Tootfinder

This is cool: A user-defined string literal operator that turns string literals into their (compile-time) hash codes. Might be useful in some codebases to avoid having to maintain unique numerical values for a bunch of identifiers.
(But it might be that in large codebases, the time to run all these compile-time hash functions adds up to be unbearable.)
(I was specifically thinking of the "slot IDs" in

C++11 compile-time hash of literal strings.
C++11 compile-time hash of literal strings. GitHub Gist: instantly share code, notes, and snippets.

Don't Hash Me Like That: Exposing and Mitigating Hash-Induced Unfairness in Local Differential Privacy
Berkay Kemal Balioglu, Alireza Khodaie, Mehmet Emre Gursoy
https://arxiv.org/abs/2506.20290

Don't Hash Me Like That: Exposing and Mitigating Hash-Induced Unfairness in Local Differential Privacy
Local differential privacy (LDP) has become a widely accepted framework for privacy-preserving data collection. In LDP, many protocols rely on hash functions to implement user-side encoding and perturbation. However, the security and privacy implications of hash function selection have not been previously investigated. In this paper, we expose that the hash functions may act as a source of unfairness in LDP protocols. We show that although users operate under the same protocol and privacy budge…

from my link log —
Thoughts on hashing in Rust.
https://purplesyringa.moe/blog/thoughts-on-rust-hashing/
saved 2024-12-13

Thoughts on Rust hashing
In languages like Python, Java, or C++, values are hashed by calling a “hash me” method on them, implemented by the type author. This fixed-hash size is then immediately used by the hash table or what have you. This design suffers from some obvious problems, like: How do you hash an integer? If you use a no-op hasher (booo), DoS attacks on hash tables are inevitable. If you hash it thoroughly, consumers that only cache hashes to optimize equality checks lose out of performance.

Ah fuck, people are using LLMs for kernel code. They really are going to fuck over everything, aren't they?
https://lwn.net/SubscriberLink/1026558/9f3079fb392c4a9a/

Choosing a hash function for 2030 and beyond: SHA-2 vs SHA-3 vs BLAKE3
As everyone knows, "temporary fixes" are nothing but temporary.
Unfortunately, the same is true for cryptography: unless security is your core value-proposition, crypto algorithms are almost never updated, and that's how we end up with SHA1-hashed password in 2024 🤦‍♂️
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NeRF-based CBCT Reconstruction needs Normalization and Initialization
Zhuowei Xu, Han Li, Dai Sun, Zhicheng Li, Yujia Li, Qingpeng Kong, Zhiwei Cheng, Nassir Navab, S. Kevin Zhou
https://arxiv.org/abs/2506.19742

NeRF-based CBCT Reconstruction needs Normalization and Initialization
Cone Beam Computed Tomography (CBCT) is widely used in medical imaging. However, the limited number and intensity of X-ray projections make reconstruction an ill-posed problem with severe artifacts. NeRF-based methods have achieved great success in this task. However, they suffer from a local-global training mismatch between their two key components: the hash encoder and the neural network. Specifically, in each training step, only a subset of the hash encoder's parameters is used (local sparse…

Do you have somewhere, by any rare chance, a Windows NT 4.0 kernel32.dll library that you can share? Or the MD5 hash.

An old friend of mine is a Hasher. Shout-out to Pollywaffle from Canberra. https://sudomakecake.com/@woollypigs/114640950327552671

This https://arxiv.org/abs/2505.16133 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. Our queries directly learn binary hash codes from knowledgebase code, eliminating intermediate feature extraction steps, and significantly reducing storage and computational overhead. Building upon this hash-based eff…

InfoWorld contributor Joydip Kanjilal reviews a group of read-only data structures in .NET Core that are optimized to allow very fast thread-safe reads over their data in comparison to plain dictionaries, lists, and hash sets.
Namely: FrozenSet and FrozenDictionary.
He also shows benchmark results, and includes a tutorial written in C#.
"How to use frozen collections in C#"

Elementary Cellular Automata as Non-Cryptographic Hash Functions
Daniel McKinley
https://arxiv.org/abs/2506.06551 https://arxiv.org/p…

Elementary Cellular Automata as Non-Cryptographic Hash Functions
A subset of 10 of the 256 elementary cellular automata (ECA) are implemented as a hash function using an error minimization lossy compression algorithm operating on wrapped 4x4 neighborhood cells. All 256 rules are processed and 10 rules in two subsets of 8 are found to have properties that include both error minimization and maximization, unique solutions, a lossy inverse, efficient retroactive hashing, and an application to edge detection. The algorithm parallels the nested powers-of-two stru…

Modern Minimal Perfect Hashing: A Survey
Hans-Peter Lehmann, Thomas Mueller, Rasmus Pagh, Giulio Ermanno Pibiri, Peter Sanders, Sebastiano Vigna, Stefan Walzer
https://arxiv.org/abs/2506.06536

Modern Minimal Perfect Hashing: A Survey
Given a set $S$ of $n$ keys, a perfect hash function for $S$ maps the keys in $S$ to the first $m \geq n$ integers without collisions. It may return an arbitrary result for any key not in $S$ and is called minimal if $m = n$. The most important parameters are its space consumption, construction time, and query time. Years of research now enable modern perfect hash functions to be extremely fast to query, very space-efficient, and scale to billions of keys. Different approaches give different tr…

Technical Evaluation of a Disruptive Approach in Homomorphic AI
Eric Filiol
https://arxiv.org/abs/2506.11954 https://arxiv.org/pdf/25…

Technical Evaluation of a Disruptive Approach in Homomorphic AI
We present a technical evaluation of a new, disruptive cryptographic approach to data security, known as HbHAI (Hash-based Homomorphic Artificial Intelligence). HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rely on. As a main claim, HbHAI makes now possible to analyze and process data in its cryptographically secure form while using existing native AI algorithms without modification, with unprecedented perf…

Time For 9 o'clock #HashTagGames hosted by @… Let's play!
#FashionAndStyleSongsOrPoems

crikey, on compiler explorer you can hash include a url https://github.com/compiler-explorer/compiler-explorer/issues/1442

How do you include files from urls? · Issue #1442 · compiler-explorer/compiler-explorer
I have seen some talk where compiler explorer accepts includes from URL. For example #include "https://...." Is there an option to allow this and make it work?

CHIP: Chameleon Hash-based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control
Chaohui Xu, Qi Cui, Chip-Hong Chang
https://arxiv.org/abs/2505.24536

CHIP: Chameleon Hash-based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control
The pervasion of large-scale Deep Neural Networks (DNNs) and their enormous training costs make their intellectual property (IP) protection of paramount importance. Recently introduced passport-based methods attempt to steer DNN watermarking towards strengthening ownership verification against ambiguity attacks by modulating the affine parameters of normalization layers. Unfortunately, neither watermarking nor passport-based methods provide a holistic protection with robust ownership proof, hig…

Neural Visibility Cache for Real-Time Light Sampling
Jakub Bok\v{s}ansk\'y, Daniel Meister
https://arxiv.org/abs/2506.05930 https://

Neural Visibility Cache for Real-Time Light Sampling
Direct illumination with many lights is an inherent component of physically-based rendering, remaining challenging, especially in real-time scenarios. We propose an online-trained neural cache that stores visibility between lights and 3D positions. We feed light visibility to weighted reservoir sampling (WRS) to sample a light source. The cache is implemented as a fully-fused multilayer perceptron (MLP) with multi-resolution hash-grid encoding, enabling online training and efficient inference o…