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General General

Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection

ArXiv Preprint

We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action associated with a randomly generated contextual covariate. This setting finds essential applications such as personalized recommendation, online advertisement, and personalized medicine. However, it is very challenging as we need to balance exploration and exploitation. We propose doubly growing epochs and estimating the parameter using the best subset selection method, which is easy to implement in practice. This approach achieves $ \tilde{\mathcal{O}}(s\sqrt{T})$ regret with high probability, which is nearly independent in the ``ambient'' regression model dimension $d$. We further attain a sharper $\tilde{\mathcal{O}}(\sqrt{sT})$ regret by using the \textsc{SupLinUCB} framework and match the minimax lower bound of low-dimensional linear stochastic bandit problems. Finally, we conduct extensive numerical experiments to demonstrate the applicability and robustness of our algorithms empirically.

Yining Wang, Yi Chen, Ethan X. Fang, Zhaoran Wang, Runze Li


General General

Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images.

In The journal of physical chemistry. B

The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high resolution images acquired via scanning electron microscopy (SEM) or atomic force microscopy (AFM). While direct and visual, the determination has been made by directly looking at the images by researchers. Using pattern recognition, in conjunction with cellular biophysical knowledge of dendritic cells, this work reports a machine learning approach to determine the maturation status of dendritic cells automatically. The determination from AFM images reaches 100% accuracy. The results from SEM images reaches 94.9%. The results demonstrate the accuracy of using machine learning for accelerating data analysis, extracting information, and drawing conclusions from high-resolution cellular images, paving the way for future applications requiring high-throughput and automation, such as cellular sorting and selection based on morphology, quantification of cellular structure, and DC-based immunotherapy.

Lohrer Michael, Liu Yang, Hanna Darrin M, Wang Kang-Hsin, Liu Fu-Tong, Laurence Ted A, Liu Gang-Yu


Surgery Surgery

Indirect visual guided fracture reduction robot based on external markers.

In The international journal of medical robotics + computer assisted surgery : MRCAS

BACKGROUND : Traditional fracture reduction surgery cannot ensure the accuracy of the reduction while consuming the physical strength of the surgeon. Although monitoring the fracture reduction process through radiography can improve the accuracy of the reduction, it will bring radiation harm to both patients and surgeons.

METHODS : We proposed a novel fracture reduction solution that parallel robot is used for fracture reduction surgery. The binocular camera indirectly obtains the position and posture of the fragment wrapped by the tissue by measuring the posture of the external markers. According to the clinical experience of fracture reduction, a path is designed for fracture reduction. Then using position-based visual serving control the robot to fracture reduction surgery. The study is approved by the ethics committee of the Rehabilitation Hospital, National Research Center for Rehabilitation Technical Aids, Beijing, China.

RESULTS : Ten virtual cases of fracture were used for fracture reduction experiments. The simulation and model bone experiments are designed respectively. In model bone experiments, the fragments are reduced without collision. The angulation error after the reduction of this method is 3.3° ± 1.8°, and the axial rotation error is 0.8° ± 0.3°, the transverse stagger error and the axial direction error after reduction is 2 ± 0.5 mm and 2.5 ± 1 mm. After the reduction surgery, the external fixator is used to assist the fixing, and the deformity will be completely corrected.

CONCLUSIONS : The solution can perform fracture reduction surgery with certain accuracy and effectively reduce the number of radiographic uses during surgery, and the collision between fragments is avoided during surgery.

Fu Zhuoxin, Sun Hao, Dong Xinyu, Chen Jianwen, Rong Hongtao, Guo Yue, Lin Shengxin


fracture reduction, indirect visual servo, parallel robot, trajectory planning

Surgery Surgery

Three dimensional virtual surgical planning in the oncologic treatment of the mandible - Current routines and clues for optimisation.

In Oral diseases

OBJECTIVES : In case of surgical removal of oral squamous cell carcinomas, a resection of mandibular bone is frequently part of the treatment. Nowadays, such resections frequently include the application of 3D virtual surgical planning (VSP) and guided surgery techniques. In this paper current methods for 3D VSP, leads for optimisation of the workflow, and patient specific application of guides and implants are reviewed.

RECENT FINDINGS : Current methods for 3D VSP enable multi-modality fusion of images. This fusion of images is not restricted to a specific software package or workflow. New strategies for 3D VSP in Oral and Maxillofacial Surgery include finite element analysis, deep learning and advanced augmented reality techniques. These strategies aim to improve the treatment in terms of accuracy, predictability and safety.

CONCLUSIONS : Application of the discussed novel technologies and strategies will improve the accuracy and safety of mandibular resection and reconstruction planning. Accurate, easy-to-use, safe and efficient three-dimensional VSP can be applied for every patient with malignancies needing resection of the mandible.

Kraeima J, Glas H H, Merema B J, Vissink A, Spijkervet F K L, Witjes M J H


CAD/CAM, data fusion, head and neck cancer, mandible, optimisation, virtual surgical planning

General General

PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints.

In Proteins

We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptions of peptide characteristics, as well as empirical descriptors based on experimental evidence on peptide stability and interaction propensity. The PeptiDesCalculator software provides a user-friendly Graphical User Interface (GUI) and is parallelized to maximize the use of computational resources available in current work stations. The PeptiDesCalculator indices are employed in modeling 8 peptide bioactivity endpoints demonstrating satisfactory behavior. Moreover, we compare the performance of a support vector machine (SVM) classifier built using 15 PeptiDesCalculator indices with that of a recently reported deep neural network (DNN) antimicrobial activity classifier, demonstrating comparable test set performance notwithstanding the remarkably lower degree of freedom for the former. This software will facilitate the development of in silico models for the prediction of peptide properties.

Barigye Stephen J, Gómez-Ganau Sergi, Serrano-Candelas Eva, Gozalbes Rafael


PeptiDesCalculator, antimicrobial, machine learning, peptide

General General

An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study.

In Pain and therapy

INTRODUCTION : Chronic pain (CP) is a complex multidimensional experience severely affecting individuals' quality of life. Multiple cognitive, affective, emotional, and interpersonal factors play a major role in CP. Furthermore, the psychological, social, and physical circumstances leading to CP show high inter-individual variability, thus making it difficult to identify core syndrome characteristics. In a biopsychosocial perspective, we aim at identifying a pattern of psycho-physical impairments that can reliably discriminate between CP individuals and healthy controls (HC) with high accuracy and estimated generalizability using machine learning.

METHODS : A total of 118 CP and 86 HC were recruited. All individuals were administered several scales assessing quality of life, physical and mental health, personal functioning, anxiety, depression, beliefs about medical treatments, and cognitive ability. These features were trained to separate CP from HC using support vector classification and repeated nested cross-validation.

RESULTS : Our psycho-physical classifier was able to discriminate CP from HC with 86.5% balanced accuracy and significance (p = 0.0001). The most reliable features characterizing CP were anxiety and depression scores, and belief of harm from prolonged pharmacological treatments; for HP, the most reliable features were physical and occupational functioning, and vitality levels.

CONCLUSION : Our findings suggest that, using psychological and physical assessments, it is possible to classify CP from HC with high reliability and estimated generalizability via (i) a pattern of psychological symptoms and cognitive beliefs characteristic of CP, and (ii) a pattern of intact physical functioning characteristic of HC. We think that our algorithm enables novel insights into potential individualized targets for CP-related early intervention programs.

Antonucci Linda A, Taurino Alessandro, Laera Domenico, Taurisano Paolo, Losole Jolanda, Lutricuso Sara, Abbatantuono Chiara, Giglio Mariateresa, De Caro Maria Fara, Varrassi Giustino, Puntillo Filomena


Chronic pain, Cognition, Machine learning, Physical health, Psychological health