A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis

Time series data, often characterized by unique composition and complex multi-scale temporal variations, requires special consideration of decomposition and multi-scale modeling in its analysis. Existing deep learning methods on this best fit to only univariate time series, and have not sufficiently accounted for sub-series level modeling and decomposition completeness. To address this, we propose MSD-Mixer, a Multi-Scale Decomposition MLP-Mixer which learns to explicitly decompose the input time series into different components, and represents the components in different layers. To handle multi-scale temporal patterns and inter-channel dependencies, we propose a novel temporal patching approach to model the time series as multi-scale sub-series, i.e., patches, and employ MLPs to mix intra- and inter-patch variations and channel-wise correlations. In addition, we propose a loss function to constrain both the magnitude and autocorrelation of the decomposition residual for decomposition completeness. Through extensive experiments on various real-world datasets for five common time series analysis tasks (long- and short-term forecasting, imputation, anomaly detection, and classification), we demonstrate that MSD-Mixer consistently achieves significantly better performance in comparison with other state-of-the-art task-general and task-specific approaches

Signatures of a gapless quantum spin liquid in the Kitaev material Na3_3Co2−x_{2-x}Znx_xSbO6_6

The honeycomb-lattice cobaltate Na$_3$Co$_2$SbO$_6$ has recently been proposed to be a proximate Kitaev quantum spin liquid~(QSL) candidate. However, non-Kitaev terms in the Hamiltonian lead to a zigzag-type antiferromagnetic~(AFM) order at low temperatures. Here, we partially substitute magnetic Co$^{2+}$ with nonmagnetic Zn$^{2+}$ and investigate the chemical doping effect in tuning the magnetic ground states of Na$_3$Co$_{2-x}$Zn$_x$SbO$_6$. X-ray diffraction characterizations reveal no structural transition but quite tiny changes on the lattice parameters over our substitution range $0\leq x\leq0.4$. Magnetic susceptibility and specific heat results both show that AFM transition temperature is continuously suppressed with increasing Zn content $x$ and neither long-range magnetic order nor spin freezing is observed when $x\geq0.2$. More importantly, a linear term of the specific heat representing fermionic excitations is captured below 5~K in the magnetically disordered regime, as opposed to the $C_{\rm m}\propto T^3$ behavior expected for bosonic excitations in the AFM state. Based on the data above, we establish a magnetic phase diagram of Na$_3$Co$_{2-x}$Zn$_x$SbO$_6$. Our results indicate the presence of gapless fractional excitations in the samples with no magnetic order, evidencing a potential QSL state induced by doping in a Kitaev system.Comment: 10 pages, 5 figure

Effects of Configurations of Internal Walls on the Threshold Value of Operation Hours for Intermittent Heating Systems

The heating load of intermittent heating is not always lower than that of continuous heating for heat storage and release of internal walls. Therefore, the threshold value of daily operation hours exists, and is affected by the configuration of internal walls. A comparative study is performed between continuous and intermittent heating modes to investigate the threshold value of daily operation hours for different internal wall configurations by employing computational fluid dynamic (CFD) models. Meanwhile, field tests on the temperature distribution within a thermal mass was carried out to validate the simulation. The results show that the heating load index of intermittent heating is larger than that of continuous heating with increased amplitude ranging from 31.58% to 152.63%. The threshold value of daily operation hours is, respectively, 18.04 h, 15.80 h, 14.59 h, and 13.46 h for four internal wall configurations. Moreover, with the increase in the insulation level of internal walls, the threshold value of daily operation hours decreases. In addition, the results indicate that it is more economical to use continuous heating when the daily operation hours are more than the threshold values

Focal Mechanisms of the 2016 Central Italy Earthquake Sequence Inferred from High-Rate GPS and Broadband Seismic Waveforms

Numerous shallow earthquakes, including a multitude of small shocks and three moderate mainshocks, i.e., the Amatrice earthquake on 24 August, the Visso earthquake on 26 October and the Norcia earthquake on 30 October, occurred throughout central Italy in late 2016 and resulted in many casualties and property losses. The three mainshocks were successfully recorded by high-rate Global Positioning System (GPS) receivers located near the epicenters, while the broadband seismograms in this area were mostly clipped due to the strong shaking. We retrieved the dynamic displacements from these high-rate GPS records using kinematic precise point positioning analysis. The focal mechanisms of the three mainshocks were estimated both individually and jointly using high-rate GPS waveforms in a very small epicentral distance range (<100 km) and unclipped regional broadband waveforms (100~600 km). The results show that the moment magnitudes of the Amatrice, Visso, and Norcia events are Mw 6.1, Mw 5.9, and Mw 6.5, respectively. Their focal mechanisms are dominated by normal faulting, which is consistent with the local tectonic environment. The moment tensor solution for the Norcia earthquake demonstrates a significant non-double-couple component, which suggests that the faulting interface is complicated. Sparse network tests were conducted to retrieve stable focal mechanisms using a limited number of GPS records. Our results confirm that high-rate GPS waveforms can act as a complement to clipped near-field long-period seismic waveform signals caused by the strong motion and can effectively constrain the focal mechanisms of moderate- to large-magnitude earthquakes. Thus, high-rate GPS observations extremely close to the epicenter can be utilized to rapidly obtain focal mechanisms, which is critical for earthquake emergency response operations

NIR initiated and pH sensitive single-wall carbon nanotubes for doxorubicin intracellular delivery

