/**
* Calculates the maximum number of partitions after performing operations
* that allow up to `k` distinct characters per partition.
*
* @param {string} s - Input string consisting of lowercase letters.
* @param {number} k - Maximum number of distinct characters allowed per partition.
* @return {number} - Maximum number of partitions achievable.
*/
var maxPartitionsAfterOperations = function(s, k) {
const n = s.length;
// L[i] stores [leftPartitions, leftMask, leftCount] for p
-Xms12G
-Xmx12G
-XX:+UnlockExperimentalVMOptions
-XX:+UseG1GC
-XX:+ParallelRefProcEnabled
-XX:MaxGCPauseMillis=50
-XX:+DisableExplicitGC
-XX:+AlwaysPreTouch
-XX:G1NewSizePercent=30
-XX:G1MaxNewSizePercent=40
-XX:G1HeapRegionSize=16M
-XX:G1ReservePercent=20
-XX:InitiatingHeapOccupancyPercent=15
-XX:G1MixedGCCountTarget=4
-XX:G1MixedGCLiveThresholdPercent=90
-XX:G1RSetUpdatingPauseTimePercent=5
-XX:SurvivorRatio=8
-XX:MaxTenuringThreshold=15
-XX:SoftRefLRUPolicyMSPerMB=10000
-XX
{% comment %}
Helper: retrieves all values for a given query key from the current URL.
Usage:
{% render 'get-parameter-from-url', key: 'filter.p.m.attributes.apparel_size' %}
{% endcomment %}
{%- liquid
# Capture page URL from header
capture content_for_query_string
echo content_for_header
endcapture
# Extract path after shop domain
assign page_url = content_for_query_string | split: '"pageurl":"' | last | split: '"' | first | split: '.myshopify.com' | last | replace: '\/'
// backend/b2bCheckoutTest.web.js
import { Permissions, webMethod } from 'wix-web-module';
import wixStores from 'wix-stores-backend';
import { contacts } from 'wix-crm-backend';
import wixFetch from 'wix-fetch';
import { getSecret } from 'wix-secrets-backend';
/* ---------------------------------- Helpers ---------------------------------- */
function pseudoUuid() {
return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
const r = (Math.random() * 16) | 0; // es
select
[Superseded].Title [Superseded Title]
, [Superseded].CI_ID [Superseded CI_ID]
, [Superseded].CI_UniqueID [Superseded CI_UniqueID]
, [Superseded].ArticleID [Superseded ArticleID]
, [Superseded].InfoURL [Superseded InfoURL]
, [Superseding].Title [Superseding Title]
, [Superseding].CI_ID [Superseding CI_ID]
, [Superseding].CI_UniqueID [Superseding CI_UniqueID]
, [Superseding].ArticleID [Superseding ArticleID]
, [Superseding].InfoURL [Superseding InfoURL]
, [PSCmd] =
import { session } from 'wix-storage-frontend';
import { currentMember } from 'wix-members-frontend';
import wixLocation from 'wix-location';
import { computeDiscount } from 'backend/utils/discount';
import { shippingNet } from 'backend/utils/shipping';
import {
createDealerOrderFromProfile,
fetchMemberDefaultAddress
} from 'backend/b2bCheckout.web';
const CART_KEY = 'b2bCart';
const LAST_ORDER_KEY = 'b2bLastOrder';
function getCart() {
try {
return JSON.parse(session.
ALGORITHM DijkstraNFLStadiums(stadiums, distances, startStadium, endStadium)
INPUT:
- stadiums: list of all stadium names
- distances: matrix of distances between stadiums
- startStadium: starting stadium name
- endStadium: destination stadium name
OUTPUT:
- shortest path as list of stadium names
- total distance in miles
BEGIN
// Initialize data structures
distanceMap = empty map
previousStadium = empty map
unvisitedStadiums = set of all stadiums
vis
This script conditionally loads an external JavaScript file only when certain UI elements (header/footer) are missing, helping avoid duplicate or conflicting scripts on pages that already include them.
```// Creates and injects a script tag into the page
const initialize = () => {
const script = document.createElement("script");
script.id = "footer-header-script"; // Unique ID to prevent duplicate injection
script.type = "text/javascript";
script.src = "https://www.example.com
function ConvertFrom-ByteArray {
[CmdletBinding()]
param (
[Alias('appid')]
[Parameter(Mandatory = $true, Position = 0, ValueFromPipeline = $true, ValueFromPipelineByPropertyName = $true)]
[byte[]]
$byteArray
)
begin {}
process {
try {
[System.Text.Encoding]::UTF8.GetString($byteArray)
}
catch {
throw $_
}
}
end {}
}
/**
* @param {number[]} nums
* @param {number} value
* @return {number}
*/
var findSmallestInteger = function(nums, value) {
// Step 1: Create a frequency map to count how many times each remainder appears
const freq = new Map();
for (let num of nums) {
// Normalize the remainder to always be non-negative
let mod = ((num % value) + value) % value;
// Count how many times each remainder appears
freq.set(mod, (freq.get(mod) || 0) + 1);
}
/
PLUGINS
β’ https://www.kadencewp.com/kadence-blocks/
β’ https://www.kadencewp.com/kadence-blocks/
# model.py
# ChildLangNet: Interaction Pattern Recognition for Early Childhood Language
# PyTorch >= 1.10
from typing import Optional, Tuple, Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
# ---------------------------
# Small building blocks
# ---------------------------
class MLP(nn.Module):
def __init__(self, in_dim: int, hidden: int, out_dim: int, p: float = 0.1):
super().__init__()
self.net = nn.Sequential(
nn.Li
import torch
import torch.nn as nn
import torch.nn.functional as F
# Define the Vocational Curriculum Alignment Network (VCAN)
class VCAN(nn.Module):
def __init__(self, curriculum_dim, enterprise_dim, hidden_dim, output_dim):
super(VCAN, self).__init__()
# Convolutional layer to process curriculum and enterprise data
self.curriculum_conv = nn.Conv1d(curriculum_dim, hidden_dim, kernel_size=3, padding=1)
self.enterprise_conv = nn.Conv1d(enterprise_d
import torch
import torch.nn as nn
import torch.nn.functional as F
# Define the Temporal Skill Progression Network (TSPNet)
class TSPNet(nn.Module):
def __init__(self, skill_dim, hidden_dim, output_dim):
super(TSPNet, self).__init__()
# Recurrent neural network (LSTM) to model temporal dependencies in skill progression
self.lstm = nn.LSTM(skill_dim, hidden_dim, batch_first=True)
# Attention mechanism to focus on the most relevant
# model.py
# TourFusionNet: Multi-Source Data Fusion for Sports Tourism Preference Prediction
# Implements: modality encoders, hierarchical fusion (self & cross attention),
# graph propagation, and Adaptive Preference Integration Strategy (APIS).
# PyTorch >= 1.10 recommended.
from typing import Dict, Optional, List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
# -----------------------------
# Utility blocks
# ----------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
# Define the Harmonic Scene Integration Network (HSIN)
class HSIN(nn.Module):
def __init__(self, music_dim, scene_dim, embedding_dim, output_dim):
super(HSIN, self).__init__()
# Define the multi-modal encoder for music and scene data
self.music_encoder = nn.Sequential(
nn.Linear(music_dim, embedding_dim),
nn.ReLU(),
nn.Dropout(0.5)
)