Predicting online video popularity faces a critical challenge: prediction drift, where models trained on historical data rapidly degrade due to evolving viral trends and user behaviors. To address this temporal distribution shift, we propose an Anchored Multi-modal Clustering and Feature Generation (AMCFG) framework that discovers temporally-invariant patterns across data distributions. Our approach employs multi-modal clustering to reveal content structure, then leverages Large Language Models (LLMs) to generate semantic Anchor Features-high-level concepts such as audience demographics, content themes, and engagement patterns-that transcend superficial trend variations. These semantic anchors, combined with cluster-derived statistical features, enable prediction based on stable principles rather than ephemeral signals. Experiments demonstrate that AMCFG significantly enhances both predictive accuracy and temporal robustness, achieving superior performance on out-of-distribution data and providing a viable solution for real-world video popularity prediction.

2023 ICCAD CAD Contest Problem B is an extended problem from 2022 ICCAD CAD Contest Problem B [1]–[2] for addressing more complex real world 3D implementation constraints. In the chiplet era, the benefits from multiple factors can be observed by splitting a large single die into multiple small, chiplet, dies. By having the multiple small, chiplet, dies with die-to-die (D2D) vertical connections, the benefits including: 1) better yield, 2) better timing/performance, 3) better cost, and 4) faster time-to-market. How to do the netlist partitioning, cell placement for both the standard cells and the macros in each of the chiplet dies, and nevertheless how to determine the location of the D2D inter-connection terminals becomes a new topic. To address this chiplet era physical implementation problem, ICCAD-2023 contest encourages the research in the techniques of multi-die netlist partitioning and placement of both standard cells and macros with D2D vertical connections. We provided (i) a set of benchmarks and (ii) an evaluation metric of multiple objectives that facilitate contestants to develop, test, and evaluate their new algorithms.

Heart rate variability (HRV) has been used in assessing mental workload (MW) level. Compared with ECG, photoplethysmogram (PPG) provides convenient in assessing MW with wearable devices, which is more suitable for daily usage. However, PPG collected by smartwatches are prone to suffer from artifacts. Those signal corruptions cause invalid Inter-beat Intervals (IBI), making it challenging to evaluate the HRV feature. Hence, the PPG-based MW assessment system is difficult to obtain a sustainable and reliable assessment of MW. In this paper, we propose a pre- and post- processing technique, called outlier removal and uncertainty estimation, respectively, to reduce the negative influences of invalid IBIs. The proposed method helps to acquire accurate HRV features and evaluate the reliability of incoming IBIs, rejecting possibly misclassified data. We verified our approach in two open datasets, which are CLAS and MAUS. Experiment results show proposed method achieved higher accuracy (66.7% v.s. 74.2%) and lower variance (11.3% v.s. 10.8%) among users, which has comparable performance to an ECG-based MW system.

In the chiplet era, the benefits from multiple factors can be observed by splitting a large single die into multiple small dies. By having the multiple small dies with die-to-die (D2D) vertical connections, the benefits including: 1) better yield, 2) better timing/performance, and 3) better cost. How to do the netlist partitioning, cell placement in each of the small dies, and also how to determine the location of the D2D inter-connection terminals becomes a new topic.

Deep neural networks are shown to be vulnerable to adversarial attacks. This motivates robust learning techniques, such as the adversarial training, whose goal is to learn a network that is robust against adversarial attacks. However, the sample complexity of robust learning can be significantly larger than that of “standard” learning. In this paper, we propose improving the adversarial robustness of a network by leveraging the potentially large test data seen at runtime. We devise a new defense method, called runtime masking and cleansing (RMC), that adapts the network at runtime before making a prediction to dynamically mask network gradients and cleanse the model of the non-robust features inevitably learned during the training process due to the size limit of the training set. We conduct experiments on real-world datasets and the results demonstrate the effectiveness of RMC empirically.