Nature-based solution enhances resilience to flooding and catalyzes multi-benefits in coastal cities in the Global South.

Coastal cities are facing a rise in groundwater levels induced by sea level rise, further triggering saturation excess flooding where groundwater levels reach the topographic surface or reduce the storage capacity of the soil, thus stressing the existing infrastructure. Lowering groundwater levels is a priority for sustaining the long-term livelihood of coastal cities. In the absence of studies assessing the possibility of using tree-planting as a measure of alleviating saturation excess flooding in the context of rising groundwater levels, the multi-benefit nature of tree-planting programs as sustainable Nature-based solutions (NBSs) in coastal cities in the Global South is discussed.

A number sense as an emergent property of the manipulating brain.

The ability to understand and manipulate numbers and quantities emerges during childhood, but the mechanism through which humans acquire and develop this ability is still poorly understood. We explore this question through a model, assuming that the learner is able to pick up and place small objects from, and to, locations of its choosing, and will spontaneously engage in such undirected manipulation. We further assume that the learner's visual system will monitor the changing arrangements of objects in the scene and will learn to predict the effects of each action by comparing perception with a supervisory signal from the motor system.

Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling.

An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Here we propose a neural algorithm that can solve all these problems and operates reliably in diverse and complex environments. At its core, the mechanism makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source.

Anatomically distributed neural representations of instincts in the hypothalamus.

Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity.

A Foundation Model for Cell Segmentation.

Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models in use are specialist models that work well for specific domains. Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive.

Splitting probabilities and mean first-passage times across multiple thresholds of jump-and-drift transition paths.

We apply stochastic-trajectory analysis to derive exact expressions for the mean first-passage times of jump-and-drift transition paths across two or more consecutive thresholds. We perform the analysis of the crossing statistics in terms of dimensionless quantities and show that, for particles starting between two thresholds, such statistics are directly related to the probability of not crossing one threshold and to the splitting probability of crossing the second one. We additionally derive a relationship for the mean first-passage time of the transition paths crossing two consecutive thresholds for particles starting outside them.

Eco-morphodynamic carbon pumping by the largest rivers in the Neotropics.

The eco-morphodynamic activity of large tropical rivers in South and Central America is analyzed to quantify the carbon flux from riparian vegetation to inland waters. We carried out a multi-temporal analysis of satellite data for all the largest rivers in the Neotropics (i.e, width > 200 m) in the period 2000-2019, at 30 m spatial resolution.

Self-Supervised Keypoint Discovery in Behavioral Videos.

We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder architecture with a geometric bottleneck to reconstruct the spatiotemporal difference between video frames. By focusing only on regions of movement, our approach works directly on input videos without requiring manual annotations.

The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions.

Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay.

The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice.

The study of naturalistic social behavior requires quantification of animals' interactions. This is generally done through manual annotation-a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals.

Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration.

Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it.

Task Programming: Learning Data Efficient Behavior Representations.

Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation effort, we present TREBA: a method to learn annotation-sample efficient trajectory embedding for behavior analysis, based on multi-task self-supervised learning.

Nonlinear reconstruction of bioclimatic outdoor-environment dynamics for the Lower Silesia region (SW Poland).

Measured meteorological time series are frequently used to obtain information about climate dynamics. We use time series analysis and nonlinear system identification methods in order to assess outdoor-environment bioclimatic conditions starting from the analysis of long historical meteorological data records. We investigate and model the stochastic and deterministic properties of 117 years (1891-2007) of monthly measurements of air temperature, precipitation and sunshine duration by separating their slow and fast components of the dynamics.

Computer Vision in the Operating Room: Opportunities and Caveats.

Effectiveness of computer vision techniques has been demonstrated through a number of applications, both within and outside healthcare. The operating room environment specifically is a setting with rich data sources compatible with computational approaches and high potential for direct patient benefit. The aim of this review is to summarize major topics in computer vision for surgical domains.

