Recent research in Visual Question Answering (VQA) has revealed state-of-the-art models to be inconsistent in their understanding of the world -- they answer seemingly difficult questions requiring reasoning correctly but get simpler associated sub-questions wrong. These sub-questions pertain to lower level visual concepts in the image that models ideally should understand to be able to answer the higher level question correctly. To address this, we first present a gradient-based interpretability approach to determine the questions most strongly correlated with the reasoning question on an image, and use this to evaluate VQA models on their ability to identify the relevant sub-questions needed to answer a reasoning question. Next, we propose a contrastive gradient learning based approach called Sub-question Oriented Tuning (SOrT) which encourages models to rank relevant sub-questions higher than irrelevant questions for an <$image, reasoning-question$> pair. We show that SOrT improves model consistency by upto 6.5% points over existing baselines, while also improving visual grounding.

In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency division multiplexing (OFDM) receivers. We first train DeepWiPHY with a synthetic dataset, which is generated using representative indoor channel models and includes typical radio frequency (RF) impairments that are the source of nonlinearity in wireless systems. To further train and evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based data collection testbed composed of Universal Software Radio Peripherals (USRPs) and commercially available IEEE 802.11ax products. The comprehensive evaluation of DeepWiPHY with synthetic and real-world datasets (110 million synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that, even without fine-tuning the neural network's architecture parameters, DeepWiPHY achieves comparable performance to or outperforms the conventional WLAN receivers, in terms of both bit error rate (BER) and packet error rate (PER), under a wide range of channel models, signal-to-noise (SNR) levels, and modulation schemes.

Many businesses and industries require accurate forecasts for weekly time series nowadays. The forecasting literature however does not currently provide easy-to-use, automatic, reproducible and accurate approaches dedicated to this task. We propose a forecasting method that can be used as a strong baseline in this domain, leveraging state-of-the-art forecasting techniques, forecast combination, and global modelling. Our approach uses four base forecasting models specifically suitable for forecasting weekly data: a global Recurrent Neural Network model, Theta, Trigonometric Box-Cox ARMA Trend Seasonal (TBATS), and Dynamic Harmonic Regression ARIMA (DHR-ARIMA). Those are then optimally combined using a lasso regression stacking approach. We evaluate the performance of our method against a set of state-of-the-art weekly forecasting models on six datasets. Across four evaluation metrics, we show that our method consistently outperforms the benchmark methods by a considerable margin with statistical significance. In particular, our model can produce the most accurate forecasts, in terms of mean sMAPE, for the M4 weekly dataset.

With Covid-19 cases surging in parts of the U.S. at the start of flu season, developers of artificial intelligence tools are about to face their biggest test of the pandemic: Can they help doctors differentiate between the two respiratory illnesses, and accurately predict which patients will become severely ill? Numerous AI models are promising to do exactly that by sifting data on symptoms and analyzing chest X-rays and CT scans. For now, the increased availability of coronavirus testing means AI is unlikely to be relied upon for frontline detection and diagnosis. But it will become increasingly important for figuring out how aggressively to treat patients and which ones are likely to need intensive care beds, ventilators, and other equipment that could become scarce if there's a Covid-flu "twindemic." "That's on the forefront of everyone's mind right now," said Anna Yaffee, an emergency medicine physician at Emory University who helped build an online symptom checker to assess Covid-19 patients.

HitchBot, a friendly-looking talking robot with a bucket for a body and pool-noodle limbs, first arrived on American soil back in 2015. This "hitchhiking" robot was an experiment by a pair of Canadian researchers who wanted to investigate people's trust in, and attitude towards, technology. The researchers wanted to see "whether a robot could hitchhike across the country, relying only on the goodwill and help of strangers." With rudimentary computer vision and a limited vocabulary but no independent means of locomotion, HitchBot was fully dependent on the participation of willing passers-by to get from place to place. Fresh off its successful journey across Canada, where it also picked up a fervent social media following, HitchBot was dropped off in Massachusetts and struck out towards California. But HitchBot never made it to the Golden State.

