Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

We study contextual linear bandit problems under uncertainty on features; they are noisy with missing entries. To address the challenges from the noise, we analyze Bayesian oracles given observed noisy features. Our Bayesian analysis finds that the optimal hypothesis can be far from the underlying realizability function, depending on noise characteristics, which is highly non-intuitive and does not occur for classical noiseless setups. This implies that classical approaches cannot guarantee a non-trivial regret bound. We thus propose an algorithm aiming at the Bayesian oracle from observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret bound with respect to feature dimension $d$ and time horizon $T$. We demonstrate the proposed algorithm using synthetic and real-world datasets.Comment: 30 page

Genotypic Characterization of Vibrio vulnificus Clinical Isolates in Korea

AbstractObjectivesVibrio vunificus is known to cause septicemia and severe wound infections in patients with chronic liver diseases or an immuno-compromised condition. We carried out the molecular characterization of V. vulnificus isolates from human Vibrio septicemia cases based on pulsed-field gel electrophoresis (PFGE) using NotI and SfiI.Methods and ResultsPFGE was used to characterize a total of 78 strains from clinical cases after NotI or SfiI digestion. The geographical distribution of PFGE patterns for the strains from the southern part of Korea, a high-risk region for Vibrio septicemia, indicated that the isolates from southeastern Korea showed a comparatively higher degree of homology than those from southwestern Korea.ConclusionsWe report the genetic distribution of V. vulnficus isolated from Vibrio septicemia cases during 2000–2004 in Korea. This method has potential use as a subspecies-typing tool for V. vulnificus strains isolated from distant geographic regions

Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors

As the physical size of recent CMOS image sensors (CIS) gets smaller, the latest mobile cameras are adopting unique non-Bayer color filter array (CFA) patterns (e.g., Quad, Nona, QxQ), which consist of homogeneous color units with adjacent pixels. These non-Bayer sensors are superior to conventional Bayer CFA thanks to their changeable pixel-bin sizes for different light conditions but may introduce visual artifacts during demosaicing due to their inherent pixel pattern structures and sensor hardware characteristics. Previous demosaicing methods have primarily focused on Bayer CFA, necessitating distinct reconstruction methods for non-Bayer patterned CIS with various CFA modes under different lighting conditions. In this work, we propose an efficient unified demosaicing method that can be applied to both conventional Bayer RAW and various non-Bayer CFAs' RAW data in different operation modes. Our Knowledge Learning-based demosaicing model for Adaptive Patterns, namely KLAP, utilizes CFA-adaptive filters for only 1% key filters in the network for each CFA, but still manages to effectively demosaic all the CFAs, yielding comparable performance to the large-scale models. Furthermore, by employing meta-learning during inference (KLAP-M), our model is able to eliminate unknown sensor-generic artifacts in real RAW data, effectively bridging the gap between synthetic images and real sensor RAW. Our KLAP and KLAP-M methods achieved state-of-the-art demosaicing performance in both synthetic and real RAW data of Bayer and non-Bayer CFAs

Fully Quantized Always-on Face Detector Considering Mobile Image Sensors

Despite significant research on lightweight deep neural networks (DNNs) designed for edge devices, the current face detectors do not fully meet the requirements for "intelligent" CMOS image sensors (iCISs) integrated with embedded DNNs. These sensors are essential in various practical applications, such as energy-efficient mobile phones and surveillance systems with always-on capabilities. One noteworthy limitation is the absence of suitable face detectors for the always-on scenario, a crucial aspect of image sensor-level applications. These detectors must operate directly with sensor RAW data before the image signal processor (ISP) takes over. This gap poses a significant challenge in achieving optimal performance in such scenarios. Further research and development are necessary to bridge this gap and fully leverage the potential of iCIS applications. In this study, we aim to bridge the gap by exploring extremely low-bit lightweight face detectors, focusing on the always-on face detection scenario for mobile image sensor applications. To achieve this, our proposed model utilizes sensor-aware synthetic RAW inputs, simulating always-on face detection processed "before" the ISP chain. Our approach employs ternary (-1, 0, 1) weights for potential implementations in image sensors, resulting in a relatively simple network architecture with shallow layers and extremely low-bitwidth. Our method demonstrates reasonable face detection performance and excellent efficiency in simulation studies, offering promising possibilities for practical always-on face detectors in real-world applications.Comment: Accepted to ICCV 2023 Workshop on Low-Bit Quantized Neural Networks (LBQNN), Ora

Effect of blood pressure and glycemic control on the plasma cell-free DNA in hemodialysis patients

