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1. Learning Recourse Costs from Pairwise Feature Comparisons

Author: Rawal, Kaivalya and Lakkaraju, Himabindu
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Statistics - Machine Learning
Abstract: This paper presents a novel technique for incorporating user input when learning and inferring user preferences. When trying to provide users of black-box machine learning models with actionable recourse, we often wish to incorporate their personal preferences about the ease of modifying each individual feature. These recourse finding algorithms usually require an exhaustive set of tuples associating each feature to its cost of modification. Since it is hard to obtain such costs by directly surveying humans, in this paper, we propose the use of the Bradley-Terry model to automatically infer feature-wise costs using non-exhaustive human comparison surveys. We propose that users only provide inputs comparing entire recourses, with all candidate feature modifications, determining which recourses are easier to implement relative to others, without explicit quantification of their costs. We demonstrate the efficient learning of individual feature costs using MAP estimates, and show that these non-exhaustive human surveys, which do not necessarily contain data for each feature pair comparison, are sufficient to learn an exhaustive set of feature costs, where each feature is associated with a modification cost., Comment: "Recourse for Humans", paper 49 from the Participatory Approaches to Machine Learning workshop at the International Conference on Machine Learning (ICML) 2020. For workshop website, see https://participatoryml.github.io/#49
Published: 2024

2. LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments

Author: Chen, Ruirui, Jiang, Weifeng, Qin, Chengwei, Rawal, Ishaan Singh, Tan, Cheston, Choi, Dongkyu, Xiong, Bo, and Ai, Bo
Subjects: Computer Science - Computation and Language
Abstract: The rapid obsolescence of information in Large Language Models (LLMs) has driven the development of various techniques to incorporate new facts. However, existing methods for knowledge editing still face difficulties with multi-hop questions that require accurate fact identification and sequential logical reasoning, particularly among numerous fact updates. To tackle these challenges, this paper introduces Graph Memory-based Editing for Large Language Models (GMeLLo), a straitforward and effective method that merges the explicit knowledge representation of Knowledge Graphs (KGs) with the linguistic flexibility of LLMs. Beyond merely leveraging LLMs for question answering, GMeLLo employs these models to convert free-form language into structured queries and fact triples, facilitating seamless interaction with KGs for rapid updates and precise multi-hop reasoning. Our results show that GMeLLo significantly surpasses current state-of-the-art knowledge editing methods in the multi-hop question answering benchmark, MQuAKE, especially in scenarios with extensive knowledge edits.
Published: 2024

3. Sapiens: Foundation for Human Vision Models

Author: Khirodkar, Rawal, Bagautdinov, Timur, Martinez, Julieta, Zhaoen, Su, James, Austin, Selednik, Peter, Anderson, Stuart, and Saito, Shunsuke
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual tasks by simply fine-tuning models pretrained on over 300 million in-the-wild human images. We observe that, given the same computational budget, self-supervised pretraining on a curated dataset of human images significantly boosts the performance for a diverse set of human-centric tasks. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. Our simple model design also brings scalability -- model performance across tasks improves as we scale the number of parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks. We achieve significant improvements over the prior state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1 mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5% relative angular error. Project page: https://about.meta.com/realitylabs/codecavatars/sapiens., Comment: ECCV 2024 (Oral)
Published: 2024

4. UNMuTe: Unifying Navigation and Multimodal Dialogue-like Text Generation

Author: Rawal, Niyati, Bigazzi, Roberto, Baraldi, Lorenzo, and Cucchiara, Rita
Subjects: Computer Science - Robotics
Abstract: Smart autonomous agents are becoming increasingly important in various real-life applications, including robotics and autonomous vehicles. One crucial skill that these agents must possess is the ability to interact with their surrounding entities, such as other agents or humans. In this work, we aim at building an intelligent agent that can efficiently navigate in an environment while being able to interact with an oracle (or human) in natural language and ask for directions when it is unsure about its navigation performance. The interaction is started by the agent that produces a question, which is then answered by the oracle on the basis of the shortest trajectory to the goal. The process can be performed multiple times during navigation, thus enabling the agent to hold a dialogue with the oracle. To this end, we propose a novel computational model, named UNMuTe, that consists of two main components: a dialogue model and a navigator. Specifically, the dialogue model is based on a GPT-2 decoder that handles multimodal data consisting of both text and images. First, the dialogue model is trained to generate question-answer pairs: the question is generated using the current image, while the answer is produced leveraging future images on the path toward the goal. Subsequently, a VLN model is trained to follow the dialogue predicting navigation actions or triggering the dialogue model if it needs help. In our experimental analysis, we show that UNMuTe achieves state-of-the-art performance on the main navigation tasks implying dialogue, i.e. Cooperative Vision and Dialogue Navigation (CVDN) and Navigation from Dialogue History (NDH), proving that our approach is effective in generating useful questions and answers to guide navigation.
Published: 2024

5. SMLT-MUGC: Small, Medium, and Large Texts -- Machine versus User-Generated Content Detection and Comparison

Author: Rawal, Anjali, Wang, Hui, Zheng, Youjia, Lin, Yu-Hsuan, and Sushmita, Shanu
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Large language models (LLMs) have gained significant attention due to their ability to mimic human language. Identifying texts generated by LLMs is crucial for understanding their capabilities and mitigating potential consequences. This paper analyzes datasets of varying text lengths: small, medium, and large. We compare the performance of machine learning algorithms on four datasets: (1) small (tweets from Election, FIFA, and Game of Thrones), (2) medium (Wikipedia introductions and PubMed abstracts), and (3) large (OpenAI web text dataset). Our results indicate that LLMs with very large parameters (such as the XL-1542 variant of GPT2 with 1542 million parameters) were harder (74%) to detect using traditional machine learning methods. However, detecting texts of varying lengths from LLMs with smaller parameters (762 million or less) can be done with high accuracy (96% and above). We examine the characteristics of human and machine-generated texts across multiple dimensions, including linguistics, personality, sentiment, bias, and morality. Our findings indicate that machine-generated texts generally have higher readability and closely mimic human moral judgments but differ in personality traits. SVM and Voting Classifier (VC) models consistently achieve high performance across most datasets, while Decision Tree (DT) models show the lowest performance. Model performance drops when dealing with rephrased texts, particularly shorter texts like tweets. This study underscores the challenges and importance of detecting LLM-generated texts and suggests directions for future research to improve detection methods and understand the nuanced capabilities of LLMs.
Published: 2024

6. DEM: Distribution Edited Model for Training with Mixed Data Distributions

Author: Ram, Dhananjay, Rawal, Aditya, Hardalov, Momchil, Pappas, Nikolaos, and Zha, Sheng
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning, 68T50, F.2.2, I.2.7
Abstract: Training with mixed data distributions is a common and important part of creating multi-task and instruction-following models. The diversity of the data distributions and cost of joint training makes the optimization procedure extremely challenging. Data mixing methods partially address this problem, albeit having a sub-optimal performance across data sources and require multiple expensive training runs. In this paper, we propose a simple and efficient alternative for better optimization of the data sources by combining models individually trained on each data source with the base model using basic element-wise vector operations. The resulting model, namely Distribution Edited Model (DEM), is 11x cheaper than standard data mixing and outperforms strong baselines on a variety of benchmarks, yielding up to 6.2% improvement on MMLU, 11.5% on BBH, 16.1% on DROP, and 9.3% on HELM with models of size 3B to 13B. Notably, DEM does not require full re-training when modifying a single data-source, thus making it very flexible and scalable for training with diverse data sources.
Published: 2024

7. CinePile: A Long Video Question Answering Dataset and Benchmark

Author: Rawal, Ruchit, Saifullah, Khalid, Basri, Ronen, Jacobs, David, Somepalli, Gowthami, and Goldstein, Tom
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multimedia
Abstract: Current datasets for long-form video understanding often fall short of providing genuine long-form comprehension challenges, as many tasks derived from these datasets can be successfully tackled by analyzing just one or a few random frames from a video. To address this issue, we present a novel dataset and benchmark, CinePile, specifically designed for authentic long-form video understanding. This paper details our innovative approach for creating a question-answer dataset, utilizing advanced LLMs with human-in-the-loop and building upon human-generated raw data. Our comprehensive dataset comprises 305,000 multiple-choice questions (MCQs), covering various visual and multimodal aspects, including temporal comprehension, understanding human-object interactions, and reasoning about events or actions within a scene. Additionally, we evaluate recent video-centric LLMs, both open-source and proprietary, on the test split of our dataset. The findings reveal that even state-of-the-art video-centric LLMs significantly lag behind human performance in these tasks, highlighting the complexity and challenge inherent in video understanding. The dataset is available at https://hf.co/datasets/tomg-group-umd/cinepile, Comment: Project page with all the artifacts - https://ruchitrawal.github.io/cinepile/. Updated version with results on Gemini Flash model and additional related work
Published: 2024

8. Generalizable Neural Human Renderer

Author: Masuda, Mana, Park, Jinhyung, Iwase, Shun, Khirodkar, Rawal, and Kitani, Kris
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: While recent advancements in animatable human rendering have achieved remarkable results, they require test-time optimization for each subject which can be a significant limitation for real-world applications. To address this, we tackle the challenging task of learning a Generalizable Neural Human Renderer (GNH), a novel method for rendering animatable humans from monocular video without any test-time optimization. Our core method focuses on transferring appearance information from the input video to the output image plane by utilizing explicit body priors and multi-view geometry. To render the subject in the intended pose, we utilize a straightforward CNN-based image renderer, foregoing the more common ray-sampling or rasterizing-based rendering modules. Our GNH achieves remarkable generalizable, photorealistic rendering with unseen subjects with a three-stage process. We quantitatively and qualitatively demonstrate that GNH significantly surpasses current state-of-the-art methods, notably achieving a 31.3% improvement in LPIPS.
Published: 2024

9. AIGeN: An Adversarial Approach for Instruction Generation in VLN

Author: Rawal, Niyati, Bigazzi, Roberto, Baraldi, Lorenzo, and Cucchiara, Rita
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Robotics
Abstract: In the last few years, the research interest in Vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal. Recent work in literature focuses on different ways to augment the available datasets of instructions for improving navigation performance by exploiting synthetic training data. In this work, we propose AIGeN, a novel architecture inspired by Generative Adversarial Networks (GANs) that produces meaningful and well-formed synthetic instructions to improve navigation agents' performance. The model is composed of a Transformer decoder (GPT-2) and a Transformer encoder (BERT). During the training phase, the decoder generates sentences for a sequence of images describing the agent's path to a particular point while the encoder discriminates between real and fake instructions. Experimentally, we evaluate the quality of the generated instructions and perform extensive ablation studies. Additionally, we generate synthetic instructions for 217K trajectories using AIGeN on Habitat-Matterport 3D Dataset (HM3D) and show an improvement in the performance of an off-the-shelf VLN method. The validation analysis of our proposal is conducted on REVERIE and R2R and highlights the promising aspects of our proposal, achieving state-of-the-art performance., Comment: Accepted to 7th Multimodal Learning and Applications Workshop (MULA 2024) at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024
Published: 2024

10. MUGC: Machine Generated versus User Generated Content Detection

Author: Xie, Yaqi, Rawal, Anjali, Cen, Yujing, Zhao, Dixuan, Narang, Sunil K, and Sushmita, Shanu
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: As advanced modern systems like deep neural networks (DNNs) and generative AI continue to enhance their capabilities in producing convincing and realistic content, the need to distinguish between user-generated and machine generated content is becoming increasingly evident. In this research, we undertake a comparative evaluation of eight traditional machine-learning algorithms to distinguish between machine-generated and human-generated data across three diverse datasets: Poems, Abstracts, and Essays. Our results indicate that traditional methods demonstrate a high level of accuracy in identifying machine-generated data, reflecting the documented effectiveness of popular pre-trained models like RoBERT. We note that machine-generated texts tend to be shorter and exhibit less word variety compared to human-generated content. While specific domain-related keywords commonly utilized by humans, albeit disregarded by current LLMs (Large Language Models), may contribute to this high detection accuracy, we show that deeper word representations like word2vec can capture subtle semantic variances. Furthermore, readability, bias, moral, and affect comparisons reveal a discernible contrast between machine-generated and human generated content. There are variations in expression styles and potentially underlying biases in the data sources (human and machine-generated). This study provides valuable insights into the advancing capacities and challenges associated with machine-generated content across various domains., Comment: 11 pages, 16 figures
Published: 2024

11. Real-Time Simulated Avatar from Head-Mounted Sensors

Author: Luo, Zhengyi, Cao, Jinkun, Khirodkar, Rawal, Winkler, Alexander, Huang, Jing, Kitani, Kris, and Xu, Weipeng
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Robotics
Abstract: We present SimXR, a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets. Due to the challenging viewpoint of head-mounted cameras, the human body is often clipped out of view, making traditional image-based egocentric pose estimation challenging. On the other hand, headset poses provide valuable information about overall body motion, but lack fine-grained details about the hands and feet. To synergize headset poses with cameras, we control a humanoid to track headset movement while analyzing input images to decide body movement. When body parts are seen, the movements of hands and feet will be guided by the images; when unseen, the laws of physics guide the controller to generate plausible motion. We design an end-to-end method that does not rely on any intermediate representations and learns to directly map from images and headset poses to humanoid control signals. To train our method, we also propose a large-scale synthetic dataset created using camera configurations compatible with a commercially available VR headset (Quest 2) and show promising results on real-world captures. To demonstrate the applicability of our framework, we also test it on an AR headset with a forward-facing camera., Comment: CVPR 2024 Hightlight. Website: https://www.zhengyiluo.com/SimXR/
Published: 2024

