bluuebunny/crossref_metadata_2025 huggingface dataset:
TwinTex: Geometry-Aware Texture Generation for Abstracted 3D Architectural Models
Authors: Xiong Weidan , Zhang Hongqian , Peng Botao , Hu Ziyu , Wu Yongli , Guo Jianwei , Huang Hui
Published: December 2023

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present
TwinTex
, the first automatic texture mapping framework to generate a photorealistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort.
Efficient Visualization of Light Pollution for the Night Sky
Authors: Dobashi Yoshinori , Ishikawa Naoto , Iwasaki Kei
Published: December 2023

Artificial light sources make our daily life convenient, but cause a severe problem called
light pollution.
We propose a novel system for efficient visualization of light pollution in the night sky. Numerous methods have been proposed for rendering the sky, but most of these focus on rendering of the daytime or the sunset sky where the sun is the only, or dominant light source. For the visualization of the light pollution, however, we must consider many city light sources on the ground, resulting in excessive computational cost. We address this problem by precomputing a set of intensity distributions for the sky illuminated by city light at various locations and with different atmospheric conditions. We apply a principal component analysis and fast Fourier transform to the precomputed distributions, allowing us to efficiently visualize the extent of the light pollution. Using this method, we can achieve one to two orders of magnitudes faster computation compared to a naive approach that simply accumulates the scattered intensity for each viewing ray. Furthermore, the fast computation allows us to interactively solve the inverse problem that determines the city light intensity needed to reduce light pollution. Our system provides the user with both a forward and inverse investigation tool for the study and minimization of light pollution.
SLANG.D: Fast, Modular and Differentiable Shader Programming
Authors: Bangaru Sai Praveen , Wu Lifan , Li Tzu-Mao , Munkberg Jacob , Bernstein Gilbert , Ragan-Kelley Jonathan , Durand Frédo , Lefohn Aaron , He Yong
Published: December 2023

We introduce SLANG.D, an extension to the Slang shading language that incorporates first-class automatic differentiation support. The new shading language allows us to transform a Direct3D-based path tracer to be fully differentiable with minor modifications to existing code. SLANG.D enables a shared ecosystem between machine learning frameworks and pre-existing graphics hardware API-based rendering systems, promoting the interchange of components and ideas across these two domains.

Our contributions include a differentiable type system designed to ensure type safety and semantic clarity in codebases that blend differentiable and non-differentiable code, language primitives that automatically generate both forward and reverse gradient propagation methods, and a compiler architecture that generates efficient derivative propagation shader code for graphics pipelines. Our compiler supports differentiating code that involves arbitrary control-flow, dynamic dispatch, generics and higher-order differentiation, while providing developers flexible control of checkpointing and gradient aggregation strategies for best performance. Our system allows us to differentiate an existing real-time path tracer, Falcor, with minimal change to its shader code. We show that the compiler-generated derivative kernels perform as efficiently as handwritten ones. In several benchmarks, the SLANG.D code achieves significant speedup when compared to prior automatic differentiation systems.
Diffusion Posterior Illumination for Ambiguity-Aware Inverse Rendering
Authors: Lyu Linjie , Tewari Ayush , Habermann Marc , Saito Shunsuke , Zollhöfer Michael , Leimkühler Thomas , Theobalt Christian
Published: December 2023

Inverse rendering, the process of inferring scene properties from images, is a challenging inverse problem. The task is ill-posed, as many different scene configurations can give rise to the same image. Most existing solutions incorporate priors into the inverse-rendering pipeline to encourage plausible solutions, but they do not consider the inherent ambiguities and the multi-modal distribution of possible decompositions. In this work, we propose a novel scheme that integrates a denoising diffusion probabilistic model pre-trained on natural illumination maps into an optimization framework involving a differentiable path tracer. The proposed method allows sampling from combinations of illumination and spatially-varying surface materials that are, both, natural and explain the image observations. We further conduct an extensive comparative study of different priors on illumination used in previous work on inverse rendering. Our method excels in recovering materials and producing highly realistic and diverse environment map samples that faithfully explain the illumination of the input images.
Manifold Path Guiding for Importance Sampling Specular Chains
Authors: Fan Zhimin , Hong Pengpei , Guo Jie , Zou Changqing , Guo Yanwen , Yan Ling-Qi
Published: December 2023

