Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks. Even Counter-Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation of CSGO players, a task important to teams, media, bettors and fans, is difficult. To address this, we introduce (1) a data model for CSGO with an open-source implementation; (2) a graph distance measure for defining distances in CSGO; and (3) a context-aware framework to value players' actions based on changes in their team's chances of winning. Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence compared to existing valuation frameworks. We also provide use cases demonstrating high-impact play identification and uncertainty estimation.

Navigation is an essential ability for mobile agents to be completely autonomous and able to perform complex actions. However, the problem of navigation for agents with limited (or no) perception of the world, or devoid of a fully defined motion model, has received little attention from research in AI and Robotics. One way to tackle this problem is to use guided navigation, in which other autonomous agents, endowed with perception, can combine their distinct viewpoints to infer the localisation and the appropriate commands to guide a sensory deprived agent through a particular path. Due to the limited knowledge about the physical and perceptual characteristics of the guided agent, this task should be conducted on a level of abstraction allowing the use of a generic motion model, and high-level commands, that can be applied by any type of autonomous agents, including humans. The main task considered in this work is, given a group of autonomous agents perceiving their common environment with their independent, egocentric and local vision sensors, the development and evaluation of algorithms capable of producing a set of high-level commands (involving qualitative directions: e.g. move left, go straight ahead) capable of guiding a sensory deprived robot to a goal location.

Modeling place functions from a computational perspective is a prevalent research topic. The technology of embedding enables a new approach that allows modeling the function of a place by its chronological context as part of a trajectory. The embedding similarity was previously proposed as a new metric for measuring the similarity of place functions, with some preliminary results. This study explores if this approach is meaningful for geographical units at a much smaller geographical granularity compared to previous studies. In addition, this study investigates if the geographical distance can influence the embedding similarity. The empirical evaluations based on a big vehicle trajectory data set confirm that the embedding similarity can be a metric proxy for place functions. However, the results also show that the embedding similarity is still bounded by the distance at the local scale.

A month ago we finished Why R? 2020 conference. We had an pleasure to host Roger Bivand, a professor at Norwegian School of Economics and Member of R Foundation. This post contains a biography of the speaker and an abstract of his talk: Applied Spatial Data Analysis with R – retrospect and prospect. When we began over 20 years ago, spatial data was usually found in proprietary software, usually geographical information systems, and most positional data was very hard to acquire. Statistics for spatial data existed, but largely without convenient access to positional data.

Spatial knowledge is a fundamental building block for the development of advanced perceptive and cognitive abilities. Traditionally, in robotics, the Euclidean (x, y, z) coordinate system and the agent's forward model are defined a priori. We show that a naive agent can autonomously build an internal coordinate system, with the same dimension and metric regularity as the external space, simply by learning to predict the outcome of sensorimotor transitions in a self-supervised way.

Collections of probability distributions arise in a variety of statistical applications ranging from user activity pattern analysis to brain connectomics. In practice these distributions are represented by histograms over diverse domain types including finite intervals, circles, cylinders, spheres, other manifolds, and graphs. This paper introduces an approach for detecting differences between two collections of histograms over such general domains. To this end, we introduce the intrinsic slicing construction that yields a novel class of Wasserstein distances on manifolds and graphs. These distances are Hilbert embeddable, which allows us to reduce the histogram collection comparison problem to the comparison of means in a high-dimensional Euclidean space. We develop a hypothesis testing procedure based on conducting t-tests on each dimension of this embedding, then combining the resulting p-values using recently proposed p-value combination techniques. Our numerical experiments in a variety of data settings show that the resulting tests are powerful and the p-values are well-calibrated. Example applications to user activity patterns, spatial data, and brain connectomics are provided.

Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. Such questions express various kinds of spatial and non-spatial constraints, necessitating a combination of textual and spatial reasoning. In response, we develop the first joint spatio-textual reasoning model, which combines geo-spatial knowledge with information in textual corpora to answer questions. We first develop a modular spatial-reasoning network that uses geo-coordinates of location names mentioned in a question, and of candidate answer POIs, to reason over only spatial constraints. We then combine our spatial-reasoner with a textual reasoner in a joint model and present experiments on a real world POI recommendation task. We report substantial improvements over existing models with-out joint spatio-textual reasoning.

