New Constructions of Zero-Correlation Zone Sequences

In this paper, we propose three classes of systematic approaches for constructing zero correlation zone (ZCZ) sequence families. In most cases, these approaches are capable of generating sequence families that achieve the upper bounds on the family size ($K$) and the ZCZ width ($T$) for a given sequence period ($N$). Our approaches can produce various binary and polyphase ZCZ families with desired parameters $(N,K,T)$ and alphabet size. They also provide additional tradeoffs amongst the above four system parameters and are less constrained by the alphabet size. Furthermore, the constructed families have nested-like property that can be either decomposed or combined to constitute smaller or larger ZCZ sequence sets. We make detailed comparisons with related works and present some extended properties. For each approach, we provide examples to numerically illustrate the proposed construction procedure.Comment: 37 pages, submitted to IEEE Transactions on Information Theor

Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation

Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addressed: (a) improving label efficiency and (b) enhancing robustness in realistic scenarios where modalities are missing at the test time. To address these challenges, we first propose a simple yet efficient multi-modal fusion mechanism Linear Fusion, that performs better than the state-of-the-art multi-modal models even with limited supervision. Second, we propose M3L: Multi-modal Teacher for Masked Modality Learning, a semi-supervised framework that not only improves the multi-modal performance but also makes the model robust to the realistic missing modality scenario using unlabeled data. We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities. Our proposal shows an absolute improvement of up to 10% on robust mIoU above the most competitive baselines. Our code is available at https://github.com/harshm121/M3

Risk Analysis of Regions with Suspicious Illegal Logging and Their Trade Flows

To eradicate illegally harvested wood sources in its domestic market, it is critical to conduct risk assessments on wood sourcing in regions with illegal loggings. It is not reliable to use a single indicator to analyze suspicious illegal logging. This study integrates three key global indicators: CPI (Corruption Perceptions Index), HDI (Human Development Indicator), and WGI (The Worldwide Governance Indicators) by applying the entropy weight method to establish a new risk indicator to rank suspicious illegal logging regions. This study aims to establish better risk indicators by considering more factors to assess the risks of illegal logging and its trade flow more reliably. By analyzing roundwood production, Myanmar, Congo, and Nigeria are rated high-risk. Countries such as the U.S., Germany, Canada, and Finland are rated low-risk