Incremental (k, z)-Clustering on Graphs
Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
https://arxiv.org/abs/2602.08542 https://arxiv.org/pdf/2602.08542 https://arxiv.org/html/2602.08542
arXiv:2602.08542v1 Announce Type: new
Abstract: Given a weighted undirected graph, a number of clusters $k$, and an exponent $z$, the goal in the $(k, z)$-clustering problem on graphs is to select $k$ vertices as centers that minimize the sum of the distances raised to the power $z$ of each vertex to its closest center. In the dynamic setting, the graph is subject to adversarial edge updates, and the goal is to maintain explicitly an exact $(k, z)$-clustering solution in the induced shortest-path metric.
While efficient dynamic $k$-center approximation algorithms on graphs exist [Cruciani et al. SODA 2024], to the best of our knowledge, no prior work provides similar results for the dynamic $(k,z)$-clustering problem. As the main result of this paper, we develop a randomized incremental $(k, z)$-clustering algorithm that maintains with high probability a constant-factor approximation in a graph undergoing edge insertions with a total update time of $\tilde O(k m^{1 o(1)} k^{1 \frac{1}{\lambda}} m)$, where $\lambda \geq 1$ is an arbitrary fixed constant. Our incremental algorithm consists of two stages. In the first stage, we maintain a constant-factor bicriteria approximate solution of size $\tilde{O}(k)$ with a total update time of $m^{1 o(1)}$ over all adversarial edge insertions. This first stage is an intricate adaptation of the bicriteria approximation algorithm by Mettu and Plaxton [Machine Learning 2004] to incremental graphs. One of our key technical results is that the radii in their algorithm can be assumed to be non-decreasing while the approximation ratio remains constant, a property that may be of independent interest.
In the second stage, we maintain a constant-factor approximate $(k,z)$-clustering solution on a dynamic weighted instance induced by the bicriteria approximate solution. For this subproblem, we employ a dynamic spanner algorithm together with a static $(k,z)$-clustering algorithm.
toXiv_bot_toot

Had to remove --incremental from my Eleventy watch command because my RSS feed were not being regenerated after a post creation on this blog, it’s a bit sad because full rebuild just to update the RSS feed is a lot.
https://rmendes.net/content/notes/2026-02-05-4be88

Ricardo Mendes
5 February 2026 Eleventy Had to remove --incremental from my Eleventy watch command because my RSS feed were not being regenerated after a post creation on this blog, it’s a bit sad because full rebui...

Sources: Nvidia delays the release of its incremental gaming GPU upgrade, codenamed Kicker, marking the first year in three decades without a new GPU for gaming (The Information)
https://www.theinformation.com/articles/nvidia-delay-new-gaming-chi…

Nvidia to Delay New Gaming Chip Due to Memory Chip Shortage
Nvidia won’t release a new graphics chip for gamers this year due to a deepening global shortage of memory chips, prompted by the AI boom, according to two people with direct knowledge of the matter. It would be the first year in three decades that Nvidia hasn’t released a new graphics ...

with the latest change, git-pages is now fully incremental: whether you're deploying from a git repositroy or from a directory via the CLI, it doesn't reupload files that are already a part of your site (± some corner cases)

always wild to see folks keep saying "this protest will not end a war so I won't do it".
I'm not going to pretend I go to enough protests but small incremental change is rarely fun, satisfying, or immediately evident.

#GitPages now supports incremental site updates when uploading via the CLI!
pictured are logs for two individual uploads:
1a. PUT probe (says blobs are missing)
1b. PUT upload (uploads all the blobs)
2. PUT probe (says all the blobs are already there)

Had Fun

Bought a car/micro-camper

Bought a van to do up as a micro-camper, and did a temporary rush job of that conversion myself while waiting in the list for the pro to do it.
Then the pro gave himself a health criss the week it was booked so I took apart my temp job and only got another temp kit-job in it's place.
Went out in it like four times during that and then broke my wrist and couldn't really use it or improve it.
Then had to take it apart even more to try
and figure out where the ad-blue hole was.
I will do a proper permanent job of the
floor and walls and ceiling and adjustments to the kit-job to make it the nicest it's been so far during the spring next year.
My assumption that the prior conversion
into a van and for wheelchair-access meant the microcamper conversion was half-done already turned out to be false.
If I buy a new one, it'll be one that has never been wheelchair adapted.
But it's going okay. Only scraped it once so far.
Fewer than aimed for or booked, but I broke my wrist and had to cancel the second half of the summer.
Went to a conference about money and computers and fringe decentralized social media and it wasn't as boring as you might expect and felt pretty much like a festival.
Exactly the target number! It's lovely.
Took 3 times longer than I'd hoped and
50% more money than I'd planned for really.
Still improvements to make but they will
be incremental and gradual over the coming year or two now.
It's been interest-only for 20 years so a big old lump sum payment that I never really expected to be able to make. Expected to have to sell and move at the end of the mortgage term.
But surprisingly the stocks ISA got high enough to pay it off after all, so I did that.
Cash-flow ruined by that and the bedroom but should start to feel a bit richer next year.

Having discovered that 2025 marked the 10 year anniversary of my return to the indie web, I've spent the last six months making small, incremental improvements in just about every part of my web life.
Felt like the right thing to do, on the final day of the year, to write something about these little side quests: #IndieWeb #HappyNewYear #Blogging

I'm porting a bunch of code that delves heavily into index math from Python to Lua for my incremental/reversible RNG library. In Lua, indices start at 1. I have never in my life been more glad to have a robust suite of unit tests, because the number of off-by-one errors I'm introducing is astronomical.
#coding

Defector reports Year 5 revenue up 1% YoY to $4.65M, including $3.8M from subscriptions; all 19 original co-founders continue to work at the worker-owned outlet (Defector)
https://defector.com/defector-annual-report-year-five

Defector Annual Report, September 2024 – August 2025 | Defector
Published on November 19, 2025 Purpose of this report Defector celebrated our fifth birthday in September 2025. It is a great honor, and something of a burden, to be publishing a fifth Annual Report. The year-to-year story of our business is one of general stability—an incredible luxury in digital media—with incremental and hard-won improvements. But…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
https://arxiv.org/abs/2506.10892 https://mastoxiv.page/@arXiv_csLG_bot/114675526577078472
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
https://arxiv.org/abs/2506.20494 https://mastoxiv.page/@arXiv_csLG_bot/114749113025183688
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
https://arxiv.org/abs/2506.20584 https://mastoxiv.page/@arXiv_csLG_bot/114749118923266356
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
https://arxiv.org/abs/2506.22376 https://mastoxiv.page/@arXiv_csLG_bot/114771735361664528
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
https://arxiv.org/abs/2507.18242 https://mastoxiv.page/@arXiv_csLG_bot/114913322736512937
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
https://arxiv.org/abs/2508.17702 https://mastoxiv.page/@arXiv_csLG_bot/115095014405732247
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
https://arxiv.org/abs/2508.19009 https://mastoxiv.page/@arXiv_csLG_bot/115100269482762688
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011 https://mastoxiv.page/@arXiv_csLG_bot/115100270137397046
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
https://arxiv.org/abs/2508.20705 https://mastoxiv.page/@arXiv_csLG_bot/115111565155687451
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
https://arxiv.org/abs/2509.25977 https://mastoxiv.page/@arXiv_csLG_bot/115298721327100391
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
https://arxiv.org/abs/2510.01384 https://mastoxiv.page/@arXiv_csLG_bot/115309690976554356
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
https://arxiv.org/abs/2510.09775 https://mastoxiv.page/@arXiv_csLG_bot/115372387779061015
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
https://arxiv.org/abs/2510.14386 https://mastoxiv.page/@arXiv_csLG_bot/115389079527543821
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
https://arxiv.org/abs/2510.16882 https://mastoxiv.page/@arXiv_csLG_bot/115412243355962887
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
https://arxiv.org/abs/2510.23117 https://mastoxiv.page/@arXiv_csLG_bot/115451891042176876
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
https://arxiv.org/abs/2510.23501 https://mastoxiv.page/@arXiv_csLG_bot/115451942159737549
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
https://arxiv.org/abs/2511.00040 https://mastoxiv.page/@arXiv_csLG_bot/115490555013124989
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
https://arxiv.org/abs/2511.04469 https://mastoxiv.page/@arXiv_csLG_bot/115507943827841017
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
https://arxiv.org/abs/2511.09902 https://mastoxiv.page/@arXiv_csLG_bot/115547587245365920
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
toXiv_bot_toot

Just finished up final polish on first release of my anarchy RNG library in Lua! Have you ever wanted an incremental or reversible shuffle? I've got one for you :)
Lua REAME page w/ links: #coding #rng #random