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1. Investigating alexithymia, empathy, and resilience in medical students during pandemic era: a cross-sectional study in northern Iran

Author: Forouzan Elyasi, Yeganeh Amirsoleimani, Romina Hamzehpour, Mahmood Moosazadeh, Mehran Zarghami, Maryam Vajdi, Elham Motevalli Alamouti, and Fatemeh Alizadeh Arimi
Subjects: Alexithymia, Empathy, Medical, Pandemic, Students, Resilience, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Abstract Background and aim Alexithymia is defined as emotional response inhibition. As well, empathy refers to the ability to put oneself in someone’s position and resilience is the capacity to recover from a series of negative emotional experiences. Considering the psychological distress induced by the coronavirus disease 2019 (COVID-19) pandemic together with academic stress and the role of empathy in physician–patient relationships, the present study was to investigate alexithymia, empathy, and resilience in Iranian medical interns and residents. Materials and methods This cross-sectional study was fulfilled in northern Iran in 2021–2022. In total, 394 medical interns and residents were initially recruited for this purpose. Then, an online sociodemographic survey form (SDSF), the Jefferson Scale of Empathy (JSE), the Toronto Alexithymia Scale (TAS-20), and the Connor–Davidson Resilience Scale (CD-RISC) were completed. The data analysis was performed using the IBM SPSS Statistics (ver.26) software in regard to the p
Published: 2024
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2. Environmental impact and damage assessment of oil trunkline transportation by life cycle assessment method

Author: Amir hosein Saedi, Yeganeh Aliabadi, Abolfazl Ahmadi, and Hamed Mirzavand
Subjects: enviromental impact, gate to gate, life cycle assessment, oil company, pipeline transportation, Technology, Science
Abstract: Abstract As climate issues gradually attract public attention worldwide, the operation of crude oil pipelines has been attached with new emission‐reduction targets. Though previous studies concerning life cycle assessment (LCA) of oil and gas pipelines have estimated the carbon footprint to some extent, there is a lack of research on life cycle carbon emissions that take the process of crude oil pipelines transportation. the main objective of this study is to build a comprehensive LCA model of carbon footprint for crude oil pipeline systems. A gate‐to‐gate analysis is provided for the LCA model, which means that the entire pipeline's life cycle from the extraction to disposal is considered. The LCA approach used both midpoint and endpoint impact assessment methods to evaluate the environmental impacts of crude oil pipeline transportation in Iran. The study analyzed the major environmental impact categories, such as greenhouse gas emissions, energy consumption, water usage, and solid waste generation. The results showed that the crude oil pipelines transportation has significant environmental impacts in Iran. The three highest impacts on the midpoint scale are marine ecotoxicity by 61.54 kg 1,4‐DCB, terrestrial ecotoxicity by 52.26 kg 1,4‐DCB and fossil resource scarcity by 40.22 kg oil eq. The endpoint impacts of three stations were also compared. The Chamran, Abadan and Zanganeh stations have 95.44%, 94.22%, and 6% impacts on human health, respectively. Also, Abadan station has the largest amount of damage to ecosystems and resources with 96.65% and 80.02%, respectively. The findings of this study could be used by policymakers, oil companies, and other stakeholders to make informed decisions and implement strategies to reduce the environmental impact of oil transportation in Iran.
Published: 2024
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3. Evaluation of the ergonomics of the office work environment using two specific and general evaluation techniques

Author: Yahya Khosravi, Narmin Hassanzadeh-Rangi, Saman Roshani, Ali Asadikaram, mahsa alfi, yeganeh Akhtari, and Ali Asghar Farshad
Subjects: working environments, physical ergonomics, risk assessments, Environmental technology. Sanitary engineering, TD1-1066
Abstract: Background: Due to the increasing spread of musculoskeletal problems (MSDs), it is necessary to examine and compare the ergonomic risk factors of the workplace with general and specific methods. The present study was conducted to evaluate the ergonomics of the office work environment using two specific and general evaluation techniques in two universities of medical sciences in the country. Methods: The prevalence of MSDs was evaluated using the Nordic standard questionnaire and the ergonomic assessment of employees in the workplace using ROSA's quick assessment of administrative stress and REBA's quick assessment of the whole body. Descriptive statistics and chi-score, independent t-tests, and logistic regression tests were used to describe and analyze the data. Results: The difference in mean ROSA score and mean monitor height between two groups of employees who have experienced MSDs during the last 12 months and the group who have not experienced them is significant. For one unit increase in ROSA score and one centimeter of monitor height, considering other intervening variables, the chance of MSDs increases by 45% and 13%, respectively. Conclusion: The ROSA tool is a more reliable method to evaluate the ergonomics of the office work environment than REBA. The overall score of the ROSA method and workstation parameters such as monitor height can be used in office jobs to design interventions to reduce MSDs. More studies on the reliability of ROSA are needed to increase the validity of ergonomic interventions based on this method.
Published: 2023

4. The Impact of Nanobubble Gases in Enhancing Soil Moisture, Nutrient Uptake Efficiency and Plant Growth: A Review

Author: Yeganeh Arablousabet and Arvydas Povilaitis
Subjects: nanobubbles, soil moisture retention, plant growth enhancement, nutrient absorption, microbial activity, Hydraulic engineering, TC1-978, Water supply for domestic and industrial purposes, TD201-500
Abstract: Nanobubble (NB) technology in agriculture has received increased interest due to its potential to promote soil moisture retention and plant development. Therefore, this review investigates the impact of various types of NBs—such as oxygen, carbon dioxide, and air—on soil and plant systems. Various studies revealed that nanobubble-saturated water (NBSW) increases moisture retention, microbial activity, and nutrient absorption, which contribute to better plant development. However, there are still gaps in understanding the specific roles of different gases regarding their stability, interactions with soil, and long-term agricultural impacts. This review aims to combine previous research by focusing on various types of NBs impact on soil moisture, water quality, and nutrient retention. Challenges include the quick dissolution of particular gases, limited field studies, and scalability. The analysis showed that despite these challenges, NBs have potential for enhancing agriculture by improving soil structure and crop yield. More study is needed to maximize their application, particularly in determining the most effective gas types and concentrations for certain agricultural areas.
Published: 2024
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5. The CHECH study: A prospective pregnancy cohort study on CHemical exposure and children's health in Tianjin, China

