Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

MR-Based PET Attenuation Correction for PET/MR Imaging

This paper addressesthe problemsof automatically planning AutonomousUnderwater Vehicle (AUV)pathswhichbestexploit complex current data, from computational estuarine modelforecasts, whilealsoavoiding obstacles. Inparticular weexamine thepossibilities foranovel typeofAUV mission deployment infastflowing tidal river regions whichexperience bi-directional current flow. These environments are interesting in that,by choosing an appropriate pathinspaceandtime, anAUV maybothbypass adverse currents whicharetoofasttobeovercome bythe vehicle's motorsandalsoexploit favorable currents toachieve fargreater speeds thanthemotorscouldotherwise provide, while substantially saving energy. TheAUV can"ride" currents bothupanddowntheriver, enabling extended monitoring of otherwise energy-exhausting, fastflowenvironments. The paperdiscusses suitable pathparameterizations, costfunctions andoptimization techniques whichenable optimal AUV paths to be efficiently generated. Thesepathstakemaximum advantage oftheriver currents inordertominimize energy expenditure, journey timeandothercostparameters. The resulting pathplannercan automatically suggest useful alternative mission start andendtimes andlocations tothose specified bytheuser. Examples arepresented fornavigation in a simple simulation ofthefastflowing HudsonRiverwaters around Manhattan.

Optimal AUV pathplanning forextended missions incomplex, fast-flowing estuarine environments.

Attenuation correction is an essential component of the long chain of data correction techniques required to achieve the full potential of quantitative positron emission tomography (PET) imaging. The development of combined PET/magnetic resonance imaging (MRI) systems mandated the widespread interest in developing novel strategies for deriving accurate attenuation maps with the aim to improve the quantitative accuracy of these emerging hybrid imaging systems. The attenuation map in PET/MRI should ideally be derived from anatomical MR images; however, MRI intensities reflect proton density and relaxation time properties of biological tissues rather than their electron density and photon attenuation properties. Therefore, in contrast to PET/computed tomography, there is a lack of standardized global mapping between the intensities of MRI signal and linear attenuation coefficients at 511 keV. Moreover, in standard MRI sequences, bones and lung tissues do not produce measurable signals owing to their low proton density and short transverse relaxation times. MR images are also inevitably subject to artifacts that degrade their quality, thus compromising their applicability for the task of attenuation correction in PET/MRI. MRI-guided attenuation correction strategies can be classified in three broad categories: (i) segmentation-based approaches, (ii) atlas-registration and machine learning methods, and (iii) emission/transmission-based approaches. This paper summarizes past and current state-of-the-art developments and latest advances in PET/MRI attenuation correction. The advantages and drawbacks of each approach for addressing the challenges of MR-based attenuation correction are comprehensively described. The opportunities brought by both MRI and PET imaging modalities for deriving accurate attenuation maps and improving PET quantification will be elaborated. Future prospects and potential clinical applications of these techniques and their integration in commercial systems will also be discussed.

/pdf/vision-20-20-magnetic-resonance-imaging-guided-attenuation-h366uo6cri.pdf

Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities

In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding to how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically,we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.

Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade. We argue that a hindrance is the high cost of experimentation on real systems, in terms of both time and money. We address this problem by proposing an open-source robotic platform which can safely operate without human supervision. The hardware is inexpensive (about \SI{5000}[\$]{}) yet highly dynamic, robust, and capable of complex interaction with external objects. The software operates at 1-kilohertz and performs safety checks to prevent the hardware from breaking. The easy-to-use front-end (in C++ and Python) is suitable for real-time control as well as deep reinforcement learning. In addition, the software framework is largely robot-agnostic and can hence be used independently of the hardware proposed herein. Finally, we illustrate the potential of the proposed platform through a number of experiments, including real-time optimal control, deep reinforcement learning from scratch, throwing, and writing.

TriFinger: An Open-Source Robot for Learning Dexterity.

Recommendation systems (RS) are an integral part of artificial intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for RS provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network architecture for recommendation and a neighbourhood based scheme for generating explanations in the model. We demonstrate the effectiveness of our approach through experiments on the Netflix dataset by jointly optimizing for both prediction accuracy and explainability.

Explanations for Temporal Recommendations

Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for recommendation systems (RS) provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network (RNN) architecture for recommendation and a neighbourhood-based scheme for generating explanations in the model. We demonstrate the effectiveness of our approach through experiments on the Netflix dataset by jointly optimizing for both prediction accuracy and explainability.

Explanations for Temporal Recommendations.

This paper considers the problem of online optimal trajectory design under time-varying environments. Of particular interest is the design of energy-efficient trajectories under strong and uncertain disturbances in ocean environments and time-varying goal location. We formulate the problem within the constrained online convex optimization formalism, and a modified online gradient descent algorithm is motivated. The mobility constraints are met using a carefully chosen stepsize, and the proposed algorithm is shown to incur sublinear regret. Different from the state-of-the-art algorithms that entail planning and re-planning the full trajectory using forecast data at each time instant, the proposed algorithm is entirely online and relies mostly on the current ocean velocity measurements at the vehicle locations. The trade-off between excess delay incurred in reaching the goal and the overall energy consumption is examined via numerical tests carried out on real data obtained from the regional ocean modelling system. As compared to the state-of-the-art algorithms, the proposed algorithm is not only energy-efficient but also several orders of magnitude computationally efficient.

Shruti Joshi

Papers

TriFinger: An Open-Source Robot for Learning Dexterity.

Explanations for Temporal Recommendations

Explanations for Temporal Recommendations.

Online Utility-Optimal Trajectory Design for Time-Varying Ocean Environments

TriFinger: An Open-Source Robot for Learning Dexterity