Enter (create) the new password (Can be any combination of letters or numbers) in the text-area labelled New Password. Confirm the new password (Please don’t copy-paste), by re-typing the password in the text-area labelled Confirm Password. Click Change Password to effect the changes Clic...
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Test Frequency response Noise level Dynamic range THD IMD + Noise Stereo crosstalk LG KP500 Cookie +0.13, -0.32 -87.5 81.9 0.125 0.150 -63.0 Apple iPod Touch 2G +0.04, -0.05 -91.4 91.5 0.0027 0.012 -90.0 LG KF750 Secret +0.53, -1.02 -87.1 87.3 0.0099 0.023 -85.8 LG KU990 Viewty +0....
BACKGROUND: Traditionally, blood group typing has been performed with serologic techniques, the classical method being the hemagglutination test. Serotyping, however, may present important limitations such as scarce availability of rare antisera, typing of recently transfused patients, and those with a ...
The LG Cookie scored an Excellent mark in our loudspeaker performance test. More info on our test can be found here. Speakerphone test Voice, dB Pink noise/ Music, dB Ringing phone, dB Overall score Apple iPhone 3G 66.1 62.1 71.7 Below Average Nokia 5800 XpressMusic 75.7 66.5 68.5 Good LG...
Bacterial strain typing For strain typing, the first and last archived KPC-KP isolates recovered from the same patient during the study period were compared with pulsed-field gel electrophoresis (PFGE), and the time between the two isolates was used to calculate the duration of colonization. For...
proxy.TestCase To setup and tear down proxy.py for your Python unittest classes, simply use proxy.TestCase instead of unittest.TestCase. Example: import proxy class TestProxyPyEmbedded(proxy.TestCase): def test_my_application_with_proxy(self) -> None: self.assertTrue(True) Note that: proxy...
The statistical analysis was performed using the Brown-Forsythe ANOVA test and Welch’s ANOVA test (GraphPad Prism 8). The Dunnett's T3 test was run for multiple comparisons with adjustedP-value < 0.001 (indicated as two asterisks) andPvalues between 0.01 and 0.001 (indicated as one aste...
In essence, the goal of prompt-tuning is to reduce semantic differences and mitigate overfitting, thereby improving model performance on both the training and test sets. The primary concept underlying prompt-tuning is the insertion of a concise textual template containing a [MASK] token into the ...
Traceback (most recent call last): File "test.py", line 12, in <module> nested(0) └ <function nested at 0x7f5c755322f0> > File "test.py", line 8, in nested func(5, c) │ └ 0 └ <function func at 0x7f5c79fc2e18> File "test.py", line 4, in func return a / b │ ...