bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficiency-aware multiple importance sampling for bidirectional rendering algorithms
Authors: Grittmann Pascal , Yazici Ömercan , Georgiev Iliyan , Slusallek Philipp
Published: July 2022

Multiple importance sampling (MIS) is an indispensable tool in light-transport simulation. It enables robust Monte Carlo integration by combining samples from several techniques. However, it is well understood that such a combination is not always more efficient than using a single sampling technique. Thus a major criticism of complex combined estimators, such as bidirectional path tracing, is that they can be significantly less efficient on common scenes than simpler algorithms like forward path tracing. We propose a general method to improve MIS efficiency: By cheaply estimating the efficiencies of various technique and sample-count combinations, we can pick the best one. The key ingredient is a numerically robust and efficient scheme that uses the samples of one MIS combination to compute the efficiency of multiple other combinations. For example, we can run forward path tracing and use its samples to decide which subset of VCM to enable, and at what sampling rates. The sample count for each technique can be controlled per-pixel or globally. Applied to VCM, our approach enables robust rendering of complex scenes with caustics, without compromising efficiency on simpler scenes.
EyeNeRF
Authors: Li Gengyan , Meka Abhimitra , Mueller Franziska , Buehler Marcel C. , Hilliges Otmar , Beeler Thabo
Published: July 2022

A unique challenge in creating high-quality animatable and relightable 3D avatars of real people is modeling human eyes, particularly in conjunction with the surrounding periocular face region. The challenge of synthesizing eyes is multifold as it requires 1) appropriate representations for the various components of the eye and the periocular region for coherent viewpoint synthesis, capable of representing diffuse, refractive and highly reflective surfaces, 2) disentangling skin and eye appearance from environmental illumination such that it may be rendered under novel lighting conditions, and 3) capturing eyeball motion and the deformation of the surrounding skin to enable re-gazing.

These challenges have traditionally necessitated the use of expensive and cumbersome capture setups to obtain high-quality results, and even then, modeling of the full eye region holistically has remained elusive. We present a novel geometry and appearance representation that enables high-fidelity capture and photorealistic
animation, view synthesis and relighting of the eye region
using only a sparse set of lights and cameras. Our hybrid representation combines an explicit parametric surface model for the eyeball surface with implicit deformable volumetric representations for the periocular region and the interior of the eye. This novel hybrid model has been designed specifically to address the various parts of that exceptionally challenging facial area - the explicit eyeball surface allows modeling refraction and high frequency specular reflection at the cornea, whereas the implicit representation is well suited to model lower frequency skin reflection via spherical harmonics and can represent non-surface structures such as hair (i.e. eyebrows) or highly diffuse volumetric bodies (i.e. sclera), both of which are a challenge for explicit surface models. Tightly integrating the two representations in a joint framework allows controlled photoreal image synthesis and joint optimization of both the geometry parameters of the eyeball and the implicit neural network in continuous 3D space. We show that for high-resolution close-ups of the human eye, our model can synthesize high-fidelity animated gaze from novel views under unseen illumination conditions, allowing to generate visually rich eye imagery.
Free2CAD
Authors: Li Changjian , Pan Hao , Bousseau Adrien , Mitra Niloy J.
Published: July 2022

CAD modeling, despite being the industry-standard, remains restricted to usage by skilled practitioners due to two key barriers. First, the user must be able to mentally parse a final shape into a valid sequence of supported CAD commands; and second, the user must be sufficiently conversant with CAD software packages to be able to execute the corresponding CAD commands. As a step towards addressing both these challenges, we present Free2CAD wherein the user can simply sketch the final shape and our system parses the input strokes into a sequence of commands expressed in a simplified CAD language. When executed, these commands reproduce the sketched object. Technically, we cast sketch-based CAD modeling as a sequence-to-sequence translation problem, for which we leverage the powerful Transformers neural network architecture. Given the sequence of pen strokes as input, we introduce the new task of grouping strokes that correspond to individual CAD operations. We combine stroke grouping with geometric fitting of the operation parameters, such that intermediate groups are geometrically corrected before being reused, as context, for subsequent steps in the sequence inference. Although trained on synthetically-generated data, we demonstrate that Free2CAD generalizes to sketches created from real-world CAD models as well as to sketches drawn by novice users.

