Telebiometric authentication using biosignals

Deepfakes threats advance, and technology is challenged to keep up.

"There is power in quantity, but how we talk about our return on the nation’s investment is an area I’m looking to try to work on," Michael Sulmeyer said.

The post Pentagon’s first cyber policy chief targets better metrics for cybersecurity success first appeared on Federal News Network.

Public trust hinges on the resilience of critical infrastructure and government agencies against cyber threats.

The post Rethinking continuous risk metrics to fortify federal cybersecurity first appeared on Federal News Network.

Location: Electronic Resource- 

Location: Engineering Library- TK7882.B56D367 2016

Re-release; Geological Survey of Canada. 1989, 81 pages, https://doi.org/10.4095/130681
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/of_1978.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/of_1978.jpg" title=" 1989, 81 pages, https://doi.org/10.4095/130681" height="150" border="1" /></a>

A new collection of fishnet stockings by MusicLegs®.

Geometric fishnet thigh high with Lycra. Wide comfort band.

Today’s methods for computing orientation are quaternion and rotation matrix. However, their efficiencies are tarnished by the complexity of the rotation matrix and the counterintuitivity of quaternion. A better method is presented here. It uses complex multiplication for rotating vectors in 3D space and can compute orientation without angle and trigonometric functions, which is simple,...

Marcelo Campos, Matthew Jenssen, Marcus Michelen and Julian Sahasrabudhe
J. Amer. Math. Soc. 38 (), 179-224.
Abstract, references and article information

Leigh Anderson
Apr 1, 2006; 5:573-588
Research

M el-Saadani
Apr 1, 1989; 30:627-630
Articles

T. De Ryck and S. Mishra
Math. Comp. 93 (), 2643-2677.
Abstract, references and article information

Otto Overkamp
Trans. Amer. Math. Soc. 377 (), 5863-5903.
Abstract, references and article information

Ahmed Elshafei, Ana Cristina Ferreira, Miguel Sánchez and Abdelghani Zeghib
Trans. Amer. Math. Soc. 377 (), 5837-5862.
Abstract, references and article information

Ángel D. Martínez
Trans. Amer. Math. Soc. 377 (), 5411-5444.
Abstract, references and article information

Chris A.J. Klaassen
Theor. Probability and Math. Statist. 111 (), 9-19.
Abstract, references and article information

Lasse L. Wolf and Hong-Wei Zhang
Proc. Amer. Math. Soc. 152 (), 5445-5453.
Abstract, references and article information

Visual Abstract

Complex protein glycosylation occurs through biosynthetic steps in the secretory pathway that create macro- and microheterogeneity of structure and function.  Required for all life forms, glycosylation diversifies and adapts protein interactions with binding partners that underpin interactions at cell surfaces and pericellular and extracellular environments. Because these biological effects arise from heterogeneity of structure and function, it is necessary to measure their changes as part of the quest to understand nature.  Quite often, however, the assumption behind proteomics that post-translational modifications are discrete additions that can be modeled using the genome as a template does not apply to protein glycosylation.  Rather, it is necessary to quantify the glycosylation distribution at each glycosite and to aggregate this information into a population of mature glycoproteins that exist in a given biological system.  To date, mass spectrometric methods for assigning singly glycosylated peptides are well-established.  But it is necessary to quantify glycosylation heterogeneity accurately in order to gauge the alterations that occur during biological processes.  The task is to quantify the glycosylated peptide forms as accurately as possible and then apply appropriate bioinformatics algorithms to the calculation of micro- and macro-similarities.  In this review, we summarize current approaches for protein quantification as they apply to this glycoprotein similarity problem.