Author: Yuxuan Zhao, Jing Wang, Gongshu Liu, Yeganeh Ataei, Carl-Gustaf Bornehag, Wei Liu, Qinghao Zhang, Jianjun Huang, Peizhe Sun, and Yuexia Sun
Subjects: Birth cohort study, Endocrine disruptors, Health, Infant, Methodology, Public aspects of medicine, RA1-1270, Environmental sciences, GE1-350
Abstract: The CHemical Exposure and Children's Health (CHECH) study is an ongoing pregnancy cohort study in Tianjin, China. This paper describes the background, aim and the study design, which can be followed by future researchers to design and conduct similar studies. The abundance and the potential adverse health outcomes of endocrine disrupting chemicals (EDCs) is concerning. More notably, developing fetuses and infants are more vulnerable to EDCs exposure. The CHECH study aims to investigate the importance of early life exposure to multiple EDCs (phthalates and their metabolites, bisphenol A and their substitutes, perfluorinated compounds and poly brominated diphenyl ethers) for multiple health outcomes in Chinese children, namely sexual development, neurodevelopment, metabolism and growth, as well as asthma and allergy. A total of 2238 pregnant women were recruited in Tianjin from May 2017 to April 2021 with a response rate of 90 %. Among these women, 2255 children were born with available information, including 47 pairs of twins. Urine samples were collected from pregnant women and children, while air and dust samples were obtained from the home environment during pregnancy and infancy periods. Information on children's health was gathered through physical examinations and questionnaires. The CHECH study, which collected exposure information and health outcomes at multiple time points, will contribute to the understanding of prenatal exposure to EDCs and their impact on children's health, thereby facilitating the development of risk assessments aimed at reducing exposure and associated health risks.
Published: 2024
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6. Isolated greater omentum solid mass, a rare manifestation of hydatid disease; A Case Report and Review of Literature

Author: Ali Jangjoo, Sina Norouzi Asl, Yeganeh Azadmanesh, and Tooraj Zandbaf
Subjects: hydatid disease, hydatid cyst, echinococcus granulosus, greater omentum, echinococcosis, case report, Internal medicine, RC31-1245
Abstract: Background: Echinococcus granulosus causes hydatid disease, which is found in various countries of the world, including Iran. The liver and lungs are prevalent involved structures in hydatid disease. One of the least common sites in hydatid disease seems to be the omentum. Seven cases of mesenteric, diaphragmatic, omental, pelvic, and retroperitoneal hydatid cysts have been reported in Iran within last 20 years. The appearance of hydatid disease as a primary mass in the greater omentum without hepatic involvement is very rare and no similar case was introduced in Iran in our searches. Case Presentation: Our patient was a 33-year-old woman who underwent a diagnostic laparoscopy due to abdominal pain and an abdominal mass. During laparoscopy, there was a solid mass with a size of about 10 × 5 cm in the greater omentum that was resected. The histopathological examination of the mass showed the hydatid disease. Conclusion: The hydatid cyst can appear anywhere on the body, and no part of the body is guarded. Since these uncommon locations often cause nonspecific symptoms, the hydatid cyst should be included in the differential diagnosing of omental cysts, particularly in endemic countries like Iran.
Published: 2023

7. Early Versus Late Tracheostomy in Mechanically Ventilated COVID-19 Patients: A Comparative Study

Author: Samrand Fattah Ghazi, Nima Nazari, Mohammad Taghi Beigmohammadi, Masoud Ramezani, Mohammadreza Salehi, and Yeganeh Abedipour
Subjects: Coronavirus disease 2019 (COVID-19), Late tracheostomy, Early tracheostomy, Airway management, Medicine (General), R5-920
Abstract: The decision to perform tracheostomy during the COVID-19 pandemic has been based mainly on practical and empirical standards. Also, the amount of scientific evidence to determine the exact timing of tracheostomy in patients with severe COVID-19 on mechanical ventilation is not significant. We conducted a retrospective cohort study on mechanically ventilated COVID-19 patients from April 25, 2021 to January 25, 2022 in intensive care units of Imam Khomeini Hospital Complex, Tehran, Iran. The 30-day survival of patients was calculated and compared between patients under tracheostomy and those without tracheostomy. A number of 135 COVID-19 cases (75 patients in the intubated group and 60 patients in the tracheostomy group) were included in this study. The mean age of the population was 53.6±12.4. The overall mortality rate was 101 (74.8%). The 30-day mortality rate was significantly higher in the intubated group (90.6%) than in the tracheostomy group (55%) (P7 days) group. This difference was not statistically significant (P> 0.05). Tracheostomy is a preferred method in airway management of severe COVID-19 patients under mechanical ventilation; however, early tracheostomy during the first week of intubation may not be superior to late tracheostomy in decreasing the mortality rate.
Published: 2023
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8. Ionic Liquid Supported on Magnetic Graphene Oxide as a Highly Efficient and Stable Catalyst for the Synthesis of Triazolopyrimidines

Author: Azar Jahanbakhshi, Mahnaz Farahi, and Yeganeh Aghajani
Subjects: ionic liquid, graphen oxide, triazolopyrimidines, fe3o4, Chemistry, QD1-999
Abstract: A novel sulfonic acid functionalized ionic liquid was prepared by anchoring 1-(propyl-3-sulfonate) vinylimidazolium hydrogen sulfate ([(CH2)3SO3HVIm]HSO4) on Fe3O4@GO. The prepared heterogeneous catalyst was characterized by XRD, FT-IR, EDX, SEM, VSM and TGA techniques. The results show that [(CH2)3SO3HVIm]HSO4 was successfully deposited on the surface of Fe3O4@GO and the prepared ionic liquid catalyst exhibited good thermal stability. The activity of the prepared catalyst was investigated in the synthesis of triazolo[1,5-a]pyrimidine derivatives by a one-pot three-component reaction of active methylene compound (malononitrile or ethyl cyanoacetate), 3-amine-1H-1,2,4-triazole and aryl aldehydes under solvent-free conditions. This catalyst could be rapidly separated by an external magnet and recycled seven times without significant loss of catalytic activity.
Published: 2022
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9. Spontaneous Tension Pneumothorax is a Rare and Life-Threatening Complication of COVID-19 Infection: A Case Report and Literature Review

