network – Macbook 2007 (A1181) Wi-Fi issues in Windows

I have:

  • Macbook A1181
  • Windows 8.1 in Bootcamp with the latest patches
  • All bootcamp drivers installed
  • Keenetic Viva router with 2.4/5 GHz dual-band Wi-Fi, located in ~1m from the Macbook

The issue is that download/upload speed via Wi-Fi is very low in any app (~60kbps) and often interrupts. On the other hand, ethernet connection works great.

And the same Macbook works great with Wi-Fi when I boot macOS, but, unfortunately, I need Windows.

Other devices, like M1 Macbook Pro (late 2020), iPhones and lots of Android phones (even 2.4 GHz) work fine too, so it seems to be a driver issue.

Any ideas on how to fix it?

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mobile network – Able to text and use internet, but no phone calls

It is a refurbished Yureka Note.

I added a newly activated sim in it. It’s sending and receiving text messages. After saving the gprs/mms settings sent by mobile operator, I am able to use internet (which is pretty fast) as well.

However, despite trying variety of things, I am unable to make or receive calls on it. When I tried calling it from another number it says: outside network coverage.

Given that signal is full, my guess is that this has something to do with no VoLTE support in this phone.

I tried many options from ##4636## also. If I select WCDMA/GSM only then there is no signal. When I use LTE only, there is signal but calling to it says Line Busy.

Is there any way to make calling work on this device with this sim?

TensorFlow 2 Python 3 Fully Connected Neural Network

I would really appreciate constructive feedback and suggestions for this fully-connected neural network I have written with TensorFlow 2, Python 3. It estimates the period and amplitude of a sine curve given 100 sample y-points on the curve.

I am mainly interested in how this can be optimised for speed, improving the styling & readability, if I have made any strange programming choices, if there’s a powerful DL technique I am missing which might be useful (etc). Snippets of this script will be used in a pedagogical document to introduce DNN concepts alongside the TensorFlow methods so improvements given this context would be very beneficial.

One immediate flaw I will can address later is that currently the model size is far too big so it will not actually generalise outside the range of parameters in the training examples. That’s fine; for now I just wanted to get everything working and will tune hyperparameters and do regularisation later.

Any advice would be deeply appreciated. Additionally, I am yet to implement a normalisation layer (I would like this to normalise (in the context of them being curves however) using the whole training data and automatically inputs to the model once trained. I am also yet to vectorise the make_curve function. Suggestions for either of these next steps would be fantastic also.

This is of course a toy problem and I will adapt the network to a different problem in which I will be interested in the efficiency and high-dimensional inputs. I have access to both cluster CPU and GPU cores, as well as my fairly laptop with GeForce GTX 1050 Ti Max-Q GPU so I would be interested in optimising this for taking advantage of the parallel computing availability.

The 3d plot is just for fun and shows how the squared error of a prediction blows up for degenerate cases such as zero period or amplitude. Would I be right to assume that a network which has generalised well would have better error specifically at the boundaries?

With the current settings, this take 2.5 mins to run on my 2018 Dell XPS laptop (‘s CPU?) with the following output:

Average test loss:  8.497130045554286
Average val loss:  7.136056077585638
Time taken:  146.38214015960693

Here is the code:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
import tensorflow.keras.backend as kb
from tensorflow import math as tm

import math, time
import numpy as np
from datetime import datetime

#import warnings
#warnings.filterwarnings("ignore")
kb.set_floatx('float64')

start = time.time()

num_sample_pts = 100
train_size     = (5*10**2)**2  # Funky format so it is a square and easily configurable during development
train_sqrt     = int(train_size**0.5)
epoch_nums     = 2**3
minibatch      = 2**6          # Remember to have minibatch << train_size*epochs
callbacks      = False         # TensorBoard logging is much slower than the learning itself 

learning_rate  = 0.00063
num_layers     = 28
reg1_rate      = 0.001
reg2_rate      = 0.001
act_func       = 'elu'
dropout        = 0.2
units          = 37

def make_curve(period, amp, aug=False):
    if False:
        a = 2
        # Make this vectorised so randoms are a vector of randoms.
    return ( amp * np.sin(( np.linspace(-4,4, num_sample_pts) + aug*np.random.rand() ) * (2*np.pi/period))
            + aug*np.random.rand() ).reshape(num_sample_pts)

def data(sample_interval = (-4, 4), amp_interval = (0,30), aug=False, gridsize=train_sqrt):
    sample = np.linspace(*sample_interval, num_sample_pts)
    mesh   = np.meshgrid(0.05 + 10*np.pi*np.random.rand(gridsize),          # Periods
                         amp_interval(0) + (amp_interval(1) - amp_interval(0)) *
                         np.random.rand(gridsize))                          # Amplitudes
    pairs  = np.array(mesh).T.reshape(-1, 2)
    curves = np.array((make_curve(w,a,aug) for w,a in pairs))               # Change when make_curve is vectorised
    glob_centre, glob_max = np.mean(curves), max(amp_interval)              # Globally centre all curves within pm 1
    curves = (curves - glob_centre) / glob_max                              # - Replace with a normalisation layer
    return (curves, pairs)
# Returns list of 2 arrays (x,y):
# - x is an array of each curve sample list
# - y is an array of the period-amplitude pairs corresponding to curve samples in x

df = data(aug=True)

