End-to-End Paired Ambisonic-Binaural Audio Rendering

Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function(HRTF)datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.

Synaptic Transmission of Primary Hippocampal Neurons was Enhanced after Terahertz Waves Exposure

Terahertz(THz)waves,also known as T-rays,encompass frequencies ranging from 0.1 to 10 THz and possess unique properties that render them applicable in various biomedical domains,particularly in neurobiology[1].Synaptic transmission,the process through which signals propagate between neurons at synapses,is pivotal for brain function and information processing.

Gelatin and lipidoid integrate to create gelasomes to enhance siRNA delivery with low toxicity

RNAi therapeutics possess the potential to cure many uncurable human diseases.For instance,RNAi therapeutics using liposomes showed remarkable survival benefits in patients with liver diseases.However,the extension of liposomes to deliver RNA to cure other ailments has largely been unsuccessful.Therefore,researchers are focusing on designing and testing different combinations of materials for versatile RNA delivery applications.Yet,an efficient and safe RNA delivery platform has not been identified.In this work,we have developed a new class of RNA-delivery vehicle called“Gelasomes,”using an incongruous combination of gelatin and lipidoid to exploit each material’s unique properties while overcoming their inherent limitations.The low in vivo toxicity of Gelasomes is attributed to the exterior gelatin layers that shield the exposure of cationic lipidoid-siRNA clusters and yet present a biocompatible surface.Indeed,toxicity studies in mice indicate that repeated administration of Gelasomes(up to 48 mg/kg BW)is well-tolerated with no notable changes in body weight,hematology,or serum chemistry.Interestingly,the gelatin outer layer efficiently protects siRNA from serum degradation(48 h),preserving its functionality beyond two months of storage.Notably,Gelasomes possess dual siRNA conjugation modes,i.e.,electrostatic binding with lipidoid core and covalent attachment to gelatin surface.The bivalency coupled with lipidoids’high transfection efficiency rendered Gelasomes with remarkably high gene silencing efficiency(>90%)at very low treatment doses in vitro(40μg/mL).In vivo studies further confirmed the high gene silencing ability of Gelasomes in non-small cell lung tumor mouse models.This new platform is tunable on all fronts:size,degree of surface coating,and biomolecule functionalization.Truncating the lipidoid C14-tail to a C8-tail yielded Gelasomes of reduced size.As lipidoids with different carbon lengths are synthesizable,we can develop a library of Gelasomes with different sizes.The surface coating with less gelatin resulted in high transfection efficiency at low doses of Gelasomes.The structure of Gelasomes offers chemical handles to couple target-specific molecules like antibodies to tune their properties for efficient biological application.

Intertwined depressive and cognitive trajectories and the risk of dementia and death in older adults:a competing risk analysis

Background Depressive symptoms and cognitive impairment often interact,rendering their associations controversial.To date,their joint trajectories and associations with dementia and death remain underexplored.Aims To explore the interactions between depressive symptoms and cognitive function,their developmental trajectories and the associations with all-cause dementia,Alzheimer’s disease(AD)and all-cause death in older adults.Methods Data were from the Health and Retirement Study.Depressive symptoms and cognitive function were measured using the 8-item Centre for Epidemiologic Studies Depression Scale and the Telephone Interview of Cognitive Status,respectively.All-cause dementia and AD were defined by self-reported or proxy-reported physician diagnoses.All-cause death was determined by interviews.The restricted cubic spline,group-based trajectory modelling and subdistribution hazard regression were used.Results Significant interactions between depressive symptoms and cognitive function in 2010 in their association with new-onset all-cause dementia and AD from 2010 to 2020 were found,especially in women(p for interaction<0.05).Independent trajectory analysis showed that emerging or high(vs no)depressive trajectories and poor or rapidly decreased cognitive trajectories(vs very good)from 1996 to 2010 were at significantly higher risk of subsequent all-cause dementia,AD and all-cause death.15 joint trajectories of depressive symptoms and cognitive function from 1996 to 2010 were determined,where rapidly decreased cognitive function was more common in those with no depressive symptoms.Compared with older adults with the trajectory of no depressive symptoms and very good cognitive function,those with the trajectory of no depressive symptoms but rapidly decreased cognitive function were much more likely to develop new-onset all-cause dementia and death,with subdistribution hazard ratios(95%confidence intervals)of 4.47(2.99 to 6.67)and 1.84(1.43 to 2.36),especially in women.Conclusions To effectively mitigate the risk of dementia and death,it is crucial to acknowledge the importance of preventing cognitive decline in older adults without depressive symptoms,particularly in women.

Digital Twins:A New Model for Urban Governance

Avivid three-dimensional rendering of a bridge appeared on the intelligent monitor and evaluation platform at Beijing Yunlu Technology Co.,Ltd.,pointing to key factors of bridge health.Real-time status,events,transportation navigation,and traffic conditions were all tracked on the screen.Numerous sensors installed on bridge track every minor dynamic change on the bridge surface and perform digital analysis.