Code and data are at https://github.com/Enigma-li/Free2CAD.
Grid-free Monte Carlo for PDEs with spatially varying coefficients
Authors: Sawhney Rohan , Seyb Dario , Jarosz Wojciech , Crane Keenan
Published: July 2022

Partial differential equations (PDEs) with spatially varying coefficients arise throughout science and engineering, modeling rich heterogeneous material behavior. Yet conventional PDE solvers struggle with the immense complexity found in nature, since they must first discretize the problem---leading to spatial aliasing, and global meshing/sampling that is costly and error-prone. We describe a method that approximates neither the domain geometry, the problem data, nor the solution space, providing the exact solution (in expectation) even for problems with extremely detailed geometry and intricate coefficients. Our main contribution is to extend the
walk on spheres (WoS)
algorithm from constant- to variable-coefficient problems, by drawing on techniques from volumetric rendering. In particular, an approach inspired by
null-scattering
yields unbiased Monte Carlo estimators for a large class of 2nd order elliptic PDEs, which share many attractive features with Monte Carlo rendering: no meshing, trivial parallelism, and the ability to evaluate the solution at any point without solving a global system of equations.
VEMPIC
Authors: Tao Michael , Batty Christopher , Ben-Chen Mirela , Fiume Eugene , Levin David I. W.
Published: July 2022

The comprehensive visual modeling of fluid motion has historically been a challenging task, due in no small part to the difficulties inherent in geometries that are non-manifold, open, or thin. Modern geometric cut-cell mesh generators have been shown to produce, both robustly and quickly, workable volumetric elements in the presence of these problematic geometries, and the resulting volumetric representation would seem to offer an ideal infrastructure with which to perform fluid simulations. However, cut-cell mesh elements are general polyhedra that often contain holes and are non-convex; it is therefore difficult to construct the explicit function spaces required to employ standard functional discretizations, such as the Finite Element Method. The Virtual Element Method (VEM) has recently emerged as a functional discretization that successfully operates with complex polyhedral elements through a weak formulation of its function spaces. We present a novel cut-cell fluid simulation framework that exactly represents boundary geometry during the simulation. Our approach enables, for the first time, detailed fluid simulation with "in-the-wild" obstacles, including ones that contain non-manifold parts, self-intersections, and extremely thin features. Our key technical contribution is the generalization of the Particle-In-Cell fluid simulation methodology to arbitrary polyhedra using VEM. Coupled with a robust cut-cell generation scheme, this produces a fluid simulation algorithm that can operate on previously infeasible geometries without requiring any additional mesh modification or repair.
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.
Ecoclimates
Authors: Pałubicki Wojtek , Makowski Miłosz , Gajda Weronika , Hädrich Torsten , Michels Dominik L. , Pirk Sören
Published: July 2022

One of the greatest challenges to mankind is understanding the underlying principles of climate change. Over the last years, the role of forests in climate change has received increased attention. This is due to the observation that not only the atmosphere has a principal impact on vegetation growth but also that vegetation is contributing to local variations of weather resulting in diverse microclimates. The interconnection of plant ecosystems and weather is described and studied as ecoclimates. In this work we take steps towards simulating ecoclimates by modeling the feedback loops between vegetation, soil, and atmosphere. In contrast to existing methods that only describe the climate at a global scale, our model aims at simulating local variations of climate. Specifically, we model tree growth interactively in response to gradients of water, temperature and light. As a result, we are able to capture a range of ecoclimate phenomena that have not been modeled before, including geomorphic controls, forest edge effects, the Foehn effect and spatial vegetation patterning. To validate the plausibility of our method we conduct a comparative analysis to studies from ecology and climatology. Consequently, our method advances the state-of-the-art of generating highly realistic outdoor landscapes of vegetation.
Moving level-of-detail surfaces
Authors: Mercier Corentin , Lescoat Thibault , Roussillon Pierre , Boubekeur Tamy , Thiery Jean-Marc
Published: July 2022

We present a simple, fast, and smooth scheme to approximate Algebraic Point Set Surfaces using non-compact kernels, which is particularly suited for filtering and reconstructing point sets presenting large missing parts. Our key idea is to consider a moving level-of-detail of the input point set which is adaptive w.r.t. to the evaluation location, just such as the samples weights are output sensitive in the traditional moving least squares scheme. We also introduce an adaptive progressive octree refinement scheme, driven by the resulting implicit surface, to properly capture the modeled geometry even far away from the input samples. Similarly to typical compactly-supported approximations, our operator runs in logarithmic time while defining high quality surfaces even on challenging inputs for which only global optimizations achieve reasonable results. We demonstrate our technique on a variety of point sets featuring geometric noise as well as large holes.
Scalable neural indoor scene rendering
Authors: Wu Xiuchao , Xu Jiamin , Zhu Zihan , Bao Hujun , Huang Qixing , Tompkin James , Xu Weiwei
Published: July 2022