12. Extreme Miscalibration and the Illusion of Adversarial Robustness

Author: Raina, Vyas, Tan, Samson, Cevher, Volkan, Rawal, Aditya, Zha, Sheng, and Karypis, George
Subjects: Computer Science - Computation and Language
Abstract: Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify. Adversarial Training (AT) is often used to increase model robustness. However, we have discovered an intriguing phenomenon: deliberately or accidentally miscalibrating models masks gradients in a way that interferes with adversarial attack search methods, giving rise to an apparent increase in robustness. We show that this observed gain in robustness is an illusion of robustness (IOR), and demonstrate how an adversary can perform various forms of test-time temperature calibration to nullify the aforementioned interference and allow the adversarial attack to find adversarial examples. Hence, we urge the NLP community to incorporate test-time temperature scaling into their robustness evaluations to ensure that any observed gains are genuine. Finally, we show how the temperature can be scaled during \textit{training} to improve genuine robustness.
Published: 2024

13. Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO$_2$ by CuPt/TiO$_2$

Author: Sumaria, Vaidish, Rawal, Takat B., Li, Young Feng, Sommer, David, Vikoren, Jake, Bondi, Robert J., Rupp, Matthias, Prasad, Amrit, and Prasad, Deeptanshu
Subjects: Condensed Matter - Materials Science, Physics - Chemical Physics
Abstract: The photoconversion of CO$_2$ to hydrocarbons is a sustainable route to its transformation into value-added compounds and, thereby, crucial to mitigating the energy and climate crises. CuPt nanoparticles on TiO$_2$ surfaces have been reported to show promising photoconversion efficiency. For further progress, a mechanistic understanding of the catalytic properties of these CuPt/TiO$_2$ systems is vital. Here, we employ $\textit{ab-initio}$ calculations, machine learning, and photocatalysis experiments to explore their configurational space and examine their reactivity and find that the interface plays a key role in stabilizing *CO$_2$, *CO, and other CH-containing intermediates, facilitating higher activity and selectivity for methane. A bias-corrected machine-learning interatomic potential trained on density functional theory data enables efficient exploration of the potential energy surfaces of numerous CO$_2$@CuPt/TiO$_2$ configurations via basin-hopping Monte Carlo simulations, greatly accelerating the study of these photocatalyst systems. Our simulations show that CO$_2$ preferentially adsorbs at the interface, with C atom bonded to a Pt site and one O atom occupying an O-vacancy site. The interface also promotes the formation of *CH and *CH$_2$ intermediates. For confirmation, we synthesize CuPt/TiO$_2$ samples with a variety of compositions and analyze their morphologies and compositions using scanning electron microscopy and energy-dispersive X-ray spectroscopy, and measure their photocatalytic activity. Our computational and experimental findings qualitatively agree and highlight the importance of interface design for selective conversion of CO$_2$ to hydrocarbons., Comment: Main text: 16 pages and 7 figures; Supporting information: 10 pages and 9 figures; Page 1, affiliation re-ordering; Page 4, typos corrected and abbreviation defined; Page 5, Table 1 caption revised and typos corrected; Page 16 typos corrected
Published: 2024

14. PhosNetVis: a web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Author: Rawal, Osho, Turhan, Berk, Peradejordi, Irene Font, Chandrasekar, Shreya, Kalayci, Selim, Gnjatic, Sacha, Johnson, Jeffrey, Bouhaddou, Mehdi, and Gümüş, Zeynep H.
Subjects: Quantitative Biology - Molecular Networks
Abstract: Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/, Comment: Added new author, added references, changed fig1 and fig4
Published: 2024

15. A survey of experimental stimulus presentation code sharing in major areas of psychology

Author: Rawal, Amit, Truong, Vuong, Lo, Yu-Hui, Tseng, Lin-Yuan, and Duncan, Niall W.
Published: 2024
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16. Investigating single-event effects in recess gate GaN/AlN p-channel HEMTs for radiation-hardened application

Author: Chanchal, Kumari, Vandana, Rawal, D. S., and Saxena, Manoj
Published: 2024
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17. Fabrication, Characterization and Micro-machinability of Stir-Cast Al6061/GNPs + Mg Nanocomposite

Author: Rawal, Sunil, Prakash, Harsh, and Sidpara, Ajay M.
Published: 2024
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18. Genome-Wide Characterization of Fennel (Anethum foeniculum) MiRNome and Identification of its Potential Targets in Homo sapiens and Arabidopsis thaliana: An Inter and Intra-species Computational Scrutiny

Author: Trivedi, Tithi S., Shaikh, Aafrinbanu M., Mankad, Archana U., Rawal, Rakesh M., and Patel, Saumya K.
Published: 2024
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19. Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras

Author: Li, Yu-Jhe, Xu, Yan, Khirodkar, Rawal, Park, Jinhyung, and Kitani, Kris
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We tackle the task of multi-view, multi-person 3D human pose estimation from a limited number of uncalibrated depth cameras. Recently, many approaches have been proposed for 3D human pose estimation from multi-view RGB cameras. However, these works (1) assume the number of RGB camera views is large enough for 3D reconstruction, (2) the cameras are calibrated, and (3) rely on ground truth 3D poses for training their regression model. In this work, we propose to leverage sparse, uncalibrated depth cameras providing RGBD video streams for 3D human pose estimation. We present a simple pipeline for Multi-View Depth Human Pose Estimation (MVD-HPE) for jointly predicting the camera poses and 3D human poses without training a deep 3D human pose regression model. This framework utilizes 3D Re-ID appearance features from RGBD images to formulate more accurate correspondences (for deriving camera positions) compared to using RGB-only features. We further propose (1) depth-guided camera-pose estimation by leveraging 3D rigid transformations as guidance and (2) depth-constrained 3D human pose estimation by utilizing depth-projected 3D points as an alternative objective for optimization. In order to evaluate our proposed pipeline, we collect three video sets of RGBD videos recorded from multiple sparse-view depth cameras and ground truth 3D poses are manually annotated. Experiments show that our proposed method outperforms the current 3D human pose regression-free pipelines in terms of both camera pose estimation and 3D human pose estimation., Comment: 17 pages including appendix
Published: 2024

20. Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency

Author: Constas, Pavlos, Rawal, Vikram, Oliveira, Matthew Honorio, Constas, Andreas, Khan, Aditya, Cheung, Kaison, Sultani, Najma, Chen, Carrie, Altomare, Micol, Akzam, Michael, Chen, Jiacheng, He, Vhea, Altomare, Lauren, Murqi, Heraa, Khan, Asad, Bhanshali, Nimit Amikumar, Rachad, Youssef, and Guerzhoy, Michael
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: We propose a reinforcement learning (RL)-based system that would automatically prescribe a hypothetical patient medication that may help the patient with their mental health-related speech disfluency, and adjust the medication and the dosages in response to zero-cost frequent measurement of the fluency of the patient. We demonstrate the components of the system: a module that detects and evaluates speech disfluency on a large dataset we built, and an RL algorithm that automatically finds good combinations of medications. To support the two modules, we collect data on the effect of psychiatric medications for speech disfluency from the literature, and build a plausible patient simulation system. We demonstrate that the RL system is, under some circumstances, able to converge to a good medication regime. We collect and label a dataset of people with possible speech disfluency and demonstrate our methods using that dataset. Our work is a proof of concept: we show that there is promise in the idea of using automatic data collection to address speech disfluency., Comment: In Proc. Machine Learning for Cognitive and Mental Health Workshop (ML4CMH) at AAAI 2024
Published: 2023

21. Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Author: Grauman, Kristen, Westbury, Andrew, Torresani, Lorenzo, Kitani, Kris, Malik, Jitendra, Afouras, Triantafyllos, Ashutosh, Kumar, Baiyya, Vijay, Bansal, Siddhant, Boote, Bikram, Byrne, Eugene, Chavis, Zach, Chen, Joya, Cheng, Feng, Chu, Fu-Jen, Crane, Sean, Dasgupta, Avijit, Dong, Jing, Escobar, Maria, Forigua, Cristhian, Gebreselasie, Abrham, Haresh, Sanjay, Huang, Jing, Islam, Md Mohaiminul, Jain, Suyog, Khirodkar, Rawal, Kukreja, Devansh, Liang, Kevin J, Liu, Jia-Wei, Majumder, Sagnik, Mao, Yongsen, Martin, Miguel, Mavroudi, Effrosyni, Nagarajan, Tushar, Ragusa, Francesco, Ramakrishnan, Santhosh Kumar, Seminara, Luigi, Somayazulu, Arjun, Song, Yale, Su, Shan, Xue, Zihui, Zhang, Edward, Zhang, Jinxu, Castillo, Angela, Chen, Changan, Fu, Xinzhu, Furuta, Ryosuke, Gonzalez, Cristina, Gupta, Prince, Hu, Jiabo, Huang, Yifei, Huang, Yiming, Khoo, Weslie, Kumar, Anush, Kuo, Robert, Lakhavani, Sach, Liu, Miao, Luo, Mi, Luo, Zhengyi, Meredith, Brighid, Miller, Austin, Oguntola, Oluwatumininu, Pan, Xiaqing, Peng, Penny, Pramanick, Shraman, Ramazanova, Merey, Ryan, Fiona, Shan, Wei, Somasundaram, Kiran, Song, Chenan, Southerland, Audrey, Tateno, Masatoshi, Wang, Huiyu, Wang, Yuchen, Yagi, Takuma, Yan, Mingfei, Yang, Xitong, Yu, Zecheng, Zha, Shengxin Cindy, Zhao, Chen, Zhao, Ziwei, Zhu, Zhifan, Zhuo, Jeff, Arbelaez, Pablo, Bertasius, Gedas, Crandall, David, Damen, Dima, Engel, Jakob, Farinella, Giovanni Maria, Furnari, Antonino, Ghanem, Bernard, Hoffman, Judy, Jawahar, C. V., Newcombe, Richard, Park, Hyun Soo, Rehg, James M., Sato, Yoichi, Savva, Manolis, Shi, Jianbo, Shou, Mike Zheng, and Wray, Michael
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions -- including a novel "expert commentary" done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community. Project page: http://ego-exo4d-data.org/, Comment: Expanded manuscript (compared to arxiv v1 from Nov 2023 and CVPR 2024 paper from June 2024) for more comprehensive dataset and benchmark presentation, plus new results on v2 data release
Published: 2023

22. Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment

Author: Rawal, Parth, Sompura, Mrunal, and Hintze, Wolfgang
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Robotics
Abstract: Synthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation. Domain randomization hereby plays an important role in reducing the simulation to reality gap. However, this generalization might not be effective in specialized domains like a production environment involving complex assemblies. Either the individual parts, trained with synthetic images, are integrated in much larger assemblies making them indistinguishable from their counterparts and result in false positives or are partially occluded just enough to give rise to false negatives. Domain knowledge is vital in these cases and if conceived effectively while generating synthetic data, can show a considerable improvement in bridging the simulation to reality gap. This paper focuses on synthetic data generation procedures for parts and assemblies used in a production environment. The basic procedures for synthetic data generation and their various combinations are evaluated and compared on images captured in a production environment, where results show up to 15% improvement using combinations of basic procedures. Reducing the simulation to reality gap in this way can aid to utilize the true potential of robot assisted production using artificial intelligence., Comment: 17 pages, 9 figures, LaTeX; typos corrected; has not been presented in any conference or published in journal
Published: 2023

23. Perturbed examples reveal invariances shared by language models

Author: Rawal, Ruchit and Toneva, Mariya
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: The rapid growth in natural language processing (NLP) research has led to numerous new models, outpacing our understanding of how they compare to established ones. One major reason for this difficulty is saturating benchmarks, which may not well reflect differences in model performance in the wild. In this work, we introduce a novel framework to compare two NLP models by revealing their shared invariance to interpretable input perturbations targeting a specific linguistic capability. Via experiments on models from the same and different architecture families, this framework offers insights about how changes in models (e.g., distillation, size increase) affect linguistic capabilities. Furthermore, our framework enables evaluation of invariances between commercial black-box models (e.g., InstructGPT family) and models that are better understood (e.g., GPT-2). Across experiments, we observe that large language models share many invariances encoded by models of various sizes, whereas the invariances by large models are only shared by other large models. Possessing a wide variety of invariances may be key to the recent successes of large language models, and our framework can shed light on the types of invariances retained or emerging in new models. We make the code publicly available., Comment: Accepted at ACL 2024 (Findings)
Published: 2023

24. GC-VTON: Predicting Globally Consistent and Occlusion Aware Local Flows with Neighborhood Integrity Preservation for Virtual Try-on