Author: Tooraj Zandbaf, Yeganeh Azadmanesh, and Ali Akbar Bagherzadeh
Subjects: Coronavirus disease 2019 (COVID-19), Corona virus, Pneumothorax, Tension pneumothorax, Spontaneous pneumothorax, Case report, Medicine (General), R5-920
Abstract: The coronavirus disease is a recent pandemic infection, with the first case being discovered in Wuhan, China, in December 2019. Iran is one of the countries that has been afflicted by this pandemic. A 34-year-old man with no history of pre-existing disease and smoking has been hospitalized in another hospital for the past two weeks due to COVID-19 infection and symptoms of fever, cough, and dyspnea. He received standard medical treatments in that hospital. After two weeks, he was discharged from the hospital with his consent. In the afternoon of the same day, he came to the emergency department of our hospital with severe dyspnea. Tension pneumothorax in the left hemithorax, pulmonary collapse, and shifting mediastinal elements to the contralateral side were observed in his chest x-ray. Tension pneumothorax is an unusual symptom in patients with SARS-CoV-2. Pneumothorax has been reported in 1% of COVID-19 patients who need hospitalization, 2% of those who need intensive care unit admission, and 1% of those who die from the infection. Spontaneous pneumothorax must be ruled out in COVID-19 patients with dyspnea and chest pain, tachycardia, tachypnea, or hypoxemia. It is critical to be familiar with these symptoms to make an accurate diagnosis and treatment plan. A thorough physical examination and serial chest X-rays can be helpful tools in the diagnosis and management of the disease.
Published: 2023
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10. Analysis of energy generation from MSW with auxiliary feed in the north of Iran

Author: Yeganeh Aliabadi, Ahmad Hajinezhad, Reza Fattahi, and Seyed Farhan Moosavian
Subjects: Additional fuel, Thermal treatment, Waste-to-Energy, MSW, Auxiliary fuel, Levelized cost of energy, Technology
Abstract: Incineration is a suitable method for municipal solid waste disposal; however, many developing countries have not yet effectively used incineration technologies. In some of these communities, the high humidity of waste is one of the primary problems. This paper aimed to evaluate the feasibility of generating energy from the incineration of MSW with auxiliary feed in the north of Iran. This study demonstrates how an incinerator waste-to-energy system is possible in northern Iran and provides major benefits by dramatically improving waste management. The total amount of waste and lower calorific values was used to estimate power and energy generation. According to the calorific value of the waste, a proper waste incineration system was designed. The results indicate that the estimated energy produced in the desired state is 62,000 MWh. Economic feasibility was evaluated using levelized cost of energy (LCOE), net present value (NPV), and internal rate of return (IRR). The NPV of this technology is estimated to be approximately 96.4 m$, and the uniform cost of the electricity unit is 0.1 $kWh. Moreover, the obtained data showed that by substituting landfilling with incineration technology and generating electricity through it, an annual reduction of CO2 pollution of 2,440,000 tons can be achieved.
Published: 2023
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11. Comparison of the views of librarians of university and public libraries in Tehran about the dissemination of valid information during the epidemic

Author: Mahdiyeh Ramezanzadeh, Yeganeh Amini, and Somayyeh Jafari Baghiabadi
Subjects: university library (s), public library (s), epidemic, information dissemination, Information theory, Q350-390, Bibliography. Library science. Information resources
Abstract: Objective: The purpose of this study is to compare the views of librarians of the university and public libraries in Tehran about information dissemination services and valid news during the epidemic in terms of services and tools used and influential factors and variables.Materials and Methods: In terms of purpose, this research has been applied with a descriptive-analytical approach using documentary and survey methods. The research community is librarians working in university and public libraries in Tehran. Data were analyzed using descriptive and analytical statistics and statistical software "SPSS" version 26.Results: The average information services provided in academic and public libraries are between 3 and 4, and the average of virtual services provided in the context of social networks in academic libraries is higher than 4. In terms of tools used, social media tools in public libraries and database tools, social networks, and media in university libraries have an average higher than 4. Influential variables can be tracked in three parts of the organization and infrastructure, human capital, and active use. From the librarians' point of view, the average of the influential variables in the organization and infrastructure, human capital, and active using sectors is very high and higher than 4. In the area of organizational and infrastructure factors, for librarians of public libraries, the variable "working hours and number of manpower in the library" is a more effective factor in providing these services. In the active using section, the variables of literacy level and age group of users in academic and public libraries were less important than other variables. Conclusion: There is a significant difference between the attitudes of librarians of the university and public libraries in Tehran towards the indicators related to the human capital variable among the variables affecting valid information dissemination services.
Published: 2021
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12. VISAGE: Video Synthesis using Action Graphs for Surgery

Author: Yeganeh, Yousef, Lazuardi, Rachmadio, Shamseddin, Amir, Dari, Emine, Thirani, Yash, Navab, Nassir, and Farshad, Azade
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Surgical data science (SDS) is a field that analyzes patient data before, during, and after surgery to improve surgical outcomes and skills. However, surgical data is scarce, heterogeneous, and complex, which limits the applicability of existing machine learning methods. In this work, we introduce the novel task of future video generation in laparoscopic surgery. This task can augment and enrich the existing surgical data and enable various applications, such as simulation, analysis, and robot-aided surgery. Ultimately, it involves not only understanding the current state of the operation but also accurately predicting the dynamic and often unpredictable nature of surgical procedures. Our proposed method, VISAGE (VIdeo Synthesis using Action Graphs for Surgery), leverages the power of action scene graphs to capture the sequential nature of laparoscopic procedures and utilizes diffusion models to synthesize temporally coherent video sequences. VISAGE predicts the future frames given only a single initial frame, and the action graph triplets. By incorporating domain-specific knowledge through the action graph, VISAGE ensures the generated videos adhere to the expected visual and motion patterns observed in real laparoscopic procedures. The results of our experiments demonstrate high-fidelity video generation for laparoscopy procedures, which enables various applications in SDS., Comment: Accepted at MICCAI 2024 Embodied AI and Robotics for HealTHcare (EARTH) Workshop
Published: 2024

13. FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation

Author: Rezvani, Sadjad, Fateh, Mansoor, Jalali, Yeganeh, and Fateh, Amirreza
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Early detection of lung cancer is crucial as it increases the chances of successful treatment. Automatic lung image segmentation assists doctors in identifying diseases such as lung cancer, COVID-19, and respiratory disorders. However, lung segmentation is challenging due to overlapping features like vascular and bronchial structures, along with pixel-level fusion of brightness, color, and texture. New lung segmentation methods face difficulties in identifying long-range relationships between image components, reliance on convolution operations that may not capture all critical features, and the complex structures of the lungs. Furthermore, semantic gaps between feature maps can hinder the integration of relevant information, reducing model accuracy. Skip connections can also limit the decoder's access to complete information, resulting in partial information loss during encoding. To overcome these challenges, we propose a hybrid approach using the FusionLungNet network, which has a multi-level structure with key components, including the ResNet-50 encoder, Channel-wise Aggregation Attention (CAA) module, Multi-scale Feature Fusion (MFF) block, self refinement (SR) module, and multiple decoders. The refinement sub-network uses convolutional neural networks for image post-processing to improve quality. Our method employs a combination of loss functions, including SSIM, IOU, and focal loss, to optimize image reconstruction quality. We created and publicly released a new dataset for lung segmentation called LungSegDB, including 1800 CT images from the LIDC-IDRI dataset (dataset version 1) and 700 images from the Chest CT Cancer Images from Kaggle dataset (dataset version 2). Our method achieved an IOU score of 98.04, outperforming existing methods and demonstrating significant improvements in segmentation accuracy. https://github.com/sadjadrz/FusionLungNet
Published: 2024

14. Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis

Author: Lüpke, Sven, Yeganeh, Yousef, Adeli, Ehsan, Navab, Nassir, and Farshad, Azade
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets limits the general applicability of the synthetic data they produce. To address this, we present a novel physics-informed generative model capable of synthesizing a variable number of brain MRI modalities, including those not present in the original dataset. Our approach utilizes latent diffusion models and a two-step generative process: first, unobserved physical tissue property maps are synthesized using a latent diffusion model, and then these maps are combined with a physical signal model to generate the final MRI scan. Our experiments demonstrate the efficacy of this approach in generating unseen MR contrasts and preserving physical plausibility. Furthermore, we validate the distributions of generated tissue properties by comparing them to those measured in real brain tissue., Comment: 5th International Workshop on Multiscale Multimodal Medical Imaging (MICCAI 2024), Project page: https://sven-luepke.github.io/phy-ldm-mri/
Published: 2024

15. Microshear Bond Strength and Microleakage of a Restorative Composite Resin with Salivary Contamination at Different Time Intervals

Author: rasoul sahebalam, Yeganeh Arian, alireza boruziniat, and Abdolrasoul Rangrazi
Subjects: saliva, contamination, composite resin, microshear, microleakage, Dentistry, RK1-715
Abstract: Introduction : Saliva contamination is an inevitable and common challenge in the field of restorative dentistry. Recognizing and considering the key time of isolation is an effective strategy to prevent the deleterious effects of salivary contamination. The purpose of this study was to evaluate the effect of salivary contamination in the course of light curing on microshear bond strength and microleakage of a restorative composite resin. Methods: 140 human third molars were divided into seven groups each containing 10 samples for measuring the microleakage and the microshear bond strength. The specimen of each group was contaminated with human saliva at a certain time, while group1 was contaminated in prior to light curing. The samples in groups 2 to 7 were contaminated with saliva at 2, 5, 10, 15, and 20 s after the start of light curing, respectively. The specimens of group7 were light cured and contaminated afterwards with human saliva. Results: According to the gathered results, the time of saliva contamination had significant negative effects on the microshear bond strength to the dentin and enamel in the course of light curing throughout the first 2s and 5s, respectively. It was indicated by the microleacage test that the saliva contamination in the first 2s, 5s, and 10s during light curing had a higher microleakage than the other times. Conclusion: In conclusion, during light curing of the composite resin, the first 10s was high sensitive to saliva contamination and therefore the isolation is very important in this time.
Published: 2021
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16. Inverse k-visibility for RSSI-based Indoor Geometric Mapping

Author: Kim, Junseo, Lisondra, Matthew, Bahoo, Yeganeh, and Saeedi, Sajad
Subjects: Computer Science - Robotics
Abstract: In recent years, the increased availability of WiFi in indoor environments has gained an interest in the robotics community to leverage WiFi signals for enhancing indoor SLAM (Simultaneous Localization and Mapping) systems. SLAM technology is widely used, especially for the navigation and control of autonomous robots. This paper discusses various works in developing WiFi-based localization and challenges in achieving high-accuracy geometric maps. This paper introduces the concept of inverse k-visibility developed from the k-visibility algorithm to identify the free space in an unknown environment for planning, navigation, and obstacle avoidance. Comprehensive experiments, including those utilizing single and multiple RSSI signals, were conducted in both simulated and real-world environments to demonstrate the robustness of the proposed algorithm. Additionally, a detailed analysis comparing the resulting maps with ground-truth Lidar-based maps is provided to highlight the algorithm's accuracy and reliability., Comment: This work has been submitted to the IEEE Sensors Journal for possible publication
Published: 2024

17. Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines

Author: Yeganeh, Yavar Taheri, Jafari, Mohsen, and Matta, Andrea
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: We investigate the application of active inference in developing energy-efficient control agents for manufacturing systems. Active inference, rooted in neuroscience, provides a unified probabilistic framework integrating perception, learning, and action, with inherent uncertainty quantification elements. Our study explores deep active inference, an emerging field that combines deep learning with the active inference decision-making framework. Leveraging a deep active inference agent, we focus on controlling parallel and identical machine workstations to enhance energy efficiency. We address challenges posed by the problem's stochastic nature and delayed policy response by introducing tailored enhancements to existing agent architectures. Specifically, we introduce multi-step transition and hybrid horizon methods to mitigate the need for complex planning. Our experimental results demonstrate the effectiveness of these enhancements and highlight the potential of the active inference-based approach., Comment: Accepted at the 10th International Conference on Machine Learning, Optimization, and Data Science
Published: 2024

18. Steroid-Responsive Post-Traumatic Persistent Neutrophilic Meningitis

Author: Mahboubeh Haddad, Fereshte Sheybani, Nahid Olfati, and Yeganeh Azhdari
Subjects: Medicine
Abstract: Post-traumatic meningitis is a potentially fatal condition that presents as a diagnostic and therapeutic challenge. The vast majority of post-traumatic meningitides are caused by infectious pathogens, most commonly multi-drug-resistant (MDR) bacterial pathogens. However, aseptic meningitis occurs less frequently due to tissue response to injury or stimulation by noninfectious agents, such as blood breakdown products or chemicals. Here, we present a case of post-traumatic persistent neutrophilic meningitis who was found to be steroid responsive. Diagnostic evaluation in our patient did not reveal any infectious pathogen, and the patient did not respond to broad-spectrum antimicrobial treatment. We suggest that physicians who treat patients with post-traumatic meningitis should consider steroid-responsive post-traumatic persistent neutrophilic meningitis (SPNM) in the list of differential diagnosis particularly when no infectious etiology is found and the patient does not respond to empirical antimicrobial treatment. Brain injury-induced immune dysregulation causing exaggerated inflammatory reaction might play a role in the pathogenesis of SPNM; however, further neuropathological studies are absolutely necessary to evaluate and characterize trauma-induced immune dysregulation.
Published: 2022
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19. Importance of the COVID-19 Vaccine Booster Dose in Protection and Immunity

Author: Alireza Abdollahi, Yeganeh Afsharyzad, Atefeh Vaezi, and Alipasha Meysamie
Subjects: SARS-CoV-2, vaccination, booster dose, RBD antibody, neutralizing antibody, spike antibody, Medicine
Abstract: Background: There is debate on the necessity of booster doses of COVID-19 vaccination, especially in countries with limited resources. Methods: This cross-sectional study was conducted in a referral laboratory in Tehran, Iran. The level of COVID-19 antibodies was measured and compared between individuals regarding the number of COVID-19 vaccine shots. Results: In this study, 176 individuals with a mean age of 36.3 (±11.7) years participated. A total of 112 individuals received two doses of the COVID-19 vaccine, and 64 individuals received three doses. Level of all antibodies was higher in those who received three doses than in those who received two doses of the COVID-19 vaccine. Considering the SARS-CoV-2 Spike IgG, the difference was not statistically significant but for the SARS-CoV-2 RBD IgG and SARS-CoV-2 NAB the difference was statistically significant. Regarding to the background variables, receiving influenza vaccine in the past year, history of autoimmune diseases and past medical history of chicken pox showed a significant association with the number of vaccine doses received. Their effects on the outcome variables assessed with multivariate logistic regression analysis. Conclusion: The results of our study show that a booster dose of the COVID-19 vaccine enhances the antibody response.
Published: 2022
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20. Tur[k]ingBench: A Challenge Benchmark for Web Agents

Author: Xu, Kevin, Kordi, Yeganeh, Nayak, Tanay, Asija, Ado, Wang, Yizhong, Sanders, Kate, Byerly, Adam, Zhang, Jingyu, Van Durme, Benjamin, and Khashabi, Daniel
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction
Abstract: Can advanced multi-modal models effectively tackle complex web-based tasks? Such tasks are often found on crowdsourcing platforms, where crowdworkers engage in challenging micro-tasks within web-based environments. Building on this idea, we present TurkingBench, a benchmark consisting of tasks presented as web pages with textual instructions and multi-modal contexts. Unlike previous approaches that rely on artificially synthesized web pages, our benchmark uses natural HTML pages originally designed for crowdsourcing workers to perform various annotation tasks. Each task's HTML instructions are instantiated with different values derived from crowdsourcing tasks, creating diverse instances. This benchmark includes 32.2K instances spread across 158 tasks. To support the evaluation of TurkingBench, we have developed a framework that links chatbot responses to actions on web pages (e.g., modifying a text box, selecting a radio button). We assess the performance of cutting-edge private and open-source models, including language-only and vision-language models (such as GPT4 and InternVL), on this benchmark. Our results show that while these models outperform random chance, there is still significant room for improvement. We hope that this benchmark will drive progress in the evaluation and development of web-based agents.
Published: 2024

21. DPPE: Dense Pose Estimation in a Plenoxels Environment using Gradient Approximation

Author: Kolios, Christopher, Bahoo, Yeganeh, and Saeedi, Sajad
Subjects: Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract: We present DPPE, a dense pose estimation algorithm that functions over a Plenoxels environment. Recent advances in neural radiance field techniques have shown that it is a powerful tool for environment representation. More recent neural rendering algorithms have significantly improved both training duration and rendering speed. Plenoxels introduced a fully-differentiable radiance field technique that uses Plenoptic volume elements contained in voxels for rendering, offering reduced training times and better rendering accuracy, while also eliminating the neural net component. In this work, we introduce a 6-DoF monocular RGB-only pose estimation procedure for Plenoxels, which seeks to recover the ground truth camera pose after a perturbation. We employ a variation on classical template matching techniques, using stochastic gradient descent to optimize the pose by minimizing errors in re-rendering. In particular, we examine an approach that takes advantage of the rapid rendering speed of Plenoxels to numerically approximate part of the pose gradient, using a central differencing technique. We show that such methods are effective in pose estimation. Finally, we perform ablations over key components of the problem space, with a particular focus on image subsampling and Plenoxel grid resolution. Project website: https://sites.google.com/view/dppe, Comment: 8 pages, 4 figures, conference
Published: 2024

22. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author: Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.
Published: 2024

23. Structure from WiFi (SfW): RSSI-based Geometric Mapping of Indoor Environments

Author: Kim, Junseo, Zalat, Jill Aghyourli, Bahoo, Yeganeh, and Saeedi, Sajad
Subjects: Computer Science - Robotics
Abstract: With the rising prominence of WiFi in common spaces, efforts have been made in the robotics community to take advantage of this fact by incorporating WiFi signal measurements in indoor SLAM (Simultaneous Localization and Mapping) systems. SLAM is essential in a wide range of applications, especially in the control of autonomous robots. This paper describes recent work in the development of WiFi-based localization and addresses the challenges currently faced in achieving WiFi-based geometric mapping. Inspired by the field of research into k-visibility, this paper presents the concept of inverse k-visibility and proposes a novel algorithm that allows robots to build a map of the free space of an unknown environment, essential for planning, navigation, and avoiding obstacles. Experiments performed in simulated and real-world environments demonstrate the effectiveness of the proposed algorithm., Comment: Accepted at American Control Conference 2024
Published: 2024

24. Conservation in heavily urbanized biodiverse regions requires urgent management action and attention to governance

Author: Laura J. Kehoe, Jessie Lund, Lia Chalifour, Yeganeh Asadian, Eric Balke, Sean Boyd, Deborah Carlson, James M. Casey, Brendan Connors, Nicolai Cryer, Mark C. Drever, Scott Hinch, Colin Levings, Misty MacDuffee, Heidi McGregor, John Richardson, David C. Scott, Daniel Stewart, Ross G. Vennesland, Chad E. Wilkinson, Pamela Zevit, Julia K. Baum, and Tara G. Martin
Subjects: conservation planning, cost‐effectiveness, decision science, estuary, expert elicitation, governance, Ecology, QH540-549.5, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract: Abstract Throughout history, humans have settled in areas of high biodiversity. Today these areas are home to our biggest urban centers with biodiversity at increasing risk from escalating cumulative threats. Identifying the management strategies to conserve species within such regions, and ensuring effective governance to oversee their implementation, presents enormous challenges. Using a novel Priority Threat Management (PTM) approach that calculates the cost‐effectiveness of conservation action and co‐governance, we discover that the 102 species at risk of local extinction within Canada's most diverse, heavily urbanized coastal region, the Fraser River estuary, require urgent investment in management strategies costing an estimated CAD$381 M over 25 years. Our study also suggests that co‐governance underpins conservation success in urban areas, by increasing the feasibility of management strategies. This study underscores that biodiversity conservation in heavily urbanized areas is not a lost cause but does require strategic planning, attention to governance, and large‐scale investment.
Published: 2021
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25. Treatment appropriateness of direct oral anticoagulants in patients with atrial fibrillation for stroke prevention: A real-world prospective study

Author: Pasebani, Yeganeh, Rafati, Ali, Dalouchi, Saied, Bahadori, Mohammad Javad, Ghoshouni, Hamed, Haghjoo, Majid, Fazelifar, Amir Farjam, Alizadeh‐Diz, Abolfath, Madadi, Shabnam, Kamali, Farzad, Hadavand, Naser, Talasaz, Azita H, Lip, Gregory Y. H., Emkanjoo, Zahra, and Sadeghipour, Parham
Published: 2024
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26. Photocrosslinkable Antibacterial Bioadhesives Derived from Soybean Oil-Based Hydroxyurethane Methacrylates

Author: Rabiee, Tina, Yeganeh, Hamid, Khorasani, Saied Nouri, and Mohammadpoor-Baltork, Iraj
Published: 2024
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27. Harnessing Nanotechnology for Idarubicin Delivery in Cancer Therapy: Current Approaches and Future Perspectives

Author: Safari, Fatemeh, Jalalian, Yeganeh, Abdouss, Hamidreza, Pourmadadi, Mehrab, Zahedi, Payam, Abdouss, Majid, Rahdar, Abbas, Fathi-karkan, Sonia, and Pandey, Sadanand
Published: 2024
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28. Highly efficient green-synthesized sucrose-derived graphene silica and carbonate sand composites for copper (II) ions removal

Author: Omdehghiasi, H., Korayem, A. H., and Yeganeh-Bakhtiary, A.
Published: 2024
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29. Why structural solutions for flood control should be adapted to climate change?

Author: Hosseinipoor, Mahdi, Mollaei Rudsary, Armin, Danesh-Yazdi, Mohammad, Kazempour, Zahra, and Yeganeh, Yasna
Published: 2024
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30. Recent Advances in Applications of Aptasensors/Nanomaterials Platform for Food and Biomedical: a Review

Author: Abedi-Firouzjah, Reza, Tavassoli, Milad, Khezerlou, Arezou, Mazaheri, Yeganeh, Alizadeh-Sani, Mahmoud, Ehsani, Ali, and Moore, Matthew D.
Published: 2024
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31. Tumor editing suppresses innate and adaptive antitumor immunity and is reversed by inhibiting DNA methylation

Author: Zhang, Ying, Naderi Yeganeh, Pourya, Zhang, Haiwei, Wang, Simon Yuan, Li, Zhouyihan, Gu, Bowen, Lee, Dian-Jang, Zhang, Zhibin, Ploumakis, Athanasios, Shi, Ming, Wu, Hao, Greer, Eric Lieberman, Hide, Winston, and Lieberman, Judy
Published: 2024
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32. A comprehensive review of drying techniques and quality for saffron

Author: Gandomzadeh, Danial, Saeidirad, Mohammad Hossein, Sabeghi, Yeganeh, Rohani, Abbas, Azarpazhooh, Elham, Saeidirad, Yasaman, and Ramaswamy, Hosahalli S.
Published: 2024
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33. Enhancing multi-class diabetic retinopathy detection using tuned hyper-parameters and modified deep transfer learning