Our objective is to explore quantitative imaging markers for early prediction of treatment response in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) undergoing [¹⁷⁷Lu]Lu-DOTATATE therapy. By doing so, we aim to enable timely switching to more effective therapies in order to prevent time-resource waste and minimize toxicities. Methods: Patients diagnosed with unresectable or metastatic, progressive, well-differentiated, receptor-positive GEP-NETs who received 4 sessions of [¹⁷⁷Lu]Lu-DOTATATE were retrospectively selected. Using SPECT/CT images taken at the end of treatment sessions, we counted all visible tumors and measured their largest diameters to calculate the tumor burden score (TBS). Up to 4 target lesions were selected and semiautomatically segmented. Target lesion peak counts and spleen peak counts were measured, and normalized peak counts were calculated. Changes in TBS (TBS) and changes in normalized peak count (nPC) throughout treatment sessions in relation to the first treatment session were calculated. Treatment responses were evaluated using third-month CT and were binarized as progressive disease (PD) or non-PD. Results: Twenty-seven patients were included (7 PD, 20 non-PD). Significant differences were observed in TBS_second-first, TBS_third-first, and TBS_fourth-first (where second-first, third-first, and fourth-first denote scan number between the second and first, third and first, and fourth and first [¹⁷⁷Lu]Lu-DOTATATE treatment cycles), respectively) between the PD and non-PD groups (median, 0.043 vs. –0.049, 0.08 vs. –0.116, and 0.109 vs. –0.123 [P = 0.023, P = 0.002, and P < 0.001], respectively). nPC_second-first showed significant group differences (mean, –0.107 vs. –0.282; P = 0.033); nPC_third-first and nPC_fourth-first did not reach statistical significance (mean, –0.122 vs. –0.312 and –0.183 vs. –0.405 [P = 0.117 and 0.067], respectively). At the optimal threshold, TBS_fourth-first exhibited an area under the curve (AUC) of 0.957, achieving 100% sensitivity and 80% specificity. TBS_second-first and TBS_third-first reached AUCs of 0.793 and 0.893, sensitivities of 71.4%, and specificities of 85% and 95%, respectively. nPC_second-first, nPC_third-first, and nPC_fourth-first showed AUCs of 0.764, 0.693, and 0.679; sensitivities of 71.4%, 71.4%, and 100%; and specificities of 75%, 70%, and 35%, respectively. Conclusion: TBS and nPC can predict [¹⁷⁷Lu]Lu-DOTATATE response by the second treatment session.

Detecting illicit financial flows require much more than using traditional business methods. At this point, using centrality metrics in investigation and analytical models will provide wider detection approaches.

Using centrality metrics to detect illicit financial flows was published on SAS Users.

Dear all,

I defined two design variables in ADE Assembler, say V1 and V2, that define the voltage 1 and voltage 2 of a "vpulse" voltage source in my schematic.

Then, I define V1 = 1.0, and V2 = 2.0, run a transient simulation, and everything is as expexcted. The source provides pulses between 1.0 V and 2.0 V.

Next, I set V1 = 1.0:0.5:1.5, thereby creating a parametric sweep with 1.0 V and 1.5 V for V1. I keep V2 at 2.0 V. Then the simulation fails, and all I get is "netl err" in my Output Expressions and an error message that the results directory does not exist and nothing can be plotted: This is reasonable, as the results directory is deleted on starting a new simulation, and as there is no simulation result, none of my output expressions can be plotted.

WARNING (OCN-6040): The specified directory does not exist, or the directory does not contain valid PSF results.
        Ensure that the path to the directory is correct and the directory has a logFile and PSF result files.
WARNING (ADE-1065): No simulation results are available.
ERROR (WIA-1175): Cannot plot waveform signals because no waveform data is available for plotting.
One of the possible reasons can be that 'Save' check box for these signals are not selected in the Outputs Setup pane. Ensure that these check boxes are selected before you run the simulation.

Normally, this kind of para,metric sweep is not a problem, I have done this many times before. There must be something special in THIS PARTICULAR test bench or simulator setup. The trouble is, I don't get any useful error messages.

Does anyone know what might be the problem here OR where to find useful information to investigate further (log files stored somewhere)? Thank you!

Regards,

Volker

P.S. Using Corners instead does not help either. Running it through all values by hand works, though.

I'm working on the code coverage. Doing a metrics analysis by default we see overall average grade and overall covered. But when i do a block analysis on an instance i see overall covered grade, code covered grade, block covered grade, statement covered grade, expression covered grade, toggle covered grade.

As I dont know the difference I started to read the IMC user guide and came to know there are 3 things we come across while doing a code coverage local, covered, average

From my understanding

local - child instances metrics doesnt reach the parent level. For example, we have an instance Q and its sub instances like Q.a, Q.b. Block Local grade of Q can be 100% even when its instances Q.a and Q.b a block local grades isnt at 100%.

In the attached image there is formula 

The key difference between average and covered is the weights.

Average : Mathematically taking the above scenario where Q.a, and Q.b has 10 blocks each. Q.a has covered 8 blocks and q.b has covered 2 blocks. Now if we take the normal average it should be total covered/ totatl number = 8+2/10+10 yielding 50%. But when we add weights saying Q.a is 70% and Q.b is 30% the new number would be (8*0.7+2*0.3) / (10*0.7+10*0.3) resulting 62%. Because of the weights we see 12% bump.

Covered: there is no role of weights.

Among these 3 metrics i've changed my default view to this in the image to get more realistic picture when i do analyze metrics. Do you guys agree with the approach?

As noted in white papers, posts on the Team Specman Blog, and the Specman documentation, IntelliGen is a totally new stimulus generator than the original "Pgen" and, as a result, there is some amount of effort needed to migrate an existing verification environment to fully leverage the power of IntelliGen.  One of the main steps in migrating code is running the linters on your code and adressing the issues highlighted. 

Included below is a simple utility you can include in your environment that allows you to collect some valuable statistics about your code base to allow you to better gauge the amount of work that might be required to migrate from Pgen to IntelliGen.  The ICFS statistics reported are of particular benefit as the utility not only identifies the approximate number of ICFSs in the environment, it also breaks the total number down according to generation contexts (structs/units and gen-on-the-fly statements) allowing you to better focus your migration efforts. 

IMPORTANT: Sometimes a given environment can trigger a large number of IntelliGen linting messages right off the bat.  Don't let this freak you out!  This does not mean that migration will be a long effort as quite often some slight changes to an environment remove a large number of identified issues.  I recently encountered a situation where a simple change to three locations in the environment, removed 500+ ICFSs!

The methods included in the utility can be used to report information on the following:
- Number of e modules
- Number of lines in the environment (including blanks and comments)
- Number and type of IntelliGen Guidelines linting messages
- Number of Inconsistently Connected Field Sets (ICFSs)
- Number of ICFS contexts and how many ICFSs per context
- Number of soft..select overlays found in the envioronment
- Number of Laces identified in the environment

To use the code below, simply load it before/after loading e-code and then
you can execute any of the following methods:

- sys.print_file_stats()             : prints # of lines and files
- sys.print_constraint_stats()   : prints # of constraints in the environment
- sys.print_guideline_stats()    : prints # of each type of linting message
- sys.print_icfs_stats()            : prints # of ICFSs, contexts and #ICFS/context
- sys.print_soft_select_stats() : prints # of soft select overlay issues
- sys.print_lace_stats()           : *Only works for SPMNv6.2s4 and later* prints # of laces identified in the environment

Each of the above calls to methods produces it's own log files (stored in the current working directory) containing relevant information for more detailed analysis.
- file_stats_log.elog : Output of "show modules" command
- constraint_log.elog : Output of the "show constraint" command
- guidelines_log.elog : Output of "gen lint -g" (with notification set to MAX_INT in order to get all warnings)
- icfs_log.elog       : Output of "gen lint -i" command
- soft_select_log.elog: Output of the "gen lint -s" command
- lace_log.elog       : Output of the "show lace" command

Happy generating!

Corey Goss

In this study, the nonclinical pharmacokinetics of OLX702A-075-16, an RNA interference therapeutic currently in development, were investigated. OLX702A-075-16 is a novel N-acetylgalactosamine conjugated asymmetric small-interfering RNA (GalNAc-asiRNA) used for the treatment of an undisclosed liver disease. Its unique 16/21-mer asymmetric structure reduces nonspecific off-target effects without compromising efficacy. We investigated the plasma concentration, tissue distribution, metabolism, and renal excretion of OLX702A-075-16 following a subcutaneous administration in mice and rats. For bioanalysis, high-performance liquid chromatography with fluorescence detection was used. The results showed rapid clearance from plasma (0.5 to 1.5 hours of half-life) and predominant distribution to the liver and/or kidney. Less than 1% of the liver concentration of OLX702A-075-16 was detected in the other tissues. Metabolite profiling using liquid chromatography coupled with high-resolution mass spectrometry revealed that the intact duplex OLX702A-075-16 was the major compound in plasma. The GalNAc moiety was predominantly metabolized from the sense strand in the liver, with the unconjugated sense strand of OLX702A-075-16 accounting for more than 95% of the total exposure in the rat liver. Meanwhile, the antisense strand was metabolized by the sequential loss of nucleotides from the 3'-terminus by exonuclease, with the rat liver samples yielding the most diverse truncated forms of metabolites. Urinary excretion over 96 hours was less than 1% of the administered dose in rats. High plasma protein binding of OLX702A-075-16 likely inhibited its clearance through renal filtration.

BACKGROUND AND PURPOSE:

Previous studies have reported metal accumulation and microstructure changes in deep gray nuclei (DGN) in Wilson disease (WD). However, there are limited studies that investigate whether there is metal accumulation and microstructure changes in DGN of patients with WD with normal-appearing routine MRI. This study aimed to evaluate multiparametric changes in DGN of WD and whether the findings correlate with clinical severity in patients with WD.

BACKGROUND AND PURPOSE:

Brain atrophy occurs in the late stage of dementia, yet structural MRI is widely used in the work-up. Atrophy patterns can suggest a diagnosis of Alzheimer disease (AD) or frontotemporal dementia (FTD) but are difficult to assess visually. We hypothesized that the availability of a quantitative volumetric brain MRI report would increase neuroradiologists’ accuracy in diagnosing AD, FTD, or healthy controls compared with visual assessment.

The Geometric Clog is a classic Crocs design, but with a vibrant raised pattern reminiscent of diamonds, spanning the entire shoe.

[Los Angeles] : UCLA Cotsen Institute of Archaeology Press, [2019]