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"Hou, Yao"
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Spatial–Temporal Transformer Networks for Traffic Flow Forecasting Using a Pre-Trained Language Model
Most current methods use spatial–temporal graph neural networks (STGNNs) to analyze complex spatial–temporal information from traffic data collected from hundreds of sensors. STGNNs combine graph neural networks (GNNs) and sequence models to create hybrid structures that allow for the two networks to collaborate. However, this collaboration has made the model increasingly complex. This study proposes a framework that relies solely on original Transformer architecture and carefully designs embeddings to efficiently extract spatial–temporal dependencies in traffic flow. Additionally, we used pre-trained language models to enhance forecasting performance. We compared our new framework with current state-of-the-art STGNNs and Transformer-based models using four real-world traffic datasets: PEMS04, PEMS08, METR-LA, and PEMS-BAY. The experimental results demonstrate that our framework outperforms the other models in most metrics.
Journal Article
ميلاد القرد
ذهب سان تسانغ وتلاميذه إلى جبل شيتوه، وتنكر الوزير جين شينغ في هيئة رجل مسن وحذرهم من الشياطين الثلاثة، فذهب وو كونغ يتقصى الأمر، ودعا با جبه ليذهبا للقتال فابتلعهما الشيطان الأسد، انتهز القرد وو كونغ الفرصة لربط حبل في بطن الوحش الذي تألم كثيرا. لم يرض الشيطان الفيل والأمر، وطلب من القرد القتال وهزم أيضا، فاضطر للموافقة على عبور الراهب سان تسانغ الجبل على محفة فكر الشيطان النسر في خطة جديدة، ودارت معاركة كبيرة، أسر فيها المعلم وتلاميذه. لكن القرد استطاع الهرب، وذهب إلى جبل الأرواح لدعوة الحكيم الأكبر. وبمساعدته نجحوا في إخضاع الشيطان الأسد والشيطان الفيل وحولوهما إلى أسد وفيل، ثم أخضعوا الشيطان النسر وواصلوا رحلتهم إلى الغرب.
Book
Early-Life Origins of Metabolic Syndrome: Mechanisms and Preventive Aspects
One of the leading global public-health burdens is metabolic syndrome (MetS), despite the many advances in pharmacotherapies. MetS, now known as “developmental origins of health and disease” (DOHaD), can have its origins in early life. Offspring MetS can be programmed by various adverse early-life conditions, such as nutrition imbalance, maternal conditions or diseases, maternal chemical exposure, and medication use. Conversely, early interventions have shown potential to revoke programming processes to prevent MetS of developmental origins, namely reprogramming. In this review, we summarize what is currently known about adverse environmental insults implicated in MetS of developmental origins, including the fundamental underlying mechanisms. We also describe animal models that have been developed to study the developmental programming of MetS. This review extends previous research reviews by addressing implementation of reprogramming strategies to prevent the programming of MetS. These mechanism-targeted strategies include antioxidants, melatonin, resveratrol, probiotics/prebiotics, and amino acids. Much work remains to be accomplished to determine the insults that could induce MetS, to identify the mechanisms behind MetS programming, and to develop potential reprogramming strategies for clinical translation.
Journal Article
Variational learning algorithms for quantum query complexity
Quantum query complexity is pivotal in the analysis of quantum algorithms, encompassing well-known examples like search and period-finding algorithms. These algorithms typically involve a sequence of unitary operations and oracle calls dependent on an input variable. In this study, we introduce a variational learning approach to explore quantum query complexity. Our method employs an efficient parameterization of the unitary operations and utilizes a loss function derived from the algorithm’s error probability. We apply this technique to various quantum query complexities, notably devising a new algorithm that resolves the 5-bit Hamming modulo problem with four queries, addressing an open question from Cornelissen et al (2021 arXiv: 2112.14682 ). This finding is corroborated by a semidefinite programming (SDP) approach. Our numerical method exhibits superior memory efficiency compared to SDP and can identify quantum query algorithms (QQAs) that require a smaller workspace register dimension, an aspect not optimized by SDP. These advancements present a significant step forward in the practical application and understanding of QQAs.