Complex visual effects such as caustics are often produced by light paths containing multiple consecutive specular vertices (dubbed
specular chains)
, which pose a challenge to unbiased estimation in Monte Carlo rendering. In this work, we study the light transport behavior within a sub-path that is comprised of a specular chain and two non-specular separators. We show that the specular manifolds formed by all the sub-paths could be exploited to provide coherence among sub-paths. By reconstructing continuous energy distributions from historical and coherent sub-paths, seed chains can be generated in the context of importance sampling and converge to admissible chains through manifold walks. We verify that importance sampling the seed chain in the continuous space reaches the goal of importance sampling the discrete admissible specular chain. Based on these observations and theoretical analyses, a progressive pipeline,
manifold path guiding
, is designed and implemented to importance sample challenging paths featuring long specular chains. To our best knowledge, this is the first general framework for importance sampling discrete specular chains in regular Monte Carlo rendering. Extensive experiments demonstrate that our method outperforms state-of-the-art unbiased solutions with up to 40 × variance reduction, especially in typical scenes containing long specular chains and complex visibility.
An Implicit Neural Representation for the Image Stack: Depth, All in Focus, and High Dynamic Range
Authors: Wang Chao , Serrano Ana , Pan Xingang , Wolski Krzysztof , Chen Bin , Myszkowski Karol , Seidel Hans-Peter , Theobalt Christian , Leimkühler Thomas
Published: December 2023

In everyday photography, physical limitations of camera sensors and lenses frequently lead to a variety of degradations in captured images such as saturation or defocus blur. A common approach to overcome these limitations is to resort to image stack fusion, which involves capturing multiple images with different focal distances or exposures. For instance, to obtain an all-in-focus image, a set of multi-focus images is captured. Similarly, capturing multiple exposures allows for the reconstruction of high dynamic range. In this paper, we present a novel approach that combines neural fields with an expressive camera model to achieve a unified reconstruction of an all-in-focus high-dynamic-range image from an image stack. Our approach is composed of a set of specialized implicit neural representations tailored to address specific sub-problems along our pipeline: We use neural implicits to predict flow to overcome misalignments arising from lens breathing, depth, and all-in-focus images to account for depth of field, as well as tonemapping to deal with sensor responses and saturation - all trained using a physically inspired supervision structure with a differentiable thin lens model at its core. An important benefit of our approach is its ability to handle these tasks simultaneously or independently, providing flexible post-editing capabilities such as refocusing and exposure adjustment. By sampling the three primary factors in photography within our framework (focal distance, aperture, and exposure time), we conduct a thorough exploration to gain valuable insights into their significance and impact on overall reconstruction quality. Through extensive validation, we demonstrate that our method outperforms existing approaches in both depth-from-defocus and all-in-focus image reconstruction tasks. Moreover, our approach exhibits promising results in each of these three dimensions, showcasing its potential to enhance captured image quality and provide greater control in post-processing.
Low-Light Image Enhancement with Wavelet-Based Diffusion Models
Authors: Jiang Hai , Luo Ao , Fan Haoqiang , Han Songchen , Liu Shuaicheng
Published: December 2023

Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Specifically, we present a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity. Additionally, it also takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information. To avoid chaotic content and diversity, we perform both forward diffusion and denoising in the training phase of WCDM, enabling the model to achieve stable denoising and reduce randomness during inference. Moreover, we further design a high-frequency restoration module (HFRM) that utilizes the vertical and horizontal details of the image to complement the diagonal information for better fine-grained restoration. Extensive experiments on publicly available real-world benchmarks demonstrate that our method outperforms the existing state-of-the-art methods both quantitatively and visually, and it achieves remarkable improvements in efficiency compared to previous diffusion-based methods. In addition, we empirically show that the application for low-light face detection also reveals the latent practical values of our method. Code is available at https://github.com/JianghaiSCU/Diffusion-Low-Light.
ToRoS: A Topology Optimization Approach for Designing Robotic Skins
Authors: Maestre Juan Montes , Hinchet Ronan , Coros Stelian , Thomaszewski Bernhard
Published: December 2023

Soft robotics offers unique advantages in manipulating fragile or deformable objects, human-robot interaction, and exploring inaccessible terrain. However, designing soft robots that produce large, targeted deformations is challenging. In this paper, we propose a new methodology for designing soft robots that combines optimization-based design with a simple and cost-efficient manufacturing process. Our approach is centered around the concept of robotic skins---thin fabrics with 3D-printed reinforcement patterns that augment and control plain silicone actuators. By decoupling shape control and actuation, our approach enables a simpler and cost-efficient manufacturing process. Unlike previous methods that rely on empirical design heuristics for generating desired deformations, our approach automatically discovers complex reinforcement patterns without any need for domain knowledge or human intervention. This is achieved by casting reinforcement design as a nonlinear constrained optimization problem and using a novel, three-field topology optimization approach tailored to fabrics with 3D-printed reinforcements. We demonstrate the potential of our approach by designing soft robotic actuators capable of various motions such as bending, contraction, twist, and combinations thereof. We also demonstrate applications of our robotic skins to robotic grasping with a soft three-finger gripper and locomotion tasks for a soft quadrupedal robot.
Adaptive Shells for Efficient Neural Radiance Field Rendering
Authors: Wang Zian , Shen Tianchang , Nimier-David Merlin , Sharp Nicholas , Gao Jun , Keller Alexander , Fidler Sanja , Müller Thomas , Gojcic Zan
Published: December 2023

