Empowering Low-Light Image Enhancer through Customized Learnable Priors

Deep neural networks have achieved remarkable progress in enhancing low-light images by improving their brightness and eliminating noise. However, most existing methods construct end-to-end mapping networks heuristically, neglecting the intrinsic prior of image enhancement task and lacking transparency and interpretability. Although some unfolding solutions have been proposed to relieve these issues, they rely on proximal operator networks that deliver ambiguous and implicit priors. In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm. Motivated by the powerful feature representation capability of Masked Autoencoder (MAE), we customize MAE-based illumination and noise priors and redevelop them from two perspectives: 1) \textbf{structure flow}: we train the MAE from a normal-light image to its illumination properties and then embed it into the proximal operator design of the unfolding architecture; and m2) \textbf{optimization flow}: we train MAE from a normal-light image to its gradient representation and then employ it as a regularization term to constrain noise in the model output. These designs improve the interpretability and representation capability of the model.Extensive experiments on multiple low-light image enhancement datasets demonstrate the superiority of our proposed paradigm over state-of-the-art methods. Code is available at https://github.com/zheng980629/CUE.Comment: Accepted by ICCV 202

Random Weights Networks Work as Loss Prior Constraint for Image Restoration

In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''. Inspired by Functional theory, we provide several alternative solutions to implement our belief in the strict mathematical manifolds including Taylor's Unfolding Network, Invertible Neural Network, Central Difference Convolution and Zero-order Filtering as ``random weights network prototype'' with respect of the following four levels: 1) the different random weights strategies; 2) the different network architectures, \emph{eg,} pure convolution layer or transformer; 3) the different network architecture depths; 4) the different numbers of random weights network combination. Furthermore, to enlarge the capability of the randomly initialized manifolds, we devise the manner of random weights in the following two variants: 1) the weights are randomly initialized only once during the whole training procedure; 2) the weights are randomly initialized at each training iteration epoch. Our propose belief can be directly inserted into existing networks without any training and testing computational cost. Extensive experiments across multiple image restoration tasks, including image de-noising, low-light image enhancement, guided image super-resolution demonstrate the consistent performance gains obtained by introducing our belief. To emphasize, our main focus is to spark the realms of loss function and save their current neglected status. Code will be publicly available

Cognitive Changes following Bilateral Deep Brain Stimulation of Subthalamic Nucleus in Parkinson's Disease: A Meta-Analysis

Background. Nowadays, it has been largely acknowledged that deep brain stimulation of subthalamic nucleus (STN DBS) can alleviate motor symptoms of Parkinson's disease, but its effects on cognitive function remain unclear, which are not given enough attention by many clinical doctors and researchers. To date, 3 existing meta-analyses focusing on this issue included self-control studies and have not drawn consistent conclusions. The present study is the first to compare effect sizes of primary studies that include control groups, hoping to reveal the net cognitive outcomes after STN DBS and the clinical significance. Methods. A structured literature search was conducted using strict criteria. Only studies with control group could be included. Data on age, duration of disease, levodopa equivalent dosage (LED), and multiple cognitive scales were collected and pooled. Results. Of 172 articles identified, 10 studies (including 3 randomized controlled trials and 7 nonrandomized controlled studies) were eligible for inclusion. The results suggest that STN DBS results in decreased global cognition, memory, verbal fluency, and executive function compared with control group. No significant difference is found in other cognitive domains. Conclusions. STN DBS seems relatively safe with respect to cognitive function, and further studies should focus on the exact mechanisms of possible verbal deterioration after surgery in the future

A new model to downscale urban and rural surface and air temperatures evaluated in Shanghai, China

