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    How AI helps programming a quantum computer

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Generative models like diffusion model s are one of the most important recent developments in Machine Learning (ML), wi th models as Stable Diffusion and Dall.e revolutionizing the field of image gene ration. These models are able to produce high quality images based on some text description. “Our new model for programming quantum computers does the same but, instead of generating images, it generates quantum circuits based on the text d escription of the quantum operation to be performed”, explains Gorka Munoz-Gil f rom the Department of Theoretical Physics of the University of Innsbruck, Austri a. To prepare a certain quantum state or execute an algorithm on a quantum computer , one needs to find the appropriate sequence of quantum gates to perform such op erations. While this is rather easy in classical computing, it is a great challe nge in quantum computing, due to the particularities of the quantum world. Recen tly, many scientists have proposed methods to build quantum circuits with many r elying machine learning methods. However, training of these ML models is often v ery hard due to the necessity of simulating quantum circuits as the machine lear ns. Diffusion models avoid such problems due to the way how they are trained. “T his provides a tremendous advantage”, explains Gorka Munoz-Gil, who developed th e novel method together with Hans J. Briegel and Florian Furrutter. “Moreover, w e show that denoising diffusion models are accurate in their generation and also very flexible, allowing to generate circuits with different numbers of qubits, as well as types and numbers of quantum gates.” The models also can be tailored to prepare circuits that take into consideration the connectivity of the quantum hardware, i.e. how qubits are connected in the quantum computer. “As producing new circuits is very cheap once the model is trained, one can use it to discover new insights about quantum operations of interest”, Gorka Munoz-Gil names anoth er potential of the new method.
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    Reports on Artificial Intelligence Findings from Government First Grade College Provide New Insights (Artificial Intelligence and Machine Learning for Disaster Prediction: a Scientometric Analysis of Highly Cited Papers)

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ar tificial Intelligence. According to news reporting out of Karnataka, India, by N ewsRx editors, research stated, “This study conducts an analysis of artificial i ntelligence (AI) and machine learning (ML) applications in natural disaster pred iction using a scientometric approach. The Web of Science Core Collection served as the primary data source, yielding 38,456 records spanning from 2003 to 2022. ” Our news journalists obtained a quote from the research from Government First Gr ade College, “The analysis concentrated on highly influential research, defined by papers garnering 100 or more citations, resulting in a final set of 1,637 pub lications. VOSviewer software facilitated the exploration of collaboration patte rns among authors, institutions, and countries, along with the identification of emerging research topics and the most impactful articles. These highly cited pa pers were distributed across various sources (625). A total of 443,502 citations were counted, with an average of 270.92 citations per document. Interestingly, the average annual citation growth rate exhibited a negative trend (-1.02% ), suggesting a potential shift in citation patterns over time. The average docu ment age of 6.9 years indicates that the majority of the research is relatively recent. Collaboration emerges as a prominent feature within the field, with an a verage of 5.09 co-authors per document and 46.55% of collaboration s being international. This underscores the collaborative nature inherent in res earch within this domain. Scholarly articles (1263) represent the predominant do cument type, followed by reviews (323), indicative of the field’s solid foundati on in peer-reviewed literature. The study’s findings hold significant implicatio ns for future research and practical applications, identifying gaps in the liter ature and underscoring the necessity for further exploration in developing AI an d ML models tailored to specific types of natural disasters, as well as assessin g these models in real-world scenarios. International collaboration and interdis ciplinary approaches are highlighted as pivotal components in advancing this cri tical field. While providing valuable insights, this approach acknowledges limit ations associated with its focus on highly cited papers and a single database.”
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    Aix-Marseille University Reports Findings in Robotics (Shaping the energy curves of a servomotor-based hexapod robot)

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news originating from Marseille, France, by NewsRx c orrespondents, research stated, “The advantageous versatility of hexapod robots is often accompanied by high power consumption, while animals have evolved an en ergy efficient locomotion. However, there are a lack of methods able to compare and apply animals’ energetic optimizations to robots.” Our news journalists obtained a quote from the research from Aix-Marseille Unive rsity, “In this study, we applied our method to a full servomotor-based hexapod robot to evaluate its energetic performance. Using an existing framework based o n the laws of thermodynamics, we estimated four metrics using a dedicated test b ench and a simulated robotic leg. We analyzed the characteristics of a single le g to shape the energetic profile of the full robot to a given task. Energy savin g is improved by 10% through continuous duty factor adjustment wit h a 192% increase in power maximization. Moreover, adjusting the r obot’s velocity by the step length and associating this with gait switching, red uces the power loss by a further 10% at low-speed locomotion. Howe ver, unlike in animals, only one unique optimal operating point has been reveale d, which is a disadvantage caused by the low energetic efficiency of servomotorbased hexapods.”
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    Xi’an Jiaotong University Reports Findings in Machine Learning (Different machin e learning methods based on maxillary sinus in sex estimation for northwestern C hinese Han population)

