Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.

He has demanded "goddamned steam" to power the Navy's aircraft carriers and prefers a wall to drones and other technology to secure the country's southern border. He has rejected the scientific consensus on climate change and repeatedly, wrongly, pointed to occasional wintry weather as proof that he's right. And this week, amid a safety scare involving Boeing's 737 MAX 8 and MAX 9 airplanes, President Trump complained that modern jets are "too complex to fly." He added: "I see it all the time in many products. Always seeking to go one unnecessary step further, when often old and simpler is far better."

A drone has been likened to a grasshopper because of its unique ability to leap into the air using its specially designed legs. The cargo-carrying automated vehicle is equipped with legs that let it jump 6.5 feet (two metres) into the air, taking off almost vertically. The craft, dubbed the Sparrow, costs £30,000 ($40,000) and can fly up to 62 miles (100km) at a speed of 112 mph (180kph). Delivery firms are pioneering a host of new technologies to tackle the last mile of deliveries. It is hoped the vehicles can cut the inefficiencies, and hence costs, of the final stage of delivery, in which packages are taken from a central hub to your door.

HEJERE, ETHIOPIA - Canada joined much of the world in barring the Boeing 737 Max 8 jet from its airspace on Wednesday, saying satellite tracking data show possible but unproven similarities between the Ethiopian Airliner crash that killed 157 people and a previous crash involving the model five months ago. The decision left the U.S. as one of the few remaining countries to allow the planes to keep flying. Canadian Transport Minister Marc Garneau said a comparison of vertical fluctuations found a "similar profile" to the Lion Air crash that killed 187 people in October. Garneau emphasized that the data are not conclusive but crossed a threshold that prompted Canada to bar the Max 8. He said the new information indicated the Ethiopian Airliner jet's automatic system kicked in to force the nose of the aircraft down after computer software determined it was too high.

An extended 5km (3.1 miles) no-fly zone for drones has come into force around airports in the UK after reported sightings at Gatwick, Heathrow and Dublin airports in recent months grounded hundreds of flights and left thousands stranded. Previously, only a 1km (0.6 mile) exclusion zone was in place. But despite the negative reputation they have received, the use of drones isn't all bad. From finding missing people to delivering takeaways, here are some of the ways the unmanned aircraft can be beneficial. A Norfolk man who went missing in June last year was only found when a police drone spotted him stuck on a marsh.

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training. We show that the proposed cross-cancer approach outperforms transfer learning from ImageNet dataset.

Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized such that outputs from the model over a set of input samples most closely match the samples' true malicious/benign (1/0) target labels. However, there are often a number of other sources of contextual metadata for each malware sample, beyond an aggregate malicious/benign label, including multiple labeling sources and malware type information (e.g., ransomware, trojan, etc.), which we can feed to the classifier as auxiliary prediction targets. In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss. We find that incorporating multiple auxiliary loss terms yields a marked improvement in performance on the main detection task. We also demonstrate that these gains likely stem from a more informed neural network representation and are not due to a regularization artifact of multi-target learning. Our auxiliary loss architecture yields a significant reduction in detection error rate (false negatives) of 42.6% at a false positive rate (FPR) of $10^{-3}$ when compared to a similar model with only one target, and a decrease of 53.8% at $10^{-5}$ FPR.

MIT has responded to Donald Trump's bizarre suggestion that its scientists are better at flying planes than trained pilots. In a strange tweet posted on Tuesday morning, the president had suggested that computer scientists from the Massachusetts Institute of Technology were required to to fly new planes, because they are so "complicated". "Pilots are no longer needed," he wrote. The post appeared to be a response to the Ethiopian Airlines disaster, in which 157 people were killed in a crash that had strange parallels to another accident involving the same model of plane just months before. Authorities across Europe have temporarily grounded all of the same planes in an attempt to stop further disasters.

Many facial recognition systems are being trained using millions of online photos uploaded by everyday people and, more often than not, the photos are being taken without users' consent, an NBC News investigation has found. In one worrying case, IBM scraped almost a million photos from unsuspecting users on Flickr to build its facial recognition database. The practice not only raises privacy concerns, but also fuels fears that the systems could one day be used to disproportionately target minorities. Many facial recognition systems are being trained using millions of online photos uploaded by everyday people and, more often than not, the photos are being taken without users' consent IBM's database, called'Diversity in Faces,' was released in January as part of the company's efforts to'advance the study of fairness and accuracy in facial recognition technology.' The database was released following a study from MIT Media Lab researcher Joy Buolamwini, which found that popular facial recognition services from Microsoft, IBM and Face vary in accuracy based on gender and race.

The recently prevalent tensor train (TT) and tensor ring (TR) decompositions can be graphically interpreted as (locally) linear interconnected latent factors and possess exponential decay of correlation. The projected entangled pair state (PEPS, also called two-dimensional TT) extends the spatial dimension of TT and its polycyclic structure can be considered as a square grid. Compared with TT, its algebraic decay of correlation means the enhancement of interaction between tensor modes. In this paper we adopt the PEPS and develop a tensor grid (TG) decomposition with its efficient realization termed splitting singular value decomposition (SSVD). By utilizing the alternating least squares (ALS) a method called TG-ALS is used to interpolate the missing entries of a tensor from its partial observations. Different kinds of data are used in the experiments, including synthetic data, color images and real-world videos. Experimental results demonstrate that the TG has much power of representation than TT and TR.