Tootfinder

from my link log —
Modern minimal perfect hashing: a survey.
https://arxiv.org/abs/2506.06536
saved 2025-06-11 https://dotat.at/:…

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…

So I've found my answer after maybe ~30 minutes of effort. First stop was the first search result on Startpage (https://millennialhawk.com/does-poop-have-calories/), which has some evidence of maybe-AI authorship but which is better than a lot of slop. It actually has real links & cites research, so I'll start by looking at the sources.
It claims near the top that poop contains 4.91 kcal per gram (note: 1 kcal = 1 Calorie = 1000 calories, which fact I could find/do trust despite the slop in that search). Now obviously, without a range or mention of an average, this isn't the whole picture, but maybe it's an average to start from? However, the citation link is to a study (https://pubmed.ncbi.nlm.nih.gov/32235930/) which only included 27 people with impaired glucose tolerance and obesity. Might have the cited stat, but it's definitely not a broadly representative one if this is the source. The public abstract does not include the stat cited, and I don't want to pay for the article. I happen to be affiliated with a university library, so I could see if I have access that way, but it's a pain to do and not worth it for this study that I know is too specific. Also most people wouldn't have access that way.
Side note: this doing-the-research protect has the nice benefit of letting you see lots of cool stuff you wouldn't have otherwise. The abstract of this study is pretty cool and I learned a bit about gut microbiome changes from just reading the abstract.
My next move was to look among citations in this article to see if I could find something about calorie content of poop specifically. Luckily the article page had indicators for which citations were free to access. I ended up reading/skimming 2 more articles (a few more interesting facts about gut microbiomes were learned) before finding this article whose introduction has what I'm looking for: https://pmc.ncbi.nlm.nih.gov/articles/PMC3127503/
Here's the relevant paragraph:
"""
The alteration of the energy-balance equation, which is defined by the equilibrium of energy intake and energy expenditure (1–5), leads to weight gain. One less-extensively-studied component of the energy-balance equation is energy loss in stools and urine. Previous studies of healthy adults showed that ≈5% of ingested calories were lost in stools and urine (6). Individuals who consume high-fiber diets exhibit a higher fecal energy loss than individuals who consume low-fiber diets with an equivalent energy content (7, 8). Webb and Annis (9) studied stool energy loss in 4 lean and 4 obese individuals and showed a tendency to lower the fecal energy excretion in obese compared with lean study participants.
"""
And there's a good-enough answer if we do some math, along with links to more in-depth reading if we want them. A Mayo clinic calorie calculator suggests about 2250 Calories per day for me to maintain my weight, I think there's probably a lot of variation in that number, but 5% of that would be very roughly 100 Calories lost in poop per day, so maybe an extremely rough estimate for a range of humans might be 50-200 Calories per day. Interestingly, one of the AI slop pages I found asserted (without citation) 100-200 Calories per day, which kinda checks out. I had no way to trust that number though, and as we saw with the provenance of the 4.91 kcal/gram, it might not be good provenance.
To double-check, I visited this link from the paragraph above: https://www.sciencedirect.com/science/article/abs/pii/S0022316622169853?via=ihub
It's only a 6-person study, but just the abstract has numbers: ~250 kcal/day pooped on a low-fiber diet vs. ~400 kcal/day pooped on a high-fiber diet. That's with intakes of ~2100 and ~2350 kcal respectively, which is close to the number from which I estimated 100 kcal above, so maybe the first estimate from just the 5% number was a bit low.
Glad those numbers were in the abstract, since the full text is paywalled... It's possible this study was also done on some atypical patient group...
Just to come full circle, let's look at that 4.91 kcal/gram number again. A search suggests 14-16 ounces of poop per day is typical, with at least two sources around 14 ounces, or ~400 grams. (AI slop was strong here too, with one including a completely made up table of "studies" that was summarized as 100-200 grams/day). If we believe 400 grams/day of poop, then 4.91 kcal/gram would be almost 2000 kcal/day, which is very clearly ludicrous! So that number was likely some unrelated statistic regurgitated by the AI. I found that number in at least 3 of the slop pages I waded through in my initial search.

Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Puru Vaish, Felix Meister, Tobias Heimann, Christoph Brune, Jelmer M. Wolterink
https://arxiv.org/abs/2509.13846

Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Many recent approaches in representation learning implicitly assume that uncorrelated views of a data point are sufficient to learn meaningful representations for various downstream tasks. In this work, we challenge this assumption and demonstrate that meaningful structure in the latent space does not emerge naturally. Instead, it must be explicitly induced. We propose a method that aligns representations from different views of the data to align complementary information without inducing false…

A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings
Xuyuan Kang, Xiao Wang, Jingjing An, Da Yan
https://arxiv.org/abs/2509.13371

A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings
Thermal energy storage (TES) is an effective method for load shifting and demand response in buildings. Optimal TES control and management are essential to improve the performance of the cooling system. Most existing TES systems operate on a fixed schedule, which cannot take full advantage of its load shifting capability, and requires extensive investigation and optimization. This study proposed a novel integrated load prediction and optimized control approach for ice-based TES in commercial bu…

Weakly Supervised Vulnerability Localization via Multiple Instance Learning
Wenchao Gu, Yupan Chen, Yanlin Wang, Hongyu Zhang, Cuiyun Gao, Michael R. Lyu
https://arxiv.org/abs/2509.11312

Weakly Supervised Vulnerability Localization via Multiple Instance Learning
Software vulnerability detection has emerged as a significant concern in the field of software security recently, capturing the attention of numerous researchers and developers. Most previous approaches focus on coarse-grained vulnerability detection, such as at the function or file level. However, the developers would still encounter the challenge of manually inspecting a large volume of code inside the vulnerable function to identify the specific vulnerable statements for modification, indica…

Decay of small energy solutions in the ABCD Boussinesq model under the influence of an uneven bottom
Christopher Maul\'en, Claudio Mu\~noz, Felipe Poblete
https://arxiv.org/abs/2507.06487 https://arxiv.org/pdf/2507.06487 https://arxiv.org/html/2507.06487
arXiv:2507.06487v1 Announce Type: new
Abstract: The Boussinesq $abcd$ system is a 4-parameter set of equations posed in $\mathbb{R}_t\times\mathbb{R}_x$, originally derived by Bona, Chen and Saut as first order 2-wave approximations of the incompressible and irrotational, two dimensional water wave equations in the shallow water wave regime, in the spirit of the original Boussinesq derivation. Among many particular regimes, depending each of them in terms of the value of the parameters $(a,b,c,d)$ present in the equations, the generic regime is characterized by the setting $b,d>0$ and $a,c<0$. If additionally $b=d$, the $abcd$ system is hamiltonian. Previously, sharp local in space $H^1\times H^1$ decay properties were proved in the case of a large class of $abcd$ model under the small data assumption. In this paper, we generalize [C. Kwak, et. al., The scattering problem for Hamiltonian ABCD Boussinesq systems in the energy space. J. Math. Pures Appl. (9) 127 (2019), 121--159] by considering the small data $abcd$ decay problem in the physically relevant variable bottom regime described by M. Chen. The nontrivial bathymetry is represented by a smooth space-time dependent function $h=h(t,x)$, which obeys integrability in time and smallness in space. We prove first the existence of small global solutions in $H^1\times H^1$. Then, for a sharp set of dispersive $abcd$ systems (characterized only in terms of parameters $a, b$ and $c$), all $H^1\times H^1$ small solutions must decay to zero in proper subset of the light cone $|x|\leq |t|$.
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Augmentation approaches for Mixed Integer Programming
Justo Puerto, Jose A. Ruiz-Alba
https://arxiv.org/abs/2507.08525 https://arxiv.…

Augmentation approaches for Mixed Integer Programming
We are interested in studying the structure of the feasible sets of MIPs and its relation with the existence of a finite cardinality test set which can be applied in augmentation algorithms. This paper characterizes a computable, finite test set for MIP which can be embedded in an augmentation algorithm. Several examples illustrate the structure of this set and its relationship with previous approaches in the literature.

An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Byeonghee Lee, Joonsung Kang
https://arxiv.org/abs/2509.10527 https://

An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Biomarker discovery in neurodegenerative diseases requires robust, interpretable frameworks capable of integrating high-dimensional multi-omics data under low-sample conditions. We propose a novel ensemble approach combining Graph Attention Networks (GAT), MultiOmics Variational AutoEncoder (MOVE), Elastic-net sparse regression, and Storey's False Discovery Rate (FDR). This framework is benchmarked against state-of-the-art methods including DIABLO, MOCAT, AMOGEL, and MOMLIN. We evaluate perform…

AI for the Routine, Humans for the Complex: Accuracy-Driven Data Labelling with Mixed Integer Linear Programming
Mohammad Hossein Amini, Mehrdad Sabetzadeh, Shiva Nejati
https://arxiv.org/abs/2507.04990

AI for the Routine, Humans for the Complex: Accuracy-Driven Data Labelling with Mixed Integer Linear Programming
The scarcity of accurately labelled data remains a major challenge in deep learning (DL). Many DL approaches rely on semi-supervised methods, which focus on constructing large datasets that require only a minimal amount of human-labelled data. Since DL training algorithms can tolerate moderate label noise, it has generally been acceptable for the accuracy of labels in large training datasets to fall well short of a perfect 100%. However, when it comes to testing DL models, achieving high label …