Author: Rawal, Hamza, Ahmad, Muhammad Junaid, and Zaman, Farooq
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Flow based garment warping is an integral part of image-based virtual try-on networks. However, optimizing a single flow predicting network for simultaneous global boundary alignment and local texture preservation results in sub-optimal flow fields. Moreover, dense flows are inherently not suited to handle intricate conditions like garment occlusion by body parts or by other garments. Forcing flows to handle the above issues results in various distortions like texture squeezing, and stretching. In this work, we propose a novel approach where we disentangle the global boundary alignment and local texture preserving tasks via our GlobalNet and LocalNet modules. A consistency loss is then employed between the two modules which harmonizes the local flows with the global boundary alignment. Additionally, we explicitly handle occlusions by predicting body-parts visibility mask, which is used to mask out the occluded regions in the warped garment. The masking prevents the LocalNet from predicting flows that distort texture to compensate for occlusions. We also introduce a novel regularization loss (NIPR), that defines a criteria to identify the regions in the warped garment where texture integrity is violated (squeezed or stretched). NIPR subsequently penalizes the flow in those regions to ensure regular and coherent warps that preserve the texture in local neighborhoods. Evaluation on a widely used virtual try-on dataset demonstrates strong performance of our network compared to the current SOTA methods., Comment: accepted at WACV 2024
Published: 2023

25. Polypyrrole nanofibers/phosphorus-doped graphene nanocomposite for efficient room temperature real-time monitoring of ammonia

Author: Singh, Ravinder, Agrohiya, Sunil, Rawal, Ishpal, Ohlan, Anil, Dahiya, Sajjan, Punia, R., and Maan, A. S.
Published: 2024
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26. Sapiens: Foundation for Human Vision Models

Author: Khirodkar, Rawal, Bagautdinov, Timur, Martinez, Julieta, Zhaoen, Su, James, Austin, Selednik, Peter, Anderson, Stuart, Saito, Shunsuke, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor
Published: 2025
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27. Sustained inactivation of the Polycomb PRC1 complex induces DNA repair defects and genomic instability in epigenetic tumors

Author: Rawal, Chetan C., Loubiere, Vincent, Butova, Nadejda L., Gracia, Juliette, Parreno, Victoria, Merigliano, Chiara, Martinez, Anne-Marie, Cavalli, Giacomo, and Chiolo, Irene
Published: 2024
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28. Synthesis, DFT Studies, and Biological Evaluation of New Quinazoline-1,3,4-Thiadiazole Derivatives as Anti-proliferative Agents

Author: Sidat, Parin S., Noolvi, Malleshappa N., Brahmbhatt, Jpan G., Rathod, Sanket S., Beldar, Vishal G., Rawal, Rakesh M., and Choudhari, Prafulla B.
Published: 2024
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29. Optical dromions with polarization–mode dispersion having parabolic law of self–phase modulation with multiplicative white noise

Author: Zayed, Elsayed M. E., Alurrfi, Khaled A. E., El–Shater, Mona, Biswas, Anjan, Yildirim, Yakup, Rawal, Bharat S., and Alshomrani, Ali Saleh
Published: 2024
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30. Highly dispersive optical solitons with differential group delay and multiplicative white noise for Fokas–Lenells equation

Author: Zayed, Elsayed M. E., Arnous, Ahmed H., Biswas, Anjan, Yildirim, Yakup, Rawal, Bharat S., Moraru, Luminita, and Alshomrani, Ali Saleh
Published: 2024
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31. Penicillin-binding proteins: the master builders and breakers of bacterial cell walls and its interaction with β-lactam antibiotics

Author: Dabhi, Milan, Patel, Rohit, Shah, Vidhi, Soni, Richa, Saraf, Meenu, Rawal, Rakesh, and Goswami, Dweipayan
Published: 2024
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32. Tailoring of EMI Shielding Properties of Camphor Sulphonic Acid-Mediated Polypyrrole Nanomaterials

Author: Rajpal, Aarti, Goyal, Parveen K., Rawal, Ishpal, Sharma, Anita, and Kumar, Vijay
Published: 2024
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33. Curcumin interferes with chitin synthesis in Aedes aegypti: a computational and experimental investigation

Author: Rao, Priyashi, Ninama, Jinal, Dudhat, Mansi, Goswami, Dweipayan, and Rawal, Rakesh M.
Published: 2024
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34. Establishment of GLORIA sites in Indian Himalayan Region: diversity and distribution of lichens

Author: Bisht, Kapil, Upadhyay, Shashi, Sekar, K. Chandra, Mehta, Poonam, Rawal, Renu, Joshi, Yogesh, and Kumar, Vishal
Published: 2024
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35. Qualitative and quantitative phytochemical screening and chemical fingerprint analysis of Conocarpus lancifolius plant using HPTLC

Author: Prajapati, Poojaben, Maitreya, Bharat B., and Rawal, Rakesh M.
Published: 2024
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36. Transient loss of Polycomb components induces an epigenetic cancer fate

Author: Parreno, V., Loubiere, V., Schuettengruber, B., Fritsch, L., Rawal, C. C., Erokhin, M., Győrffy, B., Normanno, D., Di Stefano, M., Moreaux, J., Butova, N. L., Chiolo, I., Chetverina, D., Martinez, A.-M., and Cavalli, G.
Published: 2024
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37. Dietary impact of Alfalfa on growth performance, biochemical profile, and resistance against Aeromonas hydrophila in fingerlings of common carp

Author: Ali, Basharat, Rawal, Yogesh Kumar, Dhillon, Oshin, Thayes, Chhomo, Sidharth, and Mittal, Gagan
Published: 2024
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38. Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

Author: Klee, Matthias, Aho, V. T. E., May, P., Heintz-Buschart, A., Landoulsi, Z., Jónsdóttir, S. R., Pauly, C., Pavelka, L., Delacour, L., Kaysen, A., Krüger, R., Wilmes, P., Leist, A. K., Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ali, Muhammad, Ammerlann, Wim, Arena, Giuseppe, Bassis, Michele, Batutu, Roxane, Beaumont, Katy, Béchet, Sibylle, Berchem, Guy, Bisdorff, Alexandre, Boussaad, Ibrahim, Bouvier, David, Castillo, Lorieza, Contesotto, Gessica, De Bremaeker, Nancy, Dewitt, Brian, Diederich, Nico, Dondelinger, Rene, Ramia, Nancy E, Ferrari, Angelo, Frauenknecht, Katrin, Fritz, Joëlle, Gamio, Carlos, Gantenbein, Manon, Gawron, Piotr, Georges, Laura, Ghosh, Soumyabrata, Giraitis, Marijus, Glaab, Enrico, Goergen, Martine, Gómez De Lope, Elisa, Graas, Jérôme, Graziano, Mariella, Groues, Valentin, Grünewald, Anne, Hammot, Gaël, Hanff, Anne-Marie, Hansen, Linda, Heneka, Michael, Henry, Estelle, Henry, Margaux, Herbrink, Sylvia, Herzinger, Sascha, Hundt, Alexander, Jacoby, Nadine, Jónsdóttir, Sonja, Klucken, Jochen, Kofanova, Olga, Krüger, Rejko, Lambert, Pauline, Landoulsi, Zied, Lentz, Roseline, Longhino, Laura, Lopes, Ana Festas, Lorentz, Victoria, Marques, Tainá M., Marques, Guilherme, Martins Conde, Patricia, May, Patrick, Mcintyre, Deborah, Mediouni, Chouaib, Meisch, Francoise, Mendibide, Alexia, Menster, Myriam, Minelli, Maura, Mittelbronn, Michel, Mtimet, Saïda, Munsch, Maeva, Nati, Romain, Nehrbass, Ulf, Nickels, Sarah, Nicolai, Beatrice, Nicolay, Jean-Paul, Noor, Fozia, Gomes, Clarissa P. C., Pachchek, Sinthuja, Pauly, Claire, Pauly, Laure, Pavelka, Lukas, Perquin, Magali, Pexaras, Achilleas, Rauschenberger, Armin, Rawal, Rajesh, Reddy Bobbili, Dheeraj, Remark, Lucie, Richard, Ilsé, Roland, Olivia, Roomp, Kirsten, Rosales, Eduardo, Sapienza, Stefano, Satagopam, Venkata, Schmitz, Sabine, Schneider, Reinhard, Schwamborn, Jens, Severino, Raquel, Sharify, Amir, Soare, Ruxandra, Soboleva, Ekaterina, Sokolowska, Kate, Theresine, Maud, Thien, Hermann, Thiry, Elodie, Ting Jiin Loo, Rebecca, Trouet, Johanna, Tsurkalenko, Olena, Vaillant, Michel, Vega, Carlos, Vilas Boas, Liliana, Wilmes, Paul, Wollscheid-Lengeling, Evi, and Zelimkhanov, Gelani
Published: 2024
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39. Solution to Advanced Manufacturing Process Problems using Cohort Intelligence Algorithm with Improved Constraint Handling Approaches

Author: Nargundkar, Aniket, Rawal, Madhav, Patel, Aryaman, Kulkarni, Anand J, and Shastri, Apoorva S
Subjects: Computer Science - Neural and Evolutionary Computing
Abstract: Recently, various Artificial Intelligence (AI) based optimization metaheuristics are proposed and applied for a variety of problems. Cohort Intelligence (CI) algorithm is a socio inspired optimization technique which is successfully applied for solving several unconstrained & constrained real-world problems from the domains such as design, manufacturing, supply chain, healthcare, etc. Generally, real-world problems are constrained in nature. Even though most of the Evolutionary Algorithms (EAs) can efficiently solve unconstrained problems, their performance degenerates when the constraints are involved. In this paper, two novel constraint handling approaches based on modulus and hyperbolic tangent probability distributions are proposed. Constrained CI algorithm with constraint handling approaches based on triangular, modulus and hyperbolic tangent is presented and applied for optimizing advanced manufacturing processes such as Water Jet Machining (WJM), Abrasive Jet Machining (AJM), Ultrasonic Machining (USM) and Grinding process. The solutions obtained using proposed CI algorithm are compared with contemporary algorithms such as Genetic Algorithm, Simulated Annealing, Teaching Learning Based Optimization, etc. The proposed approaches achieved 2%-127% maximization of material removal rate satisfying hard constraints. As compared to the GA, CI with Hyperbolic tangent probability distribution achieved 15%, 2%, 2%, 127%, and 4% improvement in MRR for AJMB, AJMD, WJM, USM, and Grinding processes, respectively contributing to the productivity improvement. The contributions in this paper have opened several avenues for further applicability of the proposed constraint handling approaches for solving complex constrained problems.
Published: 2023

40. DAD++: Improved Data-free Test Time Adversarial Defense

Author: Nayak, Gaurav Kumar, Khatri, Inder, Randive, Shubham, Rawal, Ruchit, and Chakraborty, Anirban
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real-world scenarios. A plethora of works based on adversarial training and regularization-based techniques have been proposed to make these deep networks robust against adversarial attacks. However, these methods require either retraining models or training them from scratch, making them infeasible to defend pre-trained models when access to training data is restricted. To address this problem, we propose a test time Data-free Adversarial Defense (DAD) containing detection and correction frameworks. Moreover, to further improve the efficacy of the correction framework in cases when the detector is under-confident, we propose a soft-detection scheme (dubbed as "DAD++"). We conduct a wide range of experiments and ablations on several datasets and network architectures to show the efficacy of our proposed approach. Furthermore, we demonstrate the applicability of our approach in imparting adversarial defense at test time under data-free (or data-efficient) applications/setups, such as Data-free Knowledge Distillation and Source-free Unsupervised Domain Adaptation, as well as Semi-supervised classification frameworks. We observe that in all the experiments and applications, our DAD++ gives an impressive performance against various adversarial attacks with a minimal drop in clean accuracy. The source code is available at: https://github.com/vcl-iisc/Improved-Data-free-Test-Time-Adversarial-Defense, Comment: IJCV Journal (Under Review)
Published: 2023

41. Performance analysis of InAlN/GaN HEMT and optimization for high frequency applications

Author: Raychaudhuri, Jagori, Mukherjee, Jayjit, Malik, Amit, Kumar, Sudhir, Rawal, D. S., Mishra, Meena, and Ghosh, Santanu
Subjects: Physics - Applied Physics
Abstract: An InAlN/GaN HEMT device was studied using extensive temperature dependent DC IV measurements and CV measurements. Barrier traps in the InAlN layer were characterized using transient analysis. Forward gate current was modelled using analytical equations. RF performance of the device was also studied and device parameters were extracted following small signal equivalent circuit model. Extensive simulations in Silvaco TCAD were also carried out by varying stem height, gate length and incorporating back barrier to optimize the suitability of this device in Ku-band by reducing the detrimental Short Channel Effects (SCEs). In this paper a novel structure i.e., a short length T gate with recess, on thin GaN buffer to achieve high cut-off frequency (f$_T$) and high maximum oscillating frequency (f$_{max}$) apt for Ku-band applications is also proposed.
Published: 2023

42. Investigation of RF performance of Ku-band GaN HEMT device and an in-depth analysis of short channel effects

Author: Raychaudhuri, Jagori, Mukherjee, Jayjit, Kumar, Sudhir, Rawal, D. S., Mishra, Meena, and Ghosh, Santanu
Subjects: Physics - Applied Physics
Abstract: In this paper, we have characterized an AlGaN/GaN High Electron Mobility Transistor (HEMT) with a short gate length (Lg $\approx$ 0.15$\mu$m). We have studied the effect of short gate length on the small signal parameters, linearity parameters and gm-gd ratio in GaN HEMT devices. To understand how scaling results in the variation of the above-mentioned parameters a comparative study with higher gate length devices on similar heterostructure is also presented here. We have scaled down the gate length but the barrier thickness(t$_{bar}$) remained same which affects the aspect ratio (L$_{g}$/t$_{bar}$) of the device and its inseparable consequences are the prominent short channel effects (SCEs) barring the optimum output performance of the device. These interesting phenomena were studied in detail and explored over a temperature range of -40$^\circ$C to 80$^\circ$C. To the best of our knowledge this paper explores temperature dependence of SCEs of GaN HEMT for the first time. With an approach to reduce the impact of SCEs a simulation study in Silvaco TCAD was carried out and it is observed that a recessed gate structure on conventional heterostructure successfully reduces SCEs and improves RF performance of the device. This work gives an overall view of gate length scaling on conventional AlGaN/GaN HEMTs.
Published: 2023
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43. Antipsoriatic Effect of Silymarin NLCs Based Gel: In Vitro and In Vivo Activity

Author: Momin, Faijmahmad, Kevlani, Vijay, Rawal, Shruti, Patel, Riya, Acharya, Sheetal, and Shah, Shreeraj
Published: 2024
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44. Measurement of the background in the CMS muon detector in pps=13TeV-collisions at pps=13TeV pps=13TeV

Author: Tytgat, M., Muhammad, A., De Lentdecker, G., Jaramillo, J., Moureaux, L., Pétré, L., Yang, Y., Rendón, C., Gokbulut, G., Hong, Y., Samalan, A., Alves, G. A., Marujo da Silva, F., Alves Coelho, E., Barroso Ferreira Filho, M., Da Costa, E. M., De Jesus Damiao, D., Ferreira, B. C., Fonseca De Souza, S., Mota Amarilo, K., Nogima, H., Santoro, A., Thiel, M., Aleksandrov, A., Dimitrov, L., Hadjiiska, R., Iaydjiev, P., Misheva, M., Mitev, G., Ratchev, L., Rashevski, G., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Keshri, S., Thakur, S., Chen, M., Dong, X., Gong, W., Hou, Q., Jiang, C., Kou, H., Liu, Z.-A., Luo, W., Song, J., Sun, L., Wang, N., Wang, Y., Wang, Z., Zhang, C., Zhang, Y., Zhang, H., Zhao, J., Agapitos, A., Ban, Y., Levin, A., Li, Q., Qian, S. J., Wang, D., Wang, K., You, Z., Avila, C., Barbosa Trujillo, D. A., Cabrera, A., Florez, C. A., Fraga, J., Reyes Vega, J. A., Ramirez, F., Rodriguez, M., Ruiz, J. D., Vanegas, N., Abdalla, H., Abdelalim, A. A., Assran, Y., Radi, A., Crotty, I., Mahmoud, M. A., Balleyguier, L., Chen, X., Combaret, C., Galbit, G., Gouzevitch, M., Grenier, G., Laktineh, I. B., Luciol, A., Mirabito, L., Tromeur, W., Bagaturia, I., Lomidze, I., Kemularia, O., Tsamalaidze, Z., Böttger, U., Eliseev, D., Hebbeker, T., Hoepfner, K., Merschmeyer, M., Ivone, F., Mukherjee, S., Nowotny, F., Philipps, B., Reithler, H., Sharma, A., Torres Da Silva De Araujo, F., Wiedenbeck, S., Zaleski, S., Zantis, F. P., Abbas, M., Mallows, S., Bencze, G., Beni, N., Molnar, J., Szillasi, Z., Teyssier, D., Ujvari, B., Zilizi, G., Babbar, J., Bansal, S., Bhatnagar, V., Chauhan, S., Kaur, A., Kaur, H., Kaur Sahota, A., Kumar, S., Sheokand, T., Singh, J., Choudhary, B. C., Kumar, A., Kumar Saini, M., Naimuddin, M., Majumdar, N., Mukhopadhyay, S., Rout, P., Amoozegar, V., Boghrati, B., Ebraimi, M., Mohammadi Najafabadi, M., Zareian, E., Abbrescia, M., Aly, R., Buonsante, M., Colaleo, A., De Filippis, N., Dell’Olio, D., De Robertis, G., Elmetenawee, W., Ferrara, N., Franco, M., Iaselli, G., Lacalamita, N., Licciulli, F., Loddo, F., Maggi, M., Martiradonna, S., Nuzzo, S., Longo, L., Pellecchia, A., Pugliese, G., Radogna, R., Ramos, D., Ranieri, A., Simone, F. M., Stamerra, A., Troiano, D., Venditti, R., Verwilligen, P., Zaza, A., Abbiendi, G., Baldanza, C., Battilana, C., Benvenuti, A., Borgonovi, L., Cafaro, V., Cavallo, F. R., Crupano, A., Cuffiani, M., Dallavalle, G. M., Fabbri, F., Fanfani, A., Fasanella, D., Giacomelli, P., Giordano, V., Guandalini, C., Guiducci, L., Lo Meo, S., Lunerti, L., Marcellini, S., Masetti, G., Navarria, F. L., Paggi, G., Perrotta, A., Primavera, F., Rossi, A. M., Rovelli, T., Torromeo, G., Benussi, L., Bianco, S., Campagnola, R., Caponero, M., Colafranceschi, S., Meola, S., Passamonti, L., Piccolo, D., Pierluigi, D., Raffone, G., Russo, A., Saviano, G., Buontempo, S., Cagnotta, A., Carnevali, F., Cassese, F., Cavallo, N., De Iorio, A., Fabozzi, F., Iorio, A. O. M., Lista, L., Paolucci, P., Passeggio, G., Rossi, B., Barcellan, L., Bellato, M., Benettoni, M., Bergnoli, A., Bragagnolo, A., Carlin, R., Castellani, L., Checchia, P., Ciano, L., Colombo, A., Corti, D., Gasparini, F., Gasparini, U., Gonella, F., Gozzelino, A., Griggio, A., Grosso, G., Gulmini, M., Isocrate, R., Lusiani, E., Maron, G., Margoni, M., Meneguzzo, A. T., Migliorini, M., Modenese, L., Montecassiano, F., Negrello, M., Passaseo, M., Pazzini, J., Ramina, L., Rampazzo, M., Rebeschini, M., Ronchese, P., Rossin, R., Simonetto, F., Toffano, M., Toniolo, N., Triossi, A., Ventura, S., Zanetti, M., Zatti, P. G., Zotto, P., Zucchetta, A., AbuZeid, S., Aimè, C., Braghieri, A., Calzaferri, S., Fiorina, D., Gigli, S., Montagna, P., Riccardi, C., Salvini, P., Vai, I., Vitulo, P., Amapane, N., Cotto, G., Dattola, D., De Remigis, P., Kiani, B., Mariotti, C., Maselli, S., Pelliccioni , M., Rotondo, F., Staiano, A., Trocino, D., Umoret, G., Asilar, E., Kim, T. J., Merlin, J. A., Choi, S., Hong, B., Lee, K. S., Goh, J., Choi, J., Kim, J., Yang, U., Yoon, I., Jang, W., Heo, J., Kang, D., Kang, Y., Kim, D., Kim, S., Ko, B., Lee, J. S. H., Park, I. C., Watson, I. J., Yang, S., Jeong, Y., Lee, Y., Yu, I., Alasfour, G., Beyrouthy, T., Gharbia, Y., Maghrbi, Y., Otkur, M., Castilla-Valdez, H., Crotte Ledesma, H., Lopez-Fernandez, R., Sánchez Hernández, A., Ramírez García, M., Vazquez, E., Shah, M. A., Zaganidis, N., Pedraza, I., Uribe Estrada, C., Ahmad, A., Ahmed, W., Asghar, M. I., Hoorani, H. R., Muhammad, S., Wajid, A., Alcaraz Maestre, J., Álvarez Fernández, A., Fernandez Bedoya, Cristina F., Blanco Ramos, L. C., Calvo, E., Carrillo Montoya, C. A., Cela Ruiz, J. M., Cepeda, M., Cerrada, M., Colino, N., Cuadrado Calzada, S., Cuchillo Ortega, J., De La Cruz, B., de Lara Rodríguez, C. I., Fernández Del Val, D., Fernández Ramos, J. P., Fouz, M. C., Francia Ferrero, D., García Romero, J., Gonzalez Lopez, O., Goy Lopez, S., Josa, M. I., León Holgado, J., Manzanilla Carretero, O., Martín Martín, I., Martínez Morales, J. J., Martín Viscasillas, E., Moran, D., Navarro Tobar, Á., Paz Herrera, R., Puras Sánchez, J. C., Puerta Pelayo, J., Pulido Ferrero, S., Redondo, I., Redondo Ferrero, D. D., Salto Parra, V., Sánchez Navas, S., Sastre, J., Urda Gómez, L., Vazquez Escobar, J., de Trocóniz, J. F., Frias Garcia-Lago, F., Reyes-Almanza, R., Alvarez Gonzalez, B., Cuevas, J., Fernandez Menendez, J., Folgueras, S., Gonzalez Caballero, I., Leguina López, P., Palencia Cortezon, E., Ramón Álvarez, C., Prado Pico, J., Rodríguez Bouza, V., Soto Rodríguez, A., Trapote, A., Villalba, C. Vico, Kailasapathy, B., Malagalage, K., Sonnadara, D. U. J., Wickramarathna, D. D. C., Dharmaratna, W. G. D., Liyanage, K., Perera, N., Wickramage, N., Aspell, P., Bianco, M., Bozzato, D., Brachet, S., Conde Garcia, A., Dabrowski, A., De Oliveira, R., Fallavollita, F., Kicsiny, P., Sharma, A., Hazen, E., May, S., Peck, A., Salyer, K., Suarez, I., Abbott, S., Bonilla, J., Breedon, R., Cai, H., Cox, P. T., Erbacher, R., Kukral, O., McLean, C., Mocellin, G., Mulhearn, M., Regnery, B., Tripathi, M., Waegel, G., Yao, Y., Carlson, J., Cousins, R., Dasgupta, A., Datta, A., Hauser, J., Ignatenko, M., Iqbal, M. A., Lo, C., Saltzberg, D., Schnaible, C., Valuev, V., Clare, R., Gordon, M., Hanson, G., Amin, N., Bradmiller-Feld, J., Campagnari, C., Danielson, T., Dishaw, A., Dorsett, A., Kim, J., Marsh, B., Mei, H., Oshiro, M., Richman, J., Setti, F., Sevilla, M. F., Siddireddy, P., Wang, S., Aruta, C., Barashko, V., Cherepanov, V., Dittrich, M., Korytov, A., Kuznetsova, E., Madorsky, A., Mitselmakher, G., Muthirakalayil Madhu, A., Rawal, N., Terentyev, N., Wang, J., Alsufyani, B., Butalla, S., Elkafrawy, T., Hohlmann, M., Yanes, E., Eysermans, J., Barberis, E., Haddad, Y., Han, Y., Madigan, G., Morse, D. M., Nguyen, V., Wood, D., Bhattacharya, S., Bueghly, J., Chen, Z., Hahn, K. A., Liu, Y., Miao, Y., Monk, D. G., Schmitt, M. H., Taliercio, A., Velasco, M., Bylsma, B., Carrigan, M., De Los Santos, R., Durkin, L. S., Hill, C., Banicz, K., Liu, J., Matveev, M., Padley, B. P., Aebi, D., Ahmad, M., Akhter, T., Bolshov, A., Bouhali, O., Eusebi, R., Gilmore, J., Huang, T., Juska, E., Kamon, T., Kim, H., Kizlov, M., Malhotra, S., Mueller, R., Rabadan, R., Rathjens, D., Safonov, A., Karchin, P. E., Aravind, A., Black, K., De Bruyn, I., Everaerts, P., Galloni, C., Herndon, M., Lanaro, A., Loveless, R., Madhusudanan Sreekala, J., Mondal, S., Teague, D., Vetens, W., Warden, A., Azhgirey, I., Borshch, V., Chtchipunov, L., Egorov, A., Gavrilov, G., Golovtcov, V., Ivanov, M., Ivantchenko, V., Ivanov, Y., Karjavine, V., Khodinov, A., Kim, V., Kurochkin, I. A., Levchenko, P., Murzin, V., Nasybulin, S., Oreshkin, V., Palichik, V., Perelygin, V., Riabchikova, A., Sosnov, D., Sulimov, V., Uvarov, L., Vavilov, S., and Vorobyev, A.
Published: 2024
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45. Search for new physics in high-mass diphoton events from proton-proton collisions at s = 13 TeV

