2,260 research outputs found
Two -operational equations and Hahn polynomials
Motivated by Liu's recent work in \cite{Liu2022}. We shall reveal the
essential feature of Hahn polynomials by presenting two new -exponential
operators. These lead us to use a systematic method to study identities
involving Hahn polynomials. As applications, we use the method of
-exponential operator to prove the bilinear generating function of Hahn
polynomials and Heine's second transformation formula. Moreover, a
generalization of -Gaussian summation is given, too
Integrable Open Spin Chains from Flavored ABJM Theory
We compute the two-loop anomalous dimension matrix in the scalar sector of
planar flavored ABJM theory. Using coordinate Bethe ansatz, we
obtain the reflection matrix and confirm that the boundary Yang-Baxter
equations are satisfied. This establishes the integrability of this theory in
the scalar sector at the two-loop order.Comment: v2, 25 pages, 2 figures, minor corrections, references adde
Marine Ecological Disasters and Their Physical Controlling Mechanisms in Jiangsu Coastal Area
The studies in this chapter are focused on marine ecological disasters in Jiangsu coastal area. Three kinds of algal blooms occurred in this region, namely, red tide associated with Dinoflagellate, green tide associated with Ulvaprolifera and golden tide associated with Sargassum. Numerical model results demonstrated that red tides in Haizhou Bay originated locally, because most of Dinoflagellates near Zhoushan Islands would be transported northeastward by the Changjiang diluted water, and even the lucky ones that entered the south of Jiangsu coastal area would die in the Subei Shoal due to high turbidity there. Due to the Changjiang diluted water and the prevailing southerly wind, Ulvaprolifera could not drift southward, either. Seawater with high turbidity in the Subei Shoal limited sunlight penetration into deep water column, and further inhibited the growth of Ulvaprolifera suspending in the water column. In this chapter, we use drift bottles and satellite-tracked Argos drifters to provide solid direct dynamic evidence that Ulvaprolifera could drift from the Subei Shoal to Qingdao coastal area and even further north. The sand ridges limited the traveling path of Ulvaprolifera in the Subei Shoal, and wind-driven currents and other baroclinic processes helped Ulvaprolifera travel farther to the north
Vernier spectrometer using counter-propagating soliton microcombs
Acquisition of laser frequency with high resolution under continuous and
abrupt tuning conditions is important for sensing, spectroscopy and
communications. Here, a single microresonator provides rapid and broad-band
measurement of frequencies across the optical C-band with a relative frequency
precision comparable to conventional dual frequency comb systems. Dual-locked
counter-propagating solitons having slightly different repetition rates are
used to implement a Vernier spectrometer. Laser tuning rates as high as 10
THz/s, broadly step-tuned lasers, multi-line laser spectra and also molecular
absorption lines are characterized using the device. Besides providing a
considerable technical simplification through the dual-locked solitons and
enhanced capability for measurement of arbitrarily tuned sources, this work
reveals possibilities for chip-scale spectrometers that greatly exceed the
performance of table-top grating and interferometer-based devices
Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs
Text-attributed Graphs (TAGs) are commonly found in the real world, such as
social networks and citation networks, and consist of nodes represented by
textual descriptions. Currently, mainstream machine learning methods on TAGs
involve a two-stage modeling approach: (1) unsupervised node feature extraction
with pre-trained language models (PLMs); and (2) supervised learning using
Graph Neural Networks (GNNs). However, we observe that these representations,
which have undergone large-scale pre-training, do not significantly improve
performance with a limited amount of training samples. The main issue is that
existing methods have not effectively integrated information from the graph and
downstream tasks simultaneously. In this paper, we propose a novel framework
called G-Prompt, which combines a graph adapter and task-specific prompts to
extract node features. First, G-Prompt introduces a learnable GNN layer
(\emph{i.e.,} adaptor) at the end of PLMs, which is fine-tuned to better
capture the masked tokens considering graph neighborhood information. After the
adapter is trained, G-Prompt incorporates task-specific prompts to obtain
\emph{interpretable} node representations for the downstream task. Our
experiment results demonstrate that our proposed method outperforms current
state-of-the-art (SOTA) methods on few-shot node classification. More
importantly, in zero-shot settings, the G-Prompt embeddings can not only
provide better task interpretability than vanilla PLMs but also achieve
comparable performance with fully-supervised baselines.Comment: Under revie
Demonstration of efficient scheme for generation of "Event Ready" entangled photon pairs from single photon source
We present a feasible and efficient scheme, and its proof-of-principle
demonstration, of creating entangled photon pairs in an event-ready way using
only simple linear optical elements and single photons. The quality of
entangled photon pair produced in our experiment is confirmed by a strict
violation of Bell's inequality. This scheme and the associated experimental
techniques present an important step toward linear optics quantum computation.Comment: 4 pages, 4 figure
Can GNN be Good Adapter for LLMs?