We propose a scalable neural scene reconstruction and rendering method to support distributed training and interactive rendering of large indoor scenes. Our representation is based on
tiles.
Tile appearances are trained in parallel through a background sampling strategy that augments each tile with distant scene information via a proxy global mesh. Each tile has two low-capacity MLPs: one for view-independent appearance (diffuse color and shading) and one for view-dependent appearance (specular highlights, reflections). We leverage the phenomena that complex view-dependent scene reflections can be attributed to virtual lights underneath surfaces at the total ray distance to the source. This lets us handle sparse samplings of the input scene where reflection highlights do not always appear consistently in input images. We show interactive free-viewpoint rendering results from five scenes, one of which covers an area of more than 100 m
2
. Experimental results show that our method produces higher-quality renderings than a single large-capacity MLP and five recent neural proxy-geometry and voxel-based baseline methods. Our code and data are available at project webpage https://xchaowu.github.io/papers/scalable-nisr.
Automatic quantization for physics-based simulation
Authors: Liu Jiafeng , Shi Haoyang , Zhang Siyuan , Yang Yin , Ma Chongyang , Xu Weiwei
Published: July 2022

Quantization has proven effective in high-resolution and large-scale simulations, which benefit from bit-level memory saving. However, identifying a quantization scheme that meets the requirement of both precision and memory efficiency requires trial and error. In this paper, we propose a novel framework to allow users to obtain a quantization scheme by simply specifying either an error bound or a memory compression rate. Based on the error propagation theory, our method takes advantage of auto-diff to estimate the contributions of each quantization operation to the total error. We formulate the task as a constrained optimization problem, which can be efficiently solved with analytical formulas derived for the linearized objective function. Our workflow extends the Taichi compiler and introduces dithering to improve the precision of quantized simulations. We demonstrate the generality and efficiency of our method via several challenging examples of physics-based simulation, which achieves up to 2.5× memory compression without noticeable degradation of visual quality in the results. Our code and data are available at https://github.com/Hanke98/AutoQantizer.
Spelunking the deep
Authors: Sharp Nicholas , Jacobson Alec
Published: July 2022

Neural implicit representations, which encode a surface as the level set of a neural network applied to spatial coordinates, have proven to be remarkably effective for optimizing, compressing, and generating 3D geometry. Although these representations are easy to fit, it is not clear how to best evaluate geometric queries on the shape, such as intersecting against a ray or finding a closest point. The predominant approach is to encourage the network to have a signed distance property. However, this property typically holds only approximately, leading to robustness issues, and holds only at the conclusion of training, inhibiting the use of queries in loss functions. Instead, this work presents a new approach to perform queries directly on
general
neural implicit functions for a wide range of existing architectures. Our key tool is the application of range analysis to neural networks, using automatic arithmetic rules to bound the output of a network over a region; we conduct a study of range analysis on neural networks, and identify variants of affine arithmetic which are highly effective. We use the resulting bounds to develop geometric queries including ray casting, intersection testing, constructing spatial hierarchies, fast mesh extraction, closest-point evaluation, evaluating bulk properties, and more. Our queries can be efficiently evaluated on GPUs, and offer concrete accuracy guarantees even on randomly-initialized networks, enabling their use in training objectives and beyond. We also show a preliminary application to inverse rendering.
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.
Unbiased and consistent rendering using biased estimators
Authors: Misso Zackary , Bitterli Benedikt , Georgiev Iliyan , Jarosz Wojciech
Published: July 2022

We introduce a general framework for transforming biased estimators into unbiased and consistent estimators for the same quantity. We show how several existing unbiased and consistent estimation strategies in rendering are special cases of this framework, and are part of a broader debiasing principle. We provide a recipe for constructing estimators using our generalized framework and demonstrate its applicability by developing novel unbiased forms of transmittance estimation, photon mapping, and finite differences.
StyleGAN-NADA
Authors: Gal Rinon , Patashnik Or , Maron Haggai , Bermano Amit H. , Chechik Gal , Cohen-Or Daniel
Published: July 2022

Can a generative model be trained to produce images from a specific domain, guided only by a text prompt, without seeing any image? In other words: can an image generator be trained "blindly"? Leveraging the semantic power of large scale Contrastive-Language-Image-Pre-training (CLIP) models, we present a text-driven method that allows shifting a generative model to new domains, without having to collect even a single image. We show that through natural language prompts and a few minutes of training, our method can adapt a generator across a multitude of domains characterized by diverse styles and shapes. Notably, many of these modifications would be difficult or infeasible to reach with existing methods. We conduct an extensive set of experiments across a wide range of domains. These demonstrate the effectiveness of our approach, and show that our models preserve the latent-space structure that makes generative models appealing for downstream tasks. Code and videos available at: stylegan-nada.github.io/
R2E2
Authors: Fouladi Sadjad , Shacklett Brennan , Poms Fait , Arora Arjun , Ozdemir Alex , Raghavan Deepti , Hanrahan Pat , Fatahalian Kayvon , Winstein Keith
Published: July 2022