Neural radiance fields achieve unprecedented quality for novel view synthesis, but their volumetric formulation remains expensive, requiring a huge number of samples to render high-resolution images. Volumetric encodings are essential to represent fuzzy geometry such as foliage and hair, and they are well-suited for stochastic optimization. Yet, many scenes ultimately consist largely of solid surfaces which can be accurately rendered by a single sample per pixel. Based on this insight, we propose a neural radiance formulation that smoothly transitions between volumetric- and surface-based rendering, greatly accelerating rendering speed and even improving visual fidelity. Our method constructs an explicit mesh envelope which spatially bounds a neural volumetric representation. In solid regions, the envelope nearly converges to a surface and can often be rendered with a single sample. To this end, we generalize the NeuS [Wang et al. 2021] formulation with a learned spatially-varying kernel size which encodes the spread of the density, fitting a wide kernel to volume-like regions and a tight kernel to surface-like regions. We then extract an explicit mesh of a narrow band around the surface, with width determined by the kernel size, and fine-tune the radiance field within this band. At inference time, we cast rays against the mesh and evaluate the radiance field only within the enclosed region, greatly reducing the number of samples required. Experiments show that our approach enables efficient rendering at very high fidelity. We also demonstrate that the extracted envelope enables downstream applications such as animation and simulation.
Slippage-Preserving Reshaping of Human-Made 3D Content
Authors: Araújo Chrystiano , Vining Nicholas , Burla Silver , De Oliveira Manuel Ruivo , Rosales Enrique , Sheffer Alla
Published: December 2023

Artists often need to
reshape
3D models of human-made objects by changing the relative proportions or scales of different model parts or elements while preserving the look and structure of the inputs. Manually reshaping inputs to satisfy these criteria is highly time-consuming; the edit in our teaser took an artist 5 hours to complete. However, existing methods for 3D shape editing are largely designed for other tasks and produce undesirable outputs when repurposed for reshaping. Prior work on 2D curve network reshaping suggests that in 2D settings the user-expected outcome is achieved when the reshaping edit keeps the
orientations
of the different model elements and when these elements
scale as-locally-uniformly-as-possible (ALUP).
However, our observations suggest that in 3D viewers are tolerant of
non-uniform
tangential scaling if and when this scaling preserves
slippage
and reduces changes in element size, or
scale
, relative to the input. Slippage preservation requires surfaces which are locally
slippable
with respect to a given rigid motion to retain this property post-reshaping (a motion is slippable if when applied to the surface, it slides the surface along itself without gaps). We build on these observations by first extending the 2D ALUP framework to 3D and then modifying it to allow non-uniform scaling while promoting slippage and scale preservation. Our 3D ALUP extension produces reshaped outputs better aligned with viewer expectations than prior alternatives; our slippage-aware method further improves the outcome producing results on par with manual reshaping ones. Our method does not require any user input beyond specifying control handles and their target locations. We validate our method by applying it to over one hundred diverse inputs and by comparing our results to those generated by alternative approaches and manually. Comparative study participants preferred our outputs over the best performing traditional deformation method by a 65% margin and over our 3D ALUP extension by a 61% margin; they judged our outputs as at least on par with manually produced ones.
Fluid Simulation on Neural Flow Maps
Authors: Deng Yitong , Yu Hong-Xing , Zhang Diyang , Wu Jiajun , Zhu Bo
Published: December 2023

We introduce Neural Flow Maps, a novel simulation method bridging the emerging paradigm of implicit neural representations with fluid simulation based on the theory of flow maps, to achieve state-of-the-art simulation of in-viscid fluid phenomena. We devise a novel hybrid neural field representation, Spatially Sparse Neural Fields (SSNF), which fuses small neural networks with a pyramid of overlapping, multi-resolution, and spatially sparse grids, to compactly represent long-term spatiotemporal velocity fields at high accuracy. With this neural velocity buffer in hand, we compute long-term, bidirectional flow maps and their Jacobians in a mechanistically symmetric manner, to facilitate drastic accuracy improvement over existing solutions. These long-range, bidirectional flow maps enable high advection accuracy with low dissipation, which in turn facilitates high-fidelity incompressible flow simulations that manifest intricate vortical structures. We demonstrate the efficacy of our neural fluid simulation in a variety of challenging simulation scenarios, including leapfrogging vortices, colliding vortices, vortex reconnections, as well as vortex generation from moving obstacles and density differences. Our examples show increased performance over existing methods in terms of energy conservation, visual complexity, adherence to experimental observations, and preservation of detailed vortical structures.
BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis
Authors: Duan Hao-Bin , Wang Miao , Shi Jin-Chuan , Chen Xu-Chuan , Cao Yan-Pei
Published: December 2023

