Quantum computing exploits quantum phenomena such as superposition and entanglement to realize a form of parallelism that is not available to traditional computing. It offers the potential of significant computational speed-ups in quantum chemistry, materials science, cryptography, and machine learning. The dominant approach to programming quantum computers is to provide an existing high-level language with libraries that allow for the expression of quantum programs. This approach can permit computations that are meaningless in a quantum context; prohibits succint expression of interaction between classical and quantum logic; and does not provide important constructs that are required for quantum programming. We present Q#, a quantum-focused domain-specific language explicitly designed to correctly, clearly and completely express quantum algorithms. Q# provides a type system; a tightly constrained environment to safely interleave classical and quantum computations; specialized syntax; symbolic code manipulation to automatically generate correct transformations of quantum operations; and powerful functional constructs which aid composition.
R. Aaij, A. Abdelmotteleb, C. Abellan Beteta
et al.
The first simultaneous test of muon-electron universality using B^{+}→K^{+}ℓ^{+}ℓ^{-} and B^{0}→K^{*0}ℓ^{+}ℓ^{-} decays is performed, in two ranges of the dilepton invariant-mass squared, q^{2}. The analysis uses beauty mesons produced in proton-proton collisions collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of 9 fb^{-1}. Each of the four lepton universality measurements reported is either the first in the given q^{2} interval or supersedes previous LHCb measurements. The results are compatible with the predictions of the Standard Model.
Wai-Ling Macrina Lam, Gisela Gabernet, Tanja Poth
et al.
Abstract Ductular reaction (DR) is the hallmark of cholestatic diseases manifested in the proliferation of bile ductules lined by biliary epithelial cells (BECs). It is commonly associated with an increased risk of fibrosis and liver failure. The receptor for advanced glycation end products (RAGE) was identified as a critical mediator of DR during chronic injury. Yet, the direct link between RAGE-mediated DR and fibrosis as well as the mode of interaction between BECs and hepatic stellate cells (HSCs) to drive fibrosis remain elusive. Here, we delineate the specific function of RAGE on BECs during DR and its potential association with fibrosis in the context of cholestasis. Employing a biliary lineage tracing cholestatic liver injury mouse model, combined with whole transcriptome sequencing and in vitro analyses, we reveal a role for BEC-specific Rage activity in fostering a pro-fibrotic milieu. RAGE is predominantly expressed in BECs and contributes to DR. Notch ligand Jagged1 is secreted from activated BECs in a Rage -dependent manner and signals HSCs in trans, eventually enhancing fibrosis during cholestasis.
with small, protruding marginal tubercles. Abdominal dorsum with large marginal sclerites on tergites II-IV and small postsiphuncular ones in addition to the sclerites developed in apterae. The scleroites on tergites I-III and VII usually into small plates and sometimes transversal bars. Abdominal tergite VIII usually with 4, rarely 5 hairs. Antennae 0.93-0.99 of body length. Processus terminalis 4.2-5.1 times as long as base of segment VI. Secondary rhinaria 46-70 on the whole length of segment III and sometimes 3-5 on basal half of segment IV. Ultimate rostral segment with 5 or 6 subsidiary hairs. Cauda with 16-17 hairs only. Other characters as in apterous viviparous female.
Very low frequency communication systems (3 kHz–30 kHz) enable applications not feasible at higher frequencies. However, the highest radiation efficiency antennas require size at the scale of the wavelength (here, >1 km), making portable transmitters extremely challenging. Facilitating transmitters at the 10 cm scale, we demonstrate an ultra-low loss lithium niobate piezoelectric electric dipole driven at acoustic resonance that radiates with greater than 300x higher efficiency compared to the previous state of the art at a comparable electrical size. A piezoelectric radiating element eliminates the need for large impedance matching networks as it self-resonates at the acoustic wavelength. Temporal modulation of this resonance demonstrates a device bandwidth greater than 83x beyond the conventional Bode-Fano limit, thus increasing the transmitter bitrate while still minimizing losses. These results will open new applications for portable, electrically small antennas. Designing high radiation efficiency antennas for portable transmitters in low frequency communication systems remains a challenge. Here, the authors report on using piezoelectricity to more efficiently radiate while achieving a bandwidth eighty three times higher than the passive Bode-Fano limit.