In this paper we explore the viability of path tracing massive scenes using a "supercomputer" constructed on-the-fly from thousands of small, serverless cloud computing nodes. We present R2E2 (Really Elastic Ray Engine) a scene decomposition-based parallel renderer that rapidly acquires thousands of cloud CPU cores, loads scene geometry from a pre-built scene BVH into the aggregate memory of these nodes in parallel, and performs full path traced global illumination using an inter-node messaging service designed for communicating ray data. To balance ray tracing work across many nodes, R2E2 adopts a service-oriented design that statically replicates geometry and texture data from frequently traversed scene regions onto multiple nodes based on estimates of load, and dynamically assigns ray tracing work to lightly loaded nodes holding the required data. We port pbrt's ray-scene intersection components to the R2E2 architecture, and demonstrate that scenes with up to a terabyte of geometry and texture data (where as little as 1/250th of the scene can fit on any one node) can be path traced at 4K resolution, in tens of seconds using thousands of tiny serverless nodes on the AWS Lambda platform.
MatFormer
Authors: Guerrero Paul , Hašan Miloš , Sunkavalli Kalyan , Měch Radomír , Boubekeur Tamy , Mitra Niloy J.
Published: July 2022

Procedural material graphs are a compact, parameteric, and resolution-independent representation that are a popular choice for material authoring. However, designing procedural materials requires significant expertise and publicly accessible libraries contain only a few thousand such graphs. We present MatFormer, a generative model that can produce a diverse set of high-quality procedural materials with complex spatial patterns and appearance. While procedural materials can be modeled as directed (operation) graphs, they contain arbitrary numbers of heterogeneous nodes with unstructured, often long-range node connections, and functional constraints on node parameters and connections. MatFormer addresses these challenges with a multi-stage transformer-based model that sequentially generates nodes, node parameters, and edges, while ensuring the semantic validity of the graph. In addition to generation, MatFormer can be used for the auto-completion and exploration of partial material graphs. We qualitatively and quantitatively demonstrate that our method outperforms alternative approaches, in both generated graph and material quality.
Robust computation of implicit surface networks for piecewise linear functions
Authors: Du Xingyi , Zhou Qingnan , Carr Nathan , Ju Tao
Published: July 2022

Implicit surface networks, such as arrangements of implicit surfaces and materials interfaces, are used for modeling piecewise smooth or partitioned shapes. However, accurate and numerically robust algorithms for discretizing either structure on a grid are still lacking. We present a unified approach for computing both types of surface networks for piecewise linear functions defined on a tetrahedral grid. Both algorithms are guaranteed to produce a correct combinatorial structure for any number of functions. Our main contribution is an exact and efficient method for partitioning a tetrahedron using the level sets of linear functions defined by barycentric interpolation. To further improve performance, we designed look-up tables to speed up processing of tetrahedra involving few functions and introduced an efficient algorithm for identifying nested 3D regions.
NeROIC
Authors: Kuang Zhengfei , Olszewski Kyle , Chai Menglei , Huang Zeng , Achlioptas Panos , Tulyakov Sergey
Published: July 2022

We present a novel method to acquire object representations from online image collections, capturing high-quality geometry and material properties of arbitrary objects from photographs with varying cameras, illumination, and backgrounds. This enables various object-centric rendering applications such as novel-view synthesis, relighting, and harmonized background composition from challenging in-the-wild input. Using a multi-stage approach extending neural radiance fields, we first infer the surface geometry and refine the coarsely estimated initial camera parameters, while leveraging coarse foreground object masks to improve the training efficiency and geometry quality. We also introduce a robust normal estimation technique which eliminates the effect of geometric noise while retaining crucial details. Lastly, we extract surface material properties and ambient illumination, represented in spherical harmonics with extensions that handle transient elements,
e.g.
sharp shadows. The union of these components results in a highly modular and efficient object acquisition framework. Extensive evaluations and comparisons demonstrate the advantages of our approach in capturing high-quality geometry and appearance properties useful for rendering applications.
NeuralTailor
Authors: Korosteleva Maria , Lee Sung-Hee
Published: July 2022

