My progress toward a truly "smart" home has been painfully slow. I'm sick of unreliable voice commands, flaky smart switches, and poorly designed apps. Where's the seamless experience that was promised? As my wife often points out, if it's not easier than flicking the regular old switch or drawing the curtains yourself, it is not an improvement. Smart home automation dangles the prospect of convenience but rarely delivers.

After teasing us for weeks with trailers showing off the Pixel 8 series, Google is now ready to give us all the details about its latest flagships. The Pixel 8 and Pixel 8 Pro look largely the same as their predecessors, with a couple of key differences. The regular Pixel 8 is slightly smaller, which makes it easier to use with one hand. Meanwhile, the Pro model has a new matte finish, upgraded cameras and an intriguing temperature sensor. So, you might actually be able to hang on to your Pixel flagship for a lot longer than before.

On the nights I have trouble sleeping, I ask my husband to explain the Legend of Zelda timelines to me. I ask him to do this for a variety of reasons. One, I'm a newer Zelda fan (my first foray into the series was when Wind Waker came to the Wii U, closely followed by Breath of the Wild) and I enjoy hearing about the lore from a longtime fan. Two, it provides a sense of comfort as I dive into a story I already love and am invested in and hear facts about familiar characters. And finally, I like annoying my husband by asking him to repeat the same story.

Buying clothes in person can be a frustrating experience. You go to the fitting room, try on the item, and find you've picked the wrong size. You then have to get dressed, go back onto the shop floor, get the right-sized item, and go through the whole process again in the fitting room. Finally, you find the right item in the right size -- but now you have to wait in a long line to make your purchase. What you thought was going to be a quick and easy procedure has turned into a bit of a slog.

We consider the problem of estimating the latent state of a spatiotemporally evolving continuous function using very few sensor measurements. We show that layering a dynamical systems prior over temporal evolution of weights of a kernel model is a valid approach to spatiotemporal modeling, and that it does not require the design of complex nonstationary kernels. Furthermore, we show that such a differentially constrained predictive model can be utilized to determine sensing locations that guarantee that the hidden state of the phenomena can be recovered with very few measurements. We provide sufficient conditions on the number and spatial location of samples required to guarantee state recovery, and provide a lower bound on the minimum number of samples required to robustly infer the hidden states. Our approach outperforms existing methods in numerical experiments.

Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the availability of large-scale data from social networks and sensor networks offers an unprecedented opportunity to predict state-changing events at the individual level. Examples of such events include disease transmission, opinion transition in elections, and rumor propagation. Unlike previous research focusing on the collective effects of social systems, this study makes efficient inferences at the individual level. In order to cope with dynamic interactions among a large number of individuals, we introduce the stochastic kinetic model to capture adaptive transition probabilities and propose an efficient variational inference algorithm the complexity of which grows linearly -- rather than exponentially-- with the number of individuals. To validate this method, we have performed epidemic-dynamics experiments on wireless sensor network data collected from more than ten thousand people over three years. The proposed algorithm was used to track disease transmission and predict the probability of infection for each individual. Our results demonstrate that this method is more efficient than sampling while nonetheless achieving high accuracy.

Measurement of spatial fields is of interest in environment monitoring. Recently mobile sensing has been proposed for spatial field reconstruction, which requires a smaller number of sensors when compared to the traditional paradigm of sensing with static sensors. A challenge in mobile sensing is to overcome the location uncertainty of its sensors. While GPS or other localization methods can reduce this uncertainty, we address a more fundamental question: can a location-unaware mobile sensor, recording samples on a directed non-uniform random walk, learn the statistical distribution (as a function of space) of an underlying random process (spatial field)? The answer is in the affirmative for Lipschitz continuous fields, where the accuracy of our distribution-learning method increases with the number of observed field samples (sampling rate). To validate our distribution-learning method, we have created a dataset with 43 experimental trials by measuring soundlevel along a fixed path using a location-unaware mobile sound-level meter.

As the title suggests, this paper uses learning-theoretic tools to study a problem of estimating a (Lipschitz) spatial field using sensors which are location-unaware. The main contributions are the formulation of the sensing problem, a proposed algorithm, an analysis of its sample complexity, and some proof-of concept experiments. While in the future this may involve new "contributions to statistical learning theory," the present study does not really develop new techniques. Overall, the problem is interesting but the paper could be strengthened significantly in several directions as noted by the reviewers. In particular some more specific motivating examples would help ground the paper -- the authors mention "spatial sensing... in smart cities or IoT or climatology" but do not elaborate.

Weaknesses: 1) The task of enhancing the target coverage in Directional Sensor Networks (DSNs) is important and challenging. However, as far as I am concerned, it is not a standard benchmark environment for studying multi-agent reinforcement learning. The proposed method/model design targets at a specific problem, limiting its significance. There already exist some popular environments for multi-agent cooperation. If experiments are conducted on these standard benchmarks, the significance of this work for the machine learning (ML) or reinforcement learning (RL) community can be improved.

Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs). This problem is challenging as the targets usually move randomly but the coverage range of sensors is limited in angle and distance. Thus, it is required to coordinate sensors to get ideal target coverage with low power consumption, e.g.