A Two-level Prediction Model for Deep Reactive Ion Etch (DRIE)

We contribute a quantitative and systematic model to capture etch non-uniformity in deep reactive ion etch of microelectromechanical systems (MEMS) devices. Deep reactive ion etch is commonly used in MEMS fabrication where high-aspect ratio features are to be produced in silicon. It is typical for many supposedly identical devices, perhaps of diameter 10 mm, to be etched simultaneously into one silicon wafer of diameter 150 mm. Etch non-uniformity depends on uneven distributions of ion and neutral species at the wafer level, and on local consumption of those species at the device, or die, level. An ion–neutral synergism model is constructed from data obtained from etching several layouts of differing pattern opening densities. Such a model is used to predict wafer-level variation with an r.m.s. error below 3%. This model is combined with a die-level model, which we have reported previously, on a MEMS layout. The two-level model is shown to enable prediction of both within-die and wafer-scale etch rate variation for arbitrary wafer loadings.Singapore-MIT Alliance (SMA

Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction

Time series anomaly detection is challenging due to the complexity and variety of patterns that can occur. One major difficulty arises from modeling time-dependent relationships to find contextual anomalies while maintaining detection accuracy for point anomalies. In this paper, we propose a framework for unsupervised time series anomaly detection that utilizes point-based and sequence-based reconstruction models. The point-based model attempts to quantify point anomalies, and the sequence-based model attempts to quantify both point and contextual anomalies. Under the formulation that the observed time point is a two-stage deviated value from a nominal time point, we introduce a nominality score calculated from the ratio of a combined value of the reconstruction errors. We derive an induced anomaly score by further integrating the nominality score and anomaly score, then theoretically prove the superiority of the induced anomaly score over the original anomaly score under certain conditions. Extensive studies conducted on several public datasets show that the proposed framework outperforms most state-of-the-art baselines for time series anomaly detection.Comment: NeurIPS 2023 (https://neurips.cc/virtual/2023/poster/70582

Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network

We propose a recurrent neural network for a "model-free" simulation of a dynamical system with unknown parameters without prior knowledge. The deep learning model aims to jointly learn the nonlinear time marching operator and the effects of the unknown parameters from a time series dataset. We assume that the time series data set consists of an ensemble of trajectories for a range of the parameters. The learning task is formulated as a statistical inference problem by considering the unknown parameters as random variables. A latent variable is introduced to model the effects of the unknown parameters, and a variational inference method is employed to simultaneously train probabilistic models for the time marching operator and an approximate posterior distribution for the latent variable. Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model. The approximate posterior distribution makes an inference on a trajectory to identify the effects of the unknown parameters. The time marching operator is approximated by a recurrent neural network, which takes a latent state sampled from the approximate posterior distribution as one of the input variables, to compute the time evolution of the probability distribution conditioned on the latent variable. In the numerical experiments, it is shown that the proposed variational inference model makes a more accurate simulation compared to the standard recurrent neural networks. It is found that the proposed deep learning model is capable of correctly identifying the dimensions of the random parameters and learning a representation of complex time series data

Chronic and Cumulative Adverse Life Events in Women with Primary Ovarian Insufficiency:An Exploratory Qualitative Study

BACKGROUND AND PURPOSE: Primary ovarian insufficiency (POI) has serious physical and psychological consequences due to estradiol deprivation, leading to increased morbidity and mortality. However, the causes of most POI cases remain unknown. Psychological stress, usually caused by stressful life events, is known to be negatively associated with ovarian function. It is important to explore high-frequency adverse life events among women with POI for future interventions. METHODS: Forty-three women (mean age=33·8 years) were recruited who were newly- diagnosed with idiopathic POI (FSH levels >40 IU/L) to participate in semi-structured interviews through convenience sampling. The main questions covered by the topic guide were designed to explore adverse life events prior to POI diagnosis. Interviews were audio recorded, transcribed and analyzed thematically. Data were analyzed from June 2019 to August 2020. RESULTS: Among the women with POI, mean age at diagnosis of POI was 33·8 years (range from 19 to 39 years), and the average time between the onset of irregular menstruation and POI diagnosis was 2.3 years. These women with POI had a relatively normal menstrual cycle before the diagnosis. A number of stressful life events prior to POI diagnosis were discussed by them as important factors influencing their health. Four core themes emerged: 1) persistent exposure to workplace stress, 2) persistent exposure to family-related adverse life events, 3) sleep problem/disturbance existed in women with POI before diagnosis, and 4) participants’ general cognition and concerns about POI. CONCLUSIONS: Persistent exposures to adverse life events related to work stress, family stress and sleep problem existed in women with POI. Our findings are consistent with the hypothesis that adverse life events play a role in the development of POI. Future research should investigate how social environmental factors influence POI disease risks, and whether provision of tailored interventions (i.e. preventing or mitigating impact of adverse life events) aimed at high-risk populations may help prevent new POI cases and improve conditions of women with POI. We gained an in-depth understanding of the experiences of these women via 1:1 qualitative method, and find adverse life events are frequent in women with POI prior to the diagnosis

A secretory hexokinase plays an active role in the proliferation of Nosema bombycis

The microsporidian Nosema bombycis is an obligate intracellular parasite of Bombyx mori, that lost its intact tricarboxylic acid cycle and mitochondria during evolution but retained its intact glycolysis pathway. N. bombycis hexokinase (NbHK) is not only a rate-limiting enzyme of glycolysis but also a secretory protein. Indirect immunofluorescence assays and recombinant HK overexpressed in BmN cells showed that NbHK localized in the nucleus and cytoplasm of host cell during the meront stage. When N. bombycis matured, NbHK tended to concentrate at the nuclei of host cells. Furthermore, the transcriptional profile of NbHK implied it functioned during N. bombycis’ proliferation stages. A knock-down of NbHK effectively suppressed the proliferation of N. bombycis indicating that NbHK is an important protein for parasite to control its host

