18,271 research outputs found
Massive Overlap Fermions on Anisotropic Lattices
We formulate the massive overlap fermions on anisotropic lattices.
We find that the dispersion relation for the overlap fermion resembles the
continuum form in the low-momentum region once the bare parameters are properly
tuned. The quark self-energy and the quark field renormalization constants are
calculated to one-loop in bare lattice perturbation theory.
We argue that massive domain wall quarks might be helpful in lattice QCD
studies on heavy-light hadron spectroscopy.Comment: 21 pages, 5 figures, one reference added compared with v.
High brightness fully coherent X-ray amplifier seeded by a free-electron laser oscillator
X-ray free-electron laser oscillator (XFELO) is expected to be a cutting edge
tool for fully coherent X-ray laser generation, and undulator taper technique
is well-known for considerably increasing the efficiency of free-electron
lasers (FELs). In order to combine the advantages of these two schemes, FEL
amplifier seeded by XFELO is proposed by simply using a chirped electron beam.
With the right choice of the beam parameters, the bunch tail is within the gain
bandwidth of XFELO, and lase to saturation, which will be served as a seeding
for further amplification. Meanwhile, the bunch head which is outside the gain
bandwidth of XFELO, is preserved and used in the following FEL amplifier. It is
found that the natural "double-horn" beam current as well as residual energy
chirp from chicane compressor are quite suitable for the new scheme. Inheriting
the advantages from XFELO seeding and undulator tapering, it is feasible to
generate nearly terawatt level, fully coherent X-ray pulses with unprecedented
shot-to-shot stability, which might open up new scientific opportunities in
various research fields.Comment: 8 pages, 8 figure
Isolation and Structure Identification of Flavonoids
Flavonoids, which possess a basic C15 phenyl‐benzopyrone skeleton, refer to a series of compounds in which two benzene rings (ring A and B) are connected to each other through three carbon atoms. Based on their core structure, flavonoids can be grouped into different flavonoid classes, such as flavonols, flavones, flavanones, flavanonols, anthocyanidins, isoflavones and chalcones. Flavonoids are often hydroxylated in positions 3, 5, 7, 3′, 4′ and/or 5′. Frequently, one or more of these hydroxyl groups are methylated, acetylated, prenylated or sulfated. In plants, flavonoids are often present as O‐ or C‐glycosides. The O‐glycosides have sugar substituents bound to a hydroxyl group of the aglycone, usually located at position 3 or 7, whereas the C‐glycosides have sugar groups bound to a carbon of the aglycone, usually 6‐C or 8‐C. The most common carbohydrates are rhamnose, glucose, galactose and arabinose. This chapter mainly introduces the methods of isolation and structure identification of flavonoids
1-(3,4-Dimethylbenzylidene)-4-ethylthiosemicarbazide
The title compound, C12H17N3S, was prepared by the reaction of 4-ethylthiosemicarbazide and 3,4-dimethylbenzaldehyde. The dihedral angle between the thiourea unit and the benzene ring is 7.09 (8)°. In the crystal, inversion dimers linked by pairs of N—H⋯S hydrogen bonds occur
How to Retrain Recommender System? A Sequential Meta-Learning Method
Practical recommender systems need be periodically retrained to refresh the
model with new interaction data. To pursue high model fidelity, it is usually
desirable to retrain the model on both historical and new data, since it can
account for both long-term and short-term user preference. However, a full
model retraining could be very time-consuming and memory-costly, especially
when the scale of historical data is large. In this work, we study the model
retraining mechanism for recommender systems, a topic of high practical values
but has been relatively little explored in the research community.
Our first belief is that retraining the model on historical data is
unnecessary, since the model has been trained on it before. Nevertheless,
normal training on new data only may easily cause overfitting and forgetting
issues, since the new data is of a smaller scale and contains fewer information
on long-term user preference. To address this dilemma, we propose a new
training method, aiming to abandon the historical data during retraining
through learning to transfer the past training experience. Specifically, we
design a neural network-based transfer component, which transforms the old
model to a new model that is tailored for future recommendations. To learn the
transfer component well, we optimize the "future performance" -- i.e., the
recommendation accuracy evaluated in the next time period. Our Sequential
Meta-Learning(SML) method offers a general training paradigm that is applicable
to any differentiable model. We demonstrate SML on matrix factorization and
conduct experiments on two real-world datasets. Empirical results show that SML
not only achieves significant speed-up, but also outperforms the full model
retraining in recommendation accuracy, validating the effectiveness of our
proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202
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