Laplacian eigenmaps perform non-linear dimensionality reduction ("manifold learning") by... embedding data points using the eigenvectors of a graph Laplacian as coordinates.show more

I wrote this fun interactive article explaining Isomap, a... classic non-linear dimensionality reduction technique. Isomap captures the local similarity structure of data through a graph and computes an embedding that preserves this structure. Check it out! Link below.show more

Many dimensionality reduction algorithms share a few central principles.... 1. Construct a graph that captures the data's local structure 2. Measure "geodesic" distances between points using the graph 3. Project the points to a lower dimension while preserving these distancesshow more

Can we make Transformers better and more efficient for... robot learning? Excited to introduce Body Transformer (BoT), an architecture that leverages robot embodiment in the attention mechanism, by treating it as a graph of sensors and actuators.show more

Footage, released by Hamas, of a Hamas terrorist using... a civilian area as a launching point. Here is the criteria for Hamas’ rocket launching points: Anywhere. Areas with civilians preferred.show more

#cellbiology #bioart The actin #cytoskeleton (green) is linked to... a variety of transmembrane proteins through alpha-actinin (magenta), with such linkages being particularly dense in membrane ruffles and filopodia. As seen at 3 frames/min for 8 min by linear and non-linear structured illumination microscopy (97 and 60 nm resolution, respectively).show more

Amazon’s machine learning model collects 300 million data points... per season and can now predict which players are likely to blitz before the snap. This is a look into the future of broadcasting.show more

WalkTheDog Cross-Morphology Motion Alignment via Phase Manifolds We present... a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike existing methods using an overly sparse high-dimensional latent, we propose a phase manifold consisting of multiple closed curves, each corresponding to a latent amplitude. With our proposed vector quantized periodic autoencoder, we learn a shared phase manifold for multiple characters, such as a human and a dog, without any supervision. This is achieved by exploiting the discrete structure and a shallow network as bottlenecks, such that semantically similar motions are clustered into the same curve of the manifold, and the motions within the same component are aligned temporally by the phase variable. In combination with an improved motion matching framework, we demonstrate the manifold's capability of timing and semantics alignment in several applications, including motion retrieval, transfer and stylization.show more

Russian Geran-4 jet-drone strike on a building in the... city of Lozova, Kharkiv Oblast, claimed to be used by the Ukrainian 156th Mechanised Brigade as a command post. Coordinates: 48.888971, 36.314785show more

Non-binary actress Emma Corrin dresses like a bird in... bizarre feather outfit during Paris fashion show. The star of Netflix ‘The Crown’ identifies as queer non-binary using they/them pronouns.show more

How can we address the scarcity of data required... for specialized AI? Learn about Simula, a framework that reframes synthetic data generation as dataset-level mechanism design. By using reasoning to architect datasets from first principles, Simula enables fine-grained control over coverage, complexity, and quality. More →show more

Dry Needling: Ultrasound Diagnosis A patient presents with pain... along the semitendinosus muscle and a suspected muscle fiber tear. A thigh ultrasound is ordered. Ultrasound reveals multiple linear tracts of increased echogenicity in the proximal third of the semitendinosus muscle, which correlate with a history of dry needling performed 3 days before the radiological study. Dry needling is an invasive technique used in physiotherapy to eliminate painful muscle points (or trigger points) that can develop due to overuse. It is essential not to perform this treatment within 5 days of a diagnostic ultrasound or MRI, as it can hinder or prevent the diagnosis of a muscle injury, especially a grade I injury.show more

For the Godot Shaders Bible, we’re taking a different... approach: starting with simple shaders and learning as we go. Topics will include: - Profiling and optimization. - Compute shaders. - Particle systems. - Ray marching. - Maths (quaternions, matrices, etc.) - VFX. Everything will be presented in a linear, step-by-step way, just as always! Subscribe today! 👉 #GODOT #GodotEngine #IndieDevs #indiegameshow more

The Authority continues to enhance road safety sensitizations and... encourages pedestrians to make safe choices when using the roads. We urge all pedestrians to prioritize their safety by using designated footbridges and crossing points. Avoid risking your life as a few extra steps could save a life #PedestrianSafetyshow more

The Thomas attractor is a mathematical concept in dynamical... systems theory representing a type of chaotic space phase that exhibits cyclic symmetry. It was named after René Thomas, the mathematician who introduced it in 1979. The system is strictly related to the Lorenz attractor another famous chaotic evolutionary space manifold. The 3D Thomas attractor is represented by the combination of three differential equations where the symmetry is invariant under rotations of 120° (or 2π/3 radians) around the x y z axis which are the space coordinates, plus μ (mu) that is a parameter controlling the behavior. The attractor is characterized by a chaotic tendency and it’s sensitive to initial conditions, meaning that as μ increases, the motion undergoes period-doubling bifurcations leading to chaos. A good visualization of this spacetime manifold is a twisted, spiral-shaped structure with 3-fold symmetry where each spiral arm is symmetric with respect to the central axis, like the one displayed below. The chaotic evolution of this attractor demonstrates the geometric beauty and complexity of unpredictable systems.show more

Posted a full preview of this in my discord,... here's a snippet! This is the culmination of all the stuff I've been learning the last few months. There's a Daz3d model in there being driven by motion capture data, with a physics driven hair rig and some basic jiggle on the boobs.show more

Imagine: Parallel Neural Networks with Shaders 🤔 (GLSL instead... of CUDA) Parallelization by encoding the weights and activations as Buffer Objects or Textures, using fs to perform matmul and activation functions in parallel. Each fragm shader instance parproc 1..n neuron.show more

SOMEONE FROM TOKYO IS MAPPING BIRD LANGUAGE INTO REAL... DATA PATTERNS AND THE VISUALIZATION LOOKS LIKE A NEURAL NETWORK DREAMING Every bird sound - frequency, duration, amplitude and modulation - gets converted into 3D space coordinates and rendered as a cluster of colored points in real time through Deepen AI. A microphone captures the acoustic signal, FFT breaks it down into frequency components from 1 kHz to 8 kHz where most birds communicate, an ML model classifies the pattern and the mapped data hits a graph where each call type gets its own color and position in space. Birds have a vocal repertoire of 5 to 200+ unique signals depending on the species - and every signal carries different information about predators, food, territory and mates that humans simply can't hear. The same technology that decodes bird language detects anomalies in industrial machinery, cardiac rhythms and structural vibrations in buildings - he just trained it on nature first.show more

My data has been exhausted once again, but this... time, my mobile data isn’t sharing as expected since I don’t have a bundle. This means yesterday, my data bundle was being deducted at a rate of 10MB per second, even though my internet speed or the data my device was using wasn’t even up to 2MB per second. Inaccurate deductions!show more