This section provides a comprehensive overview of the CSMV dataset. This extensive time range allows for the inclusion of a diverse set of content, capturing the evolution of sentiments over the course of more than two years. The distribution of labels in our CSMV dataset is shown in Figure 1. In Figure 1a, the opinion labels are distributed as follows: positive - 47%, neutral - 42%, and negative - 11%. Negative comments are clearly in the minority.

In light of this, we introduces a novel research task, M ulti-modal S entiment A nalysis for C omment R esponse of V ideo I nduced( MSA-CRVI), aims to infer opinions and emotions according to comments response to micro video.

MF mechanism consistently performs at least as well as the best prior methods.

In mechanical structures like airplanes, cars and houses, noise is generated and transmitted through vibrations. To take measures to reduce this noise, vibrations need to be simulated with expensive numerical computations. Deep learning surrogate models present a promising alternative to classical numerical simulations as they can be evaluated magnitudes faster, while trading-off accuracy. To quantify such trade-offs systematically and foster the development of methods, we present a benchmark on the task of predicting the vibration of harmonically excited plates. The benchmark features a total of 12,000 plate geometries with varying forms of beadings, material, boundary conditions, load position and sizes with associated numerical solutions. To address the benchmark task, we propose a new network architecture, named Frequency-Query Operator, which predicts vibration patterns of plate geometries given a specific excitation frequency. Applying principles from operator learning and implicit models for shape encoding, our approach effectively addresses the prediction of highly variable frequency response functions occurring in dynamic systems. To quantify the prediction quality, we introduce a set of evaluation metrics and evaluate the method on our vibrating-plates benchmark. Our method outperforms Deep-ONets, Fourier Neural Operators and more traditional neural network architectures and can be used for design optimization.

Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving.

Simultaneous behavioral and electrophysiological recordings call for new methods to reveal the interactions between neural activity and behavior. A milestone would be an interpretable model of the co-variability of spiking activity and behavior across trials. Here, we model a mouse cortical sensory-motor pathway in a tactile detection task reported by licking with a large recurrent spiking neural network (RSNN), fitted to the recordings via gradient-based optimization. We focus specifically on the difficulty to match the trial-to-trial variability in the data. Our solution relies on optimal transport to define a distance between the distributions of generated and recorded trials. The technique is applied to artificial data and neural recordings covering six cortical areas. We find that the resulting RSNN can generate realistic cortical activity and predict jaw movements across the main modes of trial-to-trial variability. Our analysis also identifies an unexpected mode of variability in the data corresponding to task-irrelevant movements of the mouse.

Fossil evidence shows a case of mistaken big cat identity. Breakthroughs, discoveries, and DIY tips sent six days a week. Present-day Japan may see its fair share of bears, but the islands' big cat populations are long gone. Between 129,000 and 11,700 years ago, temporary land bridges allowed the ancient predators to migrate between mainland Asia and the islands. Paleobiologists have long believed tigers were the primary cats to make this trek, but recently analyzed evidence published in the suggests a different timeline.

Unfortunately, choosing the best OPE algorithm for each task and domain is still unclear.