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"Pebble Mine: The Changing Minds" Keynote Panel set to provide an in-depth discussion of the recent Army Corp of Engineers' Draft Environmental Impact Statement on Pebble Mine Development in Bristol Bay at 2019 Pacific Marine Expo.

Ms. Chaker's legal scholarship in the "Chimaera" series of law review articles and on Carpenter v. United States is due to be published in top-tier journals, including The University of Florida Levin College of Law Journal of Technology Law & Policy.

Vantagepoint's Artificial Intelligence provides insight into this new asset class with its highly accurate predictive forecasting so traders can jump into trading immediately with confidence.

Live Online Interactive Stock Analysis Training Wednesday, April 1st, 2020 at 4pm PDT (7pm EDT)

In 1979, Professor Michael E. Porter published “How Competitive Forces Shape Strategy” in the Harvard Business Review. Thirty-five years later, the framework developed by Professor Porter is still being used by business valuators to assess the competitive position of an… Read More

The post A Framework To Analyze Industry Competition appeared first on Anders CPAs.

Derek take a closer look at a 70's classic.

In one embodiment, a machine-implemented method programs a heterogeneous multi-processor computer system to run a plurality of program modules, wherein each program module is to be run on one of the processors The system includes a plurality of processors of two or more different processor types. According to the recited method, machine-implemented offline processing is performed using a plurality of SIET tools of a scheduling information extracting toolkit (SIET) and a plurality of SBT tools of a schedule building toolkit (SBT). A program module applicability analyzer (PMAA) determines whether a first processor of a first processor type is capable of running a first program module without compiling the first program module. Machine-implemented online processing is performed using realtime data to test the scheduling software and the selected schedule solution.

A centrifuge to which sample tubes can be introduced while the centrifuge is in motion. The centrifuge comprises a carousel having an upper portion and a lower portion. The upper portion of the carousel has a plurality of positions for sample tubes for a centrifugation operation, a plurality of drive mechanisms attached to the upper portion of the carousel, a movable element mounted upon each drive mechanism, the movable element capable of traversing the length of the drive mechanism when the drive mechanism is actuated, a sample tube-holding assembly comprising a sample tube holder and a bearing attached to each movable element, and at least one balancing element capable of contributing to a force vector that cancels an imbalance vector generated by rotation of the centrifuge.

Ellipsometry systems and ellipsometry data collection methods with improved stabilities are disclosed. In accordance with the present disclosure, multiple predetermined, discrete analyzer angles are utilized to collect ellipsometry data for a single measurement, and data regression is performed based on the ellipsometry data collected at these predetermined, discrete analyzer angles. Utilizing multiple discrete analyzer angles for a single measurement improves the stability of the ellipsometry system.

A laser gas analyzer includes a wavelength-variable laser having a wide wavelength-variable width, a light-split module configured to split an output light of the wavelength-variable laser into a measurement light and a reference light, a first gas cell into which gases to be measured are introduced, and the measurement light is made to be incident, and a data processor configured to obtain an absorption spectrum of each of the gases to be measured based on a reference signal related to the reference light and an absorption signal related to an output light of the first gas cell, and to obtain concentrations of the respective gases to be measured.

A liquid-metering device comprising a droplet generator including a reservoir and, connected to the latter, a displacement space which is modifiable by an electromechanical transducer and which has an outlet opening and, upon excitation of the transducer, shoots a liquid droplet from a cold area into a heatable area through or counter to a gas stream generated by a gas source. To make the device suitable for automatic and quasi-continuous liquid metering in process analysis, a heatable evaporation chamber is provided through which the liquid to be metered flows via valves, and, between the evaporation chamber and the reservoir, a condensate chamber is connected via further valves. The condensate chamber and the reservoir are connected via additional valves and a pressure regulator to the gas source.

An electronic arrangement and method for providing a signal characterized by reduced phase noise having a signal source for providing a stimulus signal, a modulator coupled to the signal source for generating a modulated signal as function of the stimulus signal and a local oscillator signal, and a mixer combining the stimulus and modulated signals to generate a mixed signal that includes a component characterized by a mathematical difference of the stimulus signal and the modulated signal. The modulated signal is substantially identical to the stimulus signal and offset by a frequency of the local oscillator signal, so that the difference component of the mixed signal results in a local oscillator signal wherein the stimulus signal phase noise generated by the signal source has been mathematically cancelled.

