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/**
 * @param {number[]} nums
 * @param {number} k
 * @return {boolean}
 */
var hasIncreasingSubarrays = function(nums, k) {
    // Helper function to check if a subarray is strictly increasing
    function isStrictlyIncreasing(start, end) {
        for (let i = start; i < end; i++) {
            if (nums[i] >= nums[i + 1]) {
                return false; // Not strictly increasing
            }
        }
        return true;
    }

    // Total length needed for two adjacent subarrays of length

<div class="pp-stats-countup" data-id="pp-{{Matrix.MatrixId}}">
   <div class="section-copy">{{Module.FieldValues.SectionCopy}}</div>
   <ul class="stats-countup-list" data-layout="{{Module.FieldValues.Layout | default: 'quarter'}}">
      {% for item in List.Items %}
         {% assign el = item.FieldValues %}
         <li
            class="single-stat"
            data-start-value="{{el.StartingValue}}"
            data-value="{{el.Value}}"
         >
            <h3>
             

```python
# model.py
# SFAN + CMEFS unified model for semantic music culture modeling
# Based on Feng Liu (2025)​:contentReference[oaicite:11]{index=11}​:contentReference[oaicite:12]{index=12}

import torch
import torch.nn as nn
import torch.nn.functional as F


class MultiHeadAttention(nn.Module):
    """Multi-head self-attention for multimodal fusion."""
    def __init__(self, dim, heads=4):
        super().__init__()
        self.qkv = nn.Linear(dim, dim * 3)
      

```python
# model.py
# GEMFEN + AKIS for Intelligent Rural Evaluation System
# Based on Zhu et al. (2025)​:contentReference[oaicite:11]{index=11}

import torch
import torch.nn as nn
import torch.nn.functional as F


class GraphConvLayer(nn.Module):
    """Basic GNN layer (Eq. 1 & 4)."""
    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.linear = nn.Linear(in_dim, out_dim)

    def forward(self, x, adj):
        h = torch.matmul(adj, x)
      

```python
# model.py
# Implementation of EVCAN + AVPAM for Art Style Recognition
# Based on equations and architecture in Shi & He (2025) :contentReference[oaicite:9]{index=9}

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class MultiScaleConv(nn.Module):
    """Multi-scale feature extraction (Eq. 7–10)."""
    def __init__(self, in_ch, out_ch, scales=(3,5,7)):
        super().__init__()
        self.branches = nn.ModuleList([
            nn.Conv2

```python
# model.py
# TSLM + AKIS (minimal PyTorch implementation)
# References to equations and sections follow the uploaded paper. :contentReference[oaicite:10]{index=10}

from typing import Optional, Tuple
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


class SinusoidalPositionalEncoding(nn.Module):
    """
    Classic sinusoidal positional encodings (paper Eq. (9)-(10)). :contentReference[oaicite:11]{index=11}
    """
    def __init__(self, d

```python
# model.py
# MPFN-AMFS: Multimodal Pose Fusion Network + Adaptive Multimodal Fusion Strategy
# Author: Chen Niyun (Macao Polytechnic University, 2024) :contentReference[oaicite:8]{index=8}

import torch
import torch.nn as nn
import torch.nn.functional as F


# ======== Encoders ========

class CNNEncoder(nn.Module):
    """Visual feature extractor (2D Conv + BN + ReLU)​:contentReference[oaicite:9]{index=9}"""
    def __init__(self, in_ch=3, out_dim=128):
        su

```python
# model.py
# DCMFN-AMFS: Deep Convolutional Multi-Scale Fusion Network with Adaptive Multi-Scale Fusion Strategy
# Based on Zhang et al. (2024), Comprehensive Evaluation of Green Building Indoor Environment :contentReference[oaicite:9]{index=9}

import torch
import torch.nn as nn
import torch.nn.functional as F


class ConvBlock(nn.Module):
    """Convolution + BN + ReLU"""
    def __init__(self, in_ch, out_ch, k=3, s=1, p=1):
        super().__init__()
        self.conv 

```python
# model.py
# RSAN-ARSAS: Residual Self-Attention Network with Adaptive Residual Strategy
# Based on: Zhou & Li, "Data-Driven Fault Diagnosis..." Jiangsu Normal University, 2024 :contentReference[oaicite:12]{index=12}

import torch
import torch.nn as nn
import torch.nn.functional as F


class ResidualBlock(nn.Module):
    """Standard residual block with GELU activation"""
    def __init__(self, dim):
        super().__init__()
        self.fc1 = nn.Linear(dim, dim)
      

