify the correct tactic to be applied to a proof state 23.4% of the time. Our proof searcher can fully automatically prove 39.3% of the lemmas. When combined with the CoqHammer system, the two systems together prove 56.7% of the library's lemmas.

Police in Spain have turned to drones to encourage people to stay indoors and practice social distancing during the country's now surging COVID-19 outbreak. The drones have been equipped with speakers that officers can use to broadcast live messages from their squad cars. The drones are part of neighborhood sweeps police have been implementing to enforce a country-wide lockdown that began on Saturday. The drones have been used in Madrid to help clear parks and other public spaces where many in the country had continued to gather in spite of growing health concerns, according to a report in Popular Mechanics. Under the country's lockdown, which was implemented the same day Prime Minister Pedro Sánchez's wife Begoña Gómez tested positive for COVID-19, people are banned from leaving home for any reason other than to buy essential supplies and medicine or to go to work. As with many other countries around the world, Spain has required schools and all non-essential businesses to close, including museums, sporting events, and restaurants, which are restricted to delivery and takeout orders.

Over 4,000 blood drives have been canceled; Bryan Llenas reports. Hospitals in Thailand have begun using "ninja robots" to ease the burden on medical workers and doctors fighting to curb the spread of the coronavirus. The robots, which were originally built to monitor recovering stroke patients, have been repurposed to measure patients' fevers, AFP reported. In this photo taken on March 18, 2020 a robot modified to screen and observe COVID-19 coronavirus patients is photographed at the Regional Center of Robotics Technology at Chulalongkorn University in Bangkok. The machines have, as of Thursday, been used at four hospitals in and around Bangkok, according to the outlet.

When VentureBeat asked Andrew Burt why he was starting an AI-focused law firm, Burt was quick to clarify that it's about AI and analytics. But that didn't answer the underlying question of why the world needs a law firm focused so precisely on this one key area. "The thesis behind the law firm is that traditional legal expertise on its own is not sufficient," said Burt, a Yale Law School alum. His partner is data scientist Patrick Hall, and together they aim to provide legal acumen around AI and analytics that's bolstered by technical understanding. "If we are going to successfully manage the risks of AI and advanced analytics, we need both of these types of expertise commingled," added Burt. Called bnh.ai (techy shorthand for "Burt and Hall"), the firm is located in Washington, D.C., which Burt says confers a key advantage.

As people make efforts to stay apart from each other physically, video games are filling the socializing gap. As people make efforts to stay apart from each other physically, video games are filling the socializing gap. Some people look at the weeks ahead and wonder how they will keep themselves from going stir crazy. Across the U.S., new restrictions have limited in-person gatherings in an effort to stem the spread of coronavirus infection, as concern grows from watching its effects on the hard-hit populations of China and Italy, where thousands have died. But other Americans already have a plan to help combat social isolation: video games.

An AI startup is suing Facebook and one of its employees for allegedly stealing proprietary software that allows machine learning workloads to run faster on standard processors, eliminating the need for more expensive custom hardware. Neural Magic, founded in 2017 by Nir Shavit and Alex Matveev, describes itself as a "no-hardware AI" company. Instead of relying on GPU chips that are able to crunch through matrix maths operations to run machine-learning models quickly, the Boston-based upstart employs nifty software tricks to achieve similar speeds on CPUs. Court documents filed (PDF) in the District Court of Massachusetts last week claim that Neural Magic's first employee, Aleksandar Zlateski, breached the non-disclosure and non-competition agreement he signed when he joined as the company's technology director. Zlateski left to join Facebook and allegedly stole his former employer's secret algorithms to give to his new team. That code, describing how to perform low-precision matrix multiplication to run trained computer vision models, was then published by Facebook engineers on GitHub last year in November.

In 2015, I got obsessed with the idea of driverless trucks and started Starsky Robotics. In 2016, we became the first street-legal vehicle to be paid to do real work without a person behind the wheel. In 2018, we became the first street-legal truck to do a fully unmanned run, albeit on a closed road. In 2019, our truck became the first fully-unmanned truck to drive on a live highway. I remain incredibly proud of the product, team, and organization we were able to build; one where PhDs and truck drivers worked side by side, where generational challenges were solved by people with more smarts than pedigree, and where we discovered how the future of logistics will work.

Venus Bliss is cleared by the FDA and licensed by Health Canada for non-invasive lipolysis of the abdomen and flanks in individuals with a Body Mass Index (BMI) of 30 or less, with the diode laser applicators. The (MP)2 applicator is cleared by the FDA for temporary reduction in the appearance of cellulite, and licensed by Health Canada for temporary increase of skin tightening, temporary circumferential reduction, and temporary cellulite reduction. Venus Bliss has CE Mark as a non-invasive medical aesthetic device enabling a comprehensive approach leading to body contouring, addressing fat reduction, skin tightening, circumference reduction, and cellulite reduction. Venus Versa is cleared by the FDA, licensed by Health Canada, and has CE Mark as a multi-application device intended to be used in aesthetic and cosmetic procedures. The SR515 and SR580 applicators are cleared by the FDA, licensed by Health Canada, and have CE Mark for the treatment of benign pigmented epidermal and cutaneous lesions and treatment of benign cutaneous vascular lesions.