This study is Institutional Review Board exempt from the University of Pennsylvania as no actual patient data was obtained or analyzed. CT-guided percutaneous needle targeting was simulated on a phantom model (071B, CIRS, Norfolk, VA) containing multiple targets of various sizes. A CT grid (Guidelines 117, Beekley Medical, Bristol, CT) commonly used in clinical practice was placed on the anterior surface of the phantom for planning and to serve as a fiducial target for registration.

A preoperative CT scan of the phantom was performed at 120 kVp and 2 mm slice thickness on Siemens SOMATOM Force (Fig. 1). An 11 mm lesion was selected for targeting. Manual and semi-automated segmentations of the lesions, CT grid and bony structures, and skin surface were performed with ITK-SNAP using threshold masking and iterative region growing^{11}. Segmentation meshes were exported in STL file format followed by mesh decimation using Meshmixer (Autodesk, San Rafael, CA) to eliminate redundant vertices and reduce mesh size to improve 3D rendering performance. Reduced meshes were then exported in OBJ file format and material textures, including colors and transparencies, were applied using Blender (Amsterdam, Netherlands). The target lesion was colored in green; all other nontargeted lesions were colored in red. The final 3D surface-rendered model was exported in FBX file format (Fig. 2). Total model generation time was less than 45 min.

A long, out-of-plane trajectory with a narrow-window access was intentionally chosen to the 11-mm target from a skin entry site along the inferior aspect between CT gridlines 3 and 4 (Fig. 3). This trajectory angle was beyond the maximum gantry tilt for potential compensation by the CT scanner.

Holographic 3D AR visualization and interaction were performed using a HoloLens 2 headset device. A custom HoloLens application was developed in Unity 2019.2.21 and Mixed Reality Toolkit Foundation 2.3.0. Automated registration of the 3D model to CT grid was performed using computer vision and Vuforia 9.0.12 with the CT grid as the image target. Features on the CT grid can be reliably and quickly detected by Vuforia^{12}, and studies have validated the accuracy of Vuforia on HoloLens (v1)^{10,13,14,15,16,17}. Registration accuracies were not directly validated in this study; registration fidelity was confirmed visually by the operator based on complete alignment of the virtual gridlines with the physical gridlines. A virtual needle trajectory was added into the 3D model based on the ideal trajectory. This virtual guide allowed the user to easily trace the ideal trajectory using a real needle (Fig. 4).

All simulations were performed on a Siemens SOMATOM Force CT scanner at 120 kVp and 2 mm slice thickness. CT scanner operation was performed within the guidelines and regulations of the Department of Radiology at the University of Pennsylvania. After applying a surgical drape over the phantom, percutaneous CT-guided targeting using a 21G-20 cm Chiba needle was simulated in the same standard fashion performed clinically. Following a topogram, an initial CT scan of the phantom was performed and reviewed for trajectory planning. The needle was then passed into the phantom and iteratively advanced, redirected, or retracted, as many times as needed, until the tip of the needle was in the target. Interval CT scans were performed following any needle adjustment. Each adjustment was counted as a needle pass, and these passes were cumulatively documented.

A total of 8 participants simulated CT-guided needle targeting: 2 attendings, 3 interventional radiology (IR) residents, and 3 medical students. Both attendings had greater than 5 years of experience. 2 residents were in their final year of training. All 3 medical students had never previously seen nor performed a CT-guided intervention. Aside from 1 resident, all other participants had no prior experience wearing or interacting with HoloLens 2. In order to limit bias, participants were randomized into cohorts: CT-guided targeting 1) without AR and then repeated with AR or 2) with AR and then repeated without AR (Fig. 5).

Total number of needle passes were recorded. Total CT dose index (CTDIvol) and dose-length product (DLP) were obtained from the CT dose report. Procedure duration was measured from the acquisition time, or image metadata DICOM tag (0008,0032), of the CT scan following the 1^{st} needle pass to the acquisition time of the final CT scan with the needle tip in the target. Vector analysis of the CT scan after the first, initial needle pass was performed (Fig. 6). These CT scans were resampled into isotropic volumes (1 × 1 × 1 mm) using 3D Slicer 4.10.1 and linear interpolation^{18}. Voxel locations at the skin entry site, needle tip, and target centroid were recorded. Distances and angles were calculated using vector magnitude and dot product, respectively. All CT scans were reviewed to record needle passes that unintentionally punctured or traversed through a nontargeted lesion.