The fields of SocialVR, performance capture, and virtual try-on are often faced with a need to faithfully reproduce real garments in the virtual world. One critical task is the disentanglement of the intrinsic garment shape from deformations due to fabric properties, physical forces, and contact with the body. We propose to use a garment sewing pattern, a realistic and compact garment descriptor, to facilitate the intrinsic garment shape estimation. Another major challenge is a high diversity of shapes and designs in the domain. The most common approach for Deep Learning on 3D garments is to build specialized models for individual garments or garment types. We argue that building a unified model for various garment designs has the benefit of generalization to novel garment types, hence covering a larger design domain than individual models would. We introduce NeuralTailor, a novel architecture based on point-level attention for set regression with variable cardinality, and apply it to the task of reconstructing 2D garment sewing patterns from the 3D point cloud garment models. Our experiments show that NeuralTailor successfully reconstructs sewing patterns and generalizes to garment types with pattern topologies unseen during training.
SPCBPT
Authors: Su Fujia , Li Sheng , Wang Guoping
Published: July 2022

Bidirectional path tracing (BDPT) can be accelerated by selecting appropriate light sub-paths for connection. However, existing algorithms need to perform frequent distribution reconstruction and have expensive overhead. We present a novel approach, SPCBPT, for probabilistic connections that constructs the light selection distribution in sub-path space. Our approach bins the sub-paths into multiple subspaces and keeps the sub-paths in the same subspace of low discrepancy, wherein the light sub-paths can be selected by a subspace-based two-stage sampling method, i.e., first sampling the light subspace and then resampling the light sub-paths within this subspace. The subspace-based distribution is free of reconstruction and provides efficient light selection at a very low cost. We also propose a method that considers the Multiple Importance Sampling (MIS) term in the light selection and thus obtain an MIS-aware distribution that can minimize the upper bound of variance of the combined estimator. Prior methods typically omit this MIS weights term. We evaluate our algorithm using various benchmarks, and the results show that our approach has superior performance and can significantly reduce the noise compared with the state-of-the-art method.
Seeing through obstructions with diffractive cloaking
Authors: Shi Zheng , Bahat Yuval , Baek Seung-Hwan , Fu Qiang , Amata Hadi , Li Xiao , Chakravarthula Praneeth , Heidrich Wolfgang , Heide Felix
Published: July 2022

Unwanted camera obstruction can severely degrade captured images, including both scene occluders near the camera and partial occlusions of the camera cover glass. Such occlusions can cause catastrophic failures for various scene understanding tasks such as semantic segmentation, object detection, and depth estimation. Existing camera arrays capture multiple redundant views of a scene to see around thin occlusions. Such multi-camera systems effectively form a large synthetic aperture, which can suppress nearby occluders with a large defocus blur, but significantly increase the overall form factor of the imaging setup. In this work, we propose a
monocular single-shot
imaging approach that
optically cloaks
obstructions by emulating a large array. Instead of relying on different camera views, we learn a diffractive optical element (DOE) that performs depth-dependent optical encoding, scattering nearby occlusions while allowing paraxial wavefronts to be focused. We computationally reconstruct unobstructed images from these superposed measurements with a neural network that is trained jointly with the optical layer of the proposed imaging system. We assess the proposed method in simulation and with an experimental prototype, validating that the proposed computational camera is capable of recovering occluded scene information in the presence of severe camera obstruction.
Modeling and rendering non-euclidean spaces approximated with concatenated polytopes
Authors: Kim Seung-Wook , Doh Jaehyung , Han Junghyun
Published: July 2022

A non-Euclidean space is characterized as a manifold with a specific structure that violates Euclid's postulates. This paper proposes to approximate a manifold with polytopes. Based on the scene designer's specification, the polytopes are automatically concatenated and embedded in a higher-dimensional Euclidean space. Then, the scene is navigated and rendered via novel methods tailored to concatenated polytopes. The proof-of-concept implementation and experiments with it show that the proposed methods bring the virtual-world users unusual and fascinating experiences, which cannot be provided in Euclidean-space applications.
DIRE and its Data: Neural Decompiled Variable Renamings with Respect to Software Class
Authors: Dramko Luke , Lacomis Jeremy , Yin Pengcheng , Schwartz Ed , Allamanis Miltiadis , Neubig Graham , Vasilescu Bogdan , Le Goues Claire
Published: July 2022

