Query: Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals

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Neuroimaging: Decoding mental states from brain activity in humans <=SEP=> Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.

Content-based multimedia information retrieval: State of the art and challenges <=SEP=> Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

Distributed and overlapping representations of faces and objects in ventral temporal cortex. <=SEP=> The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.

Learning to Decode Cognitive States from Brain Images <=SEP=> Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as (1) whether the human subject is looking at a picture or a sentence, (2) whether the subject is reading an ambiguous or non-ambiguous sentence, and (3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (105 features), extremely sparse (tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings.

Machine learning classifiers and fMRI: A tutorial overview <=SEP=> Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).

Evidence in Management and Organizational Science : Assembling the Field ’ s Full Weight of Scientific Knowledge Through Syntheses <=SEP=> This chapter advocates the good scientific practice of systematic research syntheses in Management and Organizational Science (MOS). A research synthesis is the systematic accumulation, analysis and reflective interpretation of the full body of relevant empirical evidence related to a question. It is the * Corresponding author. Email: rousseau@andrew.cmu.edu 476 • The Academy of Management Annals critical first step in effective use of scientific evidence. Synthesis is not a conventional literature review. Literature reviews are often position papers, cherrypicking studies to advocate a point of view. Instead, syntheses systematically identify where research findings are clear (and where they aren’t), a key first step to establishing the conclusions science supports. Syntheses are also important for identifying contested findings and productive lines for future research. Uses of MOS evidence, that is, the motives for undertaking a research synthesis include scientific discovery and explanation, improved management practice guidelines, and formulating public policy. We identify six criteria for establishing the evidentiary value of a body of primary studies in MOS. We then pinpoint the stumbling blocks currently keeping the field from making effective use of its ever-expanding base of empirical studies. Finally, this chapter outlines (a) an approach to research synthesis suitable to the domain of MOS; and (b) supporting practices to make synthesis a collective MOS project. Evidence in Management and Organizational Science It is the nature of the object that determines the form of its possible science. (Bhaskar, 1998, p. 3) Uncertain knowledge is better than ignorance. (Mitchell, 2000, p. 9) This chapter is motivated by the failure of Management and Organizational Science (MOS) to date to make full effective use of its available research evidence. Failure to make effective use of scientific evidence is a problem both management scholars and practitioners face. Effective use of evidence, as we employ the term here, means to assemble and interpret a body of primary studies relevant to a question of fact, and then take appropriate action based upon the conclusions drawn. For science, appropriate action might be to direct subsequent research efforts elsewhere if the science is clear, or to recommend a new tact if findings are inconclusive. For practice, appropriate action might begin with a summary of key findings to share with educators, thought leaders, consultants, and the broader practice community. Unfortunately, bodies of evidence in MOS are seldom assembled or interpreted in the systematic fashion needed to permit their confident use. A systematic review of the full body of evidence is the key first step in formulating a science-based conclusion. As a consequence at present, neither the MOS scholar nor the practitioner can readily claim to be well-informed. This lapse has many causes. Two are central to our failure to use MOS evidence well: (1) overvaluing novelty to the detriment of accumulating convergent findings; and (2) the general absence of systematic research syntheses. These two causes are intertwined in that, as we shall show, use of research syntheses ties closely with how a field gauges the value of its research. This chapter’s subject, the systematic research synthesis, is not to be confused Evidence in Management and Organizational Science • 477 with a conventional literature review, its less systematic, non-representative counterpart. Systematic research syntheses assemble, analyze and interpret a comprehensive body of evidence in a highly reflective fashion according to six evidentiary criteria we describe. The why, what, and how of research synthesis in MOS is this chapter’s focus. The explosion of management research since World War II has created knowledge products at a rate far outpacing our current capacity for recall, sense-making, and use. In all likelihood, MOS’s knowledge base will continue to expand. We estimate over 200 peer-reviewed journals currently publish MOS research. These diverse outlets reflect the fact that MOS is not a discipline; it is an area of inter-related research activities cutting across numerous disciplines and subfields. The area’s expansion translates into a body of knowledge that is increasingly fragmented (Rousseau, 1997), transdisciplinary (Whitley, 2000), and interdependent with advancements in other social sciences (Tranfield, Denyer & Smart, 2003). The complicated state of MOS research makes it tough to know what we know, especially as specialization spawns research communities that often don’t and sometimes can’t talk with each other.

