Talita's Daycare 

Talita's Daycare is a home daycare in San Mateo, California. Visit our website: [Talita's Daycare](https://talitasdaycare.com)

Sync celery-worker and celery-beat variables with the web service on Railway 

# railway_sync_celery_vars.ps1
#
# Sync celery-worker and celery-beat variables with the DjangoTest4 (web) service on Railway,
# as *references* (${{DjangoTest4.VAR}}), so they always follow the web service's value.
# Sets every variable that is missing or has a different value; never touches RAILWAY_* or DJANGO_SETTINGS_MODULE.
# Each service redeploys once after its variables change.
#
# Make sure the CLI is logged in and pointed at the right project.
# Run railway login in a normal te

OptimalFutureConfidence

# OptimalFutureConfidence — reconstructed research implementation This is a **new implementation reconstructed from the supplied 62-page manuscript**, not the original research software. It includes runnable core components and research-workflow scaffolding. It **has not reproduced** any reported PISA/C19 numerical results because the raw analytic datasets, exact validated feature lists, and historical experimental artifacts were not supplied. Do not describe it as a verified reproduction until the procedures are executed and outputs checked. ## Manuscript mapping - Sec. 3.3/4.4: encoder 256→128, entropic OT + barycentric projection, one-step latent transformation (stochastic dimension 64), confidence gate. - Sec. 3.4/4.4.5: BCE + OT + representation preservation + Brier + hinge constraint. - Sec. 4.1: country-specific, weighted ESCS first quartile; each of ten math plausible values compared with the corresponding weighted 75th percentile of **all eligible** students in that country; majority vote >=5. - Sec. 4.5.3: BRS six items, reverse 2/4/6, mean score >4.30 high resilience; **independent** C19 fitting. - Sec. 4.3: AUROC, AUPRC, F1, balanced accuracy, Brier, 10-bin ECE, NLL; paired t tests, 95% t CI, Holm adjustment. - Sec. 4.4: batch 256, AdamW, cosine annealing, patience 20, seeds 42/52/62/72/82. ## Installation ```bash python -m venv .venv source .venv/bin/activate pip install -r requirements.txt python -m pytest -q ``` ## Prepare legal public-use data 1. Download the original PISA 2018 / PISA 2022 files from official OECD sources, and C19 ISWS from https://doi.org/10.5281/zenodo.5946964, observing their data-use conditions. 2. Decode original questionnaire data using the relevant source codebook. Merge PISA student and school files **by country and school identifier** if using school-level predictors; retain unique student identifiers. Resolve all year-specific column names and answer-code differences using the official year-specific dictionaries. 3. Inspect `configs/pisa_template.json` or `configs/c19_template.json`. Replace each example field with actual column names; **set a fully audited set of non-achievement predictors** and, only after validating the mappings, switch `verified_mapping` to `true`. 4. Inputs: CSV, Parquet, Stata DTA or SPSS SAV. The full manuscript harmonized feature inventory and exact cross-cycle category recodes were **not contained in the supplied PDF**, so they cannot be reconstructed without extra documentation. ## Run a verified dataset ```bash python scripts/run_experiment.py --config configs/pisa_template.json --model OptimalFutureConfidence --seed 42 --out outputs/pisa18_ofc_42 python scripts/run_experiment.py --config configs/pisa_template.json --model XGBoost --seed 42 --out outputs/pisa18_xgb_42 python scripts/run_experiment.py --config configs/pisa_template.json --model OptimalFutureConfidence --seed 42 --group-by-school --out outputs/pisa18_school_42 python scripts/run_experiment.py --config configs/pisa_template.json --model OptimalFutureConfidence --seed 42 --remove future --out outputs/pisa18_no_future_42 python scripts/run_experiment.py --config configs/c19_template.json --model OptimalFutureConfidence --seed 42 --out outputs/c19_ofc_42 python scripts/aggregate.py outputs/*/metrics.json --output outputs/summary.json ``` Templates deliberately fail before real data/feature mappings are supplied. This prevents pretending that an arbitrary feature list reproduces the study. Run each model with seeds 42,52,62,72,82 to obtain paired comparisons. `--remove` supports OT, future, calibration, constraint, and parameters include `--epsilon`, `--delta`, `--stochastic-dim`. ## Scope and limitations **Implemented:** core network; balanced minibatch Sinkhorn; sample-wise out-of-sample transport with frozen training-derived target duals; major loss terms; training/validation/test partitioning, group-disjoint school split; train-only imputation/scaling/one-hot; PISA and BRS label construction; four conventional baselines; calibration metrics; significance aggregation; basic tests. **Not complete:** source data inventory and true cross-cycle harmonization mappings; missing PISA design nuances / plausible-value sampling uncertainty and replicate-weight inference; full hyperparameter search; true TabNet and FT-Transformer comparisons; per-table plotting, source-matched model complexity/FLOPs; end-to-end replication of Tables 2–12; direct PISA 2018→2022 input alignment/transfer. A group shuffle split maintains school separation but does **not** guarantee the paper's claimed close preservation of country/class distributions. Per-model baseline hyperparameter tuning is simplified. ### Implementation choices that cannot be verified against the manuscript The paper does not specify exact reference bank size or target-potential maintenance procedure. The reconstruction uses a capped 512-point training-only bank (configurable), refreshed after each epoch, and Sinkhorn target dual values fitted on a training-only subset at each validation checkpoint. This is a practical approximation, not proof of algorithmic equivalence. For inference, a deterministic zero-noise latent realization is used; the paper gives a conceptual stochastic expectation but no Monte Carlo sample count. The training objective and formulas are structurally similar, but historical results should not be expected to match. The code should be audited against the full original source and official datasets before public claims of reproducibility. ## Data governance No participant data or results are bundled. Only use authorized, de-identified releases. Do not publicly upload identifying or restricted records. 
{
  "verified_mapping": false,
  "kind": "c19",
  "source": "/absolute/path/to/c19_netherlands.parquet",
  "brs_items": ["BRS1", "BRS2", "BRS3", "BRS4", "BRS5", "BRS6"],
  "features": ["EXAMPLE_NON_BRS_FEATURE_REPLACE_WITH_VERIFIED_LIST"]
}