The
decompiler
is one of the most common tools for examining executable binaries without the corresponding source code. It transforms binaries into high-level code, reversing the compilation process. Unfortunately, decompiler output is far from readable because the decompilation process is often incomplete. State-of-the-art techniques use machine learning to predict missing information like variable names. While these approaches are often able to suggest good variable names in context, no existing work examines how the selection of training data influences these machine learning models. We investigate how data provenance and the quality of training data affect performance, and how well, if at all, trained models generalize across software domains. We focus on the variable renaming problem using one such machine learning model,
DIRE
. We first describe
DIRE
in detail and the accompanying technique used to generate training data from raw code. We also evaluate
DIRE
’s overall performance without respect to data quality. Next, we show how training on more popular, possibly higher quality code (measured using
GitHub
stars) leads to a more generalizable model because popular code tends to have more diverse variable names. Finally, we evaluate how well
DIRE
predicts domain-specific identifiers, propose a modification to incorporate domain information, and show that it can predict identifiers in domain-specific scenarios 23% more frequently than the original
DIRE
model.
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.
Editors’ Choice—Methods—Pressure Control Apparatus for Lithium Metal Batteries
Authors: Lu Bingyu , Bao Wurigumula , Yao Weiliang , Doux Jean-Marie , Fang Chengcheng , Meng Ying Shirley
Published: July 2022

Lithium (Li) metal anodes are essential for developing next-generation high-energy-density batteries. However, Li dendrite/whisker formation caused short-circuiting issue and short cycle life have prevented lithium metal from being viably used in rechargeable batteries. Numerous works have been done to study how to regulate the Li growth in electrochemical cycling by using external stacking forces. While it is widely agreed that stack pressure positively affects the lithium plating/stripping process, the optimized pressure range provided by different works varies greatly because of the difference in the pressure control setup. In this work, a pressure control apparatus is designed for Li metal batteries with liquid and solid-state electrolytes (SSE). With considerations of minimizing cell to cell variation, a reusable split cell and pressure load cell are made for testing electrochemical cells with high precision pressure control. The capability of the designed setup is demonstrated by studying the pressure effect on the Li plating/stripping process.
In Situ High-Temperature TEM Observation of Inconel Corrosion by Molten Chloride Salts with N2, O2, or H2O
Authors: Pragnya Prachi , Gall Daniel , Hull Robert
Published: July 2022

In situ transmission electron microscopy (TEM) diffraction and imaging techniques are used to monitor and quantify corrosion of Inconel-625 by pure molten chloride salts (MgCl2 − NaCl − KCl) at 500 °C–800 °C in 1.0 atm inert N2 or pure O2, or by salts which are controllably hydrated in a high vacuum chamber. The isothermal corrosion rate R in inert N2 increases from 203 ± 30 μm year−1 at 700 °C to 463 ± 30 μm year−1 at 800 °C. An oxygen ambient causes a six-fold increase to R = 1261 ± 170 μm year−1 at 700 °C. Salt hydration dramatically accelerates corrosion to R> 3 × 105
μm year−1 at 700 °C while it leads to a more moderate R = 95 ± 20 and 486 ± 30 μm year−1 at 500 °C and 600 °C, respectively. These isothermal corrosion rates indicate that the molten chloride corrosion is significantly accelerated by salt hydration at temperatures above 600 °C, where corrosion is aggravated by increased generation and solubility of corrosive HCl gases. Hence, to reduce rate of corrosion it is important to both avoid incorporation of H2O into the system at each stage and ensure proper flushing of the system before increasing the temperature beyond 600 °C. Compositional analysis of the corroded cells indicate that corrosion in O2 ambient is dominated by oxidation of metals by O2 gas dissolved in the chloride melt, but corrosion in H2O ambients is caused by chlorination of metals by dissolved HCl gas and MgOH+ ions. So, to reduce rate of corrosion, steps should be taken to tailor chloride melt compositions that has low solubility for HCl and O2. All of our corroded samples exhibit passive-protective oxide layers of Cr, Mg, and Ni. In addition, distinct volatile compounds of Ni, Mo and Cr involving NiCl2, (Na,K)2MoO4 and CrO2(OH)2 are detected in N2, H2O, and O2 ambients, respectively. We believe that corrosion acceleration can be minimized by minimizing formation of volatile by-products or promoting reactions that could convert these volatile compounds to solid phases, as these volatile compounds led to destruction of protective oxide layers.
Synthesis and Characterization of Mesoporous Silica Templates (KIT-6, SBA-15) and Mesoporous Platinum
Authors: Parkash Anand , Solangi Nizamuddin , Wahocho Shafeeque Ahmed , Channa Abdul Sami , Inam Muhammad Ali , Ali Shafqat
Published: July 2022

