bluuebunny/crossref_metadata_2025_split huggingface dataset:
Postoperative cerebral infarction risk is related to lobectomy site in lung cancer: a retrospective cohort study of nationwide data in Japan
Authors: Nishizawa Natsumasa , Okawara Makoto , Mori Masataka , Fujino Yoshihisa , Matsuda Shinya , Fushimi Kiyohide , Tanaka Fumihiro 
Published: July 2022

ObjectivesTo determine the incidence of cerebral infarction after lobectomy at different sites using inpatient data from a diagnosis procedure combination database.DesignRetrospective cohort study.SettingData were retrieved from the Japanese diagnosis procedure combination inpatient database for patients who underwent lobectomies for lung cancer between April 2018 and March 2020.ParticipantsThe analysis included 37 352 patients from 556 institutions who underwent lobectomies for lung cancer.Main outcomes and measuresThe occurrence of cerebral infarction after lobectomy during hospitalisation was estimated using multilevel logistic regression models adjusted for sex, age, body mass index, smoking history, activity of daily living, surgical approach (thoracotomy or video-assisted), clinical cancer stage, comorbidities and hospital-level factors to describe the association between cerebral infarction and different lobectomy sites.ResultsOverall cerebral infarction after lobectomy occurred in 99 patients (0.27%): 29 with left upper lobectomy (0.39%), 19 with left lower lobectomy (0.34%), 32 with right upper lobectomy (0.24%), 6 with right middle lobectomy (0.21%) and 13 with right lower lobectomy (0.16%). The multilevel multivariate logistic regression analysis revealed high ORs for the left upper lobectomy and left lower lobectomy groups. In both univariate and multivariate analyses, left upper lobectomy had the highest OR for the occurrence of cerebral infarction compared with lobectomies at other sites.Conclusions and relevanceLeft upper lobectomy had the highest OR for the occurrence of cerebral infarction after lung cancer lobectomy during hospitalisation. There is an urgent need to investigate the specific mechanisms underlying postoperative cerebral infarction after left upper lobectomy and to establish preventive measures such as altering surgical methods, using radiological examinations for early detection and better use of anticoagulants.
Changes in STI and HIV testing and testing need among men who have sex with men during the UK’s COVID-19 pandemic response
Authors: Brown Jack RG , Reid David , Howarth Alison R , Mohammed Hamish , Saunders John , Pulford Caisey V , Hughes Gwenda , Mercer Catherine H 
Published: July 2022

ObjectivesWe examined the impact of COVID-19-related restrictions on sexual behaviours, STI and HIV testing and testing need among men who have sex with men (MSM) in the UK.MethodsWe used social media and dating applications to recruit to three cross-sectional surveys (S1–S3) during the UK’s pandemic response (S1: 23 June–14 July 2020; S2: 23 November–12 December 2020; S3: 23 March–14 April 2021). Surveys included lookback periods of around 3–4 months (P1–P3, respectively). Eligible participants were UK resident men (cisgender/transgender) and gender-diverse people assigned male at birth (low numbers of trans and gender-diverse participants meant restricting these analyses to cisgender men), aged ≥16 years who reported sex with men (cisgender/transgender) in the last year (S1: N=1950; S2: N=1463; S3: N=1487). Outcomes were: recent STI/HIV testing and unmet testing need (new male and/or multiple condomless anal sex partners without a recent STI/HIV test). Crude and adjusted associations with each outcome were assessed using logistic regression.ResultsParticipants’ sociodemographic characteristics were similar across surveys. The proportion reporting a recent STI and/or HIV test increased between P1 and P2 (25.0% to 37.2% (p<0.001) and 29.7% to 39.4% (p<0.001), respectively), then stabilised in P3 (40.5% reporting HIV testing). Unmet STI testing need increased across P1 and P2 (26.0% to 32.4%; p<0.001), but trends differed between groups, for example, unmet STI testing need was higher in bisexually-identifying (vs gay-identifying) MSM across periods (adjusted OR (aOR): P1=1.64; P2=1.42), but declined in HIV-positive (vs HIV-negative/unknown) MSM (aOR: P1=2.06; P2=0.68). Unmet HIV testing need increased across P1 and P2 (22.9% to 31.0%; p<0.001) and declined in P3 (25.1%; p=0.001). During P3, MSM reporting a low life-satisfaction level (vs medium–very high) had greater unmet need (aOR: 1.44), while from P2 onwards HIV pre-exposure prophylaxis users (vs non-users) had lower unmet need (aOR: P2=0.32; P3=0.50).ConclusionConsiderable unmet STI/HIV testing need occurred among MSM during COVID-19-related restrictions, especially in bisexually-identifying men and those reporting low life satisfaction. Improving access to STI/HIV testing in MSM is essential to prevent inequalities being exacerbated.
A deformed supersymmetric $$w_{1+\infty }$$ symmetry in the celestial conformal field theory
Authors: Ahn Changhyun 
Published: July 2022

