Should firms that apply machine learning algorithms in their decision-making make their algorithms transparent to the users they affect? Despite growing calls for algorithmic transparency, most firms have kept their algorithms opaque, citing potential gaming by users that may negatively affect the algorithm's predictive power. We develop an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights. We identify a broad set of conditions under which making the algorithm transparent benefits the firm. We show that, in some cases, even the predictive power of machine learning algorithms may increase if the firm makes them transparent. By contrast, users may not always be better off under algorithmic transparency. The results hold even when the predictive power of the opaque algorithm comes largely from correlational features and the cost for users to improve on them is close to zero. Overall, our results show that firms should not view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever to motivate users to invest in more desirable features.

Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this paper, we shift the attention to multi-label classification, where the availability of domain knowledge on the relationships among the considered classes may offer a natural way to spot incoherent predictions, i.e., predictions associated to adversarial examples lying outside of the training data distribution. We explore this intuition in a framework in which first-order logic knowledge is converted into constraints and injected into a semi-supervised learning problem. Within this setting, the constrained classifier learns to fulfill the domain knowledge over the marginal distribution, and can naturally reject samples with incoherent predictions. Even though our method does not exploit any knowledge of attacks during training, our experimental analysis surprisingly unveils that domain-knowledge constraints can help detect adversarial examples effectively, especially if such constraints are not known to the attacker. While we also show that an adaptive attack exploiting knowledge of the constraints may still deceive our classifier, it remains an open issue to understand how hard for an attacker would be to infer such constraints in practical cases. For this reason, we believe that our approach may provide a significant step towards designing robust multi-label classifiers.

Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because fouling increases the drag on vessels as they move through the water, resulting in higher fuel costs, and presents a biosecurity risk by providing a pathway for marine non-indigenous species (NIS) to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply so-called deep learning to automate the classification of images from in-water inspections for the presence and severity of biofouling. We combined images collected from in-water surveys conducted by the Australian Department of Agriculture, Water and the Environment, the New Zealand Ministry for Primary Industries and the California State Lands Commission, and annotated them using the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We compared the annotations from three biofouling experts on a 120-sample subset of these images, and found that for two tasks, identifying images containing fouling, and identifying images containing heavy fouling, they showed 89% agreement (95% CI: 87-92%). It was found that the MTurk labelling approach achieved similar agreement with experts, which we defined as performing at most 5% worse than experts (p=0.004-0.020). Our deep learning model trained with the MTurk annotations also showed reasonable performance in comparison to expert agreement, although at a lower significance level (p=0.071-0.093). We also demonstrate that significantly better performance than expert agreement can be achieved if a classifier with high recall or precision was required.

We consider offline detection of a single changepoint in binary and count time-series. We compare exact tests based on the cumulative sum (CUSUM) and the likelihood ratio (LR) statistics, and a new proposal that combines exact two-sample conditional tests with multiplicity correction, against standard asymptotic tests based on the Brownian bridge approximation to the CUSUM statistic. We see empirically that the exact tests are much more powerful in situations where normal approximations driving asymptotic tests are not trustworthy: (i) small sample settings; (ii) sparse parametric settings; (iii) time-series with changepoint near the boundary. We also consider a multichannel version of the problem, where channels can have different changepoints. Controlling the False Discovery Rate (FDR), we simultaneously detect changes in multiple channels. This "local" approach is shown to be more advantageous than multivariate global testing approaches when the number of channels with changepoints is much smaller than the total number of channels. As a natural application, we consider network-valued time-series and use our approach with (a) edges as binary channels and (b) node-degrees or other local subgraph statistics as count channels. The local testing approach is seen to be much more informative than global network changepoint algorithms.

Deep Neural networks have gained lots of attention in recent years thanks to the breakthroughs obtained in the field of Computer Vision. However, despite their popularity, it has been shown that they provide limited robustness in their predictions. In particular, it is possible to synthesise small adversarial perturbations that imperceptibly modify a correctly classified input data, making the network confidently misclassify it. This has led to a plethora of different methods to try to improve robustness or detect the presence of these perturbations. In this paper, we perform an analysis of $\beta$-Variational Classifiers, a particular class of methods that not only solve a specific classification task, but also provide a generative component that is able to generate new samples from the input distribution. More in details, we study their robustness and detection capabilities, together with some novel insights on the generative part of the model.

NASA's Mars rovers have been one of the great scientific and space successes of the past two decades. Four generations of rovers have traversed the red planet gathering scientific data, sending back evocative photographs, and surviving incredibly harsh conditions--all using on-board computers less powerful than an iPhone 1. The latest rover, Perseverance, was launched on July 30, 2020, and engineers are already dreaming of a future generation of rovers. While a major achievement, these missions have only scratched the surface (literally and figuratively) of the planet and its geology, geography, and atmosphere. "The surface area of Mars is approximately the same as the total area of the land on Earth," said Masahiro (Hiro) Ono, group lead of the Robotic Surface Mobility Group at the NASA Jet Propulsion Laboratory (JPL)--which has led all the Mars rover missions--and one of the researchers who developed the software that allows the current rover to operate.

Fox Business Flash top headlines are here. Check out what's clicking on FoxBusiness.com. The U.S. Navy could possibly operate thousands of combat ships in the coming years as the service seeks to combine surface, air and undersea drones into its fleet. It is part of a formal Integrated Force Structure Assessment in which analysis teams led by the chief of naval operations and Marine Corps commandant explored questions of fleet size in relation to fast-emerging man-unmanned teaming integration. "[W]e came up with a discreet number of ships which was more than 355 and then command and control drone networking separately unmanned," Admiral Michael Gilday, chief of naval operations, said earlier this year at the Navy's 2020 West Conference in San Diego, California. The assessment, Gilday explained, was not so much "coordinated" as "integrated," taking up a blend between a specific number of planned manned ships and a still "conceptual" number of drones.

One of the world's smallest microrobots is able to carry 2.6 times its own body weight thanks to a muscular system powered by alcohol. Conventionally, the "muscles" of small robots have been tethered to an external power source. Alternatively, they have been powered by batteries, the weight and size of which have limited efficiency and how small the robots can be. Top-of-the-range batteries have an energy density of around 1.8 megajoules per kilogram, a fraction of what you get from animal fat, which is about 38 MJ/kg. The methanol-powered muscles used by RoBeetle, an 88-milligram-long microrobot, can use catalytic combustion to reach energy levels up to 20 MJ/kg.

A breakthrough with biomorphic batteries could allow robots to store up to 72-times more energy through a system similar to biological fat reserves. Researchers at the University of Michigan – funded by the US Department of Defense – developed a new rechargeable zinc battery that integrates into the structure of a robot in order to free up space and reduce weight that conventional lithium-ion batteries create. "Robot designs are restricted by the need for batteries that often occupy 20 per cent or more of the available space inside a robot, or account for a similar proportion of the robot's weight," said Nicholas Kotov, a professor of engineering who led the research. "We don't have a single sac of fat, which would be bulky and require a lot of costly energy transfer. Distributed energy storage, which is the biological way, is the way to go for highly efficient biomorphic devices."

The US Army is pulling inspiration from the popular film'Terminator 2' in designing material capable of shape-shifting and autonomously healing. Made of polymer, the 3D printable component has a dynamic bond that allows it to go from liquid to solid multiple times. The new material is also equipped with a unique shape memory behavior, providing users with the ability to be program and trigger it to return to a previous form. The military group foresees the innovation being used to create morphing unmanned air vehicles and shape-shifting robotic platforms. The US Army is pulling inspiration from the popular science fiction film'Terminator 2' in designing material capable of shape-shifting and autonomously healing.