Using unsupported catalysts also improved stability during electrochemical reactions and high durability due to their non-corrosive component, carbon. Advanced mesoporous architectures were created in which the pore and metal composition are controlled at the nanoscale level. Rigid template-assisted synthesis, which makes periodic porosity in the solid, is used to create mesoporous platinum (Pt) and Pt bimetallic catalyst. The ability to control the composition, shape, and porous architecture of Pt and Pt bimetallic combinations, eliminating the carbon corrosion problem, improved the activity of the catalyst. Hence, 3D bicontinuous mesoporous silica KIT-6 and 2D mesoporous silica SBA-15 were synthesized. Ordered mesoporous silica prepared has uniform mesopores (7.9 and 7.3 nm for KIT-6 and SBA-15, respectively) and high specific surface areas 772 m2.g−1 (for KIT-6) and 943 m2.g−1 (for SBA-15). These rigid silica templates were employed to produce mesoporous metal particles for fuel cell electrocatalyst.
Antibiofilm Synergistic Activity of Streptomycin in Combination with Thymol-Loaded Poly (Lactic-co-glycolic Acid) Nanoparticles against Klebsiella pneumoniae Isolates
Authors: Ndezo Bisso Borel , Tokam Kuaté Christian Ramsès , Boulens Nathalie , Allémann Eric , Delie Florence , Dzoyem Jean Paul
Published: July 2022

Background. Thymol is an important component of essential oils found in the oil of thyme, is extracted mainly from Thymus vulgaris, and was shown to act synergistically with streptomycin against Klebsiella pneumoniae biofilms. Additionally, thymol could be encapsulated into poly (lactic-co-glycolic acid) (PLGA) nanoparticles to overcome issues related to its low water solubility and high volatility. The present study aimed to investigate the antibiofilm activity of thymol-loaded PLGA nanoparticles (Thy-NPs) alone and in combination with streptomycin against biofilms of K. pneumoniae isolates. Methods. The broth microdilution method was used to determine the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). The antibiofilm activities were determined by the safranin dye assay. The synergistic effect of Thy-NPs with streptomycin was assessed by the checkerboard method. The kinetic study of the biofilm biomass and time-kill assay were further performed. Results. Thy-NPs exhibited the highest antibacterial activity against K. pneumoniae isolates, with MIC values ranging from 1 to 8 µg/mL. Additionally, Thy-NPs showed the highest antibiofilm activity against K. pneumoniae isolates with minimal biofilm inhibitory concentration (MBIC) and minimal biofilm eradication concentration (MBEC) values ranging from 16 to 64 µg/mL and from 32 to 128 µg/Ml, respectively. The combination treatment combining Thy-NPs with streptomycin showed a synergistic effect against the inhibition of biofilm formation and eradication of biofilms of K. pneumoniae isolates with fractional inhibitory concentration index values ranging from 0.13 to 0.28. In addition, the MBIC and MBEC values of streptomycin against K. pneumoniae isolates were dramatically reduced (up to 128-fold) in combination with Thy-NPs, suggesting that Thy-NPs would enhance the antibiofilm activity of streptomycin. The biomass and time-kill kinetics analysis confirmed the observed synergistic interactions and showed the bactericidal activity of streptomycin in combination with Thy-NPs. Conclusions. Our results indicate that the synergistic bactericidal effect between streptomycin and Thy-NPs could be a promising approach in the control of biofilm-associated infections caused by K. pneumoniae.
Application of Multimodal Magnetic Resonance Imaging in Green Channel of Acute and Hyperacute Ischemic Stroke
Authors: Zhou Jianguo , Li Guifen , Meng Yun , Fu Dayong , Lu Mingcong , Tang Zhi
Published: July 2022

The purpose of this study was to observe the effects of multimodal magnetic resonance imaging (MRI) in the green channel of acute and hyperacute ischemic strokes, in order to better guide clinical treatment. The clinical data of 126 patients with acute and hyperacute ischemic stroke who received interventional treatment in the emergency green channel was collected retrospectively. The patients who received multimodal computed tomography (CT) were included in the CT group. Patients who underwent multimodal MRI examinations were included in the MRI group, and the door‐to‐needle time (DNT), neurological function, and prognosis of the two groups were compared. The result turned out that among the 126 patients included, 40 patients underwent CT examination (CT group) and 86 patients underwent MRI examination (MRI group). A comparison of general data between the CT group and the MRI group showed P > 0.05. The MRI group’s DNT time (61.23 ± 9.32) min was shorter than that of the CT group (87.22 ± 14.26) min, P < 0.05. Through comparison, the P values of mRS scores and NIHSS scores in both groups were greater than 0.05. After treatment, the mRS score and NIHSS score of the MRI group was lower, with P < 0.05. According to the results of this study, it can be concluded that emergency multimodal MRI could shorten the DNT time of patients with acute and hyperacute ischemic stroke, reduce the degree of neurological impairment, and improve the prognosis.
Dynamic Synchronization Modeling and Simulation of Video Sensing Nodes Based on Internet of Things
Authors: Wang Zeyu , Wang Yu Qing
Published: July 2022

