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

The Structure on Invariant Measures of C1C^1 generic diffeomorphisms

Let $\Lambda$ be an isolated non-trival transitive set of a $C^1$ generic diffeomorphism f\in\Diff(M). We show that the space of invariant measures supported on $\Lambda$ coincides with the space of accumulation measures of time averages on one orbit. Moreover, the set of points having this property is residual in $\Lambda$ (which implies the set of irregular$^+$ points is also residual in $\Lambda$). As an application, we show that the non-uniform hyperbolicity of irregular$^+$ points in $\Lambda$ with totally 0 measure (resp., the non-uniform hyperbolicity of a generic subset in $\Lambda$) determines the uniform hyperbolicity of $\Lambda$

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

Brake or Step On the Gas? Empirical Analyses of Credit Effects on Individual Consumption

Understanding the effects of credit on consumption is crucial for guiding users’ consumption behavior, designing financial marketing strategies, and identifying credit\u27s value in stimulating the economy. Whereas several studies have endeavored on this issue, most simply utilize observations of a single credit channel and/or focus on an overall effect without considering the potentially heterogeneous short-term and long-term consumption changes. This study, leveraging a quasi-experimental design with high-resolution transaction data, examines how people respond to credit in both short- and long-term periods. Results show that credit users’ consumption amount significantly expand by 51.74% after getting access to credit in the short term. However, they ultimately cut their consumption by 4.02% to cope with financial constraints in the long term. We also reveal and quantify the spillover effects of credit on consumption with savings channels. We draw on regulatory focus theory to rationalize the changes on consumers’ consumption behavior after credit activation

Electrochemical interfacial influences on deoxygenation and hydrogenation reactions in CO reduction on a Cu(100) surface.

Electroreduction of CO2 to hydrocarbons on a copper surface has attracted much attention in the last few decades for providing a sustainable way for energy storage. During the CO2 and further CO electroreduction processes, deoxygenation that is C-O bond dissociation, and hydrogenation that is C-H bond formation, are two main types of surface reactions catalyzed by the copper electrode. In this work, by performing the state-of-the-art constrained ab initio molecular dynamics simulations, we have systematically investigated deoxygenation and hydrogenation reactions involving two important intermediates, COHads and CHOads, under various conditions of (i) on a Cu(100) surface without water molecules, (ii) at the water/Cu(100) interface and (iii) at the charged water/Cu(100) interface, in order to elucidate the electrochemical interfacial influences. It has been found that the electrochemical interface can facilitate considerably the C-O bond dissociation via changing the reaction mechanisms. However, C-H bond formation has not been affected by the presence of water or electrical charge. Furthermore, the promotional roles of an aqueous environment and negative electrode potential in deoxygenation have been clarified, respectively. This fundamental study provides an atomic level insight into the significance of the electrochemical interface towards electrocatalysis, which is of general importance for understanding electrochemistry

Elucidation of the surface structure-selectivity relationship in ethanol electro-oxidation over platinum by density functional theory

We have successfully built a general framework to comprehend the structure-selectivity relationship in ethanol electrooxidation on platinum by density functional theory calculations. Based on the reaction mechanisms on three basal planes and five stepped surfaces, it was found that only (110) and n(111) × (110) sites can enhance CO2 selectivity but other non-selective step sites are more beneficial to activity

Continuously Controllable Facial Expression Editing in Talking Face Videos

Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand in the industry. The challenge is that speech-related expressions and emotion-related expressions are often highly coupled. Meanwhile, traditional image-to-image translation methods cannot work well in our application due to the coupling of expressions with other attributes such as poses, i.e., translating the expression of the character in each frame may simultaneously change the head pose due to the bias of the training data distribution. In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously. We present a new perspective for this task as a special case of motion information editing, where we use a 3DMM to capture major facial movements and an associated texture map modeled by a StyleGAN to capture appearance details. Both representations (3DMM and texture map) contain emotional information and can be continuously modified by neural networks and easily smoothed by averaging in coefficient/latent spaces, making our method simple yet effective. We also introduce a mouth shape preservation loss to control the trade-off between lip synchronization and the degree of exaggeration of the edited expression. Extensive experiments and a user study show that our method achieves state-of-the-art performance across various evaluation criteria.Comment: Demo video: https://youtu.be/WD-bNVya6k

Homo-binding character of LMO2 isoforms and their both synergic and antagonistic functions in regulating hematopoietic-related target genes

<p>Abstract</p> <p>Background</p> <p>The human <it>lmo2 </it>gene plays important roles in hematopoiesis and is associated with acute T lymphocyte leukemia. The gene encodes two protein isoforms, a longer form LMO2-L and a shorter form LMO2-S. Both isoforms function as bridge molecules to assemble their partners together to regulate their target genes. A typical LMO2 binding site consists of two elements, a GATA site and an E-box, with an interval of 9~12 bp.</p> <p>Methods</p> <p>In this study, the combination of MBP pulldown assay and mammalian two hybrid assay were used to confirm the homo-binding character of LMO2-L/-S isoforms. Luciferase reporter assay and Real-time PCR assay were used to detect expression levels and relative promoter activities of LMO2-L/-S isoforms. Co-transfection and Luciferase reporter assay were used to reveal the detailed regulatory pattern of LMO2-L/-S isoforms on their targets.</p> <p>Results</p> <p>Herein we report the homo-interaction character of LMO2-L and LMO2-S and their major difference in manner of regulating their target genes. Our results showed that LMO2-L and LMO2-S could only bind to themselves but not each other. It was also demonstrated that LMO2-L could either positively or negatively regulate the transcription of its different target genes, depending on the arrangement and strand location of the two elements GATA site and E-box, LMO2-S, however, performed constitutively transcriptional inhibiting function on all target genes.</p> <p>Conclusion</p> <p>These results suggest that LMO2 isoforms have independent functions while there is no interaction between each other and they could play synergetic or antagonistic roles precisely in regulating their different genes involved in normal and aberrant hematopoiesis.</p

Designing Pt-based electrocatalysts with high surface energy

The reactivity of an electrocatalyst depends strongly on its surface structure. Pt-based electrocatalysts of nanocrystals (NCs) enclosed with high-index facets contain a large density of catalytically active sites formed from step and kink atoms on the facets and exhibit intrinsically superior activity. However, the Pt-based NCs of high-index facets do possess a high surface energy and are thermodynamically metastable, leading to a big challenge in their shape-controlled synthesis. To overcome the challenge, kinetic–thermodynamic control of crystal growth is indispensable and is currently realized mainly by electrochemical methods and surfactant-based wet chemical approaches. This Perspective reviews recent progresses in Pt-based electrocatalysts of monometallic and bimetallic NCs of high surface energy with different morphologies of convex or concave tetrahexahedron, trapezohedron, trisoctahedron, hexoctahedron, etc. Remarkable electrocatalytic performance of these NCs has been demonstrated. Despite the considerable progress already made, the electrocatalysts of NCs with high surface energy still hold significant future opportunities in both fundamental understanding and practical applications