True happiness: The role of morality in the folk concept of happiness

Recent scientific research has settled on a purely descriptive definition of happiness that is focused solely on agents’ psychological states (high positive affect, low negative affect, high life satisfaction). In contrast to this understanding, recent research has suggested that the ordinary concept of happiness is also sensitive to the moral value of agents’ lives. Five studies systematically investigate and explain the impact of morality on ordinary assessments of happiness. Study 1 demonstrates that moral judgments influence assessments of happiness not only for untrained participants, but also for academic researchers and even in those who study happiness specifically. Studies 2 and 3 then respectively ask whether this effect may be explained by general motivational biases or beliefs in a just world. In both cases, we find evidence against these explanations. Study 4 shows that the impact of moral judgments cannot be explained by changes in the perception of descriptive psychological states. Finally, Study 5 compares the impact of moral and non-moral value, and provides evidence that unlike non-moral value, moral value is part of the criteria that govern the ordinary concept of happiness. Taken together, these studies provide a specific explanation of how and why the ordinary concept of happiness deviates from the definition used by researchers studying happiness

Quantum adiabatic machine learning by zooming into a region of the energy surface

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks

Quantum adiabatic machine learning by zooming into a region of the energy surface

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks

Towards Global Characterization of Environmental and Climatic Determinants for Seasonal Influenza

No abstract availabl

Call-Tracking Data and the Public Health Response to Bioterrorism-Related Anthrax

After public notification of confirmed cases of bioterrorism-related anthrax, the Centers for Disease Control and Prevention’s Emergency Operations Center responded to 11,063 bioterrorism-related telephone calls from October 8 to November 11, 2001. Most calls were inquiries from the public about anthrax vaccines (58.4%), requests for general information on bioterrorism prevention (14.8%), and use of personal protective equipment (12.0%); 882 telephone calls (8.0%) were referred to the state liaison team for follow-up investigation. Of these, 226 (25.6%) included reports of either illness clinically confirmed to be compatible with anthrax or direct exposure to an environment known to be contaminated with Bacillus anthracis. The remaining 656 (74.4%) included no confirmed illness but reported exposures to “suspicious” packages or substances or the receipt of mail through a contaminated facility. Emergency response staff must handle high call volumes following suspected or actual bioterrorist attacks. Standardized health communication protocols that address contact with unknown substances, handling of suspicious mail, and clinical evaluation of suspected cases would allow more efficient follow-up investigations of clinically compatible cases in high-risk groups

Captures d'écran : la photographie de presse et l'image télévisée

Influenza-associated disease burden among children in tropical sub-Saharan Africa is not well established, particularly outside of the 2009 pandemic period. We estimated the burden of influenza in children aged 0-4 years through population-based surveillance for influenza-like illness (ILI) and acute lower respiratory tract illness (ALRI). Household members meeting ILI or ALRI case definitions were referred to health facilities for evaluation and collection of nasopharyngeal and oropharyngeal swabs for influenza testing by real-time reverse transcription polymerase chain reaction. Estimates were adjusted for health-seeking behavior and those with ILI and ALRI who were not tested. During 2008-2012, there were 9,652 person-years of surveillance among children aged 0-4 years. The average adjusted rate of influenza-associated hospitalization was 4.3 (95% CI 3.0-6.0) per 1,000 person-years in children aged 0-4 years. Hospitalization rates were highest in the 0-5 month and 6-23 month age groups, at 7.6 (95% CI 3.2-18.2) and 8.4 (95% CI 5.4-13.0) per 1,000 person-years, respectively. The average adjusted rate of influenza-associated medically attended (inpatient or outpatient) ALRI in children aged 0-4 years was 17.4 (95% CI 14.2-19.7) per 1,000 person-years. Few children who had severe laboratory-confirmed influenza were clinically diagnosed with influenza by the treating clinician in the inpatient (0/33, 0%) or outpatient (1/109, 0.9%) settings. Influenza-associated hospitalization rates from 2008-2012 were 5-10 times higher than contemporaneous U.S. estimates. Many children with danger signs were not hospitalized; thus, influenza-associated severe disease rates in Kenyan children are likely higher than hospital-based estimates suggest

Estimation of the national disease burden of influenza-associated severe acute respiratory illness in Kenya and Guatemala : a novel methodology

Background: Knowing the national disease burden of severe influenza in low-income countries can inform policy decisions around influenza treatment and prevention. We present a novel methodology using locally generated data for estimating this burden. Methods and Findings: This method begins with calculating the hospitalized severe acute respiratory illness (SARI) incidence for children <5 years old and persons ≥5 years old from population-based surveillance in one province. This base rate of SARI is then adjusted for each province based on the prevalence of risk factors and healthcare-seeking behavior. The percentage of SARI with influenza virus detected is determined from provincial-level sentinel surveillance and applied to the adjusted provincial rates of hospitalized SARI. Healthcare-seeking data from healthcare utilization surveys is used to estimate non-hospitalized influenza-associated SARI. Rates of hospitalized and non-hospitalized influenza-associated SARI are applied to census data to calculate the national number of cases. The method was field-tested in Kenya, and validated in Guatemala, using data from August 2009–July 2011. In Kenya (2009 population 38.6 million persons), the annual number of hospitalized influenza-associated SARI cases ranged from 17,129–27,659 for children <5 years old (2.9–4.7 per 1,000 persons) and 6,882–7,836 for persons ≥5 years old (0.21–0.24 per 1,000 persons), depending on year and base rate used. In Guatemala (2011 population 14.7 million persons), the annual number of hospitalized cases of influenza-associated pneumonia ranged from 1,065–2,259 (0.5–1.0 per 1,000 persons) among children <5 years old and 779–2,252 cases (0.1–0.2 per 1,000 persons) for persons ≥5 years old, depending on year and base rate used. In both countries, the number of non-hospitalized influenza-associated cases was several-fold higher than the hospitalized cases. Conclusions: Influenza virus was associated with a substantial amount of severe disease in Kenya and Guatemala. This method can be performed in most low and lower-middle income countries

Solving a Higgs optimization problem with quantum annealing for machine learning

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics