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The urgent, first book from global phenomenon Joshua Wong - leader of the Hong Kong protests, Nobel prize nominee and TIME, Forbes and Fortune world leader - who will tell us how he took on the biggest country in the world, and why we all have a stake in the global fight for democracy. Introduction by Ai WeiweiAn urgent manifesto for global democracy from the leading 23-year-old Hong Kong activist - Nobel Peace Prize nominee and TIME, Forbes and Fortune world leader. At what point do you stand up to power?Age 14, Joshua Wong made history. While the adults stayed silent, Joshua staged the first ever student protest in Hong Kong to oppose National Education - and won. Since then Joshua founded Demosisto, led the Umbrella Revolution and spearheaded the Extradition Bill protests, which have seen an estimated 2 million people - more than a quarter of the population - take to Hong Kong's streets. His actions have sparked worldwide attention, a Nobel Peace Prize nomination and over 100 days in jail. In Unfree Speech, Joshua tells his story for the first time. Composed in three parts, Joshua chronicles his path to politics, collects the letters he wrote as a political prisoner under the Chinese state, and closes with a powerful and urgent call for all of us around the world to defend our democratic rights. Hong Kong is the canary in the coal mine. When we stay silent, no-one is safe: when we free our speech, they can't stop all of us

Generating ion-photon entanglement is a crucial step for scalable trapped-ion quantum networks. To avoid the crosstalk on memory qubits carrying quantum information, it is common to use a different ion species for ion-photon entanglement generation such that the scattered photons are far off-resonant for the memory qubits. However, such a dual-species scheme can be subject to inefficient sympathetic cooling due to the mass mismatch of the ions. Here we demonstrate a trapped-ion quantum network node in the dual-type qubit scheme where two types of qubits are encoded in the S and F hyperfine structure levels of 171 Yb + ions. We generate ion photon entanglement for the S -qubit in a typical timescale of hundreds of milliseconds, and verify its small crosstalk on a nearby F -qubit with coherence time above seconds. Our work demonstrates an enabling function of the dual-type qubit scheme for scalable quantum networks. In ion-photon quantum network platforms, usually memory qubits and communication qubits are encoded in ions of different species. Here, instead, the authors show how to realise ion-photon entanglement within the same-species-dual-encoding scheme.

\"An urgent manifesto for global democracy from Joshua Wong, the twenty-three-year-old phenomenon leading Hong Kong's protests, and Nobel Peace Prize nominee\"-- Provided by publisher.

A new type of fermion, corresponding to a three-fold degeneracy in the electronic band structure of crystalline molybdenum phosphide, is observed, which lies conceptually between Dirac and Weyl fermions. Triple-point fermions Quantum field theory predicts three types of fermion—Dirac, Weyl and Majorana—but so far only the first type has been detected experimentally as an elementary particle in high-energy physics. However, in recent years quasiparticle analogues of all three types have been observed in crystalline materials with non-trivial topological energy structure. These topological systems could potentially also host new types of fermionic quasiparticle that go beyond the standard description from quantum field theory. Hong Ding and colleagues use spectroscopic measurements to study the electronic band structure of the topological semimetal molybdenum phosphide and observe 'triple points'—states with three-fold degeneracy. These states lie conceptually between Dirac points (four-fold degeneracy) and Weyl points (two-fold degeneracy) and are associated with a new type of three-component fermionic quasiparticle. The authors also observe Weyl points in the system, which suggests that it could be used to study the interplay between different types of fermion. In quantum field theory, Lorentz invariance leads to three types of fermion—Dirac, Weyl and Majorana. Although the existence of Weyl and Majorana fermions as elementary particles in high-energy physics is debated, all three types of fermion have been proposed to exist as low-energy, long-wavelength quasiparticle excitations in condensed-matter systems 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 . The existence of Dirac and Weyl fermions in condensed-matter systems has been confirmed experimentally 13 , 14 , 15 , 16 , 17 , 18 , and that of Majorana fermions is supported by various experiments 19 , 20 . However, in condensed-matter systems, fermions in crystals are constrained by the symmetries of the 230 crystal space groups rather than by Lorentz invariance, giving rise to the possibility of finding other types of fermionic excitation that have no counterparts in high-energy physics 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 . Here we use angle-resolved photoemission spectroscopy to demonstrate the existence of a triply degenerate point in the electronic structure of crystalline molybdenum phosphide. Quasiparticle excitations near a triply degenerate point are three-component fermions, beyond the conventional Dirac–Weyl–Majorana classification, which attributes Dirac and Weyl fermions to four- and two-fold degenerate points, respectively. We also observe pairs of Weyl points in the bulk electronic structure of the crystal that coexist with the three-component fermions. This material thus represents a platform for studying the interplay between different types of fermions. Our experimental discovery opens up a way of exploring the new physics of unconventional fermions in condensed-matter systems.