Vector analyses, means, paired t-tests, and F-tests were performed using Google Sheets (Mountain View, CA). Post hoc power analysis suggested a total sample size of 8 for a power of 0.8 and effect size of 1 to achieve a statistical significance level of 0.05.

IC2IT2021: The 17th International Conference on Computing and Information Technology

Overview

The 17th International Conference on Computing and Information Technology takes place at a time of rapid and unprecedented change that has long since rendered trusty benchmarks like Moore’s law irrelevant. To quote best-selling author and engineer Christopher Steiner, “algorithms have already written symphonies as moving as Beethoven, picker through legalese with the deftness of a senior lawyer and written news articles with the smooth hand of a seasoned reporter”. Indeed, it may not be long before an algorithm joins the Board of a venture capital firm (outperforming its humanoid peers, naturally). Key the many of these achievements stem from research in the areas of artificial intelligence, machine learning, natural language processing, speech recognition, and image processing among others that have already delivered solutions to the complex user and societal problems: helping architects design environmentally sustainable, structurally challenging and aesthetically interesting buildings; allowing scientists to split atoms and categorize human genomes; and enabling engineers to 3D print autonomous cars to name but a few. Such advances mirror changing economies, increasingly dominated by globalization and competition where constant high-speed innovation is now the only strategy that matters.

Conference topics included (but not limited to):

Architecture and Application

Business Intelligence

Decision Support

Evolutinary Computation

Hybrid Systems

Knowledge Discovery

Knowledge Management

Knowledge Transfer

Ontology and Semantic

Web Optimization

Swarm Intelligence

Particle Swarm Optimization

Recommender System

Data Science and Machine Learning

Affective Computing

Artificial Neural Network

Behavior Analytics

Big Data Analysis

Community Analysis

Computational Intelligence

Data Mining

Deep Learning

Feature Selection

Fuzzy Systems

Geographic/Spatial Data Mining

User Behavior Prediction

Image and Video Processing

Biometric Quality

Computer Vision

Image and Video Forensic

Image Enhancement

Image Compression

Image Processing

Image Recognition/Classification

Human-Computer Interface

Multiple Object Tracking

Virtual Reality

Natural Language Processing and Text Mining

Computational Linguistics

Information Retrieval

Natural Language Processing

Natural Language Understanding

Question Answering

Semantic Mining

Social Network Mining

Text Mining

Topic Segmentation & Recognition

Web Mining

Network and Security

Ad Hoc Networks

Content Delivery

Cloud and Grid Computing

P2P Networks and Protocols

Real Time Streaming Networks

Security and Forensic

Self-Organization and Emergence in Computer

Networks

Sensor Networks

Wired and Wireless Networks

All accepted papers will be published by Springer in the Recent Advances in Information and Communication Technology 2021, which is in the Advances in Intelligent Systems and Computing series, ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **.

Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent’s location within it. SLAM algorithms are widely and successfully used in navigation, robotic mapping, environment reconstruction, and odometry for virtual reality or augmented reality.

SLAM algorithms are tailored to the available resources, and hence not aimed at perfection but at operational compliance, and thus, they often require various optimizations. Recent advances in deep learning, mobile technology, 5G communications, and sensors are continually changing and pushing the limits of traditional SLAM algorithms so that SLAM becomes more accurate, fast, and even intelligent. The focus of this Special Issue of the ETRI Journal is to publish recent outstanding results in the rapidly progressing subject of SLAM and its applications. The topics of particular interest include but are not limited to:

• Visual SLAM

• Sensor fusion for SLAM

• AI/Deep learning for SLAM

• Semantic SLAM

• SLAM-based reconstruction

• SLAM for dynamic objects and environments

• 2D/3D mapping and data association

• Relocalization, loop closure, and realignment techniques

• Performance optimization for SLAM

• Cloud-based SLAM

• SLAM-based applications (self-driving cars, drones, autonomous robots, augmented reality, and virtual reality)

Important Dates (tentative)

Paper submission due: February 21, 2021

First decision: April 19, 2021

Revised submission due: May 25, 2021

Final decision: June 1, 2021

Final paper due: June 6, 2021

Tentative publication date: Aug 10, 2021

Paper Submission

Papers should be submitted at https://ift.tt/2HScjKx and should adhere to the journal’s Author Guidelines.