```python
# model.py
# SARP-ASAUE: Sparse Attention & Uncertainty Estimation for Risk Prediction
# Based on Bin Zhan (Zhejiang A&F University) 2024 :contentReference[oaicite:10]{index=10}

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple


class SparseAttention(nn.Module):
    """Sparse multi-head attention selecting top-k keys per query."""

    def __init__(self, dim: int, num_heads: int = 4, topk: int = 16):
        super()._

```python
# model.py
# CMARL-COS: Collaborative Multi-Agent RL with Constraint-Aware Optimization
# Author: Legend Co., Ltd. (Sharon's workspace)
# Inspired by: Yu Caixia, "Multi-Agent Collaboration and Reinforcement Learning-Driven Framework..."  :contentReference[oaicite:5]{index=5}

from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F


def mlp(sizes: List[int], activation=nn.ReLU, out_

```python
# model.py
# Hybrid Attention-Fusion Network (HAFN)
# Based on Wang & Wu (2024)

import torch
import torch.nn as nn
import torch.nn.functional as F


# ----- Attention Modules -----
class CrossAttention(nn.Module):
    def __init__(self, in_e, in_g, hidden):
        super().__init__()
        self.W_e = nn.Linear(in_e, hidden)
        self.W_g = nn.Linear(in_g, hidden)

    def forward(self, He, Hg):
        # Compute attention maps
        Ae = F.softmax(torch.matmu

```python
# model.py
# Temporal-Spatial Attention-Enhanced Reading Model (TSA-ERM)
# Based on Cuiqin Sun (2024): integrates BiRNN, CNN spatial encoding, and reinforcement learning optimization.

import torch
import torch.nn as nn
import torch.nn.functional as F


# ---------- Temporal Attention ----------
class TemporalAttention(nn.Module):
    def __init__(self, hidden_dim):
        super().__init__()
        self.W = nn.Linear(hidden_dim, hidden_dim)
        self.v = nn.Paramete

```python
# model.py
# Adaptive Channel Attention and Feature Fusion Network (ACAFNet)
# Implements ACAM, HFFM, CPL, and ACAFFS as per the paper.

import torch
import torch.nn as nn
import torch.nn.functional as F


# ---------- Adaptive Channel Attention Module ----------
class AdaptiveChannelAttention(nn.Module):
    def __init__(self, channels, reduction=16):
        super().__init__()
        self.fc1 = nn.Linear(channels, channels // reduction)
        self.fc2 = nn.Linear(ch

```python
# model.py
# Cross-Space Graph Attention Network (CSGAN)
# Implements multimodal encoding, cross-space attention, and graph convolution.

import torch
import torch.nn as nn
import torch.nn.functional as F


class CrossSpaceAttention(nn.Module):
    """Computes cross-space attention weights."""
    def __init__(self, in_dim, attn_dim):
        super().__init__()
        self.query = nn.Linear(in_dim, attn_dim)
        self.key = nn.Linear(in_dim, attn_dim)
        self.v

```python
# model.py
# Reference implementation of MACEN:
# Multi-Scale Attention + Contextual Embedding + Feature Fusion + ACAS
# Input: (B, C=1, T, F) spectrogram-like tensors
# Output: class logits (or you can adapt for regression)

from typing import Sequence, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F


def _to_tokens(x: torch.Tensor) -> torch.Tensor:
    """
    Convert (B, C, T, F) -> (B, N, C) tokens where N = T*F
    We keep channels as feat