Code Snippets on Cacher

3666. Minimum Operations to Equalize Binary String

You are given a binary string s, and an integer k. In one operation, you must choose exactly k different indices and flip each '0' to '1' and each '1' to '0'. Return the minimum number of operations required to make all characters in the string equal to '1'. If it is not possible, return -1.

/**
 * @param {string} s
 * @param {number} k
 * @return {number}
 *
 * This solution is based on analyzing how many zeros can be flipped per operation.
 * Each operation flips exactly k bits, so the parity of the zero count changes
 * depending on k. The editorial shows that the minimum number of operations can be
 * computed using two candidate formulas depending on parity constraints.
 */
var minOperations = function (s, k) {
    const n = s.length;

    // Count how many zeros are in the str

PHP static kešování / memoizace

<?php

declare(strict_types=1);

/**
 * ============================================================
 * VARIANTA 1
 * Jednoduchá request-level cache pomocí static proměnných
 * ============================================================
 */

function getExpensiveData(): mixed
{
    static $loaded = false;
    static $data;

    if (!$loaded) {
        $data = loadFromDatabase();
        $loaded = true;
    }

    return $data;
}



/**
 * =============================

サーバーサイド言語の公開/非公開ディレクトリ設計

# 公開ディレクトリ分離

Webサーバーのドキュメントルートを `public/` に限定し、それ以外のファイルをHTTPアクセスから遮断する。

## ディレクトリ構造

```
project-root/
├── bootstrap.php       # プロジェクトルートのパス定数を定義
├── public/             # ドキュメントルート（Webアクセス可能）
│   ├── index.php
│   ├── api/
│   └── assets/         # CSS, JS, 画像
├── config/             # DB接続情報、環境変数等の設定ファイル
├── lib/                # クラス定義、ユーティリティ関数
├── templates/          # HTMLテンプレート、レイアウト
└── vendor/             # 外部ライブラリ、依存パッケージ
```

## public/ に置くもの

- エントリポイント（index.php 等）
- 静的リソース（CSS,

Chaining Background properties

add more than one background image to an element

background: url('../images/table-left-top.png') left top/12rem 10rem no-repeat,
            url('../images/table-right-top.png') right top/12rem 10rem no-repeat,
            url('../images/table-left-bottom.png') left bottom/12rem 16rem no-repeat,
            url('../images/table-right-bottom.png') right bottom/12rem 14rem no-repeat;

