Proactive dialogue systems aim to empower chatbots with the capability of leading conversations towards specific targets, thereby enhancing user engagement and service autonomy. Existing systems typically target pre-defined keywords or entities, neglecting user attributes and preferences implicit in dialogue history, hindering the development of long-term user intimacy. To address these challenges, we take a radical step towards building a more human-like conversational agent by integrating proactive dialogue systems with long-term memory into a unified framework. Specifically, we define a novel task named Memory-aware Proactive Dialogue (MapDia). By decomposing the task, we then propose an automatic data construction method and create the first Chinese Memory-aware Proactive Dataset (ChMapData). Furthermore, we introduce a joint framework based on Retrieval Augmented Generation (RAG), featuring three modules: Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation, designed to steer dialogues towards relevant historical topics at the right time. The effectiveness of our dataset and models is validated through both automatic and human evaluations. We release the open-source framework and dataset at https://github.com/FrontierLabs/MapDia.

Semantic communication, as a revolutionary communication architecture, is considered a promising novel communication paradigm. Unlike traditional symbol-based error-free communication systems, semantic-based visual communication systems extract, compress, transmit, and reconstruct images at the semantic level. However, widely used image similarity evaluation metrics, whether pixel-based MSE or PSNR or structure-based MS-SSIM, struggle to accurately measure the loss of semantic-level information of the source during system transmission. This presents challenges in evaluating the performance of visual semantic communication systems, especially when comparing them with traditional communication systems. To address this, we propose a semantic evaluation metric -- SeSS (Semantic Similarity Score), based on Scene Graph Generation and graph matching, which shifts the similarity scores between images into semantic-level graph matching scores. Meanwhile, semantic similarity scores for tens of thousands of image pairs are manually annotated to fine-tune the hyperparameters in the graph matching algorithm, aligning the metric more closely with human semantic perception. The performance of the SeSS is tested on different datasets, including (1)images transmitted by traditional and semantic communication systems at different compression rates, (2)images transmitted by traditional and semantic communication systems at different signal-to-noise ratios, (3)images generated by large-scale model with different noise levels introduced, and (4)cases of images subjected to certain special transformations. The experiments demonstrate the effectiveness of SeSS, indicating that the metric can measure the semantic-level differences in semantic-level information of images and can be used for evaluation in visual semantic communication systems.

Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity. However, existing sparse Bayesian learning mainly employs Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to significant performance degradation for sparse Bayesian learning method. The goal of this paper is to propose a new robust implementation for sparse Bayesian learning, so that robustness and sparseness can be realized simultaneously. Motivated by the great robustness of maximum correntropy criterion (MCC), we proposed an integration of MCC into the sparse Bayesian learning regime. To be specific, we derived the explicit error assumption inherent in the MCC and then leveraged it for the likelihood function. Meanwhile, we used the automatic relevance determination (ARD) technique for the sparse prior distribution. To fully evaluate the proposed method, a synthetic dataset and a real-world muscle activity reconstruction task with two different brain modalities were employed. Experimental results showed that our proposed sparse Bayesian correntropy learning framework improves significantly the robustness in a noisy regression task. The proposed method can realize higher correlation coefficient and lower root mean squared error in the real-world muscle activity reconstruction tasks. Sparse Bayesian correntropy learning provides a powerful tool for neural decoding which can promote the development of brain-computer interfaces.

The mathematical concept of a tensor could be broadly explained in the following way: If a scalar has the lowest dimensionality and is followed by a vector and then by a matrix. A tensor would be the next object in the line. Scalar, vectors and matrices are all tensors of rank 0, 1 and 2 respectively. Tensors are simply a generalization of the concepts we have seen so far. At first, computation in TensorFlow may seem needlessly complicated.

First, we're going to take a look at the tensor object type. Then we'll have a graphical understanding of TensorFlow to define computations. Finally, we'll run the graphs with sessions, showing how to substitute intermediate values. In TensorFlow, data isn't stored as integers, floats, or strings. These values are encapsulated in an object called a tensor, a fancy term for multidimensional arrays.

In the previous article, we studied Linear Regression. One thing that I believe is that if we can correlate anything with us or our life, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.

Click here to skip the intro and dive right in! When I started the residency program in the summer of 2017, I had a lot of experience programming, and a good understanding of machine learning, but I had never used Tensorflow before. I figured that given my background I'd be able to pick it up quickly. To my surprise, the learning curve was fairly steep, and even months into the residency, I would occasionally find myself confused about how to turn ideas into Tensorflow code. I'm writing this blog post as a message-in-a-bottle to my former self: it's the introduction that I wish I had been given before starting on my journey. Hopefully, it will also be a helpful resource for others. In the three years since its release, Tensorflow has cemented itself as a cornerstone of the deep learning ecosystem.

We start by importing TensorFlow as tf. Then we print the version of TensorFlow that we are using. We are using TensorFlow 1.5.0. In this video, we're going to use tf.reshape to change the shape of a TensorFlow tensor as long as the number of elements stay the same. We will do three examples to show how reshape works. Let's start out with an initial TensorFlow constant tensor shaped 2x3x4 with numerical integer values between 1 and 24, all of whom have the data type of int32.

First, we're going to take a look at the tensor object type. Then we'll have a graphical understanding of TensorFlow to define computations. Finally, we'll run the graphs with sessions, showing how to substitute intermediate values. In TensorFlow, data isn't stored as integers, floats, or strings. These values are encapsulated in an object called a tensor, a fancy term for multidimensional arrays.

One of the popular ways is by using stochastic gradient decent (SGD). SGD has a hyper parameter called learning rate or step size that determines the scale of walking in negative direction of gradient. We use an exponentially decaying learning rate: lr tf.constant(.001,