Biomarkers that predict disease progression might assist the development of better therapeutic strategies for aggressive cancers, such as ovarian cancer. Here, we investigated the role of collagen type XI alpha 1 (COL11A1) in cell invasiveness and tumor formation and the prognostic impact of COL11A1 expression in ovarian cancer. Microarray analysis suggested that COL11A1 is a disease progression-associated gene that is linked to ovarian cancer recurrence and poor survival. Small interference RNA-mediated specific reduction in COL11A1 protein levels suppressed the invasive ability and oncogenic potential of ovarian cancer cells and decreased tumor formation and lung colonization in mouse xenografts. A combination of experimental approaches, including real-time RT–PCR, casein zymography and chromatin immunoprecipitation (ChIP) assays, showed that COL11A1 knockdown attenuated MMP3 expression and suppressed binding of Ets-1 to its putative MMP3 promoter-binding site, suggesting that the Ets-1–MMP3 axis is upregulated by COL11A1. Transforming growth factor (TGF)-beta (TGF-β1) treatment triggers the activation of smad2 signaling cascades, leading to activation of COL11A1 and MMP3. Pharmacological inhibition of MMP3 abrogated the TGF-β1-triggered, COL11A1-dependent cell invasiveness. Furthermore, the NF-YA-binding site on the COL11A1 promoter was identified as the major determinant of TGF-β1-dependent COL11A1 activation. Analysis of 88 ovarian cancer patients indicated that high COL11A1 mRNA levels are associated with advanced disease stage. The 5-year recurrence-free and overall survival rates were significantly lower ( P =0.006 and P =0.018, respectively) among patients with high expression levels of tissue COL11A1 mRNA compared with those with low expression. We conclude that COL11A1 may promote tumor aggressiveness via the TGF-β1–MMP3 axis and that COL11A1 expression can predict clinical outcome in ovarian cancer patients.

High-temperature superconductivity emerges on doping holes or electrons into antiferromagnetic copper oxides. The large energy scale of magnetic excitations, for example, compared with phonon energies, is thought to drive superconductivity with high transition temperatures ( T c ). Comparing high-energy magnetic excitations of hole- and electron-doped superconductors provides an opportunity to test this hypothesis. Here, we use resonant inelastic X-ray scattering at the Cu L 3 -edge to reveal collective excitations in the electron-doped cuprate Nd 2− x Ce x CuO 4 . Surprisingly, magnetic excitations harden significantly across the antiferromagnetic high-temperature superconductivity phase boundary despite short-ranged antiferromagnetic correlations, in contrast to the hole-doped cuprates. Furthermore, we find an unexpected branch of collective modes in superconducting compounds, absent in hole-doped cuprates. These modes emanate from the zone centre and possess a higher temperature scale than T c , signalling a distinct quantum phase. Despite their differences, the persistence of magnetic excitations and the existence of a distinct quantum phase are apparently universal in both hole- and electron-doped cuprates. Cuprate superconductors are created by adding electrons or holes to a ‘parent’ compound. They have dissimilar phase diagrams and the asymmetry is further highlighted by unexpected collective modes measured using resonant inelastic X-ray scattering.

Sleep is critical to a person's physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.

An experimental setup has been constructed to measure the collection efficiency (CE) of sub-micrometer aerosol particles by cloud droplets. Droplets of a dilute aqueous ammonium sulfate solution with an average radius of 21.6 μm fall freely into a chamber and collide with sub-micrometer polystyrene latex (PSL) sphere particles of known sizes and concentrations. Two relative humidity (RH) conditions, 15 ± 3 % and 88 ± 3 %, hereafter termed \"low\" and \"high\", respectively, were varied with different particles sizes and concentrations. After passing through the chamber, the droplets and aerosol particles were sent to the Particle Analysis by Laser Mass Spectrometry (PALMS) instrument to determine chemical compositions on a single-droplet basis. \"Coagulated droplets\" (droplets that collected aerosols) had mass spectra that contained signatures from both an aerosol particle and a droplet residual. CE values range from 2.0 × 10−1 to 1.6 for the low-RH case and from 1.5 × 10−2 to 9.0 × 10−2 for the high-RH case. CE values were, within experimental uncertainty, independent of the aerosol concentrations. CE values in this study were found to be in agreement with previous experimental and theoretical studies. To our knowledge, this is the first collection experiment performed on a single-droplet basis with atmospherically relevant conditions such as droplet sizes, droplet charges and flow.

Trapped ions constitute one of the most promising systems for implementing quantum computing and networking 1 , 2 . For large-scale ion-trap-based quantum computers and networks, it is critical to have two types of qubit: one for computation and storage, and another for auxiliary operations such as qubit detection 3 , sympathetic cooling 4 – 7 and entanglement generation through photon links 8 , 9 . Although the two qubit types can be implemented using two different ion species 3 , 10 – 13 , this approach introduces substantial complexity into creating and controlling each qubit type 14 , 15 . Here we resolve these challenges by implementing two coherently convertible qubit types using one ion species. We encode the qubits into two pairs of clock states of the 171 Yb + ions, and achieve microsecond-level conversion rates between the two types with one-way fidelities of 99.5%. We further demonstrate that operations on one qubit type, including sympathetic laser cooling, single-qubit gates and qubit detection, have crosstalk errors less than 0.06% on the other type, which is below the best-known error threshold of ~1% for fault-tolerant quantum computing using the surface code 1 , 16 . Our work establishes the feasibility and advantages of using coherently convertible dual-type qubits with the same ion species for large-scale quantum computing and networking. Quantum computing with trapped ions requires qubits that can store and manipulate quantum information, and others that can be used for destructive incoherent operations. Different states of ytterbium-171 ions can be used to realize both qubit types