Author: Hayrapetyan, A., Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Damanakis, K., Dragicevic, M., Hussain, P. S., Jeitler, M., Krammer, N., Li, A., Liko, D., Mikulec, I., Schieck, J., Schöfbeck, R., Schwarz, D., Sonawane, M., Templ, S., Waltenberger, W., Wulz, C.-E., Darwish, M. R., Janssen, T., Van Laer, T., Van Mechelen, P., Breugelmans, N., D’Hondt, J., Dansana, S., De Moor, A., Delcourt, M., Heyen, F., Lowette, S., Makarenko, I., Müller, D., Tavernier, S., Tytgat, M., Van Onsem, G. P., Van Putte, S., Vannerom, D., Bilin, B., Clerbaux, B., Das, A. K., De Lentdecker, G., Evard, H., Favart, L., Gianneios, P., Jaramillo, J., Khalilzadeh, A., Khan, F. A., Lee, K., Mahdavikhorrami, M., Malara, A., Paredes, S., Shahzad, M. A., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., De Coen, M., Dobur, D., Gokbulut, G., Hong, Y., Knolle, J., Lambrecht, L., Marckx, D., Mota Amarilo, K., Samalan, A., Skovpen, K., Van Den Bossche, N., van der Linden, J., Wezenbeek, L., Benecke, A., Bethani, A., Bruno, G., Caputo, C., De Favereau De Jeneret, J., Delaere, C., Donertas, I. S., Giammanco, A., Guzel, A. O., Jain, Sa., Lemaitre, V., Lidrych, J., Mastrapasqua, P., Tran, T. T., Wertz, S., Alves, G. A., Alves Gallo Pereira, M., Coelho, E., Correia Silva, G., Hensel, C., Menezes De Oliveira, T., Moraes, A., Rebello Teles, P., Soeiro, M., Vilela Pereira, A., Aldá Júnior, W. L., Barroso Ferreira Filho, M., Brandao Malbouisson, H., Carvalho, W., Chinellato, J., Da Costa, E. M., Da Silveira, G. G., De Jesus Damiao, D., Fonseca De Souza, S., Gomes De Souza, R., Macedo, M., Martins, J., Mora Herrera, C., Mundim, L., Nogima, H., Pinheiro, J. P., Santoro, A., Sznajder, A., Thiel, M., Bernardes, C. A., Calligaris, L., Fernandez Perez Tomei, T. R., Gregores, E. M., Maietto Silverio, I., Mercadante, P. G., Novaes, S. F., Orzari, B., Padula, Sandra S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Keshri, S., Thakur, S., Cheng, T., Javaid, T., Yuan, L., Hu, Z., Liang, Z., Liu, J., Yi, K., Chen, G. M., Chen, H. S., Chen, M., Iemmi, F., Jiang, C. H., Kapoor, A., Liao, H., Liu, Z.-A., Sharma, R., Song, J. N., Tao, J., Wang, C., Wang, J., Wang, Z., Zhang, H., Zhao, J., Agapitos, A., Ban, Y., Deng, S., Guo, B., Jiang, C., Levin, A., Li, C., Li, Q., Mao, Y., Qian, S., Qian, S. J., Qin, X., Sun, X., Wang, D., Yang, H., Zhang, L., Zhao, Y., Zhou, C., Yang, S., You, Z., Jaffel, K., Lu, N., Bauer, G., Li, B., Zhang, J., Gao, X., Lin, Z., Lu, C., Xiao, M., Avila, C., Barbosa Trujillo, D. A., Cabrera, A., Florez, C., Fraga, J., Reyes Vega, J. A., Ramirez, F., Rendón, C., Rodriguez, M., Ruales Barbosa, A. A., Ruiz Alvarez, J. D., Giljanovic, D., Godinovic, N., Lelas, D., Sculac, A., Kovac, M., Petkovic, A., Sculac, T., Bargassa, P., Brigljevic, V., Chitroda, B. K., Ferencek, D., Jakovcic, K., Mishra, S., Starodumov, A., Susa, T., Attikis, A., Christoforou, K., Hadjiagapiou, A., Leonidou, C., Mousa, J., Nicolaou, C., Paizanos, L., Ptochos, F., Razis, P. A., Rykaczewski, H., Saka, H., Stepennov, A., Finger, M., Finger, Jr., M., Kveton, A., Carrera Jarrin, E., Assran, Y., El-mahdy, B., Elgammal, S., Mahmoud, M. A., Mohammed, Y., Ehataht, K., Kadastik, M., Lange, T., Nandan, S., Nielsen, C., Pata, J., Raidal, M., Tani, L., Veelken, C., Kirschenmann, H., Osterberg, K., Voutilainen, M., Bharthuar, S., Bin Norjoharuddeen, N., Brücken, E., Garcia, F., Inkaew, P., Kallonen, K. T. S., Lampén, T., Lassila-Perini, K., Lehti, S., Lindén, T., Martikainen, L., Myllymäki, M., Rantanen, M. m., Siikonen, H., Tuominiemi, J., Luukka, P., Petrow, H., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Faure, J. L., Ferri, F., Ganjour, S., Gras, P., Hamel de Monchenault, G., Kumar, M., Lohezic, V., Malcles, J., Orlandi, F., Portales, L., Rosowsky, A., Sahin, M. Ö., Savoy-Navarro, A., Simkina, P., Titov, M., Tornago, M., Beaudette, F., Boldrini, G., Busson, P., Cappati, A., Charlot, C., Chiusi, M., Damas, F., Davignon, O., De Wit, A., Ehle, I. T., Fontana Santos Alves, B. A., Ghosh, S., Gilbert, A., Granier de Cassagnac, R., Hakimi, A., Harikrishnan, B., Kalipoliti, L., Liu, G., Nguyen, M., Ochando, C., Salerno, R., Sauvan, J. B., Sirois, Y., Urda Gómez, L., Vernazza, E., Zabi, A., Zghiche, A., Agram, J.-L., Andrea, J., Apparu, D., Bloch, D., Brom, J.-M., Chabert, E. C., Collard, C., Falke, S., Goerlach, U., Haeberle, R., Le Bihan, A.-C., Meena, M., Poncet, O., Saha, G., Sessini, M. A., Van Hove, P., Vaucelle, P., Di Florio, A., Amram, D., Beauceron, S., Blancon, B., Boudoul, G., Chanon, N., Contardo, D., Depasse, P., Dozen, C., El Mamouni, H., Fay, J., Gascon, S., Gouzevitch, M., Greenberg, C., Grenier, G., Ille, B., Jourd‘huy, E., Laktineh, I. B., Lethuillier, M., Mirabito, L., Perries, S., Purohit, A., Vander Donckt, M., Verdier, P., Xiao, J., Lomidze, I., Toriashvili, T., Tsamalaidze, Z., Botta, V., Consuegra Rodríguez, S., Feld, L., Klein, K., Lipinski, M., Meuser, D., Pauls, A., Pérez Adán, D., Röwert, N., Teroerde, M., Diekmann, S., Dodonova, A., Eich, N., Eliseev, D., Engelke, F., Erdmann, J., Erdmann, M., Fackeldey, P., Fischer, B., Hebbeker, T., Hoepfner, K., Ivone, F., Jung, A., Lee, M. y., Mausolf, F., Merschmeyer, M., Meyer, A., Mukherjee, S., Noll, D., Nowotny, F., Pozdnyakov, A., Rath, Y., Redjeb, W., Rehm, F., Reithler, H., Sarkisovi, V., Schmidt, A., Sharma, A., Spah, J. L., Stein, A., Torres Da Silva De Araujo, F., Wiedenbeck, S., Zaleski, S., Dziwok, C., Flügge, G., Kress, T., Nowack, A., Pooth, O., Stahl, A., Ziemons, T., Zotz, A., Aarup Petersen, H., Aldaya Martin, M., Alimena, J., Amoroso, S., An, Y., Bach, J., Baxter, S., Bayatmakou, M., Becerril Gonzalez, H., Behnke, O., Belvedere, A., Bhattacharya, S., Blekman, F., Borras, K., Campbell, A., Cardini, A., Cheng, C., Colombina, F., De Silva, M., Eckerlin, G., Eckstein, D., Estevez Banos, L. I., Filatov, O., Gallo, E., Geiser, A., Guglielmi, V., Guthoff, M., Hinzmann, A., Jeppe, L., Kaech, B., Kasemann, M., Kleinwort, C., Kogler, R., Komm, M., Krücker, D., Lange, W., Leyva Pernia, D., Lipka, K., Lohmann, W., Lorkowski, F., Mankel, R., Melzer-Pellmann, I.-A., Mendizabal Morentin, M., Meyer, A. B., Milella, G., Moral Figueroa, K., Mussgiller, A., Nair, L. P., Niedziela, J., Nürnberg, A., Otarid, Y., Park, J., Ranken, E., Raspereza, A., Rastorguev, D., Rübenach, J., Rygaard, L., Saggio, A., Scham, M., Schnake, S., Schütze, P., Schwanenberger, C., Selivanova, D., Sharko, K., Shchedrolosiev, M., Stafford, D., Vazzoler, F., Ventura Barroso, A., Walsh, R., Wang, D., Wang, Q., Wen, Y., Wichmann, K., Wiens, L., Wissing, C., Yang, Y., Zimermmane Castro Santos, A., Albrecht, A., Albrecht, S., Antonello, M., Bein, S., Benato, L., Bollweg, S., Bonanomi, M., Connor, P., El Morabit, K., Fischer, Y., Garutti, E., Grohsjean, A., Haller, J., Jabusch, H. R., Kasieczka, G., Keicher, P., Klanner, R., Korcari, W., Kramer, T., Kuo, C. c., Kutzner, V., Labe, F., Lange, J., Lobanov, A., Matthies, C., Moureaux, L., Mrowietz, M., Nigamova, A., Nissan, Y., Paasch, A., Pena Rodriguez, K. J., Quadfasel, T., Raciti, B., Rieger, M., Savoiu, D., Schindler, J., Schleper, P., Schröder, M., Schwandt, J., Sommerhalder, M., Stadie, H., Steinbrück, G., Tews, A., Wolf, M., Brommer, S., Burkart, M., Butz, E., Chwalek, T., Dierlamm, A., Droll, A., Faltermann, N., Giffels, M., Gottmann, A., Hartmann, F., Hofsaess, R., Horzela, M., Husemann, U., Kieseler, J., Klute, M., Koppenhöfer, R., Lawhorn, J. M., Link, M., Lintuluoto, A., Maier, B., Maier, S., Mitra, S., Mormile, M., Müller, Th., Neukum, M., Oh, M., Pfeffer, E., Presilla, M., Quast, G., Rabbertz, K., Regnery, B., Shadskiy, N., Shvetsov, I., Simonis, H. J., Sowa, L., Stockmeier, L., Tauqeer, K., Toms, M., Trevisani, N., Von Cube, R. F., Wassmer, M., Wieland, S., Wittig, F., Wolf, R., Zuo, X., Anagnostou, G., Daskalakis, G., Kyriakis, A., Papadopoulos, A., Stakia, A., Kontaxakis, P., Melachroinos, G., Painesis, Z., Papavergou, I., Paraskevas, I., Saoulidou, N., Theofilatos, K., Tziaferi, E., Vellidis, K., Zisopoulos, I., Bakas, G., Chatzistavrou, T., Karapostoli, G., Kousouris, K., Papakrivopoulos, I., Siamarkou, E., Tsipolitis, G., Zacharopoulou, A., Adamidis, K., Bestintzanos, I., Evangelou, I., Foudas, C., Kamtsikis, C., Katsoulis, P., Kokkas, P., Kosmoglou Kioseoglou, P. G., Manthos, N., Papadopoulos, I., Strologas, J., Hajdu, C., Horvath, D., Márton, K., Rádl, A. J., Sikler, F., Veszpremi, V., Csanád, M., Farkas, K., Fehérkuti, A., Gadallah, M. M. A., Kadlecsik, Á., Major, P., Pásztor, G., Veres, G. I., Ujvari, B., Zilizi, G., Bencze, G., Czellar, S., Molnar, J., Szillasi, Z., Novak, T., Babbar, J., Bansal, S., Beri, S. B., Bhatnagar, V., Chaudhary, G., Chauhan, S., Dhingra, N., Kaur, A., Kaur, A., Kaur, H., Kaur, M., Kumar, S., Sandeep, K., Sheokand, T., Singh, J. B., Singla, A., Ahmed, A., Bhardwaj, A., Chhetri, A., Choudhary, B. C., Kumar, A., Kumar, A., Naimuddin, M., Ranjan, K., Saini, M. K., Saumya, S., Baradia, S., Barman, S., Bhattacharya, S., Das Gupta, S., Dutta, S., Dutta, S., Sarkar, S., Ameen, M. M., Behera, P. K., Behera, S. C., Chatterjee, S., Dash, G., Jana, P., Kalbhor, P., Kamble, S., Komaragiri, J. R., Kumar, D., Pujahari, P. R., Saha, N. R., Sharma, A., Sikdar, A. K., Singh, R. K., Verma, P., Verma, S., Vijay, A., Dugad, S., Mohanty, G. B., Parida, B., Shelake, M., Suryadevara, P., Bala, A., Banerjee, S., Chatterjee, R. M., Guchait, M., Jain, Sh., Jaiswal, A., Kumar, S., Majumder, G., Mazumdar, K., Parolia, S., Thachayath, A., Bahinipati, S., Kar, C., Maity, D., Mal, P., Mishra, T., Muraleedharan Nair Bindhu, V. K., Naskar, K., Nayak, A., Nayak, S., Pal, K., Sadangi, P., Swain, S. K., Varghese, S., Vats, D., Acharya, S., Alpana, A., Dube, S., Gomber, B., Hazarika, P., Kansal, B., Laha, A., Sahu, B., Sharma, S., Vaish, K. Y., Bakhshiansohi, H., Jafari, A., Zeinali, M., Bashiri, S., Chenarani, S., Etesami, S. M., Hosseini, Y., Khakzad, M., Khazaie, E., Mohammadi Najafabadi, M., Tizchang, S., Felcini, M., Grunewald, M., Abbrescia, M., Colaleo, A., Creanza, D., D’Anzi, B., De Filippis, N., De Palma, M., Elmetenawee, W., Fiore, L., Iaselli, G., Longo, L., Louka, M., Maggi, G., Maggi, M., Margjeka, I., Mastrapasqua, V., My, S., Nuzzo, S., Pellecchia, A., Pompili, A., Pugliese, G., Radogna, R., Ramos, D., Ranieri, A., Silvestris, L., Simone, F. M., Sözbilir, Ü., Stamerra, A., Troiano, D., Venditti, R., Verwilligen, P., Zaza, A., Abbiendi, G., Battilana, C., Bonacorsi, D., Capiluppi, P., Cavallo, F. R., Cuffiani, M., Dallavalle, G. M., Diotalevi, T., Fabbri, F., Fanfani, A., Fasanella, D., Giacomelli, P., Giommi, L., Grandi, C., Guiducci, L., Lo Meo, S., Lorusso, M., Lunerti, L., Marcellini, S., Masetti, G., Navarria, F. L., Paggi, G., Perrotta, A., Primavera, F., Rossi, A. M., Rossi Tisbeni, S., Rovelli, T., Siroli, G. P., Costa, S., Di Mattia, A., Lapertosa, A., Potenza, R., Tricomi, A., Tuve, C., Assiouras, P., Barbagli, G., Bardelli, G., Camaiani, B., Cassese, A., Ceccarelli, R., Ciulli, V., Civinini, C., D’Alessandro, R., Focardi, E., Kello, T., Latino, G., Lenzi, P., Lizzo, M., Meschini, M., Paoletti, S., Papanastassiou, A., Sguazzoni, G., Viliani, L., Benussi, L., Bianco, S., Meola, S., Piccolo, D., Chatagnon, P., Ferro, F., Robutti, E., Tosi, S., Benaglia, A., Brivio, F., Cetorelli, F., De Guio, F., Dinardo, M. E., Dini, P., Gennai, S., Gerosa, R., Ghezzi, A., Govoni, P., Guzzi, L., Lucchini, M. T., Malberti, M., Malvezzi, S., Massironi, A., Menasce, D., Moroni, L., Paganoni, M., Palluotto, S., Pedrini, D., Perego, A., Pinolini, B. S., Pizzati, G., Ragazzi, S., Tabarelli de Fatis, T., Buontempo, S., Cagnotta, A., Carnevali, F., Cavallo, N., Fabozzi, F., Iorio, A. O. M., Lista, L., Paolucci, P., Rossi, B., Ardino, R., Azzi, P., Bacchetta, N., Bisello, D., Bortignon, P., Bortolato, G., Bragagnolo, A., Bulla, A. C. M., Carlin, R., Checchia, P., Dorigo, T., Gasparini, F., Lusiani, E., Margoni, M., Maron, G., Meneguzzo, A. T., Migliorini, M., Pazzini, J., Ronchese, P., Rossin, R., Simonetto, F., Tosi, M., Triossi, A., Ventura, S., Zanetti, M., Zotto, P., Zucchetta, A., Zumerle, G., Aimè, C., Braghieri, A., Calzaferri, S., Fiorina, D., Montagna, P., Re, V., Riccardi, C., Salvini, P., Vai, I., Vitulo, P., Ajmal, S., Ascioti, M. E., Bilei, G. M., Carrivale, C., Ciangottini, D., Fanò, L., Magherini, M., Mariani, V., Menichelli, M., Moscatelli, F., Rossi, A., Santocchia, A., Spiga, D., Tedeschi, T., Alexe, C. A., Asenov, P., Azzurri, P., Bagliesi, G., Bhattacharya, R., Bianchini, L., Boccali, T., Bossini, E., Bruschini, D., Castaldi, R., Ciocci, M. A., Cipriani, M., D’Amante, V., Dell’Orso, R., Donato, S., Giassi, A., Ligabue, F., Marini, A. C., Matos Figueiredo, D., Messineo, A., Musich, M., Palla, F., Rizzi, A., Rolandi, G., Roy Chowdhury, S., Sarkar, T., Scribano, A., Spagnolo, P., Tenchini, R., Tonelli, G., Turini, N., Vaselli, F., Venturi, A., Verdini, P. G., Baldenegro Barrera, C., Barria, P., Basile, C., Campana, M., Cavallari, F., Cunqueiro Mendez, L., Del Re, D., Di Marco, E., Diemoz, M., Errico, F., Longo, E., Mijuskovic, J., Organtini, G., Pandolfi, F., Paramatti, R., Quaranta, C., Rahatlou, S., Rovelli, C., Santanastasio, F., Soffi, L., Amapane, N., Arcidiacono, R., Argiro, S., Arneodo, M., Bartosik, N., Bellan, R., Bellora, A., Biino, C., Borca, C., Cartiglia, N., Costa, M., Covarelli, R., Demaria, N., Finco, L., Grippo, M., Kiani, B., Legger, F., Luongo, F., Mariotti, C., Markovic, L., Maselli, S., Mecca, A., Menzio, L., Meridiani, P., Migliore, E., Monteno, M., Mulargia, R., Obertino, M. M., Ortona, G., Pacher, L., Pastrone, N., Pelliccioni, M., Ruspa, M., Siviero, F., Sola, V., Solano, A., Staiano, A., Tarricone, C., Trocino, D., Umoret, G., White, R., Belforte, S., Candelise, V., Casarsa, M., Cossutti, F., De Leo, K., Della Ricca, G., Dogra, S., Hong, J., Huh, C., Kim, B., Kim, J., Lee, D., Lee, H., Lee, S. W., Moon, C. S., Oh, Y. D., Ryu, M. S., Sekmen, S., Tae, B., Yang, Y. C., Kim, M. S., Bak, G., Gwak, P., Kim, H., Moon, D. H., Asilar, E., Choi, J., Kim, D., Kim, T. J., Merlin, J. A., Ryou, Y., Choi, S., Han, S., Hong, B., Lee, K., Lee, K. S., Lee, S., Yoo, J., Goh, J., Yang, S., Kim, H. S., Kim, Y., Lee, S., Almond, J., Bhyun, J. H., Choi, J., Choi, J., Jun, W., Kim, J., Ko, S., Kwon, H., Lee, H., Lee, J., Lee, J., Oh, B. H., Oh, S. B., Seo, H., Yang, U. K., Yoon, I., Jang, W., Kang, D. Y., Kang, Y., Kim, S., Ko, B., Lee, J. S. H., Lee, Y., Park, I. C., Roh, Y., Watson, I. J., Ha, S., Yoo, H. D., Choi, M., Kim, M. R., Lee, H., Lee, Y., Yu, I., Beyrouthy, T., Gharbia, Y., Dreimanis, K., Gaile, A., Pikurs, G., Potrebko, A., Seidel, M., Sidiropoulos Kontos, D., Strautnieks, N. R., Ambrozas, M., Juodagalvis, A., Rinkevicius, A., Tamulaitis, G., Yusuff, I., Zolkapli, Z., Benitez, J. F., Castaneda Hernandez, A., Encinas Acosta, H. A., Gallegos Maríñez, L. G., León Coello, M., Murillo Quijada, J. A., Sehrawat, A., Valencia Palomo, L., Ayala, G., Castilla-Valdez, H., Crotte Ledesma, H., De La Cruz-Burelo, E., Heredia-De La Cruz, I., Lopez-Fernandez, R., Mejia Guisao, J., Mondragon Herrera, C. A., Sánchez Hernández, A., Oropeza Barrera, C., Ramirez Guadarrama, D. L., Ramírez García, M., Bautista, I., Pedraza, I., Salazar Ibarguen, H. A., Uribe Estrada, C., Bubanja, I., Raicevic, N., Butler, P. H., Ahmad, A., Asghar, M. I., Awais, A., Awan, M. I. M., Hoorani, H. R., Khan, W. A., Avati, V., Grzanka, L., Malawski, M., Bialkowska, H., Bluj, M., Górski, M., Kazana, M., Szleper, M., Zalewski, P., Bunkowski, K., Doroba, K., Kalinowski, A., Konecki, M., Krolikowski, J., Muhammad, A., Pozniak, K., Zabolotny, W., Araujo, M., Bastos, D., Beirão Da Cruz E Silva, C., Boletti, A., Bozzo, M., Camporesi, T., Da Molin, G., Faccioli, P., Gallinaro, M., Hollar, J., Leonardo, N., Marozzo, G. B., Niknejad, T., Petrilli, A., Pisano, M., Seixas, J., Varela, J., Wulff, J. 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S., Zarubin, A., Zhizhin, I., and Zhokin, A.
Published: 2024
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46. Multiplicity and transverse momentum dependence of charge-balance functions in pPb and PbPb collisions at LHC energies

