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I built a PINN implementation in Python to solve two problems as part of a physics exam project: the damped harmonic oscillator (2nd-order ODE) and the 1D viscid Burgers' equation (nonlinear PDE). Both forward and inverse problems (to estimate unknown equation parameters from data) are implemented for each problem. The repo includes source code, sample outputs, and the written exam report (PDF). Beyond the standard PINN training setup, I ran a comparison against non-physics-informed baselines and specifically investigated extrapolation behavior, i.e. how well the models generalize outside the training domain, and finally made statistical analyses of the parameter estimation performance. GitHub: https://github.com/desdb6/pinn-dho-burgers Ready-to-run demo scripts are included, and the modules are structured to be importable so you can write your own training scripts for more customization. This is not novel research, just a clean student implementation, but hopefully useful to others learning about PINNs. Happy to answer questions or receive feedback in the comments. submitted by /u/Reversed456 [link] [留言]

submitted by /u/Cool_Technician_6380 [link] [留言]

Today is the final day to apply or nominate a startup for Startup Battlefield 200. Once the clock strikes 11:59 p.m. PT, the window closes on your chance to compete for $100,000 in equity-free funding, gain global visibility, connect directly with investors, and launch on the TechCrunch Disrupt stage.

ElevenLabs' new model will let users regenerate a section of a song without affecting the rest of the track.

With the new scissors icon, you can clip favorite moments from podcasts and share them with your audience.

The 2025 Internet Crime Report was published a few weeks ago, but I only just saw it. Lots of interesting statistics. Press release . News articles .

There are only 3 days left to save up to $410 on your ticket to TechCrunch Disrupt 2026. Early Bird pricing ends May 29 at 11:59 p.m. PT, and once the deadline passes, ticket prices increase. If you plan to attend one of the most influential gatherings in tech this year, now is the time to lock in your pass before rates go up again.

Google I/O made it official: AI-generated answers are now front and center in search, and most brands have almost no visibility into how AI is describing them to their customers. For anyone who has spent years building a strategy around 10 blue links, the rules just changed in a pretty significant way. On this episode of TechCrunch’s Equity podcast, Rebecca […]

Trajectory is betting the rapid iteration cycle that supercharged vibe-coding can help all kinds of companies build AI products that learn continuously.

These display smart glasses can connect to a phone, laptop, or gaming handheld and project the screen to your eyeballs.

China's AI boom is producing world-class talent, and Beijing is increasingly reluctant to let them go elsewhere.

The office was created a year ago and seemingly named for a far right European plan to expel minorities and immigrants from Western nations. It now works, a source says, with little to no oversight.

If you've ever tried to pick an STT vendor for a phone-based voice agent or call center product, you've probably hit this wall: you have plenty of real production audio, but it's unlabeled, so you can't compute WER on it. And the annotated public datasets (FLEURS, CommonVoice, LibriSpeech) are clean studio recordings that have nothing to do with how STT models actually handle your G.711 encoded noisy phone calls. Annotating production audio is slow, expensive, and usually a privacy headache. So most teams end up benchmarking on clean data, picking a vendor, then discovering in prod which one actually survives noise. noisekit fills that gap. Take a clean annotated dataset, apply degradations that approximate your production conditions, end up with a noisy annotated corpus you can run WER on across every STT candidate. uvx noisekit generate \ --dataset google/fleurs --config en_us --split test \ --samples 100 \ --output ./noisy-fleurs Feed ./noisy-fleurs through each STT candidate, normalize, and compute WER with the existing transcripts. The output is HuggingFace AudioFolder-compatible, so load_dataset("audiofolder", data_dir="./noisy-fleurs") works. Presets cover the conditions that actually matter for voice products: telecom: G.711 narrowband bandpass + 8-bit BitCrush + 16-32 kbps MP3 (sounds like a real phone call, not a synthetic low-pass filter) noise: real ambient mixed at 5-15 dB SNR (auto-downloads a MUSAN noise-only subset, or bring your own --noise-dir matching your domain: call center, cafe, car, street) reverb: pyroomacoustics far-field at 1-3 m mic distance low_bitrate: wideband MP3 at 16-32 kbps clipping: ADC / mic saturation clean_reference: control / WER floor compound chains stack realistically. noise_telecom = noisy room then phone codec, which is what an actual support call sounds like. Each output gets PESQ, SNR and NISQA scores in metadata.jsonl alongside the original transcript, so you can correlate WER with measured signal quality after the fac