AbstractBy using the K-free complex bosons and the K-free complex fermions, we construct the $$\mathcal {N}\,{=}\,2$$

N

=

2

supersymmetric $$W_{\infty }^{K,K}$$

W

∞

K
,
K

algebra which is the matrix generalization of previous $${{\mathcal {N}}}\,{=}\,2$$

N

=

2

supersymmetric $$W_{\infty }$$

W
∞

algebra. By twisting this $${{\mathcal {N}}}\,{=}\,2$$

N

=

2

supersymmetric $$W_{\infty }^{K,K}$$

W

∞

K
,
K

algebra, we obtain the $${{\mathcal {N}}}\,{=}\,1$$

N

=

1

supersymmetric $$W_{\infty }^{K}$$

W

∞

K

algebra which is the matrix generalization of known $${{\mathcal {N}}}\,{=}\,1$$

N

=

1

supersymmetric topological $$W_{\infty }$$

W
∞

algebra. From this two-dimensional symmetry algebra, we propose the operator product expansion (OPE) between the soft graviton and gravitino (as a first example), at nonzero deformation parameter, in the supersymmetric Einstein–Yang–Mills theory explicitly. Other six OPEs between the graviton, gravitino, gluon and gluino can be determined completely. At vanishing deformation parameter, we reproduce the known result of Fotopoulos, Stieberger, Taylor and Zhu on the above OPEs via celestial holography.
Joint neural phase retrieval and compression for energy- and computation-efficient holography on the edge
Authors: Wang Yujie , Chakravarthula Praneeth , Sun Qi , Chen Baoquan 
Published: July 2022

Recent deep learning approaches have shown remarkable promise to enable high fidelity holographic displays. However, lightweight wearable display devices cannot afford the computation demand and energy consumption for hologram generation due to the limited onboard compute capability and battery life. On the other hand, if the computation is conducted entirely remotely on a cloud server, transmitting lossless hologram data is not only challenging but also result in prohibitively high latency and storage.
In this work, by distributing the computation and optimizing the transmission, we propose the first framework that jointly generates and compresses high-quality phase-only holograms. Specifically, our framework asymmetrically separates the hologram generation process into high-compute remote encoding (on the server), and low-compute decoding (on the edge) stages. Our encoding enables light weight latent space data, thus faster and efficient transmission to the edge device. With our framework, we observed a reduction of 76% computation and consequently 83% in energy cost on edge devices, compared to the existing hologram generation methods. Our framework is robust to transmission and decoding errors, and approach high image fidelity for as low as 2 bits-per-pixel, and further reduced average bit-rates and decoding time for holographic videos.
Adjoint nonlinear ray tracing
Authors: Teh Arjun , O'Toole Matthew , Gkioulekas Ioannis 
Published: July 2022

Reconstructing and designing media with continuously-varying refractive index fields remains a challenging problem in computer graphics. A core difficulty in trying to tackle this inverse problem is that light travels inside such media along curves, rather than straight lines. Existing techniques for this problem make strong assumptions on the shape of the ray inside the medium, and thus limit themselves to media where the ray deflection is relatively small. More recently, differentiable rendering techniques have relaxed this limitation, by making it possible to differentiably simulate curved light paths. However, the automatic differentiation algorithms underlying these techniques use large amounts of memory, restricting existing differentiable rendering techniques to relatively small media and low spatial resolutions.
We present a method for optimizing refractive index fields that both accounts for curved light paths and has a small, constant memory footprint. We use the adjoint state method to derive a set of equations for computing derivatives with respect to the refractive index field of optimization objectives that are subject to nonlinear ray tracing constraints. We additionally introduce discretization schemes to numerically evaluate these equations, without the need to store nonlinear ray trajectories in memory, significantly reducing the memory requirements of our algorithm. We use our technique to optimize high-resolution refractive index fields for a variety of applications, including creating different types of displays (multiview, lightfield, caustic), designing gradient-index optics, and reconstructing gas flows.
Procedural texturing of solid wood with knots
Authors: Larsson Maria , Ijiri Takashi , Yoshida Hironori , Huber Johannes A. J. , Fredriksson Magnus , Broman Olof , Igarashi Takeo 
Published: July 2022