In nonparametric regression and spatial process modeling, it is common for the inputs to fall in a restricted subset of Euclidean space. For example, the locations at which spatial data are collected may be restricted to a narrow non-linear subset, such as near the edge of a lake. Typical kernel-based methods that do not take into account the intrinsic geometric of the domain across which observations are collected may produce sub-optimal results. In this article, we focus on solving this problem in the context of Gaussian process (GP) models, proposing a new class of diffusion-based GPs (DB-GPs), which learn a covariance that respects the geometry of the input domain. We use the term `diffusion-based' as the idea is to measure intrinsic distances between inputs in a restricted domain via a diffusion process. As the heat kernel is intractable computationally, we approximate the covariance using finitely-many eigenpairs of the Graph Laplacian (GL). Our proposed algorithm has the same order of computational complexity as current GP algorithms using simple covariance kernels. We provide substantial theoretical support for the DB-GP methodology, and illustrate performance gains through toy examples, simulation studies, and applications to ecology data.

Manila – The head of the Philippines' military said Tuesday that the country is considering partnering with Japan to beef up its cyberdefense and drone capability as part of its force modernization program. Building cyberdefense and security infrastructure "is one aspect we are focusing on now and I think we can partner with Japan in this area," Chief of Staff Gen. Gilbert Gapay said during a media forum in Manila, noting a similar thrust for force upgrades within his country. The general said the military is also considering acquiring drones and other unmanned aerial vehicles from Japan to raise its maritime surveillance and monitoring capabilities. Japan has always been among the countries shortlisted for sourcing military hardware, based on studies conducted by different technical working groups, according to Gapay. In August, the Philippines signed a $103.5 million contract with Mitsubishi Electric Corp. for an air radar system, marking the first export of a newly made complete defense product since Japan eased its post-World War II arms export ban in 2014.

Manila – The head of the Philippines' military said Tuesday that the country is considering partnering with Japan to beef up its cyber defense and drone capability as part of its force modernization program. Building cyber defense and security infrastructure "is one aspect we are focusing on now and I think we can partner with Japan in this area," Chief of Staff Gen. Gilbert Gapay said during a media forum in Manila, noting a similar thrust for force upgrades within his country. The general said the military is also considering acquiring drones and other unmanned aerial vehicles from Japan to raise its maritime surveillance and monitoring capabilities. Japan has always been among the countries shortlisted for sourcing military hardware, based on studies conducted by different technical working groups, according to Gapay. In August, the Philippines signed a $103.5 million contract with Mitsubishi Electric Corp. for an air radar system, marking the first export of a newly made complete defense product since Japan eased its post-World War II arms export ban in 2014.

Trainers and coaches can use Exer Studio to provide more interaction and direct feedback in virtual classes. The pandemic happened to be perfect timing for the launch of three friends' artificial intelligence fitness platform. Denver-based Exer Labs, which launched its first app in May, uses AI on mobile and tablet devices as well as computer vision to help fitness users experience a more interactive and affordable at-home workout. "Our goal is to not make fitness such a luxury," said Exer co-founder Zaw Thet. "We want to focus on the hardware that people already have at home and make it accessible for everyone."

Today, Global Navigation Satellite Systems (GNSS) are used to provide position information as a driver navigational aid. This provides an attractive solution, as it offers global positioning using relatively lowcost hardware with lightweight computational load. In recent years, accuracy and robustness have increased, thanks to the availability of substantially more GNSS satellites, multiple civil frequencies such as L5, multi-frequency capable mass market receivers, and continental-scale coverage of corrections services like networked Real-Time Kinematic (RTK), Precise Point Positioning (PPP), and other model based approaches such as PPP-RTK [2]. One of the challenges facing adoption of RTK and other precision GNSS solutions in next-generation automotive systems is understanding the environment that vehicles will be operating in, as this could potentially be used as a core component of a safety critical system. General Motor's (GM) Super Cruise is an example use of GNSS as a core input to the feature activation criteria, only allowing the feature to be active on divided highways [3]. In order to address the integrity of such a system, the GNSS conditions on roads in terms of service denials must be understood. Some of the factors that affect the performance of GNSS and RTK use on highways include obstructions (e.g.