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Bill Fulkerson

http://www.crypto.com/papers/blaze-govtreform-20171129.pdf - 0 views

www.crypto.com/...blaze-govtreform-20171129.pdf

voting security

shared by Bill Fulkerson on 29 Nov 17 - No Cached
  • Bill Fulkerson on 29 Nov 17
     
    I offer three specific recommendations: * Paperless DRE voting machines should be immediately phased out from US elections in favor of systems, such as precinct-counted optical scan ballots, that leave a direct artifact of the voter's choice. * Statistical "risk limiting audits" should be used after every election to detect software failures and attacks. * Additional resources, infrastructure, and training should be made available to state and local voting officials to help them more effectively defend their systems against increasingly sophisticated adversaries.
Bill Fulkerson

DNA damage caused by migrating light energy - 0 views

phys.org/...0-09-dna-migrating-energy.html

energy damage dna light

shared by Bill Fulkerson on 15 Sep 20 - No Cached
  • Bill Fulkerson on 15 Sep 20
     
    Ultraviolet light endangers the integrity of human genetic information and may cause skin cancer. For the first time, researchers at Karlsruhe Institute of Technology (KIT) have demonstrated that DNA damage may also occur far away from the point of incidence of the radiation. They produced an artificially modeled DNA sequence in new architecture and succeeded in detecting DNA damage at a distance of 30 DNA building blocks. The results are reported in Angewandte Chemie.
Bill Fulkerson

SARS-CoV-2 viral load predicts COVID-19 mortality - The Lancet Respiratory Medicine - 0 views

www.thelancet.com/...fulltext

mortality medicine respiratory viral

shared by Bill Fulkerson on 06 Aug 20 - No Cached
  • Bill Fulkerson on 06 Aug 20
     
    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection platforms currently report qualitative results. However, technology based on RT-PCR allows for calculation of viral load, which is associated with transmission risk and disease severity in other viral illnesses.1 Viral load in COVID-19 might correlate with infectivity, disease phenotype, morbidity, and mortality. To date, no studies have assessed the association between viral load and mortality in a large patient cohort.2, 3, 4 To our knowledge, we are the first to report on SARS-CoV-2 viral load at diagnosis as an independent predictor of mortality in a large hospitalised cohort (n=1145).
Bill Fulkerson

The Soil Talks Back - 0 views

wis-wander.weizmann.ac.il/...soil-talks-back

soil

shared by Bill Fulkerson on 19 May 20 - No Cached
  • Bill Fulkerson on 19 May 20
     
    ]. "The narrow strip of soil around the plant's root teems with millions of microorganisms, making it one of the most complex ecosystems on earth. To determine whether the composition of this "root microbiome" triggers changes within the plant, postdoctoral fellow Dr. Elisa Korenblum and other members of a team headed by Prof. Asaph Aharoni of Weizmann's Plant and Environmental Sciences Department, created a hydroponic set-up in which they split the roots of tomato seedlings in two. In a series of experiments, the researchers placed one side of the split roots in vials, progressively diluting the soil suspensions several times. Each dilution altered the soil's microbial composition and reduced the diversity within the microbial community, so that the different suspensions ended up containing root microbiomes with high, medium and low diversity levels. The other side of the roots was submerged in a vial with a clean, soil-free solution. If the soil microbes communicate with the plant, one would expect to detect signs of their messages on both sides of the root system. That was exactly what the scientists found…. 'Our ultimate goal is to decipher the chemical language - one could call it 'Plantish' - used by plants and the soil to interact with one another,' Korenblum
Bill Fulkerson

Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Netwo... - 0 views

ieeexplore.ieee.org/...9117073

detection network coronavirus regions wireless cellular

shared by Bill Fulkerson on 26 Jun 20 - No Cached
  • Bill Fulkerson on 26 Jun 20
     
    Goal: The purpose of this article is to introduce a new strategy to identify areas with high human density and mobility, which are at risk for spreading COVID-19. Crowded regions with actively moving people (called at-risk regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. Methods: Our scheme identifies at-risk regions using existing cellular network functionalities-handover and cell (re)selection-used to maintain seamless coverage for mobile end-user equipment (UE). The frequency of handover and cell (re)selection events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. Results: These measurements, which are accumulated over very many UEs, allow us to identify the at-risk regions without compromising the privacy and anonymity of individuals. Conclusions: The inferred at-risk regions can then be subjected to further monitoring and risk mitigation.
Bill Fulkerson

The next-generation bots interfering with the US election - 0 views

www.nature.com/...d41586-020-03034-5

election bots

shared by Bill Fulkerson on 31 Oct 20 - No Cached
  • Bill Fulkerson on 31 Oct 20
     
    Data scientist Emilio Ferrara tells Nature that fake social-media accounts are harder to detect than ever before.
Steve Bosserman

