An Open-Source 7-Axis, Robotic Platform to Enable Dexterous Procedures within CT Scanners

This paper describes the design, manufacture, and performance of a highly dexterous, low-profile, 7 Degree-of-Freedom (DOF) robotic arm for CT-guided percutaneous needle biopsy. Direct CT guidance allows physicians to localize tumours quickly; however, needle insertion is still performed by hand. This system is mounted to a fully active gantry superior to the patient's head and teleoperated by a radiologist. Unlike other similar robots, this robot's fully serial-link approach uses a unique combination of belt and cable drives for high-transparency and minimal-backlash, allowing for an expansive working area and numerous approach angles to targets all while maintaining a small in-bore cross-section of less than $16cm^2$. Simulations verified the system's expansive collision free work-space and ability to hit targets across the entire chest, as required for lung cancer biopsy. Targeting error is on average $<1mm$ on a teleoperated accuracy task, illustrating the system's sufficient accuracy to perform biopsy procedures. The system is designed for lung biopsies due to the large working volume that is required for reaching peripheral lung lesions, though, with its large working volume and small in-bore cross-sectional area, the robotic system is effectively a general-purpose CT-compatible manipulation device for percutaneous procedures. Finally, with the considerable development time undertaken in designing a precise and flexible-use system and with the desire to reduce the burden of other researchers in developing algorithms for image-guided surgery, this system provides open-access, and to the best of our knowledge, is the first open-hardware image-guided biopsy robot of its kind.Comment: 8 pages, 9 figures, final submission to IROS 201

Topological Excitations in Spinor Bose-Einstein Condensates

We investigate the properties of skyrmion in the ferromagnetic state of spin-1 Bose-Einstein condensates by means of the mean-field theory and show that the size of skyrmion is fixed to the order of the healing length. It is shown that the interaction between two skyrmions with oppositely rotating spin textures is attractive when their separation is large, following a unique power-law behavior with a power of -7/2.Comment: 4 pages, 5 figure

Josephson Current between Triplet and Singlet Superconductors

The Josephson effect between triplet and singlet superconductors is studied. Josephson current can flow between triplet and singlet superconductors due to the spin-orbit coupling in the spin-triplet superconductor but it is finite only when triplet superconductor has $L_z=-S_z=\pm 1$, where $L_z$ and $S_z$ are the perpendicular components of orbital angular momentum and spin angular momentum of the triplet Cooper pairs, respectively. The recently observed temperature and orientational dependence of the critical current through a Josephson junction between UPt$_3$ and Nb is investigated by considering a non-unitary triplet state.Comment: 4 pages, no figure

Block CUR: Decomposing Matrices using Groups of Columns

A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individual rows or columns of the matrix is limited by either system constraints or cost. In this paper, we consider matrix approximation by sampling predefined \emph{blocks} of columns (or rows) from the matrix. We present an algorithm for sampling useful column blocks and provide novel guarantees for the quality of the approximation. This algorithm has application in problems as diverse as biometric data analysis to distributed computing. We demonstrate the effectiveness of the proposed algorithms for computing the Block CUR decomposition of large matrices in a distributed setting with multiple nodes in a compute cluster, where such blocks correspond to columns (or rows) of the matrix stored on the same node, which can be retrieved with much less overhead than retrieving individual columns stored across different nodes. In the biometric setting, the rows correspond to different users and columns correspond to users' biometric reaction to external stimuli, {\em e.g.,}~watching video content, at a particular time instant. There is significant cost in acquiring each user's reaction to lengthy content so we sample a few important scenes to approximate the biometric response. An individual time sample in this use case cannot be queried in isolation due to the lack of context that caused that biometric reaction. Instead, collections of time segments ({\em i.e.,} blocks) must be presented to the user. The practical application of these algorithms is shown via experimental results using real-world user biometric data from a content testing environment.Comment: shorter version to appear in ECML-PKDD 201

Fracture Energy Measurement in Different Concrete Grades

Fracture energy is regarded as an intrinsic (material) property to dominate crack mechanisms and associated crack growth to concrete damage under applied stress. More recently, huge evolution in computing technology leading to finite element analysis (FEA) approaches to require incorporation of constitutive model, such as traction-separation relationship derived from state-of-the-art fracture mechanics fundamental. A physically-based models requires fracture energy values; therefore, properly measured fracture energy value is essential to exhibit better structures response within FEA models. Large arrays of parameters involved during concrete mixture such as beam size effect, aggregate size and concrete grade to affect the flexural resistance in concrete. The fracture and failure in concrete ahead of crack tip is represented by fracture energy values where micro-damage events occurred such as interfacial failure, fiber-bridging and matrix cracking. This study aims to investigate the fracture energy of concrete specimens with combination of notch depth ao at mid-span, design concrete strength as specified in the testing series. Independent compression strength, fc and measured load-displacement profiles under three-points bending test were used to determine fracture energy by incorporating three available fracture energy expressions such as Bazant, Hillerborg and CEB-FIP models

Fracture Energy Measurement in Different Concrete Grades

Fracture energy is regarded as an intrinsic (material) property to dominate crack mechanisms and associated crack growth to concrete damage under applied stress. More recently, huge evolution in computing technology leading to finite element analysis (FEA) approaches to require incorporation of constitutive model, such as traction-separation relationship derived from state-of-the-art fracture mechanics fundamental. A physically-based models requires fracture energy values; therefore, properly measured fracture energy value is essential to exhibit better structures response within FEA models. Large arrays of parameters involved during concrete mixture such as beam size effect, aggregate size and concrete grade to affect the flexural resistance in concrete. The fracture and failure in concrete ahead of crack tip is represented by fracture energy values where micro-damage events occurred such as interfacial failure, fiber-bridging and matrix cracking. This study aims to investigate the fracture energy of concrete specimens with combination of notch depth ao at mid-span, design concrete strength as specified in the testing series. Independent compression strength, fc and measured load-displacement profiles under three-points bending test were used to determine fracture energy by incorporating three available fracture energy expressions such as Bazant, Hillerborg and CEB-FIP models

Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values

In high-stakes applications of data-driven decision-making such as healthcare, it is of paramount importance to learn a policy that maximizes the reward while avoiding potentially dangerous actions when there is uncertainty. There are two main challenges usually associated with this problem. First, learning through online exploration is not possible due to the critical nature of such applications. Therefore, we need to resort to observational datasets with no counterfactuals. Second, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features. In this article, we consider the problem of constructing personalized policies using logged data when there are missing values in the attributes of features in both training and test data. The goal is to recommend an action (treatment) when ~X, a degraded version of Xwith missing values, is observed. We consider three strategies for dealing with missingness. In particular, we introduce the conservative strategy where the policy is designed to safely handle the uncertainty due to missingness. In order to implement this strategy, we need to estimate posterior distribution p(X|~X) and use a variational autoencoder to achieve this. In particular, our method is based on partial variational autoencoders (PVAEs) that are designed to capture the underlying structure of features with missing values

Magnetic Field Effect on the Supercurrent of an SNS junction

In this paper we study the effect of a Zeeman field on the supercurrent of a mesoscopic SNS junction. It is shown that the supercurrent suppression is due to a redistribution of current-carrying states in energy space. A dramatic consequence is that (part of the) the suppressed supercurrent can be recovered with a suitable non-equilibrium distribution of quasiparticles.Comment: 4 figures in postscrip