Author: Modaresnia, Yeganeh, Abedinzadeh Torghabeh, Farhad, and Hosseini, Seyyed Abed
Published: 2024
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34. Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Author: Marghi, Yeganeh, Gala, Rohan, Baftizadeh, Fahimeh, and Sümbül, Uygar
Published: 2024
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35. Application of metalated covalent organic frameworks (MCOFs) for adsorption and separation of pollutants: a review study

Author: Kurd, Nematullah, Afkhami, Abbas, Yeganeh, Reza, and Ganji, Hanieh
Published: 2024
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36. In Vitro Evaluation and Characterization of Double Emulsion Based on Polyvinylpyrrolidone/Agarose/ZIF-8 Hydrogel Nanocomposite Platform for Quercetin Delivery to Breast Cancer Cells

Author: Seddighi, Yeganeh, Pourmadadi, Mehrab, Abdouss, Majid, Mazinani, Saeedeh, Rahdar, Abbas, and Ghotekar, Suresh
Published: 2024
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37. Antibacterial Polyhydroxyurethane-Gelatin Wound Dressings with In Situ-Generated Silver Nanoparticles or Hyperthermia Induced by Near-Infrared Light Absorption

Author: Babaahmadi, Masoud and Yeganeh, Hamid
Published: 2024
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38. LIPSTICK: Corruptibility-Aware and Explainable Graph Neural Network-based Oracle-Less Attack on Logic Locking

Author: Aghamohammadi, Yeganeh and Rezaei, Amin
Subjects: Computer Science - Cryptography and Security
Abstract: In a zero-trust fabless paradigm, designers are increasingly concerned about hardware-based attacks on the semiconductor supply chain. Logic locking is a design-for-trust method that adds extra key-controlled gates in the circuits to prevent hardware intellectual property theft and overproduction. While attackers have traditionally relied on an oracle to attack logic-locked circuits, machine learning attacks have shown the ability to retrieve the secret key even without access to an oracle. In this paper, we first examine the limitations of state-of-the-art machine learning attacks and argue that the use of key hamming distance as the sole model-guiding structural metric is not always useful. Then, we develop, train, and test a corruptibility-aware graph neural network-based oracle-less attack on logic locking that takes into consideration both the structure and the behavior of the circuits. Our model is explainable in the sense that we analyze what the machine learning model has interpreted in the training process and how it can perform a successful attack. Chip designers may find this information beneficial in securing their designs while avoiding incremental fixes., Comment: Proceedings of 29th Asia and South Pacific Design Automation Conference (ASP-DAC 2024)
Published: 2024

39. Multi-BD Symbiotic Radio-Aided 6G IoT Network: Energy Consumption Optimization with QoS Constraint Approach

Author: Yeganeh, Rahman Saadat, Omidi, Mohammad Javad, and Ghavami, Mohammad
Subjects: Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract: The commensal symbiotic radio (CSR) system is proposed as a novel solution for connecting systems through green communication networks. This system enables us to establish secure, ubiquitous, and unlimited connectivity, which is a goal of 6G. The base station uses MIMO antennas to transmit its signal. Passive IoT devices, called symbiotic backscatter devices (SBDs), receive the signal and use it to charge their power supply. When the SBDs have data to transmit, they modulate the information onto the received ambient RF signal and send it to the symbiotic user equipment, which is a typical active device. The main purpose is to enhance energy efficiency in this network by minimizing energy consumption (EC) while ensuring the minimum required throughput for SBDs. To achieve this, we propose a new scheduling scheme called Timing-SR that optimally allocates resources to SBDs. The main optimization problem involves non-convex objective functions and constraints. To solve this, we use mathematical techniques and introduce a new approach called sequential quadratic and conic quadratic representation to relax and discipline the problem, leading to reducing its complexity and convergence time. The simulation results demonstrate that the proposed approach outperforms other outlined schemes in reducing EC.
Published: 2024

40. Sum Throughput Maximization in Multi-BD Symbiotic Radio NOMA Network Assisted by Active-STAR-RIS

Author: Yeganeh, Rahman Saadat, Omidi, Mohammad Javad, Zeinali, Farshad, Mili, Mohammad Robat, and Ghavami, Mohammad
Subjects: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract: In this paper, we employ active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASRIS) to aid in establishing and enhancing communication within a commensal symbiotic radio (CSR) network. Unlike traditional RIS, ASRIS not only ensures coverage in an omni directional manner but also amplifies received signals, consequently elevating overall network performance. in the first phase, base station (BS) with active massive MIMO antennas, send ambient signal to SBDs. In the first phase, the BS transmits ambient signals to the symbiotic backscatter devices (SBDs), and after harvesting the energy and modulating their information onto the signal carrier, the SBDs send Backscatter signals back to the BS. In this scheme, we employ the Backscatter Relay system to facilitate the transmission of information from the SBDs to the symbiotic User Equipments (SUEs) with the assistance of the BS. In the second phase, the BS transmits information signals to the SUEs after eliminating interference using the Successive Interference Cancellation (SIC) method. ASRIS is employed to establish communication among SUEs lacking a line of sight (LoS) and to amplify power signals for SUEs with a LoS connection to the BS. It is worth noting that we use NOMA for multiple access in all network. The main goal of this paper is to maximize the sum throughput between all users. To achieve this, we formulate an optimization problem with variables including active beamforming coefficients at the BS and ASRIS, as well as the phase adjustments of ASRIS and scheduling parameters between the first and second phases. To model this optimization problem, we employ three deep reinforcement learning (DRL) methods, namely PPO, TD3, and A3C. Finally, the mentioned methods are simulated and compared with each other., Comment: This article will be submitted to the Transactions journal
Published: 2024

41. Gemini: A Family of Highly Capable Multimodal Models

Author: Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, 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Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
Published: 2023

42. Association Between Noise-Induced Hearing Loss and Vestibular Dysfunction in Workers Chronically Exposed to Occupational Noise