Until we get something like ::nth-letter , there are still some really cool text effects we can make from existing CSS features, like letter-spacing , ::first-word and ::first-line . Revealing Text With CSS letter-spacing originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Vision Transformers waste 90% of their compute recalculating stationary asphalt. NeuroFlow tracks semantic surprise in embedding space, physically eliminating background tokens before the encoder. Result: 55.8x wall-clock speedup for ViTs on high-res video (1792p) with 97% fidelity. No fine-tuning required. NeuroFlow is a dynamic routing framework for Vision Transformer video inference. It exploits temporal redundancy by tracking per-patch semantic surprise via an Exponential Moving Average (EMA) of patch-level embeddings, effectively answering the architectural mismatch between O(N2) self-attention and highly redundant natural video streams. Key Contributions Architecture C (Dual-Memory Reconstruction): A completely training-free inference engine that combines a Layer 0 Retinal Gate with a Layer 12 Cortical Cache. It achieves 71.55% zero-shot top-1 accuracy at 84.0% token sparsity on SigLIP, retaining 92.4% of dense accuracy without modifying any weights. Architecture B (Extreme Wall-Clock Speedup): Physically eliminates stationary tokens before the encoder. With sparse manifold distillation, it reduces 1792p SigLIP 2 inference from 678 ms to 11.9 ms—a 55.80× wall-clock speedup at 97.37% embedding fidelity. LLM Ablation: Characterises the architectural boundaries of applying similarity-gated bypass to autoregressive language models (Phi-3-mini), demonstrating 0% token drift in syntactically constrained generation. Code and paper: https://github.com/ynnk-research/-NeuroFlow submitted by /u/Bobby-Ly [link] [留言]

I built a bot trained on my own X bookmarks and likes. Around 50,000 of them, accumulated over years...

Follow-up to my earlier post on learning rules vs. human fMRI. Same five conditions (BP, FA, PC, STDP, untrained), same model weights, now evaluated against macaque V1/V2 (FreemanZiemba2013, single-unit) and macaque V4/IT (MajajHong2015, multi-electrode). Main findings: Early visual alignment is qualitatively conserved across species. STDP (ρ ≈ 0.30) and PC (ρ ≈ 0.28) lead at macaque V1/V2, consistent with their position in human V1. The pattern isn't an fMRI artifact. The untrained baseline result doesn't replicate cleanly. In human fMRI, Random ≥ BP at V1. In macaque, STDP and PC pull ahead of Random (electrophysiology has enough SNR to resolve the difference fMRI can't). IT alignment scales with capacity, not learning rule. ResNet-50 (pretrained, ImageNet): ρ ≈ 0.25 at macaque IT. Custom 3-conv CNN across all learning rules: ρ = 0.07–0.14. The IT convergence from the companion paper looks like a capacity floor. Cross-species IT rankings: Kendall's τ = 0.00 (p = 1.00) but n = 5 only has power at τ = ±1.0, so this is uninformative rather than evidence of non-conservation. Limitations worth noting: V1/V2 and V4/IT come from different macaque datasets with different stimulus sets (textures vs. objects): the V2→V4 drop is confounded by this switch Stimulus control shows IT rankings are weakly inverted across stimulus sets (τ = −0.40), so cross-species IT differences may be partially stimulus-driven Companion paper: arxiv.org/abs/2604.16875 Cross-species paper: https://arxiv.org/abs/2605.22401 Code: github.com/nilsleut/cross-species-rsa Happy to discuss the stimulus confound issue or the capacity control in more detail. submitted by /u/ConfusionSpiritual19 [link] [留言]

Profiling PyTorch training has an interesting measurement problem: the more you measure, the more you can change the behavior of the run itself. A simple example is torch.cuda.synchronize() . It gives cleaner timing boundaries, but it also inserts synchronization points into an otherwise asynchronous CUDA workload. An alternative is to use CUDA events around selected boundaries and read them later, so timing can be captured without forcing synchronization in the hot path. This does not replace PyTorch Profiler or Nsight, but it can work as a lightweight first pass before deeper operator-level profiling. I wrote a short technical note about this while working on an open-source PyTorch training diagnostics tool: https://medium.com/p/19adf1054bcf submitted by /u/traceml-ai [link] [留言]

submitted by /u/No-Comfortable-4920 [link] [留言]

Pretty little phones with pretty big price tags.