A Bridging Framework for Model Optimization and Deep Propagation <=SEP=> Optimizing task-related mathematical model is one of the most fundamental methodologies in statistic and learning areas. However, generally designed schematic iterations may hard to investigate complex data distributions in real-world applications. Recently, training deep propagations (i.e., networks) has gained promising performance in some particular tasks. Unfortunately, existing networks are often built in heuristic manners, thus lack of principled interpretations and solid theoretical supports. In this work, we provide a new paradigm, named Propagation and Optimization based Deep Model (PODM), to bridge the gaps between these different mechanisms (i.e., model optimization and deep propagation). On the one hand, we utilize PODM as a deeply trained solver for model optimization. Different from these existing network based iterations, which often lack theoretical investigations, we provide strict convergence analysis for PODM in the challenging nonconvex and nonsmooth scenarios. On the other hand, by relaxing the model constraints and performing end-to-end training, we also develop a PODM based strategy to integrate domain knowledge (formulated as models) and real data distributions (learned by networks), resulting in a generic ensemble framework for challenging real-world applications. Extensive experiments verify our theoretical results and demonstrate the superiority of PODM against these state-of-the-art approaches.

Fast Alternating LS Algorithms for High Order CANDECOMP/PARAFAC Tensor Factorizations <=SEP=> CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas such as in chemometrics, telecommunication, data mining, neuroscience, separated representations. For an order- N tensor, most CP algorithms can be computationally demanding due to computation of gradients which are related to products between tensor unfoldings and Khatri-Rao products of all factor matrices except one. These products have the largest workload in most CP algorithms. In this paper, we propose a fast method to deal with this issue. The method also reduces the extra memory requirements of CP algorithms. As a result, we can accelerate the standard alternating CP algorithms 20-30 times for order-5 and order-6 tensors, and even higher ratios can be obtained for higher order tensors (e.g., N ≥ 10). The proposed method is more efficient than the state-of-the-art ALS algorithm which operates two modes at a time (ALSo2) in the Eigenvector PLS toolbox, especially for tensors with order N ≥ 5 and high rank.

Planning With Agents : An Efficient Approach Using Hierarchical Dynamic Decision Networks <=SEP=> To be useful in solving real world problems, agents need to be able to act in environments in which it may not be possible to be completely aware of the current state and where actions do not always work as planned. Additional complexity is added to the problem when one considers groups of agents working together. By casting the agent planning problem as a partially observable Markov decision problem (POMDP), optimal policies can be generated for partially observable and stochastic environments. Exact solutions, however, are notoriously difficult to find for problems of a realistic nature. We introduce a hierarchical decision network-based planning algorithm that can generate high quality plans during execution while demonstrating significant time savings. We also discuss how this approach is particularly applicable to planning in a multiagent environment as compared to other POMDP-based planning algorithms. We present experimental results comparing our algorithm with results obtained by current POMDP and hierarchical POMDP (HPOMDP) methods.

Multi-label learning: a review of the state of the art and ongoing research <=SEP=> Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. The paper presents an up-todate overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed and ongoing research are presented.

Occlusion-capable multiview volumetric three-dimensional display. <=SEP=> Volumetric 3D displays are frequently purported to lack the ability to reconstruct scenes with viewer-position-dependent effects such as occlusion. To counter these claims, a swept-screen 198-view horizontal-parallax-only 3D display is reported here that is capable of viewer-position-dependent effects. A digital projector illuminates a rotating vertical diffuser with a series of multiperspective 768 x 768 pixel renderings of a 3D scene. Evidence of near-far object occlusion is reported. The aggregate virtual screen surface for a stationary observer is described, as are guidelines to construct a full-parallax system and the theoretical ability of the present system to project imagery outside of the volume swept by the screen.

