Query: One-DOF Superimposed Rigid Origami with Multiple States

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Review of Inflatable Booms for Deployable Space Structures : Packing and Rigidization <=SEP=> Inflatable structures offer the potential of compactly stowing lightweight structures, which assume a fully deployed state in space. An important category of space inflatables are cylindrical booms, which may form the structural members of trusses or the support structure for solar sails. Two critical and interdependent aspects of designing inflatable cylindrical booms for space applications are i) packaging methods that enable compact stowage and ensure reliable deployment, and ii) rigidization techniques that provide long-term structural ridigity after deployment. The vast literature in these two fields is summarized to establish the state of the art.

Simulation of Rigid Origami <=SEP=> This paper presents a system for computer based interactive simulation of origami, based on rigid origami model. Our system shows the continuous process of folding a piece of paper into a folded shape by calculating the configuration from the crease pattern. The configuration of the model is represented by crease angles, and the trajectory is calculated by projecting angles motion to the constrained space. Additionally, polygons are triangulated and crease lines are adjusted in order to make the model more flexible. Flexible motion and comprehensible representation of origami help users to understand the structure of the model and to fold the model from a crease pattern.

One-DOF Cylindrical Deployable Structures with Rigid Quadrilateral Panels <=SEP=> In this paper,we present a novel cylindrical deployable structure and variations of its design with the following characteristics: 1. Flat-foldable: The shape flattens into a compact 2D configuration. 2. Rigid-foldable: Each element does not deform throughout the transformation. 3. One-DOF: The mechanism has exactly one degree of freedom. 4. Thick: Facets can be substituted with thick or multilayered panels without introducing the distortion of elements.

Robotic origami folding <=SEP=> Origami, the human art of paper sculpture, is a fresh challenge for the field of robotic manipulation, and provides a concrete example for many difficult and general manipulation problems. This thesis will present some initial results, including the world’s first origami-folding robot, some new theorems about foldability, definition of a simple class of origami for which I have designed a complete automatic planner, analysis of the kinematics of more complicated folds, and some observations about the configuration spaces of compound spherical closed chains.

Microrobot Design Using Fiber Reinforced Composites <=SEP=> Mobile microrobots with characteristic dimensions on the order of 1cm are difficult to design using either MEMS (microelectromechanical systems) technology or precision machining. This is due to the challenges associated with constructing the high strength links and high-speed, low-loss joints with micron scale features required for such systems. Here we present an entirely new framework for creating microrobots which makes novel use of composite materials. This framework includes a new fabrication process termed Smart Composite Microstructures (SCM) for integrating rigid links and large angle flexure joints through a laser micromachining and lamination process. We also present solutions to actuation and integrated wiring issues at this scale using SCM. Along with simple design rules that are customized for this process, our new complete microrobotic framework is a cheaper, quicker, and altogether superior method for creating microrobots that we hope will become the paradigm for robots at this scale.

Swarm Control of UAVs for Cooperative Hunting with DDDAS <=SEP=> Swarm control is a problem of increasing importance with technological advancements. Recently, governments have begun employing UAVs for reconnaissance, including swarms of drones searching for evasive targets. An agent-based simulation for dynamic cooperative cleaning is augmented with additional behaviors and implemented into a Dynamic Data-Driven Application System (DDDAS) framework for dynamic swarm control.

Machine-to-machine communications for home energy management system in smart grid <=SEP=> Machine-to-machine (M2M) communications have emerged as a cutting edge technology for next-generation communications, and are undergoing rapid development and inspiring numerous applications. This article presents an investigation of the application of M2M communications in the smart grid. First, an overview of M2M communications is given. The enabling technologies and open research issues of M2M communications are also discussed. Then we address the network design issue of M2M communications for a home energy management system (HEMS) in the smart grid. The network architecture for HEMS to collect status and power consumption demand from home appliances is introduced. Then the optimal HEMS traffic concentration is presented and formulated as the optimal cluster formation. A dynamic programming algorithm is applied to obtain the optimal solution. The numerical results show that the proposed optimal traffic concentration can minimize the cost of HEMS.

