Astronomers Are Closing In on the Kuiper Belt's Secrets As next-generation telescopes map this outer frontier, astronomers are bracing for discoveries that could reveal hidden planets, strange structures, and clues to the solar system's chaotic youth. Out beyond the orbit of Neptune lies an expansive ring of ancient relics, dynamical enigmas, and possibly a hidden planet--or two. The Kuiper Belt, a region of frozen debris about 30 to 50 times farther from the sun than the Earth is--and perhaps farther, though nobody knows--has been shrouded in mystery since it first came into view in the 1990s. Over the past 30 years, astronomers have cataloged about 4,000 Kuiper Belt objects (KBOs), including a smattering of dwarf worlds, icy comets, and leftover planet parts. But that number is expected to increase tenfold in the coming years as observations from more advanced telescopes pour in.

Since 2011, rafts of floating \emph{Sargassum} seaweed have frequently obstructed the coasts of the Intra-Americas Seas. The motion of the rafts is represented by a high-dimensional nonlinear dynamical system. Referred to as the eBOMB model, this builds on the Maxey--Riley equation by incorporating interactions between clumps of \emph{Sargassum} forming a raft and the effects of Earth's rotation. The absence of a predictive law for the rafts' centers of mass suggests a need for machine learning. In this paper, we evaluate and contrast Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) and Sparse Identification of Nonlinear Dynamics (SINDy). In both cases, a physics-inspired closure modeling approach is taken rooted in eBOMB. Specifically, the LSTM model learns a mapping from a collection of eBOMB variables to the difference between raft center-of-mass and ocean velocities. The SINDy model's library of candidate functions is suggested by eBOMB variables and includes windowed velocity terms incorporating far-field effects of the carrying flow. Both LSTM and SINDy models perform most effectively in conditions with tightly bonded clumps, despite declining precision with rising complexity, such as with wind effects and when assessing loosely connected clumps. The LSTM model delivered the best results when designs were straightforward, with fewer neurons and hidden layers. While LSTM model serves as an opaque black-box model lacking interpretability, the SINDy model brings transparency by discerning explicit functional relationships through the function libraries. Integration of the windowed velocity terms enabled effective modeling of nonlocal interactions, particularly in datasets featuring sparsely connected rafts.

The growing demand for innovative research in the food industry is driving the adoption of robots in large-scale experimentation, as it offers increased precision, replicability, and efficiency in product manufacturing and evaluation. To this end, we introduce a robotic system designed to optimize food product quality, focusing on powdered cappuccino preparation as a case study. By leveraging optimization algorithms and computer vision, the robot explores the parameter space to identify the ideal conditions for producing a cappuccino with the best foam quality. The system also incorporates computer vision-driven feedback in a closed-loop control to further improve the beverage. Our findings demonstrate the effectiveness of robotic automation in achieving high repeatability and extensive parameter exploration, paving the way for more advanced and reliable food product development.

Interacting individuals in complex systems often give rise to coherent motion exhibiting coordinated global structures. Such phenomena are ubiquitously observed in nature, from cell migration, bacterial swarms, animal and insect groups, and even human societies. Primary mechanisms responsible for the emergence of collective behavior have been extensively identified, including local alignments based on average or relative velocity, non-local pairwise repulsive-attractive interactions such as distance-based potentials, interplay between local and non-local interactions, and cognitive-based inhomogeneous interactions. However, discovering how to adapt these mechanisms to modulate emergent behaviours remains elusive. Here, we demonstrate that it is possible to generate coordinated structures in collective behavior at desired moments with intended global patterns by fine-tuning an inter-agent interaction rule. Our strategy employs deep neural networks, obeying the laws of dynamics, to find interaction rules that command desired collective structures. The decomposition of interaction rules into distancing and aligning forces, expressed by polynomial series, facilitates the training of neural networks to propose desired interaction models. Presented examples include altering the mean radius and size of clusters in vortical swarms, timing of transitions from random to ordered states, and continuously shifting between typical modes of collective motions. This strategy can even be leveraged to superimpose collective modes, resulting in hitherto unexplored but highly practical hybrid collective patterns, such as protective security formations. Our findings reveal innovative strategies for creating and controlling collective motion, paving the way for new applications in robotic swarm operations, active matter organisation, and for the uncovering of obscure interaction rules in biological systems.

Scientists have developed tiny robots using human cells that could one day patrol our bodies, searching for and healing diseased cells and tissue. So-called'anthrobots,' assembled from human cells can repair damage to brain cells in a dish, according to a study published Thursday in the journal Advanced Science. Scientists at Tufts University in Massachusetts developed the SIZE robots to heal diseases, but foresee the technology repairing cell and tissue damage from conditions such as Alzheimer's. These bots - whose name means'human robots' - were made from human airway cells. To build the anthrobots, scientists started with samples of the cells that line human lungs.

We consider the well-studied problem of learning a linear combination of $k$ ReLU activations with respect to a Gaussian distribution on inputs in $d$ dimensions. We give the first polynomial-time algorithm that succeeds whenever $k$ is a constant. All prior polynomial-time learners require additional assumptions on the network, such as positive combining coefficients or the matrix of hidden weight vectors being well-conditioned. Our approach is based on analyzing random contractions of higher-order moment tensors. We use a multi-scale analysis to argue that sufficiently close neurons can be collapsed together, sidestepping the conditioning issues present in prior work. This allows us to design an iterative procedure to discover individual neurons.

This paper describes an approach, called centered object integrated seg(cid:173) mentation and recognition (COISR). The application is hand-printed character recognition. One uses a backpropagation network that scans exhaus(cid:173) tively over a field of characters and is trained to recognize whether it is centered over a single character or between characters. When it is centered over a character, the net classifies the cnaracter. The approach is tested on a dataset of hand-printed digits.

The xenobots – made from frog cells – are the first multicellular organisms found to reproduce in this way. Xenobots were first created last year, using cells taken from the embryo of the frog species Xenopus laevis. Under the right lab conditions, the cells formed small structures that could self-assemble, move in groups and sense their environment. Now, the researchers behind the work have found that xenobots can also self-replicate. Josh Bongard at the University of Vermont and Michael Levin at Tufts University in Massachusetts and their colleagues began by extracting rapidly dividing stem cells that are destined to become skin cells from frog embryos.

TL;DR -- Recent research shows that'wide' neural networks change very little when they are trained, while'narrow' networks change the weights of their synapses dramatically. This is a consequence of the fact that those wide-nets tend to all turn into the same network, statistically. Because all initializations are the same, this reduces the'distance' a network must travel to minimize loss. So, less change occurs during training. Though this makes them train faster and with greater expressiveness, it's actually counter-productive to generalization and following symbolic constraints and analogies, as well as for concatenating sub-tasks into goals. We'll need a Mixture of Experts for those problems.

The best way to tell if someone owns a dog is to look at what they wear every day. If the answer is "anything that matches my pet's hair," then they probably have a dog or cat at home. One question we get asked a lot is: Can a robot vacuum help? The short answer is yes--but there are things you should know before purchasing an automated cleaner to help turn the tide in your battle against pet hair. We've been testing robot vacuums for years--and have done specific research on the best robot vacuums for pet hair--so we've got answers to the most common issues.