mining pools – TT-Miner 5.0.3 Win, 5.0.2 Linux KAWPOW, ProgPoW, MTP, EAGLESONG, EPIC, ETHASH

TT-Miner version 5.0.3 for Windows, 5.0.1 for Linux available
Please note that the Linux version of TT-Miner requires Ubuntu 16.04 or later!

To get independent information about the performance of a miner, you should always compare hash rates @ pool, shares @ pool, or just the profit made.

Download

Supported algorithms:

  • PROGPOW (Zano, Sero & EPIC)
  • ETHASH (ETH, ETC, Music, Callisto, etc.)
  • UBQHASH (UBIQ)
  • MTP (Zcoin, Tecra)
  • LYRA2REV3 (Vertcoin)
  • EAGLESONG (CKB-Nerves)

TT-Miner supports Ethash mining on Nicehash.

In the current version, the miner supports cuda 9.2, 10.0, 10.1, 10.2 and 11.0.

If you want to make sure TT-Miner is using a specific version of cuda, add one of these values, if you don’t add any of them, the miner will always use the algorithm for the latest version of cuda:

-92 for cuda 9.20 (ETHASH-92, UBQHASH-92)
-100 for where 10.00 (ETHASH-100, PROGPOW-100)
-101 for cuda 10.10 (ETHASH-101, MTP-101)
-102 for cuda 10.20 (ETHASH-102, MTP-102)
-110 for cuda 11.00 (ETHASH-110, MTP-110)

Please note the following requirements for different Cuda Toolkit releases:

Cuda-Toolkit for Linux Windows

CUDA 11.0.189 RC> = 450.36.06> = 451.22
CUDA 10.2.89> = 440.33> = 441.22
CUDA 10.1.105> = 418.39> = 418.96
CUDA 10.0.130> = 410.48> = 411.31
CUDA 9.2.148> = 396.37> = 398.26

If you are looking for possible command line options, please start the miner with the -h or –help option: TT-Miner.exe -h

TT-Miner is NOT bug free, so if you find anything please help make TT-Miner better and let me know so I can fix it.

Happy mining!

linux – MSSQL, Ubuntu server – failure: remote connection from Win 7

Our analytical lab (chemistry) has an information management system (LIMS) that uses an MSSQL database. Technicians interact with the system through a Windows form application that is closed-source and permits only SQLOLEDB.1, MSOLEDBSQL, and SQLNCLI11.1 (Native Client 11) connections (choices are picked from a dropdown box at login). Our production server is MSSQL 2016 running on Windows Server 2012.

The lab has several analytical instruments that are constrained to Win 7 and Win XP for data acquisition. This is an immutable constraint.

On the production setup, all machines make a connection with legacy SQLOLEDB.1 but Win 10 Machines can alternatively connect with MSOLEDBSQL, and SQLNCLI11.1 as well. Win 7 and XP machines cannot.

I am prototyping an MSSQL Server 2019 upgrade on a Ubuntu server. As configured, no machines can connect to it using the legacy SQLOLEDB.1 connection. The LIMS application is able to connect to it from Win 10 using either MSOLEDBSQL or SQLNCLI11.1. The Win 7 and XP machines cannot connect with any of the protocols.

If this is to work, I have two choices:

  1. Somehow configure MSSQL on the Ubuntu server to accept SQLOLEDB.1 connections.
  2. Somehow configure XP and Win 7 machines to make MSOLEDBSQL or SQLNCLI11.1 connections.

I cannot figure out how to do either one. Please help.

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RX Post Production Suite 5 includes the most intelligent iZotope tools for postproduction

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App Windows – OverTone DSP EQ500 v3.0.0 WIN | NulledTeam UnderGround

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Digital EQ no longer has to sound digital. The EQ500 plug-in emulates all the desirable qualities of a professional analogue channel equalizer, while the elegantly simple design makes adding texture and colour – or taming problem frequencies – both intuitive and instinctive.

Styled as a virtual 500-series module, the EQ500 uses innovative analogue filter modelling to open up the frequency response and accurately replicate the more natural sound of analogue designs, without additional latency or high CPU demand. More efficient use of CPU and system resources allows more plug-in instances in a typical audio workstation project.
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windows – Is it possible to make a mbr legacy bootcamp win 10 from a gpt partitioned catalina hdd 10.15.7 with bootcamp 6?

