A complex system with cluttered observations may be a coupled mixture of multiple simple sub-systems corresponding to latent entities. Such sub-systems may hold distinct dynamics in the continuous-time domain; therein, complicated interactions between sub-systems also evolve over time. This setting is fairly common in the real world but has been less considered. In this paper, we propose a sequential learning approach under this setting by decoupling a complex system for handling irregularly sampled and cluttered sequential observations. Such decoupling brings about not only subsystems describing the dynamics of each latent entity but also a meta-system capturing the interaction between entities over time. Specifically, we argue that the meta-system evolving within a simplex is governed by projected differential equations (ProjDEs). We further analyze and provide neural-friendly projection operators in the context of Bregman divergence. Experimental results on synthetic and real-world datasets show the advantages of our approach when facing complex and cluttered sequential data compared to the state-of-the-art.
Wind energy resource assessment typically requires numerical models, but such models are too computationally intensive to consider multi-year timescales. Increasingly, unsupervised machine learning techniques are used to identify a small number of representative weather patterns to simulate long-term behaviour. Here we develop a novel wind energy workflow that for the first time combines weather patterns derived from unsupervised clustering techniques with numerical weather prediction models (here WRF) to obtain efficient and accurate long-term predictions of power and downstream wakes from an entire wind farm. We use ERA5 reanalysis data clustering not only on low altitude pressure but also, for the first time, on the more relevant variable of wind velocity. We also compare the use of large-scale and local-scale domains for clustering. A WRF simulation is run at each of the cluster centres and the results are aggregated using a novel post-processing technique. By applying our workflow to two different regions, we show that our long-term predictions agree with those from a year of WRF simulations but require less than 2% of the computational time. The most accurate results are obtained when clustering on wind velocity. Moreover, clustering over the Europe-wide domain is sufficient for predicting wind farm power output, but downstream wake predictions benefit from the use of smaller domains. Finally, we show that these downstream wakes can affect the local weather patterns. Our approach facilitates multi-year predictions of power output and downstream farm wakes, by providing a fast, accurate and flexible methodology that is applicable to any global region. Moreover, these accurate long-term predictions of downstream wakes provide the first tool to help mitigate the effects of wind energy loss downstream of wind farms, since they can be used to determine optimum wind farm locations.
Multi-agent robotic systems are increasingly operating in real-world environments in close proximity to humans, yet are largely controlled by policy models with inscrutable deep neural network representations. We introduce a method for incorporating interpretable concepts from a domain expert into models trained through multi-agent reinforcement learning, by requiring the model to first predict such concepts then utilize them for decision making. This allows an expert to both reason about the resulting concept policy models in terms of these high-level concepts at run-time, as well as intervene and correct mispredictions to improve performance. We show that this yields improved interpretability and training stability, with benefits to policy performance and sample efficiency in a simulated and real-world cooperative-competitive multi-agent game.
Fine-grained anomaly detection has recently been dominated by segmentation based approaches. These approaches first classify each element of the sample (e.g., image patch) as normal or anomalous and then classify the entire sample as anomalous if it contains anomalous elements. However, such approaches do not extend to scenarios where the anomalies are expressed by an unusual combination of normal elements. In this paper, we overcome this limitation by proposing set features that model each sample by the distribution its elements. We compute the anomaly score of each sample using a simple density estimation method. Our simple-to-implement approach outperforms the state-of-the-art in image-level logical anomaly detection (+3.4%) and sequence-level time-series anomaly detection (+2.4%).
While providing low latency is a fundamental requirement in deploying recommendation services, achieving high resource utility is also crucial in cost-effectively maintaining the datacenter. Co-locating multiple workers of a model is an effective way to maximize query-level parallelism and server throughput, but the interference caused by concurrent workers at shared resources can prevent server queries from meeting its SLA. Hera utilizes the heterogeneous memory requirement of multi-tenant recommendation models to intelligently determine a productive set of co-located models and its resource allocation, providing fast response time while achieving high throughput. We show that Hera achieves an average 37.3% improvement in effective machine utilization, enabling 26% reduction in required servers, significantly improving upon the baseline recommedation inference server.