We report a study of the processes of e^{+}e^{-}→K^{+}D_{s}^{-}D^{*0} and K^{+}D_{s}^{*-}D^{0} based on e^{+}e^{-} annihilation samples collected with the BESIII detector operating at BEPCII at five center-of-mass energies ranging from 4.628 to 4.698 GeV with a total integrated luminosity of 3.7 fb^{-1}. An excess of events over the known contributions of the conventional charmed mesons is observed near the D_{s}^{-}D^{*0} and D_{s}^{*-}D^{0} mass thresholds in the K^{+} recoil-mass spectrum for events collected at sqrt[s]=4.681 GeV. The structure matches a mass-dependent-width Breit-Wigner line shape, whose pole mass and width are determined as (3982.5_{-2.6}^{+1.8}±2.1) MeV/c^{2} and (12.8_{-4.4}^{+5.3}±3.0) MeV, respectively. The first uncertainties are statistical and the second are systematic. The significance of the resonance hypothesis is estimated to be 5.3 σ over the contributions only from the conventional charmed mesons. This is the first candidate for a charged hidden-charm tetraquark with strangeness, decaying into D_{s}^{-}D^{*0} and D_{s}^{*-}D^{0}. However, the properties of the excess need further exploration with more statistics.
Photonic crystal nanobeam cavities are versatile platforms of interest for optical communications, optomechanics, optofluidics, cavity QED, etc. In a previous work [Appl. Phys. Lett. 96, 203102 (2010)], we proposed a deterministic method to achieve ultrahigh Q cavities. This follow-up work provides systematic analysis and verifications of the deterministic design recipe and further extends the discussion to air-mode cavities. We demonstrate designs of dielectric-mode and air-mode cavities with Q > 10⁹, as well as dielectric-mode nanobeam cavities with both ultrahigh-Q (> 10⁷) and ultrahigh on-resonance transmissions (T > 95%).
High-frequency trading (HFT) uses computer algorithms to make trading decisions in short time scales (e.g., second-level), which is widely used in the Cryptocurrency (Crypto) market (e.g., Bitcoin). Reinforcement learning (RL) in financial research has shown stellar performance on many quantitative trading tasks. However, most methods focus on low-frequency trading, e.g., day-level, which cannot be directly applied to HFT because of two challenges. First, RL for HFT involves dealing with extremely long trajectories (e.g., 2.4 million steps per month), which is hard to optimize and evaluate. Second, the dramatic price fluctuations and market trend changes of Crypto make existing algorithms fail to maintain satisfactory performance. To tackle these challenges, we propose an Efficient hieArchical Reinforcement learNing method for High Frequency Trading (EarnHFT), a novel three-stage hierarchical RL framework for HFT. In stage I, we compute a Q-teacher, i.e., the optimal action value based on dynamic programming, for enhancing the performance and training efficiency of second-level RL agents. In stage II, we construct a pool of diverse RL agents for different market trends, distinguished by return rates, where hundreds of RL agents are trained with different preferences of return rates and only a tiny fraction of them will be selected into the pool based on their profitability. In stage III, we train a minute-level router which dynamically picks a second-level agent from the pool to achieve stable performance across different markets. Through extensive experiments in various market trends on Crypto markets in a high-fidelity simulation trading environment, we demonstrate that EarnHFT significantly outperforms 6 state-of-art baselines in 6 popular financial criteria, exceeding the runner-up by 30% in profitability.
Blackjack or "21" is a popular card-based game of chance and skill. The objective of the game is to win by obtaining a hand total higher than the dealer's without exceeding 21. The ideal blackjack strategy will maximize financial return in the long run while avoiding gambler's ruin. The stochastic environment and inherent reward structure of blackjack presents an appealing problem to better understand reinforcement learning agents in the presence of environment variations. Here we consider a q-learning solution for optimal play and investigate the rate of learning convergence of the algorithm as a function of deck size. A blackjack simulator allowing for universal blackjack rules is also implemented to demonstrate the extent to which a card counter perfectly using the basic strategy and hi-lo system can bring the house to bankruptcy and how environment variations impact this outcome. The novelty of our work is to place this conceptual understanding of the impact of deck size in the context of learning agent convergence.
The cytochrome bcl complex is an oligomeric membrane protein complex which is a component of the mitochondrial respiratory chain and of the electron transfer chains of numerous bacteria which use oxygen, nitrogen, and sulfur compounds as terminal electron acceptors. The cytochrome bcl complex also participates in the cyclic transfer of electrons to and from the photosynthetic reaction centers in anoxygenic photosynthetic bacteria. In all of these species the cytochrome bcl complex transfers electrons from ubiquinol to cytochrome c and links this electron transfer to translocation of protons across the membrane in which the bcl complex resides. The mechanism by which the cytochrome bcl complex links electron transfer to proton translocation is the protonmotive Q cycle (1). This protonmotive electron transfer is one of the most important mechanisms of cellular energy transduction, found in a phylogenetically diverse range of organisms (2). The purpose of this review is to explain the protonmotive Q cycle.
We propose a theory of the market impact of metaorders based on a coarse-grained approach where the microscopic details of supply and demand is replaced by a single parameter $ρ\in [0,+\infty]$ shaping the supply-demand equilibrium and the market impact process during the execution of the metaorder. Our model provides an unified explanation of most of the empirical observations that have been reported and establishes a strong connection between the excess volatility puzzle and the order-driven view of the markets through the square-root law.