{
  "verified_mapping": false,
  "kind": "pisa",
  "source": "/absolute/path/to/official/pisa_student_merged.parquet",
  "country": "CNT",
  "school": "CNTSCHID",
  "student_id": "CNTSTUID",
  "escs": "ESCS",
  "weight": "W_FSTUWT",
  "math_pvs": ["PV1MATH",

bigPromoter 

# bigPromoter

bigPromoter is a local SEO & web development agency in San Francisco, CA.

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1569. Number of Ways to Reorder Array to Get Same BST

Given an array nums that represents a permutation of integers from 1 to n. We are going to construct a binary search tree (BST) by inserting the elements of nums in order into an initially empty BST. Find the number of different ways to reorder nums so that the constructed BST is identical to that formed from the original array nums. For example, given nums = [2,1,3], we will have 2 as the root, 1 as a left child, and 3 as a right child. The array [2,3,1] also yields the same BST but [3,2,1] yields a different BST. Return the number of ways to reorder nums such that the BST formed is identical to the original BST formed from nums. Since the answer may be very large, return it modulo 109 + 7.
/**
 * @param {number[]} nums
 * @return {number}
 */
var numOfWays = function(nums) {
    const MOD = 1_000_000_007n;
    const n = nums.length;

    // Precompute Pascal's triangle for the interleaving counts.
    const choose = Array.from({ length: n + 1 }, () => Array(n + 1).fill(0n));
    for (let a = 0; a <= n; a++) {
        choose[a][0] = choose[a][a] = 1n;
        for (let b = 1; b < a; b++) {
            choose[a][b] = (choose[a - 1][b - 1] + choose[a - 1][b]) % MOD;
        }
    }

Excellence Landscaping & Paver 

# Excellence Landscaping & Paver

Excellence Landscaping & Paver provides landscaping and paver installation services.

