We report the first experimental results on spin-dependent elastic weakly interacting massive particle (WIMP) nucleon scattering from the XENON1T dark matter search experiment. The analysis uses the ...full ton year exposure of XENON1T to constrain the spin-dependent proton-only and neutron-only cases. No significant signal excess is observed, and a profile likelihood ratio analysis is used to set exclusion limits on the WIMP-nucleon interactions. This includes the most stringent constraint to date on the WIMP-neutron cross section, with a minimum of 6.3×10^{-42} cm^{2} at 30 GeV/c^{2} and 90% confidence level. The results are compared with those from collider searches and used to exclude new parameter space in an isoscalar theory with an axial-vector mediator.
We present the performance of a semantic segmentation network, sparsessnet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time ...projection chamber for the study of neutrino properties and interactions. sparsessnet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNEs νe-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are reclassified into two classes more relevant to the current analysis. The output of sparsessnet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is ≥ 99 %. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec , the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.
The second meeting for the International Consensus on Antinuclear antibody (ANA) Pattern (ICAP) was held on 22 September 2015, one day prior to the opening of the 12th Dresden Symposium on ...Autoantibodies in Dresden, Germany. The ultimate goal of ICAP is to promote harmonization and understanding of autoantibody nomenclature, and thereby optimizing ANA usage in patient care. The newly developed ICAP website www.ANApatterns.org was introduced to the more than 50 participants. This was followed by several presentations and discussions focusing on key issues including the two-tier classification of ANA patterns into competent-level versus expert-level, the consideration of how to report composite versus mixed ANA patterns, and the necessity for developing a consensus on how ANA results should be reported. The need to establish on-line training modules to help users gain competency in identifying ANA patterns was discussed as a future addition to the website. To advance the ICAP goal of promoting wider international participation, it was agreed that there should be a consolidated plan to translate consensus documents into other languages by recruiting help from members of the respective communities.
Abstract
The selection of low-radioactive construction materials is of utmost importance for the success of low-energy rare event search experiments. Besides radioactive contaminants in the bulk, the ...emanation of radioactive radon atoms from material surfaces attains increasing relevance in the effort to further reduce the background of such experiments. In this work, we present the
$$^{222}$$
222
Rn emanation measurements performed for the XENON1T dark matter experiment. Together with the bulk impurity screening campaign, the results enabled us to select the radio-purest construction materials, targeting a
$$^{222}$$
222
Rn activity concentration of
$$10\,\mathrm{\,}\upmu \mathrm{Bq}/\mathrm{kg}$$
10
μ
Bq
/
kg
in
$$3.2\,\mathrm{t}$$
3.2
t
of xenon. The knowledge of the distribution of the
$$^{222}$$
222
Rn sources allowed us to selectively eliminate problematic components in the course of the experiment. The predictions from the emanation measurements were compared to data of the
$$^{222}$$
222
Rn activity concentration in XENON1T. The final
$$^{222}$$
222
Rn activity concentration of
$$(4.5\pm 0.1)\,\mathrm{\,}\upmu \mathrm{Bq}/\mathrm{kg}$$
(
4.5
±
0.1
)
μ
Bq
/
kg
in the target of XENON1T is the lowest ever achieved in a xenon dark matter experiment.
Humans are increasingly interacting in environments mediated by algorithms that control the flow of social information, yet little is known about how algorithms impact social ...learning.Algorithm-mediated social learning is currently characterized by functional misalignment: human social learning evolved to promote adaptive behaviors that foster cooperation and collective problem-solving, but content algorithms are designed to sustain attention and engagement on platforms.Emerging evidence suggests that content algorithms exploit social-learning biases by amplifying prestigious, ingroup, moral and emotional (‘PRIME’) information and teaching users to produce more of this content via social learning.In specific contexts, such as morality and politics, these human–algorithm interactions saturate the environment with PRIME information, which leads to social misperceptions that can promote conflict and misinformation rather than cooperation and collective problem-solving.The framework of functional misalignment can shed light on how to design algorithms that foster more functional social learning in digital environments.
Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or ‘PRIME’ information) to sustain users’ attention and maximize engagement. Here, we synthesize emerging insights into ‘algorithm-mediated social learning’ and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human–algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.
Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or ‘PRIME’ information) to sustain users’ attention and maximize engagement. Here, we synthesize emerging insights into ‘algorithm-mediated social learning’ and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human–algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.
Objective Although medical management of acute uncomplicated type B aortic dissection remains the standard of care, contemporary data regarding the natural history of medically treated patients are ...sparse. The goal of this study was to evaluate the natural history of patients with acute type B aortic dissection who were initially managed with medical therapy alone. Methods All patients with acute type B aortic dissection who were initially managed medically between March 1999 and March 2011 were included. Failure of medical therapy was defined as any death or aorta-related intervention. Early failure occurred within 15 days of presentation. Predictors of long-term outcomes were determined using backward stepwise regression. Results A total of 298 patients with medically managed acute type B dissections were identified. The cohort had an average age of 65.9 years at presentation and was 61.7% male. There were 174 (58.4%) failures including 119 deaths and 87 interventions (24 endovascular, 63 open); 57 (66%) interventions were performed for aneurysmal degeneration. There were 37 (12%) early failures including 14 deaths and 25 interventions (10 endovascular, 15 open). Aneurysmal degeneration was the indication for intervention in six patients (24%). Mean follow-up was 4.2 years (range, 0.1-14.7 years). Kaplan-Meier estimate demonstrated that freedom from intervention was 77.3% ± 2.4% at 3 years and 74.2% ± 2.5% at 6 years. There were no predictors of freedom from intervention. Kaplan-Meier estimate demonstrated that the intervention-free survival was 55.0% ± 3.0% at 3 years and 41.0% ± 3.2% at 6 years. End-stage renal disease was predictive of failure of medical treatment (hazard ratio, 2.60; confidence interval, 1.19-5.66; P = .02), and age >70 years was protective against failure (hazard ratio, 0.97; confidence interval, 0.95-0.98; P < .01). Kaplan-Meier estimate demonstrated that survival after 6 years was higher in patients who underwent interventions (76% vs 58%; P = .018). Conclusions The majority of patients with acute type B dissection will fail medical therapy over time as evidenced by a 6-year intervention-free survival of 41%. Patients who underwent any aortic intervention had a significant survival advantage over those who were treated with medical management alone. Further study is necessary to determine who will benefit most from early intervention.
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