We present a procedural framework for modeling the annual ring pattern of solid wood with knots. Although wood texturing is a well-studied topic, there have been few previous attempts at modeling knots inside the wood texture. Our method takes the skeletal structure of a tree log as input and produces a three-dimensional scalar field representing the time of added growth, which defines the volumetric annual ring pattern. First, separate fields are computed around each strand of the skeleton, i.e., the stem and each knot. The strands are then merged into a single field using smooth minimums. We further suggest techniques for controlling the smooth minimum to adjust the balance of smoothness and reproduce the distortion effects observed around dead knots. Our method is implemented as a shader program running on a GPU with computation times of approximately 0.5 s per image and an input data size of 600 KB. We present rendered images of solid wood from pine and spruce as well as plywood and cross-laminated timber (CLT). Our results were evaluated by wood experts, who confirmed the plausibility of the rendered annual ring patterns.
Link to code: https://github.com/marialarsson/procedural_knots.
DR.JIT
Authors: Jakob Wenzel , Speierer Sébastien , Roussel Nicolas , Vicini Delio 
Published: July 2022

DR.JIT is a new just-in-time compiler for physically based rendering and its derivative. DR.JIT expedites research on these topics in two ways: first, it traces high-level simulation code (e.g., written in Python) and aggressively simplifies and specializes the resulting program representation, producing data-parallel kernels with state-of-the-art performance on CPUs and GPUs.
Second, it simplifies the development of differentiable rendering algorithms. Efficient methods in this area turn the derivative of a simulation into a simulation of the derivative. DR.JIT provides fine-grained control over the process of automatic differentiation to help with this transformation.
Specialization is particularly helpful in the context of differentiation, since large parts of the simulation ultimately do not influence the computed gradients. DR.JIT tracks data dependencies globally to find and remove redundant computation.
ADOP
Authors: Rückert Darius , Franke Linus , Stamminger Marc 
Published: July 2022

In this paper we present ADOP, a novel point-based, differentiable neural rendering pipeline. Like other neural renderers, our system takes as input calibrated camera images and a proxy geometry of the scene, in our case a point cloud. To generate a novel view, the point cloud is rasterized with learned feature vectors as colors and a deep neural network fills the remaining holes and shades each output pixel. The rasterizer renders points as one-pixel splats, which makes it very fast and allows us to compute gradients with respect to all relevant input parameters efficiently. Furthermore, our pipeline contains a fully differentiable physically-based photometric camera model, including exposure, white balance, and a camera response function. Following the idea of inverse rendering, we use our renderer to refine its input in order to reduce inconsistencies and optimize the quality of its output. In particular, we can optimize structural parameters like the camera pose, lens distortions, point positions and features, and a neural environment map, but also photometric parameters like camera response function, vignetting, and per-image exposure and white balance. Because our pipeline includes photometric parameters, e.g. exposure and camera response function, our system can smoothly handle input images with varying exposure and white balance, and generates high-dynamic range output. We show that due to the improved input, we can achieve high render quality, also for difficult input, e.g. with imperfect camera calibrations, inaccurate proxy geometry, or varying exposure. As a result, a simpler and thus faster deep neural network is sufficient for reconstruction. In combination with the fast point rasterization, ADOP achieves real-time rendering rates even for models with well over 100M points.
https://github.com/darglein/ADOP
Differentiable signed distance function rendering
Authors: Vicini Delio , Speierer Sébastien , Jakob Wenzel 
Published: July 2022

Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular interest but also poses severe challenges: shapes are intertwined with visibility, whose discontinuous nature introduces severe bias in computed derivatives unless costly precautions are taken. Shape representations like triangle meshes suffer from additional difficulties, since the continuous optimization of mesh parameters cannot introduce topological changes.
One common solution to these difficulties entails representing shapes using signed distance functions (SDFs) and gradually adapting their zero level set during optimization. Previous differentiable rendering of SDFs did not fully account for visibility gradients and required the use of mask or silhouette supervision, or discretization into a triangle mesh.
In this article, we show how to extend the commonly used sphere tracing algorithm so that it additionally outputs a reparameterization that provides the means to compute accurate shape parameter derivatives. At a high level, this resembles techniques for differentiable mesh rendering, though we show that the SDF representation admits a particularly efficient reparameterization that outperforms prior work. Our experiments demonstrate the reconstruction of (synthetic) objects without complex regularization or priors, using only a per-pixel RGB loss.
Generalized resampled importance sampling
Authors: Lin Daqi , Kettunen Markus , Bitterli Benedikt , Pantaleoni Jacopo , Yuksel Cem , Wyman Chris 
Published: July 2022

As scenes become ever more complex and real-time applications embrace ray tracing, path sampling algorithms that maximize quality at low sample counts become vital. Recent
resampling
algorithms building on Talbot et al.'s [2005] resampled importance sampling (RIS) reuse paths spatiotemporally to render surprisingly complex light transport with a few samples per pixel. These reservoir-based spatiotemporal importance resamplers (ReSTIR) and their underlying RIS theory make various assumptions, including sample independence. But sample reuse
introduces correlation
, so ReSTIR-style iterative reuse loses most convergence guarantees that RIS theoretically provides.

We introduce generalized resampled importance sampling (GRIS) to extend the theory, allowing RIS on correlated samples, with unknown PDFs and taken from varied domains. This solidifies the theoretical foundation, allowing us to derive variance bounds and convergence conditions in ReSTIR-based samplers. It also guides practical algorithm design and enables advanced path reuse between pixels via complex shift mappings.
We show a path-traced resampler (ReSTIR PT) running interactively on complex scenes, capturing many-bounce diffuse and specular lighting while shading just one path per pixel. With our new theoretical foundation, we can also modify the algorithm to guarantee convergence for offline renderers.
Algorithm 1030: SC-SR1: MATLAB Software for Limited-memory SR1 Trust-region Methods
Authors: Brust Johannes , Burdakov Oleg , Erway Jennifer , Marcia Roummel 
Published: July 2022

We present a MATLAB implementation of the symmetric rank-one (SC-SR1) method that solves trust-region subproblems when a limited-memory symmetric rank-one (L-SR1) matrix is used in place of the true Hessian matrix, which can be used for large-scale optimization. The method takes advantage of two shape-changing norms [Burdakov and Yuan
2002
; Burdakov et al.
2017
] to decompose the trust-region subproblem into two separate problems. Using one of the proposed norms, the resulting subproblems have closed-form solutions. Meanwhile, using the other proposed norm, one of the resulting subproblems has a closed-form solution while the other is easily solvable using techniques that exploit the structure of L-SR1 matrices. Numerical results suggest that the SC-SR1 method is able to solve trust-region subproblems to high accuracy even in the so-called “hard case.” When integrated into a trust-region algorithm, extensive numerical experiments suggest that the proposed algorithms perform well, when compared with widely used solvers, such as truncated conjugate-gradients.
Staff scheduling for a courier distribution centre using evolutionary algorithm
Authors: Si Ying Pua , Mohd Yusoh Zeratul Izzah 
Published: July 2022