Synthesizing photorealistic 4D human head avatars from videos is essential for VR/AR, telepresence, and video game applications. Although existing Neural Radiance Fields (NeRF)-based methods achieve high-fidelity results, the computational expense limits their use in real-time applications. To overcome this limitation, we introduce
BakedAvatar
, a novel representation for real-time neural head avatar synthesis, deployable in a standard polygon rasterization pipeline. Our approach extracts deformable multi-layer meshes from learned isosurfaces of the head and computes expression-, pose-, and view-dependent appearances that can be baked into static textures for efficient rasterization. We thus propose a three-stage pipeline for neural head avatar synthesis, which includes learning continuous deformation, manifold, and radiance fields, extracting layered meshes and textures, and fine-tuning texture details with differential rasterization. Experimental results demonstrate that our representation generates synthesis results of comparable quality to other state-of-the-art methods while significantly reducing the inference time required. We further showcase various head avatar synthesis results from monocular videos, including view synthesis, face reenactment, expression editing, and pose editing, all at interactive frame rates on commodity devices. Source codes and demos are available on our project page.
Assessing Effectiveness of Test Suites: What Do We Know and What Should We Do?
Authors: Zhang Peng , Wang Yang , Liu Xutong , Lu Zeyu , Yang Yibiao , Li Yanhui , Chen Lin , Wang Ziyuan , Sun Chang-Ai , Yu Xiao , Zhou Yuming
Published: December 2023

Background.
Software testing is a critical activity for ensuring the quality and reliability of software systems. To evaluate the effectiveness of different test suites, researchers have developed a variety of metrics.
Problem.
However, comparing these metrics is challenging due to the lack of a standardized evaluation framework including comprehensive factors. As a result, researchers often focus on single factors (e.g., size), which finally leads to different or even contradictory conclusions. After comparing dozens of pieces of work in detail, we have found two main problems most troubling to our community: (1) researchers tend to oversimplify the description of the ground truth they use, and (2) data involving real defects is not suitable for analysis using traditional statistical indicators.
Objective.
We aim at scrutinizing the whole process of comparing test suites for our community.
Method.
To hit this aim, we propose a framework ASSENT (ev

A

luating te

S

t

S

uite

E

ffective

N

ess me

T

rics) to guide the follow-up research for evaluating a test suite effectiveness metric. ASSENT consists of three fundamental components: ground truth, benchmark test suites, and agreement indicator. Its functioning is as follows: first, users clarify the ground truth for determining the real order in effectiveness among test suites. Second, users generate a set of benchmark test suites and derive their ground truth order in effectiveness. Third, users use the metric to derive the order in effectiveness for the same test suites. Finally, users calculate the agreement indicator between the two orders derived by two metrics.
Result.
With ASSENT, we are able to compare the accuracy of different test suite effectiveness metrics. We apply ASSENT to evaluate representative test suite effectiveness metrics, including mutation score and code coverage metrics. Our results show that, based on the real faults, mutation score, and subsuming mutation score are the best metrics to quantify test suite effectiveness. Meanwhile, by using mutants instead of real faults, test effectiveness will be overestimated by more than 20% in values.
Conclusion.
We recommend that the standardized evaluation framework ASSENT should be used for evaluating and comparing test effectiveness metrics in the future work.
Comparative Analysis of Numerical Methods for Lithium-Ion Battery Electrochemical Modeling
Authors: Xu Le , Cooper Julian , Allam Anirudh , Onori Simona
Published: December 2023

Electrochemical models provide insights into the battery internal states and have become powerful tools for battery design and management. These models consist of partial differential equations (PDEs) that are solved numerically. In this paper, we compare two spatial discretization methods commonly used to numerically solve the governing PDEs in the context of Lithium ion batteries, namely finite difference method (FDM) and finite volume method (FVM) in terms of model accuracy and mass conservation guarantee. First, we provide the mathematical details to carry out the spatial discretization for both FDM and FVM to solve the battery single particle model (SPM). SPM parameters are identified from experimental data, and sensitivity analysis is conducted to study parameter identifiability under different current input profiles, followed by model accuracy and mass conservation analysis of the two numerical schemes. Leveraging the third order Hermite extrapolation approach, an enhanced FVM scheme is proposed in this paper to improve the model accuracy of standard FVM which relies on linear extrapolation. This paper shows that the FVM scheme with Hermite extrapolation leads to accurate and robust control-oriented battery model while guaranteeing mass conservation and high accuracy.
Size-Optimized Silver Nanowires with Graphene Oxide for High-Performance Transparent Electrodes
Authors: Zhu Maolan , Yin Jun , Weng Wei , Jiang Chunhai , Zhou Jiangcong , Wang Liang , Zhong Shuiping
Published: December 2023