Security and usability: the gap in real-world online banking <=SEP=> Online banking is one of the most sensitive tasks performed by general Internet users. Most traditional banks now offer online banking services, and strongly encourage customers to do online banking with 'peace of mind.' Although banks heavily advertise an apparent '100% online security guarantee,' typically the fine print makes this conditional on users fulfilling certain security requirements. We examine some of these requirements as set by major Canadian banks, in terms of security and usability. We opened personal checking accounts at the five largest Canadian banks, and one online-only bank. We found that many security requirements are too difficult for regular users to follow, and believe that some marketing-related messages about safety and security actually mislead users. We are also interested in what kind of computer systems people really use for online banking, and whether users satisfy common online banking requirements. Our survey of 123 technically advanced users from a university environment strongly supports our view of an emerging gap between banks' expectations (or at least what their written customer policy agreements imply) and users' actions related to security requirements of online banking. Our participants, being more security-aware than the general population, arguably makes our results best-case regarding what can be expected from regular users. Yet most participants failed to satisfy common security requirements, implying most online banking customers do not (or cannot) follow banks' stated end-user security requirements and guidelines. The survey also sheds light on the security settings of systems used for sensitive online transactions. This work is intended to spur a discussion on real-world system security and user responsibilities, in a scenario where everyday users are heavily encouraged to perform critical tasks over the Internet, despite the continuing absence of appropriate tools to do so.

Sensor Log: A mobile data collection and annotation application <=SEP=> Smartphones equipped with sensors can be used for monitoring simple daily activities (e.g., walking, climbing stairs etc.). The data collected via sensors need to be partially or completely annotated in order to be used in the training phase of learning algorithms. In order to ease the time consuming and burdensome annotation process, a mobile data collection and annotation application named Sensor Log is developed. The annotation feature and its ease of use make the developed application superior to existing systems. The application can be downloaded from the Android applications distribution platform.

Analysis and design for RCD clamped snubber used in output rectifier of phase-shift full-bridge ZVS converters <=SEP=> Detailed analysis and parameter design for a resistorcapacitor-diode (RCD) clamped snubber used in the output rectifier of phase-shift full-bridge zero-voltage-switching (PS-FB-ZVS) converters are presented. Design equations and some properties of the clamped circuit are highlighted.

Mining advertiser-specific user behavior using adfactors <=SEP=> Consider an online ad campaign run by an advertiser. The ad serving companies that handle such campaigns record users' behavior that leads to impressions of campaign ads, as well as users' responses to such impressions. This is summarized and reported to the advertisers to help them evaluate the performance of their campaigns and make better budget allocation decisions.
 The most popular reporting statistics are the click-through rate and the conversion rate. While these are indicative of the effectiveness of an ad campaign, the advertisers often seek to understand more sophisticated long-term effects of their ads on the brand awareness and the user behavior that leads to the conversion, thus creating a need for the reporting measures that can capture both the duration and the frequency of the pathways to user conversions.
 In this paper, we propose an alternative data mining framework for analyzing user-level advertising data. In the aggregation step, we compress individual user histories into a graph structure, called the adgraph, representing local correlations between ad events. For the reporting step, we introduce several scoring rules, called the adfactors (AF), that can capture global role of ads and ad paths in the adgraph, in particular, the structural correlation between an ad impression and the user conversion. We present scalable local algorithms for computing the adfactors; all algorithms were implemented using the MapReduce programming model and the Pregel framework.
 Using an anonymous user-level dataset of sponsored search campaigns for eight different advertisers, we evaluate our framework with different adgraphs and adfactors in terms of their statistical fit to the data, and show its value for mining the long-term behavioral patterns in the advertising data.

An Organizational Theoretic Review of Green Supply Chain Management Literature <=SEP=> Green supply chain management (GSCM) has gained increasing attention within both academia and industry. As the literature grows, finding new directions by critically evaluating the research and identifying future directions becomes important in advancing knowledge for the field. Using organizational theories to help categorize the literature provides opportunities to address both the objectives of understanding where the field currently stands and identifying research opportunities and directions. After providing a background discussion on GSCM we categorize and review recent GSCM literature under nine broad organizational theories. Within this review framework, we also identify GSCM research questions that are worthy of investigation. Additional organizational theories which are considered valuable for future GSCM research are also identified with a conclusion for this review.

