Dutch housing update: AI, interest rates and do you buy or rent? - DutchNews.nl
Dutch housing update: AI, interest rates and do you buy or rent? DutchNews.nl
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updatetrunk/5e79c7376a212f6abc628dc596ddec1fcf67e1cb: Update third_party/kineto submodule to 4826a43 (#179492)
Includes the following commits: Remove duplicate test ignore ( pytorch/kineto#1328 ) 4826a43 Ensure that async doesn't loop while sync is active ( pytorch/kineto#1327 ) 37fada9 Authored with Claude. Pull Request resolved: #179492 Approved by: https://github.com/ryanzhang22

Cardinality Estimation for High Dimensional Similarity Queries with Adaptive Bucket Probing
arXiv:2604.04603v1 Announce Type: new Abstract: In this work, we address the problem of cardinality estimation for similarity search in high-dimensional spaces. Our goal is to design a framework that is lightweight, easy to construct, and capable of providing accurate estimates with satisfying online efficiency. We leverage locality-sensitive hashing (LSH) to partition the vector space while preserving distance proximity. Building on this, we adopt the principles of classical multi-probe LSH to adaptively explore neighboring buckets, accounting for distance thresholds of varying magnitudes. To improve online efficiency, we employ progressive sampling to reduce the number of distance computations and utilize asymmetric distance computation in product quantization to accelerate distance calc
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Improved Upper Bounds for the Directed Flow-Cut Gap
arXiv:2604.03412v1 Announce Type: new Abstract: We prove that the flow-cut gap for $n$-node directed graphs is at most $n^{1/3 + o(1)}$. This is the first improvement since a previous upper bound of $\widetilde{O}(n^{11/23})$ by Agarwal, Alon, and Charikar (STOC '07), and it narrows the gap to the current lower bound of $\widetilde{\Omega}(n^{1/7})$ by Chuzhoy and Khanna (JACM '09). We also show an upper bound on the directed flow-cut gap of $W^{1/2}n^{o(1)}$, where $W$ is the sum of the minimum fractional cut weights. As an auxiliary contribution, we significantly expand the network of reductions among various versions of the directed flow-cut gap problem. In particular, we prove near-equivalence between the edge and vertex directed flow-cut gaps, and we show that when parametrizing by $W

Cardinality Estimation for High Dimensional Similarity Queries with Adaptive Bucket Probing
arXiv:2604.04603v1 Announce Type: new Abstract: In this work, we address the problem of cardinality estimation for similarity search in high-dimensional spaces. Our goal is to design a framework that is lightweight, easy to construct, and capable of providing accurate estimates with satisfying online efficiency. We leverage locality-sensitive hashing (LSH) to partition the vector space while preserving distance proximity. Building on this, we adopt the principles of classical multi-probe LSH to adaptively explore neighboring buckets, accounting for distance thresholds of varying magnitudes. To improve online efficiency, we employ progressive sampling to reduce the number of distance computations and utilize asymmetric distance computation in product quantization to accelerate distance calc

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