Miracle-dz/UniFAR-FTUs huggingface dataset:
Resonantly driven CNOT gate for electron spins

Building an essential quantum component To build a universal quantum computer—the kind that can handle any computational task you throw at it—an essential early step is to demonstrate the so-called CNOT gate, which acts on two qubits. Zajac et al. built an efficient CNOT gate by using electron spin qubits in silicon quantum dots, an implementation that is especially appealing because of its compatibility with existing semiconductor-based electronics (see the Perspective by Schreiber and Bluhm). To showcase the potential, the authors used the gate to create an entangled quantum state called the Bell state. Science, this issue p. 439; see also p. 393 A two-qubit gate essential for quantum computing is demonstrated in silicon quantum dots. Single-qubit rotations and two-qubit CNOT operations are crucial ingredients for universal quantum computing. Although high-fidelity single-qubit operations have been achieved using the electron spin degree of freedom, realizing a robust CNOT gate has been challenging because of rapid nuclear spin dephasing and charge noise. We demonstrate an efficient resonantly driven CNOT gate for electron spins in silicon. Our platform achieves single-qubit rotations with fidelities greater than 99%, as verified by randomized benchmarking. Gate control of the exchange coupling allows a quantum CNOT gate to be implemented with resonant driving in ~200 nanoseconds. We used the CNOT gate to generate a Bell state with 78% fidelity (corrected for errors in state preparation and measurement). Our quantum dot device architecture enables multi-qubit algorithms in silicon.
Hydraulically amplified self-healing electrostatic actuators with muscle-like performance

Liquids show their strength Dielectric elastomer actuators are electrically powered muscle mimetics that offer high actuation strain and high efficiency but are limited by failure caused by high electric fields and aging. Acome et al. used a liquid dielectric, rather than an elastomeric polymer, to solve a problem of catastrophic failure in dielectric elastomer actuators. The dielectric's liquid nature allowed it to self-heal—something that would not be possible with a solid dielectric. The approach allowed the authors to exploit electrostatic and hydraulic forces to achieve muscle-like contractions in a powerful but delicate gripper. Science, this issue p. 61 Electrohydraulic soft actuators enable robust and versatile movement in next-generation soft robotic devices. Existing soft actuators have persistent challenges that restrain the potential of soft robotics, highlighting a need for soft transducers that are powerful, high-speed, efficient, and robust. We describe a class of soft actuators, termed hydraulically amplified self-healing electrostatic (HASEL) actuators, which harness a mechanism that couples electrostatic and hydraulic forces to achieve a variety of actuation modes. We introduce prototypical designs of HASEL actuators and demonstrate their robust, muscle-like performance as well as their ability to repeatedly self-heal after dielectric breakdown—all using widely available materials and common fabrication techniques. A soft gripper handling delicate objects and a self-sensing artificial muscle powering a robotic arm illustrate the wide potential of HASEL actuators for next-generation soft robotic devices.
Detection of strong convective weather based on manifold learning

With the rapid development of radar technology, the radar data people can access is growing exponentially. For a mass of high-dimensional data, it is necessary to reduce the data dimension while maintaining the data information in order to minimize the impact of the dimension disasters. The detection method of the strong convective weather (hailstone and rainstorm) is based on manifold learning algorithm in this paper. Firstly the dimension of 22-dimensional features of the strong convective weather is reduced by manifold learning algorithm-Local Tangent Space Alignment, then in low-dimensional (8-dimensional) data space the useful and hidden rules for the detection of strong convective weather is dig out, finally the effective rules are obtained to detect the strong convective weather. Compared with the non-dimensionality reduction method, the proposed method improves the detection accuracy and reduces the time complexity through experimental test.
Effect of Ginkgo biloba extract on lopinavir, midazolam and fexofenadine pharmacokinetics in healthy subjects