Ranking Attacks Based on Vulnerability Analysis <=SEP=> Now that multiple-known attacks can affect one software product at the same time, it is necessary to rank and prioritize those attacks in order to establish a better defense. The purpose of this paper is to provide a set of security metrics to rank attacks based on vulnerability analysis. The vulnerability information is retrieved from a vulnerability management ontology, which integrates commonly used standards like CVE, CWE, CVSS, and CAPEC. Among the benefits of ranking attacks through the method proposed here are: a more effective mitigation or prevention of attack patterns against systems, a better foundation to test software products, and a better understanding of vulnerabilities and attacks.

Stability analysis of negative impedance converter <=SEP=> Negative Impedance Converter (NIC) have received much attention to improve the restriction of antenna gain-bandwidth tradeoff relationship. However, a significant problem with NIC is the potential instability of unwanted oscillation due to the presence of positive feedback. For solving this problem, we propose a NIC circuit technique which is stable and has wideband characteristics of negative impedance.

Medical robotics—Regulatory, ethical, and legal considerations for increasing levels of autonomy <=SEP=> The regulatory, ethical, and legal barriers imposed on medical robots necessitate careful consideration of different levels of autonomy, as well as the context for use.

Activity recognition from acceleration data based on discrete consine transform and SVM <=SEP=> This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.

Language Problems and ADHD Symptoms: How Specific Are the Links? <=SEP=> Symptoms of inattention and hyperactivity frequently co-occur with language difficulties in both clinical and community samples. We explore the specificity and strength of these associations in a heterogeneous sample of 254 children aged 5 to 15 years identified by education and health professionals as having problems with attention, learning and/or memory. Parents/carers rated pragmatic and structural communication skills and behaviour, and children completed standardised assessments of reading, spelling, vocabulary, and phonological awareness. A single dimension of behavioural difficulties including both hyperactivity and inattention captured behaviour problems. This was strongly and negatively associated with pragmatic communication skills. There was less evidence for a relationship between behaviour and language structure: behaviour ratings were more weakly associated with the use of structural language in communication, and there were no links with direct measures of literacy. These behaviour problems and pragmatic communication difficulties co-occur in this sample, but impairments in the more formal use of language that impact on literacy and structural communication skills are tied less strongly to behavioural difficulties. One interpretation is that impairments in executive function give rise to both behavioural and social communication problems, and additional or alternative deficits in other cognitive abilities impact on the development of structural language skills.

Soft tissue cephalometric analysis: diagnosis and treatment planning of dentofacial deformity. <=SEP=> This article will present a new soft tissue cephalometric analysis tool. This analysis may be used by the orthodontist and surgeon as an aid in diagnosis and treatment planning. The analysis is a radiographic instrument that was developed directly from the philosophy expressed in Arnett and Bergman "Facial keys to orthodontic diagnosis and treatment planning, Parts I and II" (Am J Orthop Dentofacial Orthod 1993; 103:299-312 and 395-411). The novelty of this approach, as with the "Facial Keys" articles, is an emphasis on soft tissue facial measurement.

A DTN-Based Sensor Data Gathering for Agricultural Applications <=SEP=> This paper presents our field experience in data collection from remote sensors. By letting tractors, farmers, and sensors have short-range radio communication devices with delay-disruption tolerant networking (DTN), we can collect data from those sensors to our central database. Although, several implementations have been made with cellular phones or mesh networks in the past, DTN-based systems for such applications are still under explored. The main objective of this paper is to present our practical implementation and experiences in DTN-based data collection from remote sensors. The software, which we have developed for this research, has about 50 kbyte footprint, which is much smaller than any other DTN implementation. We carried out an experiment with 39 DTN nodes at the University of Tokyo assuming an agricultural scenario. They achieved 99.8% success rate for data gathering with moderate latency, showing sufficient usefulness in data granularity.