FREE SPEECH OR SINOPHOBIA? THE U.S. SENATE MUST DECIDE

The 100 men and women who make up the U.S.Senate will soon have an important choice.They will either affirm that the First Amendment matters or they will surrender to fear.The senators must decide whether to go along with their colleagues in the House of Representatives and require the popular social media app TikTok to be sold to an American company.If they do endorse the House vote,then ByteDance must divest itself of TikTok or see the app will be banned on U.S.-based social media platforms.

有关逃至准噶尔后罗布藏丹津的几份满文档案

This article is the first to translate and introduce more than ten Manchu documents related to Lobzangdanjin,and to explore in details into the historical truths reflected in the sources:escape of him in the second year of Yongzheng from Gasin Am,following north side of Kunlun Mountains heading to the west,and then entered into Jungaria via Keriye Corridor;during the reign of Emperor Yongzheng,there were several negotiations between Qing court and Jungarian Khanate regarding the deportation of Lobzangdanjin;in the 2Oth year of Qianlong reign,after Qing army conquered Dawachi and arrived in Ili,Lobzangdanjin with his son voluntarily surrendered;after being deported to Beijing,Emperor Qianlong pardoned the his sins and reused his two sons;as well as his circumstances during his stay in Jungaria.Author also corrected certain errors in Qing official records,such as the Qing Veritable Records and"Tables and Biographies of Mongolian Nobilities",as well as erroneous views in previous researches.

经阴道超声联合3D-TVS Render成像诊断子宫内膜病变的临床价值分析

目的分析经阴道超声联合三维超声(3D-TVS)Render成像诊断子宫内膜病变的临床价值。方法回顾性分析2019年11月至2022年11月我院收治的123例子宫内膜病变患者的临床资料,术前或诊刮前均行经阴道超声、3D-TVS Render成像检查,以病理检查结果作为“金标准”,分析经阴道超声联合3D-TVS Render成像对子宫内膜病变的诊断价值。结果经病理检查结果显示,123例患者中子宫内膜癌24例,子宫内膜良性病变99例;子宫内膜癌患者经阴道超声检查的子宫内膜厚度(ET)高于良性病变患者,经3D-TVS Render成像检查的子宫内膜血管指数(VI)、血流指数(FI)、血管血流指数(VFI)高于良性病变患者(P<0.05);绘制ROC曲线结果显示,ET、VI、FI、VFI联合诊断子宫内膜病变的AUC为0.968,高于各指标单独诊断。结论经阴道超声联合3D-TVS Render成像对子宫内膜病变的诊断价值较高,值得临床推广应用。

Research and Application of Caideng Model Rendering Technology for Virtual Reality

With the development of virtual reality (VR) technology, more and more industries are beginning to integrate with VR technology. In response to the problem of not being able to directly render the lighting effect of Caideng in digital Caideng scenes, this article analyzes the lighting model. It combines it with the lighting effect of Caideng scenes to design an optimized lighting model algorithm that fuses the bidirectional transmission distribution function (BTDF) model. This algorithm can efficiently render the lighting effect of Caideng models in a virtual environment. And using image optimization processing methods, the immersive experience effect on the VR is enhanced. Finally, a Caideng roaming interactive system was designed based on this method. The results show that the frame rate of the system is stable during operation, maintained above 60 fps, and has a good immersive experience.

Deep residual learning for denoising Monte Carlo renderings

Learning-based techniques have recently been shown to be effective for denoising Monte Carlo rendering methods. However, there remains a quality gap to state-of-the-art handcrafted denoisers. In this paper, we propose a deep residual learning based method that outperforms both state-of-the-art handcrafted denoisers and learning-based denoisers.Unlike the indirect nature of existing learning-based methods(which e.g., estimate the parameters and kernel weights of an explicit feature based filter), we directly map the noisy input pixels to the smoothed output. Using this direct mapping formulation, we demonstrate that even a simple-and-standard ResNet and three common auxiliary features(depth, normal,and albedo) are sufficient to achieve high-quality denoising. This minimal requirement on auxiliary data simplifies both training and integration of our method into most production rendering pipelines. We have evaluated our method on unseen images created by a different renderer. Consistently superior quality denoising is obtained in all cases.

