air travel – How to get back a backpack lost on train or airport in Germany?

We’re just strangers on the internet, so we’re not in a position to look for your bag based on your description.

You can report your lost item to the Frankfurt Airport lost & found or Deutsche Bahn lost & found (covers the S-Bahn Rhein-Main as well) depending on where you think you lost it, or both if you’re unsure. If it’s turned in, they can match it to your report and contact you to arrange a way to get it to you. You’ll likely have to pay some fees to claim it, as detailed on those pages.

If you have travel insurance, you can check to see whether this is covered under your policy, but this sort of loss may be excluded from coverage.

air travel – How to get back a backpack lost on train or airport?

We’re just strangers on the internet, so we’re not in a position to look for your bag based on your description.

You can report your lost item to the Frankfurt Airport lost & found or Deutsche Bahn lost & found (covers the S-Bahn Rhein-Main as well) depending on where you think you lost it, or both if you’re unsure. If it’s turned in, they can match it to your report and contact you to arrange a way to get it to you. You’ll likely have to pay some fees to claim it, as detailed on those pages.

If you have travel insurance, you can check to see whether this is covered under your policy, but this sort of loss may be excluded from coverage.

air travel – how to get back a black backpack lost on train or airport?

so i landed at frankfurt airport terminal, i had my luaggages unti when i had to pick up the train going to frankfurt hbf, after reaching the destination, i realized i lost my backpack and the caracterics are here: Brand
Others

Material
Cloth

Type of Closure
Zipper

Castors
Unspecified

Label
With Ethiopian Airlines tags: ET 840 and ET 706

Colour
Black

More Colour
Black

Pendant
With Ethiopian Airlines label: ET 840 and ET 706

Cash
0.00 EUR

Public description
Backpack Color: Black
The bag contains:

  • A laptop
  • A laptop charger
  • Some clothes
  • Some watches
  • 2 telephone chargers
  • Documents ( bachelor diploma of Jiangsu University , IUBH University documents…etc)

The documents are under the name: KANEMA KWATATSHEY MERVEILLE.

I’ve noticed the lost when i arrived at the main station of Frankfurt from Frankfurt airport by train it was around 7:15 am to 7:45 am today (30 oct 2020)
would anyone carry it?
thanks.

eu – Train fare 4 Euros higher for travel to airport than to its two adjacent stations

Is it legal to have a higher train fare for travel to/from airport when adjacent stops are way cheaper?

I frequently travel to an airport that is connected to its serving city by rail service.
A one way train fare to/from the airport to/from the city is 5 Euros. It’s roughly a 15 minutes train ride.
However, travelling on the same identical train that stops at the airport and boarding/alighting from any of the adjacent stops costs 1.10-1.20 Euros.

E.g.

  • 1 station before the airport’s station to the city: 1.20 Euros
  • Airport’s station to the city: 5 Euros
  • 1 station after the airport’s station to the city: 1.10 Euros

I find the 5 Euros fare quite good compared to some prices that can be found elsewhere. However, I don’t find this to be ethical and was wondering if there might be any justification for the 450% higher rate (e.g. arguing that the airport’s station infrastructure is what adds to train fare).
If this is indeed Price Discrimination, is it legal?

where on earth – What is the longest distance between two stops of a train route?

Given the vast distances and sparse population, you’d expect Australia to have some solid contenders, but somewhat surprisingly I can’t find any legs over 24h — partly because the trains cater to tourists and make stops at metropolises like Cook, WA (population 4) to break up the monotony a bit. Without this, Kalgoorlie to Adelaide on the Indian Pacific would clock in at 27.5 hours (dep 01:20 Mon, arr 07:20 Tue, -2.5 hrs time difference going from WA to SA).

But assuming we do count all stops, the longest distance between stops in Australia appears to be on the same line in the other direction: the weekly train departing Adelaide, SA on Thursdays at 21:40 arrives in Rawlinna, WA at 18:55 Fridays, for a non-stop travel time of 22 hrs 45 mins (including 2.5 hr time difference, now in the other direction). Schedules here: https://www.seat61.com/Australia.htm

statistics – Finding how crowded a given train connections is (by day of week, and by hour)

You can get a rough estimate by looking at the ticket prices a week or two in advance. The departures with the cheapest tickets will likely be least crowded. If I for example check the coming Monday (October 26th), there are for most departures saver fares available ranging from €24.90 to €39.90. For one departure, no saver fares are available, but you need a full price ticket for €41.00.

