TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks. Code is provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.Comment: Accepted by the ACM International Conference on Information and Knowledge Management (CIKM 2022

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables effective discrimination of anomalies. Reconstruction-based methods still dominate, but the representation learning with anomalies might hurt the performance with its large abnormal loss. On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection. In this paper, we propose DCdetector, a multi-scale dual attention contrastive representation learning model. DCdetector utilizes a novel dual attention asymmetric design to create the permutated environment and pure contrastive loss to guide the learning process, thus learning a permutation invariant representation with superior discrimination abilities. Extensive experiments show that DCdetector achieves state-of-the-art results on multiple time series anomaly detection benchmark datasets. Code is publicly available at https://github.com/DAMO-DI-ML/KDD2023-DCdetector

DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model

Electrical load forecasting is of great significance for the decision makings in power systems, such as unit commitment and energy management. In recent years, various self-supervised neural network-based methods have been applied to electrical load forecasting to improve forecasting accuracy and capture uncertainties. However, most current methods are based on Gaussian likelihood methods, which aim to accurately estimate the distribution expectation under a given covariate. This kind of approach is difficult to adapt to situations where temporal data has a distribution shift and outliers. In this paper, we propose a diffusion-based Seq2seq structure to estimate epistemic uncertainty and use the robust additive Cauchy distribution to estimate aleatoric uncertainty. Rather than accurately forecasting conditional expectations, we demonstrate our method's ability in separating two types of uncertainties and dealing with the mutant scenarios

LogiCoT: Logical Chain-of-Thought Instruction-Tuning

Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing. However, they fall short of helping the model handle complex reasoning tasks. To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills

Mechanism Design with Predicted Task Revenue for Bike Sharing Systems

Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called {\em TruPreTar} to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. TruPreTar possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that even when the payment budget is tight, the total revenue still exceeds or equals the budget. Otherwise, TruPreTar achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least $\sqrt{2}$ that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that TruPreTar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms.Comment: Accepted by AAAI 2020; This is the full version that contains all the proof

High-redshift galaxy groups as seen by Athena/WFI

The first massive galaxy groups in the Universe are predicted to have formed at redshifts well beyond two. Baryonic physics, like stellar and active galactic nuclei (AGN) feedback in this very active epoch, are expected to have left a strong imprint on the thermo-dynamic properties of these early galaxy groups. Therefore, observations of these groups are key to constrain the relative importance of these physical processes. However, current instruments are not sensitive enough to detect them easily and characterize their hot gas content. In this work, we quantify the observing power of the Advanced Telescope for High ENergy Astrophysics (Athena), the future large X-ray observatory of the European Space Agency (ESA), for discovering and characterizing early galaxy groups at high redshifts. We used the SImulation of X-ray TElescopes (SIXTE) simulator to mimic Athena observations, and a custom-made wavelet-based algorithm to detect galaxy groups and clusters in the redshift range $0.5 \le z \le 4$. We performed extensive X-ray spectral fitting in order to characterize their gas temperature and X-ray luminosity. We also investigate how well Athena will constrain different feedback mechanisms. In the deep Wide Field Imager (WFI) survey expected to be carried out during part of Athena's first four years (the nominal mission lifetime) more than 10,000 galaxy groups and clusters at $z\ge 0.5$ will be discovered. We find that Athena can detect $\sim20$ high-redshift galaxy groups with masses of $M_{500}\geq$ $5\times 10^{13}$ $M_{\odot}$ and $z\geq2$, and almost half of them will have a gas temperature determined to a precision of $\Delta T/T \le 25\%$. We demonstrate that high-redshift galaxy groups can be detected very efficiently as extended sources by Athena and that a key parameter determining the total number of such newly discovered sources is the area on the sky surveyed by Athena.Comment: 24 pages, 18 figures, accepted for publication in A&

Benchmarks and Custom Package for Electrical Load Forecasting

Load forecasting is of great significance in the power industry as it can provide a reference for subsequent tasks such as power grid dispatch, thus bringing huge economic benefits. However, there are many differences between load forecasting and traditional time series forecasting. On the one hand, load forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch, rather than simply pursuing prediction accuracy. On the other hand, the load is largely influenced by many external factors, such as temperature or calendar variables. In addition, the scale of predictions (such as building-level loads and aggregated-level loads) can also significantly impact the predicted results. In this paper, we provide a comprehensive load forecasting archive, which includes load domain-specific feature engineering to help forecasting models better model load data. In addition, different from the traditional loss function which only aims for accuracy, we also provide a method to customize the loss function based on the forecasting error, integrating it into our forecasting framework. Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models

Tissue-Engineered Trachea Consisting of Electrospun Patterned sc-PLA/GO-g-IL Fibrous Membranes with Antibacterial Property and 3D-Printed Skeletons with Elasticity

In this study, a tissue-engineered trachea, consisting of multilevel structural electrospun polylactide (PLA) membranes enveloping 3D-printed thermoplastic polyurethane (TPU) skeletons, was developed to create a mechanically robust, antibacterial and bioresorbable graft for the tracheal reconstruction. The study design incorporated two distinct uses of stereocomplex PLA: patterned electrospun fibers to enhance tissue integration compared to the random layered fibers, meanwhile possessing good antibacterial property; and 3D-printed TPU scaffold with elasticity to provide external support and protection. Herein, ionic liquid (IL)-functioned graphene oxide (GO) was synthesized and presented enhanced mechanical and hydrophilicity properties. More interesting, antibacterial activity of the GO-g-IL modified PLA membranes were proved by Escherichia coli and Staphylococcus aureus, showing superior antibacterial effect compared to single GO or IL. The synergistic antibacterial effect could be related to that GO break cytomembrane of bacteria by its extremely sharp edges, while IL works by electrostatic interaction between its cationic structures and electronegative phosphate groups of bacteria membranes, leading to the loss of cell electrolyte and cell death. Hence, after L929 fibroblast cells were seeded on patterned fibrous membranes with phenotypic shape, further effective cell infiltration, cell proliferation and attachment were observed. In addition, the tissue-engineered trachea scaffolds were implanted into rabbit models. The in vivo result confirmed that the scaffolds with patterned membranes manifested favorable biocompatibility and promoted tissue regeneration