Author: Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Damanakis, K., Dragicevic, M., Escalante Del Valle, A., Hussain, P. S., Jeitler, M., Krammer, N., Lechner, L., Liko, D., Mikulec, I., Schieck, J., Schöfbeck, R., Schwarz, D., Sonawane, M., Templ, S., Waltenberger, W., Wulz, C.-E., Darwish, M. R., Janssen, T., Kello, T., Van Mechelen, P., Bols, E. S., D’Hondt, J., De Moor, A., Delcourt, M., El Faham, H., Lowette, S., Morton, A., Müller, D., Sahasransu, A. R., Tavernier, S., Van Doninck, W., Van Putte, S., Vannerom, D., Clerbaux, B., Dansana, S., De Lentdecker, G., Favart, L., Hohov, D., Jaramillo, J., Lee, K., Mahdavikhorrami, M., Makarenko, I., Malara, A., Paredes, S., Pétré, L., Postiau, N., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., Dobur, D., Knolle, J., Lambrecht, L., Mestdach, G., Rendón, C., Samalan, A., Skovpen, K., Tytgat, M., Van Den Bossche, N., Vermassen, B., Wezenbeek, L., Benecke, A., Bruno, G., Bury, F., Caputo, C., David, P., Delaere, C., Donertas, I. S., Giammanco, A., Jaffel, K., Jain, Sa., Lemaitre, V., Lidrych, J., Mondal, K., Tran, T. T., Vischia, P., Wertz, S., Alves, G. A., Coelho, E., Hensel, C., Moraes, A., Rebello Teles, P., Aldá Júnior, W. L., Alves Gallo Pereira, M., Barroso Ferreira Filho, M., Brandao Malbouisson, H., Carvalho, W., Chinellato, J., Da Costa, E. M., Da Silveira, G. G., De Jesus Damiao, D., Dos Santos Sousa, V., Fonseca De Souza, S., Martins, J., Mora Herrera, C., Mota Amarilo, K., Mundim, L., Nogima, H., Santoro, A., Silva Do Amaral, S. M., Sznajder, A., Thiel, M., Vilela Pereira, A., Bernardes, C. A., Calligaris, L., Fernandez Perez Tomei, T. R., Gregores, E. M., Mercadante, P. G., Novaes, S. F., Orzari, B., Padula, Sandra S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Rodozov, M., Shopova, M., Sultanov, G., Dimitrov, A., Ivanov, T., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Keshri, S., Thakur, S., Cheng, T., Guo, Q., Javaid, T., Mittal, M., Yuan, L., Bauer, G., Hu, Z., Lezki, S., Yi, K., Chen, G. M., Chen, H. S., Chen, M., Iemmi, F., Jiang, C. H., Kapoor, A., Liao, H., Liu, Z.-A., Milosevic, V., Monti, F., Sharma, R., Tao, J., Wang, J., Zhang, H., Zhao, J., Agapitos, A., Ban, Y., Carvalho Antunes De Oliveira, A., Levin, A., Li, C., Li, Q., Lyu, X., Mao, Y., Qian, S. J., Sun, X., Wang, D., Xiao, J., Yang, H., Lu, M., You, Z., Lu, N., Gao, X., Leggat, D., Okawa, H., Zhang, Y., Lin, Z., Lu, C., Xiao, M., Avila, C., Barbosa Trujillo, D. A., Cabrera, A., Florez, C., Fraga, J., Reyes Vega, J. A., Mejia Guisao, J., Ramirez, F., Rodriguez, M., Ruiz Alvarez, J. D., Giljanovic, D., Godinovic, N., Lelas, D., Puljak, I., Sculac, A., Antunovic, Z., Kovac, M., Sculac, T., Bargassa, P., Brigljevic, V., Chitroda, B. K., Ferencek, D., Mishra, S., Roguljic, M., Starodumov, A., Susa, T., Attikis, A., Christoforou, K., Konstantinou, S., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P. A., Rykaczewski, H., Saka, H., Stepennov, A., Finger, M., Finger, Jr., M., Kveton, A., Ayala, E., Carrera Jarrin, E., Abdelalim, A. A., Salama, E., Lotfy, A., Mahmoud, M. A., Bhowmik, S., Dewanjee, R. K., Ehataht, K., Kadastik, M., Lange, T., Nandan, S., Nielsen, C., Pata, J., Raidal, M., Tani, L., Veelken, C., Eerola, P., Kirschenmann, H., Osterberg, K., Voutilainen, M., Bharthuar, S., Brücken, E., Garcia, F., Havukainen, J., Kim, M. S., Kinnunen, R., Lampén, T., Lassila-Perini, K., Lehti, S., Lindén, T., Lotti, M., Martikainen, L., Myllymäki, M., Rantanen, M. m., Siikonen, H., Tuominen, E., Tuominiemi, J., Luukka, P., Petrow, H., Tuuva, T., Amendola, C., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Faure, J. L., Ferri, F., Ganjour, S., Gras, P., Hamel de Monchenault, G., Lohezic, V., Malcles, J., Rander, J., Rosowsky, A., Sahin, M. Ö., Savoy-Navarro, A., Simkina, P., Titov, M., Baldenegro Barrera, C., Beaudette, F., Buchot Perraguin, A., Busson, P., Cappati, A., Charlot, C., Damas, F., Davignon, O., Diab, B., Falmagne, G., Fontana Santos Alves, B. A., Ghosh, S., Granier de Cassagnac, R., Hakimi, A., Harikrishnan, B., Liu, G., Motta, J., Nguyen, M., Ochando, C., Portales, L., Salerno, R., Sarkar, U., Sauvan, J. B., Sirois, Y., Tarabini, A., Vernazza, E., Zabi, A., Zghiche, A., Agram, J.-L., Andrea, J., Apparu, D., Bloch, D., Brom, J.-M., Chabert, E. C., Collard, C., Goerlach, U., Grimault, C., Le Bihan, A.-C., Van Hove, P., Beauceron, S., Blancon, B., Boudoul, G., Chanon, N., Choi, J., Contardo, D., Depasse, P., Dozen, C., El Mamouni, H., Fay, J., Gascon, S., Gouzevitch, M., Greenberg, C., Grenier, G., Ille, B., Laktineh, I. B., Lethuillier, M., Mirabito, L., Perries, S., Vander Donckt, M., Verdier, P., Viret, S., Adamov, G., Lomidze, I., Tsamalaidze, Z., Botta, V., Feld, L., Klein, K., Lipinski, M., Meuser, D., Pauls, A., Röwert, N., Teroerde, M., Diekmann, S., Dodonova, A., Eich, N., Eliseev, D., Erdmann, M., Fackeldey, P., Fischer, B., Hebbeker, T., Hoepfner, K., Ivone, F., Lee, M. y., Mastrolorenzo, L., Merschmeyer, M., Meyer, A., Mondal, S., Mukherjee, S., Noll, D., Novak, A., Nowotny, F., Pozdnyakov, A., Rath, Y., Redjeb, W., Rehm, F., Reithler, H., Schmidt, A., Schuler, S. C., Sharma, A., Stein, A., Torres Da Silva De Araujo, F., Vigilante, L., Wiedenbeck, S., Zaleski, S., Dziwok, C., Flügge, G., Haj Ahmad, W., Hlushchenko, O., Kress, T., Nowack, A., Pooth, O., Stahl, A., Ziemons, T., Zotz, A., Aarup Petersen, H., Aldaya Martin, M., Alimena, J., An, Y., Baxter, S., Bayatmakou, M., Becerril Gonzalez, H., Behnke, O., Bhattacharya, S., Blekman, F., Borras, K., Brunner, D., Campbell, A., Cardini, A., Cheng, C., Colombina, F., Consuegra Rodríguez, S., Correia Silva, G., De Silva, M., Eckerlin, G., Eckstein, D., Estevez Banos, L. I., Filatov, O., Gallo, E., Geiser, A., Giraldi, A., Greau, G., Grohsjean, A., Guglielmi, V., Guthoff, M., Jafari, A., Jomhari, N. Z., Kaech, B., Kasemann, M., Kaveh, H., Kleinwort, C., Kogler, R., Komm, M., Krücker, D., Lange, W., Leyva Pernia, D., Lipka, K., Lohmann, W., Mankel, R., Melzer-Pellmann, I.-A., Mendizabal Morentin, M., Metwally, J., Meyer, A. B., Milella, G., Mormile, M., Mussgiller, A., Nürnberg, A., Otarid, Y., Pérez Adán, D., Ranken, E., Raspereza, A., Ribeiro Lopes, B., Rübenach, J., Saggio, A., Savitskyi, M., Scham, M., Scheurer, V., Schnake, S., Schütze, P., Schwanenberger, C., Shchedrolosiev, M., Sosa Ricardo, R. E., Sreelatha Pramod, L. P., Stafford, D., Vazzoler, F., Ventura Barroso, A., Walsh, R., Wang, Q., Wen, Y., Wichmann, K., Wiens, L., Wissing, C., Wuchterl, S., Yang, Y., Zimermmane Castro Santos, A., Albrecht, A., Albrecht, S., Antonello, M., Bein, S., Benato, L., Bonanomi, M., Connor, P., De Leo, K., Eich, M., El Morabit, K., Fröhlich, A., Garbers, C., Garutti, E., Hajheidari, M., Haller, J., Hinzmann, A., Jabusch, H. R., Kasieczka, G., Keicher, P., Klanner, R., Korcari, W., Kramer, T., Kutzner, V., Labe, F., Lange, J., Lobanov, A., Matthies, C., Mehta, A., Moureaux, L., Mrowietz, M., Nigamova, A., Nissan, Y., Paasch, A., Pena Rodriguez, K. J., Quadfasel, T., Rieger, M., Savoiu, D., Schindler, J., Schleper, P., Schröder, M., Schwandt, J., Sommerhalder, M., Stadie, H., Steinbrück, G., Tews, A., Wolf, M., Brommer, S., Burkart, M., Butz, E., Chwalek, T., Dierlamm, A., Droll, A., Faltermann, N., Giffels, M., Gosewisch, J. O., Gottmann, A., Hartmann, F., Horzela, M., Husemann, U., Klute, M., Koppenhöfer, R., Link, M., Lintuluoto, A., Maier, S., Mitra, S., Müller, Th., Neukum, M., Oh, M., Quast, G., Rabbertz, K., Shvetsov, I., Simonis, H. J., Trevisani, N., Ulrich, R., van der Linden, J., Von Cube, R. F., Wassmer, M., Wieland, S., Wolf, R., Wunsch, S., Zuo, X., Anagnostou, G., Assiouras, P., Daskalakis, G., Kyriakis, A., Stakia, A., Diamantopoulou, M., Karasavvas, D., Kontaxakis, P., Manousakis-Katsikakis, A., Melachroinos, G., Panagiotou, A., Papavergou, I., Saoulidou, N., Theofilatos, K., Tziaferi, E., Vellidis, K., Zisopoulos, I., Bakas, G., Chatzistavrou, T., Karapostoli, G., Kousouris, K., Papakrivopoulos, I., Siamarkou, E., Tsipolitis, G., Zacharopoulou, A., Adamidis, K., Bestintzanos, I., Evangelou, I., Foudas, C., Gianneios, P., Kamtsikis, C., Katsoulis, P., Kokkas, P., Kosmoglou Kioseoglou, P. G., Manthos, N., Papadopoulos, I., Strologas, J., Csanád, M., Farkas, K., Gadallah, M. M. A., Major, P., Mandal, K., Pásztor, G., Rádl, A. J., Surányi, O., Veres, G. I., Bartók, M., Hajdu, C., Horvath, D., Sikler, F., Veszpremi, V., Bencze, G., Beni, N., Czellar, S., Karancsi, J., Molnar, J., Szillasi, Z., Teyssier, D., Raics, P., Ujvari, B., Zilizi, G., Csorgo, T., Nemes, F., Novak, T., Babbar, J., Bansal, S., Beri, S. B., Bhatnagar, V., Chaudhary, G., Chauhan, S., Dhingra, N., Gupta, R., Kaur, A., Kaur, A., Kaur, H., Kaur, M., Kumar, S., Kumari, P., Meena, M., Sandeep, K., Sheokand, T., Singh, J. B., Singla, A., Ahmed, A., Bhardwaj, A., Chhetri, A., Choudhary, B. C., Kumar, A., Naimuddin, M., Ranjan, K., Saumya, S., Baradia, S., Barman, S., Bhattacharya, S., Bhowmik, D., Dutta, S., Dutta, S., Gomber, B., Maity, M., Palit, P., Saha, G., Sahu, B., Sarkar, S., Behera, P. K., Behera, S. C., Chatterjee, S., Kalbhor, P., Komaragiri, J. R., Kumar, D., Muhammad, A., Panwar, L., Pradhan, R., Pujahari, P. R., Saha, N. R., Sharma, A., Sikdar, A. K., Verma, S., Naskar, K., Aziz, T., Das, I., Dugad, S., Kumar, M., Mohanty, G. B., Suryadevara, P., Bala, A., Banerjee, S., Guchait, M., Karmakar, S., Kumar, S., Majumder, G., Mazumdar, K., Mukherjee, S., Thachayath, A., Bahinipati, S., Das, A. K., Kar, C., Mal, P., Mishra, T., Muraleedharan Nair Bindhu, V. K., Nayak, A., Saha, P., Swain, S. K., Varghese, S., Vats, D., Alpana, A., Dube, S., Kansal, B., Laha, A., Pandey, S., Rastogi, A., Sharma, S., Bakhshiansohi, H., Khazaie, E., Zeinali, M., Chenarani, S., Etesami, S. M., Khakzad, M., Mohammadi Najafabadi, M., Grunewald, M., Abbrescia, M., Aly, R., Aruta, C., Colaleo, A., Creanza, D., Cristella, L., D‘ Anzi, B., De Filippis, N., De Palma, M., Di Florio, A., Elmetenawee, W., Errico, F., Fiore, L., Iaselli, G., Maggi, G., Maggi, M., Margjeka, I., Mastrapasqua, V., My, S., Nuzzo, S., Pellecchia, A., Pompili, A., Pugliese, G., Radogna, R., Ramos, D., Ranieri, A., Selvaggi, G., Silvestris, L., Simone, F. M., Sözbilir, Ü., Stamerra, A., Venditti, R., Verwilligen, P., Abbiendi, G., Battilana, C., Bonacorsi, D., Borgonovi, L., Brigliadori, L., Campanini, R., Capiluppi, P., Castro, A., Cavallo, F. R., Cuffiani, M., Dallavalle, G. M., Diotalevi, T., Fabbri, F., Fanfani, A., Fasanella, D., Giacomelli, P., Giommi, L., Grandi, C., Guiducci, L., Lo Meo, S., Lunerti, L., Marcellini, S., Masetti, G., Navarria, F. L., Perrotta, A., Primavera, F., Rossi, A. M., Rovelli, T., Siroli, G. P., Costa, S., Di Mattia, A., Potenza, R., Tricomi, A., Tuve, C., Barbagli, G., Bardelli, G., Camaiani, B., Cassese, A., Ceccarelli, R., Ciulli, V., Civinini, C., D’Alessandro, R., Focardi, E., Latino, G., Lenzi, P., Lizzo, M., Meschini, M., Paoletti, S., Sguazzoni, G., Viliani, L., Benussi, L., Bianco, S., Meola, S., Piccolo, D., Bozzo, M., Chatagnon, P., Ferro, F., Robutti, E., Tosi, S., Benaglia, A., Boldrini, G., Brivio, F., Cetorelli, F., De Guio, F., Dinardo, M. E., Dini, P., Gennai, S., Ghezzi, A., Govoni, P., Guzzi, L., Lucchini, M. T., Malberti, M., Malvezzi, S., Massironi, A., Menasce, D., Moroni, L., Paganoni, M., Pedrini, D., Pinolini, B. S., Ragazzi, S., Redaelli, N., Tabarelli de Fatis, T., Zuolo, D., Buontempo, S., Cagnotta, A., Carnevali, F., Cavallo, N., De Iorio, A., Fabozzi, F., Iorio, A. O. M., Lista, L., Paolucci, P., Rossi, B., Sciacca, C., Ardino, R., Azzi, P., Bacchetta, N., Bisello, D., Bortignon, P., Bragagnolo, A., Carlin, R., Checchia, P., Dorigo, T., Fanzago, F., Gasparini, F., Gasparini, U., Grosso, G., Layer, L., Lusiani, E., Margoni, M., Meneguzzo, A. T., Pazzini, J., Ronchese, P., Simonetto, F., Strong, G., Tosi, M., Yarar, H., Zanetti, M., Zotto, P., Zucchetta, A., Zumerle, G., Abu Zeid, S., Aimè, C., Braghieri, A., Calzaferri, S., Fiorina, D., Montagna, P., Re, V., Riccardi, C., Salvini, P., Vai, I., Vitulo, P., Asenov, P., Bilei, G. M., Ciangottini, D., Fanò, L., Magherini, M., Mantovani, G., Mariani, V., Menichelli, M., Moscatelli, F., Piccinelli, A., Presilla, M., Rossi, A., Santocchia, A., Spiga, D., Tedeschi, T., Azzurri, P., Bagliesi, G., Bertacchi, V., Bhattacharya, R., Bianchini, L., Boccali, T., Bossini, E., Bruschini, D., Castaldi, R., Ciocci, M. A., D’Amante, V., Dell’Orso, R., Donato, S., Giassi, A., Ligabue, F., Matos Figueiredo, D., Messineo, A., Musich, M., Palla, F., Parolia, S., Ramirez-Sanchez, G., Rizzi, A., Rolandi, G., Roy Chowdhury, S., Sarkar, T., Scribano, A., Spagnolo, P., Tenchini, R., Tonelli, G., Turini, N., Venturi, A., Verdini, P. G., Barria, P., Campana, M., Cavallari, F., Cunqueiro Mendez, L., Del Re, D., Di Marco, E., Diemoz, M., Longo, E., Meridiani, P., Organtini, G., Pandolfi, F., Paramatti, R., Quaranta, C., Rahatlou, S., Rovelli, C., Santanastasio, F., Soffi, L., Tramontano, R., Amapane, N., Arcidiacono, R., Argiro, S., Arneodo, M., Bartosik, N., Bellan, R., Bellora, A., Biino, C., Cartiglia, N., Costa, M., Covarelli, R., Demaria, N., Finco, L., Grippo, M., Kiani, B., Legger, F., Luongo, F., Mariotti, C., Maselli, S., Mecca, A., Migliore, E., Monteno, M., Mulargia, R., Obertino, M. M., Ortona, G., Pacher, L., Pastrone, N., Pelliccioni, M., Ruspa, M., Shchelina, K., Siviero, F., Sola, V., Solano, A., Soldi, D., Staiano, A., Tarricone, C., Tornago, M., Trocino, D., Umoret, G., Vagnerini, A., Vlasov, E., Belforte, S., Candelise, V., Casarsa, M., Cossutti, F., Della Ricca, G., Sorrentino, G., Dogra, S., Huh, C., Kim, B., Kim, D. H., Kim, G. N., Kim, J., Lee, J., Lee, S. W., Moon, C. S., Oh, Y. D., Pak, S. I., Ryu, M. S., Sekmen, S., Yang, Y. C., Kim, H., Moon, D. H., Asilar, E., Kim, T. J., Park, J., Choi, S., Han, S., Hong, B., Lee, K., Lee, K. S., Lim, J., Park, J., Park, S. K., Yoo, J., Goh, J., Kim, H. S., Kim, Y., Lee, S., Almond, J., Bhyun, J. H., Choi, J., Jeon, S., Kim, J., Kim, J. S., Ko, S., Kwon, H., Lee, H., Lee, S., Oh, B. H., Oh, S. B., Seo, H., Yang, U. K., Yoon, I., Jang, W., Kang, D. Y., Kang, Y., Kim, D., Kim, S., Ko, B., Lee, J. S. H., Lee, Y., Merlin, J. A., Park, I. C., Roh, Y., Song, D., Watson, I. J., Yang, S., Ha, S., Yoo, H. D., Choi, M., Kim, M. R., Lee, H., Lee, Y., Yu, I., Beyrouthy, T., Maghrbi, Y., Dreimanis, K., Pikurs, G., Potrebko, A., Seidel, M., Veckalns, V., Ambrozas, M., Juodagalvis, A., Rinkevicius, A., Tamulaitis, G., Bin Norjoharuddeen, N., Hoh, S. Y., Yusuff, I., Zolkapli, Z., Benitez, J. F., Castaneda Hernandez, A., Encinas Acosta, H. A., Gallegos Maríñez, L. G., León Coello, M., Murillo Quijada, J. A., Sehrawat, A., Valencia Palomo, L., Ayala, G., Castilla-Valdez, H., Heredia-De La Cruz, I., Lopez-Fernandez, R., Mondragon Herrera, C. A., Perez Navarro, D. A., Sánchez Hernández, A., Oropeza Barrera, C., Vazquez Valencia, F., Pedraza, I., Salazar Ibarguen, H. A., Uribe Estrada, C., Bubanja, I., Mijuskovic, J., Raicevic, N., Ahmad, A., Asghar, M. I., Awais, A., Awan, M. I. M., Gul, M., Hoorani, H. R., Khan, W. A., Avati, V., Grzanka, L., Malawski, M., Bialkowska, H., Bluj, M., Boimska, B., Górski, M., Kazana, M., Szleper, M., Zalewski, P., Bunkowski, K., Doroba, K., Kalinowski, A., Konecki, M., Krolikowski, J., Araujo, M., Bastos, D., Boletti, A., Faccioli, P., Gallinaro, M., Hollar, J., Leonardo, N., Niknejad, T., Pisano, M., Seixas, J., Varela, J., Adzic, P., Milenovic, P., Dordevic, M., Milosevic, J., Aguilar-Benitez, M., Alcaraz Maestre, J., Barrio Luna, M., Bedoya, Cristina F., Cepeda, M., Cerrada, M., Colino, N., De La Cruz, B., Delgado Peris, A., Fernández Del Val, D., Fernández Ramos, J. P., Flix, J., Fouz, M. C., Gonzalez Lopez, O., Goy Lopez, S., Hernandez, J. M., Josa, M. I., León Holgado, J., Moran, D., Perez Dengra, C., Pérez-Calero Yzquierdo, A., Puerta Pelayo, J., Redondo, I., Redondo Ferrero, D. D., Romero, L., Sánchez Navas, S., Sastre, J., Urda Gómez, L., Vazquez Escobar, J., Willmott, C., de Trocóniz, J. F., Alvarez Gonzalez, B., Cuevas, J., Fernandez Menendez, J., Folgueras, S., Gonzalez Caballero, I., González Fernández, J. R., Palencia Cortezon, E., Ramón Álvarez, C., Rodríguez Bouza, V., Soto Rodríguez, A., Trapote, A., Vico Villalba, C., Brochero Cifuentes, J. A., Cabrillo, I. J., Calderon, A., Duarte Campderros, J., Fernandez, M., Fernandez Madrazo, C., Gomez, G., Lasaosa García, C., Martinez Rivero, C., Martinez Ruiz del Arbol, P., Matorras, F., Matorras Cuevas, P., Piedra Gomez, J., Prieels, C., Scodellaro, L., Vila, I., Vizan Garcia, J. M., Jayananda, M. K., Kailasapathy, B., Sonnadara, D. U. J., Wickramarathna, D. D. C., Dharmaratna, W. G. D., Liyanage, K., Perera, N., Wickramage, N., Abbaneo, D., Auffray, E., Auzinger, G., Baechler, J., Barney, D., Bermúdez Martínez, A., Bianco, M., Bilin, B., Bin Anuar, A. A., Bocci, A., Brondolin, E., Caillol, C., Camporesi, T., Cerminara, G., Chernyavskaya, N., Chhibra, S. S., Choudhury, S., Cipriani, M., d’Enterria, D., Dabrowski, A., David, A., De Roeck, A., Defranchis, M. M., Deile, M., Dobson, M., Dünser, M., Dupont, N., Fallavollita, F., Florent, A., Forthomme, L., Franzoni, G., Funk, W., Ghosh, S., Giani, S., Gigi, D., Gill, K., Glege, F., Gouskos, L., Govorkova, E., Haranko, M., Hegeman, J., Innocente, V., James, T., Janot, P., Kaspar, J., Kieseler, J., Kratochwil, N., Laurila, S., Lecoq, P., Leutgeb, E., Lourenço, C., Maier, B., Malgeri, L., Mannelli, M., Marini, A. C., Meijers, F., Mersi, S., Meschi, E., Moortgat, F., Mulders, M., Orfanelli, S., Orsini, L., Pantaleo, F., Perez, E., Peruzzi, M., Petrilli, A., Petrucciani, G., Pfeiffer, A., Pierini, M., Piparo, D., Pitt, M., Qu, H., Quast, T., Rabady, D., Racz, A., Reales Gutiérrez, G., Rovere, M., Sakulin, H., Salfeld-Nebgen, J., Scarfi, S., Selvaggi, M., Sharma, A., Silva, P., Sphicas, P., Stahl Leiton, A. 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S., Zarubin, A., Zhizhin, I., and Zhokin, A.
Published: 2024
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47. Measurement of multijet azimuthal correlations and determination of the strong coupling in proton-proton collisions at s=13TeV

Author: Hayrapetyan, A., Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Damanakis, K., Dragicevic, M., Hussain, P. S., Jeitler, M., Krammer, N., Li, A., Liko, D., Mikulec, I., Schieck, J., Schöfbeck, R., Schwarz, D., Sonawane, M., Templ, S., Waltenberger, W., Wulz, C.-E., Darwish, M. R., Janssen, T., Van Mechelen, P., Bols, E. S., D’Hondt, J., Dansana, S., De Moor, A., Delcourt, M., El Faham, H., Lowette, S., Makarenko, I., Müller, D., Sahasransu, A. R., Tavernier, S., Tytgat, M., Van Onsem, G. P., Van Putte, S., Vannerom, D., Clerbaux, B., Das, A. K., De Lentdecker, G., Favart, L., Hohov, D., Jaramillo, J., Khalilzadeh, A., Lee, K., Mahdavikhorrami, M., Malara, A., Paredes, S., Pétré, L., Postiau, N., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., De Coen, M., Dobur, D., Hong, Y., Knolle, J., Lambrecht, L., Mestdach, G., Amarilo, K. 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S., Zarubin, A., Zhizhin, I., and Zhokin, A.
Published: 2024
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48. Constraints on anomalous Higgs boson couplings from its production and decay using the WW channel in proton–proton collisions at s=13TeV