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Добавление runner 

docker compose exec gitlab-runner gitlab-runner register
```

Ответы на вопросы регистрации:

| Поле | Значение |
| --- | --- |
| GitLab instance URL | `https://gitlab.example.ru` |
| Authentication token | Токен `glrt-…` из GitLab |
| Description / name | `docker-runner` |
| Executor | `docker` |
| Default Docker image | `alpine:3.22` |

3551. Minimum Swaps to Sort by Digit Sum

You are given an array nums of distinct positive integers. You need to sort the array in increasing order based on the sum of the digits of each number. If two numbers have the same digit sum, the smaller number appears first in the sorted order. Return the minimum number of swaps required to rearrange nums into this sorted order. A swap is defined as exchanging the values at two distinct positions in the array.
/**
 * @param {number[]} nums
 * @return {number}
 */
var minSwaps = function(nums) {
    const digitSum = (num) => {
        let sum = 0;

        while (num > 0) {
            sum += num % 10;
            num = Math.floor(num / 10);
        }

        return sum;
    };

    // Sort a copy into the required order.
    const sorted = [...nums].sort((a, b) => {
        const sumDifference = digitSum(a) - digitSum(b);
        return sumDifference !== 0 ? sumDifference : a - b;
    });

    // Map

Добавление пользователя 

adduser developer - добавляем
getent group docker - прверяем есть ли группа docker
usermod -aG docker developer - добавляем польователя developer в группу docker
groupadd docker - создание группы

DF Limousine Services

[DF Limousine Services](https://www.dflimo.com/) provides limousine and chauffeured transportation for airport transfers, corporate travel, weddings, and special events.
# DF Limousine Services

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Berger Tables (AI - Sonnet 4.5) 

<?php
/**
 * @param array    $teams   ID (nebo názvy) týmů
 * @param int      $cycles  1 = jednou, 2 = odvety, 4 = čtyřikrát...
 * @param string   $mode    'mirror' | 'shuffle'
 * @return array<int, array<int, array{0:mixed,1:mixed}>>
 *         [kolo][pozice] = [domácí, hosté]; u volna [tým, null]
 */
function bergerSchedule(
    array $teams,
    int $cycles = 1,
    string $mode = 'mirror',
    bool $shuffleRounds = true,
    ?int $seed = null
): array {
    $teams = array_valu

2916. Subarrays Distinct Element Sum of Squares II

You are given a 0-indexed integer array nums. The distinct count of a subarray of nums is defined as: Let nums[i..j] be a subarray of nums consisting of all the indices from i to j such that 0 <= i <= j < nums.length. Then the number of distinct values in nums[i..j] is called the distinct count of nums[i..j]. Return the sum of the squares of distinct counts of all subarrays of nums. Since the answer may be very large, return it modulo 109 + 7. A subarray is a contiguous non-empty sequence of elements within an array.
/**
 * @param {number[]} nums
 * @return {number}
 */
var sumCounts = function(nums) {
    const MOD = 1_000_000_007;
    const n = nums.length;

    const sum = new Array(4 * n).fill(0);
    const sumSquares = new Array(4 * n).fill(0);
    const lazy = new Array(4 * n).fill(0);

    function apply(node, left, right, delta) {
        const length = right - left + 1;
        const d = delta % MOD;

        // (x + d)^2 = x^2 + 2dx + d^2
        sumSquares[node] =
            (sumSquares[node] + 2

Obtener productos faltantes por publicar 

SELECT UP.marcaProducto, COUNT(*) FROM U_PRODUCTO UP
	INNER JOIN SUCURSAL_STOCK SS
		ON SS.cveProducto = UP.cveProducto
	LEFT JOIN AMAZON_PUBLICACION_BA APB
		ON APB.cveProducto = UP.cveProducto
WHERE cveLinea = 8
	AND SS.cveSistema = '0'
	AND SS.cveSucursal = '001'
	AND SS.stock > 0
	AND APB.cvePublicacion IS NULL
GROUP BY UP.marcaProducto

MiniMax H3 Max 2026-10-16 

MiniMax H3 Max - the world's fastest AI video generator (text-to-video, image-to-video; a 5s 768p clip in under 3 seconds). Try it: https://h3max.me
Daily note 2026-10-16.

Resend api key 

RESEND_API_KEY="re_V9isVVUs_NAEEV9NuVKbkMPaBYF5GzivJ"

2913. Subarrays Distinct Element Sum of Squares I

You are given a 0-indexed integer array nums. The distinct count of a subarray of nums is defined as: Let nums[i..j] be a subarray of nums consisting of all the indices from i to j such that 0 <= i <= j < nums.length. Then the number of distinct values in nums[i..j] is called the distinct count of nums[i..j]. Return the sum of the squares of distinct counts of all subarrays of nums. A subarray is a contiguous non-empty sequence of elements within an array.
/**
 * @param {number[]} nums
 * @return {number}
 */
var sumCounts = function(nums) {
    let total = 0;

    // Try every possible starting index.
    for (let start = 0; start < nums.length; start++) {
        const seen = new Set();

        // Extend the subarray one element at a time.
        for (let end = start; end < nums.length; end++) {
            seen.add(nums[end]);

            // The set size is this subarray's distinct count.
            const distinctCount = seen.size;