ABSTRACT Objective: Animal and in vitro data suggest that Ginkgo biloba extract (GBE) may modulate CYP3A4 activity. As such, GBE may alter the exposure of HIV protease inhibitors metabolized by CYP3A4. It is also possible that GBE could alter protease inhibitor pharmacokinetics (PK) secondary to modulation of P-glycoprotein (P‑gp). The primary objective of the study was to evaluate the effect of GBE on the exposure of lopinavir in healthy volunteers administered lopinavir/ritonavir. Secondary objectives were to compare ritonavir exposure pre- and post-GBE, and assess the effect of GBE on single doses of probe drugs midazolam and fexofenadine. Methods: This open-label study evaluated the effect of 2 weeks of standardized GBE administration on the steady-state exposure of lopinavir and ritonavir in 14 healthy volunteers administered lopinavir/ritonavir to steady-state. In addition, single oral doses of probe drugs midazolam and fexofenadine were administered prior to and after 4 weeks of GBE (following washout of lopinavir/ritonavir) to assess the influence of GBE on CYP3A and P‑gp activity, respectively. Results: Lopinavir, ritonavir and fexofenadine exposures were not significantly affected by GBE administration. However, GBE decreased midazolam AUC0–∞ and Cmax by 34% ( p = 0.03) and 31% ( p = 0.03), respectively, relative to baseline. In general, lopinavir/ritonavir and GBE were well tolerated. Abnormal laboratory results included mild elevations in hepatic enzymes, cholesterol and triglycerides, and mild-to-moderate increases in total bilirubin. Conclusions: Our results suggest that GBE induces CYP3A metabolism, as assessed by a decrease in midazolam concentrations. However, there was no change in the exposure of lopinavir, likely due to ritonavir's potent inhibition of CYP3A4. Thus, GBE appears unlikely to reduce the exposure of ritonavir-boosted protease inhibitors, while concentrations of unboosted protease inhibitors may be affected. Limitations to our study include the single sequence design and the evaluation of a ritonavir-boosted protease inhibitor exclusively.
U-Th dating of carbonate crusts reveals Neandertal origin of Iberian cave art

Neandertal cave art It has been suggested that Neandertals, as well as modern humans, may have painted caves. Hoffmann et al. used uranium-thorium dating of carbonate crusts to show that cave paintings from three different sites in Spain must be older than 64,000 years. These paintings are the oldest dated cave paintings in the world. Importantly, they predate the arrival of modern humans in Europe by at least 20,000 years, which suggests that they must be of Neandertal origin. The cave art comprises mainly red and black paintings and includes representations of various animals, linear signs, geometric shapes, hand stencils, and handprints. Thus, Neandertals possessed a much richer symbolic behavior than previously assumed. Science, this issue p. 912 Data from three ancient sites suggest that Neandertals were making cave paintings in Europe more than 64 thousand years ago The extent and nature of symbolic behavior among Neandertals are obscure. Although evidence for Neandertal body ornamentation has been proposed, all cave painting has been attributed to modern humans. Here we present dating results for three sites in Spain that show that cave art emerged in Iberia substantially earlier than previously thought. Uranium-thorium (U-Th) dates on carbonate crusts overlying paintings provide minimum ages for a red linear motif in La Pasiega (Cantabria), a hand stencil in Maltravieso (Extremadura), and red-painted speleothems in Ardales (Andalucía). Collectively, these results show that cave art in Iberia is older than 64.8 thousand years (ka). This cave art is the earliest dated so far and predates, by at least 20 ka, the arrival of modern humans in Europe, which implies Neandertal authorship.
LDA-Based Unified Topic Modeling for Similar TV User Grouping and TV Program Recommendation

Social TV is a social media service via TV and social networks through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned: grouping of similar TV users to create social TV communities and recommending TV programs based on group and personal interests for personalizing TV. In this paper, we propose a unified topic model based on grouping of similar TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two latent Dirichlet allocation (LDA) models. One is a topic model of TV users, and the other is a topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs, which enforces the grouping of similar TV users and associated description words for watched TV programs at the same time in a unified topic modeling framework. The unified model identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overcomes an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with similar tastes can be grouped as topics, which can then be recommended as social TV communities. To verify our proposed method of unified topic-modeling-based TV user grouping and TV program recommendation for social TV services, in our experiments, we used real TV viewing history data and electronic program guide data from a seven-month period collected by a TV poll agency. The experimental results show that the proposed unified topic model yields an average 81.4% precision for 50 topics in TV program recommendation and its performance is an average of 6.5% higher than that of the topic model of TV users only. For TV user prediction with new TV programs, the average prediction precision was 79.6%. Also, we showed the superiority of our proposed model in terms of both topic modeling performance and recommendation performance compared to two related topic models such as polylingual topic model and bilingual topic model.
Influence of Prescribed Blood Products on the Incidence of Deep Vein Thrombosis and Pulmonary Embolism in Surgical Patients