Silver nanowires (AgNWs)-based transparent electrode is one critical component for many modern electronic devices, however, preparation of high-performance AgNWs-based transparent electrode with appealing light transmittance and long-term stability is still challenging. Herein, by combining the size-optimized AgNWs with graphene oxide (GO), the composite AgNWs/GO film electrode with high light-transmittance and appealing stability is prepared. For the size-controlled preparation of AgNWs, well-defined one-dimensional structure (average diameter = 62 nm) is obtained by elaborately adjusting the Br− concentration (0.05 mM), Cl− concentration (1 mM), ratio of PVP/AgNO3 (5:1) and PVP-type selection (PVP1300k: PVP58k = 2:1), contributing to a high light transmittance of 84% for the corresponding AgNWs-based electrode. After further introducing the GO, the film electrode presents much enhanced stability and increased film-substrate adhesive strength, namely only 3.6% increase in resistance after 1000 cycles of bending and being stable even under ultrasonic vibration. The results can provide new insights for designing electrode with ultrastability and high light transmittance.
Modeling Contact Resistance and Water Transport within a Cathode Liquid-Fed Proton Exchange Membrane Electrolyzer
Authors: Gerhardt Michael R. , Østenstad Jenny S. , Barnett Alejandro O. , Thomassen Magnus S.
Published: December 2023

Conventional proton-exchange membrane (PEM) water electrolyzers use thicker membranes (>175 μm) than their PEM fuel cell counterparts (<25 μm), which reduces hydrogen crossover but also reduces electrolyzer efficiency due to the increased resistance. Reduction of hydrogen crossover is critical in conventional systems to avoid buildup of hydrogen in the anode above the lower flammability limit. New concepts for operating PEM water electrolyzers are emerging, such as the patented concept involving liquid water supply at the cathode while operating the anode with air, which reduces the safety concern related to hydrogen crossover using thin membranes. Experimental work has demonstrated the viability of this approach, but open questions remain regarding the interplay between water transport, water consumption, and cell performance, as well as identifying the components and material properties that enable high performance. In this work, a physics-based computational model of a cathode-fed PEM water electrolyzer was developed. The model highlights the importance of limiting contact resistance and explores the effect of cell compression on non-uniformity of current distributions. Sensitivity studies found that membranes up to 50 μm thick can be used without significant water transport limitations.
cAMP-dependent protein kinase signaling is required for (2R,6R)-hydroxynorketamine to potentiate hippocampal glutamatergic transmission
Authors: Riggs Lace M. , Pereira Edna F. R. , Thompson Scott M. , Gould Todd D.
Published: December 2023

Ketamine is a rapid-acting antidepressant and its preclinical effects are mimicked by its ( 2R,6R)-(HNK) metabolite. We found that ( 2R,6R)-HNK initiates acute adaptations in fast excitatory synaptic transmission by potentiating glutamate release via cAMP-PKA signaling at hippocampal Schaffer collateral synapses. This cAMP-PKA-dependent potentiation was not dependent on TrkB activation by BDNF, which functionally delimits the rapid synaptic effects of ( 2R,6R)-HNK from its sustained BDNF-dependent actions that are thought to maintain antidepressant action in vivo.
Study of Fractional-Order Boundary Value Problems Using Nondiscretization Numerical Method
Authors: Khan Zareen A. , Ahmad Sajjad , Zeb Salman , Yousaf Muhammad
Published: December 2023

This paper is devoted to present a numerical scheme based on operational matrices to compute approximate solutions to fractional-order boundary value problems. For the mentioned operational matrices, we utilize fractional-order Bernoulli polynomials. Since fractional-order problems are usually difficult to treat for their corresponding analytical or exact solutions, therefore, we need sophisticated methods to find their best numerical solutions. The presented numerical scheme has the ability to reduce the proposed problem to the corresponding algebraic equations. The obtained algebraic equations are then solved by using the computational software MATLAB for the corresponding numerical results. The used method has the ability to save much more time and also is reliable to secure the proper amount of memory. Several examples are solved by using the considered method. Also, the solutions are compared with their exact solution graphically. In addition, the absolute errors for different scale values are presented graphically. In addition, we compare our results with the results of the shifted Legendre polynomials spectral method.
ALICA: A Multi‐S‐Box Lightweight Cryptographic Algorithm Based on Generalized Feistel Structure
Authors: Ye Jun , Chen Yabing , An Fanglin , Jiang Weili
Published: December 2023