AbstractBackgroundThe plasma levels of cell-free DNA (cfDNA) are known to be elevated under inflammatory or apoptotic conditions. Increased cfDNA levels have been reported in hemodialysis (HD) patients. The aim of this study was to investigate the clinical significance of cfDNA in HD patients.MethodsA total of 95 patients on HD were enrolled. We measured their predialysis cfDNA levels using real-time EIF2C1 gene sequence amplification and analyzed its association with certain clinical parameters.ResultsThe mean plasma cfDNA level in the HD patients was 3,884 ± 407 GE/mL, and the mean plasma cfDNA level in the control group was 1,420 ± 121 GE/mL (P < 0.05). Diabetic patients showed higher plasma cfDNA levels compared with nondiabetic patients (P < 0.01). Patients with cardiovascular complications also showed higher plasma cfDNA levels compared with those without cardiovascular complication (P < 0.05). In univariable analysis, the cfDNA level was associated with 3-month mean systolic blood pressure (SBP), white blood cell, serum albumin, creatinine (Cr), normalized protein catabolic rate in HD patients. In diabetic patients, it was significantly correlated with SBP, hemoglobin A1c, and serum albumin. In multivariate analysis, SBP was the independent determinant for the cfDNA level. In diabetic patients, cfDNA level was independently associated with hemoglobin A1c and SBP.ConclusionsIn patients with HD, cfDNA is elevated in diabetic patients and patients with cardiovascular diseases. Uncontrolled hypertension and poor glycemic control are independent determinants for the elevated cfDNA. Our data suggest that cfDNA might be a marker of vascular injury rather than proinflammatory condition in HD patients

Deficiency of peroxiredoxin 2 exacerbates angiotensin II-induced abdominal aortic aneurysm

Abdominal aortic aneurysm: Potential enzyme biomarker identified An enzyme with antioxidant properties may provide a biomarker and therapeutic agent to help treat abdominal aortic aneurysm (AAA). AAA involves the structural deterioration of the aorta through chronic inflammation and oxidative stress, and can trigger life-threatening artery rupture. An antioxidant enzyme called peroxiredoxin 2 (PRDX2) is increased in patients with ruptures, but whether its role in AAA is beneficial or detrimental is unclear. Goo Taeg Oh at the Ewha Womans University in Seoul, Jong-Gil Park at the Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea, and co-workers examined the effect of PRDX2 on AAA progression. PRDX2 suppressed structural damage in mice, limiting artery dilation and protein degradation. Loss of PRDX2 accelerated AAA development. Measuring levels of PRDX2 may indicate AAA severity in patients, while boosting the enzyme could repair aortic damage

The effect of low-dose intravenous bisphosphonate treatment on osteoporosis in children with quadriplegic cerebral palsy

PurposeQuadriplegic children with cerebral palsy are more susceptible to osteoporosis because of various risk factors that interfere with bone metabolism. Pamidronate is effective for pediatric osteoporosis, but there are no guidelines for optimal dosage or duration of treatment in quadriplegic children with osteoporosis. We aimed to evaluate the efficacy of low-dose pamidronate treatment in these patients.MethodsTen quadriplegic patients on antiepileptic drugs (6 male, 4 female patients; mean age, 10.9±5.76 years), with osteoporosis and gross motor function classification system level V, were treated with pamidronate (0.5–1.0 mg/kg/day, 2 consecutive days) every 3–4 months in a single institution. The patients received oral supplements of calcium and vitamin D before and during treatment. The lumbar spine bone mineral density (BMD) z score and biochemical markers of bone metabolism were measured regularly during treatment.ResultsThe main underlying disorder was perinatal hypoxic brain damage (40%, 4 of 10). The mean cumulative dose of pamidronate was 4.49±2.22 mg/kg/yr, and the mean treatment period was 10.8±3.32 months. The BMD z score of the lumbar spine showed a significant increase from −4.22±1.24 before treatment to −2.61±1.69 during treatment (P=0.008). Alkaline phosphatase decreased during treatmentn (P=0.037). Significant adverse drug reactions and new fractures were not reported.ConclusionLow-dose pamidronate treatment for quadriplegic children with cerebral palsy increased lumbar BMD and reduced the incidence of fracture

Evaluation of the pathogenicity of GJB3 and GJB6 variants associated with nonsyndromic hearing loss

AbstractA number of genes responsible for hearing loss are related to ion recycling and homeostasis in the inner ear. Connexins (Cx26 encoded by GJB2, Cx31 encoded by GJB3 and Cx30 encoded by GJB6) are core components of gap junctions in the inner ear. Gap junctions are intercellular communication channels and important factors that are associated with hearing loss. To date, a molecular genetics study of GJB3 and GJB6 as a causative gene for hearing loss has not been performed in Korea. This study was therefore performed to elucidate the genetic characteristics of Korean patients with nonsyndromic sensorineural hearing loss and to determine the pathological mechanism of hearing loss by analyzing the intercellular communication function of Cx30 and Cx31 variants. Sequencing analysis of the GJB3 and GJB6 genes in our population revealed a total of nine variants, including four novel variants in the two genes. Three of the novel variants (Cx31-p.V27M, Cx31-p.V43M and Cx-30-p.I248V) and two previously reported variants (Cx31-p.V84I and Cx30-p.A40V) were selected for functional studies using a pathogenicity prediction program and assessed for whether the mutations were located in a conserved region of the protein. The results of biochemical and ionic coupling tests showed that both the Cx31-p.V27M and Cx31-p.V84I variants did not function normally when each was expressed as a heterozygote with the wild-type Cx31. This study demonstrated that two variants of Cx31 were pathogenic mutations with deleterious effect. This information will be valuable in understanding the pathogenic role of GJB3 and GJB6 mutations associated with hearing loss