In this paper we present ensembles of classifiers for automated animal audio classification, exploiting different data augmentation techniques for training Convolutional Neural Networks (CNNs). The specific animal audio classification problems are i) birds and ii) cat sounds, whose datasets are freely available. We tra…
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48 results for “animal studies”
Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasetsq-bio.NC
NeuroPaint infers missing brain area dynamics from multi-animal datasets.
problem Leveraging multi-animal datasets to understand interactions between brain areas.
method Masked autoencoding approach trained across animals with partial observations.
result Models can successfully reconstruct dynamics of unrecorded brain areas.
Emergenet predicts animal influenza strain emergence, outperforming current methods.
problem Limited ability to quantitatively assess animal influenza strain emergence.
method Infer digital twin of sequence evolution using 220,151 HA sequences.
result Emergenet predictions outperform WHO seasonal vaccine recommendations and CDC IRAT scores.
New framework learns interaction rules from animal trajectories.
problem Challenges in extracting interaction rules from animal movement data.
method Augmented behavioral models with neural networks and theory-guided regularization.
result Improved performance over baselines and novel biological insights.
Learning the activities of animals is important for the purpose of monitoring their welfare vis a vis their behaviour with respect to their environment and conspecifics. While previous works have largely focused on activity recognition in a single animal, little or no work has been done in learning the collective behav…
Shape analysis methods have in the past few years become very popular, both for theoretical exploration as well as from an application point of view. Originally developed for planar curves, these methods have been expanded to higher dimensional curves, surfaces, activities, character motions and many other objects. In …
Improved animated faces using audiovisual and modality dropout.
problem Creating realistic animated faces using speech and visual cues.
method Training a deep learning model with modality dropout to balance audio and visual inputs.
result Modality dropout improves viewer preference for audiovisual-driven animation.
New method ranks competitors from multiple types of comparisons.
problem Ranking individuals or teams from multiple conflicting comparison types.
method Combination of expectation-maximization algorithm and modified Bradley-Terry model.
result A ranking can be computed from multiple conflicting comparison types.
In this article, we will formulate a mathematical framework that allows us to treat character animations as points on infinite dimensional Hilbert manifolds. Constructing geodesic paths between animations on those manifolds allows us to derive a distance function to measure similarities of different motions. This appro…
LISBET automates social behavior analysis using machine learning.
problem Manual annotation of social behaviors is time-consuming, biased, and misses subtle interactions.
method Self-supervised learning on body tracking data.
result Automated detection and segmentation of social interactions.
Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune a…
We present an instance segmentation algorithm trained and applied to a CCTV recording of beef cattle during a winter finishing period. A fully convolutional network was transformed into an instance segmentation network that learns to label each instance of an animal separately. We introduce a conceptually simple framew…
This paper introduces a novel deep learning framework for image animation. Given an input image with a target object and a driving video sequence depicting a moving object, our framework generates a video in which the target object is animated according to the driving sequence. This is achieved through a deep architect…
The existing graph neural networks (GNNs) based on the spectral graph convolutional operator have been criticized for its performance degradation, which is especially common for the models with deep architectures. In this paper, we further identify the suspended animation problem with the existing GNNs. Such a problem …
Deep learning classifies animal behavior from wearable accelerometers.
problem Classifying animal behavior from accelerometer data.
method End-to-end deep neural network with IIR and FIR filters.
result Outperforms state-of-the-art algorithms in real-time classification.
Generative AI reduces herd behavior in trading, but can also lead to optimal herding.
problem Impact of generative AI on financial stability and herd behavior.
method Laboratory experiments with large language models replicating human trading behavior.
result AI agents make more rational decisions than humans, reducing herd behavior but also potentially leading to optimal herding.
Classical topological concepts are applied to understand high performance computing simulations of molecules writhing in three dimensional space. These simulations produce peta-bytes of floating point data, to describe 3 dimensional changes in molecular structure. A zero-th order analysis is achieved by viewing a compu…
We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to da…
A generalised framework for detailed classification of swimming paths inside the Morris Water Mazeq-bio.QM
The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents. Over the years, various methods of analysing rodent data collected in this task have been proposed. These methods span from classical performance measurements (e.g. escape latency, rodent speed, quadrant p…
Paper introduces method to estimate animal motion on unknown submanifolds using Koopman operator.
problem Estimating animal motion on unknown submanifolds in high-dimensional space.
method Data-dependent approximation of Koopman operator in RKHS over ambient space.
result Strong rates of convergence derived for estimates in terms of fill distance.
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other …
The paper learns pose variations within shape populations using constrained mixtures of factor analyzers.
problem Learning pose variations within a shape population with articulated parts and relative rotations.
method Formulated as mixtures of factor analyzers, segmentation by component posterior probabilities, and constraints on factor loading matrices for rotation matrices.
result Automatic learning of pose variations from shape populations, resulting in smooth and realistic animations.
Existing graph neural networks may suffer from the "suspended animation problem" when the model architecture goes deep. Meanwhile, for some graph learning scenarios, e.g., nodes with text/image attributes or graphs with long-distance node correlations, deep graph neural networks will be necessary for effective graph re…
Paper uses RL for high-level character control in 3D environments.
problem Creating intelligent characters with generalizable behavior.
method Combines traditional animations, heuristics, and reinforcement learning.
result Demonstrates learning of complex behaviors in a 3D environment.
