60 research outputs found
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Engineered potentials and dynamics of ultracold quantum gases under the microscope
In this thesis, I present experiments on making and probing strongly correlated gases of ultracold atoms in an optical lattice with engineered potentials and dynamics. The quantum gas microscope first developed in our lab enables single-site resolution imaging and manipulation of atoms in a two-dimensional lattice, offering an ideal platform for quantum simulation of condensed matter systems. Here we demonstrate our abilities to generate optical potential with high precision and high resolution, and engineer coherent dynamics using photon assisted tunneling. We also create a system of bilayer quantum gases that brings new imaging capabilities and extends the possible range of our quantum simulation.Physic
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning
The diffusion of rumors on microblogs generally follows a propagation tree
structure, that provides valuable clues on how an original message is
transmitted and responded by users over time. Recent studies reveal that rumor
detection and stance detection are two different but relevant tasks which can
jointly enhance each other, e.g., rumors can be debunked by cross-checking the
stances conveyed by their relevant microblog posts, and stances are also
conditioned on the nature of the rumor. However, most stance detection methods
require enormous post-level stance labels for training, which are
labor-intensive given a large number of posts. Enlightened by Multiple Instance
Learning (MIL) scheme, we first represent the diffusion of claims with
bottom-up and top-down trees, then propose two tree-structured weakly
supervised frameworks to jointly classify rumors and stances, where only the
bag-level labels concerning claim's veracity are needed. Specifically, we
convert the multi-class problem into a multiple MIL-based binary classification
problem where each binary model focuses on differentiating a target stance or
rumor type and other types. Finally, we propose a hierarchical attention
mechanism to aggregate the binary predictions, including (1) a bottom-up or
top-down tree attention layer to aggregate binary stances into binary veracity;
and (2) a discriminative attention layer to aggregate the binary class into
finer-grained classes. Extensive experiments conducted on three Twitter-based
datasets demonstrate promising performance of our model on both claim-level
rumor detection and post-level stance classification compared with
state-of-the-art methods.Comment: Accepted by SIGIR 202
Measuring entanglement entropy through the interference of quantum many-body twins
Entanglement is one of the most intriguing features of quantum mechanics. It
describes non-local correlations between quantum objects, and is at the heart
of quantum information sciences. Entanglement is rapidly gaining prominence in
diverse fields ranging from condensed matter to quantum gravity. Despite this
generality, measuring entanglement remains challenging. This is especially true
in systems of interacting delocalized particles, for which a direct
experimental measurement of spatial entanglement has been elusive. Here, we
measure entanglement in such a system of itinerant particles using quantum
interference of many-body twins. Leveraging our single-site resolved control of
ultra-cold bosonic atoms in optical lattices, we prepare and interfere two
identical copies of a many-body state. This enables us to directly measure
quantum purity, Renyi entanglement entropy, and mutual information. These
experiments pave the way for using entanglement to characterize quantum phases
and dynamics of strongly-correlated many-body systems.Comment: 14 pages, 12 figures (6 in the main text, 6 in supplementary
material
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
The truth is significantly hampered by massive rumors that spread along with
breaking news or popular topics. Since there is sufficient corpus gathered from
the same domain for model training, existing rumor detection algorithms show
promising performance on yesterday's news. However, due to a lack of training
data and prior expert knowledge, they are poor at spotting rumors concerning
unforeseen events, especially those propagated in different languages (i.e.,
low-resource regimes). In this paper, we propose a unified contrastive transfer
framework to detect rumors by adapting the features learned from well-resourced
rumor data to that of the low-resourced. More specifically, we first represent
rumor circulated on social media as an undirected topology, and then train a
Multi-scale Graph Convolutional Network via a unified contrastive paradigm. Our
model explicitly breaks the barriers of the domain and/or language issues, via
language alignment and a novel domain-adaptive contrastive learning mechanism.
To enhance the representation learning from a small set of target events, we
reveal that rumor-indicative signal is closely correlated with the uniformity
of the distribution of these events. We design a target-wise contrastive
training mechanism with three data augmentation strategies, capable of unifying
the representations by distinguishing target events. Extensive experiments
conducted on four low-resource datasets collected from real-world microblog
platforms demonstrate that our framework achieves much better performance than
state-of-the-art methods and exhibits a superior capacity for detecting rumors
at early stages.Comment: A significant extension of the first contrastive approach for
low-resource rumor detection (arXiv:2204.08143
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
In recent years, we witness the explosion of false and unconfirmed
information (i.e., rumors) that went viral on social media and shocked the
public. Rumors can trigger versatile, mostly controversial stance expressions
among social media users. Rumor verification and stance detection are different
yet relevant tasks. Fake news debunking primarily focuses on determining the
truthfulness of news articles, which oversimplifies the issue as fake news
often combines elements of both truth and falsehood. Thus, it becomes crucial
to identify specific instances of misinformation within the articles. In this
research, we investigate a novel task in the field of fake news debunking,
which involves detecting sentence-level misinformation. One of the major
challenges in this task is the absence of a training dataset with
sentence-level annotations regarding veracity. Inspired by the Multiple
Instance Learning (MIL) approach, we propose a model called Weakly Supervised
Detection of Misinforming Sentences (WSDMS). This model only requires bag-level
labels for training but is capable of inferring both sentence-level
misinformation and article-level veracity, aided by relevant social media
conversations that are attentively contextualized with news sentences. We
evaluate WSDMS on three real-world benchmarks and demonstrate that it
outperforms existing state-of-the-art baselines in debunking fake news at both
the sentence and article levels
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