@arXiv_hepph_bot@mastoxiv.page2025-07-11 08:12:21
Polarized Deep Inelastic Scattering as $x \to 1$ using Soft Collinear Effective Theory
Jaipratap Singh Grewal, Aneesh V. Manohar, Jyotirmoy Roy
https://arxiv.org/abs/2507.07175
@tiotasram@kolektiva.social2025-07-04 20:14:31
Long; central Massachusetts colonial history
Today on a whim I visited a site in Massachusetts marked as "Huguenot Fort Ruins" on OpenStreetMaps. I drove out with my 4-year-old through increasingly rural central Massachusetts forests & fields to end up on a narrow street near the top of a hill beside a small field. The neighboring houses had huge lawns, some with tractors.
Appropriately for this day and this moment in history, the history of the site turns out to be a microcosm of America. Across the field beyond a cross-shaped stone memorial stood an info board with a few diagrams and some text. The text of the main sign (including typos/misspellings) read:
"""
Town Is Formed
Early in the 1680's, interest began to generate to develop a town in the area west of Natick in the south central part of the Commonwealth that would be suitable for a settlement. A Mr. Hugh Campbell, a Scotch merchant of Boston petitioned the court for land for a colony. At about the same time, Joseph Dudley and William Stoughton also were desirous of obtaining land for a settlement. A claim was made for all lands west of the Blackstone River to the southern land of Massachusetts to a point northerly of the Springfield Road then running southwesterly until it joined the southern line of Massachusetts.
Associated with Dudley and Stoughton was Robert Thompson of London, England, Dr. Daniel Cox and John Blackwell, both of London and Thomas Freak of Hannington, Wiltshire, as proprietors. A stipulation in the acquisition of this land being that within four years thirty families and an orthodox minister settle in the area. An extension of this stipulation was granted at the end of the four years when no group large enough seemed to be willing to take up the opportunity.
In 1686, Robert Thompson met Gabriel Bernor and learned that he was seeking an area where his countrymen, who had fled their native France because of the Edict of Nantes, were desirous of a place to live. Their main concern was to settle in a place that would allow them freedom of worship. New Oxford, as it was the so-named, at that time included the larger part of Charlton, one-fourth of Auburn, one-fifth of Dudley and several square miles of the northeast portion of Southbridge as well as the easterly ares now known as Webster.
Joseph Dudley's assessment that the area was capable of a good settlement probably was based on the idea of the meadows already established along with the plains, ponds, brooks and rivers. Meadows were a necessity as they provided hay for animal feed and other uses by the settlers. The French River tributary books and streams provided a good source for fishing and hunting. There were open areas on the plains as customarily in November of each year, the Indians burnt over areas to keep them free of underwood and brush. It appeared then that this area was ready for settling.
The first seventy-five years of the settling of the Town of Oxford originally known as Manchaug, embraced three different cultures. The Indians were known to be here about 1656 when the Missionary, John Eliott and his partner Daniel Gookin visited in the praying towns. Thirty years later, in 1686, the Huguenots walked here from Boston under the guidance of their leader Isaac Bertrand DuTuffeau. The Huguenot's that arrived were not peasants, but were acknowledged to be the best Agriculturist, Wine Growers, Merchant's, and Manufacter's in France. There were 30 families consisting of 52 people. At the time of their first departure (10 years), due to Indian insurrection, there were 80 people in the group, and near their Meetinghouse/Church was a Cemetery that held 20 bodies. In 1699, 8 to 10 familie's made a second attempt to re-settle, failing after only four years, with the village being completely abandoned in 1704.
The English colonist made their way here in 1713 and established what has become a permanent settlement.
"""
All that was left of the fort was a crumbling stone wall that would have been the base of a higher wooden wall according to a picture of a model (I didn't think to get a shot of that myself). Only trees and brush remain where the multi-story main wooden building was.
This story has so many echoes in the present:
- The rich colonialists from Boston & London agree to settle the land, buying/taking land "rights" from the colonial British court that claimed jurisdiction without actually having control of the land. Whether the sponsors ever actually visited the land themselves I don't know. They surely profited somehow, whether from selling on the land rights later or collecting taxes/rent or whatever, by they needed poor laborers to actually do the work of developing the land (& driving out the original inhabitants, who had no say in the machinations of the Boston court).
- The land deal was on condition that there capital-holders who stood to profit would find settlers to actually do the work of colonizing. The British crown wanted more territory to be controlled in practice not just in theory, but they weren't going to be the ones to do the hard work.
- The capital-holders actually failed to find enough poor suckers to do their dirty work for 4 years, until the Huguenots, fleeing religious persecution in France, were desperate enough to accept their terms.