With the development of science and technology, IoT devices have already become ubiquitous in the public eye. Through the perception layer, the collected data are displayed or transmitted to the server backend for analysis. Due to the increasing integration of IoT devices into people’s daily lives, privacy issues, such as data leaks, have received more attention. Most sensor nodes, such as temperature and pressure sensors in marine environments, have low computing power, storage capacity, and significant underlying heterogeneity, making it challenging to implement a standardized data security protection solution. Data security in the nodes is seriously challenged as a result. It is of great significance to design a lightweight block cipher for the Internet of Things (IoT) environment, which can ensure the security of node information. A new lightweight block cipher algorithm called ALICA is proposed in this paper, which is well‐suited for the low computing power and heterogeneous device environment at the lower layers. A generalized Feistel structure and a linear structure with XOR and shift operations are used to achieve ease of software implementation. Two different S‐boxes are used to create the nonlinear structure of the password, thereby enhancing the robust security of the cipher. In addition, the cipher adopts a design approach that prioritizes software efficiency while also considering hardware implementation efficiency. This makes it more suitable for low computing power, storage capacity, and resource‐limited IoT sensor nodes at the lower layers.
Development of Modified-Release Diclofenac Sodium Capsules Using Blends of Pectin-Clay Multiparticulate Hybrid Systems as Release Retardants
Authors: Adi-Dako Ofosua , Owusu Frederick William Akuffo , Attah Isaac Yaw , Kumadoh Doris , Acquah Jnr Prince George , Nelson Issaka , Hutton-Mills Nii Odartey , Obese Karen Yaa Yeboaa , Sarkodie Joseph Adusei , N’guessan Benoit Banga , Kwapong Awo Afi , Osei-Asare Christina , Adase Emmanuel , Archer Mary-Ann
Published: December 2023

A combination of inorganic and organic hybrid systems is of high research interest as they provide novel hybrid systems for the improvement of existing properties, overcoming limitations of the parent materials, and for the optimization of their controlled release potential. This study sorted to develop and pharmaceutically assess the release profile of diclofenac sodium using cocoa pod husk (CPH) blended with different proportions of either talc or bentonite as multiparticulate composite release modifiers. Preformulation investigations of the multiparticulate hybrid systems included pH, swelling index, moisture content, elemental contents, and flow properties. The FTIR was also used to investigate the compatibilities between pectin and bentonite (PB), pectin and talc (PT), and diclofenac and pectin-talc (DPT), as well as diclofenac and pectin-bentonite (DPB). The diclofenac content, uniformity of the weight of capsules, in vitro drug release, and the kinetics and mechanism of release of diclofenac from the hybrid systems were also investigated using mathematical models. The pectin yield was 23.3%, with the water-holding capacities of pectin-talc (PT) and pectin-bentonite (PB) hybrid systems being 6.4% and 5.0%, respectively. The swelling indices of PT and PB were 110.0 and 130.0 in 0.1 M HCL at pH 1.2 and 130.0 and 149.0 in phosphate buffer at pH 6.8, respectively. This system was also found to exhibit excellent flow properties, and there were no diclofenac-excipient interactions. All formulated batches passed the pharmacopoeial and nonpharmacopoeial tests. They also demonstrated controlled release properties via different release kinetics and mechanisms. This study shows that the pectin-talc and pectin-bentonite multiparticulate composites could be used as release modifiers in pharmaceutical preparations.
Chemical Compositions and Antioxidant Activities of Thirty-Seven Typical Pomegranates Grown in China
Authors: Zheng Wenhao , Gao Boyan , Zhu Hanshu , Zhu Lin , Lu Zhenhua , Chen Lina , Li Mo , Luo Yinghua , Yu Liangli (Lucy)
Published: December 2023

The chemical compositions of juice and peel from 37 cultivars of pomegranates grown in China were identified using UPLC-Q TOF MS. The total phenolic contents and antioxidant activities of pomegranate juice and peel samples were also determined to make comparisons of their potential nutraceutical values. A total of 20 and 25 compounds were tentatively identified from the juice and peel samples. For the antioxidant activities of juice samples, No. 32 from Italy showed greatest abilities in scavenging DPPH and ABTS free radicals, and a registered novel cultivar No. 24 showed greatest value in scavenging oxygen radical. For the peel samples, No. 8 from Anhui Province of China showed generally greater values in all the three assays. All these results showed that different cultivars of pomegranate have different amounts of bioactive components and antioxidant activities, and development about the byproduct of these pomegranates after industrial processing should be focused in the future.
Phase I/II Study of the WEE1 Inhibitor Adavosertib (AZD1775) in Combination with Carboplatin in Children with Advanced Malignancies: Arm C of the AcSé-ESMART Trial
Authors: Gatz Susanne A. , Harttrampf Anne C. , Brard Caroline , Bautista Francisco , André Nicolas , Abbou Samuel , Rubino Jonathan , Rondof Windy , Deloger Marc , Rübsam Marc , Marshall Lynley V. , Hübschmann Daniel , Nebchi Souad , Aerts Isabelle , Thebaud Estelle , De Carli Emilie , Defachelles Anne Sophie , Paoletti Xavier , Godin Robert , Miah Kowser , Mortimer Peter G.S. , Vassal Gilles , Geoerger Birgit
Published: December 2023

Purpose:

AcSé-ESMART Arm C aimed to define the recommended dose and activity of the WEE1 inhibitor adavosertib in combination with carboplatin in children and young adults with molecularly enriched recurrent/refractory malignancies.