Single cells continuously experience and react to mechanical challenges in three-dimensional tissues. Spatial constraints in dense tissues, physical activity, and injury all impose changes in cell shape. How cells can measure shape deformations to ensure correct tissue development and homeostasis remains largely unknown (see the Perspective by Shen and Niethammer). Working independently, Venturini et al. and Lomakin et al. now show that the nucleus can act as an intracellular ruler to measure cellular shape variations. The nuclear envelope provides a gauge of cell deformation and activates a mechanotransduction pathway that controls actomyosin contractility and migration plasticity. The cell nucleus thereby allows cells to adapt their behavior to the local tissue microenvironment. Science , this issue p. [eaba2644][1], p. [eaba2894][2]; see also p. [295][3] ### INTRODUCTION The human body is a crowded place. This crowding is even more acute when the regulation of cell growth and proliferation fails during the formation of a tumor. Dealing with the lack of space in crowded environments presents cells with a challenge. This is especially true for immune cells, whose task is to patrol tissues, causing them to experience both acute and sustained deformation as they move. Although changes in tissue crowding and associated cell shape alterations have been known by pathologists to be key diagnostic traits of late-stage tumors since the 19th century, the impact of these changes on the biology of cancer and immune cells remains unclear. Moreover, it is not known whether cells can detect and adaptively respond to deformations in densely packed spaces. ### RATIONALE To test the hypothesis that cells possess an ability to detect and respond to environmentally induced changes in their shape, we fabricated artificial microenvironments that mimic the conditions experienced by tumor and immune cells in a crowded tissue. By combining dynamic confinement, force measurements, and live cell imaging, we were able to quantify cell responses to precisely controlled physical perturbations of their shape. ### RESULTS Our results show that, although cells are surprisingly resistant to compressive forces, they monitor their own shape and develop an active contractile response when deformed below a specific height. Notably, we find that this is achieved by cells monitoring the deformation of their largest internal compartment: the nucleus. We establish that the nucleus provides cells with a precise measure of the extent of their deformation. Once cell compression exceeds the size of the nucleus, it causes the bounding nuclear envelope (NE) to unfold and stretch. The onset of the contractile response occurs when the NE reaches a fully unfolded state. This transition in the mechanical state of the NE and its membranes permits calcium release from internal membrane stores and activates the calcium-dependent phospholipase cPLA2, an enzyme known to operate as a molecular sensor of nuclear membrane tension and a critical regulator of signaling and metabolism. Activated cPLA2 catalyzes the formation of arachidonic acid, an omega-6 fatty acid that, among other processes, potentiates the adenosine triphosphatase activity of myosin II. This induces contractility of the actomyosin cortex, which produces pushing forces to resist physical compression and to rapidly squeeze the cell out of its compressive microenvironment in an “evasion reflex” mechanism. ### CONCLUSION Although the nucleus has traditionally been considered a passive storehouse for genetic material, our work identifies it as an active compartment that rapidly convers mechanical inputs into signaling outputs, with a critical role of its envelope in this sensing function. The nucleus is able to detect environmentally imposed compression and respond to it by generating a signal that is used to change cell behaviors. This phenomenon plays a critical role in ensuring that cells, such as the immune cells within a tumor, can adapt, survive, and efficiently move through a crowded and mechanically heterogeneous microenvironment. Characterizing the full spectrum of signals triggered by nuclear compression has the potential to elucidate mechanisms underlying signaling, epigenetic, and metabolic adaptations of cells to their mechanoenvironment and is thus an exciting avenue for future research. ![Figure][4] The nuclear ruler and its contribution to the “life cycle” of a confined cell. (1) Cell confinement below resting nucleus size, leading to nuclear deformation and to unfolding, and stretching of the nuclear envelope. (2) Nuclear membrane tension increase, which triggers calcium release, cPLA2 activation, and arachidonic acid (ARA) production. (3) Actomyosin force ( F ) generation. (4) Increased cell migratory capacity and escape from confinement. The microscopic environment inside a metazoan organism is highly crowded. Whether individual cells can tailor their behavior to the limited space remains unclear. In this study, we found that cells measure the degree of spatial confinement by using their largest and stiffest organelle, the nucleus. Cell confinement below a resting nucleus size deforms the nucleus, which expands and stretches its envelope. This activates signaling to the actomyosin cortex via nuclear envelope stretch-sensitive proteins, up-regulating cell contractility. We established that the tailored contractile response constitutes a nuclear ruler–based signaling pathway involved in migratory cell behaviors. Cells rely on the nuclear ruler to modulate the motive force that enables their passage through restrictive pores in complex three-dimensional environments, a process relevant to cancer cell invasion, immune responses, and embryonic development. [1]: /lookup/doi/10.1126/science.aba2644 [2]: /lookup/doi/10.1126/science.aba2894 [3]: /lookup/doi/10.1126/science.abe3881 [4]: pending:yes

Recent wildfires in the United States have resulted in loss of life and billions of dollars, destroying countless structures and forests. Fighting wildfires is extremely complex. It is difficult to observe the true state of fires due to smoke and risk associated with ground surveillance. There are limited resources to be deployed over a massive area and the spread of the fire is challenging to predict. This paper proposes a decision-theoretic approach to combat wildfires. We model the resource allocation problem as a partially-observable Markov decision process. We also present a data-driven model that lets us simulate how fires spread as a function of relevant covariates. A major problem in using data-driven models to combat wildfires is the lack of comprehensive data sources that relate fires with relevant covariates. We present an algorithmic approach based on large-scale raster and vector analysis that can be used to create such a dataset. Our data with over 2 million data points is the first open-source dataset that combines existing fire databases with covariates extracted from satellite imagery. Through experiments using real-world wildfire data, we demonstrate that our forecasting model can accurately model the spread of wildfires. Finally, we use simulations to demonstrate that our response strategy can significantly reduce response times compared to baseline methods.

Given a multi-microphone recording of an unknown number of speakers talking concurrently, we simultaneously localize the sources and separate the individual speakers. At the core of our method is a deep network, in the waveform domain, which isolates sources within an angular region $\theta \pm w/2$, given an angle of interest $\theta$ and angular window size $w$. By exponentially decreasing $w$, we can perform a binary search to localize and separate all sources in logarithmic time. Our algorithm allows for an arbitrary number of potentially moving speakers at test time, including more speakers than seen during training. Experiments demonstrate state-of-the-art performance for both source separation and source localization, particularly in high levels of background noise.