- Of course, the land was only so ripe for settlement because of careful tending over centuries by the natives who were eventually driven off, and whose land management practices are abandoned today. Given the mention of praying towns (& dates), this was after King Phillip's war, which resulted in at least some forced resettlement of native tribes around the area, but the descendants of those "Indians" mentioned in this sign are still around. For example, this is the site of one local band of Nipmuck, whose namesake lake is about 5 miles south of the fort site: #LandBack.
@arXiv_astrophSR_bot@mastoxiv.page2025-06-11 09:11:35
The VMC Survey -- LIV. Anomalous Cepheids in the Magellanic Clouds Period-Luminosity relations in the near-infrared bands
Teresa Sicignano, Vincenzo Ripepi, Marina Rejkuba, Martino Romaniello, Marcella Marconi, Roberto Molinaro, Anupam Bhardwaj, Giulia De Somma, Maria-Rosa Cioni, Felice Cusano, Gisella Clementini, Richard de Grijs, Valentin Ivanov, Jesper Storm, Martin Groenewegen
@arXiv_hepph_bot@mastoxiv.page2025-07-10 08:12:01
Positivity bounds in scalar-QED EFT at one-loop level
Yunxiao Ye, Xiao Cao, Yu-Hang Wu, Jiayin Gu
https://arxiv.org/abs/2507.06302 https://
@arXiv_mathFA_bot@mastoxiv.page2025-07-08 10:49:21
Composition ideals of Lip-Linear operators and a Hilbert space characterization
Athmane Ferradi, Khalil Saadi
https://arxiv.org/abs/2507.04492 https://
@tiotasram@kolektiva.social2025-07-28 13:06:20
How popular media gets love wrong
Now a bit of background about why I have this "engineered" model of love:
First, I'm a white straight cis man. I've got a few traits that might work against my relationship chances (e.g., neurodivergence; I generally fit pretty well into the "weird geek" stereotype), but as I was recently reminded, it's possible my experience derives more from luck than other factors, and since things are tilted more in my favor than most people on the planet, my advice could be worse than useless if it leads people towards strategies that would only have worked for someone like me. I don't *think* that's the case, but it's worth mentioning explicitly.
When I first started dating my now-wife, we were both in graduate school. I was 26, and had exactly zero dating/romantic experience though that point in my life. In other words, a pretty stereotypical "incel" although I definitely didn't subscribe to incel ideology at all. I felt lonely, and vaguely wanted a romantic relationship (I'm neither aromantic nor asexual), but had never felt socially comfortable enough to pursue one before. I don't drink and dislike most social gatherings like parties or bars; I mostly hung around the fringes of the few college parties I attended, and although I had a reasonable college social life in terms of friends, I didn't really do anything to pursue romance, feeling too awkward to know where to start. I had the beginnings of crushes in both high school and college, but never developed a really strong crush, probably correlated with not putting myself in many social situations outside of close all-male friend gatherings. I never felt remotely comfortable enough to act on any of the proto-crushes I did have. I did watch porn and masturbate, so one motivation for pursuing a relationship was physical intimacy, but loneliness was as much of a motivating factor, and of course the social pressure to date was a factor too, even though I'm quite contrarian.
When I first started dating my now-wife, we were both in graduate school. I was 26, and had exactly zero dating/romantic experience though that point in my life. In other words, a pretty stereotypical "incel" although I definitely didn't subscribe to incel ideology at all. I felt lonely, and vaguely wanted a romantic relationship (I'm neither aromantic nor asexual), but had never felt socially comfortable enough to pursue one before. I don't drink and dislike most social gatherings like parties or bars; I mostly hung around the fringes of the few college parties I attended, and although I had a reasonable college social life in terms of friends, I didn't really do anything to pursue romance, feeling too awkward to know where to start. I had the beginnings of crushes in both high school and college, but never developed a really strong crush, probably correlated with not putting myself in many social situations outside of close all-male friend gatherings. I never felt remotely comfortable enough to act on any of the proto-crushes I did have. I did watch porn and masturbate, so one motivation for pursuing a relationship was physical intimacy, but loneliness was as much of a motivating factor, and of course the social pressure to date was a factor too, even though I'm quite contrarian.
I'm lucky in that I had some mixed-gender social circles already like intramural soccer and a graduate-student housing potluck. Graduate school makes a *lot* more of these social spaces accessible, so I recognize that those not in school of some sort have a harder time of things, especially if like me they don't feel like they fit in in typical adult social spaces like bars.