Patients and Methods:

Adavosertib was administered orally, twice every day on Days 1 to 3 and carboplatin intravenously on Day 1 of a 21-day cycle, starting at 100 mg/m2/dose and AUC 5, respectively. Patients were enriched for molecular alterations in cell cycle and/or homologous recombination (HR).

Results:

Twenty patients (median age: 14.0 years; range: 3.4–23.5) were included; 18 received 69 treatment cycles. Dose-limiting toxicities were prolonged grade 4 neutropenia and grade 3/4 thrombocytopenia requiring transfusions, leading to two de-escalations to adavosertib 75 mg/m2/dose and carboplatin AUC 4; no recommended phase II dose was defined. Main treatment-related toxicities were hematologic and gastrointestinal. Adavosertib exposure in children was equivalent to that in adults; both doses achieved the cell kill target. Overall response rate was 11% (95% confidence interval, 0.0–25.6) with partial responses in 2 patients with neuroblastoma. One patient with medulloblastoma experienced unconfirmed partial response and 5 patients had stable disease beyond four cycles. Seven of these eight patients with clinical benefit had alterations in HR, replication stress, and/or RAS pathway genes with or without TP53 alterations, whereas TP53 pathway alterations alone (8/10) or no relevant alterations (2/10) were present in the 10 patients without benefit.

Conclusions:

Adavosertib–carboplatin combination exhibited significant hematologic toxicity. Activity signals and identified potential biomarkers suggest further studies with less hematotoxic DNA-damaging therapy in molecularly enriched pediatric cancers.
Roles of cGAS-STING Pathway in Radiotherapy Combined with Immunotherapy for Hepatocellular Carcinoma
Authors: Ma Jianing , Xin Yuning , Wang Qiang , Ding Lijuan
Published: December 2023

Although great strides have been made in the management and treatment of hepatocellular carcinoma (HCC), its prognosis is still poor yielding a high mortality. Immunotherapy is recommended for treating advanced HCC, but its efficiency is hampered because of hepatic immunosuppression. Stimulator of interferon genes (STING) pathway, serving as a critical cytoplasmic DNA-sensing process, is reported to initiate the antitumor immune response, and link the innate immunity to the adaptive immune system. Radiotherapy has been well acknowledged to induce destruction and release of tumor-derived DNA into the cytoplasm, which then activates the cGAS-STING pathway. On this basis, radiotherapy can be used as a sensitizer for immunotherapy, and its combination with immunotherapy may bring in changes to the suboptimal efficacy of immune checkpoint inhibitor monotherapy. In this review, we summarized the roles of cGAS-STING pathway in regulation of radiotherapy combined with immunotherapy for treating HCC.
Effects of Exclusive Breastfeeding Promotion Interventions on Child Outcomes: A Systematic Review and Meta-Analysis
Authors: Dib Sarah , Fair Frankie Joy , McCann Lucy Jane , Nicholls Antonia , Kalea Anastasia Z. , Soltani Hora , Fewtrell Mary
Published: December 2023

Introduction: Interventions promoting exclusive breastfeeding (EBF) may benefit infant health outcomes, but evidence is inconsistent. The objective of this review was to assess the effect of interventions promoting EBF on health outcomes in infants and children under 7 years of age. Methods: A literature search was conducted using EMBASE, MEDLINE, CINAHL, Cochrane Central, Cochrane Database of Systematic Reviews, and WHO International Clinical Trials Registry Platform from inception to April 2022. Inclusion criteria were randomized or cluster-randomized controlled trials aiming to increase EBF that reported effects on offspring growth, morbidity, and/or mortality up to age 7 years. The primary outcome was infant/child growth. Secondary outcomes were infant morbidity and mortality and EBF rates. Data were pooled using a random-effects model. Results: Thirty-two studies (40 papers) were identified. No effect on infant/child growth was observed. EBF promotion interventions significantly improved EBF rates up to 6 months (n = 25; OR 3.15; 95% CI: 2.36, 4.19) and significantly reduced the odds of respiratory illness at 0–3 months by 59% (n = 2; OR 0.41; 95% CI: 0.20, 0.84) but not at later time-points. A borderline significant effect was observed for diarrhea (n = 12; OR 0.84; 95% CI: 0.70, 1.00). Effects on hospitalizations or mortality were not significant. Discussion/Conclusion: EBF promotion interventions improve EBF rates and might yield modest reductions in infant morbidity without affecting infant/child growth. Future studies should investigate the cost-effectiveness of these interventions and examine potential benefits on other health outcomes.
TP53-Mutated Myelodysplastic Syndrome and Acute Myeloid Leukemia: Current Guidelines, Therapies, and Future Considerations
Authors: DiGennaro Jeremy , Sallman David A.
Published: December 2023