🌊 ECO-STORM & AQUA-GOV

🌊 ECO-STORM & AQUA-GOV A Computational Intelligence Framework for Sustainable Marine Resource Management Overview This repository implements ECO-STORM (Ecologically-Constrained Optimization via Stochastic Temporal Resource Management) and AQUA-GOV (Adaptive Quota and Uncertainty-Aware Governance), a unified computational intelligence framework for multimodal oceanic data analysis and sustainable marine resource management. The framework integrates: Multimodal ocean sensing data Stochastic ecological modeling Reinforcement learning–based policy optimization Fairness-aware multi-agent quota negotiation Enforcement-aware governance modeling The goal is to provide an adaptive, uncertainty-aware, and governance-integrated decision support system for sustainable marine ecosystems. 🔬 Key Contributions 1️⃣ ECO-STORM: Stochastic Ecological Optimization ECO-STORM models marine ecosystems as a stochastic controlled dynamic system, combining: Biomass evolution with logistic growth Spatial dispersal across marine cells Harvest dynamics driven by fishing effort Environmental stochasticity Enforcement-adjusted reward modeling Key features: Markov Decision Process (MDP) formulation Neural function approximation for value iteration Softmax-parameterized effort allocation Multi-objective optimization: Ecological sustainability Economic profitability Social equity 2️⃣ AQUA-GOV: Fairness-Aware Governance AQUA-GOV operationalizes ECO-STORM within real-world governance structures. It introduces: Decentralized multi-agent quota negotiation Regret-based adaptive quota updates Uncertainty-aware precautionary regulation Enforcement modeling (illegal fishing & detection) Gini-based fairness regularization Reinforcement-driven policy adaptation This ensures: Ecological threshold compliance Balanced stakeholder benefits Reduced inequality in quota allocation Lower ecological violation rates 🏗 Framework Architecture The framework forms a closed-loop pipeline: Multimodal Ocean Data ↓ Data Integration & Fusion ↓ Ecological Simulation (ECO-STORM) ↓ Adaptive Policy Optimization ↓ Governance & Quota Allocation (AQUA-GOV) ↓ Feedback & Reinforcement Update As illustrated in the framework diagram (Figure 1 in the paper), ecological dynamics, policy control, and governance interact in a continuous feedback loop . 📊 Supported Datasets The framework has been evaluated on multiple ocean-related datasets, including: Oceanic Multimodal Sensor Dataset Marine Resource Utilization Dataset Ocean Temperature & Salinity Dataset Sustainable Fisheries Prediction Dataset The model consistently outperforms traditional object detection and multimodal baselines in: Accuracy F1-score AUC Robustness under noisy and missing data Fairness-aware allocation metrics See experimental tables in the paper for full comparisons . 🧠 Technical Highlights Multimodal State Representation System state: s_t = (x_t, e_t) Where: x_t = biomass vector e_t = ecological indicators State transition: s_{t+1} = T(s_t, a_t, ξ_t) with stochastic drivers modeled as Gaussian processes. Biomass Dynamics Biomass evolution per region: x_{t+1} = growth + dispersal − harvest + stochastic noise Includes: Logistic growth Spatial diffusion Effort-based harvest Environmental uncertainty Adaptive Policy Optimization Neural basis functions approximate value functions Bellman optimality formulation Softmax-based region effort allocation Reward integrates ecological, economic, and compliance signals Fairness Mechanism Quota inequality measured via Gini coefficient: F(a_t) = fairness penalty across users Full AQUA-GOV reduces: Gini coefficient Utility variance Ecological violation rate ⚙️ Implementation Details Framework: PyTorch Hardware: NVIDIA A100 GPUs Optimizer: AdamW Learning rate: 5e-4 (cosine annealing) Mixed precision training (fp16) Transformer-based multimodal encoder Early & late fusion strategies Gradient clipping for temporal stability 📈 Scalability The framework demonstrates: Linear training time scaling with dataset size Stable GPU memory footprint Robust performance under partial data Strong generalization across datasets 🔐 Governance-Oriented Design Unlike conventional predictive models, this framework: ✔ Integrates ecological constraints directly into optimization ✔ Models enforcement uncertainty ✔ Incorporates fairness into objective functions ✔ Unifies prediction and decision-making ✔ Supports decentralized multi-agent negotiation 🚀 Future Work Planned extensions include: Real-time deployment in live marine monitoring systems Human-in-the-loop governance feedback Cooperative game-theoretic quota allocation Distributed ledger integration for transparent governance 📄 Citation If you use this framework, please cite: Xuan Liu. A Computational Intelligence Framework for Multimodal Oceanic Data Analysis and Predictive Modeling in Sustainable Marine Resource Management.

"""
ECO-STORM + AQUA-GOV (Reference Implementation Skeleton)
========================================================

This is a *research-oriented* code template in PyTorch that mirrors the paper's
core ideas:

- ECO-STORM: stochastic ecological dynamics + MDP + neural value approximation
            + softmax effort allocation + enforcement-aware reward
- AQUA-GOV: decentralized multi-agent quota negotiation + uncertainty-aware
            quota reduction + enforcement penalties + fa

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FSM Data Migration from ServiceChannel

!Important! Don't judge this code as though it should be optimized. It's not. This process was built from inherited partially functioning code from several different developers, subject to a number of urgent last minute requirement changes/additions, and ultimately evolved from the bottom-up into spaghetti. It's not perfectly orchestrated from the top-down, so don't evaluate it in those terms. When reusing any of this code, optimize it from the top-down at that time.

// Modify these start and end variables for the dates you wish to load
var startDate = '2026-02-24';
var endDate = '2026-02-27';
// Optionally use deltaStart when loading work orders to only load records which have been updated since this date
var deltaStart = '2026-02-25';

var gdtStartDate = new GlideDate(startDate);
var gdtEndDate = new GlideDate(endDate);

var daysIncrement = 1;

var startDateCurrent = startDate;
var endDateCurrent = addDays(startDateCurrent, daysIncrement);
var

