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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. 

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.

 

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. 

Deep learning can learn the complex physics of energetic materials. 

Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials design problems. This paper aims to review recent advances in AI‐driven materials‐by‐design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro‐morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials‐by‐design, namely representation learning of microstructure morphology (i. e., shape descriptors), structure‐property‐performance (S−P−P) linkage estimation, and optimization/design exploration. We leave out “process” as much work remains to be done to establish the connectivity between process and structure. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials‐by‐design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials‐by‐design, such as meta‐learning, active learning, Bayesian learning, and semi‐/weakly‐supervised learning, to bridge the gap between machine learning research and EM research.

 

Accurate and efficient power demand forecasting in urban settings is essential for making decisions related to planning, managing and operations in electricity supply. This task, however, is complicated due to many sources of uncertainty such as due to the variation in weather conditions and household or other needs that influence the inherent stochastic and nonlinear characteristics of electricity demand. Due to the modeling flexibility and computational efficiency afforded by it, a Gaussian process model is employed in this study for energy demand prediction as a function of temperature. A Gaussian process model is a Bayesian non-parametric regression method that models data using a joint Gaussian distribution with mean and covariance functions. The selected mean function is modeled as a polynomial function of temperature, whereas the covariance function is appropriately selected to reflect the actual data patterns. We employ real data sets of daily temperature and electricity demand from Austin, Texas, USA to assess the effectiveness of the proposed method for load forecasting. The accuracy of the model prediction is evaluated using metrics such as mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and 95% confidence interval (95% CI). A numerical study undertaken demonstrates that the proposed method has promise for energy demand prediction.