Section Editor

Jounghyun Gerard Kim, Korea University, Rep. of Korea, gjkim@korea.ac.kr

Guest Editors

Jong-Il Park, Hanyang University, Rep. of Korea, jipark@hanyang.ac.kr¬¬

Soon-Yong Park, Kyungpook National University, Rep. of Korea, sypark@knu.ac.kr

Junho Kim, Kookmin University, Rep. of Korea, junho@kookmin.ac.kr

Sejin Lee, Kongju National University, Rep. of Korea, sejiny3@kongju.ac.kr

Seonghun Hong, Keimyung University, Rep. of Korea, sh.hong@kmu.ac.kr

The Editorial Office can be contacted at etrij@etri.re.kr.

We present exact bright, dark and rogue soliton solutions of generalized

higherorder nonlinear Schrodinger equation, describing the ultrashort beam

propagation in tapered waveguide amplifier, via a similarity transformation

connected with the constant-coefficient Sasa-Satsuma and Hirota equations. Our

exact analysis takes recourse to identify the allowed tapering profile in

conjunction with appropriate gain function which corresponds to PT-symmetric

waveguide. We extend our analysis to study the effect of tapering profiles and

higher-order terms on the evolution of self-similar waves and thus enabling one

to control the self-similar wave structure and dynamical behavior.

The service life as hard tissue implantation for clinical application needs compatible mechanical

properties, e.g. strength, modulus, etc, and certain self-healing in case of internal infection.

Therefore, for sake of improving the properties of Ti-Cu alloy, the microstructure, mechanical

properties, corrosion resistance and antibacterial properties of Ti- x Cu alloy ( x = 2, 5, 7 and 10

wt.%) prepared by Ar-arc melting followed by heat treatment were studied. The results show that the

Ti-Cu alloy was mainly composed of α -Ti matrix and precipitated Ti 2 Cu phase. The Cu element

mainly accumulates in the lamellar structure and forms the precipitated Ti 2 Cu phase. As the

increase of Cu content, the lamellar Ti 2 Cu phase increases, the compressive strength and elastic

modulus also were altered. The Ti-7Cu alloy exhibited the higher compressive strength (2169 MPa) and

the lower elastic modulus (108 GPa) compared with other Ti-Cu …

We propose a feasible waveguide design optimized for harnessing Stimulated

Brillouin Scattering with long-lived phonons. The design consists of a fully

suspended ridge waveguide surrounded by a 1D phononic crystal that mitigates

losses to the substrate while providing the needed homogeneity for the build-up

of the optomechanical interaction. The coupling factor of these structures was

calculated to be 0.54 (W.m)$^{-1}$ for intramodal backward Brillouin scattering

with its fundamental TE-like mode and 4.5(W.m)$^{-1}$ for intramodal forward

Brillouin scattering. The addition of the phononic crystal provides a 30 dB

attenuation of the mechanical displacement after only five unitary cells,

possibly leading to a regime where the acoustic losses are only limited by

fabrication. As a result, the total Brillouin gain, which is proportional to

the product of the coupling and acoustic quality factors, is nominally equal to

the idealized fully suspended waveguide.

In this paper, we present a hybrid position/force controller for operating

joint robots. The hybrid controller has two goals—motion tracking and force

regulating. As long as these two goals are not mutually exclusive, they can be

decoupled in some way. In this work, we make use of the smooth and invertible

mapping from joint space to task space to decouple the two control goals and

design controllers separately. The traditional motion controller in task space

is used for motion control, while the force controller is designed through

manipulating the desired trajectory to regulate the force indirectly. Two case

studies—contour tracking/polishing surfaces and grabbing boxes with two

robotic arms—are presented to show the efficacy of the hybrid controller, and

simulations with physics engines are carried out to validate the efficacy of

the proposed method.

