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That's just link aggregator of everything I consider interesting, especially DL and topological condensed matter physics. @EvgeniyZh
🧽 Diffusion Video Inpainting 🧽
👉#Alibaba unveils a technical report about DiffuEraser, a video inpainting model based on stable diffusion, designed to fill masked regions with greater details and more coherent structures. Code & weights released under Apache💙
👉Review https://t.ly/7rEll
👉Paper arxiv.org/pdf/2501.10018
👉Project lixiaowen-xw.github.io/DiffuEraser-page/
👉Repo github.com/lixiaowen-xw/DiffuEraser
The Devil is in Temporal Token: High Quality Video Reasoning Segmentation https://arxiv.org/abs/2501.08549
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Universal ratios along a line of critical points. The Ashkin--Teller model https://arxiv.org/abs/hep-th/0309129
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Homomorphic Encryption in iOS 18 (❄️ Score: 151+ in 3 days)
Link: https://readhacker.news/s/6kXxZ
Comments: https://readhacker.news/c/6kXxZ
Finite size analysis for interacting bosons at the one-two dimensional crossover https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.013021
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IPDN: Image-enhanced Prompt Decoding Network for 3D Referring Expression Segmentation https://arxiv.org/abs/2501.04995
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Consistency Checks for Language Model Forecasters https://arxiv.org/abs/2412.18544
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https://rowansci.com/publications/the-charlottes-web-of-dft
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Soft symmetries of topological orders https://arxiv.org/abs/2501.03314
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Time-reversal symmetry breaking fractional quantum spin Hall insulator in moiré MoTe2 https://arxiv.org/abs/2501.02525
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Playing Chess with 84,688 Regular Expressions (🔥 Score: 151+ in 3 hours)
Link: https://readhacker.news/s/6kGte
Comments: https://readhacker.news/c/6kGte
CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings https://arxiv.org/abs/2501.01257
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Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning https://openreview.net/forum?id=YXnwlZe0yf&noteId=yrsGpHd0Sf
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POEX: Policy Executable Embodied AI Jailbreak Attacks https://arxiv.org/abs/2412.16633
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Physics IQ Benchmark: Do generative video models learn physical principles from watching videos? https://physics-iq.github.io/
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Suppressing Counter-Rotating Errors for Fast Single-Qubit Gates with Fluxonium https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.040342
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Point Transformer V3: Simpler Faster Stronger https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Point_Transformer_V3_Simpler_Faster_Stronger_CVPR_2024_paper
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Beating cuBLAS in Single-Precision General Matrix Multiplication https://salykova.github.io/sgemm-gpu
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Efficient CPA Attack on Hardware Implementation of ML-DSA in Post-Quantum Root of Trust https://eprint.iacr.org/2025/009
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Multi-Context Temporal Consistent Modeling for Referring Video Object Segmentation https://arxiv.org/abs/2501.04939
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How to Minimize the Decoherence Caused by Black Holes https://arxiv.org/abs/2501.04773
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Thermally driven quantum refrigerator autonomously resets a superconducting qubit https://www.nature.com/articles/s41567-024-02708-5
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First-order quantum breakdown of superconductivity in an amorphous superconductor https://www.nature.com/articles/s41567-024-02713-8
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I talked about toric code at a group meeting, so here are my notes. They are a bit terse, but I think give a nice perspective on main ideas
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Encrypted Qubits can be Cloned https://arxiv.org/abs/2501.02757
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Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment https://arxiv.org/abs/2412.19326
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Show HN: Watch 3 AIs compete in real-time stock trading (Score: 154+ in 5 hours)
Link: https://readhacker.news/s/6kmms
Comments: https://readhacker.news/c/6kmms
A live dashboard where you can watch GPT-4, Claude 3, and Gemini analyze market data and make daily stock trades with real money. Each AI explains its reasoning, and you can compare their different approaches to the same data.
Link: https://trading.snagra.com?utm_source=hn (no signup required)
What you can try right now:
- Watch live trades from GPT-4, Claude 3, and Gemini
- Read each AI's full analysis and reasoning
- Compare their different interpretations of the same market data
- Track their real-time performance and win rates
- View historical trades and performance metrics
Built this over the holidays to study how different AI models approach financial decisions. Each morning at 9:30 AM EST, the AIs analyze market data and make real trades with $5 stakes.
Technical Implementation:
- Next.js frontend with real-time updates
- Node.js/Lambda backend for AI processing
- PostgreSQL for trade tracking
- Alpaca API for automated trading
- Consistent prompts for all models
Data Flow:
1. Daily market analysis (9:30 AM EST)
2. Each AI gets identical inputs:
- Financial headlines
- Market summaries
- Technical indicators
- Earnings reports
3. AIs provide:
- Stock picks with reasoning
- Entry/exit conditions
- Risk assessment
4. Automated trade execution
Note: This is an experiment in AI behavior, not investment advice. The goal is to study how different LLMs interpret financial data and make decisions with real consequences.
I'll be around to answer questions about the implementation.
NBLFQ: a lock-free MPMC queue optimized for low contention https://inria.hal.science/hal-04851700/document
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