answer:Here is a Python class named 'RestaurantBill' that meets your requirements. The class includes error handling to validate the inputs to prevent invalid inputs.```pythonclass RestaurantBill: def __init__(self, meal_items, tip_percentage=0.18): self.meal_items = meal_items self.tip_percentage = tip_percentage def add_item(self, item_name, item_price, item_tax): if item_price < 0 or item_tax < 0 or item_tax > 1: raise ValueError('Invalid price or tax percentage') self.meal_items[item_name] = (item_price, item_tax) def remove_item(self, item_name): if item_name not in self.meal_items: raise ValueError('Item does not exist') del self.meal_items[item_name] def change_tip_percentage(self, new_tip_percentage): if new_tip_percentage < 0 or new_tip_percentage > 1: raise ValueError('Invalid tip percentage') self.tip_percentage = new_tip_percentage def calculate_total_bill(self): total = 0 for price, tax in self.meal_items.values(): total += price * (1 + tax) total += total * self.tip_percentage return total# Test the classmeal_items = {'steak': (35, 0.09), 'wine': (50, 0.12)}bill = RestaurantBill(meal_items)bill.add_item('dessert', 10, 0.08)bill.remove_item('wine')bill.change_tip_percentage(0.2)print(bill.calculate_total_bill())```Note: This solution assumes that the tax rate and tip percentage are expressed as fractions of 1 (i.e., 0.09 for 9%, rather than 9). If they're expressed as percentage values (9, 20), just divide them by 100 in the code.

answer:Quantum entanglement, one-half of Einstein's "spooky action at a distance," is arguably the cornerstone of quantum cryptography. This principle, conceived from the womb of scientific uncertainty and dissent, offers a remarkable confluence of theory and application within the rapidly evolving era of quantum cryptography. It all began at the bedrock of quantum mechanics as part of the Copenhagen interpretation in the early 20th century. Particles that were once in contact remained inextricably intertwined or entangled, regardless of the spatial divide. This entanglement, involving interdependencies of quantum states, was initially a tough sell to a scientific community steeped in classical physics. However, entanglement eventually prevailed as a runaway phenomenon of quantum mechanics, rendering the classical paradigm obsolete.The defining contribution of the quantum entanglement to quantum cryptography is the EPR paradox (Einstein, Podolsky, Rosen), a cornerstone of quantum mechanics. The entanglement of particles generates a complex, secure key, which bolsters encrypted information against malicious threats — thus creating quantum cryptography.However, quantum entanglement isn't above imperfections. Destructive interference, coupled with an annoying propensity to degrade under environmental decoherence, provides major stumbling blocks. These setbacks have prompted exhaustive research into crafting reliable error correction algorithms, local hidden variable theories, and exploration into the boundaries of non-locality.Within quantum cryptography, a robust structure is formed by intricately walking the tightrope between maintaining entanglement and rectifying these inconsistencies. Moreover, the executions involve a sublime blend of quantum key distribution, quantum teleportation, and quantum dense coding to name a few.Unyielding constant monitoring is vital to ensure the preservation of entanglement, thereby preserving encryption. These technical exigencies and their potential solutions foster an environment of perpetual experimentation and improvement, infusing resilience into the theory and practice of quantum entanglement.The implications of quantum entanglement reach far beyond the peripheries of quantum cryptography – they play a central role in uniting distinct theoretical systems within the kaleidoscopic domain of quantum mechanics. Bell's Theorem and quantum superposition are prime examples of this influence.As we gaze into the future, quantum entanglement is the North Star in the expanse of quantum cryptography. Its potential to shape theoretical constructs in tandem with practical processes sets the course for a timeline that dwarfs the conventional in scope and magnitude, epitomizing breathtaking innovation and remarkable resilience. In conclusion, while quantum entanglement presents both stumbling blocks and solutions within quantum mechanics, its critical contribution to quantum cryptography cannot be undervalued. It is its symbiotic relationship with the very essence of quantum mechanics that allows for the creation of a robust and precise cryptographic framework that can keep pace with the rapidly evolving computational theoretical challenges. Quantum entanglement, regardless of its inherent limitations, is a testament to the beauty of scientific perseverance and innovative spirit. It is undeniably the hinge on which the future of quantum cryptography and quantum mechanics will sway.