Author: Hayrapetyan, A., Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Damanakis, K., Dragicevic, M., Hussain, P. S., Jeitler, M., Krammer, N., Li, A., Liko, D., Mikulec, I., Schieck, J., Schöfbeck, R., Schwarz, D., Sonawane, M., Templ, S., Waltenberger, W., Wulz, C.-E., Darwish, M. R., Janssen, T., Van Mechelen, P., Bols, E. S., D’Hondt, J., Dansana, S., De Moor, A., Delcourt, M., El Faham, H., Lowette, S., Makarenko, I., Müller, D., Sahasransu, A. R., Tavernier, S., Tytgat, M., Van Onsem, G. P., Van Putte, S., Vannerom, D., Clerbaux, B., Das, A. K., De Lentdecker, G., Favart, L., Gianneios, P., Hohov, D., Jaramillo, J., Khalilzadeh, A., Lee, K., Mahdavikhorrami, M., Malara, A., Paredes, S., Pétré, L., Postiau, N., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., De Coen, M., Dobur, D., Hong, Y., Knolle, J., Lambrecht, L., Mestdach, G., Mota Amarilo, K., Rendón, C., Samalan, A., Skovpen, K., Van Den Bossche, N., van der Linden, J., Wezenbeek, L., Benecke, A., Bethani, A., Bruno, G., Caputo, C., Delaere, C., Donertas, I. S., Giammanco, A., Jaffel, K., Jain, Sa., Lemaitre, V., Lidrych, J., Mastrapasqua, P., Mondal, K., Tran, T. T., Wertz, S., Alves, G. A., Coelho, E., Hensel, C., De Oliveira, T. Menezes, Moraes, A., Rebello Teles, P., Soeiro, M., Aldá Júnior, W. L., Alves Gallo Pereira, M., Barroso Ferreira Filho, M., Brandao Malbouisson, H., Carvalho, W., Chinellato, J., Da Costa, E. M., Da Silveira, G. G., De Jesus Damiao, D., Fonseca De Souza, S., Gomes De Souza, R., Martins, J., Mora Herrera, C., Mundim, L., Nogima, H., Pinheiro, J. P., Santoro, A., Sznajder, A., Thiel, M., Vilela Pereira, A., Bernardes, C. A., Calligaris, L., Fernandez Perez Tomei, T. R., Gregores, E. M., Mercadante, P. G., Novaes, S. F., Orzari, B., Padula, Sandra S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Keshri, S., Thakur, S., Cheng, T., Javaid, T., Yuan, L., Hu, Z., Liu, J., Yi, K., Chen, G. M., Chen, H. S., Chen, M., Iemmi, F., Jiang, C. H., Kapoor, A., Liao, H., Liu, Z.-A., Sharma, R., Song, J. N., Tao, J., Wang, C., Wang, J., Wang, Z., Zhang, H., Agapitos, A., Ban, Y., Levin, A., Li, C., Li, Q., Mao, Y., Qian, S. 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F., Vetens, W., Warden, A., Afanasiev, S., Andreev, V., Andreev, Yu., Aushev, T., Azarkin, M., Babaev, A., Belyaev, A., Blinov, V., Boos, E., Borshch, V., Budkouski, D., Chekhovsky, V., Chistov, R., Danilov, M., Dermenev, A., Dimova, T., Druzhkin, D., Dubinin, M., Dudko, L., Ershov, A., Gavrilov, G., Gavrilov, V., Gninenko, S., Golovtcov, V., Golubev, N., Golutvin, I., Gorbunov, I., Gribushin, A., Ivanov, Y., Kachanov, V., Karjavine, V., Karneyeu, A., Kim, V., Kirakosyan, M., Kirpichnikov, D., Kirsanov, M., Klyukhin, V., Kodolova, O., Korenkov, V., Kozyrev, A., Krasnikov, N., Lanev, A., Levchenko, P., Lychkovskaya, N., Makarenko, V., Malakhov, A., Matveev, V., Murzin, V., Nikitenko, A., Obraztsov, S., Oreshkin, V., Palichik, V., Perelygin, V., Petrushanko, S., Polikarpov, S., Popov, V., Radchenko, O., Savina, M., Savrin, V., Shalaev, V., Shmatov, S., Shulha, S., Skovpen, Y., Slabospitskii, S., Smirnov, V., Snigirev, A., Sosnov, D., Sulimov, V., Tcherniaev, E., Terkulov, A., Teryaev, O., Tlisova, I., Toropin, A., Uvarov, L., Uzunian, A., Vorobyev, A., Voytishin, N., Yuldashev, B. S., Zarubin, A., Zhizhin, I., and Zhokin, A.
Published: 2024
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49. Double Trouble: Novel Digenic CD19-RABEP2 Deletion in Predominantly Antibody Deficiency with Syndromic Features

Author: Jodhawat, Neha, Bargir, Umair Ahmed, Rawal, Goutham, Kamble, Priti, Shinde-Vhatkar, Shweta, Dalvi, Aparna, Kulkarni, Suhas, and Madkaikar, Manisha
Published: 2024
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50. Metal-free Borylation of α‑Naphthamides and Phenylacetic Acid Drug

Author: Suman Maji, Parveen Rawal, Animesh Ghosh, Karishma Pidiyar, Shaeel A. Al-Thabaiti, Puneet Gupta, and Debabrata Maiti
Subjects: Chemistry, QD1-999
Published: 2024
Full Text: View/download PDF

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