Two Simple Algorithms For Displaying Orthographic Projections of Surfaces

Work reported herein was conducted at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research Contract number N00014-75C-0643Two simple algorithms are described for displaying orthographic projections of surfaces. The first, called RELIEF-PLOT, produces a three-dimensional plot of a surface z = f(x,y). The second, called SHADED-IMAGE, adds information about surface reflectivity and source illumination to produce a grey level image of a surface z = f(x,y). Both algorithms demonstrate how a systematic profile expansion can be used to do hidden surface elimination essentially for free.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc

Looking in the Shadows

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643.The registration of an image with a model of the surface being imaged is an important prerequisite to many image understanding tasks. Once registration is achieved, new image analysis techniques can be explored. One approach is to compare the real image with an image synthesized from the surface model. But, accurate comparison requires and accurate synthetic image. More realistic synthetic images can be obtained once shadow information is included. Accurate shadow regions can be determined when a hidden-surface algorithm is applied to the surface model in order to calculate which surface elements can be seen from the light source. We illustrate this technique using LANDSAT imagery registered with digital terrain models. Once shadow information is included, the effect of sky illumination and atmospheric haze can be measured.MIT Artificial Intelligence Laborator

The TRACK Program Package

Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0005. Vision Flashes are informal papers intended for internal use.A collection of LISP functions has been written to provide vidisector users with the following three line-oriented vision primitives: (i) given an initial point and an estimated initial direction, track a line in that direction until the line terminates. (ii) given two points, verify the existence of a line joining those two points. (iii) given the location of a vertex, find suspect directions for possible lines emanating from that vertex.MIT Artificial Intelligence Laboratory Robotics Section Department of Defense Advanced Research Projects Agenc

Loss of strumpellin in the melanocytic lineage impairs the WASH Complex but does not affect coat colour

The five-subunit WASH complex generates actin networks that participate in endocytic trafficking, migration and invasion in various cell types. Loss of one of the two subunits WASH or strumpellin in mice is lethal, but little is known about their role in mammals in vivo. We explored the role of strumpellin, which has previously been linked to hereditary spastic paraplegia, in the mouse melanocytic lineage. Strumpellin knockout in melanocytes revealed abnormal endocytic vesicle morphology but no impairment of migration in vitro or in vivo and no change in coat colour. Unexpectedly, WASH and filamentous actin could still localize to vesicles in the absence of strumpellin, although the shape and size of vesicles was altered. Blue native PAGE revealed the presence of two distinct WASH complexes, even in strumpellin knockout cells, revealing that the WASH complex can assemble and localize to endocytic compartments in cells in the absence of strumpellin

Parallel-propagating Fluctuations at Proton-kinetic Scales in the Solar Wind are Dominated by Kinetic Instabilities

We use magnetic helicity to characterise solar wind fluctuations at proton-kinetic scales from Wind observations. For the first time, we separate the contributions to helicity from fluctuations propagating at angles quasi-parallel and oblique to the local mean magnetic field, $\mathbf{B}_0$. We find that the helicity of quasi-parallel fluctuations is consistent with Alfv\'en-ion cyclotron and fast magnetosonic-whistler modes driven by proton temperature anisotropy instabilities and the presence of a relative drift between $\alpha$-particles and protons. We also find that the helicity of oblique fluctuations has little dependence on proton temperature anisotropy and is consistent with fluctuations from the anisotropic turbulent cascade. Our results show that parallel-propagating fluctuations at proton-kinetic scales in the solar wind are dominated by proton temperature anisotropy instabilities and not the turbulent cascade. We also provide evidence that the behaviour of fluctuations at these scales is independent of the origin and macroscopic properties of the solar wind.Comment: Accepted for publication in ApJL. 6 Pages, 3 figures, 1 tabl

COPe-support - a multi-component digital intervention for family carers for people affected by psychosis: study protocol for a randomized controlled trial.

BACKGROUND: Psychosis often causes significant distress and impacts not only in the individuals, but also those close to them. Many relatives and friends ('carers') provide long-term support and need resources to assist them. We have co-produced a digital mental health intervention called COPe-support (Carers fOr People with Psychosis e-support) to provide carers with flexible access to high quality psychoeducation and interactive support from experts and peers. This study evaluates the effectiveness of COPe-support to promote mental wellbeing and caregiving experiences in carers. METHODS: This study is a single-blind, parallel arm, individually randomized controlled trial (RCT) comparing COPe-support, with attention control. Both groups continue to receive usual care. COPe-support provides interactive web-based psychoeducation on psychosis-related issues, wellbeing-promotion and network support through forums. The attention-control is a non-interactive online information resource pack. Carers living in England are eligible if they provide at least weekly support to a family member or close friend affected by psychosis, and use internet communication (including emails) daily. All trial procedures are run online, including collection of outcome measurements which participants will directly input into our secure platform. Following baseline assessment, a web-based randomization system will be used to allocate 360 carers to either arm. Participants have unlimited access to the allocated condition for 40 weeks. Data collection is at three time points (10, 20, and 40 weeks after randomization). Analyses will be conducted by trial statisticians blinded to allocation. The primary outcome is mental wellbeing measured by Warwick Edinburgh Mental Wellbeing Scale (WEMWBS), at 20 weeks. As well as an intention-to-treat analysis, a complier average causal effect (CACE) analysis will be conducted to estimate the intervention effect in participants who have accessed COPe-support content twice or more. The secondary objectives and analysis will examine other health and caregiving-related outcomes and explore mechanisms. In a process evaluation, we will interview 20% of the intervention arm participants regarding the acceptability of COPe-support. We will explore in detail participants' usage patterns. DISCUSSION: The results of this trial will provide valuable information about the effectiveness of COPe-support in promoting wellbeing and caregiving experiences in carers. TRIAL REGISTRATION: The RCT is registered with the Current Controlled Trials registration (ISRCTN 89563420, registration date: 02/03/2018)

A Program to Output Stored Pictures

Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0003. Vision flashes are informal papers intended for internal use.A program called LPTSEE has been written for use with the MIT vision system. LPTSEE makes use of the overprint capability of the line printer to allow the user to output a stored picture image.MIT Artificial Intelligence Laboratory Robotics Section Department of Defense Advanced Research Projects Agenc

Visual Feedback in a Coordinated Hand-Eye System

This paper reproduces a thesis proposal of the same title submitted to the Dept. of Electrical Engineering for the degree of Master of Science. Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0003. Vision flashes are informal papers intended for internal use.A system is proposed for the development of new techniques for the control and monitoring of a mechanical arm-hand. The use of visual feedback is seen to provide new interactive capabilities in a machine hand-eye system. The proposed system explores the use of visual feedback in such operations as the pouring and stirring of liquids, the location of objects for grasping, and the simple rote learning of new arm motions.MIT Artificial Intelligence Laboratory Robotics Section Department of Defense Advanced Research Projects Agenc

Automating the assessment of biofouling in images using expert agreement as a gold standard

Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because it increases fuel costs and presents a biosecurity risk by providing a pathway for non-indigenous marine species to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply deep learning to automate the classification of images from in-water inspections to identify the presence and severity of fouling. We combined several datasets to obtain over 10,000 images collected from in-water surveys which were annotated by a group biofouling experts. We compared the annotations from three experts on a 120-sample subset of these images, and found that they showed 89% agreement (95% CI: 87-92%). Subsequent labelling of the whole dataset by one of these experts achieved similar levels of agreement with this group of experts, which we defined as performing at most 5% worse (p=0.009-0.054). Using these expert labels, we were able to train a deep learning model that also agreed similarly with the group of experts (p=0.001-0.014), demonstrating that automated analysis of biofouling in images is feasible and effective using this method.Comment: 12 page