Bioclimatic conditions of the Lower Silesia region (South-West Poland) from 1966 to 2017.

This work analyses the temporal and spatial characteristics of bioclimatic conditions in the Lower Silesia region. The daily time values (12UTC) of meteorological variables in the period 1966-2017 from seven synoptic stations of the Institute of Meteorology and Water Management (IMGW) (Jelenia Góra, Kłodzko, Legnica, Leszno, Wrocław, Opole, Śnieżka) were used as the basic data to assess the thermal stress index UTCI (Universal Thermal Climate Index). The UTCI can be interpreted by ten different thermal classes, representing the bulk of these bioclimatic conditions.

Computational Neuroethology: A Call to Action.

The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical.

Parametric transitions between bare and vegetated states in water-driven patterns.

Conditions for vegetation spreading and pattern formation are mathematically framed through an analysis encompassing three fundamental processes: flow stochasticity, vegetation dynamics, and sediment transport. Flow unsteadiness is included through Poisson stochastic processes whereby vegetation dynamics appears as a secondary instability, which is addressed by Floquet theory. Results show that the model captures the physical conditions heralding the transition between bare and vegetated fluvial states where the nonlinear formation and growth of finite alternate bars are accounted for by Center Manifold Projection.

The Attentional Drift Diffusion Model of Simple Perceptual Decision-Making.

Perceptual decisions requiring the comparison of spatially distributed stimuli that are fixated sequentially might be influenced by fluctuations in visual attention. We used two psychophysical tasks with human subjects to investigate the extent to which visual attention influences simple perceptual choices, and to test the extent to which the attentional Drift Diffusion Model (aDDM) provides a good computational description of how attention affects the underlying decision processes. We find evidence for sizable attentional choice biases and that the aDDM provides a reasonable quantitative description of the relationship between fluctuations in visual attention, choices and reaction times.

Primary Postnatal Dorsal Root Ganglion Culture from Conventionally Slaughtered Calves.

Neurological disorders in ruminants have an important impact on veterinary health, but very few host-specific in vitro models have been established to study diseases affecting the nervous system. Here we describe a primary neuronal dorsal root ganglia (DRG) culture derived from calves after being conventionally slaughtered for food consumption. The study focuses on the in vitro characterization of bovine DRG cell populations by immunofluorescence analysis.

Generalized regressive motion: a visual cue to collision.

Brains and sensory systems evolved to guide motion. Central to this task is controlling the approach to stationary obstacles and detecting moving organisms. Looming has been proposed as the main monocular visual cue for detecting the approach of other animals and avoiding collisions with stationary obstacles.

Vision without the Image.

Novel image sensors transduce the stream of photons directly into asynchronous electrical pulses, rather than forming an image. Classical approaches to vision start from a good quality image and therefore it is tempting to consider image reconstruction as a first step to image analysis. We propose that, instead, one should focus on the task at hand (e.

Speed versus accuracy in visual search: Optimal performance and neural architecture.

Searching for objects among clutter is a key ability of the visual system. Speed and accuracy are the crucial performance criteria. How can the brain trade off these competing quantities for optimal performance in different tasks? Can a network of spiking neurons carry out such computations, and what is its architecture? We propose a new model that takes input from V1-type orientation-selective spiking neurons and detects a target in the shortest time that is compatible with a given acceptable error rate.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors.

Fast Feature Pyramids for Object Detection.

Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid.

Behavioral responses to a repetitive visual threat stimulus express a persistent state of defensive arousal in Drosophila.

The neural circuit mechanisms underlying emotion states remain poorly understood. Drosophila offers powerful genetic approaches for dissecting neural circuit function, but whether flies exhibit emotion-like behaviors has not been clear. We recently proposed that model organisms may express internal states displaying "emotion primitives," which are general characteristics common to different emotions, rather than specific anthropomorphic emotions such as "fear" or "anxiety.