Deep vein thrombosis (DVT) and pulmonary embolisms (PEs) are common complications after surgical procedures. The influence of prescribed blood products on the occurrence of DVT and PE was evaluated in postsurgical patients in this retrospective case–control study. The records of 286 surgical patients were analyzed: DVT (n = 52), PE (n = 92), and a control group (n = 142). The amounts of prescribed blood, blood products, and vitamin K were reviewed, together with appropriate prescribing of low-molecular-weight heparins. The influence of prescribed blood products on the occurrence of DVT or PE was analyzed using multinomial logistic regression. We demonstrated a significant difference between the test and control groups (P < .05) in relation to receiving packed red blood cells. Treatment with red blood cells was associated with an increased risk of PE but not DVT. Patients who developed PE after surgery were hospitalized for longer (median 10 days) than patients with DVT (median 6 days). There was no difference between the test and control groups concerning treatment with fresh frozen plasma. Inadequate thromboprophylaxis significantly increased the likelihood of DVT. There is a connection between receiving packed red blood cells and occurrence of postoperative PE in surgical patients. Thus, patients receiving red blood cells should be monitored more closely after surgery, as they are more likely to develop PE postoperatively.
Infectious Complications of Lung Transplantation

Infections of the lungs, pleura, and mediastinum are the most common causes of mortality and morbidity following lung or heart-lung transplantation. Compared with patients receiving other solid organ transplants, recipients of lung or heart-lung allografts suffer a disproportionately high incidence of pulmonary infection. For single-lung recipients, the transplanted lung is at excess risk for infectious complications compared to the native lung. Therefore, factors other than systemic immunosuppression must account in part for increased susceptibility to pulmonary infection. Several mechanisms probably underlie the increased risk for pulmonary infections.
An Integrated Framework for Human–Robot Collaborative Manipulation

This paper presents an integrated learning framework that enables humanoid robots to perform human-robot collaborative manipulation tasks. Specifically, a table-lifting task performed jointly by a human and a humanoid robot is chosen for validation purpose. The proposed framework is split into two phases: 1) phase I-learning to grasp the table and 2) phase II-learning to perform the manipulation task. An imitation learning approach is proposed for phase I. In phase II, the behavior of the robot is controlled by a combination of two types of controllers: 1) reactive and 2) proactive. The reactive controller lets the robot take a reactive control action to make the table horizontal. The proactive controller lets the robot take proactive actions based on human motion prediction. A measure of confidence of the prediction is also generated by the motion predictor. This confidence measure determines the leader/follower behavior of the robot. Hence, the robot can autonomously switch between the behaviors during the task. Finally, the performance of the human-robot team carrying out the collaborative manipulation task is experimentally evaluated on a platform consisting of a Nao humanoid robot and a Vicon motion capture system. Results show that the proposed framework can enable the robot to carry out the collaborative manipulation task successfully.
A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering

Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an “enhanced multivariate degradation modeling,” which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.
Topic Model for Graph Mining

Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graph-structured data due to the “bag-of-word” assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.
Inhibitor Risk Stratification and Individualized Treatment in Patients With Nonsevere Hemophilia A: A Single-Institution Practice Audit

Inhibitor risk in nonsevere hemophilia A increases with cumulative factor VIII (FVIII) exposure days and high-risk mutations. A standardized approach to minimize inhibitor risk is warranted. Following establishment of a systematic approach to reduce inhibitor risk in nonsevere hemophilia, we evaluated the uptake of these strategies into clinical practice. All adult males with nonsevere hemophilia A followed by British Columbia Adult Hemophilia Program from 2004 to 2016 were included in this retrospective audit. Quality-of-care indicators on inhibitor prevention were examined. Of 108 patients, 18 patients had high-risk FVIII mutations for inhibitor development. Rates of FVIII genotyping and 1-deamino-8-d-arginine-vasopressin (DDAVP) testing in mild patients without contraindications were both over 90%, although DDAVP was used for surgical prophylaxis in only 70% of procedures. Inhibitor testing and clinic visits occurred at a median interval of 22 months. Over 80% of patients with high-risk mutations had documentation and education on their inhibitor risk. Our practice audit demonstrated a high level of recognition and patient education of individual inhibitor risk. Impact of our standardized approach on the incidence of inhibitor development is yet to be determined.
A study of a beam-like electromagnetic vibration absorber

