d0rc

USE_ADAPTIVE_THRESHOLD

#ifndef USE_ADAPTIVE_THRESHOLD
#  define USE_ADAPTIVE_THRESHOLD 1
#endif
#ifndef ADAPTIVE_THRESHOLD_ALPHA
#  define ADAPTIVE_THRESHOLD_ALPHA 0.1f
#endif

d0rc

#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <math.h>

#define ORIGINAL_TABLE_SIZE 256
#define PROPOSED_TABLE_SIZE 512

int32_t original_table[ORIGINAL_TABLE_SIZE];
int32_t proposed_table[PROPOSED_TABLE_SIZE];

d0rc

To capture a video stream from a Logitech C270 HD Webcam using libusb, you'll need to interface with the webcam's USB Video Class (UVC) interface, as the C270 is a UVC-compliant device. Below is a step-by-step guide to achieve this. Note that this process requires familiarity with C/C++ programming, USB protocols, and handling video data (e.g., MJPEG or YUYV formats). The approach assumes you're working on a Linux system, as libusb is commonly used there, but it can be adapted for other platforms.Prerequisites

1. Install libusb: Ensure libusb-1.0 is installed (sudo apt install libusb-1.0-0-dev on Debian/Ubuntu or equivalent).
2. Development Environment: Set up a C/C++ environment with a compiler (e.g., gcc) and necessary headers.
3. Permissions: Ensure you have permissions to access the webcam (e.g., add your user to the video group or run as root, though root is not recommended).
4. Dependencies: Optionally, install v4l2loopback to create a virtual video device for testing or piping the stream to other appl

d0rc

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

if __name__ == '__main__':
    # Constants and parameters
    hbar = 1.0  # Reduced Planck's constant
    m = 1.0     # Particle mass
    V0 = 1.0    # Barrier height

d0rc

package main

import (
    "math"
    "github.com/d0rc/mcts"
    "time"
)

// Sequence and other types remain as provided
type Sequence struct {

d0rc

func removeBordersWithTolerance(inputPath, outputPath string, tolerance, minBorderWidth int) error {
	// Open the input file
	file, err := os.Open(inputPath)
	if err != nil {
		return err
	}
	defer file.Close()

	// Decode the image
	img, format, err := image.Decode(file)

d0rc

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import numpy as np
from scipy.special import softmax
from scipy.stats import entropy
from collections import deque
import os

# ANSI color codes
BLUE = "\033[94m"

d0rc

package main

import (
	"fmt"
	"strconv"
)

// TokenType represents the type of a token
type TokenType int

d0rc

The Role of GTO Strategies in Poker:

Balancing Strategic Play with Biological Risk-Seeking

Introduction

In the world of poker, players often find themselves navigating a fine line between strategic play based on mathematical models and decisions influenced by biological and emotional factors. While it is tempting to focus on interpersonal dynamics and risk-seeking behavior, research has shown that Game Theory Optimal (GTO) strategies are not dependent on opponent modeling. This essay explores the importance of maintaining a clear distinction between playing poker as a strategic game and engaging in a social learning process driven by biological and emotional factors.

Poker as a Strategic Game

d0rc

from flask import Flask, request, send_file
import torch
from diffusers import FluxPipeline
from io import BytesIO
import ssl
import os

app = Flask(__name__)

# Initialize the pipeline