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Abstract Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods. Pioneering work has demonstrated that ML provides solutions to governing systems of equations with comparable accuracy to those obtained using direct numerical methods, but with significantly faster computational speed. These high-speed, high-fidelity estimations can facilitate the solving of complex multiscale systems by providing a better initial solution to traditional solvers. This paper provides a perspective on the opportunities and challenges of using ML for complex multiscale modeling and simulation. We first outline the current state-of-the-art ML approaches for simulating multiscale systems and highlight some of the landmark developments. Next, we discuss current challenges for ML in multiscale computational modeling, such as the data and discretization dependence, interpretability, and data sharing and collaborative platform development. Finally, we suggest several potential research directions for the future. 

The arms race between Internet freedom advocates and censors has catalyzed the emergence of sophisticated blocking techniques and directed significant research emphasis toward the development of automated censorship measurement and evasion tools based on packet manipulation. However, we observe that the probing process of censorship middleboxes using state-of-the-art evasion tools can be easily fingerprinted by censors, necessitating detection-resilient probing techniques. We validate our hypothesis by developing a real-time detection approach that utilizes Machine Learning (ML) to detect flow-level packet-manipulation and an algorithm for IP-level detection based on Threshold Random Walk (TRW). We then take the first steps toward detection-resilient censorship evasion by presenting DeResistor, a system that facilitates detection-resilient probing for packet-manipulation-based censorship-evasion. DeResistor aims to defuse detection logic employed by censors by performing detection-guided pausing of censorship evasion attempts and interleaving them with normal user-driven network activity. We evaluate our techniques by leveraging Geneva, a state-of-the-art evasion strategy generator, and validate them against 11 simulated censors supplied by Geneva, while also testing them against real-world censors (i.e., China’s Great Firewall (GFW), India and Kazakhstan). From an adversarial perspective, our proposed real-time detection method can quickly detect clients that attempt to probe censorship middle-boxes with manipulated packets after inspecting only two probing flows. From a defense perspective, DeResistor is effective at shielding Geneva training from detection while enabling it to narrow the search space to produce less detectable traffic. Importantly, censorship evasion strategies generated using DeResistor can attain a high success rate from different vantage points against the GFW (up to 98%) and 100% in India and Kazakhstan. Finally, we discuss detection countermeasures and extensibility of our approach to other censor-probing-based tools. 

Abstract A crucial step in functional genomics is identifying actively translated open reading frames (ORFs) and linking them to biological functions. The challenge lies in identifying short ORFs, as their identification is greatly influenced by data quality and depth. Here, we improved the coverage of super-resolution Ribo-seq in Arabidopsis (Arabidopsis thaliana), revealing uncharacterized translation events for nuclear, chloroplastic, and mitochondrial genes. Assisted by a transcriptome assembly, we identified 7,751 unconventional translation events, comprising 6,996 upstream ORFs (uORFs) and 209 downstream ORFs on annotated protein-coding genes, as well as 546 ORFs in presumed non-coding RNAs. Proteomics data confirmed the production of stable proteins from some of these unannotated translation events. We present evidence of active translation from primary transcripts of tasiRNAs (TAS1–4) and microRNAs (pri-MIR163, pri-MIR169), and periodic ribosome stalling supporting co-translational decay. Additionally, we developed a method for identifying extremely short uORFs, including 370 minimum uORFs (AUG-stop), and 2,921 tiny uORFs (2–10 amino acids), and 681 uORFs that overlap with each other. Remarkably, these short uORFs exhibit strong translational repression as do longer uORFs. We also systematically discovered 594 uORFs regulated by alternative splicing, suggesting widespread isoform-specific translational control. Finally, these prevalent uORFs are associated with numerous important pathways. In summary, our improved Arabidopsis translational landscape provides valuable resources to study gene expression regulation. 

Deep learning can learn the complex physics of energetic materials.