A simple model, TsT2m (Surface Temperature and near surface air Temperature (at 2 m) model), is developed to downscale numerical model output (such as from ECMWF) to obtain higher temporal and spatial resolution surface and near surface air temperature. It is evaluated in Shanghai, China. Surface temperature (TS) and near surface air temperature (Ta) sub-models account for variations in land covers and their different thermal properties, resulting in spatial variations of surface and air temperature. The Net All Wave Radiation Parameterization (NARP) scheme is used to compute net wave radiation for the surface temperature sub-model, the Objective Hysteresis Model (OHM) is used to calculate the net storage heat fluxes, and the surface temperature is obtained by the force-restore method. The near surface air temperature sub-model considers the horizontal and vertical energy changes for a column of well mixed air above the surface. Modeled surface temperatures reproduce the general pattern of MODIS images well, while providing more detailed patterns of the surface urban heat island. However, the simulated surface temperatures capture the warmer urban land cover and are 10.3°C warmer on average than those derived from the coarser MODIS data. For other land cover types values are more similar. Downscaled, higher temporal and spatial resolution air temperatures are compared to observations at 110 Automatic Weather Stations across Shanghai. After downscaling with the TsT2m model, the average forecast accuracy of near surface air temperature is improved by about 20%. The scheme developed has considerable potential for prediction and mitigation of urban climate conditions, particularly for weather and climate services related to heat stres

Diagnostic value of cerebrospinal fluid human epididymis protein 4 for leptomeningeal metastasis in lung adenocarcinoma

BackgroundThe diagnosis of lung adenocarcinoma (LUAD) leptomeningeal metastasis (LM) remains a clinical challenge. Human epididymis protein 4 (HE4) functions as a novel tumor biomarker for cancers. This study aimed to assess the diagnostic value of cerebrospinal fluid (CSF) HE4, and combined with CEACAM6, for LUAD LM.MethodsThe CSF HE4 protein level was measured in two independent cohorts by electrochemiluminescence. Test cohort included 58 LUAD LM patients, 22 LUAD patients without LM (Wiot-LM), and 68 normal controls. Validation cohort enrolled 50 LUAD LM patients and 40 normal controls, in parallel with Wiot-LM patients without brain metastases (19 Wiot-LM/BrM patients) or with BrM (26 BrM patients). The CSF level of CEA, CA125, CA153, CA199, CA724, NSE and ProGRP of these samples was measured by electrochemiluminescence, whereas the CSF CEACAM6 level was detected by enzyme-linked immunosorbent assay (ELISA). In addition, the serum level of these biomarkers was detected by same method as CSF.ResultsThe level of HE4 or CEACAM6 in CSF samples from LUAD LM patients was significantly higher than those from normal controls and Wiot-LM patients. The HE4 or CEACAM6 level in CSF was higher than that in serum of LM patient. The CSF HE4 or CEACAM6 level for distinguished LM from Wiot-LM showed good performance by receiver-operating characteristic analysis. The better discriminative power for LM was achieved when HE4 was combined with CEACAM6. In addition, the CSF HE4 and CEACAM6 level showed little or no difference between Wiot-LM/BrM and BrM patients, the BrM would not significantly influence the HE4 or CEACAM6 level in CSF. The diagnostic power of CSF CA125, CA153, CA199, CA724, NSE and ProGRP for LUAD LM were not ideal.ConclusionThe combination with HE4 and CEACAM6 has the promising application for the diagnosis of LUAD LM

Cooperative ecological adaptive cruise control for plug-in hybrid electric vehicle based on approximate dynamic programming

Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. Additionally, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions

Ice-nucleating particles from multiple aerosol sources in the urban environment of Beijing under mixed-phase cloud conditions