In this paper, we introduce two methods to solve the American-style option pricing problem and its dual form at the same time using neural networks. Without applying nested Monte Carlo, the first method uses a series of neural networks to simultaneously compute both the lower and upper bounds of the option price, and the second one accomplishes the same goal with one global network. The avoidance of extra simulations and the use of neural networks significantly reduce the computational complexity and allow us to price Bermudan options with frequent exercise opportunities in high dimensions, as illustrated by the provided numerical experiments. As a by-product, these methods also derive a hedging strategy for the option, which can also be used as a control variate for variance reduction.
We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. Each household earns income and engages in consumption at each time step while aiming to maximize a concave utility subject to the underlying market conditions. The households aim to find the optimal saving strategy that maximizes their discounted cumulative utility given the market condition, while the firms determine the market conditions through maximizing corporate profit based on the household population behavior. The model captures a wide range of applications in macroeconomic studies, and we propose a data-driven reinforcement learning framework that finds the regularized competitive equilibrium of the model. The proposed algorithm enjoys theoretical guarantees in converging to the equilibrium of the market at a sub-linear rate.
Blazars are active galactic nuclei with relativistic jets pointed almost directly at Earth. Blazars are characterized by strong, apparently stochastic flux variability at virtually all observed wavelengths and timescales, from minutes to years, the physical origin of which is still poorly understood. In the high-energy gamma-ray band, the Large Area Telescope aboard the Fermi space telescope (Fermi-LAT) has conducted regular monitoring of thousands of blazars since 2008. Deep learning can help uncover structure in gamma-ray blazars' complex variability patterns that traditional methods based on parametric statistical modeling or manual feature engineering may miss. In this work, we propose using a self-supervised Transformer encoder architecture to construct an effective representation of blazar gamma-ray variability. Measurement errors, upper limits, and missing data are accommodated using learned encodings. The model predicts a set of quantiles for the flux probability distribution at each time step, an architecture naturally suited for describing data generated by a stochastic process. As a proof of concept for how the model output can be analyzed to extract scientifically relevant information, a preliminary search for weekly-timescale time-reversal asymmetry in gamma-ray blazar light curves was conducted, finding no significant evidence for asymmetry.
The increasing availability of audio editing software altering digital audios and their ease of use allows create forgeries at low cost. A copy-move forgery (CMF) is one of easiest and popular audio forgeries, which created by copying and pasting audio segments within the same audio, and potentially post-processing it. Three main approaches to audio copy-move detection exist nowadays: samples/frames comparison, acoustic features coherence searching and dynamic time warping. But these approaches will suffer from computational complexity and/or sensitive to noise and post-processing. In this paper, we propose a new local feature tensors-based copy-move detection algorithm that can be applied to transformed duplicates detection and localization problem to a special locality sensitive hash like procedure. The experimental results with massive online real-time audios datasets reveal that the proposed technique effectively determines and locating copy-move forgeries even on a forged speech segment are as short as fractional second. This method is also computational efficient and robust against the audios processed with severe nonlinear transformation, such as resampling, filtering, jsittering, compression and cropping, even contaminated with background noise and music. Hence, the proposed technique provides an efficient and reliable way of copy-move forgery detection that increases the credibility of audio in practical forensics applications
Every year, about 4 million people die from upper respiratory infections. Mask-wearing is crucial in preventing the spread of pathogen-containing droplets, which is the primary cause of these illnesses. However, most techniques for mask efficacy evaluation are expensive to set up and complex to operate. In this work, a novel, low-cost, and quantitative metrology to visualize, track, and analyze orally-generated fluid droplets is developed. The project has four stages: setup optimization, data collection, data analysis, and application development. The metrology was initially developed in a dark closet as a proof of concept using common household materials and was subsequently implemented into a portable apparatus. Tonic water and UV darklight tube lights are selected to visualize fluorescent droplet and aerosol propagation with automated analysis developed using open-source software. The dependencies of oral fluid droplet generation and propagation on various factors are studied in detail and established using this metrology. Additionally, the smallest detectable droplet size was mathematically correlated to height and airborne time. The efficacy of different types of masks is evaluated and associated with fabric microstructures. It is found that masks with smaller-sized pores and thicker material are more effective. This technique can easily be constructed at home using materials that total to a cost of below \$60, thereby enabling a low-cost and accurate metrology.