A vector network analyzer (VNA) for analyzing the response of a device under test (DUT), the VNA comprising a plurality of VNA ports configured to be connected to the DUT; a plurality of source ports configured to be connected to the VNA ports; a plurality of couplers for coupling a plurality of coupled signals, wherein said plurality of coupled signals are combined to provide a sum signal; and a receiver configured to receive said forward sum signal.

A fluid analyzer includes an optical source and an optical detector defining an optical beam path through an interrogation region of a fluid flow cell. Flow-control devices conduct analyte and reference fluids through a channel and the interrogation region, and manipulate fluid flow in response to control signals to move a fluid boundary separating the analyte and reference fluids across the interrogation region. A controller generates control signals to (1) cause the fluid boundary to be moved across the interrogation region accordingly, (2) sample an output signal from the optical detector at a first interval during which the interrogation region contains more analyte fluid than reference fluid and at a second interval during which the interrogation region contains more reference fluid than analyte fluid, and (3) determine from samples of the output signal a measurement value indicative of an optically measured characteristic of the analyte fluid.

IBM i2 Analyze is an extensible, scalable, and service-oriented analytical environment that is designed to provide organizations with access to intelligence when and where they need it, so they can make faster and more informed decisions.

Bill Curry:  “In some ways, you would be [surprised that the polls in Connecticut are busy today]. Last week, on the Democratic side, every story said that it’s all over for Bernie; it’s so difficult to put this nomination together. … Continue reading →

At the Smarter Cities Forum in Shanghai today, IBM announced a collaboration with South China's largest hospital, Guang Dong Hospital of Traditional Chinese Medicine, to apply new analytics technology to help doctors uncover trends and new knowledge about disease treatment from thousands of anonymous Electronic Medical Records (EMR)The tool will also enable clinicians to perform empirical studies on the efficacy of certain traditional Chinese treatments.

(Harvard Medical School) An international research team has conducted the first in-depth, wide-scale study of the genomic history of ancient civilizations in the central Andes mountains and coast before European contact. The findings reveal early genetic distinctions between groups in nearby regions, population mixing within and beyond the Andes, surprising genetic continuity amid cultural upheaval, and ancestral cosmopolitanism among some of the region's most well-known ancient civilizations.

Gianluca Mastrantonio, Clara Grazian, Sara Mancinelli, Enrico Bibbona.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2483--2508.

Abstract:
Improved communication systems, shrinking battery sizes and the price drop of tracking devices have led to an increasing availability of trajectory tracking data. These data are often analyzed to understand animal behavior. In this work, we propose a new model for interpreting the animal movent as a mixture of characteristic patterns, that we interpret as different behaviors. The probability that the animal is behaving according to a specific pattern, at each time instant, is nonparametrically estimated using the Logistic-Gaussian process. Owing to a new formalization and the way we specify the coregionalization matrix of the associated multivariate Gaussian process, our model is invariant with respect to the choice of the reference element and of the ordering of the probability vector components. We fit the model under a Bayesian framework, and show that the Markov chain Monte Carlo algorithm we propose is straightforward to implement. We perform a simulation study with the aim of showing the ability of the estimation procedure to retrieve the model parameters. We also test the performance of the information criterion we used to select the number of behaviors. The model is then applied to a real dataset where a wolf has been observed before and after procreation. The results are easy to interpret, and clear differences emerge in the two phases.

  Mobile applications can behave differently between emulator and device and, as an app grows more and more complex, debugging performance bottlenecks can become extremely difficult. The GPA System Anal...

  When developing an Android* application, you usually need to test, optimize, and debug on many different platforms. While basically every hardware and chip manufacturer provides a set of custom tools...

The less distance from Kepler 1649 c's host red-dwarf star, scientists believe, puts the exoplanet in the habitable zone where liquid water could exist on the world's surface.

In the former blog about Python and Specman: Specman: Python Is here!, we described the technical information around Specman-Python integration. Since Python provides so many easy to use existing libraries in various fields, it is very tempting to leverage these cool Python apps.

Coverage has always been the center of the verification methodology, however in the last few years it gets even more focus as people develop advanced utilities, usually using Machine Learning aids. Anyhow, any attempt to leverage your coverage usually starts with some analysis of the behavior and trends of some typical tests. Visualizing the data makes it easier to understand, analyze, and communicate. Fortunately, Python has many Visualization libraries.