Detection of Unhealthy Region of plant leaves using Neural Network <=SEP=> A leaf is an organ of vascular plant and is the principal lateral appendage of the stem. Each leaf has a set of features that differentiate it from the other leaves, such as margin and shape. This work proposes a comparison of supervised plant leaves classification using different approaches, based on different representations of these leaves, and the chosen algorithm. Beginning with the representation of leaves, we presented leaves by a fine-scale margin feature histogram, by a Centroid Contour Distance Curve shape signature, or by an interior texture feature histogram in 64 element vector for each one, after we tried different combination among these features to optimize results. We classified the obtained vectors. Then we evaluate the classification using cross validation. The obtained results are very interesting and show the importance of each feature. The classification of plant leaf images with biometric features. Traditionally, the trained taxonomic perform this process by following various tasks. The taxonomic usually classify the plants based on flowering and associative phenomenon. It was found that this process was time consuming and difficult. The biometric features of plants leaf like venation make this classification easy. Leaf biometric feature are analyzed using computer based method like morphological feature analysis and artificial neural network and Naive bayes based classifier. KNN classification model take input as the leaf venation morphological feature and classify them into four different species. The result of this classification based on leaf venation is achieved 96.53% accuracy in the training of the model for classification of leaves provide the 91% accuracy in testing to classify the leaf images. Keyword: Artificial neural network, K-NN Classification, Leaf venation pattern, Morphological Features

Earlier Identification of Children with Autism Spectrum Disorder: An Automatic Vocalisation-Based Approach <=SEP=> Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study, we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD. We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD.

High sigma measurement of random threshold voltage variation in 14nm Logic FinFET technology <=SEP=> Random variation of threshold voltage (Vt) in MOSFETs plays a central role in determining the minimum operating voltage of products in a given process technology. Properly characterizing Vt variation requires a large volume of measurements of minimum size devices to understand the high sigma behavior. At the same time, a rapid measurement approach is required to keep the total measurement time practical. Here we describe a new test structure and measurement approach that enables practical characterization of Vt distributions to high sigma and its application to 14nm Logic FinFET technology. We show that both NMOS and PMOS single fin devices have very low random Vt variation of 19mV and 24mV respectively, normally distributed out to +/-5σ.

Medicinal and pharmaceutical uses of seaweed natural products: A review <=SEP=> In the last three decades the discovery of metabolites with biological activities from macroalgae has increased significantly. However, despite the intense research effort by academic and corporate institutions, very few products with real potential have been identified or developed. Based on Silverplatter MEDLINE and Aquatic Biology, Aquaculture & Fisheries Resources databases, the literature was searched for natural products from marine macroalgae in the Rhodophyta, Phaeophyta and Chlorophyta with biological and pharmacological activity. Substances that currently receive most attention from pharmaceutical companies for use in drug development, or from researchers in the field of medicine-related research include: sulphated polysaccharides as antiviral substances, halogenated furanones from Delisea pulchra as antifouling compounds, and kahalalide F from a species of Bryopsis as a possible treatment of lung cancer, tumours and AIDS. Other substances such as macroalgal lectins, fucoidans, kainoids and aplysiatoxins are routinely used in biomedical research and a multitude of other substances have known biological activities. The potential pharmaceutical, medicinal and research applications of these compounds are discussed.

