We could all use a little help around the kitchen, and luckily for those of us in need of a sous chef, Korean Tech Giant, Samsung agrees. As part of the company's many showcases at CES in Las Vegas, Samsung gave attendees an unprecedented look at Bot Chef, a robotic kitchen assistance that uses two plucky arms to assemble, stir, pour, and prep pre-planned recipes. As described by Samsung live demonstration of Bot Chef's abilities, the bot is'an AI-powered collaborative robotic arm that can use everyday kitchen tools.' The bot is designed to cut, mix, and season dishes and uses voice-recognition technology to carry out user commands. For instance, a demonstrator for Samsung uttered the command'Hey Bot Chef, let's make a salad' to which the system replied'OK, which one?' After selecting the'sesame tofu salad' Bot Chef began its mission which started by analyzing the selected recipe and figuring out which step it needed to begin with.

While many automakers are in a rush to nix traditional driving in favor of fully autonomous vehicles, Honda is holding on tight to the steering wheel in a new'augmented' experience that blends the best both worlds. The concept, which is on display at CES in Las Vegas, combines several novel driving technologies that are designed to help drivers seamlessly switch between manual and autonomous modes, including a moveable steering wheel that doubles as an accelerator and brake. The wheel, turned brake and accelerator, which Honda provided MailOnline a simulated demo of, is controlled by either pulling (braking) or pushing (accelerating) it away from one's body. Honda's augmented driving concept was showcased at CES in Las Vegas and includes several technologies that hope to blend autonomous and manual driving In a virtual demonstration, MailOnline tested out aspects of Honda's high-tech steering wheel that also doubles as an accelerator and brake It's also equipped with sensor around the outer ring that can feel a driver's touch. When the car is in its autonomous state a passenger can swipe their hand left or right over the top of the steering wheel to make it change lanes.

In this research, an extensive literature review was performed on the recent developments of the ambulance routing problem (ARP) and ambulance location problem (ALP). Both are respective modifications of the vehicle routing problem (VRP) and maximum covering problem (MCP), with modifications to objective functions and constraints. Although alike, a key distinction is emergency service systems (EMS) are considered critical and the optimization of these has become all the more important as a result. Similar to their parent problems, these are NP-hard and must resort to approximations if the space size is too large. Much of the current work has simply been on modifying existing systems through simulation to achieve a more acceptable result. There has been attempts towards using meta-heuristics, though practical experimentation is lacking when compared to VRP or MCP. The contributions of this work are a comprehensive survey of current methodologies, summarized models, and suggested future improvements.

The entropy models have been recently adopted in many studies to evaluate the distribution of the shear stress in circular channels. However, the uncertainty in their predictions and their reliability remains an open question. We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels. The Bayesian Monte-Carlo (BMC) uncertainty method is simplified considering a 95% Confidence Bound (CB). We developed a new statistic index called as FREEopt-based OCB (FOCB) using the statistical indices Forecasting Range of Error Estimation (FREE) and the percentage of observed data in the CB (Nin), which integrates their combined effect. The Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, had the highest certainty in the calculation of shear stress values in circular channels followed by traditional uniform flow shear stress and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy with much higher values of FOCB equal to 57.726 has less certainty in the estimation of shear stress than other models. Using the presented results in this study, the amount of confidence in entropy methods in the calculation of shear stress to design and implement different types of open channels and their stability is determined.

Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite the recent progress, a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way. Here, we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network (GIN), which was recently proposed as a state-of-the-art GNN for graph classification. One important observation in this paper is that the GIN is a realization of convolutional neural network (CNN) with two-tab filters in the graph space where the shift operation is realized using the adjacent matrix. Based on this observation, we visualize the important regions of the brain by a saliency mapping method of the trained GIN. We validate our proposed framework using large-scale resting-state fMRI data for classifying the sex of the subject based on the graph structure of the brain. The experiment was consistent with our expectation such that the obtained saliency map show high correspondence with previous neuroimaging evidences related to sex differences.

In this work we define formal grammars in terms of free monoidal categories, along with a functor from the category of formal grammars to the category of automata. Generalising from the Booleans to arbitrary semirings, we extend our construction to weighted formal grammars and weighted automata. This allows us to link the categorical viewpoint on natural language to the standard machine learning notion of probabilistic language model.

Spin-glasses are universal models that can capture complex behavior of many-body systems at the interface of statistical physics and computer science including discrete optimization, inference in graphical models, and automated reasoning. Computing the underlying structure and dynamics of such complex systems is extremely difficult due to the combinatorial explosion of their state space. Here, we develop deep generative continuous spin-glass distributions with normalizing flows to model correlations in generic discrete problems. We use a self-supervised learning paradigm by automatically generating the data from the spin-glass itself. We demonstrate that key physical and computational properties of the spin-glass phase can be successfully learned, including multi-modal steady-state distributions and topological structures among metastable states. Remarkably, we observe that the learning itself corresponds to a spin-glass phase transition within the layers of the trained normalizing flows. The inverse normalizing flows learns to perform reversible multi-scale coarse-graining operations which are very different from the typical irreversible renormalization group techniques.

Time series forecasting admits a wide range of applications from signal processing, pattern recognition and weather forecasting to mathematical finance, to name only a few. Machine learning techniques for time-series forecasting have been widely studied [1, 2]. The traditional recurrent approaches towards sequence modeling tasks [1, 2] have been recently challenged by convolutional network architectures [3-6]. Latter compete in the categories speed and precision and regularly outperform conventional recurrent approaches such as LSTM's, GRU's or RNN's [7-12]. In particular, those convolutional architectures may overcome the deficiencies of recurrent networks to handle long and multi-scale sequences with increased receptive fields [3, 13, 14]. For time sequences of images convolutional LSTM's aim to combine the best of both worlds [15, 16]. In this work we present a novel approach to utilize convolutional neural networks for image sequence as well as general sequence forecasting tasks. In contrast to the recent serge in causal "dilated" convolutional networks [3-6, 13, 14, 17, 18] our approach is closer in spirit to non-casual architectures [19-22]. However, our architecture distinguishes itself by its composite design for time series forecasting, see fig.

Count data take on non-negative integer values and are challenging to properly analyze using standard linear-Gaussian methods such as linear regression and principal components analysis. Generalized linear models enable direct modeling of counts in a regression context using distributions such as the Poisson and negative binomial. When counts contain only relative information, multinomial or Dirichlet-multinomial models can be more appropriate. We review some of the fundamental connections between multinomial and count models from probability theory, providing detailed proofs. These relationships are useful for methods development in applications such as topic modeling of text data and genomics.

New mobility concepts are at the forefront of research and innovation in smart cities. The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing. Specifically, knowing the origin and destination of each agent in the network can allow for real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being "altruistic" i.e., being willing to accept an alternative non-preferred route in order to achieve a network-level performance goal. In this work, we conduct a study to compare different levels of agent altruism and the resulting effect on the network-level traffic performance. Specifically, this study compares the effects of different underlying urban structures on the overall network performance, and investigates which characteristics of the network make it possible to realize routing improvements using a decentralized optimization router. The main finding is that, with increased vehicle altruism, it is possible to balance traffic flow among the links of the network. We show evidence that the decentralized optimization router is more effective with networks of high load while we study the influence of cities characteristics, in particular: networks with a higher number of nodes (intersections) or edges (roads) per unit area allow for more possible alternate routes, and thus higher potential to improve network performance.