Defense: Real-time Multi-Resolution Neural Networks for Hand Simulation , Mianlun Zheng (Advisor: Jernej Barbič) from joint angle, simulate bone, tendon, muscle, vein, nerve, fascia, … application: medical education, robotics, Metaverse, physical therapy (committee) average (interpolate) 6 MRI scan of hand pose by calculating plastic strain & min energy function for equilibrium pattern matching bad: need huge data; no anatomy (dumb) tendon simulation: rod model; attach bone; emulate force from forearm; hook → tunnel to slide through fascia: triangle mesh + cloth solver; fat: FEM simulation; nail: rigid point cloud in skin high accuracy: < 1mm error against MRI real-time neural net challenge: nets in graphics slow; real-world usage need < 1ms; existing libraries overlook small model need: change level of detail (LOD) for Metaverse animation: deform character to vertices joint angle → linear blend skinning → net to reduce residue → real-time output LOD support: more vertex each level; restriction vs prolongation to downsample & upsample go up & down by level each level each region (by unity construction) separate net → small net → efficient further performance: reduce joint by perturbation simulation; PCA reduction for each region hot cache performance: lower output dimension & faster than prior work; much faster on high level Measuring context switching and memory overheads for Linux threads Native POSIX Thread Library (NPTL) & futex several times faster than old Linux thread bearable latency: on his i7-4771 in 2018, multicore context switching took ~1.5µs, same core switching ~2.5µs, launching thread ~5µs, launching process ~22.5µs lazy memory: 8 MiB virtual via ulimit (VM), ~8 KiB resident (RSS) without touching stack Optimal Protocols for Studying & Learning, Andrew Huberman neuroplasticity apply during fast eye movement sleep focus & alertness is how remember; sleep is prerequisite; meditation, NSDR are tool testing help remember; test ASAP after exposure best be open-ended, short answer, minimal prompt, require critical thinking do not matter for memory if wrong perceived familiarity hinder memory gap effect: pause in talk greatly reinforce memory ChatGPT from Scratch: How to Train an Enterprise AI Assistant • Phil Winder • GOTO 2023 post-GPT are decoder-only logistic regression is often superb for domain-specific task RLHF degrade performance parameter-efficient fine-tuning (PEFT): use adapter alongside frozen model quantization massively reduce RAM usage Falcon-7B LLM: used in demo, said to have cleaned training data, open-source fine tuning demo done on V100 in Colab How Smartphones Shrink Our Brains anticipation trump focus habit to rely on tool kill memory trying to split attention make though shallow paper is better for thinking (I think large screen help)