The departures with tickets for €24.90 at 5:58, 20:58 and 21:58 will likely have the least number of passengers. The departures with only full price tickets for €41.00 at 18:58 will likely have the most.

BTW, the service runs every hour and not only every 2 hours.

python – Can a neural network train against the loss function from past iterations?

I have a neural network that I am training on a loss function (i.e. loss1). And, as the optimizer continues iterating, the loss function continues decreasing as expected. But is it possible for me to create an additional loss function (i.e. loss2) that is based upon some combination of loss1 from previous iterations? For example, I may want to ensure that loss1 from the past 100 iterations follows a symmetric distribution. This would require me to store loss1 from the past 100 iterations (which I can do) and then train my optimizers on some new function of loss1 values from the past (which I cannot do). But even more simply, I am thus far unable to write a loss2 that includes past values of loss1 in any way.

Below I have written a minimal code whereby I construct and train a neural network on a loss function and then store the values of the loss function in a list (loss1_log). Now I want to train on the stored values in this list. But is it even computationally possible for the optimizer to train on these past values of loss1?


import numpy as np 
import tensorflow as tf

end_it = 1000 #number of iterations
layers = (2, 20, 20, 20, 1)

#Generate training data
len_data = 10000
x_x = np.array((np.linspace(0.,1.,len_data)))
x_y = np.array((np.linspace(0.,1.,len_data))) 
y_true = np.array((np.linspace(-0.2,0.2,len_data)))

N_train = int(len_data)
idx = np.random.choice(len_data, N_train, replace=False)

x_train = x_x.T(idx,:)
y_train = x_y.T(idx,:) 
v1_train = y_true.T(idx,:) 

sample_batch_size = int(0.01*N_train)

np.random.seed(1234)
tf.set_random_seed(1234)
import logging
logging.getLogger('tensorflow').setLevel(logging.ERROR)
tf.logging.set_verbosity(tf.logging.ERROR)

