210 research outputs found
Equal-time kinetic equations in a rotational field
We investigate quantum kinetic theory for a massive fermion system under a
rotational field. From the Dirac equation in curved space we derive the
complete set of kinetic equations for the spin components of the covariant and
equal-time Wigner functions. While the particles are no longer on a mass shell
in general case due to the rotation-spin coupling, there are always only two
independent components, which can be taken as the number and spin densities.
With the help from the off-shell constraint we obtain the closed transport
equations for the two independent components in classical limit and at quantum
level. The classical rotation-orbital coupling controls the dynamical evolution
of the number density, but the quantum rotation-spin coupling explicitly
changes the spin density.Comment: 12 page
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions
Large Language Models (LLMs) demonstrate impressive reasoning ability and the
maintenance of world knowledge not only in natural language tasks, but also in
some vision-language tasks such as open-domain knowledge-based visual question
answering (OK-VQA). As images are invisible to LLMs, researchers convert images
to text to engage LLMs into the visual question reasoning procedure. This leads
to discrepancies between images and their textual representations presented to
LLMs, which consequently impedes final reasoning performance. To fill the
information gap and better leverage the reasoning capability, we design a
framework that enables LLMs to proactively ask relevant questions to unveil
more details in the image, along with filters for refining the generated
information. We validate our idea on OK-VQA and A-OKVQA. Our method
continuously boosts the performance of baselines methods by an average gain of
2.15% on OK-VQA, and achieves consistent improvements across different LLMs.Comment: Accepted to EMNLP2023 Finding
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The prophages of Citrobacter rodentium represent a conserved family of horizontally-acquired mobile genetic elements associated with enteric evolution towards pathogenicity
Prophage mediated horizontal gene transfer (HGT) plays a key role in the evolution of bacteria, enabling access to new environmental niches, including pathogenicity. Citrobacter rodentium is a host-adapted intestinal mouse pathogen and important model organism for attaching and effacing (A/E) pathogens including the clinically significant enterohaemorrhagic (EHEC) and enteropathogenic (EPEC) Escherichia coli. Despite containing ten prophage genomic regions, including an active temperate phage, ΦNP, little was known regarding the nature of C. rodentium prophages in the bacterium’s evolution towards pathogenicity. In this study, our characterization of ΦNP led to the discovery of a second, fully functional temperate phage, named ΦSM. We identify the bacterial host-receptor for both phages as lipopolysaccharide (LPS). ΦNP and ΦSM are likely important mediators of HGT in C. rodentium. Bioinformatic analysis of the ten prophage regions reveals cargo genes encoding known virulence factors, including several Type III secretion system (T3SS) effectors. C. rodentium prophages are conserved across a wide range of pathogenic enteric bacteria, including EPEC and EHEC as well as pathogenic strains of Salmonella enterica, Shigella boydii, and Klebsiella pneumoniae. Phylogenetic analysis of core enteric backbone genes compared against prophage evolutionary models suggests that these prophages represent an important, conserved family of horizontally acquired enteric-associated pathogenicity determinants. In addition to highlighting the transformative role of bacteriophage mediated HGT in C. rodentium’s evolution towards pathogenicity, these data suggest that the examination of conserved families of prophages in other pathogenic bacteria and disease outbreaks might provide deeper evolutionary and pathological insights otherwise obscured by more classical analysis.BBSRC and China Scholarship Council and the Cambridge Commonwealth, European, and International Trust
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Low-rank tensor decomposition and completion have attracted significant
interest from academia given the ubiquity of tensor data. However, the low-rank
structure is a global property, which will not be fulfilled when the data
presents complex and weak dependencies given specific graph structures. One
particular application that motivates this study is the spatiotemporal data
analysis. As shown in the preliminary study, weakly dependencies can worsen the
low-rank tensor completion performance. In this paper, we propose a novel
low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework
by introducing the -norm penalty and Graph Laplacian penalty to model
the weakly dependency on graph. We further propose an efficient optimization
algorithm based on the Block Coordinate Descent for efficient estimation. A
case study based on the metro passenger flow data in Hong Kong is conducted to
demonstrate improved performance over the regular tensor completion methods.Comment: Accepted at AAAI 202
Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding
We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our
solution is built with two new techniques, namely Lightweight Attentional
Feature Fusion (LAFF) for combining diverse visual / textual features and
Bidirectional Negation Learning (BNL) for addressing queries that contain
negation cues. In particular, LAFF performs feature fusion at both early and
late stages and at both text and video ends to exploit diverse (off-the-shelf)
features. Compared to multi-head self attention, LAFF is much more compact yet
more effective. Its attentional weights can also be used for selecting fewer
features, with the retrieval performance mostly preserved. BNL trains a
negation-aware video retrieval model by minimizing a bidirectionally
constrained loss per triplet, where a triplet consists of a given training
video, its original description and a partially negated description. For video
feature extraction, we use pre-trained CLIP, BLIP, BEiT, ResNeXt-101 and irCSN.