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community? Content-based techniques can alleviate this issue but require extra infor…
This paper develops a method for estimating animal motion submanifolds using distribution-free learning.
problem Estimating submanifold models of animal motion supported on a configuration manifold.
method Formulates a novel method based on distribution-free learning over the manifold of measurements.
result Derives approximations of the unknown mapping that converge with rates dependent on sample size and dimensionality.
Systems developed in wearable devices with sensors onboard are widely used to collect data of humans and animals activities with the perspective of an on-board automatic classification of data. An interesting application of these systems is to support animals' behaviour monitoring gathered by sensors' data analysis. Th…
Bayesian method infers local rules for collective animal movement.
problem Learn local rules governing long-term group behaviors.
method Bayesian Inverse Reinforcement Learning with Linearly-Solvable Markov Decision Process.
result Recover true costs and find value of collective movement.
Facial expression analysis based on machine learning requires large number of well-annotated data to reflect different changes in facial motion. Publicly available datasets truly help to accelerate research in this area by providing a benchmark resource, but all of these datasets, to the best of our knowledge, are limi…
Manually authoring transition animations for a complete locomotion system can be a tedious and time-consuming task, especially for large games that allow complex and constrained locomotion movements, where the number of transitions grows exponentially with the number of states. In this paper, we present a novel approac…
CARL controls a quadruped to move naturally in complex environments.
problem Motion synthesis in dynamic environments with complex constraints.
method CARL uses GANs to adapt high-level controls to action distributions and deep reinforcement learning for dynamic recovery.
result CARL can be controlled with high-level directives and react naturally to dynamic environments.
AR app enhances young children's understanding of Wudhu.
problem Teaching Wudhu to young children is challenging.
method Needs analysis, system design, implementation, testing (Black Box Testing).
result AR technology improves children's learning interest and understanding of Wudhu.
New method identifies flawed internal models of the world in animals.
problem How animals make decisions with partial sensory information.
method Generalizes Inverse Rational Control to continuous nonlinear dynamics and noise.
result Identifies the best internal model explaining an agent's actions.
Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting low-dimensional representations and concatenating them, followed by a decoding step of the concatenated vector. This accounts for only first…
Emotions are intimately tied to motivation and the adaptation of behavior, and many animal species show evidence of emotions in their behavior. Therefore, emotions must be related to powerful mechanisms that aid survival, and, emotions must be evolutionary continuous phenomena. How and why did emotions evolve in nature…
Most animals possess the ability to actuate a vast diversity of movements, ostensibly constrained only by morphology and physics. In practice, however, a frequent assumption in behavioral science is that most of an animal's activities can be described in terms of a small set of stereotyped motifs. Here we introduce a m…
Simpler ε-greedy with longer action durations improves exploration.
problem Limited exploration capability of ε-greedy in complex domains.
method Temporally extended ε-greedy with repeated actions for random durations.
result Temporally extended ε-greedy outperforms sophisticated methods on various domains.
Collective motion of animal groups often undergoes changes due to perturbations. In a topological sense, we describe these changes as switching between low-dimensional embedding manifolds underlying a group of evolving agents. To characterize such manifolds, first we introduce a simple mapping of agents between time-st…
The vision systems of the eagle and the snake outperform everything that we can make in the laboratory, but snakes and eagles cannot build an eyeglass or a telescope or a microscope. (Judea Pearl)
A novel method compares 3D point clouds using information geometry.
problem Comparing 3D point clouds in machine learning applications.
method Interprets point clouds as probability density functions on a statistical manifold, using GMM and Modified Symmetric KL divergence.
result Demonstrates effectiveness through various case studies.
Generative adversarial networks (GANs) have shown outstanding performance on a wide range of problems in computer vision, graphics, and machine learning, but often require numerous training data and heavy computational resources. To tackle this issue, several methods introduce a transfer learning technique in GAN train…
We propose Progressive Structure-conditional Generative Adversarial Networks (PSGAN), a new framework that can generate full-body and high-resolution character images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resol…
We prove that Pareto theory of circulation of elites results from our wealth evolution model, Kelly criterion for optimal betting and Keynes' observation of "animal spirits" that drive the economy and cause that human financial decisions are prone to excess risk-taking.
Deep learning-based style transfer between images has recently become a popular area of research. A common way of encoding "style" is through a feature representation based on the Gram matrix of features extracted by some pre-trained neural network or some other form of feature statistics. Such a definition is based on…
This study improves lung tumor segmentation in mice MRI scans with nnU-Net, reducing annotation needs.
problem Accurate lung tumor segmentation in mice MRI scans for drug discovery.
method Optimized nnU-Net 3D model for lung tumor segmentation with minimal annotations.
result nnU-Net 3D models outperform 2D models in MRI mice scans, requiring fewer annotations.
There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physi…
This research explores inductive biases for deep learning to improve AI's higher-level cognition.
problem Current AI struggles with flexible out-of-distribution and systematic generalization.
method Examines and proposes new inductive biases for deep learning.
result Identifies specific inductive biases for higher-level sequential processing.
With the aim of creating virtual cloth deformations more similar to real world clothing, we propose a new computational framework that recasts three dimensional cloth deformation as an RGB image in a two dimensional pattern space. Then a three dimensional animation of cloth is equivalent to a sequence of two dimensiona…