class NeuralNet:
    def __init__(self, x, y, v1, layers):
        X = np.concatenate((x, y), 1)  
        self.lb = X.min(0)
        self.ub = X.max(0)
        self.X = X
        self.x = X(:,0:1)
        self.y = X(:,1:2) 
        self.v1 = v1 
        self.layers = layers 
        self.weights_v1, self.biases_v1 = self.initialize_NN(layers) 
        self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=False,
                                                     log_device_placement=False)) 
        self.x_tf = tf.placeholder(tf.float32, shape=(None, self.x.shape(1)))
        self.y_tf = tf.placeholder(tf.float32, shape=(None, self.y.shape(1))) 
        self.v1_tf = tf.placeholder(tf.float32, shape=(None, self.v1.shape(1)))  
        self.v1_pred = self.net(self.x_tf, self.y_tf)
        self.loss1_log = ()
        self.loss1 = tf.reduce_mean(tf.square(self.v1_pred)) 
        self.loss2 = 0.0*self.loss1 #ideally tf.reduce_mean(tf.square(some function of self.loss1_log)), e.g. tf.reduce_mean(self.loss1_log(-100:-1) - np.mean(self.loss1_log(-100:-1)))
        self.loss = self.loss1 + self.loss2
        self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss,
                                                                var_list=self.weights_v1+self.biases_v1,
                                                                method = 'L-BFGS-B',
                                                                options = {'maxiter': 50,
                                                                           'maxfun': 50000,
                                                                           'maxcor': 50,
                                                                           'maxls': 50,
                                                                           'ftol' : 1.0 * np.finfo(float).eps})
        self.optimizer_Adam = tf.train.AdamOptimizer()
        self.train_op_Adam_v1 = self.optimizer_Adam.minimize(self.loss, var_list=self.weights_v1+self.biases_v1) 
        init = tf.global_variables_initializer()  
        self.sess.run(init)
    def initialize_NN(self, layers):
        weights = ()
        biases = ()
        num_layers = len(layers)
        for l in range(0,num_layers-1):
            W = self.xavier_init(size=(layers(l), layers(l+1)))
            b = tf.Variable(tf.zeros((1,layers(l+1)), dtype=tf.float32), dtype=tf.float32)
            weights.append(W)
            biases.append(b) 
        return weights, biases
    def xavier_init(self, size):
        in_dim = size(0)
        out_dim = size(1)
        xavier_stddev = np.sqrt(2/(in_dim + out_dim)) 
        return tf.Variable(tf.truncated_normal((in_dim, out_dim), stddev=xavier_stddev), dtype=tf.float32)
    def neural_net(self, X, weights, biases):
        num_layers = len(weights) + 1
        H = 2.0*(X - self.lb)/(self.ub - self.lb) - 1.0
        for l in range(0,num_layers-2):
            W = weights(l)
            b = biases(l)
            H = tf.tanh(tf.add(tf.matmul(H, W), b))
        W = weights(-1)
        b = biases(-1)
        Y = tf.add(tf.matmul(H, W), b) 
        return Y
    def net(self, x, y): 
        v1_out = self.neural_net(tf.concat((x,y), 1), self.weights_v1, self.biases_v1)
        v1 = v1_out(:,0:1)
        return v1
    def callback(self, loss):
        global Nfeval
        print(str(Nfeval)+' - Loss in loop: %.3e' % (loss))
        Nfeval += 1
    def fetch_minibatch(self, x_in, y_in, v1_in, N_train_sample):  
        idx_batch = np.random.choice(len(x_in), N_train_sample, replace=False)
        x_batch = x_in(idx_batch,:)
        y_batch = y_in(idx_batch,:) 
        v1_batch = v1_in(idx_batch,:) 
        return x_batch, y_batch, v1_batch
    def train(self, end_it):
        it = 0
        while it < end_it: 
            x_res_batch, y_res_batch, v1_res_batch = self.fetch_minibatch(self.x, self.y, self.v1, sample_batch_size) # Fetch residual mini-batch
            tf_dict = {self.x_tf: x_res_batch, self.y_tf: y_res_batch,
                       self.v1_tf: v1_res_batch}
            self.sess.run(self.train_op_Adam_v1, tf_dict)
            self.optimizer.minimize(self.sess,
                                    feed_dict = tf_dict,
                                    fetches = (self.loss),
                                    loss_callback = self.callback) 
            loss1_value = self.sess.run((self.loss1), tf_dict)
            self.loss1_log.append(loss1_value)
            it = it + 1
    def predict(self, x_star, y_star): 
        tf_dict = {self.x_tf: x_star, self.y_tf: y_star}
        v1_star = self.sess.run(self.v1_pred, tf_dict)  
        return v1_star

model = NeuralNet(x_train, y_train, v1_train, layers)
 
Nfeval = 1
model.train(end_it)

buses – Where to find the bus schedules for Charleroi airport to Charleroi train station?

The answer to about any “how do I get from here to there using public transport” question can be found on the Belgian Railway’s website.

https://www.belgiantrain.be/en

The trip planner on that site can plan trips for you between any two points in the country. It understands addresses, station and bus stop names, and POI’s.

So you just enter “Charleroi Airport” in the “from” field, wherever you want to go to in Belgium (or Europe) in the “to” field. Add time and date, and presto, you’re set.

Such comprehensive public transport planners are actually rather common in Europe, but depending on where you come from you may indeed not be used to such planners being available, or indeed being comprehensive.

In the case of Belgium however the planner can be trusted. And it will reliably tell you how to get from Charleroir Airport to Liège.

united states – Where can I find what the cost of a train ticket in the USA was in 1930?

I was unable to find exactly what you’re looking for, but Streamliner Memories has a collection of old timetables, some of which have information about fares.

From Chicago to Los Angeles in 1927, the fare would be between $12.75 and $84.00 depending on the class of service, though note that the price could be higher for some faster trains, and apparently discounts were available if you were willing to accept an upper berth.

I couldn’t find any information about pricing for New York to Chicago trains at about that time, but it seems reasonable to assume it’d be less than the Chicago to Los Angeles leg, as the distance is less.

Where can I find what the cost of a train ticketin the USA was in 1930?

I am looking for the cost of a train ticket in the USA from like New York City to Los Angles for example in 1930. I found it on for an airplane ticket but not a train. Not sure if this Stack Exchange is the right place or not. I have done research online and can not seem to get an answer. The links always takes me to the Amtrak web site which is no good for me.