I4U System Description for NIST SRE'20 CTS Challenge

This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resulted from active collaboration among researchers across eight research teams - I$^2$R (Singapore), UEF (Finland), VALPT (Italy, Spain), NEC (Japan), THUEE (China), LIA (France), NUS (Singapore), INRIA (France) and TJU (China). The submission was based on the fusion of top performing sub-systems and sub-fusion systems contributed by individual teams. Efforts have been spent on the use of common development and validation sets, submission schedule and milestone, minimizing inconsistency in trial list and score file format across sites.Comment: SRE 2021, NIST Speaker Recognition Evaluation Workshop, CTS Speaker Recognition Challenge, 14-12 December 202

Isothiocyanates induce oxidative stress and suppress the metastasis potential of human non-small cell lung cancer cells

<p>Abstract</p> <p>Background</p> <p>Isothiocyanates are natural compounds found in consumable cruciferous vegetables. They have been shown to inhibit chemical carcinogenesis by a wide variety of chemical carcinogens in animal models. Recent studies have also shown that isothiocyanates have antitumor activity, inhibiting the growth of several types of cultured human cancer cells. Our previous study showed that PEITC inhibited human leukemia cells growth by inducing apoptosis. However, the effect of isothiocyanates on lung cancer cell metastasis has not been studied. In the present study, we investigated the inhibitory effects of BITC and PEITC on metastatic potential of highly metastatic human lung cancer L9981 cells.</p> <p>Methods</p> <p>Cell migration and invasion were measured by wound healing assay and transwell chemotaxis assay. Expression of metastasis-related genes was assessed by quantitative RT-PCR and Western blotting. The mechanisms of action were evaluated by flow cytometry, reporter assay and Western blotting.</p> <p>Results</p> <p>Our data showed that both BITC and PEITC inhibited L9981 cell growth in a dose-dependent manner, the IC50 values were 5.0 and 9.7 μM, respectively. Cell migrations were reduced to 8.1% and 16.5% of control, respectively; and cell invasions were reduced to 2.7% and 7.3% of control, respectively. Metastasis-related genes MMP-2, Twist and β-catenin were also modulated. BITC and PEITC inhibited cell survival signaling molecules Akt and NFκB activation. Moreover, BITC and PEITC increased ROS generation and caused GSH depletion. Pretreatment with NAC blocked BITC and PEITC induced ROS elevation and NFκB inhibition.</p> <p>Conclusion</p> <p>Our results indicated that BITC and PEITC suppress lung cancer cell metastasis potential by modulation of metastasis-related gene expression, inhibition of Akt/NFκB pathway. Induction of oxidative stress may play an important role.</p

FreDo: Frequency Domain-based Long-Term Time Series Forecasting

The ability to forecast far into the future is highly beneficial to many applications, including but not limited to climatology, energy consumption, and logistics. However, due to noise or measurement error, it is questionable how far into the future one can reasonably predict. In this paper, we first mathematically show that due to error accumulation, sophisticated models might not outperform baseline models for long-term forecasting. To demonstrate, we show that a non-parametric baseline model based on periodicity can actually achieve comparable performance to a state-of-the-art Transformer-based model on various datasets. We further propose FreDo, a frequency domain-based neural network model that is built on top of the baseline model to enhance its performance and which greatly outperforms the state-of-the-art model. Finally, we validate that the frequency domain is indeed better by comparing univariate models trained in the frequency v.s. time domain

Experimental Research on the Impactive Dynamic Effect of Gas-Pulverized Coal of Coal and Gas Outburst

Coal and gas outburst is one of the major serious natural disasters during underground coal, and the shock air flow produced by outburst has a huge threat on the mine safety. In order to study the two-phase flow of a mixture of pulverized coal and gas of a mixture of pulverized coal and gas migration properties and its shock effect during the process of coal and gas outburst, the coal samples of the outburst coal seam in Yuyang Coal Mine, Chongqing, China were selected as the experimental subjects. By using the self-developed coal and gas outburst simulation test device, we simulated the law of two-phase flow of a mixture of pulverized coal and gas in the roadway network where outburst happened. The results showed that the air in the roadway around the outburst port is disturbed by the shock wave, where the pressure and temperature are abruptly changed. For the initial gas pressure of 0.35 MPa, the air pressure in different locations of the roadway fluctuated and eventually remain stable, and the overpressure of the outburst shock wave was about 20~35 kPa. The overpressure in the main roadway and the distance from the outburst port showed a decreasing trend. The highest value of temperature in the roadway increased by 0.25 °C and the highest value of gas concentration reached 38.12% during the experiment. With the action of shock air flow, the pulverized coal transportation in the roadway could be roughly divided into three stages, which are the accelerated movement stage, decelerated movement stage and the particle settling stage respectively. Total of 180.7 kg pulverized coal of outburst in this experiment were erupted, and most of them were accumulated in the main roadway. Through the analysis of the law of outburst shock wave propagation, a shock wave propagation model considering gas desorption efficiency was established. The relationships of shock wave overpressure and outburst intensity, gas desorption rate, initial gas pressure, cross section and distance of the roadway were obtained, which can provide a reference for the protection of coal and gas outburst and control of catastrophic ventilation