Author: Nadri, Hamed, Khavanin, Ali, Kim, In-Ju, Akbari, Mehdi, Nadri, Farshad, and Yeganeh, Reza
Subjects: Noise control -- Investigations, Textile workers -- Investigations, Hearing loss -- Risk factors, Company legal issue, Environmental issues, Health
Abstract: Background: Despite the understanding of the association between noise exposure and noise-induced hearing loss (NIHL), the effects of noise on vestibular function are less well understood and neglected. Aim: The aim of this study was to investigate the function of the vestibular system in workers with NIHL. Materials and Methods: Thirty male textile workers with symmetrical NIHL (49.3 ± 4.5 dB at 4 kHz) and thirty healthy male textile office staff with normal hearing were included in this study. Pure tone audiometry using air conduction and bone conduction was used to determine hearing thresholds. The videonystagmography battery test, including the oculomotor test, caloric test, and video head impulse test (vHIT), was used for the evaluation of vestibular system function. Results: There was no difference in saccadic tracking, smooth pursuit tracking, and optokinetic nystagmus between the two groups. It was found that the abnormal vHIT was significantly higher in workers with NIHL (40%) than in the control group (3.3%). In addition, the abnormal caloric test was significantly higher in workers with NIHL (33.3%) than in the control group (0%). The association between the hearing thresholds of 3, 4, 6, and 8 kHz and vestibular dysfunction was significant. Conclusion: This study provides evidence of the simultaneous occurrence of NIHL and vestibular dysfunction. In other words, the results support the role of noise as a risk factor for the development of vestibular dysfunction in textile workers. Keywords: Noise-induced hearing loss, vestibular dysfunction, video head impulse test, Videonystagmography, Author(s): Hamed Nadri [1]; Ali Khavanin [2]; In-Ju Kim [3]; Mehdi Akbari [4]; Farshad Nadri (corresponding author) [5]; Reza Yeganeh [1] INTRODUCTION Cochlear damage, also referred to as noise-induced hearing [...]
Published: 2024
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43. Multi-pass Friction Stir Welding of Al–TiC–Zn–Mg Composite: Microstructure and Mechanical Characteristics

Author: Abdollahzadeh, Amin, Bagheri Vanani, Behrouz, Koohdar, Hamidreza, Aboutalebi Babereh, Amir, and Yeganeh, Mahdi
Published: 2024
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44. Recognition of GCK Homozygote missense (His424Tyr) variant in a female patient with neonatal hyperglycemia

Author: Pashapour Yeganeh, Amirreza, Rahimi Farahani, Marjan, Panahi, Nekoo, Mohammad Amoli, Mahsa, Nickhah Klashami, Zeynab, Aghaei Meybodi, Hamid Reza, and Soltani, Akbar
Published: 2024
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45. H2-quartz and cushion gas-quartz intermolecular interactions: implications for hydrogen geo-storage in sandstone reservoirs

Author: Sikiru, Surajudeen, Al-Yaseri, Ahmed, Yekeen, Nurudeen, Soleimani, Hassan, N, Bonnia N., Hamza, Mohammed Falalu, and Ghotbi, Mohammad Yeganeh
Published: 2024
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46. The effect of raw material (cow and chicken manure) and reactor type for improving and maximizing biogas production

Author: Karami-Boozhani, Somayeh, Yeganeh, Reza, Jahanbakhshi, Ahmad, Kheiralipour, Kamran, and Ebrahimi, Seyed Hadi
Published: 2024
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47. Phase transitions of the synergistic effects of Ba2+O2TiO2/Mn4+O6 nanofluid with integration of electromagnetic field for improved oil recovery

Author: Sikiru, Surajudeen, Soleimani, Hassan, Rahmanian, Nejat, Rostami, Amir, Khodapanah, Leila, Ghotbi, Mohammad Yeganeh, Hamza, Mohammed Falalu, Soleimani, Hojjatollah, Khodapanah, Nasrin, Sabet, Maziyar, Demiral, Birol M. R., Bonnia, N. N., Ibrahim, Norazila, and Muhammad, Nurmalessa
Published: 2024
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48. Five-Year Outcomes of Coarctoplasty with Stents in the Pediatric Population: Results from a Retrospective Single-Center Cohort with Centrally Adjudicated Outcomes

Author: Mortezaeian, Hojjat, Rezanejad, Elham, Pasebani, Yeganeh, Zamani, Raheleh, Khalili, Yasaman, Ghaemi, Hamidreza, Jafari, Farshad, Sabri, Mahshad, Moosavi, Jamal, Mohebbi, Bahram, Abdi, Amir, Montazeri Namin, Sara, Sadeghipour, Parham, Haulon, Stephan, and Fraisse, Alain
Published: 2024
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49. Investigation Cytotoxicity and Curcumin Release Behavior by Pyranopyrazole-TiO2@niosome Carrier for Breast Cancer Treatment

Author: Yeganeh, Faten Eshrati, Ghafuri, Hossein, and Azizi, Mojtaba
Published: 2024
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50. PRISM: Progressive Restoration for Scene Graph-based Image Manipulation

Author: Jahoda, Pavel, Farshad, Azade, Yeganeh, Yousef, Adeli, Ehsan, and Navab, Nassir
Subjects: Computer Science - Machine Learning
Abstract: Scene graphs have emerged as accurate descriptive priors for image generation and manipulation tasks, however, their complexity and diversity of the shapes and relations of objects in data make it challenging to incorporate them into the models and generate high-quality results. To address these challenges, we propose PRISM, a novel progressive multi-head image manipulation approach to improve the accuracy and quality of the manipulated regions in the scene. Our image manipulation framework is trained using an end-to-end denoising masked reconstruction proxy task, where the masked regions are progressively unmasked from the outer regions to the inner part. We take advantage of the outer part of the masked area as they have a direct correlation with the context of the scene. Moreover, our multi-head architecture simultaneously generates detailed object-specific regions in addition to the entire image to produce higher-quality images. Our model outperforms the state-of-the-art methods in the semantic image manipulation task on the CLEVR and Visual Genome datasets. Our results demonstrate the potential of our approach for enhancing the quality and precision of scene graph-based image manipulation.
Published: 2023

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