This paper presents a study of a beam-like semi-active electromagnetic vibration absorber (EVA). The EVA consists of a flexible ferromagnetic cantilever beam, a ferromagnetic mass attached to the free end of the beam, and an E-shaped electromagnet. By changing the current of the electromagnet, the interactive force between the ferromagnetic mass and the electromagnet is varied accordingly, hence, the stiffness of this EVA is online tunable. The working frequency of the EVA is adjusted simultaneously as well. A finite element method (FEM) analysis study has been carried out to determine the characterization of the EVA. The effective stiffness of the absorber from FEM agrees with the results from the experiment well. A 2 degree-of-freedom experimental semi-active control system is built up to test the effectiveness of the EVA design. The performance under both steady-state exciting and sweeping frequency exciting show the EVA effectiveness in vibration suppression.
The content validity and acceptability of the Evaluation of Daily Activity Questionnaire in musculoskeletal conditions

Introduction The Evaluation of Daily Activity Questionnaire is a detailed self-report measure of activity limitations. It has two parts, 10 scales of symptom severity and 14 daily activity domains, including 138 activities. It has good psychometric properties in rheumatoid arthritis. The aim was next to establish its content validity and acceptability in seven musculoskeletal conditions: ankylosing spondylitis; osteoarthritis; systemic lupus erythematosus; systemic sclerosis; chronic pain; chronic hand/upper limb musculoskeletal disorders; and primary Sjögren's syndrome. Method Participants completed the Evaluation of Daily Activity Questionnaire in their own time, followed by a cognitive debriefing interview to identify their views of importance of including each item and Evaluation of Daily Activity Questionnaire acceptability. Results Six to 12 people with each condition were interviewed (n = 70): 17 men and 53 women, 57.38 (SD 12.83) years of age and with 13.15 (SD 11.02) years condition duration. Overall, all 10 scales and 138 activities were considered important to include. Most found it: had clear instructions (93%); was easy to complete (87%); included about the right amount of activities (77%); and would help an occupational therapist gain insight into the effects of the person's conditions (87%). Conclusion The Evaluation of Daily Activity Questionnaire has good content validity and acceptability in these seven conditions.
Physician Clinical Information Technology and Health Care Disparities

The authors develop a conceptual framework regarding how information technology (IT) can alter within-physician disparities, and they empirically test some of its implications in the context of coronary heart disease. Using a random experiment on 256 primary care physicians, the authors analyze the relationships between three IT functions (feedback and two types of clinical decision support) and five process-of-care measures. Endogeneity is addressed by eliminating unobserved patient characteristics with vignettes and by proxying for omitted physician characteristics. The results indicate that IT has no effects on physicians’ diagnostic certainty and treatment of vignette patients overall. The authors find that treatment and certainty differ by patient age, gender, and race. Consistent with the framework, IT’s effects on these disparities are complex. Feedback eliminated the gender disparities, but the relationships differed for other IT functions and process measures. Current policies to reduce disparities and increase IT adoption may be in discord.
Reducing food’s environmental impacts through producers and consumers

The global impacts of food production Food is produced and processed by millions of farmers and intermediaries globally, with substantial associated environmental costs. Given the heterogeneity of producers, what is the best way to reduce food's environmental impacts? Poore and Nemecek consolidated data on the multiple environmental impacts of ∼38,000 farms producing 40 different agricultural goods around the world in a meta-analysis comparing various types of food production systems. The environmental cost of producing the same goods can be highly variable. However, this heterogeneity creates opportunities to target the small numbers of producers that have the most impact. Science, this issue p. 987 Food producer heterogeneity on a global level creates mitigation opportunities with respect to environmental damage caused by food production. Food’s environmental impacts are created by millions of diverse producers. To identify solutions that are effective under this heterogeneity, we consolidated data covering five environmental indicators; 38,700 farms; and 1600 processors, packaging types, and retailers. Impact can vary 50-fold among producers of the same product, creating substantial mitigation opportunities. However, mitigation is complicated by trade-offs, multiple ways for producers to achieve low impacts, and interactions throughout the supply chain. Producers have limits on how far they can reduce impacts. Most strikingly, impacts of the lowest-impact animal products typically exceed those of vegetable substitutes, providing new evidence for the importance of dietary change. Cumulatively, our findings support an approach where producers monitor their own impacts, flexibly meet environmental targets by choosing from multiple practices, and communicate their impacts to consumers.