407 research outputs found
Multiscale adaptive smoothing models for the hemodynamic response function in fMRI
In the event-related functional magnetic resonance imaging (fMRI) data
analysis, there is an extensive interest in accurately and robustly estimating
the hemodynamic response function (HRF) and its associated statistics (e.g.,
the magnitude and duration of the activation). Most methods to date are
developed in the time domain and they have utilized almost exclusively the
temporal information of fMRI data without accounting for the spatial
information. The aim of this paper is to develop a multiscale adaptive
smoothing model (MASM) in the frequency domain by integrating the spatial and
frequency information to adaptively and accurately estimate HRFs pertaining to
each stimulus sequence across all voxels in a three-dimensional (3D) volume. We
use two sets of simulation studies and a real data set to examine the finite
sample performance of MASM in estimating HRFs. Our real and simulated data
analyses confirm that MASM outperforms several other state-of-the-art methods,
such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Dielectric response of soft mode in ferroelectric SrTiO3
We report far-infrared dielectric properties of powder form ferroelectric
SrTiO3. Terahertz time-domain spectroscopy (THz-TDS) measurement reveals that
the low-frequency dielectric response of SrTiO3 is a consequence of the lowest
transverse optical (TO) soft mode TO1 at 2.70 THz (90.0 1/cm), which is
directly verified by Raman spectroscopy. This result provides a better
understanding of the relation of low-frequency dielectric function with the
optical phonon soft mode for ferroelectric materials. Combining THz-TDS with
Raman spectra, the overall low-frequency optical phonon response of SrTiO3 is
presented in an extended spectral range from 6.7 1/cm to 1000.0 1/cm.Comment: 14 pages; 4 figure
Composite Community-Aware Diversified Influence Maximization with Efficient Approximation
Influence Maximization (IM) is a famous topic in mobile networks and social
computing, which aims at finding a small subset of users to maximize the
influence spread through online information cascade. Recently, some careful
researchers paid attention to diversity of information dissemination,
especially community-aware diversity, and formulated the diversified IM
problem. The diversity is ubiquitous in a lot of real-world applications, but
they are all based on a given community structure. In social networks, we can
form heterogeneous community structures for the same group of users according
to different metrics. Therefore, how to quantify the diversity based on
multiple community structures is an interesting question. In this paper, we
propose the Composite Community-Aware Diversified IM (CC-DIM) problem, which
aims at selecting a seed set to maximize the influence spread and the composite
diversity over all possible community structures under consideration. To
address the NP-hardness of CC-DIM problem, we adopt the technique of reverse
influence sampling and design a random Generalized Reverse Reachable (G-RR) set
to estimate the objective function. The composition of a random G-RR set is
much more complex than the RR set used for the IM problem, which will lead to
inefficiency of traditional sampling-based approximation algorithms. Because of
this, we further propose a two-stage algorithm, Generalized HIST (G-HIST). It
can not only return a approximate solution with at least
probability, but also improve the efficiency of sampling and ease
the difficulty of searching by significantly reducing the average size of G-RR
sets. Finally, we evaluate our G-HIST on real datasets against existing
algorithms. The experimental results show the effectiveness of our proposed
algorithm and its superiority over other baseline algorithms.Comment: 15 page
Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification
Cloth-changing person reidentification (ReID) is a newly emerging research
topic that is aimed at addressing the issues of large feature variations due to
cloth-changing and pedestrian view/pose changes. Although significant progress
has been achieved by introducing extra information (e.g., human contour
sketching information, human body keypoints, and 3D human information),
cloth-changing person ReID is still challenging due to impressionable
pedestrian representations. Moreover, human semantic information and pedestrian
identity information are not fully explored. To solve these issues, we propose
a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing
person ReID, where the human semantic is fully utilized and the identity is
unchangeable to guide collaborative learning. First, we design a novel clothing
attention degradation stream to reasonably reduce the interference caused by
clothing information where clothing attention and mid-level collaborative
learning are employed. Second, we propose a human semantic attention and body
jigsaw stream to highlight the human semantic information and simulate
different poses of the same identity. In this way, the extraction features not
only focus on human semantic information that is unrelated to the background
but also are suitable for pedestrian pose variations. Moreover, a pedestrian
identity enhancement stream is further proposed to enhance the identity
importance and extract more favorable identity robust features. Most
importantly, all these streams are jointly explored in an end-to-end unified
framework, and the identity is utilized to guide the optimization. Extensive
experiments on five public clothing person ReID datasets demonstrate that the
proposed IGCL significantly outperforms SOTA methods and that the extracted
feature is more robust, discriminative, and clothing-irrelevant
Remote Sensing Evidence for Significant Variations in the Global Gross Domestic Product during the COVID-19 Epidemic
Coronavirus disease 2019 (COVID-19) has been spreading rapidly and is still threatening human health currently. A series of measures for restraining epidemic spreading has been adopted throughout the world, which seriously impacted the gross domestic product (GDP) globally. However, details of the changes in the GDP and its spatial heterogeneity characteristics on a fine scale worldwide during the pandemic are still uncertain. We designed a novel scheme to simulate a 0.1° × 0.1° resolution grid global GDP map during the COVID-19 pandemic. Simulated nighttime-light remotely sensed data (SNTL) was forecasted via a GM(1, 1) model under the assumption that there was no COVID-19 epidemic in 2020. We constructed a geographically weighted regression (GWR) model to determine the quantitative relationship between the variation of nighttime light (ΔNTL) and the variation of GDP (ΔGDP). The scheme can detect and explain the spatial heterogeneity of ΔGDP at the grid scale. It is found that a series of policies played an obvious role in affecting GDP. This work demonstrated that the global GDP, except for in a few countries, represented a remarkably decreasing trend, whereas the ΔGDP exhibited significant differences
Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic
In human conversations, individuals can indicate relevant regions within a
scene while addressing others. In turn, the other person can then respond by
referring to specific regions if necessary. This natural referential ability in
dialogue remains absent in current Multimodal Large Language Models (MLLMs). To
fill this gap, this paper proposes an MLLM called Shikra, which can handle
spatial coordinate inputs and outputs in natural language. Its architecture
consists of a vision encoder, an alignment layer, and a LLM. It is designed to
be straightforward and simple, without the need for extra vocabularies,
position encoder, pre-/post-detection modules, or external plug-in models. All
inputs and outputs are in natural language form. Referential dialogue is a
superset of various vision-language (VL) tasks. Shikra can naturally handle
location-related tasks like REC and PointQA, as well as conventional VL tasks
such as Image Captioning and VQA. Experimental results showcase Shikra's
promising performance. Furthermore, it enables numerous exciting applications,
like providing mentioned objects' coordinates in chains of thoughts and
comparing user-pointed regions similarities. Our code and model are accessed at
https://github.com/shikras/shikra
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