A Riemann solver at a junction compatible with a homogenization limit

We consider a junction regulated by a traffic lights, with n incoming roads and only one outgoing road. On each road the Phase Transition traffic model, proposed in [6], describes the evolution of car traffic. Such model is an extension of the classic Lighthill-Whitham-Richards one, obtained by assuming that different drivers may have different maximal speed. By sending to infinity the number of cycles of the traffic lights, we obtain a justification of the Riemann solver introduced in [9] and in particular of the rule for determining the maximal speed in the outgoing road.Comment: 19 page

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Comparison of the biomarkers between the patients with OPLL (case) and the controls.

<p>Comparison of the biomarkers between the patients with OPLL (case) and the controls.</p

Demographic data of the patients with OPLL (case) and the control.

<p>Demographic data of the patients with OPLL (case) and the control.</p

Serum biomarkers in patients with ossification of the posterior longitudinal ligament (OPLL): Inflammation in OPLL

<div><p>Backgroud</p><p>Ossification of the posterior longitudinal ligament (OPLL) is characterized by replacement of ligamentous tissue by ectopic new bone formation. OPLL causes narrowing of the spinal canal, resulting in neurological impairment. However, the pathogenesis of OPLL has not been fully elucidated. We investigated whether inflammation occurs in OPLL or not using high-sensitivity CRP (hs-CRP) in a case-control study.</p><p>Methods and findings</p><p>This study included 103 patients with OPLL in the patient group and 95 age- and sex-matched volunteers with degenerative spinal disease in the control group. Of the 103 OPLL patients, 88 patients who were available for more than 2 years follow-up were checked for OPLL progression. A blood sample was obtained and Hs-CRP, and other routine data, including total protein (TP), albumin (ALB), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), glucose (Glu), calcium (Ca), inorganic phosphate (Pi), white blood cell count (WBC), hemoglobin (Hb) and platelet (PLT), were analyzed. The data were compared between the patients with OPLL and the controls. The severity of the ossified lesions in the whole spine were evaluated by the ossification index (OS index) in patients with OPLL. The data were also compared between the patients with OPLL progression (the progression group) and the patients without OPLL progression (the non-progression group). In the results, the mean hs-CRP in the OPLL group was higher than that in the controls. The Pi in the OPLL group was lower than that in the control group. A negative correlation was found between the Pi and the OS index. The mean hs-CRP in the progression group was higher than that in the non-progression group. There was a positive correlation between the average length of the OPLL progression per year and the hs-CRP.</p><p>Conclusions</p><p>The results may suggest the occurrence of local inflammation in OPLL and the inflammation might cause OPLL progression. These facts are important for understanding the pathology of OPLL.</p></div

The relationship between the average length of OPLL progression per year and the serum concentration of hs-CRP in the OPLL group.

<p>A weak positive correlation was found. The data showed statistical significance (p = 0.045, r = -0.36).</p

The relationship between the serum concentration of Pi and the OS index in the OPLL group.

<p>A negative correlation was found. (p<0.001, r = -0.51).</p

CT images of a patient with ossification of the posterior longitudinal ligament (OPLL).

<p>This 60-year-old female has OPLL at C5, 5–6, 6 (A), T5, 5–6, 6 6–7,7 (B, C) L1-2 and L5-S1 (D). Her OS index is 9.</p

Demographic data of the progression group and the non-progression group.

<p>Demographic data of the progression group and the non-progression group.</p

Comparison of the biomarkers betweeen the progression group and the non-progression group.

<p>Comparison of the biomarkers betweeen the progression group and the non-progression group.</p