Targeted approaches to improve tomato fruit taste

Tomato(Solanum lycopersicum)is the most valuable fruit and horticultural crop species worldwide.Compared with the fruits of their progenitors,those of modern tomato cultivars are,however,often described as having unsatisfactory taste or lacking f lavor.The f lavor of a tomato fruit arises from a complex mix of tastes and volatile metabolites,including sugars,acids,amino acids,and various volatiles.However,considerable differences in fruit f lavor occur among tomato varieties,resulting in mixed consumer experiences.While tomato breeding has traditionally been driven by the desire for continual increases in yield and the introduction of traits that provide a long shelf-life,consumers are prepared to pay a reasonable premium for taste.Therefore,it is necessary to characterize preferences of tomato f lavor and to define its underlying genetic basis.Here,we review recent conceptual and technological advances that have rendered this more feasible,including multi-omics-based QTL and association analyses,along with the use of trained testing panels,and machine learning approaches.This review proposes how the comprehensive datasets compiled to date could allow a precise rational design of tomato germplasm resources with improved organoleptic quality for the future.

How can the Dual-atom Catalyst FeCo-NC Surpass Singleatom Catalysts Fe-NC/Co-NC in CO_(2) RR?-CO Intermediate Assisted Promotion via a Synergistic Effect

Atomically dispersed catalysts are widely adopted in CO_(2)reduction reaction(CO_(2)RR)due to maximal atomic utilization and high catalytic activity.Dual-atom catalysts(DACs),with more dispersed active sites and distinct electronic structures compared with single-atom catalysts(SACs),may exhibit diverse catalytic performance.Herein,the DAC FeCo-NC and SAC Fe-NC/Co-NC are employed as probes to explore DACs advantage in CO_(2)RR.Results show that the moderate interaction between the dual-atom center and N coordination balances structural stability and catalytic activity.CO is the only product on Fe-NC/Co-NC,and the high limiting potentials from−1.22 to−1.67 V inhibit further reduction.FeCo-NC assisted with CO intermediate exhibits low limiting potentials of−0.64 V for both CH_(3)OH and CH 4,comparable to those on Cu-based catalysts.Under circumstance of applied potentials,CO_(2)RR on FeCo-NC has greater advantages in yielding CH_(3)OH and CH 4 than that on Fe-NC/Co-NC,and hydrogen evolution reaction is severely inhibited.The intrinsic essence is that dual-atom center can provide large spin-polarization and multi-electron transfer capability,rendering CO intermediates as effective electronic and geometric modifiers in CO_(2)RR.This work highlights FeCo-NC as a high-performance CO_(2)RR catalyst toward deep-reduction C1 products and elucidates CO intermediate assisted promotion mechanism via a dual-atom synergistic effect.

A survey of real-time rendering on Web3D application

Background In recent years, with the rapid development of mobile Internet and Web3D technologies, a large number of web-based online 3D visualization applications have emerged. Web3D applications, including Web3D online tourism, Web3D online architecture, Web3D online education environment, Web3D online medical care, and Web3D online shopping are examples of these applications that leverage 3D rendering on the web. These applications have pushed the boundaries of traditional web applications that use text, sound, image, video, and 2D animation as their main communication media, and resorted to 3D virtual scenes as the main interaction object, enabling a user experience that delivers a strong sense of immersion. This paper approached the emerging Web3D applications that generate stronger impacts on people's lives through “real-time rendering technology”, which is the core technology of Web3D. This paper discusses all the major 3D graphics APIs of Web3D and the well-known Web3D engines at home and abroad and classify the real-time rendering frameworks of Web3D applications into different categories. Results Finally, this study analyzed the specific demand posed by different fields to Web3D applications by referring to the representative Web3D applications in each particular field. Conclusions Our survey results show that Web3D applications based on real-time rendering have in-depth sectors of society and even family, which is a trend that has influence on every line of industry.

3D-TVS Render成像联合OmniView技术诊断宫腔粘连的价值探究

目的:探究经阴道三维超声(3D-TVS)三维渲染(Render)成像技术与自由解剖(OmniView)成像技术联合指导宫腔粘连(IUA)诊断的价值。方法:将2019年4月-2022年4月宜春市妇幼保健院收治的临床疑诊为IUA,且均接受阴道超声检查的76例患者所有资料进行回顾性分析,检查结果以宫腔镜为准,与3D-TVS Render、OmniView对比,评价两种技术联合使用对IUA的诊断效能。结果:Render技术漏诊18例(18/63,28.57%),误诊3例(3/13,23.08%),其诊断的敏感度为71.43%,特异度为76.92%,阳性预测值为93.75%,阴性预测值为35.71%,准确率为72.37%。OmniView技术漏诊14例(14/63,22.22%),误诊2例(2/13,15.38%),其诊断的敏感度为77.78%,特异度为84.62%,阳性预测值为96.08%,阴性预测值为44.00%,准确率为78.95%。Render联合OmniView技术漏诊6例(6/63,9.52%),误诊1例(1/13,7.69%),其诊断的敏感度为90.48%,特异度为92.31%,阳性预测值为98.28%,阴性预测值为66.67%,准确率为90.79%。Render、OmniView技术与宫腔镜检查的一致性检验的Kappa值分别为0.332、0.457;联合诊断与宫腔镜检查的一致性检验的Kappa值为0.718,一致性水平好。结论:3D-TVS Render成像联合OmniView成像技术诊断IUA准确率较高,且操作简单、无创,具有临床诊断价值。

