(E)-N-Butyl-3-(3,4-dihy­droxy­phen­yl)acryl­amide hemihydrate

In the title compound, C13H17NO3·0.5H2O, a new caffeic acid amide derivative, the solvent water mol­ecule 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 ethyl­enic spacer in the caffeic acid amide mol­ecule allows the formation of a conjugated system, strongly stabilized through π-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 mol­ecules of the caffeic acid amide form a supermolecular planar structure through O—H⋯O hydrogen bonds between a hy­droxy group of one caffeic acid mol­ecule and a carbonyl O atom of another. These planes inter­act 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 $\theta_{j}$, the observational angle $\theta_{V}$, and the bulk Lorentz factor $\Gamma$ all have significant impacts on the PA rotations. For a fixed $\theta_{j}\Gamma_{0}$ value ($\Gamma_{0}$ is the normalization factor of $\Gamma$), regardless of concrete $\theta_{j}$ and $\Gamma_{0}$ values, PA rotation within $T_{90}$ ($\triangle$PA) remains roughly unchanged for a $q\equiv\theta_{V}/\theta_{j}$ value. As $\theta_{j}\Gamma_{0}$ value increases, the $q$ range for $\triangle$PA$>10^{\circ}$ becomes smaller. The most significant PA rotation with $\triangle$PA$\thicksim90^{\circ}$ will happen when $\theta_{j}\Gamma_{0}\thicksim100$ and $1.1\leq q\leq1.2$. For the top-hat jet, observations of the PA rotation within $T_{90}$ 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 $\chi_{ac}(T)$ 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