This paper aims to provide an introduction to a basic form of the Q-tensor approach to modelling liquid crystals, which has seen increased interest in recent years. The increase in interest in this type of modelling approach has been driven by investigations into the fundamental nature of defects and new applications of liquid crystals such as bistable displays and colloidal systems for which a description of defects and disorder is essential. The work in this paper is not new research, rather it is an introductory guide for anyone wishing to model a system using such a theory. A more complete mathematical description of this theory, including a description of flow effects, can be found in numerous sources but the books by Virga and Sonnet and Virga are recommended. More information can be obtained from the plethora of papers using such approaches, although a general introduction for the novice is lacking. The first few sections of this paper will detail the development of the Q-tensor approach for nematic liquid crystalline systems and construct the free energy and governing equations for the mesoscopic dependent variables. A number of device surface treatments are considered and theoretical boundary conditions are specified for each instance. Finally, an example of a real device is demonstrated.
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating capitals. However, a vast majority of existing RL methods focus on the relatively low frequency trading scenarios (e.g., day-level) and fail to capture the fleeting intraday investment opportunities due to two major challenges: 1) how to effectively train profitable RL agents for intraday investment decision-making, which involves high-dimensional fine-grained action space; 2) how to learn meaningful multi-modality market representation to understand the intraday behaviors of the financial market at tick-level. Motivated by the efficient workflow of professional human intraday traders, we propose DeepScalper, a deep reinforcement learning framework for intraday trading to tackle the above challenges. Specifically, DeepScalper includes four components: 1) a dueling Q-network with action branching to deal with the large action space of intraday trading for efficient RL optimization; 2) a novel reward function with a hindsight bonus to encourage RL agents making trading decisions with a long-term horizon of the entire trading day; 3) an encoder-decoder architecture to learn multi-modality temporal market embedding, which incorporates both macro-level and micro-level market information; 4) a risk-aware auxiliary task to maintain a striking balance between maximizing profit and minimizing risk. Through extensive experiments on real-world market data spanning over three years on six financial futures, we demonstrate that DeepScalper significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
In recent years, quantitative investment methods combined with artificial intelligence have attracted more and more attention from investors and researchers. Existing related methods based on the supervised learning are not very suitable for learning problems with long-term goals and delayed rewards in real futures trading. In this paper, therefore, we model the price prediction problem as a Markov decision process (MDP), and optimize it by reinforcement learning with expert trajectory. In the proposed method, we employ more than 100 short-term alpha factors instead of price, volume and several technical factors in used existing methods to describe the states of MDP. Furthermore, unlike DQN (deep Q-learning) and BC (behavior cloning) in related methods, we introduce expert experience in training stage, and consider both the expert-environment interaction and the agent-environment interaction to design the temporal difference error so that the agents are more adaptable for inevitable noise in financial data. Experimental results evaluated on share price index futures in China, including IF (CSI 300) and IC (CSI 500), show that the advantages of the proposed method compared with three typical technical analysis and two deep leaning based methods.
D. Vernooy, Vladimir S. Ilchenko, H. Mabuchi
et al.
Measurements of the quality factor Q approximately 8x10(9) are reported for the whispering-gallery modes (WGM's) of quartz microspheres for the wavelengths 670, 780, and 850 nm; these results correspond to finesse f approximately 2.2x10(6) . The observed independence of Q from wavelength indicates that losses for the WGM's are dominated by a mechanism other than bulk absorption in fused silica in the near infrared. Data obtained by atomic force microscopy combined with a simple model for surface scattering suggest that Q can be limited by residual surface inhomogeneities. Absorption by absorbed water can also explain why the material limit is not reached at longer wavelengths in the near infrared.
ABSTRAK : Sekolah minggu adalah suatu wadah yang didalamnya terbentuknya iman anak-anak dalam mendengarkan firman Tuhan tetapi yang bukan lah demikian banyak guru sekolah minggu yang seudah meras puas jika sudah memberikan firman Tuhan dengan persiapan yang matang tetepi belum tentu dalam persiapan itu kita menydari akan apa yang sesungguhnya dibutuhkan oleh anak sekolah minggu dan juga kita merasa senang jika sudah selesai membeikan meteri kita bertanya dan menerka memeberikan jawban seperti yang kita harapakan tetapi yang perlu kita lakukan adalah memberikan apa yang seharusnya mereka butuhkan dalam pertumbuhan iman mereka secara pribadi dalam menengarkan firman Tuhan seperti dalam Amanat Agung Tuhan Yesus.
In this report it is analyzed the focuses of the commercial dynamism of India, covering the fundamentals of growth rate, trade balance, coverage rate, openness rate, share of world indicators and then present each of them in detail.
We solve explicitly the Almgren-Chriss optimal liquidation problem where the stock price process follows a geometric Brownian motion. Our technique is to work in terms of cash and to use functional analysis tools. We show that this framework extends readily to the case of a stochastic drift for the price process and the liquidation of a portfolio.