A Special Issue:“Optimization and Key Equipments of Integrated Energy System”for Global Energy Interconnection

Energy is a critical basis for the survival and progress of humanity.Traditional energy systems,which are planned,designed,and operated in isolation,have artificially disrupted the interconnections among various energy forms.This limitation has reduced the reliability and flexibility of system operations,rendering them unsuitable for societal advancement.Integrated energy systems(IESs)dismantle the technical,market,and managerial barriers inherent in traditional systems.

Rendering algorithms for aberrated human vision simulation

Vision-simulated imagery―the process of generating images that mimic the human visual system―is a valuable tool with a wide spectrum of possible applications, including visual acuity measurements, personalized planning of corrective lenses and surgeries, vision-correcting displays, vision-related hardware development, and extended reality discomfort reduction. A critical property of human vision is that it is imperfect because of the highly influential wavefront aberrations that vary from person to person. This study provides an overview of the existing computational image generation techniques that properly simulate human vision in the presence of wavefront aberrations. These algorithms typically apply ray tracing with a detailed description of the simulated eye or utilize the point-spread func-tion of the eye to perform convolution on the input image. Based on the description of the vision simulation tech-niques, several of their characteristic features have been evaluated and some potential application areas and research directions have been outlined.

Fully Bayesian analysis of the relevance vector machine classification for imbalanced data problem

Relevance Vector Machine(RVM)is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model.Relevance Vector Machine classification suffers from theoretical limitations and computational inefficiency mainly because there is no closed-form solution for the posterior of the weight parameters.We propose two advanced Bayesian approaches for RVM classification,namely the Enhanced RVM and the Reinforced RVM,to perfect the theoretic framework of RVM and extend the algorithm to the imbalanced data problem,which has an arresting skew in data size between classes.First,the Enhanced RVM conducts a strict Bayesian sampling process instead of the approximation method in the original one to remedy its theoretic limitations,especially the nonconvergence of the iterations.Secondly,we conjecture that the hierarchical prior makes the Reinforced RVM achieve consistent estimations of the quantities of interest compared with the non-consistent estimations of the original RVM.Consistency is necessary for RVM classification since it makes the model more stable and localises the relevant vectors more accurately in the imbalanced data problem.The two-level prior also renders the Reinforced one competitive in the imbalanced data problem by building the inner connection of parameter dimensions and alloting a more vital relevance to the small class data weight parameter.The theoretic proofs and several numeric studies demonstrate the merits of our two proposed algorithms.

An Improved HVQ Algorithm for Compression and Rendering of Space Environment Volume Data with Multi-correlated Variables

Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated variables at the same time.However,existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable,not dealing with the redundant information among these variables.For space environment volume data with multi-correlated variables,based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables.The volume data associated with each variable is divided into disjoint blocks of size 43 initially.The blocks are represented as two levels,a mean level and a detail level.The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level.To both global levels,a splitting based on a principal component analysis is applied to compute initial codebooks.Then,LBG algorithm is conducted for codebook refinement and quantization.We further take advantage of progressive rendering based on GPU for real-time interactive visualization.Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data,including>5,>10,>30 and>50 MeV integrated proton flux.The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression,achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis.To overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the image.And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models.In this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and PSNR.The experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

Perceptual Optimization for Point-Based Point Cloud Rendering

Point-based rendering is a common method widely used in point cloud rendering.It realizes rendering by turning the points into the base geometry.The critical step in point-based rendering is to set an appropriate rendering radius for the base geometry,usually calculated using the average Euclidean distance of the N nearest neighboring points to the rendered point.This method effectively reduces the appearance of empty spaces between points in rendering.However,it also causes the problem that the rendering radius of outlier points far away from the central region of the point cloud sequence could be large,which impacts the perceptual quality.To solve the above problem,we propose an algorithm for point-based point cloud rendering through outlier detection to optimize the perceptual quality of rendering.The algorithm determines whether the detected points are outliers using a combination of local and global geometric features.For the detected outliers,the minimum radius is used for rendering.We examine the performance of the proposed method in terms of both objective quality and perceptual quality.The experimental results show that the peak signal-to-noise ratio(PSNR)of the point cloud sequences is improved under all geometric quantization,and the PSNR improvement ratio is more evident in dense point clouds.Specifically,the PSNR of the point cloud sequences is improved by 3.6%on average compared with the original algorithm.The proposed method significantly improves the perceptual quality of the rendered point clouds and the results of ablation studies prove the feasibility and effectiveness of the proposed method.