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Shaanxi, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “Sex estimat ion is a critical aspect of forensic expertise. Some special anatomical structur es, such as the maxillary sinus, can still maintain integrity in harsh environme ntal conditions and may be served as a basis for sex estimation.” Our news editors obtained a quote from the research from Xi’an Jiaotong Universi ty, “Due to the complex nature of sex estimation, several studies have been cond ucted using different machine learning algorithms to improve the accuracy of sex prediction from anatomical measurements. In this study, linear data of the maxi llary sinus in the population of northwest China by using Cone-Beam Computed Tom ography (CBCT) were collected and utilized to develop logistic, K-Nearest Neighb or (KNN), Support Vector Machine (SVM) and random forest (RF) models for sex est imation with R 4.3.1. CBCT images from 477 samples of Han population (75 males a nd 81 females, aged 5-17 years; 162 males and 159 females, aged 18-72) were used to establish and verify the model. Length (MSL), width (MSW), height (MSH) of b oth the left and right maxillary sinuses and distance of lateral wall between tw o maxillary sinuses (distance) were measured. 80% of the data were randomly picked as the training set and others were testing set. Besides, these samples were grouped by age bracket and fitted models as an attempt. Overall, t he accuracy of the sex estimation for individuals over 18 years old on the testi ng set was 77.78%, with a slightly higher accuracy rate for males a t 78.12% compared to females at 77.42%. However, accu racy of sex estimation for individuals under 18 was challenging. In comparison t o logistic, KNN and SVM, RF exhibited higher accuracy rates. Moreover, incorpora ting age as a variable improved the accuracy of sex estimation, particularly in the 18-27 age group, where the accuracy rate increased to 88.46%. M eanwhile, all variables showed a linear correlation with age. The linear measure ments of the maxillary sinus could be a valuable tool for sex estimation in indi viduals aged 18 and over. A robust RF model has been developed for sex estimatio n within the Han population residing in the northwestern region of China.”
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    Researcher at Beijing Institute of Technology Publishes Research in Computationa l Intelligence (Research on the Messenger UAV Mission Planning Based on Sampling Transformation Algorithm)

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on computational intelligence. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “In recent years, there has been a si gnificant development in unmanned platform technologies, specifically unmanned g round vehicles (UGVs) and unmanned aerial vehicles (UAVs).” Funders for this research include National Outstanding Youth Talents Support Pro gram; Basic Science Center Programs. The news reporters obtained a quote from the research from Beijing Institute of Technology: “As a result, their application scenarios have expanded considerably . Unmanned platforms are considered integral components of the Internet of Thing s system. However, certain challenges arise when dealing with specialized tasks, such as navigating complex urban low-altitude terrain with multiple obstacles a nd limited communication capabilities. These challenges can greatly impact the e fficiency of the system due to information isolation. To address this issue, a m essenger drone mechanism is introduced in this paper, which utilizes air superio rity to facilitate indirect communication between unmanned platforms. Additional ly, a task sequence planning algorithm based on sampling transformation is desig ned.”
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    Affiliated Zhongshan Hospital of Dalian University Reports Findings in Robotics (Experimental validation of the accuracy of roboticassisted radioactive seed im plantation for tumor treatment)

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news reporting out of Dalian, People’s Republic of C hina, by NewsRx editors, research stated, “An experimental validation of a robot ic system for radioactive iodine-125 seed implantation (RISI) in tumor treatment was conducted using customized phantom models and animal models simulating live r and lung lesions. The robotic system, consisting of planning, navigation, and implantation modules, was employed to implant dummy radioactive seeds into the m odels.” Our news journalists obtained a quote from the research from the Affiliated Zhon gshan Hospital of Dalian University, “Fiducial markers were used for target loca lization. In phantom experiments across 40 cases, the mean errors between planne d and actual seed positions were 0.98 ± 1.05 mm, 1.14 ± 0.62 mm, and 0.90 ± 1.05 mm in the x, y, and z directions, respectively. The x, y, and z directions corr espond to the left-right, anterior-posterior, and superior-inferior anatomical p lanes. Silicone phantoms exhibiting significantly smaller x-axis errors compared to liver and lung phantoms (p <0.05). Template assistance significantly reduced errors in all axes (p <0.05). No si gnificant dosimetric deviations were observed in parameters such as D90, V100, a nd V150 between plans and post-implant doses (p > 0.05). In animal experiments across 23 liver and lung cases, the mean implantation err ors were 1.28 ± 0.77 mm, 1.66 ± 0.69 mm, and 1.86 ± 0.93 mm in the x, y, and z d irections, slightly higher than in phantoms (p <0.05), wit h no significant differences between liver and lung models.”
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    New Findings from South China University of Technology in Artificial Intelligenc e Provides New Insights (Saving Face: Leveraging Artificial Intelligence-based N egative Feedback To Enhance Employee Job Performance)