I tried to install win10 to ssd via efi boot on my macbook air 2012, it ran pretty well as far as performance, but because it’s pre-uefi it makes it the garbage windows version with no sound, no proper graphics card, can’t change brightness, it’s awful, then I tried parallels, and I can tell already that it’s going to suck how the mouse moves so that’s out.

So, my only option is to try a legacy bootcamp partition install somehow sharing the same hdd as my macOS (which has plenty of room and is fast), but this is mostly a novelty thing, I have a windows laptop, so if there’s a risk of mangling my mac I’ll just get a newer macbook eventually, but if it’s doable safely, any resources would be appreciated as I looked online and it’s a mess of forum posts of peoples’ problems.

game theory – How to generate the pairwise win probability matrix according to the win probability of each competitor?

For example, I have the win probability vector (0.2, 0.5, 0.8) which mean the first user wins with a probability of 0.2 against a random user, the user 2 wins with a probability of 0.5 against a random user and so on.

I want to generate a pairwise matrix having each 1vs1 probabilities. I wrote this formula:

$$p(textrm{A wins against B}) = frac{p(textrm{A wins}) cdot (1 – p(textrm{B wins}))}{p(textrm{A wins}) cdot (1 – p(textrm{B wins})) + (1 – p(textrm{A wins})) cdot p(textrm{B wins})}$$

So, to generate the matrix, we can use this formula:

$$M_{ij} = frac{p_i cdot (1 – p_j)}{p_i cdot (1 – p_j) + (1 – p_i) cdot p_j}$$

with p the win prob vector and M the matrix I want to generate.

My question is: what is the right formula?

Because when I empirically try to “prove” the formula, I get results near the expected result (but not exact).

Here the python code of the proof:

import numpy as np
from scipy import stats
import random

def getRandomFloat(min=0.0, max=1.0, decimalMax=2):
    return round(random.uniform(min, max), decimalMax)
def truncateFloat(f, n=2):
    '''Truncates/pads a float f to n decimal places without rounding'''
    s = '{}'.format(f)
    if 'e' in s or 'E' in s:
        return float('{0:.{1}f}'.format(f, n))
    i, p, d = s.partition('.')
    return float('.'.join((i, (d+'0'*n)(:n))))

def generate_pairwise_win_prob(win_prob, float_precision=None):
    # We create the pairwise win probability (`p_win_prob`):
    p_win_prob = np.zeros((len(win_prob), len(win_prob)))
    w = win_prob
    for i in range(len(win_prob)):
        for j in range(i, len(win_prob)):
            # p_win_prob(i, j) = 1 / (1 + np.exp(w(j) - w(i))) # The Bradley-Terry-Luce model doesn't work
            p_win_prob(i, j) = (w(i) * (1 - w(j))) / (w(i) * (1 - w(j)) + (1 - w(i)) * w(j))
            if float_precision is not None:
                p_win_prob(i, j) = truncateFloat(p_win_prob(i, j), float_precision)
            p_win_prob(j, i) = 1 - p_win_prob(i,j)
    return p_win_prob

def pwp_empirical_proof(win_prob, draw_prob_interval=None):
    p_win_prob = generate_pairwise_win_prob(win_prob)
    victories = (0) * len(win_prob)
    defeats = (0) * len(win_prob)
    for i in range(100000):
        a, b = random.sample(range(len(win_prob)), 2)
        result = match(a, b, p_win_prob, draw_prob_interval=draw_prob_interval)
        if result != 0:
            if result == 1:
                victories(a) += 1
                defeats(b) += 1
            else:
                victories(b) += 1
                defeats(a) += 1
    predicted_win_prob = ()
    for i in range(len(win_prob)):
        current = victories(i) / (victories(i) + defeats(i))
        current = truncateFloat(current, 2)
        predicted_win_prob.append(current)
    print("win_prob: " + str(win_prob))
    print("predicted_win_prob: " + str(predicted_win_prob))
    print()

# We define the function that will give the result of a match:
def match(i, j, p_win_prob, draw_prob_interval=None): # draw a comparision from the model
    assert i != j
    rdf = getRandomFloat()
    if draw_prob_interval is not None and abs(p_win_prob(i, j) - rdf) <= draw_prob_interval:
        return 0 # draw
    elif rdf < p_win_prob(i,j):
        return 1 # i beats j
    else:
        return -1 # j beats i

pwp_empirical_proof((0.2, 0.5, 0.8))

And I get:

win_prob: (0.2, 0.5, 0.8)
predicted_win_prob: (0.12, 0.49, 0.87)

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