Black cattle body shape and temperature measurement using thermography and KINECT sensor <=SEP=> Black cattle body shape and temperature measurement system is introduced. It is important to evaluate the quality of Japanese black cattle periodically during their growth process. Not only the weight and size of cattle, but also the posture, shape, and temperature need to be tracked as primary evaluation criteria. In the present study, KINECT sensor and thermal camera obtains the body shape and its temperature. The whole system is calibrated to operate in a common coordinate system. Point cloud data are obtained from different angles and reconstructed in a computer. The thermal data are captured too. Both point cloud data and thermal information are combined by considering the orientation of the cow. The collected information is used to evaluate and estimate cattle conditions.

Adaptive Mobile Robot Navigation and Mapping <=SEP=> The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman lter to incorporate vehicle localization and environmental mapping. We describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. We introduce a metric for adaptive sensing which is de ned in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action which yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for 1) adaptive control of motion and 2) adaptive control of motion and scanning. The vehicle tends to explore selectively di erent objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.

A comparison of track-to-track fusion algorithms for automotive sensor fusion <=SEP=> In exteroceptive automotive sensor fusion, sensor data are usually only available as processed, tracked object data and not as raw sensor data. Applying a Kalman filter to such data leads to additional delays and generally underestimates the fused objectspsila covariance due to temporal correlations of individual sensor data as well as inter-sensor correlations. We compare the performance of a standard asynchronous Kalman filter applied to tracked sensor data to several algorithms for the track-to-track fusion of sensor objects of unknown correlation, namely covariance union, covariance intersection, and use of cross-covariance. For the simulation setup used in this paper, covariance intersection and use of cross-covariance turn out to yield significantly lower errors than a Kalman filter at a comparable computational load.

A Reverse Approach to Named Entity Extraction and Linking in Microposts <=SEP=> In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base.

The Study of a Dual-Mode Ring Oscillator <=SEP=> An analytical investigation of a dual-mode ring oscillator is presented. The ring oscillator is designed using a CMOS 0.18-μm technology with differential eight-stage delay cells employing auxiliary input devices. With a proper startup control, the oscillator operates in two different modes covering two different frequency bands. A nonlinear model, along with the linearization method, is used to obtain the transient and steady-state behaviors of the dual-mode ring oscillator. The analytical derivations are verified through HSPICE simulation. The oscillator operates at the frequency bands from 2-5 GHz and from 0.1-2 GHz, respectively.

Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil <=SEP=> Deep Learning is one of the latest approaches in the field of artificial neural networks. Since they were first proposed in mid-2006, Deep Learning models have obtained state-of-art results in some problems with classification and pattern recognition. However, such models have been little used in time series forecasting. This work aims to investigate the use of some of these architectures in this kind of problem, specifically in predicting the hourly average speed of winds in the Northeastern region of Brazil. The results showed that Deep Learning offers a good alternative for performing this task, overcoming some results of previous works.

Characterizing large-scale use of a direct manipulation application in the wild <=SEP=> Examining large-scale, long-term application use is critical to understanding how an application meets the needs of its user community. However, there have been few published analyses of long-term use of desktop applications, and none that have examined applications that support creating and modifying content using direct manipulation. In this paper, we present an analysis of 2 years of usage data from an instrumented version of the GNU Image Manipulation Program, including data from over 200 users. In the course of our analysis, we show that previous findings concerning the sparseness of command use and idiosyncrasy of users’ command vocabularies extend to a new domain and interaction style. These findings motivate continued research in adaptive and mixed-initiative interfaces. We also describe the novel application of a clustering technique to characterize a user community’s higher-level tasks from low-level logging data.

On traveling path and related problems for a mobile station in a rechargeable sensor network <=SEP=> Wireless power transfer is a promising technology to fundamentally address energy problems in a wireless sensor network. To make such a technology work effectively, a vehicle is needed to carry a charger to travel inside the network. On the other hand, it has been well recognized that a mobile base station offers significant advantages over a fixed one. In this paper, we investigate an interesting problem of co-locating the mobile base station on the wireless charging vehicle. We study an optimization problem that jointly optimizes traveling path, stopping points, charging schedule, and flow routing. Our study is carried out in two steps. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem.