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Findings from Deakin University in Artificial Intelligence Reported (Framing the Predictive Mind: Why We Should Think Again About Dreyfus)

1-1页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Artificial Intelligence is now available. According to news reporting out of Burwood, Australia, by New sRx editors, the research stated, "In this paper I return to Hubert Dreyfus' old but influential critique of artificial intelligence, redirecting it towards con temporary predictive processing models of the mind (PP). I focus on Dreyfus' arg uments about the ‘frame problem' for artificial cognitive systems, and his contr asting account of embodied human skills and expertise." Financial support for this research came from Deakin University. Our news journalists obtained a quote from the research from Deakin University, "The frame problem presents as a prima facie problem for practical work in AI an d robotics, but also for computational views of the mind in general, including f or PP. Indeed, some of the issues it presents seem more acute for PP, insofar as it seeks to unify all cognition and intelligence, and aims to do so without adm itting any cognitive processes or mechanisms outside of the scope of the theory. I contend, however, that there is an unresolved problem for PP concerning wheth er it can both explain all cognition and intelligent behavior as minimizing pred iction error with just the core formal elements of the PP toolbox, and also adeq uately comprehend (or explain away) some of the apparent cognitive differences b etween biological and prediction-based artificial intelligence, notably in regar d to establishing relevance and flexible context-switching, precisely the featur es of interest to Dreyfus' work on embodied indexicality, habits/skills, and abd uctive inference."

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Data from Norton Leatherman Spine Center Advance Knowledge in Robotics [Comparison of No Tap (Two-step) and Tapping Robotic Assisted Cortical Bone Traje ctory Screw Insertion]

2-3页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news reporting out of Louisville, Kentucky, by New sRx editors, research stated, "Workflow for cortical bone trajectory (CBT) screw s includes tapping line-to-line or under tapping by 1 mm. We describe a non-tapp ing,two-step workflow for CBT screw placement, and compare the safety profile a nd time savings to the Tap (three-step) workflow." Our news journalists obtained a quote from the research from Norton Leatherman S pine Center, "Patients undergoing robotic assisted 1-3 level posterior fusion wi th CBT screws for degenerative conditions were identified and separated into eit her a No-Tap or Tap workflow. Number of total screws, screw-related complication s, estimated blood loss, operative time, robotic time, and return to the operati ng room were collected and analyzed. There were 91 cases (458 screws) in the No- Tap and 88 cases (466 screws) in the Tap groups, with no difference in demograph ics, revision status, ASA grade, approach, number of levels fused or diagnosis b etween cohorts. Total robotic time was lower in the No-Tap (26.7 min) versus the Tap group (30.3 min, p = 0.053). There was no difference in the number of malpo sitioned screws identified intraoperatively (10 vs 6, p = 0.427), screws convert ed to freehand (3 vs 3, p = 0.699), or screws abandoned (3 vs 2, p = 1.000). No pedicle/pars fracture or fixation failure was seen in the No-Tap cohort and one in the Tap cohort (p = 1.00). No patients in either cohort were returned to OR f or malpositioned screws."

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Research Findings from University of Wisconsin Madison Update Understanding of M achine Learning (Symbolic Regression on FPGAs for Fast Machine Learning Inferenc e)

2-2页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting out of the University of Wisconsin Madison by NewsRx editors, research stated, "The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while stil l meeting data processing time constraints." The news correspondents obtained a quote from the research from University of Wi sconsin Madison: "In this contribution, we introduce a novel end-to-end procedur e that utilizes a machine learning technique called symbolic regression (SR). It searches the equation space to discover algebraic relations approximating a dat aset. We use PySR (a software to uncover these expressions based on an evolution ary algorithm) and extend the functionality of hls4ml (a package for machine lea rning inference in FPGAs) to support PySR-generated expressions for resource-con strained production environments. Deep learning models often optimize the top me tric by pinning the network size because the vast hyperparameter space prevents an extensive search for neural architecture. Conversely, SR selects a set of mod els on the Pareto front, which allows for optimizing the performance-resource tr ade-off directly. By embedding symbolic forms, our implementation can dramatical ly reduce the computational resources needed to perform critical tasks."

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Baskent University Reports Findings in Machine Learning (Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment)

3-4页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news reporting out of Ankara, Turkey, by NewsRx editors , research stated, "The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method." Financial support for this research came from Baskent Universitesi. Our news journalists obtained a quote from the research from Baskent University, "The objectives of this study were (1) to identify the best-performing model am ong seven machine learning (ML) models, which will standardize the extraction de cision and serve as a guide for inexperienced clinicians, and (2) to determine t he important variables for the extraction decision. This study included 1000 pat ients who received orthodontic treatment with or without extraction (500 extract ion and 500 non-extraction). The success criteria of the study were the decision s made by the four experienced orthodontists. Seven ML models were trained using 36 variables; including demographic information, cephalometric and model measur ements. First, the extraction decision was performed, and then the extraction ty pe was identified. Accuracy and area under the curve (AUC) of the receiver opera ting characteristics (ROC) curve were used to measure the success of ML models. The Stacking Classifier model, which consists of Gradient Boosted Trees, Support Vector Machine, and Random Forest models, showed the highest performance in ext raction decision with 91.2% AUC. The most important features deter mining extraction decision were maxillary and mandibular arch length discrepancy , Wits Appraisal, and ANS-Me length. Likewise, the Stacking Classifier showed th e highest performance with 76.3% accuracy in extraction type decis ions. The most important variables for the extraction type decision were mandibu lar arch length discrepancy, Class I molar relationship, cephalometric overbite, Wits Appraisal, and L1-NB distance. The Stacking Classifier model exhibited the best performance for the extraction decision."

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Findings from University of Patras Yields New Findings on Robotics (Collision De tection for Collaborative Assembly Operations On High-payload Robots)

4-5页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news originating from Patras, Greece, by NewsRx co rrespondents, research stated, "Human-robot collaboration for high-payload indus trial applications has the potential to unlock new applications and improve oper ator ergonomics. However, ensuring safety with closer proximity remains challeng ing due to the large payload." Funders for this research include H2020 Project "SHERLOCK Seamless and safe huma n centered robotic applications for novel collaborative workshops", HEU Project "CONVERGING Social industrial collaborative environments integrating AI, Big Dat a and Robotics for smart manufacturing" by the EC.

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Chinese Academy of Sciences Reports Findings in Lung Cancer (A machine learning- based approach to predict energy layer for each field in spot-scanning proton ar c therapy for lung cancer: A feasibility study)

5-6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 Oncology - Lung Cancer is the subject of a report. According to news reporting out of Lanzhou, People' s Republic of China, by NewsRx editors, research stated, "Determining the optima l energy layer (EL) for each field, under considering both dose constraints and delivery efficiency, is crucial to promoting the development of proton arc thera py (PAT) technology. This study aimed to explore the feasibility and potential c linical benefits of utilizing machine learning (ML) technique to automatically s elect EL for each field in PAT plans of lung cancer." Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "Proton Bragg peak position (BPP) was employed to characterize EL. The ground truth BPPs for each field were determined using the modified ELO-SPA T framework. Features in geometric, water-equivalent thicknesses (WET) and beaml et were defined and extracted. By analyzing the relationship between the extract ed features and ground truth, a polynomial regression model with L2-norm regular ization (Ridge regression) was constructed and trained. The performance of the r egression model was reported as an error between the predictions and the ground truth. Besides, the predictions were used to make PAT plans (PAT_PR ED). These plans were compared with those using the ground truth BPPs (PAT_ TRUTH) and the mid- WET of the target volumes (PAT_MID) in terms of relative biological effectiveness-weighted dose (RWD) distributions. One hundred ten patients with lung cancer, a total of 7920 samples, were enrolled retrospec tively, with 5940 cases randomly selected as the training set and the remaining 1980 cases as the testing set. Nine patients (648 samples) were collected additi onally to evaluate the regression model in terms of plan quality and robustness. With regard to the prediction errors, the root mean squared errors and mean abs olute errors between the ML-predicted and ground truth BPPs for the testing set were 9.165 and 6.572 mm, respectively, indicating differences of approximately t wo to three ELs. As for plan quality, the PAT_TRUTH and PAT_ PRED plans performed similarly in terms of plan robustness, target coverage and organs at risk (OARs) protection, with differences smaller than 0.5 Gy(RBE). Thi s trend was also observed for dose conformity and uniformity. The PAT_ MID plans produced the lowest robustness index and lowest doses to OARs, along w ith the highest heterogeneity index, indicating better protection for OARs, impr oved plan robustness, but compromised dose homogeneity. Additionally, for relati vely small tumor sizes, the PAT_MID plan demonstrated a notably poo r dose conformity index."