However, at one point I just decided that my desire for a relationship would need action on my part and so I'd try to build a relationship and see what happened. I worked up my courage and asked one of the people in my potluck if she'd like to go for a hike (pretty much clearly a date but not explicitly one; in retrospect not the best first-date modality in a lot of ways, but it made a little more sense in our setting where we could go for a hike from our front door). To emphasize this point: I was not in love with (or even infatuated with) my now-wife at that point. I made a decision to be open to building a relationship, but didn't follow the typical romance story formula beyond that. Now of course, in real life as opposed to popular media, this isn't anything special. People ask each other out all the time just because they're lonely, and some of those relationships turn out fine (although many do not).
I was lucky in that some aspects of who I am and what I do happened to be naturally comforting to my wife (natural advantage in the "appeal" model of love) but of course there are some aspects of me that annoy my wife, and we negotiate that. In the other direction, there's some things I instantly liked about my wife, and other things that still annoy me. We've figured out how to accept a little, change a little, and overall be happy with each other (though we do still have arguments; it's not like the operation/construction/maintenance of the "love mechanism" is always perfectly smooth). In particular though, I approached the relationship with the attitude of "I want to try to build a relationship with this person," at first just because of my own desires for *any* relationship, and then gradually more and more through my desire to build *this specific* relationship as I enjoyed the rewards of companionship.
So for example, while I think my wife is objectively beautiful, she's also *subjectively* very beautiful *to me* because having decided to build a relationship with her, I actively tried to see her as beautiful, rather than trying to judge whether I wanted a relationship with her based on her beauty. In other words, our relationship is more causative of her beauty-to-me than her beauty-to-me is causative of our relationship. This is the biggest way I think the "engineered" model of love differs from the "fire" and "appeal" models: you can just decide to build love independent of factors we typically think of as engendering love (NOT independent of your partner's willingness to participate, of course), and then all of those things like "thinking your partner is beautiful" can be a result of the relationship you're building. For sure those factors might affect who is willing to try building a relationship with you in the first place, but if more people were willing to jump into relationship building (not necessarily with full commitment from the start) without worrying about those other factors, they might find that those factors can come out of the relationship instead of being prerequisites for it. I think this is the biggest failure of the "appeal" model in particular: yes you *do* need to do things that appeal to your partner, but it's not just "make myself lovable" it's also: is your partner putting in the effort to see the ways that you are beautiful/lovable/etc., or are they just expecting you to become exactly some perfect person they've imagined (and/or been told to desire by society)? The former is perfectly possible, and no less satisfying than the latter.
To cut off my rambling a bit here, I'll just add that in our progress from dating through marriage through staying-married, my wife and I have both talked at times explicitly about commitment, and especially when deciding to get married, I told her that I knew I couldn't live up to the perfect model of a husband that I'd want to be, but that if she wanted to deepen our commitment, I was happy to do that, and so we did. I also rearranged my priorities at that point, deciding that I knew I wanted to prioritize this relationship above things like my career or my research interests, and while I've not always been perfect at that in my little decisions, I've been good at holding to that in my big decisions at least. In the end, *once we had built a somewhat-committed relationship*, we had something that we both recognized was worth more than most other things in life, and that let us commit even more, thus getting even more out of it in the long term. Obviously you can't start the first date with an expectation of life-long commitment, and you need to synchronize your increasing commitment to a relationship so that it doesn't become lopsided, which is hard. But if you take the commitment as an active decision and as the *precursor* to things like infatuation, attraction, etc., you can build up to something that's incredibly strong and rewarding.
I'll follow this up with one more post trying to distill some advice from my ramblings.
#relationships #love
@ErikJonker@mastodon.social2025-06-29 12:02:37
Great blog about EuroStack, we need to BUILD !
"We need to think about this first and foremost as an industrial project. With some public support but one where “industry” (the supply side) needs to be given the incentive to build, and the opportunity to scale and expand. We don’t need more “events” and pensive stuff about “our European values” and “democracy” around digital sovereignty. Industry should do its thing, or nothing will happen. Wake up."
@tiotasram@kolektiva.social2025-08-04 15:49:00
Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2
@arXiv_hepph_bot@mastoxiv.page2025-08-07 09:29:34
Two-loop renormalisation of quark and gluon fields in the SMEFT in the on-shell scheme
Claude Duhr, Giuseppe Ventura, Eleni Vryonidou
https://arxiv.org/abs/2508.04500 https://…
@arXiv_csNI_bot@mastoxiv.page2025-06-03 07:28:09
Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison
Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand, M. Hasan Najafi
https://arxiv.org/abs/2506.00924
