Credit Risk Measurement with Wrong Way Risk

I will start with introducing the corporate bond and several important components of it. The existing credit risk model can be categorized into two groups — Structural (Firm Value) Model and Reduced-form (Intensity-based) Models, followed by the risk measure and the risk measure—Value at Risk and its computation. Then I applied the previously introduced material to the given portfolio to calculate its credit VaR using two methods, S-critical and the Monte Carlo simulation. Finally, I present some advanced credit risk models with stochastic interest rate

SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections

In this work we propose a deep learning model, i.e., SAPI, to predict vehicle trajectories at intersections. SAPI uses an abstract way to represent and encode surrounding environment by utilizing information from real-time map, right-of-way, and surrounding traffic. The proposed model consists of two convolutional network (CNN) and recurrent neural network (RNN)-based encoders and one decoder. A refiner is proposed to conduct a look-back operation inside the model, in order to make full use of raw history trajectory information. We evaluate SAPI on a proprietary dataset collected in real-world intersections through autonomous vehicles. It is demonstrated that SAPI shows promising performance when predicting vehicle trajectories at intersection, and outperforms benchmark methods. The average displacement error(ADE) and final displacement error(FDE) for 6-second prediction are 1.84m and 4.32m respectively. We also show that the proposed model can accurately predict vehicle trajectories in different scenarios

Adversarial for Sequential Recommendation Walking in the Multi-Latent Space

Recently, sequential recommendation plays a critical role in our daily life, since it serves as personalized information filters to dis- cover popular users’ preferred products over time. Due to the success of the adversarial learning, a mass of research efforts start to strengthen sequential recommendation by the adversarial learning, which is able to learn complex underlying data distribution. However, existing adversarial sequential recommendation methods suffer from mode collapse and unexplained prediction. To boost the diversity, performance, and interpretability of sequential recommendation system, we propose a novel temporal-aware adversarial framework, namely TSRGAN. In principle, the input of traditional adversarial-based recommendation system is a noise variable sampled from normal distribution. We argue that it is hard to generate an item cover complex users’ preferences(e.g. price, brand and item style) using a single latent space. Therefore, our model employs multiple latent space to generate plausible item which matches user’ preferences from multiple views(e.g. Movie style, Movie release date). Besides, previous adversarial-based recommenders focus on generating active item, but they omits that user’s favour is not in- variable. With GANs terminology, the recommenders only will be rewarded when seeking the peak mode, but it neglects minor mode, in other word mode collapse. In order to alleviate this issue, we design a novel diversity reward function and diversify regularization to encourage the model exploring minor mode over time and guarantee generating diversity item with reasonable. Concretely, we propose multiple learnable latent codes to generate item matching user’s preferences from different views, then we leverage the diversity reward signal to shape the distribution of multiple latent space over time. It means that the multiple latent space are sampled form different distribution instead of Gaussian distribution. Such a manipulation of the latent space can be treated as walking from plain distribution latent space to diversity distributions latent space. Further, the reward signal is modified over time, therefore, our methods names "Temporal-aware" adversarial framework. In short, our model has two sequential stages: encode the user’ characteristics and historical behaviours under multiple latent space with the Self Attention-based generator(G), and discriminator(D) try to distinguish the generator’s output item from the ground ruth. Besides, discriminator attempt to apply reward signal to shape the latent space distribution time by time. Extensive experiments demonstrate remarkable performance with interpretability improvement against the state-of-the-art baselines

An Asynchronous LLM Architecture for Event Stream Analysis with Cameras

Event-based cameras, as bio-inspired vision sensors, record intensity changes asynchronously. The Dynamic and Active-pixel Vision Sensor (DAVIS) enhances information diversity by combining a standard camera with an event-based camera. However, current methods analyze event streams synchronously, contradicting their nature and introducing noise. To address this, most approaches accumulate events within a time interval to create synchronous frames, wasting sensitive intensity changes. This paper introduces a novel neural asynchronous approach for event stream analysis. Our method asynchronously extracts dynamic information by leveraging historical motion information and critical features of grayscale frames. Extensive experiments demonstrate our model’s significant improvements over state-of-the-art baselines

Capacity Constrained Influence Maximization in Social Networks

Influence maximization (IM) aims to identify a small number of influential individuals to maximize the information spread and finds applications in various fields. It was first introduced in the context of viral marketing, where a company pays a few influencers to promote the product. However, apart from the cost factor, the capacity of individuals to consume content poses challenges for implementing IM in real-world scenarios. For example, players on online gaming platforms can only interact with a limited number of friends. In addition, we observe that in these scenarios, (i) the initial adopters of promotion are likely to be the friends of influencers rather than the influencers themselves, and (ii) existing IM solutions produce sub-par results with high computational demands. Motivated by these observations, we propose a new IM variant called capacity constrained influence maximization (CIM), which aims to select a limited number of influential friends for each initial adopter such that the promotion can reach more users. To solve CIM effectively, we design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the $1/2$-approximation ratio. To improve the efficiency, we devise the scalable implementation named RR-OPIM+ with $(1/2-\epsilon)$-approximation and near-linear running time. We extensively evaluate the performance of 9 approaches on 6 real-world networks, and our solutions outperform all competitors in terms of result quality and running time. Additionally, we deploy RR-OPIM+ to online game scenarios, which improves the baseline considerably.Comment: The technical report of the paper entitled 'Capacity Constrained Influence Maximization in Social Networks' in SIGKDD'2

Understand Group Interaction and Cognitive State in Online Collaborative Problem Solving: Leveraging Brain-to-Brain Synchrony Data

The purpose of this study aimed to analyze the process of online collaborative problem solving (CPS) via brain-to-brain synchrony (BS) at the problem-understanding and problem-solving stages. Aiming to obtain additional insights than traditional approaches (survey and observation), BS refers to the synchronization of brain activity between two or more people, as an indicator of interpersonal interaction or common attention. Thirty-six undergraduate students participated. Results indicate the problem-understanding stage showed a higher level of BS than the problem-solving stage. Moreover, the level of BS at the problem-solving stage was significantly correlated with task performance. Groups with all high CPS skill students had the highest level of BS, while some of the mixed groups could achieve the same level of BS. BS is an effective indicator of CPS to group performance and individual interaction. Implications for the online CPS design and possible supports for the process of online CPS activity are also discussed