3,142 research outputs found
Prognostic Outcomes and Risk Factors for Patients with Renal Cell Carcinoma and Venous Tumor Thrombus after Radical Nephrectomy and Thrombectomy: The Prognostic Significance of Venous Tumor Thrombus Level.
IntroductionTo evaluate the prognostic outcomes and risk factors for renal cell carcinoma (RCC) patients with venous tumor thrombus in China.Materials and methodsWe reviewed the clinical information of 169 patients who underwent radical nephrectomy and thrombectomy. Overall and cancer-specific survival rates were analyzed. Univariate and multivariate analyses were used to investigate the potential prognostic factors.ResultsThe median survival time was 63 months. The five-year overall survival and cancer-specific survival rate were 53.6% and 54.4% for all patients. For all patients, significant survival difference was only observed between early (below hepatic vein) and advanced (above hepatic vein) tumor thrombus. However, significant differences existed between both RV/IVC and early/advanced tumor thrombus groups in N0M0 patients. Multivariate analysis demonstrated that higher tumor thrombus level (p = 0.016, RR = 1.58), N (p = 0.013, RR = 2.60), and M (p < 0.001, RR = 4.14) stages and adrenal gland invasion (p = 0.001, RR = 4.91) were the most significant negative prognostic predictors.ConclusionsIn this study, we reported most cases of RCC patients with venous extension in China. We proved that patients with RCC and venous tumor thrombus may have relative promising long-term survival rate, especially those with early tumor thrombus
Duality between the deconfined quantum-critical point and the bosonic topological transition
Recently significant progress has been made in -dimensional conformal
field theories without supersymmetry. In particular, it was realized that
different Lagrangians may be related by hidden dualities, i.e., seemingly
different field theories may actually be identical in the infrared limit. Among
all the proposed dualities, one has attracted particular interest in the field
of strongly-correlated quantum-matter systems: the one relating the easy-plane
noncompact CP model (NCCP) and noncompact quantum electrodynamics (QED)
with two flavors () of massless two-component Dirac fermions. The
easy-plane NCCP model is the field theory of the putative deconfined
quantum-critical point separating a planar (XY) antiferromagnet and a dimerized
(valence-bond solid) ground state, while noncompact QED is the theory for
the transition between a bosonic symmetry-protected topological phase and a
trivial Mott insulator. In this work we present strong numerical support for
the proposed duality. We realize the noncompact QED at a critical point
of an interacting fermion model on the bilayer honeycomb lattice and study it
using determinant quantum Monte Carlo (QMC) simulations. Using stochastic
series expansion QMC, we study a planar version of the - spin
Hamiltonian (a quantum XY-model with additional multi-spin couplings) and show
that it hosts a continuous transition between the XY magnet and the
valence-bond solid. The duality between the two systems, following from a
mapping of their phase diagrams extending from their respective critical
points, is supported by the good agreement between the critical exponents
according to the proposed duality relationships.Comment: 14 pages, 9 figure
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting
Due to the nonstationary nature, the distribution of real-world multivariate
time series (MTS) changes over time, which is known as distribution drift. Most
existing MTS forecasting models greatly suffer from distribution drift and
degrade the forecasting performance over time. Existing methods address
distribution drift via adapting to the latest arrived data or self-correcting
per the meta knowledge derived from future data. Despite their great success in
MTS forecasting, these methods hardly capture the intrinsic distribution
changes, especially from a distributional perspective. Accordingly, we propose
a novel framework temporal conditional variational autoencoder (TCVAE) to model
the dynamic distributional dependencies over time between historical
observations and future data in MTSs and infer the dependencies as a temporal
conditional distribution to leverage latent variables. Specifically, a novel
temporal Hawkes attention mechanism represents temporal factors subsequently
fed into feed-forward networks to estimate the prior Gaussian distribution of
latent variables. The representation of temporal factors further dynamically
adjusts the structures of Transformer-based encoder and decoder to distribution
changes by leveraging a gated attention mechanism. Moreover, we introduce
conditional continuous normalization flow to transform the prior Gaussian to a
complex and form-free distribution to facilitate flexible inference of the
temporal conditional distribution. Extensive experiments conducted on six
real-world MTS datasets demonstrate the TCVAE's superior robustness and
effectiveness over the state-of-the-art MTS forecasting baselines. We further
illustrate the TCVAE applicability through multifaceted case studies and
visualization in real-world scenarios.Comment: 13 pages, 6 figures, submitted to IEEE Transactions on Neural
Networks and Learning Systems (TNNLS
Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective
Performance unfairness among variables widely exists in multivariate time
series (MTS) forecasting models since such models may attend/bias to certain
(advantaged) variables. Addressing this unfairness problem is important for
equally attending to all variables and avoiding vulnerable model biases/risks.
However, fair MTS forecasting is challenging and has been less studied in the
literature. To bridge such significant gap, we formulate the fairness modeling
problem as learning informative representations attending to both advantaged
and disadvantaged variables. Accordingly, we propose a novel framework, named
FairFor, for fairness-aware MTS forecasting. FairFor is based on adversarial
learning to generate both group-independent and group-relevant representations
for the downstream forecasting. The framework first leverages a spectral
relaxation of the K-means objective to infer variable correlations and thus to
group variables. Then, it utilizes a filtering&fusion component to filter the
group-relevant information and generate group-independent representations via
orthogonality regularization. The group-independent and group-relevant
representations form highly informative representations, facilitating to
sharing knowledge from advantaged variables to disadvantaged variables to
guarantee fairness. Extensive experiments on four public datasets demonstrate
the effectiveness of our proposed FairFor for fair forecasting and significant
performance improvement.Comment: 13 pages, 5 figures, accepted by IEEE Transactions on Knowledge and
Data Engineering (TKDE
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