In this work, we synthesized a pH sensitive poly(ethylene glycol)-doxorubicin (PEG-DOX) on single-wall carbon nanotubes (SWNTs). Using multimodal nonlinear optical imaging microscopy, we found that low power (1 mW cm-2) near-infrared radiation can initiate prodrug burst release from the carbon nanotubes in seconds. The successful SWNTs capping with PEG-DOX (denoted PEG-DOX@SWNT) was determined by transmission electron microscopy and FTIR results. The in vitro release of DOX from the PEG-DOX@SWNT was evaluated upon changes of pH values and NIR treating time. The cytotoxicity of the PEG-DOX@SWNT was also evaluated. This dual-sensitive delivery system based on SWNTs provides a facile approach to promote drug release and kill cancer cells.Peer reviewed: YesNRC publication: Ye

A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting

We study the forecasting problem for traffic with dynamic, possibly periodical, and joint spatial-temporal dependency between regions. Given the aggregated inflow and outflow traffic of regions in a city from time slots 0 to t-1, we predict the traffic at time t at any region. Prior arts in the area often consider the spatial and temporal dependencies in a decoupled manner or are rather computationally intensive in training with a large number of hyper-parameters to tune. We propose ST-TIS, a novel, lightweight, and accurate Spatial-Temporal Transformer with information fusion and region sampling for traffic forecasting. ST-TIS extends the canonical Transformer with information fusion and region sampling. The information fusion module captures the complex spatial-temporal dependency between regions. The region sampling module is to improve the efficiency and prediction accuracy, cutting the computation complexity for dependency learning from $O(n^2)$ to $O(n\sqrt{n})$, where n is the number of regions. With far fewer parameters than state-of-the-art models, the offline training of our model is significantly faster in terms of tuning and computation (with a reduction of up to $90\%$ on training time and network parameters). Notwithstanding such training efficiency, extensive experiments show that ST-TIS is substantially more accurate in online prediction than state-of-the-art approaches (with an average improvement of up to $9.5\%$ on RMSE, and $12.4\%$ on MAPE)

General learning ability in perceptual learning

Developing expertise in any field usually requires acquisition of a wide range of skills. Most current studies on perceptual learning have focused on a single task and concluded that learning is quite specific to the trained task, and the ubiquitous individual differences reflect random fluctuations across subjects. Whether there exists a general learning ability that determines individual learning performance across multiple tasks remains largely unknown. In a large-scale perceptual learning study with a wide range of training tasks, we found that initial performance, task, and individual differences all contributed significantly to the learning rates across the tasks. Most importantly, we were able to extract both a task-specific but subject-invariant component of learning, that accounted for 38.6% of the variance, and a subject-specific but task-invariant perceptual learning ability, that accounted for 36.8% of the variance. The existence of a general perceptual learning ability across multiple tasks suggests that individual differences in perceptual learning are not "noise"; rather, they reflect the variability of learning ability across individuals. These results could have important implications for selecting potential trainees in occupations that require perceptual expertise and designing better training protocols to improve the efficiency of clinical rehabilitation

Identifying Long- and Short-Term Processes in Perceptual Learning

Practice makes perfect in almost all perceptual tasks, but how perceptual improvements accumulate remains unknown. Here, we developed a multicomponent theoretical framework to model contributions of both long- and short-term processes in perceptual learning. Applications of the framework to the block-by-block learning curves of 49 adult participants in seven perceptual tasks identified ubiquitous long-term general learning and within-session relearning in most tasks. More importantly, we also found between-session forgetting in the vernier-offset discrimination, face-view discrimination, and auditory-frequency discrimination tasks; between-session off-line gain in the visual shape search task; and within-session adaptation and both between-session forgetting and off-line gain in the contrast detection task. The main results of the vernier-offset discrimination and visual shape search tasks were replicated in a new experiment. The multicomponent model provides a theoretical framework to identify component processes in perceptual learning and a potential tool to optimize learning in normal and clinical populations.</p

Long noncoding RNA LUCAT1 enhances the survival and therapeutic effects of mesenchymal stromal cells post-myocardial infarction

Mesenchymal stromal cell (MSC) transplantation has been a promising therapeutic strategy for repairing heart tissues post-myocardial infarction (MI). Nevertheless, its therapeutic efficacy remains low, which is mainly ascribed to the low viability of transplanted MSCs. Recently, long noncoding RNAs (lncRNAs) have been reported to participate in diverse physiological and pathological processes, but little is known about their role in MSC survival. Using unbiased transcriptome profiling of hypoxia-preconditioned MSCs (HP-MSCs) and normoxic MSCs (N-MSCs), we identified a lncRNA named lung cancer-associated transcript 1 (LUCAT1) under hypoxia. LUCAT1 knockdown reduced the survival of engrafted MSCs and decreased the MSC-based therapeutic potency, as shown by impaired cardiac function, reduced cardiomyocyte survival, and increased fibrosis post-MI. Conversely, LUCAT1 overexpression had the opposite results. Mechanistically, LUCAT1 bound with and recruited jumonji domain-containing 6 (JMJD6) to the promoter of forkhead box Q1 (FOXQ1), which demethylated FOXQ1 at H4R3me(2(s)) and H3R2me(2(a)), thus downregulating Bax expression and upregulating Bcl-2 expression to attenuate MSC apoptosis. Therefore, our findings revealed the protective effects of LUCAT1 on MSC apoptosis and demonstrated that the LUCAT1-mediated JMJD6-FOXQ1 pathway might represent a novel target to potentiate the therapeutic effect of MSC-based therapy for ischemic cardiovascular diseases