Recently, large language models (LLMs) have demonstrated superior
capabilities in understanding and zero-shot learning on textual data, promising
significant advances for many text-related domains. In the graph domain,
various real-world scenarios also involve textual data, where tasks and node
features can be described by text. These text-attributed graphs (TAGs) have
broad applications in social media, recommendation systems, etc. Thus, this
paper explores how to utilize LLMs to model TAGs. Previous methods for TAG
modeling are based on million-scale LMs. When scaled up to billion-scale LLMs,
they face huge challenges in computational costs. Additionally, they also
ignore the zero-shot inference capabilities of LLMs. Therefore, we propose
GraphAdapter, which uses a graph neural network (GNN) as an efficient adapter
in collaboration with LLMs to tackle TAGs. In terms of efficiency, the GNN
adapter introduces only a few trainable parameters and can be trained with low
computation costs. The entire framework is trained using auto-regression on
node text (next token prediction). Once trained, GraphAdapter can be seamlessly
fine-tuned with task-specific prompts for various downstream tasks. Through
extensive experiments across multiple real-world TAGs, GraphAdapter based on
Llama 2 gains an average improvement of approximately 5\% in terms of node
classification. Furthermore, GraphAdapter can also adapt to other language
models, including RoBERTa, GPT-2. The promising results demonstrate that GNNs
can serve as effective adapters for LLMs in TAG modeling.Comment: Accepted by WWW'2
Research progress on the premature ovarian failure caused by cisplatin therapy.
Cisplatin is a common anticancer drug able to kill tumor cells, but it causes adverse reactions in the kidney, digestive tract, and other systems. The antitumor effects of cisplatin are mainly due to its ability to bind to the DNA in tumor cells to prevent replication, thereby reducing RNA and protein syntheses, leading to cell damage and death. Cisplatin has a wide range of applications; it can be used to treat cervical, thyroid, ovarian, and other cancers. Cisplatin has a beneficial therapeutic effect, but its therapeutic selectivity is poor. In addition to eliminating diseased target cells, cisplatin can damage normal cells; in women of reproductive age being treated for cancer, cisplatin can lead to ovarian function impairment, premature ovarian failure (POF), and/or infertility. Therefore, reducing the adverse effects of cisplatin on ovarian function is an important topic in clinical research. In this paper, we explore the research progress on the POF caused by cisplatin treatment
Neuronal representation of working memory in the medial prefrontal cortex of rats
Working memory is a process for short-term active maintenance of information. Behavioral neurophysiological studies in monkeys have demonstrated that the dorsolateral prefrontal cortex (dlPFC) is a key cortical region for working memory. The medial prefrontal cortex (mPFC) in rats is a cortical area similar to the dlPFC in monkeys in terms of anatomical connections, and is also required for behavioral performance on working-memory tasks. However, it is still controversial regarding whether and how mPFC neurons encode working memory. In the present study, we trained rats on a two-choice spatial delayed alternation task in Y maze, a typical working memory task for rodents, and investigated neuronal activities in the mPFC when rats performed the task. Our results show that, (1) inactivation of the mPFC severely impaired the performance of rats on the task, consistent with previous studies showing the importance of the mPFC for working-memory tasks; (2) 93.7% mPFC cells (449 in 479) exhibited changes in spiking frequency that were temporally locked with the task events, some of which, including delay-related cells, were tuned by spatial information; (3) differential delay activities in individual mPFC cells appeared transiently and sequentially along the delay, especially during the early phase of the delay; (4) some mPFC cells showed no change in discharge frequency but exhibited differential synchronization in firing during the delay. The present results suggest that mPFC neurons in rats are involved in encoding working memory, via increasing firing frequency or synchronization
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