Ice crystals occurring in mixed-phase clouds play a vital role in global precipitation and energy balance because of the unstable equilibrium between coexistent liquid droplets and ice crystals, which affects cloud lifetime and radiative properties, as well as precipitation formation. Satellite observations proved that immersion freezing, i.e., ice formation on particles immersed within aqueous droplets, is the dominant ice nucleation (IN) pathway in mixed-phase clouds. However, the impact of anthropogenic emissions on atmospheric IN in the urban environment remains ambiguous. In this study, we present in situ observations of ambient ice-nucleating particle number concentration (NINP) measured at mixed-phase cloud conditions (−30 ∘C, relative humidity with respect to liquid water RHw= 104 %) and the physicochemical properties of ambient aerosol, including chemical composition and size distribution, at an urban site in Beijing during the traditional Chinese Spring Festival. The impact of multiple aerosol sources such as firework emissions, local traffic emissions, mineral dust, and urban secondary aerosols on NINP is investigated. The results show that NINP during the dust event reaches up to 160 # L−1 (where “#” represents number of particles), with an activation fraction (AF) of 0.0036 % ± 0.0011 %. During the rest of the observation, NINP is on the order of 10−1 to 10 # L−1, with an average AF between 0.0001 % and 0.0002 %. No obvious dependence of NINP on the number concentration of particles larger than 500 nm (N500) or black carbon (BC) mass concentration (mBC) is found throughout the field observation. The results indicate a substantial NINP increase during the dust event, although the observation took place at an urban site with high background aerosol concentration. Meanwhile, the presence of atmospheric BC from firework and traffic emissions, along with urban aerosols formed via secondary transformation during heavily polluted periods, does not influence the observed INP concentration. Our study corroborates previous laboratory and field findings that anthropogenic BC emission has a negligible effect on NINP and that NINP is unaffected by heavy pollution in the urban environment under mixed-phase cloud conditions.</p

Size-dependent microwave heating and catalytic activity of fine iron particles in the deep dehydrogenation of hexadecane

Knowledge of the electromagnetic microwave radiation–solid matter interaction and ensuing mechanisms at active catalytic sites will enable a deeper understanding of microwave-initiated chemical interactions and processes, and will lead to further optimization of this class of heterogeneous catalysis. Here, we study the fundamental mechanism of the interaction between microwave radiation and solid Fe catalysts and the deep dehydrogenation of a model hydrocarbon, hexadecane. We find that the size-dependent electronic transition of particulate Fe metal from a microwave “reflector” to a microwave “absorber” lies at the heart of efficient metal catalysis in these heterogeneous processes. In this regard, the optimal particle size of a Fe metal catalyst for highly effective microwave-initiated dehydrogenation reactions is approximately 80–120 nm, and the catalytic performance is strongly dependent on the ratio of the mean radius of Fe particles to the microwave skin depth (r/δ) at the operating frequency. Importantly, the particle size of selected Fe catalysts will ultimately affect the basic heating properties of the catalysts and decisively influence their catalytic performance under microwave initiation. In addition, we have found that when two or more materials─present as a mechanical mixture─are simultaneously exposed to microwave irradiation, each constituent material will respond to the microwaves independently. Thus, the interaction between the two materials has been found to have synergistic effects, subsequently contributing to heating and improving the overall catalytic performance

The decarbonization of coal tar via microwave-initiated catalytic deep dehydrogenation

Coal tar, a major by-product of the coal industry, presents considerable difficulties in its refining and conversion into fuels due to its complex chemical composition and physical properties, such as high viscosity, corrosiveness, thermal instability, etc. Here we report a new route for producing hydrogen-rich gases together with carbonaceous materials, including carbon nanotubes, through the microwave-initiated catalytic deep dehydrogenation of coal tar using inexpensive iron catalysts. The resulting carbonaceous materials generated over the catalyst were investigated using a variety of techniques including scanning electron microscopy (SEM), transmission electron microscopy (TEM), temperature programmed oxidation (TPO) and Raman spectroscopy. Importantly, we have found that an aqueous emulsion feed of the coal tar enables considerably easier handling and an enhanced hydrogen production whilst also significantly reducing the extent of catalyst deactivation. This behaviour is shown to be assisted by the phenomenon of micro-explosion that enhances mass and heat transfer during the catalytic reactions