NodeJS + Redis

services:
  app:
    image: node:20-alpine
    working_dir: /app
    # Si usas package.json, cambia esto a: npm start
    command: sh -c "npm install && node server.js"
    environment:
      - DB_HOST=db
      - DB_USER=${DB_USER:-app_user}
      - DB_PASSWORD=${DB_PASSWORD:-secret}
      - DB_NAME=${DB_NAME:-app_db}
      - REDIS_HOST=redis
      - PORT=80
      - NODE_ENV=${NODE_ENV:-production}
    volumes:
      - app_data:/app
    depends_on:
      - db
      - redis

  db:
    image: mari

Gunicorn + Redis

services:
  app:
    image: python:3.11-slim
    working_dir: /app
    # Instala dependencias y lanza Gunicorn en el puerto 80 con 4 workers
    command: sh -c "pip install --no-cache-dir -r requirements.txt && gunicorn -w 4 -b 0.0.0.0:80 app:app"
    environment:
      - DB_HOST=db
      - DB_USER=${DB_USER:-app_user}
      - DB_PASSWORD=${DB_PASSWORD:-secret}
      - DB_NAME=${DB_NAME:-app_db}
      - REDIS_HOST=redis
      - PYTHONUNBUFFERED=1
    volumes:
      - app_data:/app
    depends_on

ASPNet + Redis

services:
  app:
    image: mcr.microsoft.com/dotnet/sdk:8.0
    working_dir: /app
    # Asume que tienes un archivo .csproj en la raíz del volumen
    command: dotnet run --urls "http://0.0.0.0:80"
    environment:
      - ASPNETCORE_ENVIRONMENT=${ASPNETCORE_ENVIRONMENT:-Production}
      - DB_HOST=db
      - DB_USER=${DB_USER:-app_user}
      - DB_PASSWORD=${DB_PASSWORD:-secret}
      - DB_NAME=${DB_NAME:-app_db}
      - REDIS_HOST=redis
    volumes:
      - app_data:/app
    depends_on:

LAMP + Redis

services:
  app:
    image: webdevops/php-apache:8.2
    environment:
      - DB_HOST=db
      - DB_USER=${DB_USER:-lamp_user}
      - DB_PASSWORD=${DB_PASSWORD:-secret}
      - DB_NAME=${DB_NAME:-lamp_db}
      - REDIS_HOST=redis
      - WEB_DOCUMENT_ROOT=${WEB_DOCUMENT_ROOT:-/app}
      - PHP_MEMORY_LIMIT=${PHP_MEMORY_LIMIT:-256M}
      - PHP_UPLOAD_MAX_FILESIZE=${PHP_UPLOAD_MAX:-50M}
      - PHP_POST_MAX_SIZE=${PHP_UPLOAD_MAX:-50M}
      - PHP_DISPLAY_ERRORS=${PHP_DISPLAY_ERRORS:-0}
      - T

vector db key

al-ln0RebtDf8yq_XlUnSEX9oZtnlidtmVIyOW8NjLnTuE

1356. Sort Integers by The Number of 1 Bits

You are given an integer array arr. Sort the integers in the array in ascending order by the number of 1's in their binary representation and in case of two or more integers have the same number of 1's you have to sort them in ascending order. Return the array after sorting it.

/**
 * @param {number[]} arr
 * @return {number[]}
 */
var sortByBits = function(arr) {
    // Helper: count how many 1-bits are in the binary representation
    function bitCount(n) {
        // Convert to binary string and count '1' characters
        return n.toString(2).split('1').length - 1;
    }

    // Sort using a custom comparator
    return arr.sort((a, b) => {
        const bitsA = bitCount(a);
        const bitsB = bitCount(b);

        // Primary sort: by number of 1-bits
        i

dev stack in ubuntu

#!/usr/bin/env bash
set -euo pipefail

# Installs on Ubuntu:
# - Docker Engine + Compose plugin
# - MongoDB Community Edition (7.0)
# - Redis
# - PostgreSQL
# - nvm (plus latest LTS Node)
# - Astral uv
#
# Usage:
#   chmod +x setup-dev-stack.sh
#   ./setup-dev-stack.sh

if ! command -v apt-get >/dev/null 2>&1; then
  echo "This script supports Ubuntu/Debian systems with apt-get."
  exit 1
fi

if [[ -f /etc/os-release ]]; then
  # shellcheck disable=SC1091
  . /etc/os-release
  if [[ "${ID:-}" !=

openai api key

OPENAI_API_KEY="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"

Prompt to analyze PRs

# Prompt d'Analyse de Pull Requests

## 🎯 Objectif Principal

**Faire ressortir les GROS TRAVAUX** : identifier et mettre en évidence les contributions majeures, les projets de fond et les efforts importants investis sur la période.


## Instructions pour l'IA

Analyse le fichier CSV `[NOM_FICHIER].csv` qui contient mes Pull Requests GitHub et génère un résumé structuré **en mettant l'accent sur les travaux d'envergure**.

Un fichier JSON associé `[NOM_FICHIER_FILES].json` (même préfixe que le C

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