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Due to spectacular gains during periods of rapid price increase and unpredictably large drops, Bitcoin has become a popular emergent asset class over the past few years. In this paper, we are interested in predicting the crashes of Bitcoin market. To tackle this task, we propose a framework for deep learning time series classification based on contrastive learning. The proposed framework is evaluated against six machine learning (ML) and deep learning (DL) baseline models, and outperforms them by 15.8% in balanced accuracy. Thus, we conclude that the contrastive learning strategy significantly enhance the model's ability of extracting informative representations, and our proposed framework performs well in predicting Bitcoin crashes. [ABSTRACT FROM AUTHOR]

By utilizing progress in millijoule-level pulsed fiber lasers operating in the 1.96 µm spectral range, we introduce a concept utilizing a spaceborne differential absorption barometric lidar designed to operate within the 1.96 µm CO2 absorption band for remote sensing of Martian atmospheric properties. Our focus is on the online wavelength situated in the trough region of two absorption lines, selected due to its insensitivity to laser frequency variations, thus mitigating the necessity for stringent laser frequency stability. Our investigation revolves around a compact lidar configuration, featuring reduced telescope dimensions and lower laser pulse energies. These adjustments are geared towards minimizing costs for potential forthcoming Mars missions. The core measurement objectives encompass the determination of column CO2 absorption optical depth, columnar CO2 abundance, surface atmospheric pressure, and vertical distributions of dust and cloud layers. Through the amalgamation of surface pressure data with atmospheric temperature insights garnered from sounders and utilizing the barometric formula, the prospect of deducing atmospheric pressure profiles becomes feasible. Simulation studies validate the viability of our approach. Notably, the precision of Martian surface pressure measurements is projected to surpass 1 Pa when the aerial dust optical depth is projected to be under 0.7, a typical airborne dust scenario on Mars, considering a horizontal averaging span of 10 km. [ABSTRACT FROM AUTHOR]

Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method. [ABSTRACT FROM AUTHOR]

Nighttime PM2.5 detection by remote sensing can expand understanding of PM2.5 spatiotemporal patterns due to wider coverage compared to ground monitors and by supplementing traditional daytime detection. However, using remote sensing data to invert PM2.5 at night is still challenging. Compared with daytime detection, which operates on sunlight, nighttime detection operates on much weaker moonlight and artificial light sources, complicating signal extraction. Moreover, as the attempts to sense PM2.5 remotely using satellite data are relatively recent, the existing nighttime models are still not mature, overlooking many important factors such as stray light, seasonality in meteorological effects, and observation angle. This paper attempts to improve the accuracy of nighttime PM2.5 detection by proposing an inversion model that takes these factors into consideration. The Visible Infrared Imaging Radiometer Suite/Day/Night Band (VIIRS/DNB) on board the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic Atmospheric Administration-20 (NOAA-20) was used to establish a nighttime PM2.5 inversion model in the Beijing area from 1 March 2018 to 28 February 2019. The model was designed by first studying the effects of these factors through a stepwise regression, then building a multivariate regression model to compensate for these effects. The results showed that the impact of satellite viewing zenith angle (VZA) was strongest, followed by seasonality and moonlight. Total accuracy was measured using correlation coefficient (R) compared to ground measurements, achieving 0.87 over the urban area and 0.74 over the suburbs. Specifically, the proposed method works efficiently at subsatellite points, which in this case correspond to VZA from 0 and 5°. In spring, summer, autumn, and winter, the R reached 0.95, 0.93, 0.94, and 0.97 at subsatellite points in the urban area, while it was 0.88, 0.82, 0.85, and 0.77 in the suburbs. [ABSTRACT FROM AUTHOR]

Considering the mixed results of the relationship between job insecurity and job performance, this study investigated the interaction effect of job insecurity and job embeddedness on job performance and examined the mediating role of affective commitment from the perspectives of conservation of resources theory and social exchange theory. A survey of 725 contract employees from two Chinese private manufacturing companies revealed that when employees had high levels of job embeddedness, job insecurity was significantly and positively related to job performance. In contrast, job insecurity was significantly and negatively related to job performance when there were low levels of job embeddedness. Furthermore, the results indicated that affective commitment mediated the interaction effect. The above conclusions not only illustrate the important role of job embeddedness in the relationship between job insecurity and job performance but also provide beneficial ideas and information to organisations and employees for managing job insecurity. [ABSTRACT FROM AUTHOR]

We present a method to determine the geometric form factor of a ground-based lidar using simultaneous lidar measurements made from the ground and from space. The theoretical basis is described. The feasibility of the method is demonstrated by applying it to the measurement data acquired by the Cloud Aerosol Lidar Infrared Pathfinder Satellite Observation (CALIPSO) lidar and a ground-based lidar located at the Hampton University (37.02[degrees]N, 76.34[degrees]W). The geometric factors with different aerosol conditions are retrieved. OCIS codes: 010.3640, 290.5860, 280.0280, 010.1280.