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligen ce have been presented. According to news reporting originating in Guangzhou, Pe ople’s Republic of China, by NewsRx journalists, research stated, “Negative perf ormance feedback is vital for stimulating employees to enhance their performance despite resulting in stress and adverse work outcomes. Fortunately, artificial intelligence (AI)-enabled automated agents have gradually assumed certain functi ons led by human leaders, such as providing feedback.” Financial support for this research came from The 2023 Central University Basic Research Business Fee Project Interdisciplinary Youth Team Project. The news reporters obtained a quote from the research from the South China Unive rsity of Technology, “Drawing from regulatory focus theory, we propose that AI-b ased feedback systems can serve as a ‘remediation’ tool, effectively mitigating employees’ apprehensions about receiving negative feedback. In two studies, we f ound that for employees who fear losing face, AI-based negative feedback motivat es promotion-focused cognition-motivation to learn-representing a learning mecha nism to promote job performance and impedes their prevention-focused cognition-i nterpersonal rumination-reducing the depletion needed for job performance.”
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    Central South University Reports Findings in Liver Fibrosis (Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum)

    8-8页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Liver Diseases and Con ditions - Liver Fibrosis is the subject of a report. According to news originati ng from Hunan, People’s Republic of China, by NewsRx correspondents, research st ated, “This study intends to use the basic information and blood routine of schi stosomiasis patients to establish a machine learning model for predicting liver fibrosis. We collected medical records of Schistosoma japonicum patients admitted to a hospital in China from June 2019 to June 2022.” Financial support for this research came from National Natural Science Foundatio n of China. Our news journalists obtained a quote from the research from Central South Unive rsity, “The method was to screen out the key variables and six different machine learning algorithms were used to establish prediction models. Finally, the opti mal model was compared based on AUC, specificity, sensitivity and other indicato rs for further modeling. The interpretation of the model was shown by using the SHAP package. A total of 1049 patients’ medical records were collected, and 10 k ey variables were screened for modeling using lasso method, including red cell d istribution width-standard deviation (RDW-SD), Mean corpuscular hemoglobin conce ntration (MCHC), Mean corpuscular volume (MCV), hematocrit (HCT), Red blood cell s, Eosinophils, Monocytes, Lymphocytes, Neutrophils, Age. Among the 6 different machine learning algorithms, LightGBM performed the best, and its AUCs in the tr aining set and validation set were 1 and 0.818, respectively. This study establi shed a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum.”
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    Research from University of Hawaii at Manoa Broadens Understanding of Machine Le arning (Machine Learning for New Physics Searches in B -> K*0 + - Decays)

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news originating from the Uni versity of Hawaii at Manoa by NewsRx editors, the research stated, “We report th e status of a neural network regression model trained to extract new physics (NP ) parameters in Monte Carlo (MC) simulation data.” The news journalists obtained a quote from the research from University of Hawai i at Manoa: “We utilize a new EvtGen NP MC generator to generate B -> K*0+- events according to the deviation of the Wilson Coefficient C9 from its S M value, dC9. We train a three-dimensional ResNet regression model, using images built from the angular observables and the invariant mass of the di-muon system , to extract values of d C9 directly from the MC data samples.”
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    New Machine Learning Research from Mohammed V University Described (Improving mo nthly precipitation prediction accuracy using machine learning models: a multi-v iew stacking learning technique)

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    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from Rabat, Moroc co, by NewsRx correspondents, research stated, “This research paper explores the implementation of machine learning (ML) techniques in weather and climate forec asting, with a specific focus on predicting monthly precipitation. The study ana lyzes the efficacy of six multivariate machine learning models: Decision Tree, R andom Forest, K-Nearest Neighbors (KNN), AdaBoost, XGBoost, and Long Short-Term Memory (LSTM).” The news editors obtained a quote from the research from Mohammed V University: “Multivariate time series models incorporating lagged meteorological variables w ere employed to capture the dynamics of monthly rainfall in Rabat, Morocco, from 1993 to 2018. The models were evaluated based on various metrics, including roo t mean square error (RMSE), mean absolute error (MAE), and coefficient of determ ination (R2). XGBoost showed the highest performance among the six individual mo dels, with an RMSE of 40.8 (mm). In contrast, Decision Tree, AdaBoost, Random Fo rest, LSTM, and KNN showed relatively lower performances, with specific RMSEs ra nging from 47.5 (mm) to 51 (mm). A novel multi-view stacking learning approach i s introduced, offering a new perspective on various ML strategies. This integrat ed algorithm is designed to leverage the strengths of each individual model, aim ing to substantially improve the precision of precipitation forecasts. The best results were achieved by combining Decision Tree, KNN, and LSTM to build the met a-base while using XGBoost as the second-level learner.”
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