How Neuroscience Can Help Us Treat Trafficked Youth - Pacific Standard - 0 views

psmag.com/...help-us-treat-trafficked-youth

neuroscience youth "human trafficking"

shared by Steve Bosserman on 04 May 18 - No Cached
  • A common misconception is that these youths are choosing to engage in the commercial sex trade. But as recent advances in neuroimaging techniques help scientists unravel the myriad ways that trauma affects the brain, emerging evidence suggests that brain changes resulting from trauma could make young people more vulnerable to exploiters and less receptive to people trying to help. Rather than making a conscious decision to rebel, these kids are simply doing their best to survive, using the adaptive strategies that their brains developed in response to a perilous world.
  • Though the human brain is adaptable throughout life, adaptability is greatest during childhood, as the developing brain responds to the surrounding environment. Survival is the brain's top priority. Young people growing up in dangerous environments will develop brains that are highly responsive to threat cues. In particular, recent studies show that children who have experienced trauma exhibit drastic changes in their amygdala, an area of the brain wired to identify signs of danger.
  • Besides the amygdala, another brain region affected by trauma is the hippocampus, an area involved with contextual learning and memory. The hippocampus takes context into account, so that a person can appropriately respond to the same cues in different situations. For instance, most people would respond differently to hearing gunfire in a shooting range than they would to hearing gunfire in an airport.
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  • McLaughlin's research shows that youth who have experienced trauma have a smaller hippocampus than those who haven't, and are less able to take context into account when they detect a threat cue. When McLaughlin presented images of faces embedded in real-world scenes to kids, those with a history of trauma exhibited less activity in their hippocampus while viewing angry facial expressions. And when given a memory test afterward, they were less able to remember scenes in which they'd identified people displaying anger.
  • Because the brain remains malleable throughout life, it's never too late to mitigate the effects of trauma, McLaughlin says. There are a variety of evidence-based treatments that have proven effective in alleviating the mental-health consequences of trauma. For example, the technique of cognitive reappraisal, a cornerstone of Trauma-Focused Cognitive Behavioral Therapy, can help victims better regulate their emotions by changing the way they view a distressing situation. They can try to imagine that the situation is occurring far away from them, or that they are viewing the event from a removed perspective, as though watching a movie screen. When McLaughlin taught such techniques to young people in her lab, they were able to decrease their emotional reactivity as well as their amygdala response to negative stimuli.
Steve Bosserman

How We Made AI As Racist and Sexist As Humans - 0 views

thewalrus.ca/as-racist-and-sexist-as-humans

artificial intelligence algorithms sexism data mining racism

shared by Steve Bosserman on 25 May 18 - No Cached
  • Artificial intelligence may have cracked the code on certain tasks that typically require human smarts, but in order to learn, these algorithms need vast quantities of data that humans have produced. They hoover up that information, rummage around in search of commonalities and correlations, and then offer a classification or prediction (whether that lesion is cancerous, whether you’ll default on your loan) based on the patterns they detect. Yet they’re only as clever as the data they’re trained on, which means that our limitations—our biases, our blind spots, our inattention—become theirs as well.
  • The majority of AI systems used in commercial applications—the ones that mediate our access to services like jobs, credit, and loans— are proprietary, their algorithms and training data kept hidden from public view. That makes it exceptionally difficult for an individual to interrogate the decisions of a machine or to know when an algorithm, trained on historical examples checkered by human bias, is stacked against them. And forget about trying to prove that AI systems may be violating human rights legislation.
  • Data is essential to the operation of an AI system. And the more complicated the system—the more layers in the neural nets, to translate speech or identify faces or calculate the likelihood someone defaults on a loan—the more data must be collected.
  • ...8 more annotations...
  • The power of the system is its “ability to recognize that correlations occur between gender and professions,” says Kathryn Hume. “The downside is that there’s no intentionality behind the system—it’s just math picking up on correlations. It doesn’t know this is a sensitive issue.” There’s a tension between the futuristic and the archaic at play in this technology. AI is evolving much more rapidly than the data it has to work with, so it’s destined not just to reflect and replicate biases but also to prolong and reinforce them.
  • And sometimes, even when ample data exists, those who build the training sets don’t take deliberate measures to ensure its diversity
  • But not everyone will be equally represented in that data.
  • Accordingly, groups that have been the target of systemic discrimination by institutions that include police forces and courts don’t fare any better when judgment is handed over to a machine.
  • A growing field of research, in fact, now looks to apply algorithmic solutions to the problems of algorithmic bias.
  • Still, algorithmic interventions only do so much; addressing bias also demands diversity in the programmers who are training machines in the first place.
  • A growing awareness of algorithmic bias isn’t only a chance to intervene in our approaches to building AI systems. It’s an opportunity to interrogate why the data we’ve created looks like this and what prejudices continue to shape a society that allows these patterns in the data to emerge.
  • Of course, there’s another solution, elegant in its simplicity and fundamentally fair: get better data.
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