Social conflict is a survival mechanism yielding both normal and pathological behaviors. Tounderstand its underlying principles, we collected behavioral and whole-brain neural data from mice advancing through stages of social conflict.

In this paper, we tackled just the first one in the list to show the effectiveness of9 ouralgorithm. Weagree that computations aresimple, i.e., elegant,once the18 aforementioned requirements have been elicited. Eliciting them, however,is definitely non-trivial and has not been19 explored in the literature so far for expectations. Our circuits are expressive enough to model larger datasets24 (see our answer to R#1.2) and learning them would scale: in manycases it is easier to learn aLC than aneural net25 (e.g., see [3]). 3. Approximate inference alternatives. Whenever we are able to compute expectations exactly for26 regression (Thm 1), we might want to consider approximations only to speed computations.

For our synthetic datasets, we conduct two additional rounds of instruction evolution to increase complexity. As shown in Figure 3, the scores of instructions across the three datasets consistently increase.

In this section, we first provide model parameters used for training the attack GANs. We then provide sample images from each cluster/class for each of the models, along with the generated noise using ourGAN models. In this section, we provide additional details for the defense approaches considered in this paper. B.1 RobustDeepClustering We provide hyperparameter values (Table 6) for training the GAN network for RUC, along with confusion matrices (Figures 37 - 39) and adversarial samples (Figures 40 - 42) obtained via our attack. Then, in Table 8 we provide the actual values used for generating the injection/detection bar plot figureinthemaintext.

Quantitative imaging methods, such as magnetic resonance fingerprinting (MRF), aim to extract interpretable pathology biomarkers by estimating biophysical tissue parameters from signal evolutions. However, the pattern-matching algorithms or neural networks used in such inverse problems often lack principled uncertainty quantification, which limits the trustworthiness and transparency, required for clinical acceptance. Here, we describe a physics-structured variational autoencoder (PS-VAE) designed for rapid extraction of voxelwise multi-parameter posterior distributions. Our approach integrates a differentiable spin physics simulator with self-supervised learning, and provides a full covariance that captures the inter-parameter correlations of the latent biophysical space. The method was validated in a multi-proton pool chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) molecular MRF study, across in-vitro phantoms, tumor-bearing mice, healthy human volunteers, and a subject with glioblastoma. The resulting multi-parametric posteriors are in good agreement with those calculated using a brute-force Bayesian analysis, while providing an orders-of-magnitude acceleration in whole brain quantification. In addition, we demonstrate how monitoring the multi-parameter posterior dynamics across progressively acquired signals provides practical insights for protocol optimization and may facilitate real-time adaptive acquisition.

Birthing a litter with several fathers may help when food is scarce. Breakthroughs, discoveries, and DIY tips sent six days a week. If a female house mouse mates with multiple male house mice, her litter could have multiple fathers. Polyandry, as this mating practice is called, is common for various species. Yet scientists are still investigating its purpose and the potential benefits of birthing half siblings within the same litter.