1,859 research outputs found
(E)-N-Butyl-3-(3,4-dihyÂdroxyÂphenÂyl)acrylÂamide hemihydrate
In the title compound, C13H17NO3·0.5H2O, a new caffeic acid amide derivative, the solvent water molÂecule lies on a twofold axis and the terminal ethyl group appears disordered with occupancy factors of 0.525 (6) and 0.475 (6). The benzene ring makes an angle of 17.3 (2)° with the C=C—C—O linker. The presence of an ethylÂenic spacer in the caffeic acid amide molÂecule allows the formation of a conjugated system, strongly stabilized through Ï€-electron delocalization. The C=C double bond in the linker is trans, similar to those previously reported in caffeic esters. The crystal is stabilized by O—H⋯O, N—H⋯O and C—H⋯O hydrogen bonds. The molÂecules of the caffeic acid amide form a supermolecular planar structure through O—H⋯O hydrogen bonds between a hyÂdroxy group of one caffeic acid molÂecule and a carbonyl O atom of another. These planes interÂact via C—H⋯O, O—H⋯O and N—H⋯O hydrogen bonds to form a three-dimensional network
Rotation of Polarization Angle in Gamma-Ray Burst Prompt Phase\uppercase\expandafter{\romannumeral2}. The Influence of The Parameters
In addition to the light curve and energy spectrum, polarization is also
important for the study of Gamma-ray burst (GRB) prompt emission. Rotation of
the polarization angle (PA) with time will cause depolarization of the
time-integrated polarization degree. However, it is rarely studied before.
Here, we use the magnetic reconnection model with a large-scale ordered aligned
magnetic field in the emitting region to study the influence of the key
parameters on the PA rotations. We find that half-opening angle of the jet
, the observational angle , and the bulk Lorentz factor
all have significant impacts on the PA rotations. For a fixed
value ( is the normalization factor of
), regardless of concrete and values, PA
rotation within (PA) remains roughly unchanged for a
value. As value
increases, the range for PA becomes smaller. The
most significant PA rotation with PA will
happen when and . For the
top-hat jet, observations of the PA rotation within will imply a
slightly off-axis observation.Comment: 9 pages, 7 figures, submitte
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
Uncertainty estimation is an essential step in the evaluation of the
robustness for deep learning models in computer vision, especially when applied
in risk-sensitive areas. However, most state-of-the-art deep learning models
either fail to obtain uncertainty estimation or need significant modification
(e.g., formulating a proper Bayesian treatment) to obtain it. Most previous
methods are not able to take an arbitrary model off the shelf and generate
uncertainty estimation without retraining or redesigning it. To address this
gap, we perform a systematic exploration into training-free uncertainty
estimation for dense regression, an unrecognized yet important problem, and
provide a theoretical construction justifying such estimations. We propose
three simple and scalable methods to analyze the variance of outputs from a
trained network under tolerable perturbations: infer-transformation,
infer-noise, and infer-dropout. They operate solely during inference, without
the need to re-train, re-design, or fine-tune the model, as typically required
by state-of-the-art uncertainty estimation methods. Surprisingly, even without
involving such perturbations in training, our methods produce comparable or
even better uncertainty estimation when compared to training-required
state-of-the-art methods.Comment: 18 pages, 13 figure
Emergent order in the spin-frustrated system DyxTb2-xTi2O7 studied by ac susceptibility measurements
We report the a.c. susceptibility study of Dy_xTb_{2-x}Ti_2O_7 with x in [0,
2]. In addition to the single-ion effect at Ts (single-ion effect peak
temperature) corresponding to the Dy3+ spins as that in spin ice Dy_2Ti_2O_7
and a possible spin freezing peak at Tf (Tf < 3 K), a new peak associated with
Tb^{3+} is observed in at nonzero magnetic field with a
characteristic temperature T^* (Tf < T^* < Ts). T^* increases linearly with x
in a wide composition range (0 < x < 1.5 at 5 kOe). Both application of a
magnetic field and increasing doping with Dy3+ enhance T^*. The T^* peak is
found to be thermally driven with an unusually large energy barrier as
indicated from its frequency dependence. These effects are closely related to
the crystal field levels, and the underlying mechanism remains to be
understood.Comment: 7 pages, 5 figure
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
Existing methods, such as concept bottleneck models (CBMs), have been
successful in providing concept-based interpretations for black-box deep
learning models. They typically work by predicting concepts given the input and
then predicting the final class label given the predicted concepts. However,
(1) they often fail to capture the high-order, nonlinear interaction between
concepts, e.g., correcting a predicted concept (e.g., "yellow breast") does not
help correct highly correlated concepts (e.g., "yellow belly"), leading to
suboptimal final accuracy; (2) they cannot naturally quantify the complex
conditional dependencies between different concepts and class labels (e.g., for
an image with the class label "Kentucky Warbler" and a concept "black bill",
what is the probability that the model correctly predicts another concept
"black crown"), therefore failing to provide deeper insight into how a
black-box model works. In response to these limitations, we propose
Energy-based Concept Bottleneck Models (ECBMs). Our ECBMs use a set of neural
networks to define the joint energy of candidate (input, concept, class)
tuples. With such a unified interface, prediction, concept correction, and
conditional dependency quantification are then represented as conditional
probabilities, which are generated by composing different energy functions. Our
ECBMs address both limitations of existing CBMs, providing higher accuracy and
richer concept interpretations. Empirical results show that our approach
outperforms the state-of-the-art on real-world datasets.Comment: Accepted by ICLR 202
Subgraph Frequency Distribution Estimation using Graph Neural Networks
Small subgraphs (graphlets) are important features to describe fundamental
units of a large network. The calculation of the subgraph frequency
distributions has a wide application in multiple domains including biology and
engineering. Unfortunately due to the inherent complexity of this task, most of
the existing methods are computationally intensive and inefficient. In this
work, we propose GNNS, a novel representational learning framework that
utilizes graph neural networks to sample subgraphs efficiently for estimating
their frequency distribution. Our framework includes an inference model and a
generative model that learns hierarchical embeddings of nodes, subgraphs, and
graph types. With the learned model and embeddings, subgraphs are sampled in a
highly scalable and parallel way and the frequency distribution estimation is
then performed based on these sampled subgraphs. Eventually, our methods
achieve comparable accuracy and a significant speedup by three orders of
magnitude compared to existing methods.Comment: accepted by KDD 2022 Workshop on Deep Learning on Graph
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