63 research outputs found
Linear Relations Among Poincare Series via Harmonic Weak Maass Forms
We discuss the problem of the vanishing of Poincar\'e series. This problem is
known to be related to the existence of weakly holomorphic forms with
prescribed principal part. The obstruction to the existence is related to the
pseudomodularity of Ramanujan's mock theta functions. We embed the space of
weakly holomorphic modular forms into the larger space of harmonic weak Maass
forms. From this perspective we discuss the linear relations between Poincar\'e
series and the connection to Ramanujan's mock theta functions
Optimal linear Glauber model
Contrary to the actual nonlinear Glauber model (NLGM), the linear Glauber
model (LGM) is exactly solvable, although the detailed balance condition is not
generally satisfied. This motivates us to address the issue of writing the
transition rate () in a best possible linear form such that the mean
squared error in satisfying the detailed balance condition is least. The
advantage of this work is that, by studying the LGM analytically, we will be
able to anticipate how the kinetic properties of an arbitrary Ising system
depend on the temperature and the coupling constants. The analytical
expressions for the optimal values of the parameters involved in the linear
are obtained using a simple Moore-Penrose pseudoinverse matrix. This
approach is quite general, in principle applicable to any system and can
reproduce the exact results for one dimensional Ising system. In the continuum
limit, we get a linear time-dependent Ginzburg-Landau (TDGL) equation from the
Glauber's microscopic model of non-conservative dynamics. We analyze the
critical and dynamic properties of the model, and show that most of the
important results obtained in different studies can be reproduced by our new
mathematical approach. We will also show in this paper that the effect of
magnetic field can easily be studied within our approach; in particular, we
show that the inverse of relaxation time changes quadratically with (weak)
magnetic field and that the fluctuation-dissipation theorem is valid for our
model.Comment: 25 pages; final version; appeared in Journal of Statistical Physic
An Alternate Approach to LC-Circuits
ABSTRACT When an Inductor of self inductance L and a fully charged capacitor o
Garcinol loaded vitamin E TPGS emulsified PLGA nanoparticles: preparation, physicochemical characterization, in vitro and in vivo studies
Garcinol (GAR) is a naturally occurring polyisoprenylated phenolic compound. It has been recently
investigated for its biological activities such as antioxidant, anti-inflammatory, anti ulcer, and
antiproliferative effect on a wide range of human cancer cell lines. Though the outcomes are very
promising, its extreme insolubility in water remains the main obstacle for its clinical application. Herein
we report the formulation of GAR entrapped PLGA nanoparticles by nanoprecipitation method using
vitamin E TPGS as an emulsifier. The nanoparticles were characterized for size, surface morphology,
surface charge, encapsulation efficiency and in vitro drug release kinetics. The MTT assay depicted a
high amount of cytotoxicity of GAR-NPs in B16F10, HepG2 and KB cells. A considerable amount of cell
apoptosis was observed in B16f10 and KB cell lines. In vivo cellular uptake of fluorescent NPs on B16F10
cells was also investigated. Finally the GAR loaded NPs were radiolabeled with technetium-99m with
>95% labeling efficiency and administered to B16F10 melanoma tumor bearing mice to investigate the
in vivo deposition at the tumor site by biodistribution and scintigraphic imaging study. In vitro cellular
uptake studies and biological evaluation confirm the efficacy of the formulation for cancer treatmen
What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations
With over 500 million tweets posted per day, in Twitter, it is difficult for
Twitter users to discover interesting content from the deluge of uninteresting
posts. In this work, we present a novel, explainable, topical recommendation
system, that utilizes social annotations, to help Twitter users discover
tweets, on topics of their interest. A major challenge in using traditional
rating dependent recommendation systems, like collaborative filtering and
content based systems, in high volume social networks is that, due to attention
scarcity most items do not get any ratings. Additionally, the fact that most
Twitter users are passive consumers, with 44% users never tweeting, makes it
very difficult to use user ratings for generating recommendations. Further, a
key challenge in developing recommendation systems is that in many cases users
reject relevant recommendations if they are totally unfamiliar with the
recommended item. Providing a suitable explanation, for why the item is
recommended, significantly improves the acceptability of recommendation. By
virtue of being a topical recommendation system our method is able to present
simple topical explanations for the generated recommendations. Comparisons with
state-of-the-art matrix factorization based collaborative filtering, content
based and social recommendations demonstrate the efficacy of the proposed
approach
Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging
The U.S. Securities and Exchange Commission (SEC) mandates all public
companies to file periodic financial statements that should contain numerals
annotated with a particular label from a taxonomy. In this paper, we formulate
the task of automating the assignment of a label to a particular numeral span
in a sentence from an extremely large label set. Towards this task, we release
a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794
labels. We benchmark the performance of the FNXL dataset by formulating the
task as (a) a sequence labelling problem and (b) a pipeline with span
extraction followed by Extreme Classification. Although the two approaches
perform comparably, the pipeline solution provides a slight edge for the least
frequent labels.Comment: Accepted to ACL'23 Findings Pape
FinRED: A Dataset for Relation Extraction in Financial Domain
Relation extraction models trained on a source domain cannot be applied on a
different target domain due to the mismatch between relation sets. In the
current literature, there is no extensive open-source relation extraction
dataset specific to the finance domain. In this paper, we release FinRED, a
relation extraction dataset curated from financial news and earning call
transcripts containing relations from the finance domain. FinRED has been
created by mapping Wikidata triplets using distance supervision method. We
manually annotate the test data to ensure proper evaluation. We also experiment
with various state-of-the-art relation extraction models on this dataset to
create the benchmark. We see a significant drop in their performance on FinRED
compared to the general relation extraction datasets which tells that we need
better models for financial relation extraction.Comment: Accepted at FinWeb at WWW'2
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