The features detected in monolayer atmospheric columns sounded by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) aboard the same satellite. Using the IIR's three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of "not confident" V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are reclassified more appropriately as confident cloud layers through the use of the IIR observations in the tropics and in the midlatitudes, respectively. IIR observations are of relatively little help in deriving high-confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those of clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke, respectively, are found to be misclassified cloud layers by the IIR measurements. [ABSTRACT FROM AUTHOR]

The belongingness literature has largely examined the antecedents of non-teleworkers' sense of belongingness, but little attention has been paid to what job-related factors could affect teleworkers' sense of belongingness. Grounded in organizational support theory, our research focuses on why feedback quality from the direct leader brings sense of belongingness and considers how conscientiousness of teleworkers shapes this effect. Based on data from 329 participants obtained at three different time points from one technology service organization in China, our results indicated that teleworkers' perceived organizational support serves as an essential mediator of the positive relationship between feedback quality from the direct leader and sense of belongingness. Additionally, the teleworkers' conscientiousness strengthened the positive direct effect of feedback quality on perceived organizational support and the indirect effect on sense of belongingness. The moderating role of conscientiousness in strengthening the link between feedback quality and perceived organizational support was significant for high levels of conscientiousness and not significant for low levels. Finally, we discussed theoretical and practical implications. [ABSTRACT FROM AUTHOR]

We present observations of cirrus clouds from June 2006 to July 2007 performed by using a two-wavelength lidar located at Hampton University. For this time period, cirrus clouds were observed mostly in 7-13.5 km altitudes. Data analyses have been performed focusing on a color-ratio retrieval. In total, 86,369 samples from 1,689 profiles (1 min average and 15 m range resolution) containing cirrus clouds with attenuated backscatter ratio (ratio of attenuated total backscatter to the molecular backscatter) larger than 10 have been selected. The cirrus color ratio distribution shows a peak value at about 0.88 and a full width at half-maximum of 0.12. OCIS codes: 280.3640, 010.1615.

Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor). On July 17, 2014, the CALIPSO satellite was tilted 30° off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraft-based high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.

Backscatter and extinction from water clouds are well-understood, both theoretically and experimentally, and thus changes to the expected measurement of layer-integrated attenuated backscatter can be used to infer the optical properties of overlying layers. In this paper we offer a first look at a new retrieval technique that uses CALIPSO measurements of opaque water clouds to derive optical depths and Ångström exponents for overlying aerosol layers.

In a previous study we evaluated the above-cloud aerosol optical depth (AOD) retrieval at 532 nm in the CALIOP version 3 (V3) data products for two selected spatial domains along the Saharan dust transport pathway and African smoke transport pathway. In that study we rescaled the V3 level-1 (L1) data to compensate for known V3 calibration biases, and then derived aerosol intrinsic properties such as lidar ratio (Sa) and particulate depolarization ratio (PDR) for comparison with the CALIOP dust and smoke aerosol models. The calibration of the recently released version 4 (V4) CALIPSO L1 data product is significantly improved over V3. So in this paper we repeat our previous analysis using the new V4 L1 data. A comparison shows that our rescaled V3 and the new V4 data are different only by ~1% in the two selected spatial domains. The retrieved AOD values decrease by ~2.6% in both domains from V3 to V4. When the data is screened to exclude weakly scattering layers, the median Sa retrieved from the V4 L1 data in the dust transport region is reduced by 1.4 sr to 43.0±8.3 sr. The median Sa value in the smoke transport region is increased by 0.8 sr to 71.2±15.1 sr. The PDR values remain almost unchanged for the screened data.

We discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photoncounting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson-distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root mean square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF, uncertainties can be reliably calculated from or for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar and tested using data from the Lidar In-space Technology Experiment. OCIS codes: 280.3640, 040.5160, 270.5290.

Although innovative work behavior (IWB) is essential to organizational success and survival, little is known about its dispositional antecedents. In this study, we invoke regulatory mode theory to examine the independent and interactive effects of two regulatory orientations, assessment and locomotion, on three aspects of employee IWB: idea generation, promotion, and realization. Results from lagged survey data generally supported our hypotheses that each orientation would exhibit distinct effect patterns. For idea generation, assessment exhibited a positive relationship, whereas locomotion exhibited a negative relationship. Meanwhile, only locomotion was significantly (positively) related to idea promotion. And for idea realization, both assessment and locomotion exhibited positive associations. Furthermore, the interaction of assessment and locomotion in relation to each aspect of IWB also revealed distinct patterns: for idea generation, higher levels of assessment buffered against the negative effects of locomotion; for idea promotion, higher assessment constrained the positive effects of locomotion; and for idea realization, assessment supported locomotion, such that we observed greater realization levels when both regulatory modes were higher. We discuss the theoretical and practical implications of our results, as well as promising directions for future research concerning both regulatory mode and IWB. [ABSTRACT FROM AUTHOR]