439 research outputs found
A novel exact solution of the 2+1-dimensional radial Dirac equation for the generalized Dirac oscillator with the inverse potentials
The generalized Dirac oscillator as one of the exact solvable model in
quantum mechanics was introduced in 2+1-dimensional world in this paper. What
is more, the general expressions of the exact solutions for these models with
the inverse cubic, quartic, quintic and sixtic power potentials in radial Dirac
equation were further given by means of the Bethe ansatz method. And finally,
the corresponding exact solutions in this paper were further discussed
TabuLa: Harnessing Language Models for Tabular Data Synthesis
Given the ubiquitous use of tabular data in industries and the growing
concerns in data privacy and security, tabular data synthesis emerges as a
critical research area. The recent state-of-the-art methods show that large
language models (LLMs) can be adopted to generate realistic tabular data. As
LLMs pre-process tabular data as full text, they have the advantage of avoiding
the curse of dimensionality associated with one-hot encoding high-dimensional
data. However, their long training time and limited re-usability on new tasks
prevent them from replacing exiting tabular generative models. In this paper,
we propose Tabula, a tabular data synthesizer based on the language model
structure. Through Tabula, we demonstrate the inherent limitation of employing
pre-trained language models designed for natural language processing (NLP) in
the context of tabular data synthesis. Our investigation delves into the
development of a dedicated foundational model tailored specifically for tabular
data synthesis. Additionally, we propose a token sequence compression strategy
to significantly reduce training time while preserving the quality of synthetic
data. Extensive experiments on six datasets demonstrate that using a language
model structure without loading the well-trained model weights yields a better
starting model for tabular data synthesis. Moreover, the Tabula model,
previously trained on other tabular data, serves as an excellent foundation
model for new tabular data synthesis tasks. Additionally, the token sequence
compression method substantially reduces the model's training time. Results
show that Tabula averagely reduces 46.2% training time per epoch comparing to
current LLMs-based state-of-the-art algorithm and consistently achieves even
higher synthetic data utility
Effects of Cations and PH on Antimicrobial Activity of Thanatin and s-Thanatin against _Escherichia coli_ ATCC25922 and _B. subtilis_ ATCC 21332
Thanatin and s-thanatin were insect antimicrobial peptides which have shown potent antimicrobial activities on a variety of microbes. In order to investigate the effect of cations and pH on the activity of these peptides against Gram-negative bacteria and Gram-positive bacteria, the antimicrobial activities of both peptides were studied in increasing concentrations of monovalent cations (K^+^ and Na^+^), divalent cations (Ca^2+^ and Mg^2+^) and H^+^. The NCCLS broth microdilution method showed that both peptides were sensitive to the presence of cations. The divalent cations showed more antagonized effect on the activity against Gram-negative bacteria than the monovalent cations, since the two peptides lost the ability to inhibit bacterial growth at a very low concentration. In addition, the activities of both peptides tested were not significantly affected by pH. Comparing to studies of other antibacterial peptide activities, our data support a hypothesis that positive ions affect the sensitivity to cation peptides
MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning
We introduce MoviePuzzle, a novel challenge that targets visual narrative
reasoning and holistic movie understanding. Despite the notable progress that
has been witnessed in the realm of video understanding, most prior works fail
to present tasks and models to address holistic video understanding and the
innate visual narrative structures existing in long-form videos. To tackle this
quandary, we put forth MoviePuzzle task that amplifies the temporal feature
learning and structure learning of video models by reshuffling the shot, frame,
and clip layers of movie segments in the presence of video-dialogue
information. We start by establishing a carefully refined dataset based on
MovieNet by dissecting movies into hierarchical layers and randomly permuting
the orders. Besides benchmarking the MoviePuzzle with prior arts on movie
understanding, we devise a Hierarchical Contrastive Movie Clustering (HCMC)
model that considers the underlying structure and visual semantic orders for
movie reordering. Specifically, through a pairwise and contrastive learning
approach, we train models to predict the correct order of each layer. This
equips them with the knack for deciphering the visual narrative structure of
movies and handling the disorder lurking in video data. Experiments show that
our approach outperforms existing state-of-the-art methods on the \MoviePuzzle
benchmark, underscoring its efficacy
Continuous and discontinous compressible flows in a converging-diverging channel solved by physics-informed neural networks without data
Physics-informed neural networks (PINNs) are employed to solve the classical
compressible flow problem in a converging-diverging nozzle. This problem
represents a typical example described by the Euler equations, thorough
understanding of which serves as a guide for solving more general compressible
flows. Given a geometry of the channel, analytical solutions for the steady
states indeed exist and they depend on the ratio between the back pressure of
the outlet and stagnation pressure of the inlet. Moreover, in the diverging
region, the solution may branch into subsonic flow, supersonic flow, and a
mixture of both with a discontinuous transition where a normal shock takes
place. Classical numerical schemes with shock-fitting/capturing methods have
been designed to solve this type of problem effectively, whereas the original
PINNs fail in front of the hyperbolic non-linear partial differential
equations. We make a first attempt to exploit the power of PINNs to directly
solve this problem by adjusting the weights of different components of the loss
function, to acquire physical solutions and meanwhile avoid trivial solutions.
With a universal setting yet no exogenous data, we are able to solve this
problem accurately, that is, for different given pressure ratios PINNs provide
different branches of solutions at both steady and unsteady states, some of
which are discontinuous in nature
Preparation and Properties of Graphene Doped TiO2 Mesoporous Materials for Photocathode Protection
© 2021 The Authors. Published by ESG (www.electrochemsci.org). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).In this study, TiO2-Graphene nanocomposites with a pore size of 10-15 nm were prepared by a sol-gel method under ultrasonic radiation environment. This kind of TiO2-Graphene nanocomposites show excellent performance in the aspects of sunlight absorption, photocathodic protection, and super hydrophobicity.Peer reviewe
Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training
We introduce CDBERT, a new learning paradigm that enhances the semantics
understanding ability of the Chinese PLMs with dictionary knowledge and
structure of Chinese characters. We name the two core modules of CDBERT as
Shuowen and Jiezi, where Shuowen refers to the process of retrieving the most
appropriate meaning from Chinese dictionaries and Jiezi refers to the process
of enhancing characters' glyph representations with structure understanding. To
facilitate dictionary understanding, we propose three pre-training tasks, i.e.,
Masked Entry Modeling, Contrastive Learning for Synonym and Antonym, and
Example Learning. We evaluate our method on both modern Chinese understanding
benchmark CLUE and ancient Chinese benchmark CCLUE. Moreover, we propose a new
polysemy discrimination task PolyMRC based on the collected dictionary of
ancient Chinese. Our paradigm demonstrates consistent improvements on previous
Chinese PLMs across all tasks. Moreover, our approach yields significant
boosting on few-shot setting of ancient Chinese understanding.Comment: To appear at ACL 2023 Finding
Feedback Control for Online Training of Neural Networks
International audienc
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