In this blog, we show an example of how you can use the plotting Python library (matplotlib) to easily display coverage information during a run. In this blog, we use the Specman Coverage API to extract coverage data, and a Python module to display coverage grades interactively during a single run and the way to connect both.

Before we look at the example, if you have read the former blog about Specman and Python and were concerned about the fact that python3 is not supported, we are glad to update that in Specman 19.09, Python3 is now supported (in addition to Python2).

The Testcase
Let’s say I have a stable verification environment and I want to make it more efficient. For example: I want to check whether I can make the tests shorter while hardly harming the coverage. I am not sure exactly how to attack this task, so a good place to start is to visually analyze the behavior of the coverage on some typical test I chose. The first thing we need to do is to extract the coverage information of the interesting entities. This can be done using the old Coverage API. 

Coverage API
Coverage API is a simple interface to extract coverage information at a certain point. It is implemented through a predefined struct type named user_cover_struct. To use it, you need to do the following:

1. Define a child of user_cover_structusing like inheritance (my_cover_struct below).
2. Extend its relevant methods (in our example we extend only the end_group() method) and access the relevant members (you can read about the other available methods and members in cdnshelp).
3. Create an instance of the user_cover_structchild and call the predefined scan_cover() method whenever you want to query the data (even in every cycle). Calling this method will result in calling the methods you extended in step 2.  

 The code example below demonstrates these three steps. We chose to extend the end_group() method and we keep the group grade in some local variable. Note that we divide it by 100,000,000 to get a number between 0 to 1 since the grade in this API is an integer from 0 to 100,000,000. 

Pass the Data to a Python Module
After we have extracted the group grade, we need to pass the grade along with the cycle and the coverage group name (assuming there are a few) to a Python module. We will take a look at the Python module itself later. For now, we will first take a look at how to pass the information from the e code to Python. Note that in addition to passing the grade at certain points (addVal method), we need an initialization method (init_plot) with the number of cycles, so that the X axis can be drawn at the beginning, and end_plot() to mark interesting points on the plot at the end. But to begin with, let’s have empty methods on the Python side and make sure we can just call them from the e code.

 # plot_i.py
def init_plot(numCycles):
    print (numCycles)
def addVal(groupName,cycle,grade):
    print (groupName,cycle,grade)
def end_plot():
    print ("end_plot") 

And add the calls from e code:

 Before running this, note that you need to ensure that Specman finds the Python include and lib directories, and Python finds our Python module. To do this, you need to define a few environment variables: SPECMAN_PYTHON_INCLUDE_DIR, SPECMAN_PYTHON_LIB_DIR, and PYTHONPATH. 

 The Python Module to Draw the Plot
After we extracted the coverage information and ensured that we can pass it to a Python module, we need to display this data in the Python module. There are many code examples out there for drawing a graph with Python, especially with matplotlib. You can either accumulate the data and draw a graph at the end of the run or draw a graph interactively during the run itself- which is very useful especially for long runs.

Below is a code that draws the coverage grade of multiple groups interactively during the run and at the end of the run it prints circles around the maximum point and adds some text to it. I am new to Python so there might be better or simpler ways to do so, but it does the work. The cool thing is that there are so many examples to rely on that you can produce this kind of code very fast.

 In the example we used, we had two interesting entities: packet and state_machine, thus we had two equivalent coverage groups. When running our example connecting to the Python module, we get the following graph which is displayed interactively during the run.

When analyzing this specific example, we can see two things. First, packet gets to a high coverage quite fast and significant part of the run does not contribute to its coverage. On the other hand, something interesting happens relating to state_machine around cycle 700 which suddenly boosts its coverage. The next step would be to try to dump graphic information relating to other entities and see if something noticeable happens around cycle 700.

To run a complete example, you can download the files from: https://github.com/okirsh/Specman-Python/

Do you feel like analyzing the coverage behavior in your environment? We will be happy to hear about your outcomes and other usages of the Python interface.

Orit Kirshenberg
Specman team

ManageEngine EventLog Analyzer version 10.0 suffers from an information disclosure vulnerability.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Microchip Announces the 53100A Phase Noise Analyzer for Precision Oscillator Characterization