In order to solve the dynamic synchronization problem of IoT video perception, the authors propose an abstract model DSAM based on IoT video perception. This method firstly establishes the abstract model DSAM of IoT video perception based on the

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network and then analyzes the state evolution, model transition, and dynamic interaction of the model; finally, the model DSAM is used to analyze and simulate the example. The result shows that when the model DSAM is tested and verified and when the number of buffer media is 50, 100, and 200, the minimum number of underflows of the DSAM model is 2 and the minimum video data packet loss rate is 1.02; the results are better than those of the MTFSC and 3TFSC models; in addition, without synchronization control, the synchronization rate between video frames is lower than that with synchronization control. The test results show that the model proposed and established by the authors can correctly handle the dynamic synchronization of IoT video perception and has certain practical value.
In‐Depth Study of Thymus vulgaris Essential Oil: Towards Understanding the Antibacterial Target Mechanism and Toxicological and Pharmacological Aspects
Authors: Akermi Sarra , Smaoui Slim , Fourati Mariam , Elhadef Khaoula , Chaari Moufida , Chakchouk Mtibaa Ahlem , Mellouli Lotfi
Published: July 2022

Questions have been raised apropos the emerging problem of microbial resistance, which may pose a great hazard to the human health. Among biosafe compounds are essential oils which captured consumer draw due to their multifunctional properties compared to chemical medication drugs. Here, we examined the chemical profile and the mechanism(s) of action of the Thymus vulgaris essential oil (TVEO) against a Gram‐negative bacterium Salmonella enterica Typhimurium ATTCC 10028 (S. enterica Typhimurium ATTCC 10028) and two Gram‐positive bacteria Staphyloccocus aureus ATCC 6538 (S. aureus ATCC 6538) and Listeria monocytogenes ATCC 19117 (L. monocytogenes ATCC 19117). Findings showed that TVEO was principally composed of thymol, o‐cymene, and γ‐terpinene with 47.44, 16.55, and 7.80%, respectively. Molecular docking simulations stipulated that thymol and β‐sesquiphellandrene (a minor compound at 1.37%) could target multiple bacterial pathways including topoisomerase II and DNA and RNA polymerases of the three tested bacteria. This result pointed plausible impairments of the pathogenic bacteria cell replication and transcription processes. Through computational approach, the VEGA quantitative structure–activity relationship (QSAR) model, we revealed that among twenty‐six TVEO compounds, sixteen had no toxic effects and could be safe for human consumption as compared to the Food and Drug Administration (FDA) approved drugs (ciprofloxacin and rifamycin SV). Assessed by the SwissADME server, the pharmacokinetic profile of all identified TVEO compounds define their absorption, distribution, metabolism, and excretion (ADME) properties and were assessed. In order to predict their biological activity spectrum based on their chemical structure, all TVEO compounds were subjected to PASS (Prediction of Activity Spectra for Substances) online tool. Results indicated that the tested compounds could have multiple biological activities and various enzymatic targets. Findings of our study support that identified compounds of TVEO can be a safe and effective alternative to synthetic drugs and can easily combats hazardous multidrug‐resistant bacteria.
Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things
Authors: Dang Tianjiao , Liu Jifa
Published: July 2022

Monitoring environmental water quality in an efficient, cheap, and sustainable way can better serve the country’s strategic requirements for water resources and water ecological protection. This paper takes the Shaanxi section of the Weihe River Basin as a pilot project and aims to use the Internet of Things technology to develop water quality monitoring sensors, so as to realize the construction of low-cost, high-reliability water quality monitoring demonstration applications. First of all, we established the design of the water quality collection terminal, designed the low-power water quality sensor node, supported the Internet of Things protocol and the collection of various water quality parameters, and used networking for data transmission. Secondly, we use the ant colony algorithm-based system clustering model to obtain a cluster map of water quality monitoring tasks in a certain section of the Weihe River Basin. We take the task clustering graph as an example for analysis, optimize the monitoring model through the ant colony algorithm, and obtain the weight of the optimization index. The weight of the scheduled task limit of the monitoring point becomes larger, so the release of the monitoring task mainly affects the limit of the scheduled task of the monitoring point. Through the above work, we designed and implemented a set of online water quality monitoring system based on the Internet of Things and data mining technology. The system can provide reference for large-scale water resource protection and water environment governance.