We introduce a new dataset for training and evaluating grounded language

models. Our data is collected within a virtual reality environment and is

designed to emulate the quality of language data to which a pre-verbal child is

likely to have access: That is, naturalistic, spontaneous speech paired with

richly grounded visuospatial context. We use the collected data to compare

several distributional semantics models for verb learning. We evaluate neural

models based on 2D (pixel) features as well as feature-engineered models based

on 3D (symbolic, spatial) features, and show that neither modeling approach

achieves satisfactory performance. Our results are consistent with evidence

from child language acquisition that emphasizes the difficulty of learning

verbs from naive distributional data. We discuss avenues for future work on

cognitively-inspired grounded language learning, and release our corpus with

the intent of facilitating research on the topic.

Thanks to virtual reality, we can run experiments that test what people will do in situations where lives are on the line. We often find people act against what they claim to regard as morally acceptable, says **Sylvia Terbeck**

Humans

| Comment

28 October 2020

YOU probably aren’t as moral as you think. Philosophers have often asked people how they would act in a given situation when lives are on the line, but it is hard to test what they would do in practice. Now, thanks to virtual reality, we are starting to find out – and what people say doesn’t match up with what they do.

There are many thought experiments and dilemmas for breaking down ethical decisions, and perhaps none is more famous than the trolley problem. The scenario begins with a runaway trolley that is on course to …

To rationalize these observations, we focus on studying harmonic propagation, with a given angular frequency \(\omega =2\pi f\), of surface water waves in the linear inviscid case. In the open wave tank, surface gravity wave motion is encoded in the velocity potential

$$\begin{aligned} \Phi (x,y,z,t)= \text {Re}\left[ \phi (x,y)\cosh {(k(z+h))} e^{-i\omega t}\right] , \end{aligned}$$

(1)

where *z* is the vertical coordinate normal to the bottom of the wave tank, as sketched in Fig. 1a. Here \(\phi (x,y)\) is the solution of the Helmholtz equation

$$\begin{aligned} (\nabla ^{2}+k^{2})\phi (x,y)=0\ , \end{aligned}$$

(2)

where \(\nabla\) is the bidimensional gradient and *k* is the wave vector chosen from the dispersion relation for surface gravity waves

$$\begin{aligned} \omega (k)=\sqrt{gk\tanh {(k h)}}\ . \end{aligned}$$

(3)

Deformations of the interface \(\eta (x,y,t)\) from the flat surface at \(z=0\) are then found by the relation

$$\begin{aligned} \eta (x,y,t)= \text {Re}\left[ -\frac{i\omega }{g}\phi (x,y) e^{-i\omega t}\right] \ . \end{aligned}$$

In our experiments, the local depth of the fluid changes from \(h=h_0\) to \(h=h_0-h_g\) over the waveguide, which changes the wave vector from \(k=k_0\) to \(k=k_g\). Thus, the ansatz (1) does not fulfill the boundary conditions where depth discontinuities develop, and the complete boundary value problem must be solved in order to find the observed waveguided modes. However, this issue can be sorted out if one deals with surface gravity waves in the shallow water limit. In this limit \(k h\ll 1\), Eq. (3) becomes \(k=\omega /\sqrt{gh}\) and the problem reduces to non-dispersive waves with different wave speeds inside (\(\sqrt{g(h_0-h_g)}\)) and outside (\(\sqrt{gh_0}\)) the waveguide region^{18}. Assuming that the wave is guided in the *x*-direction while propagating along the *y*-axis, then \(\phi (x,y)=\tilde{\phi }(x)e^{i\beta y}\), and thus Eq. (2) turns to

$$\begin{aligned} \left[ \partial _{x}^{2}+(k_0^{2}-\beta ^{2})\right] \tilde{\phi }(x)=0\ , \end{aligned}$$

(4)

outside the waveguide region, and

$$\begin{aligned} \left[ \partial _{x}^{2}+(k_g^{2}-\beta ^{2})\right] \tilde{\phi }(x)=0\ , \end{aligned}$$

(5)

inside. Here \(\tilde{\phi }(x)\) represents the transversal profile of the guided mode and \(\beta\) corresponds to its propagation constant. \(\tilde{\phi }(x)\) satisfies Eqs. (4) and (5) with \(\tilde{\phi }\) and \(\partial _x\tilde{\phi }\) continuous at the edges of the waveguide region. From these equations, we find symmetric modal solutions for \(\tilde{\phi }(x)\) when

$$\begin{aligned} \sqrt{\frac{\beta _s^{2}-k_0^{2}}{k_g^{2}-\beta _s^{2}}}=\tan {\left[ \sqrt{\left( k_g^{2}-\beta _s^{2}\right) }\ \frac{a}{2}\right] } \end{aligned}$$

(6)

is satisfied for a propagation constant \(\beta =\beta _s\), where “s” stands for “symmetric”. For asymmetric modes, this relation is similar but changing “\(\tan\)” for “\(-\cot\)” and \(\beta _s\) for \(\beta _a\). These relations allow us to find the possible mode propagation constants \(\beta =\{ \beta _s,\beta _a\}\) in the wave vector band \(k_0<\beta <k_g\). The relation above is completely analog to what is found in optical waveguides^{4,5} where waveguiding occurs in regions with a larger refractive index as the light velocity becomes smaller. Optical guided modes have a \(\cos\)-like profile inside the waveguide region and an exponentially decaying wave function (evanescent field) outside of it. Therefore, our water waveguide is completely equivalent to an optical waveguide, specifically to the case having a one dimensional step-like refractive index profile, a concept used in different physical contexts^{2,37}.

When shallow water theory can not be used, one needs to solve the above problem including the condition of zero normal derivative along the entire bottom surface for \(\Phi\)^{33,34,35}. In this case, a more complex relation between the waveguide parameters is found. We have computed \(\beta\) using shallow water theory and the complete shelf model from Miles^{35}, applied to our problem (see “Methods” section). In the case of narrow waveguides (\(a=2.0\,\hbox {cm}\)), only symmetric modes are excited. For wider waveguides (\(a=10.0\,\hbox {cm}\)) and \(f > 3.0\,\hbox {Hz}\) (\(\omega > 6\pi \,\hbox {rad/s}\)), asymmetric modes can be excited as well. This information is compiled in Tables 1 and 2, where for narrow waveguides we observe only symmetric states, while for wider ones we observe the appearance of asymmetric states above \(f > 3.0\,\hbox {Hz}\). In Fig. 4a we show the experimental \(\beta\) values as a function of \(\omega\), for \(h_g=1.5\,\hbox {cm}\) and for 4 different waveguide widths. We also show the dispersion relation (3) inside (continuous line) and outside (dotted line) the waveguide region, which sets the wave vector band \(k_0<\beta <k_g\). We observe an excellent agreement between the experimentally measured \(\beta\) values (symbols) and the numerical calculation of Eq. (6), for narrow waveguides (see dashed line in Fig. 4a). This case is simpler due to the absence of higher-order modes in the measured frequency range, as shown in Table 1. For wider waveguides, we observe a mismatch between the numerically computed \(\beta\) values (shown in Table 2) and the experimental ones for \(\omega > 21.99\,\hbox {rad/s}\) [\(f = 3.5\,\hbox {Hz}\)]. This can be the result of the development of asymmetric as well as symmetric modes, propagating along the waveguide region. In addition, Fig. 4b shows a comparison between the symmetric mode profiles obtained numerically and experimentally for a narrow waveguide (\(a= 2.5\,\hbox {cm}\)).

Following the equivalence between shallow water and optics, we compute the evolution of the guided water wave by implementing a beam propagation method (BPM) which solves numerically Eq. (2). This method, widely used in optics, allows us to track the envelope evolution along the waveguide. We include dissipation effects by multiplying directly the numerically obtained wave field on each spatial position with a damping exponential factor \(\exp {(-\alpha y/\lambda )}\). In Fig. 4c we show a numerical example, including an exponentially decaying factor due to dissipation, and compare it to its experimental counterpart in Fig. 4d. The numerical result is completely symmetric in the *y*-direction, due to the absence of inhomogeneities while the experimental image is slightly asymmetric due to different background waves propagating through the open tank. Both figures display an excellent agreement, which corroborates water waveguiding in an open fluid layer.

Finally, following Eq. (3) and considering our experimental configuration for \(\omega > 31.42\,\hbox {rad/s}\) (\(f = 5\,\hbox {Hz}\)), we get \(k_0=k_g\). Therefore, as we enter the deep water limit outside and inside the waveguide, waveguiding becomes forbidden. The absence of an effective velocity contrast does not allow the excitation of guided modes. This is in agreement with the experimentally obtained data presented in Fig. 4a, where the existence region shrinks for \(f> 5\,\hbox {Hz}\).