Merced College

Rain in the dark poses significant challenges to deploying real-world applications such as autonomous driving, surveillance systems, and night photography. Existing low-light enhancement or deraining methods struggle to brighten low-light conditions and remove rain simultaneously. Cascade approaches, like “deraining followed by low-light enhancement” and vice versa, often result in problematic rain patterns or overly blurred and overexposed images. To address these challenges, we introduce a novel two-stage model called L2RIRNet, which innovatively integrates low-light enhancement and deraining into a single framework in real-world settings. Our model comprises two key components: a Dual Degradation Representation Network (DDR-Net) and a Restoration Network. The DDR-Net independently learns degradation representations for luminance effects in dark areas and rain patterns in light areas, which are constrained by dual degradation loss and have not been discussed in the previous methods. The Restoration Network restores the degraded image using a Fourier Detail Guidance (FDG) module, which focuses on texture details in frequency and spatial domains to inform the restoration process and leverages near-rainless detailed images. Furthermore, we contribute a dataset containing both synthetic and real-world low-light-rainy images. Extensive experiments demonstrate that our L2RIRNet performs favorably against existing methods in synthetic and complex real-world scenarios. All the code and dataset can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/linxin0/Low_light_rainy</uri>.

Adam is arguably one of the most commonly used approach in deep learning and machine learning. With good regret bounds and empirical convergence proofs, the approach has produced many state-of-the-art models over a variety of problems. However, the method only uses gradient information at each iterate in addition to some moving averaged gradients and its corresponding moments from the past. In this paper, we propose a method that builds upon Adam and incorporates quasi-Newton matrices for approximating second derivatives. These Hessian approximations satisfy the so-called secant equation, which is the first-order Taylor series expansion of the gradient along the direction of the change in iterates. Judicious choices of quasi-Newton matrices can lead to guaranteed descent in the objective function and improved convergence. In this work, we integrate search directions obtained from using these quasi-Newton Hessian approximations with the Adam optimization algorithm. We provide convergence guarantees and demonstrate improved performance through an extensive experimentation on a variety of applications.

Sepsis is characterized by a severe systemic inflammatory response caused by hyperpermeability of the endothelial barrier resulting microvascular leakage, which is a leading factor to multiorgan failure. In sepsis, the hyperpermeable endothelial cells contribute to the activation of platelets, which release numerous mediators that affect coagulation, inflammatory response and are believed to directly or indirectly affect the integrity of the endothelial barrier. One such mediator is serotonin (5-hydroxytryptamine, 5-HT), a signaling molecule which mediates a number of cellular functions including regulation of cytoskeletal dynamics associated with barrier function of endothelial cells. The actions of 5-HT are mediated by different types of receptors and terminated via an uptake mechanism of a 5-HT transporter (SERT) on the platelet and endothelial cell. Earlier studies revealed unexpected discoveries concerning the impact of 5-HT signaling on the permeability of the endothelial barrier. These findings have been supported by the clinical reports on the anti-inflammatory property of 5-HT reuptake inhibitor, SSRIs in treating sepsis-related morbidity and mortality. This review focuses on a wide-range of literature to pinpoint cellular and molecular mechanisms that mediate 5-HT-induced microvascular injury in sepsis pathogenesis.

Career and college readiness interventions have been associated with numerous benefits for students. This chapter presents a case study of a developmental, comprehensive, culturally responsive career and college readiness unit. This chapter aims to demonstrate how one school counselor implemented a college and career readiness intervention through a culturally responsive lens, generating insights that potentially benefit comprehensive school counseling programming. Implications for K–12 school counselors to foster culturally responsive career and college readiness by using data to inform decisions, ensuring students at all grade levels are exposed to career awareness and exploration by using a tiered systems approach and developing a culturally affirming college culture, are discussed.

Christian Alvarado is

EVIDENCE-BASED ANSWER The lateral flow immunoassay urinary antigen tests for chlamydia have a sensitivity between 41% and 88% and a specificity between 89% and 99% (SOR: A, meta-analysis of cohort studies). The immunoassay urinary antigen test for gonorrhea has a sensitivity of 100% and a specificity between 93% and 98% (SOR: B, systematic review of cohort studies).

Unique properties of nanomaterials can be leveraged to increase the sustainability of food production and distribution.

We examined the role of transport velocity in the active assembly of microtubule spools. We found that transport velocity influences the kinetics of spool assembly, but not the steady-state properties of assembled spools.