61 research outputs found
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series
Anomaly detection of time series, especially multivariate time series(time
series with multiple sensors), has been focused on for several years. Though
existing method has achieved great progress, there are several challenging
problems to be solved. Firstly, existing method including neural network only
concentrate on the relationship in terms of timestamp. To be exact, they only
want to know how does the data in the past influence which in the future.
However, one sensor sometimes intervenes in other sensor such as the speed of
wind may cause decrease of temperature. Secondly, there exist two categories of
model for time series anomaly detection: prediction model and reconstruction
model. Prediction model is adept at learning timely representation while short
of capability when faced with sparse anomaly. Conversely, reconstruction model
is opposite. Therefore, how can we efficiently get the relationship both in
terms of both timestamp and sensors becomes our main topic. Our approach uses
GAT, which is originated from graph neural network, to obtain connection
between sensors. And LSTM is used to obtain relationships timely. Our approach
is also designed to be double headed to calculate both prediction loss and
reconstruction loss via VAE(Variational Auto-Encoder). In order to take
advantage of two sorts of model, multi-task optimization algorithm is used in
this model
Non-Abelian inverse Anderson transitions
Inverse Anderson transitions, where the flat-band localization is destroyed
by disorder, have been wildly investigated in quantum and classical systems in
the presence of Abelian gauge fields. Here, we report the first investigation
on inverse Anderson transitions in the system with non-Abelian gauge fields. It
is found that pseudospin-dependent localized and delocalized eigenstates
coexist in the disordered non-Abelian Aharonov-Bohm cage, making inverse
Anderson transitions depend on the relative phase of two internal pseudospins.
Such an exotic phenomenon induced by the interplay between non-Abelian gauge
fields and disorder has no Abelian analogy. Furthermore, we theoretically
design and experimentally fabricate nonAbelian Aharonov-Bohm topolectrical
circuits to observe the non-Abelian inverse Anderson transition. Through the
direct measurements of frequency-dependent impedance responses and voltage
dynamics, the pseudospin-dependent non-Abelian inverse Anderson transitions are
observed. Our results establish the connection between inverse Anderson
transitions and non-Abelian gauge fields, and thus comprise a new insight on
the fundamental aspects of localization in disordered non-Abelian flat-band
systems
Observation of inverse Anderson transitions in Aharonov-Bohm topolectrical circuits
It is well known that Anderson transition is a disorder-induced
metal-insulator transition.Contrary to this conventional wisdom, some
investigations have shown that disorders could destroy the phase coherence of
localized modes in flatbands, making the localized states melt into extended
states. This phenomenon is called the inverse Anderson transition. While, to
date, the experimental observation of inverse Anderson transitions is still
lacking. In this work, we report the implementation of inverse Anderson
transitions based on Aharonov-Bohm topolectrical circuits. Different types of
disorders, including symmetric-correlated, antisymmetric-correlated and
uncorrelated disorders, can be easily implemented in Aharonov-Bohm circuits by
engineering the spatial distribution of ground settings. Through the direct
measurements of frequency-dependent impedance responses and time-domain voltage
dynamics, the inverse Anderson transitions induced by antisymmetric-correlated
disorders are clearly observed. Moreover, the flat bands and associated spatial
localizations are also fulfilled in clean Aharonov-Bohm circuits or
Aharonov-Bohm circuits sustaining symmetric-correlated and uncorrelated
disorders, respectively. Our proposal provides a flexible platform to
investigate the interplay between the geometric localization and Anderson
localization, and could have potential applications in electronic signal
control.Comment: 12 pages, 4 figure
Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder
Composed image retrieval aims to find an image that best matches a given
multi-modal user query consisting of a reference image and text pair. Existing
methods commonly pre-compute image embeddings over the entire corpus and
compare these to a reference image embedding modified by the query text at test
time. Such a pipeline is very efficient at test time since fast vector
distances can be used to evaluate candidates, but modifying the reference image
embedding guided only by a short textual description can be difficult,
especially independent of potential candidates. An alternative approach is to
allow interactions between the query and every possible candidate, i.e.,
reference-text-candidate triplets, and pick the best from the entire set.
Though this approach is more discriminative, for large-scale datasets the
computational cost is prohibitive since pre-computation of candidate embeddings
is no longer possible. We propose to combine the merits of both schemes using a
two-stage model. Our first stage adopts the conventional vector distancing
metric and performs a fast pruning among candidates. Meanwhile, our second
stage employs a dual-encoder architecture, which effectively attends to the
input triplet of reference-text-candidate and re-ranks the candidates. Both
stages utilize a vision-and-language pre-trained network, which has proven
beneficial for various downstream tasks. Our method consistently outperforms
state-of-the-art approaches on standard benchmarks for the task.Comment: 14 page
Exploring topological phase transition andWeyl physics in five dimensions with electric circuits
Weyl semimetals are phases of matter with gapless electronic excitations that
are protected by topology and symmetry. Their properties depend on the
dimensions of the systems. It has been theoretically demonstrated that
five-dimensional (5D) Weyl semimetals emerge as novel phases during the
topological phase transition in analogy to the three-dimensional case. However,
experimental observation of such a phenomenon remains a great challenge because
the tunable 5D system is extremely hard to construct in real space. Here, we
construct 5D electric circuit platforms in fully real space and experimentally
observe topological phase transitions in five dimensions. Not only are Yang
monopoles and linked Weyl surfaces observed experimentally, but various phase
transitions in five dimensions are also proved, such as the phase transitions
from a normal insulator to a Hopf link of twoWeyl surfaces and then to a 5D
topological insulator. The demonstrated topological phase transitions in five
dimensions leverage the concept of higher-dimensional Weyl physics to control
electrical signals in the engineered circuits
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems
The Segment Anything Model (SAM) has demonstrated exceptional performance and
versatility, making it a promising tool for various related tasks. In this
report, we explore the application of SAM in Weakly-Supervised Semantic
Segmentation (WSSS). Particularly, we adapt SAM as the pseudo-label generation
pipeline given only the image-level class labels. While we observed impressive
results in most cases, we also identify certain limitations. Our study includes
performance evaluations on PASCAL VOC and MS-COCO, where we achieved remarkable
improvements over the latest state-of-the-art methods on both datasets. We
anticipate that this report encourages further explorations of adopting SAM in
WSSS, as well as wider real-world applications.Comment: Technique repor
Bi-directional Training for Composed Image Retrieval via Text Prompt Learning
Composed image retrieval searches for a target image based on a multi-modal
user query comprised of a reference image and modification text describing the
desired changes. Existing approaches to solving this challenging task learn a
mapping from the (reference image, modification text)-pair to an image
embedding that is then matched against a large image corpus. One area that has
not yet been explored is the reverse direction, which asks the question, what
reference image when modified as describe by the text would produce the given
target image? In this work we propose a bi-directional training scheme that
leverages such reversed queries and can be applied to existing composed image
retrieval architectures. To encode the bi-directional query we prepend a
learnable token to the modification text that designates the direction of the
query and then finetune the parameters of the text embedding module. We make no
other changes to the network architecture. Experiments on two standard datasets
show that our novel approach achieves improved performance over a baseline
BLIP-based model that itself already achieves state-of-the-art performance.Comment: 12 pages, 5 figure
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