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The diffusion model, a new generative modeling paradigm, has achieved significant success in generating images, audio, video, and text. It has been adapted for sequence-to-sequence text generation (Seq2Seq) through DiffuSeq, termed S2S Diffusion. Existing S2S-Diffusion models predominantly rely on fixed or hand-crafted rules to schedule noise during the diffusion and denoising processes. However, these models are limited by non-contextualized noise, which fails to fully consider the characteristics of Seq2Seq tasks. In this paper, we propose the Meta-DiffuB framework - a novel scheduler-exploiter S2S-Diffusion paradigm designed to overcome the limitations of existing S2S-Diffusion models. We employ Meta-Exploration to train an additional scheduler model dedicated to scheduling contextualized noise for each sentence. Our exploiter model, an S2S-Diffusion model, leverages the noise scheduled by our scheduler model for updating and generation. Meta-DiffuB achieves state-of-the-art performance compared to previous S2S-Diffusion models and fine-tuned pre-trained language models (PLMs) across four Seq2Seq benchmark datasets. We further investigate and visualize the impact of Meta-DiffuB's noise scheduling on the generation of sentences with varying difficulties. Additionally, our scheduler model can function as a "plug-and-play" model to enhance DiffuSeq without the need for fine-tuning during the inference stage.

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented and analyzed under several instances of the problem, including: use of full three-dimensional velocity-models, cases in which the earthquake-source location is unknown, as well as moment tensor inversion under model misspecification

Based on the spectral decomposition through a code of PrepSpec, the light curves (spanning 6.5 years in the observed frame) of the broad-line Balmer decrement, i.e., the flux ratio of the broad \ha to the broad \hb line, are calculated for a sample of 44 Sloan Digital Sky Survey reverberation-mapped quasars ($z<0.53$). It is found that the logarithm of the mean broad-line Balmer decrement is 0.62 with a standard deviation of 0.15 dex. The relations between the mean Balmer decrement and the SMBH accretion properties (the luminosity, black hole mass, Eddington ratio, accretion rate) are investigated and no obvious correlations are found. It is found that there are 27 quasars ($61\%$) showing strong negative correlations between the Balmer decrement variance and the continuum variance, i.e., the Balmer decrement would be smaller with larger continuum flux. Assuming that the dust obscuration leads to the variance in the Balmer decrement and the continuum, an expected slope is $-1/3$, which is not consistent with most of measured slopes. Using the interpolated cross-correlation function, the time delays between the inverse Balmer decrement and the continuum are measured for 14 quasars with the maximum correlation coefficient larger the 0.6. It suggests that the size corresponding to the Balmer decrement lag extends from the BLR size to the torus size., Comment: 10 pages, 10 figures, Accepted for publication in MNRAS

Single atoms are interesting candidates for studying quantum optics and quantum information processing. Recently, trapping and manipulation of single atoms using tight optical dipole traps have generated considerable interest. Here we report an experimental investigation of the dynamics of atoms in a modified optical dipole trap with a backward propagating dipole trap beam, where a change in the two-atom collision rate by six times has been achieved. The theoretical model presented gives a prediction of high probabilities of few-atom loading rates under proper experimental conditions. This work provides an alternative approach to the control of the few-atom dynamics in a dipole trap and the study of the collective quantum optical effects of a few atoms., Comment: 5 pages, 3 figures

We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophysical features within the state of Nevada, USA are used to define geothermal potential across a broad region. We have available a relatively small set of positive training sites (known resources or active power plants) and negative training sites (known drill sites with unsuitable geothermal conditions) and use these to constrain and optimize artificial neural networks for this classification task. The main objective is to predict the geothermal resource potential at unknown sites within a large geographic area where the defining features are known. These predictions could be used to target promising areas for further detailed investigations. We describe the evolution of our work from defining a specific neural network architecture to training and optimization trials. Upon analysis we expose the inevitable problems of model variability and resulting prediction uncertainty. Finally, to address these problems we apply the concept of Bayesian neural networks, a heuristic approach to regularization in network training, and make use of the practical interpretation of the formal uncertainty measures they provide., Comment: 27 pages, 12 figures