Rotational Cryptanalysis of MORUS

MORUS is one of the finalists of the CAESAR competition. This is an ARX construction that required investigation against rotational cryptanalysis. We investigated the power of rotational cryptanalysis against MORUS. We show that all the operations in the state update function of MORUS maintain the rotational pairs when the rotation distance is set to a multiple of the sub-word size. Our investigation also confirms that the rotational pairs can be used as distinguishers for the full version of MORUS if the constants used in MORUS are rotational-invariant. However, the actual constants used in MORUS are not rotational-invariant. The introduction of such constants in the state update function breaks the symmetry of the rotational pairs. Experimental results show that rotational pairs can be used as distinguishers for only one step of the initialization phase of MORUS. For more than one step, there are not enough known differences in the rotational pairs of MORUS to provide an effective distinguisher. This is due to the XOR-ing of the constants that are not rotational-invariant. Therefore, it is unlikely for an adversary to construct a distinguisher for the full version of MORUS by observing the rotational pairs.

Rotational-invariant speckle-scanning ultrasonography through thick bones

Ultrasonography is a major medical imaging technique that has been broadly applied in many disease diagnoses. However, due to strong aberration and scattering in the human skull, high-resolution transcranial ultrasonic imaging remains a grand challenge. Here, we explore the rotational-invariant property of ultrasonic speckle and develop high-resolution speckle-scanning ultrasonography to image sub-millimeter-sized features through thick bones. We experimentally validate the rotational invariance of ultrasonic speckle. Based on this property, we scan a random ultrasonic speckle pattern across an object sandwiched between two thick bones so that the object features can be encoded to the ultrasonic waves. After receiving the transmitted ultrasonic waves, we reconstruct the image of the object using an iterative phase retrieval algorithm. We successfully demonstrate imaging of hole and tube features sized as fine as several hundreds of microns between two 0.5 ~ 1-cm-thick bones. With 2.5-MHz excitation and the third-harmonic detection, we measure the spatial resolution as 352 µm. Rotational-invariant speckle-scanning ultrasonography offers a new approach to image through thick bones and paves an avenue towards high-resolution ultrasonic imaging of the human brain.

A Scale and Rotational Invariant Key-point Detector based on Sparse Coding

Most popular hand-crafted key-point detectors such as Harris corner, SIFT, SURF aim to detect corners, blobs, junctions, or other human-defined structures in images. Though being robust with some geometric transformations, unintended scenarios or non-uniform lighting variations could significantly degrade their performance. Hence, a new detector that is flexible with context change and simultaneously robust with both geometric and non-uniform illumination variations is very desirable. In this article, we propose a solution to this challenging problem by incorporating Scale and Rotation Invariant design (named SRI-SCK) into a recently developed Sparse Coding based Key-point detector (SCK). The SCK detector is flexible in different scenarios and fully invariant to affine intensity change, yet it is not designed to handle images with drastic scale and rotation changes. In SRI-SCK, the scale invariance is implemented with an image pyramid technique, while the rotation invariance is realized by combining multiple rotated versions of the dictionary used in the sparse coding step of SCK. Techniques for calculation of key-points’ characteristic scales and their sub-pixel accuracy positions are also proposed. Experimental results on three public datasets demonstrate that significantly high repeatability and matching score are achieved.

New ideas for handling of loop and angular integrals in D-dimensions in QCD

We discuss new ideas for consideration of loop diagrams and angular integrals in D-dimensions in QCD. In case of loop diagrams, we propose the covariant formalism of expansion of tensorial loop integrals into the orthogonal basis of linear combinations of external momenta. It gives a very simple representation for the final results and is more convenient for calculations on computer algebra systems. In case of angular integrals we demonstrate how to simplify the integration of differential cross sections over polar angles. Also we derive the recursion relations, which allow to reduce all occurring angular integrals to a short set of basic scalar integrals. All order ε-expansion is given for all angular integrals with up to two denominators based on the expansion of the basic integrals and using recursion relations. A geometric picture for partial fractioning is developed which provides a new rotational invariant algorithm to reduce the number of denominators.

Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine

Re-operations and revisions are often performed in patients who have undergone total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RTSA). This necessitates an accurate recognition of the implant model and manufacturer to set the correct apparatus and procedure according to the patient’s anatomy as personalized medicine. Owing to unavailability and ambiguity in the medical data of a patient, expert surgeons identify the implants through a visual comparison of X-ray images. False steps cause heedlessness, morbidity, extra monetary weight, and a waste of time. Despite significant advancements in pattern recognition and deep learning in the medical field, extremely limited research has been conducted on classifying shoulder implants. To overcome these problems, we propose a robust deep learning-based framework comprised of an ensemble of convolutional neural networks (CNNs) to classify shoulder implants in X-ray images of different patients. Through our rotational invariant augmentation, the size of the training dataset is increased 36-fold. The modified ResNet and DenseNet are then combined deeply to form a dense residual ensemble-network (DRE-Net). To evaluate DRE-Net, experiments were executed on a 10-fold cross-validation on the openly available shoulder implant X-ray dataset. The experimental results showed that DRE-Net achieved an accuracy, F1-score, precision, and recall of 85.92%, 84.69%, 85.33%, and 84.11%, respectively, which were higher than those of the state-of-the-art methods. Moreover, we confirmed the generalization capability of our network by testing it in an open-world configuration, and the effectiveness of rotational invariant augmentation.

Histological classification of non-small cell lung cancer using Gabor filtering Minkowski functionals and Neighborhood Component Analysis on CT images.

Information regarding the histological types of non-small cell lung cancer is essential to determine the treatment strategy. Although several radiomics studies using almost similar feature variables were reported, a considerable variation in the performances has been observed. In this study, as novel radiomic features, 2D Gabor filtering Minkowski functionals were used. They were calculated in rotational invariant and both scale and rotational invariant ways using circular shift operations of Gabor filters on nonenhanced computed tomographic images. Eighty-six patients (47 adenocarcinomas, 39 squamous cell carcinomas) were analyzed. Two independent observers manually delineated a single slice segmentation of a tumor. Feature selection was made by neighborhood component analysis. Among various classifiers, 1-nearest neighbor gave a promising performance. The observer-averaged accuracy of rotational invariant analysis was 86.28% and that of both scale and rotational invariant one was 88.27%. However, there was no common feature among the ten top-ranked features of each observer with the identical Gabor filtering type. Hence further study of the robustness is necessary to create a more reliable model.