Staff scheduling is a <span lang="EN-US">combinatorics optimization problem and companies face this complex task on daily basis in constructing a schedule fitting all conditions. In a courier distribution center, staffs are assigned to work in processes of a continuous workflow. Staffs have varying work ability for each process. Instead of generating staff schedule instinctively, it is an advantage to optimize staff’s schedule by measuring the performance of each staff. An optimized schedule improves the operation’s efficiency and fully utilize staffs’ work ability, hence, minimizing the cost. This paper proposed evolutionary algorithm, namely genetic algorithm as the solution to courier center staff scheduling. Based on the result, the produced schedule can reduce up to 30% of the staff in schedule while not affecting operation workflow. The cut down on number of working staffs could amount to a substantial reduction of operation cost every month. The generated schedule is significantly customized and take less time to complete an operation. Although the proposed solution is specific to the use case of a courier distribution center, it is however, potentially a generalize model for the logistics industry, introducing a more effective staff scheduling system to cope with the industry’s ever-rising demands.</span>
Hindi to English transliteration using multilayer gated recurrent units
Authors: Zeeshan Ansari Mohd , Ahmad Tanvir , Sufyan Beg Mirza Mohd , Ahmad Faiyaz 
Published: July 2022

Transliteration is <span lang="EN-US">the task of translating text from source script to target script provided that the language of the text remains the same. In this work, we perform transliteration on less explored Devanagari to Roman Hindi transliteration and its back transliteration. The neural transliteration model in this work is based on a sequence-to-sequence neural network that is composed of two major components, an encoder that transforms source language words into a meaningful representation and the decoder that is responsible for decoding the target language words. We utilize gated recurrent units (GRU) to design the multilayer encoder and decoder network. Among the several models, the multilayer model shows the best performance in terms of coupon equivalent rate (CER) and word error rate (WER). The method generates quite satisfactory predictions in Hindi-English bilingual machine transliteration with WER of 64.8% and CER of 20.1% which is a significant improvement over existing methods.</span>
Utilizing the right phosphor in near-ultraviolet and blue light-emitting diode devices to generate white illumination
Authors: Nguyen Thi Dieu An , Quoc Anh Nguyen Doan , Xuan Le Phan 
Published: July 2022

In the <span>scenario of solid-state lighting (SSL) gradually replacing old standard techniques; the pc-LEDs (or diodes based on conversion phosphor) becomes a common method for creating white illumination, based on SSL. As of now, both of the UV-LEDs and the blue LEDs have been still being considered for the task of creating white illumination through phosphor excitation as it hasn’t been known which LED type is truly superior. It is common that when it comes to phosphor, people will overlook the performance in LED devices with a wavelength range of 365 nm to 470 nm. Our research demonstrates the information concerning extrinsic quantum efficacy in the InxGa1-xN LED devices with the mentioned range as well as combines the information and the effectiveness of phosphor for the task of examining the performance of near-UV and blue LEDs and creating white illumination. In addition, the research demonstrates recreations for the task of assessing the white illumination mixtures under the correlated color temperature of 3000 K and 4000 K in the two LED structures.</span>
High-efficiency green and red phosphors enable a broader hue-gamut light-emitting diode backlight for brighter displays
Authors: Nguyen Thi Dieu An , Xuan Le Phan 
Published: July 2022

<p><span><span>In this article, </span></span>we suggest combining a blue InGaN chip with strait-band green <span><span>(β-sialon:Eu<sup>2+</sup>), red (K<sub>2</sub>SiF<sub>6</sub>:Mn<sup>4+</sup>) </span></span>phosphors to create WLED devices with a wide hue range and high effectiveness that may be utilized in LCD backlighting. The highest radiation wavelength of a gas-pressure sinted<span><span><br /></span></span><span>β-sialon:Eu<sup>2+ </sup></span>is 535 nm, the full width at half maximum (FWHM) is 54 nm, and the outside quantum performance is 54.0% lower than the 450 nm stimulation. We created K<sub>2</sub>SiF<sub>6</sub>:Mn<sup>4+</sup> in two steps. The phosphor possesses a sharp line radiation spectrum accompanied by the most intense maximum point under 631 nm, an FWHM reaching roughly 3 nm, as well as an exterior quantum effectiveness of 54.5%. When computed at 120 mA, the manufactured three-range wLEDs had an illuminating performance of 91–96 lm/W and a large color temperature of 11,184–13,769 K (i.e., 7,828–8,611 K in LCD screens). The hue range represented by the CIE 1931 and CIE 1976 hue gaps is 85.5-85.9% and 94.3-96.2% of the NTSC requirement, respectively. The optic characteristics outperform those of phosphor-transformed wLED backlights utilizing broad-range green or red phosphors, indicating the two strait-range phosphors studied are the best luminous substances for producing brighter and livelier screens.</p>