answer:Here is a simple C# program that accomplishes your task. The main steps are to sort the array and then find the median value. If the number of elements is even, the median is the average of the two middle numbers. If the number of elements is odd, the median is the middle number. ```csharpusing System; public class Program{ public static void Main() { int[] nums = {10, 2, 36, 14, 25}; Array.Sort(nums); double median; int size = nums.Length; // Check if the size is even or odd if (size % 2 == 0) { // If even, average the two middle elements median = ((double)nums[size / 2 - 1] + (double)nums[size / 2]) / 2; } else { // If odd, choose the middle element median = (double) nums[size / 2]; } // Print the median Console.WriteLine("Median: " + median); } }```This program first sorts the array, then checks if the size of the array is even or odd. If it's even, it finds the two middle numbers, adds them together, and divides by 2 to find the median. If it's odd, it simply picks the middle number. Then the calculated median is printed to the console.

answer:Here's a Python TensorFlow solution creating the MLP described:```pythonimport tensorflow as tffrom tensorflow import keras# Define callback function for bifurcated input layersclass BifurcatedInput(keras.layers.Layer): def call(self, inputs): input1, input2 = inputs return [input1, input2]# Define the modelinput_1 = keras.layers.Input(shape=(256,), name='input_1')input_2 = keras.layers.Input(shape=(256,), name='input_2')bifurcated_input = BifurcatedInput()([input_1, input_2])hidden_1 = keras.layers.Dense(512, activation='relu')(bifurcated_input[0])hidden_2 = keras.layers.Dense(512, activation='relu')(bifurcated_input[1])concatenate = keras.layers.Concatenate()([hidden_1, hidden_2])hidden_layer_1 = keras.layers.Dense(128, activation='relu')(concatenate)hidden_layer_2 = keras.layers.Dense(64, activation='relu')(hidden_layer_1)output = keras.layers.Dense(10, activation='softmax')(hidden_layer_2)model = keras.Model(inputs=[input_1, input_2], outputs=output)# Compile and Print the model summarymodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])model.summary()```In the code above, first we have created the inputs layers, next we created a custom bifurcated input, after that we pass both of these inputs through first dense layer (hidden layer), then we concatenate these two outputs and pass this through next two dense layers. At the end, the final layer is the output layer which consists of 10 neurons with a softmax activation function as you asked. Lastly, we compile and print the summary of the model.Remember, the numbers (like 256, 512, 128, 64, 10) I used for the shape of the Input and Dense layers are for example purposes only. You would replace these with the number of features in your dataset, the number of hidden neurons you want in your layers, and the number of classes you want to predict, respectively.

answer:Unfortunately, TensorFlow and the Transformers library are not available in Lua. They are Python libraries and cannot be directly translated into Lua. However, if you're looking for a deep learning library in Lua, you might want to check out Torch (http://torch.ch/). It's a scientific computing framework that offers wide support for machine learning algorithms. It's a powerful framework but doesn't have the same level of support for modern transformer models like BERT.Here's an example of how you might define a simple model in Torch:```luarequire 'torch'require 'nn'-- Define the modelmodel = nn.Sequential()model:add(nn.Linear(10, 5)) -- Input layermodel:add(nn.ReLU()) -- Activation functionmodel:add(nn.Linear(5, 1)) -- Output layer-- Define the loss functioncriterion = nn.MSECriterion()-- Generate some dummy datainput = torch.randn(10)target = torch.randn(1)-- Forward passoutput = model:forward(input)loss = criterion:forward(output, target)-- Backward passmodel:zeroGradParameters()gradOutput = criterion:backward(output, target)model:backward(input, gradOutput)-- Update parameterslearningRate = 0.01model:updateParameters(learningRate)```Please note that this is a very basic example and real-world models would be more complex. Also, Torch doesn't have a built-in equivalent to BERT or the Transformers library. You would need to implement such models from scratch, which can be quite complex.

answer:You can solve this problem using Java's built-in methods present in the String and Character classes. Here is a simple function that should do the job:```javaimport java.util.regex.*;public class Main { public static void main(String args[]) { String text = "Hello, World! How's everything?"; System.out.println(removePunctuation(text)); } public static String removePunctuation(String text) { return text.replaceAll("p{Punct}", ""); }}```In the `removePunctuation` method, `text.replaceAll("p{Punct}", "")` is used to remove all punctuation from the input string. The `p{Punct}` is a predefined character class in Java's regex library that includes punctuation characters like ! " # % & ' ( ) * + , - . / : ; < = > ? @ [ ] ^ _ ` { | } ~.Please test this Java method with different strings containing punctuation marks to ensure it works as expected in your specific context.