Hao Li

Bio: Hao Li is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 62, co-authored 1045 publications receiving 17772 citations. Previous affiliations of Hao Li include South China University of Technology & Guangxi Medical University.

Artificial intelligence in healthcare: past, present and future

Abstract: Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

Quantum computational advantage using photons

Abstract: Quantum computers promise to perform certain tasks that are believed to be intractable to classical computers. Boson sampling is such a task and is considered a strong candidate to demonstrate the ...

Quantum computational advantage using photons

Abstract: Gaussian boson sampling exploits squeezed states to provide a highly efficient way to demonstrate quantum computational advantage. We perform experiments with 50 input single-mode squeezed states with high indistinguishability and squeezing parameters, which are fed into a 100-mode ultralow-loss interferometer with full connectivity and random transformation, and sampled using 100 high-efficiency single-photon detectors. The whole optical set-up is phase-locked to maintain a high coherence between the superposition of all photon number states. We observe up to 76 output photon-clicks, which yield an output state space dimension of $10^{30}$ and a sampling rate that is $10^{14}$ faster than using the state-of-the-art simulation strategy and supercomputers. The obtained samples are validated against various hypotheses including using thermal states, distinguishable photons, and uniform distribution.

Robust axion insulator and Chern insulator phases in a two-dimensional antiferromagnetic topological insulator

Abstract: The intricate interplay between non-trivial topology and magnetism in two-dimensional materials can lead to the emergence of interesting phenomena such as the quantum anomalous Hall effect. Here we investigate the quantum transport of both bulk crystal and exfoliated MnBi2Te4 flakes in a field-effect transistor geometry. For the six septuple-layer device tuned into the insulating regime, we observe a large longitudinal resistance and zero Hall plateau, which are characteristics of an axion insulator state. The robust axion insulator state occurs in zero magnetic field, over a wide magnetic-field range and at relatively high temperatures. Moreover, a moderate magnetic field drives a quantum phase transition from the axion insulator phase to a Chern insulator phase with zero longitudinal resistance and quantized Hall resistance h/e2, where h is Planck’s constant and e is electron charge. Our results pave the way for using even-number septuple-layer MnBi2Te4 to realize the quantized topological magnetoelectric effect and axion electrodynamics in condensed matter systems. A large longitudinal resistance and zero Hall plateau—hallmarks of an axion insulator—are found in MnBi2Te4. Moreover, a moderate magnetic field drives a quantum phase transition to a Chern insulator phase with zero longitudinal resistance and quantized Hall resistance h/e2.

Dual Single-Atomic Ni-N4 and Fe-N4 Sites Constructing Janus Hollow Graphene for Selective Oxygen Electrocatalysis

Abstract: Nitrogen-coordinated metal single atoms in carbon have aroused extensive interest recently and have been growing as an active research frontier in a wide range of key renewable energy reactions and devices Herein, a step-by-step self-assembly strategy is developed to allocate nickel (Ni) and iron (Fe) single atoms respectively on the inner and outer walls of graphene hollow nanospheres (GHSs), realizing separate-sided different single-atom functionalization of hollow graphene The Ni or Fe single atom is demonstrated to be coordinated with four N atoms via the formation of a Ni-N4 or Fe-N4 planar configuration The developed Ni-N4 /GHSs/Fe-N4 Janus material exhibits excellent bifunctional electrocatalytic performance, in which the outer Fe-N4 clusters dominantly contribute to high activity toward the oxygen reduction reaction (ORR), while the inner Ni-N4 clusters are responsible for excellent activity toward the oxygen evolution reaction (OER) Density functional theory calculations demonstrate the structures and reactivities of Fe-N4 and Ni-N4 for the ORR and OER The Ni-N4 /GHSs/Fe-N4 endows a rechargeable Zn-air battery with excellent energy efficiency and cycling stability as an air-cathode, outperforming that of the benchmark Pt/C+RuO2 air-cathode The current work paves a new avenue for precise control of single-atom sites on carbon surface for the high-performance and selective electrocatalysts

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury

Abstract: Acute kidney injury (AKI) is a complex disorder for which currently there is no accepted definition. Having a uniform standard for diagnosing and classifying AKI would enhance our ability to manage these patients. Future clinical and translational research in AKI will require collaborative networks of investigators drawn from various disciplines, dissemination of information via multidisciplinary joint conferences and publications, and improved translation of knowledge from pre-clinical research. We describe an initiative to develop uniform standards for defining and classifying AKI and to establish a forum for multidisciplinary interaction to improve care for patients with or at risk for AKI. Members representing key societies in critical care and nephrology along with additional experts in adult and pediatric AKI participated in a two day conference in Amsterdam, The Netherlands, in September 2005 and were assigned to one of three workgroups. Each group's discussions formed the basis for draft recommendations that were later refined and improved during discussion with the larger group. Dissenting opinions were also noted. The final draft recommendations were circulated to all participants and subsequently agreed upon as the consensus recommendations for this report. Participating societies endorsed the recommendations and agreed to help disseminate the results. The term AKI is proposed to represent the entire spectrum of acute renal failure. Diagnostic criteria for AKI are proposed based on acute alterations in serum creatinine or urine output. A staging system for AKI which reflects quantitative changes in serum creatinine and urine output has been developed. We describe the formation of a multidisciplinary collaborative network focused on AKI. We have proposed uniform standards for diagnosing and classifying AKI which will need to be validated in future studies. The Acute Kidney Injury Network offers a mechanism for proceeding with efforts to improve patient outcomes.