Selective compression learning of latent representations for variable-rate image compression

Recently, many neural network-based image compression methods have shown promising results superior to the existing tool-based conventional codecs. However, most of them are often trained as separate models for different target bit rates, thus increasing the model complexity. Therefore, several studies have been conducted for learned compression that supports variable rates with single models, but they require additional network modules, layers, or inputs that often lead to complexity overhead, or do not provide sufficient coding efficiency. In this paper, we firstly propose a selective compression method that partially encodes the latent representations in a fully generalized manner for deep learning-based variable-rate image compression. The proposed method adaptively determines essential representation elements for compression of different target quality levels. For this, we first generate a 3D importance map as the nature of input content to represent the underlying importance of the representation elements. The 3D importance map is then adjusted for different target quality levels using importance adjustment curves. The adjusted 3D importance map is finally converted into a 3D binary mask to determine the essential representation elements for compression. The proposed method can be easily integrated with the existing compression models with a negligible amount of overhead increase. Our method can also enable continuously variable-rate compression via simple interpolation of the importance adjustment curves among different quality levels. The extensive experimental results show that the proposed method can achieve comparable compression efficiency as those of the separately trained reference compression models and can reduce decoding time owing to the selective compression. The sample codes are publicly available at https://github.com/JooyoungLeeETRI/SCR.Comment: Accepted as a NeurIPS 2022 paper. [Github] https://github.com/JooyoungLeeETRI/SC

Comparison of fiberoptic bronchoscopic intubation using silicone and polyvinyl chloride double-lumen tubes

Background Direct insertion of a double-lumen tube (DLT) using a flexible fiberoptic bronchoscope (FOB) is an option for DLT intubation. The difficult process of fiberoptic intubation is that the different properties of polyvinyl chloride and silicone DLTs may affect railroading differently. Therefore, we aimed to compare intubation using polyvinyl chloride and silicone DLTs over an FOB. Methods Patients aged 19–75 years who required one-lung ventilation under general anesthesia were enrolled in this study. After induction of anesthesia, the anesthesiologist intubated the DLT using FOB. The primary outcome was the difficulty of railroading over the flexible FOB scaled into five grades (I, II-1, II-2, III, and IV). Additionally, the intubation time and mucosal damage were recorded. Results A total of 46 patients participated in this study, 23 each in the silicone and polyvinyl groups. The difficulty of railroading over the FOB was significantly different between the two groups (P < 0.001). In the silicone group, the grades of difficulty in railroading were limited to I and II-1; 20 patients (87%) presented no difficulty in advancing the tube. In contrast, in the polyvinyl group, 13 patients (57%) had scores of II-2 and III. Both the intubation time and mucosal damage were significantly better in the silicone group than in the polyvinyl group. Conclusions Intubation using a silicone DLT over an FOB was easier and faster than that with a polyvinyl chloride DLT with lesser trauma around the glottis

A study of thermal decomposition of phases in cementitious systems using HT-XRD and TG

Significant variations have been reported on the temperature range of thermal decomposition of cementitious phases. Thus, this study identified temperature ranges on the phases in actual cementitious systems (portland cement (OPC) pastes, blended pastes of ground granulated blast furnace slag (GGBFS) with OPC, and Ca(OH)2-activated GGBFS) by simultaneously using thermogravimetry (TG) and high-temperature X-ray diffraction (HT-XRD) as follows: (1) 81??-91 ??C for dehydration of ettringite, (2) ???80??-240 ??C for major dehydration of C-S-H, (3) ???241??-244 ??C for hydrogarnet, (4) ???129??-138 ??C for Al2O3-Fe2O3-mono phase (AFm), (5) ???411??-427 ??C for Ca(OH)2, and (6) ???648??-691 ??C for CaCO3. The CaO layers and SiO2 chains of C-S-H likely started to decompose from 615??-630 ??C, and eventually transformed to new crystalline phases. This study also demonstrated that (a) the quantity of calcite could be overestimated due to additional carbonation when Ca(OH)2 is plentifully present in samples, and (b) the quantification of phases would be greatly affected by sample particle size when GGBFS is used in the system

Prevention of Bradycardia during Spinal Anesthesia under Dexmedetomidine Sedation in Older Adults

Older adults exhibit reduced physiological responses to beta-adrenergic stimulation and parasympathetic inhibition. This study aimed to investigate the effect of reducing the incidence of bradycardia in the atropine and ephedrine pretreatment group compared to the control group in older adults who received spinal anesthesia with intravenous dexmedetomidine. Overall, 102 older adults aged over 65 years were randomly divided into three groups, and saline (control group), atropine at 0.5 mg (atropine group), and ephedrine at 8 mg (ephedrine group) were administered intravenously to each group as pretreatment. Immediately after spinal anesthesia, dexmedetomidine loading and study drug injections were commenced. The primary outcome was the incidence of bradycardia (p = 0.035), and no difference was noted between the atropine and ephedrine groups. Therefore, if ephedrine or atropine is selected and used according to the patient’s condition and clinical situation, it may be helpful in preventing bradycardia during spinal anesthesia using dexmedetomidine in older patients

Fast Video Encoding Algorithm for the Internet of Things Environment Based on High Efficiency Video Coding

Video data for the Internet traffic is increasing, and video data transmission is important for consideration of real-time process in the Internet of Things (IoT). Thus, in the IoT environment, video applications will be valuable approach in networks of smart sensor devices. High Efficiency Video Coding (HEVC) has been developed by the Joint Collaborative Team on Video Coding (JCT-VC) as a new generation video coding standard. Recently, HEVC includes range extensions (RExt), scalable coding extensions, and multiview extensions. HEVC RExt provides high resolution video with a high bit-depth and an abundance of color formats. In this paper, a fast intraprediction unit decision method is proposed to reduce the computational complexity of the HEVC RExt encoder. To design intramode decision algorithm, Local Binary Pattern (LBP) of the current prediction unit is used as texture feature. Experimental results show that the encoding complexity can be reduced by up to 12.35% on average in the AI-Main profile configuration with only a small bit-rate increment and a PSNR decrement, compared with HEVC test model (HM) 12.0-RExt4.0 reference software

Comparison of silicone double-lumen tube and polyvinyl chloride single-lumen tube in fiberoptic tracheal intubation on a difficult airway model: a randomized controlled non-inferiority trial

Abstract The management of patients with history or suspicion of difficult intubation can be challenging, especially in surgical procedures requiring one-lung ventilation. The ease of insertion of silicone double lumen tube (DLT) have previously been shown to be comparable to polyvinyl single lumen tube (SLT) in fiberoptic bronchoscope (FOB) tracheal intubation. Hence, in difficult airway situation, we hypothesized that the performance of insertion of silicone DLT would also be non-inferior to polyvinyl SLT in FOB intubation. We used a neck collar to mimic patients with difficult airway. 80 patients who required one-lung ventilation were enrolled in a prospective, randomized, non-inferiority trial. Patients were randomly allocated to the DLT or SLT groups (SLT with bronchial blocker). Neck collar was supplied to all patients before FOB intubation. The time of insertion for FOB, railroading, tracheal intubation, and total procedure were measured. The difficulty of railroading was evaluated in 4 grades. In the DLT group, the railroading was significantly shorter and easier comparing to the SLT group. The total procedure was also simpler and faster in the DLT group. While simulated difficult airways may not fully replicate actual difficult airways, we suggest that fiberoptic intubation with silicone DLT could be a feasible first-line option for patients with expected difficult airways requiring lung separation, unless the size of the DLT relative to the patient’s airway is problematic. Trial registration: NCT03392766

Effects of CaCl2 on hydration and properties of lime(CaO)-activated slag/fly ash binder

This study presented CaCl2 as a potential additive activator to develop a new strong, price-competitive CaO-activated GGBFS binder blended with fly ash (CAS 4:4:2) to commercially replace ternary blended cements, which generally consist of 40% Portland cement, 40% GGBFS, and 20% fly ash (wt.%) (PC 4:4:2), widely used for concrete production. Despite CAS 4:4:2 having no clinker cement compound, the addition of CaCl2 not only significantly accelerated reactions of CAS 4:4:2 binders but also largely increased strengths at all curing days. Up to 72 h, the cumulative reaction heat of CAS 4:4:2 with CaCl2 was also reasonably low. Reaction products and microstructures of hardened CAS 4:4:2 pastes were considerably changed after CaCl2 addition. The CaCl2 presence markedly promoted dissolution of the glass phase of GGBFS and fly ash in early days, resulting in more production of reaction products (e.g., C-S-H, hydrocalumite) and pore-size refinement

Mechanical and microstructural properties of lightweight CaO- activated fly ash composites in the presence of magnesium nitrate

The use of industrial by-products is required to replace cement to reduce carbon emission, and it is necessary to secure the strength of cementless binder to satisfy the required mechanical perfor-mance. The purpose of this study is to develop a new binder system composed of activated fly ash (FA) with CaO by adding magnesium nitrate ((Mg(NO3)2) as a new additive. To reveal the mecha-nism of strength enhancement, the influence of the dosage of Mg(NO3)2 on compressive strength, reaction products, and pore characteristics was observed. In addition, expanded perlite (EP) and expanded vermiculite (EV) were mixed in selected samples to develop a lightweight cementless composite. The results showed that adding Mg(NO3)2 significantly increased strength of CaO-activated FA system. In the reaction process, Mg(NO3)2 promoted the solubility of quicklime (CaO) to form more C-(A)-S-H and Ca-Mg-Al-(OH)-NO3-AFm phase and increased the initial con-centration of silicon (Si) and aluminum (Al) from FA. The dense C-(A)-S-H formation enhanced mechanical strength by reducing porosity in binder matrix. When EP and EV were mixed in the selected binder, even if the strength decreased as the amount of EP and EV increased, the require-ments for the strength and specific gravity of commercial autoclave aerated concrete (AAC) were satisfied

High-accuracy rebar position detection using deep learning-based frequency-difference electrical resistance tomography

Rebar corrosion is one of the most critical mechanisms causing structural deterioration in reinforced concrete structures. However, rebar corrosion assessment is difficult in that typical non-destructive testing methods have limitations in accurately detecting rebar positioning in concrete. This study presents high-accuracy rebar position detection using a deep learning-based electrical resistance tomography (ERT) technique. Two data sets were prepared as input data: (1) the original circular ERT images in a Cartesian coordinate system and (2) the transformed rectangular ERT images in a polar coordinate system. The proposed convolutional neural network (CNN) model successfully distinguished rebar position from ERT images. Most of the radial and angular positions of the rebar were accurately identified by the model, despite rebar&apos;s wide distribution of high conductivity in the raw ERT images. Notably, the detection performance clearly depended on the coordinate types in the ERT im-ages, whether they were Cartesian or polar coordinates