As for text features, we adopt bag-of-words, word2vec, CLIP and BLIP. Our
training data consists of MSR-VTT, TGIF and VATEX that were used in our
previous participation. In addition, we automatically caption the V3C1
collection for pre-training. The 2022 edition of the TRECVID benchmark has
again been a fruitful participation for the RUCMM team. Our best run, with an
infAP of 0.262, is ranked at the second place teamwise
Distribution and Determinants of Correlation between PM2.5 and O3 in China Mainland: Dynamitic simil-Hu Lines
In recent years, China has made great efforts to control air pollution.
During the governance process, it is found that fine particulate matter (PM2.5)
and ozone (O3) change in the same trend among some areas and the opposite in
others, which brings some difficulties to take measures in a planned way.
Therefore, this study adopted multi-year and large-scale air quality data to
explore the distribution of correlation between PM2.5 and O3, and proposed a
concept called dynamic similar hu lines to replace the single fixed division in
the previous research. Furthermore, this study discussed the causes of
distribution patterns quantitatively with geographical detector and random
forest. The causes included natural factors and anthropogenic factors. And
these factors could be divided into three parts according to the
characteristics of spatial distribution: broadly changing with longitude,
changing with latitude, and having local characteristics. Overall, regions with
relatively more densely population, higher GDP, lower altitude, higher
humidity, higher atmospheric pressure, higher surface temperature, less
sunshine hours and more accumulated precipitation often corresponds to positive
correlation coefficient between PM2.5 and O3, no matter in which season. The
parts with opposite conditions that mentioned above are essentially negative
correlation coefficient. And what's more, humidity, global surface temperature,
air temperature and accumulated precipitation are four decisive factors to form
the distribution of correlation between PM2.5 and O3. In general, collaborative
governance of atmospheric pollutants should consider particular time and space
background and also be based on the local actual socio-economic situations,
geography and geomorphology, climate and meteorology and other comprehensive
factors.Comment: Our research group have decided to withdraw this preprin
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
Passenger clustering based on trajectory records is essential for
transportation operators. However, existing methods cannot easily cluster the
passengers due to the hierarchical structure of the passenger trip information,
including multiple trips within each passenger and multi-dimensional
information about each trip. Furthermore, existing approaches rely on an
accurate specification of the clustering number to start. Finally, existing
methods do not consider spatial semantic graphs such as geographical proximity
and functional similarity between the locations. In this paper, we propose a
novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can
preserve the hierarchical structure of the multi-dimensional trip information
and cluster them in a unified one-step manner with the ability to determine the
number of clusters automatically. The spatial graphs are utilized in community
detection to link the semantic neighbors. We further propose a tensor version
of Collapsed Gibbs Sampling method with a minimum cluster size requirement. A
case study based on Hong Kong metro passenger data is conducted to demonstrate
the automatic process of cluster amount evolution and better cluster quality
measured by within-cluster compactness and cross-cluster separateness. The code
is available at https://github.com/bonaldli/TensorDPMM-G.Comment: Accepted in ACM SIGSPATIAL 2023. arXiv admin note: substantial text
overlap with arXiv:2306.1379
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