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University of Oxford Reports Findings in Robotics (Oscillating latent dynamics i n robot systems during walking and reaching)

7-8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 Robotics is the subjec t of a report. According to news reporting originating in Oxford, United Kingdom , by NewsRx journalists, research stated, "Sensorimotor control of complex, dyna mic systems such as humanoids or quadrupedal robots is notoriously difficult. Wh ile artificial systems traditionally employ hierarchical optimisation approaches or black-box policies, recent results in systems neuroscience suggest that comp lex behaviours such as locomotion and reaching are correlated with limit cycles in the primate motor cortex."

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Researchers from Anhui Agricultural University Detail Research in Intelligent Sy stems (Knowledge distillation based on projector integration and classifier shar ing)

7-7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 intelligent systems is the subject of a new report. According to news reporting from Anhui Agricultura l University by NewsRx journalists, research stated, "Knowledge distillation can transfer the knowledge from the pre-trained teacher model to the student model, thus effectively accomplishing model compression." Funders for this research include Anhui Provincial Key Research And Development Plan; Independent Project of Anhui Key Laboratory of Smart Agricultural Technolo gy And Equipment. The news editors obtained a quote from the research from Anhui Agricultural Univ ersity: "Previous studies have carefully crafted knowledge representation, targe ting loss function design, and distillation location selection, but there have b een few studies on the role of classifiers in distillation. Previous experiences have shown that the final classifier of the model has an essential role in maki ng inferences, so this paper attempts to narrow the gap in performance between m odels by having the student model directly use the classifier of the teacher mod el for the final inference, which requires an additional projector to help match features of the student encoder with the teacher's classifier. However, a singl e projector cannot fully align the features, and integrating multiple projectors may result in better performance. Considering the balance between projector siz e and performance, through experiments, we obtain the size of projectors for dif ferent network combinations and propose a simple method for projector integratio n. In this way, the student model undergoes feature projection and then uses the classifiers of the teacher model for inference, obtaining a similar performance to the teacher model."

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Sandro Pertini Hospital Reports Findings in Machine Learning (Intraoperative lef t-sided colorectal anastomotic testing in clinical practice: a multi-treatment m achine-learning analysis of the iCral3 prospective cohort)

8-9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 Machine Learning is th e subject of a report. According to news originating from Rome, Italy, by NewsRx correspondents, research stated, "Current evidence about intraoperative anastom otic testing after left-sided colorectal resections is still controversial. The aim of this study was to analyze the impact of Indocyanine Green fluorescent ang iography (ICG-FA) and air-leak test (ALT) over standard assessment on anastomoti c leakage (AL) rates according to surgeon's perception of anastomosis perfusion and/or integrity in clinical practice."

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Research Data from Nanjing University of Science and Technology Update Understan ding of Robotics (Dynamic Modeling of a Soft Robotic Fish Driven By Dielectric E lastomer Based On the Ancf and Ib-lbm)

9-10页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Robotics is now availab le. According to news reporting originating in Nanjing, People's Republic of Chi na, by NewsRx journalists, research stated, "Dielectric elastomer (DE) is an int elligent and pliable material that exhibits significant deformation when subject ed to an electric field. It possesses attributes such as remarkable strain, exce ptional energy density, rapid responsiveness, and minimal weight, making it uniq uely advantageous in the propulsion of bionic fish." Funders for this research include National Natural Science Foundation of China ( NSFC), Fundamental Research Funds for the Central Universities.

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