# Not used. Another possibility could be rmse against a sample from predicted curve
def custom_mean_percentage_loss(y_true, y_pred): #Minimax on the percentage error
    diff = y_pred - y_true
    non_zero = y_true + 10**-8
    res = tm.reduce_mean(tm.abs(tm.divide(diff,non_zero)))
    return res

if callbacks:
    logdir = "logs\scalars\" + datetime.now().strftime("%Y%m%d-%H%M-%S")
    tensorboard_callback = (keras.callbacks.TensorBoard(log_dir=logdir, update_freq='epoch'))
else:
    tensorboard_callback = ()
kb.set_floatx('float64')


def model_builder():
    initializer = keras.initializers.TruncatedNormal(mean=0., stddev=0.5)
    model = keras.Sequential()
    model.add( Dense( units = num_sample_pts,                           # Number of input nodes equals input dimension
                                   kernel_initializer = initializer,    # Initialize weights
                                   activation = act_func,
                                   kernel_regularizer = keras.regularizers.l1_l2(reg1_rate, reg2_rate),
                                   dtype='float64'))
    BatchNormalization()

    for layer in range(num_layers):
        Dropout(dropout)
        model.add( Dense( units = units,                                # Number of nodes to make number of inputs
                         activation = act_func,
                         kernel_regularizer = keras.regularizers.l1_l2(reg1_rate, reg2_rate),
                         dtype='float64'))
        BatchNormalization()
        
    model.add( Dense(units = 2, activation = 'linear', dtype='float64'))
    # Outputting amplitude-period pair requires 2 nodes in the output layer.
    
    model.compile(
        optimizer = keras.optimizers.Adam(learning_rate = learning_rate),
        loss = 'mse',
        metrics = ('mse') ) # Measures train&test performance
    return model

model = model_builder()
training_history = model.fit(*df,
                             batch_size = minibatch,                               # Number of data per gradient step
                             epochs = epoch_nums,
                             verbose = 0,
                             validation_split = min(0.2, (train_sqrt**2)/5000),    # Fraction of data used for validation set
                             callbacks=tensorboard_callback)
                             
print("Average test loss: ", np.average(training_history.history('loss')(:10)))
print("Average val loss: ", np.average(training_history.history('val_loss')(:10)))
print('Time taken: ', time.time()-start)
print(' ')
    
import winsound
for i in range(2):
    winsound.Beep(1000, 250)

This is the first neural network I have written. Thank you very much for your thoughts, improvements and contributions.

Pi Network – Free Crypto with just one click per day

Hi,

I just found out about the Pi app that let’s you produce the Pi cryptocoin with just one tap everyday. If you invite more friends to your network, the amount you produce every hour increases.
SEMrush

This is the link that will send you to where you can download your app for Android or iOS:
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This is my refferal name: Skaritza
I will appreciate if you will include my name in the registering process when it appears, that will help me and you can also do that with the ones you will invite with your own code.

Good luck!

 

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vpn – Can wifi network administrators see if you are using a proxy?

If your friend is using a proxy, all the requests will look as requests towards the IP of the proxy. Depending on your friend’s parents’ knowledge of networks, they might find that as a suspicious behavior. In normal cases where the average parents don’t know much about proxies, there should not be any problem.

Modify the ip address of a network adapter interface

ifconfig shows a Network Adapter – ens37. I want to modfiy it’s ip address to 192.168.26.1/24
I tried with ifconfig ens37 192.168.26.1 netmask 255.255.255.0 But that throws an error.
Any commands or ways to modify the ip/mask of a network adapter interface.
I am running fedora as a virtual machine on VMWARE.

Below is the list of new offers that were recently added to the network…

Below is the list of new offers that were recently added to the network…

ID: 13351
Name: Viscerex – Health – SS – NO SEO – [DE] – with 1-Click Upsell
Categories: Health

ID: 13352
Name: Zenith Labs Meditation In A Bottle – VSL – Health – SS – [All GEOs]
Categories: Health

ID: 13353
Name: Gavvia – Brain Enhancement – Trial – NO SEO – [US] – with 1-Click Upsell
Categories: Brain Enhancement – Trial

ID: 13354
Name: GeeHii – Brain Enhancement – Trial – NO SEO – [US] – with 1-Click Upsell
Categories: Brain Enhancement – Trial

ID: 13355
Name: iPhone 11 Pro – Sweepstakes & Surveys – Trial – [32 GEOs]
Categories: Sweepstakes & Surveys – Trial

ID: 13356
Name: Striker Speed Blade – VSL – eCommerce – Trial – [US]
Categories: eCommerce – Trial

ID: 13357
Name: Simple Promise Glucose Shield – VSL – Health – SS – [US]
Categories: Health

ID: 13358
Name: Institute for Natural Healing Books – VSL – eCommerce – SS – [US]
Categories: eCommerce

ID: 13359
Name: Uber Watts – Presell Page – eCommerce – SS – [US]
Categories: eCommerce

ID: 13360
Name: Thrive Health Labs Gluco 20 – VSL – Health – SS – [US]
Categories: Health

ID: 13361
Name: Thrive Health Labs Joint Guard 360 – VSL – Health – SS – [US]
Categories: Health

ID: 13362
Name: Blue Light Glasses – eCommerce – Trial – [US]
Categories: eCommerce – Trial

ID: 13363
Name: FastShip Face Mask Store – eCommerce – SS – [US]
Categories: eCommerce

ID: 13364
Name: DIY Silencer DVD – eCommerce – Trial – [US]
Categories: eCommerce – Trial

ID: 13365
Name: Night Vision Cap Light – eCommerce – Trial – [US]
Categories: eCommerce – Trial

ID: 13366
Name: Rulers of Darkness Exposed Book – eCommerce – Trial – [US]
Categories: eCommerce – Trial

ID: 13367
Name: Coin Knife – eCommerce – Trial – [US]
Categories: eCommerce – Trial

ID: 13368
Name: 6 in 1 Multi Tool Kit – VSL – eCommerce – Trial – [US]
Categories: eCommerce – Trial

ID: 13369
Name: Permanent Match – eCommerce – Trial – [US]
Categories: eCommerce – Trial