Background: Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy characterized by uncontrolled proliferation and impaired differentiation of myeloid cells in the bone marrow. The tumor suppressor gene TP53 plays a crucial role in maintaining genomic integrity and preventing the development of cancer. TP53 mutations are frequently observed in AML (∼10% of patients) and are associated with aggressive disease behavior, resistance to therapy, and poor prognosis. Summary: Recent changes in classification of TP53-mutated myelodysplastic syndrome (MDS) have occurred related to the allelic status of TP53 and more importantly to harmonize MDS/AML patients as a homogeneous hematological malignancy. Current treatment regimens involve hypomethylating agents +/− venetoclax or intensive chemotherapy although unfortunately independent of treatment regimen the overall survival (OS) of this patient cohort is around 6 months with poor long-term outcomes after allogeneic stem-cell transplantation. Recent developments geared toward the treatment of TP53-mutated MDS/AML have focused on immunotherapies. Key Messages: Notably, there is optimism surrounding these new therapies that could provide breakthroughs with improving outcomes either as monotherapy or combined with established nonimmune therapies. This paper aims to provide an overview of TP53-mutated MDS/AML, including the underlying mechanisms, clinical implications, and emerging therapeutic strategies targeting this hematologic malignancy.
Cocaine Use Prediction With Tensor-Based Machine Learning on Multimodal MRI Connectome Data
Authors: Zhang Anru R. , Bell Ryan P. , An Chen , Tang Runshi , Hall Shana A. , Chan Cliburn , Al-Khalil Kareem , Meade Christina S.
Published: December 2023

This letter considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study used functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the data sets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from 275 (individuals) × 2 (fMRI and dMRI) × 246 (ROIs) × 246 (ROIs) to 275 (individuals) × 2 (fMRI and dMRI) × 6 (clusters) × 6 (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into six clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting data set was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented.

Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse.
Performance Evaluation of Matrix Factorization for fMRI Data
Authors: Endo Yusuke , Takeda Koujin
Published: December 2023

A hypothesis in the study of the brain is that sparse coding is realized in information representation of external stimuli, which has been experimentally confirmed for visual stimulus recently. However, unlike the specific functional region in the brain, sparse coding in information processing in the whole brain has not been clarified sufficiently. In this study, we investigate the validity of sparse coding in the whole human brain by applying various matrix factorization methods to functional magnetic resonance imaging data of neural activities in the brain. The result suggests the sparse coding hypothesis in information representation in the whole human brain, because extracted features from the sparse matrix factorization (MF) method, sparse principal component analysis (SparsePCA), or method of optimal directions (MOD) under a high sparsity setting or an approximate sparse MF method, fast independent component analysis (FastICA), can classify external visual stimuli more accurately than the nonsparse MF method or sparse MF method under a low sparsity setting.
The fundamental karyotype and plastid DNA of Alstroemeria piperata (Liliales, Alstroemeriaceae), a species endemic to the Valparaíso Region, Chile
Authors: PERRY CARLOS BAEZA , ROJAS GLORIA , TORO-NÚÑEZ ÓSCAR , RUIZ-PONCE EDUARDO
Published: December 2023

A recently described species of Chilean Alstroemeria, A. piperata, is endemic to the Mediterranean region and characterized by having storage roots covered with whitish hairs. It is a highly endangered species, consisting only of a single population with few individuals. Its chromosomes are 2n = 16, typical for the genus, but they possess unique secondary constrictions. A molecular analysis of plastid DNA revealed no single nucleotide polymorphisms, which suggests that processes of phenotypic stabilization and incipient genetic isolation may be occurring within the species.
On the Atlantic species of the genus Abyssianira Menzies, 1956 (Isopoda: Asellota: Paramunnidae)
Authors: DOTI BRENDA L. , ROCCATAGLIATA DANIEL
Published: December 2023

Two new Atlantic species of the genus Abyssianira are described: A. acutilobi n. sp. and A. lingula n. sp. from off Argentine and Brazilian coasts, respectively. In order to facilitate comparisons the descriptions of two previously known Atlantic species, A. dentifrons Menzies, 1956 and A. argentenensis Menzies, 1962, are completed. Four groups of dorsolateral wart-like elevations are herein described for the first time. These “warts” are present in A. acutilobi, A. lingula and A. dentifrons and they seem to consist of agglutinated sand and detritus. The diagnosis of the genus Abyssianira is emended, and a dichotomous key for these four species is offered.