Journal Article
Determining system Hamiltonian from eigenstate measurements without correlation functions
Local Hamiltonians arise naturally in physical systems. Despite their seemingly 'simple' local structures, exotic features such as non-local correlations and topological orders exhibit in eigenstates of these systems. Previous studies for recovering local Hamiltonians from measurements on an eigenstate ψ require information of nonlocal correlation functions. In this work, we argue that local measurements on ψ is enough to recover the Hamiltonian in most of the cases. Specially, we develop an algorithm to demonstrate the observation. Our algorithm is tested numerically for randomly generated local Hamiltonians of different system sizes and returns promising reconstructions with desired accuracy. Additionally, for random generated Hamiltonians (not necessarily local), our algorithm also provides precise estimations.
Journal Article
Neural networks for quantum inverse problems
Quantum inverse problem (QIP) is the problem of estimating an unknown quantum system from a set of measurements, whereas the classical counterpart is the inverse problem of estimating a distribution from a set of observations. In this paper, we present a neural-network-based method for QIPs, which has been widely explored for its classical counterpart. The proposed method utilizes the quantumness of the QIPs and takes advantage of the computational power of neural networks to achieve remarkable efficiency for the quantum state estimation. We test the method on the problem of maximum entropy estimation of an unknown state ρ from partial information both numerically and experimentally. Our method yields high fidelity, efficiency and robustness for both numerical experiments and quantum optical experiments.
Journal Article
Region analysis of H → γγ via a bottom quark loop
A
bstract
The
H
→
γγ
decay is an ideal process to study the structure of next-to-leading power logarithms induced by quarks due to its simple initial and final states. We perform a region analysis of this process up to two-loop level to inspect the origins of the logarithms. To deal with the endpoint singularities that are prevalent for the next-to-leading power logarithms, we have adopted two different kinds of regulators to exhibit the advantages and disadvantages of each regulator. In the analytic regulator we have chosen, the power of the propagator is changed by
η
. And the endpoint singularities are regulated in the form of 1/
η
. These poles cancel between the collinear and anti-collinear sectors since there is no soft mode in such a regulator. In the ∆ regulator, the soft sector is important. The leading and next-to-leading logarithms can be inferred from only this sector. Moreover, the symmetry between the collinear and anti-collinear sectors is preserved. After imposing a cut on the bottom quark transverse momentum, the leading order result is finite in each sector. We also discuss the next-to-next-to-leading power contributions and find that the potential factorization formulae involve two-dimensional endpoint singularities. Our region analysis could help to develop sophisticated factorization and resummation schemes beyond leading power.
Journal Article
The Effects of Resveratrol in the Treatment of Metabolic Syndrome
Resveratrol, also known as 3,5,4′-trihydroxystilbene, is a natural polyphenol that occurs as a phytoalexin. It is produced by plant sources such as grapes, apples, blueberries, plums, peanuts, and other oilseeds. This compound has a variety of effects on human health and diseases. This review summarizes the mounting evidence that resveratrol is helpful in treating metabolic syndrome and related disorders. Resveratrol can be provided either early as a reprogramming agent or later as part of treatment. A few of the main mechanisms underlying the beneficial effects of resveratrol on metabolic syndrome are outlined. This review also discusses the potential of resveratrol derivatives as a complementary or alternative medicine. In conclusion, resveratrol could be a useful regimen for the prevention and treatment of metabolic syndrome and its related conditions.
Journal Article
Preventive Aspects of Early Resveratrol Supplementation in Cardiovascular and Kidney Disease of Developmental Origins
The increase in the incidence of cardiovascular diseases (CVDs) and kidney disease has stimulated research for strategies that could prevent, rather than just treat, both interconnected disorders. Resveratrol, a polyphenolic compound with pleiotropic biofunctions, has shown health benefits. Emerging epidemiological data supports that early life environmental insults are regarded as increased risks of developing CVDs and kidney disease in adulthood. Conversely, both disorders could be reversed or postponed by shifting interventions from adulthood to earlier stage by so-called reprogramming. The purpose of this review is first to highlight current epidemiological studies linking cardiovascular and renal programming to resulting CVD and kidney disease of developmental origins. This will be followed by a summary of how resveratrol could exert a positive influence on CVDs and kidney disease. This review also presents an overview of the evidence documenting resveratrol as a reprogramming agent to protect against CVD and kidney disease of developmental origins from animal studies and to outline the advances in understanding the underlying molecular mechanisms. Overall, this review reveals the need for future research to further clarify the reprogramming effects of resveratrol before clinical translation.
Journal Article