A Multi-Cloud Framework for Measuring and Describing Performance Aspects of Cloud Services Across Different Application Types <=SEP=> Cloud services have emerged as an innovative IT provisioning model in the recent years. However, after their usage severe considerations have emerged with regard to their varying performance due to multitenancy and resource sharing issues. These issues make it very difficult to provide any kind of performance estimation during application design or deployment time. The aim of this paper is to present a mechanism and process for measuring the performance of various Cloud services and describing this information in machine understandable format. The framework is responsible for organizing the execution and can support multiple Cloud providers. Furthermore we present approaches for measuring service performance with the usage of specialized metrics for ranking the services according to a weighted combination of cost, performance and workload.

Tiny ImageNet Visual Recognition Challenge <=SEP=> In this work, we investigate the effect of convolutional network depth, receptive field size, dropout layers, rectified activation unit type and dataset noise on its accuracy in Tiny-ImageNet Challenge settings. In order to make a thorough evaluation of the cause of the peformance improvement, we start with a basic 5 layer model with 5×5 convolutional receptive fields. We keep increasing network depth or reducing receptive field size, and continue applying modern techniques, such as PReLu and dropout, to the model. Our model achieves excellent performance even compared to state-of-the-art results, with 0.444 final error rate on the test set.

Design of biped robot inspired by cats for fast running <=SEP=> ELECT A novel design of a biped robot inspired by domestic cats for fast running is introduced. The skeletomuscular system of a cat is analysed and applied to determine the link parameters and the linkage structure of the proposed mechanism. The linkage design of the leg mechanism is explained and a kinematic analysis based on vector loop equations is performed. The effectiveness of the proposed mechanism is verified experimentally. The biped robot runs at an average speed of 2 m/s at a step frequency of 4 Hz. This leg mechanism can facilitate the development of fast running robot systems.

Paper 265-25 Sample Size Computations and Power Analysis with the SAS System <=SEP=> Statistical power analysis characterizes the ability of a study to detect a meaningful effect size—for example, the difference between two population means. It also determines the sample size required to provide a desired power for an effect of scientific interest. Proper planning reduces the risk of conducting a study that will not produce useful results and determines the most sensitive design for the resources available. Power analysis is now integral to the health and behavioral sciences, and its use is steadily increasing wherever empirical studies are performed. SAS Institute is working to implement power analysis for common situations such as t-tests, ANOVA, comparison of binomial proportions, equivalence testing, survival analysis, contingency tables and linear models, and eventually for a wide range of models and designs. An effective graphical user interface reveals the contribution to power of factors such as effect size, sample size, inherent variability, type I error rate, and choice of design and analysis. This presentation demonstrates issues involved in power analysis, summarizes the current state of methodology and software, and outlines future directions.

Brain activation during human male ejaculation. <=SEP=> Brain mechanisms that control human sexual behavior in general, and ejaculation in particular, are poorly understood. We used positron emission tomography to measure increases in regional cerebral blood flow (rCBF) during ejaculation compared with sexual stimulation in heterosexual male volunteers. Manual penile stimulation was performed by the volunteer's female partner. Primary activation was found in the mesodiencephalic transition zone, including the ventral tegmental area, which is involved in a wide variety of rewarding behaviors. Parallels are drawn between ejaculation and heroin rush. Other activated mesodiencephalic structures are the midbrain lateral central tegmental field, zona incerta, subparafascicular nucleus, and the ventroposterior, midline, and intralaminar thalamic nuclei. Increased activation was also present in the lateral putamen and adjoining parts of the claustrum. Neocortical activity was only found in Brodmann areas 7/40, 18, 21, 23, and 47, exclusively on the right side. On the basis of studies in rodents, the medial preoptic area, bed nucleus of the stria terminalis, and amygdala are thought to be involved in ejaculation, but increased rCBF was not found in any of these regions. Conversely, in the amygdala and adjacent entorhinal cortex, a decrease in activation was observed. Remarkably strong rCBF increases were observed in the cerebellum. These findings corroborate the recent notion that the cerebellum plays an important role in emotional processing. The present study for the first time provides insight into which regions in the human brain play a primary role in ejaculation, and the results might have important implications for our understanding of how human ejaculation is brought about, and for our ability to improve sexual function and satisfaction in men.

An energy-efficient unequal clustering mechanism for wireless sensor networks <=SEP=> Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime

Cyber Security and Power System Communication—Essential Parts of a Smart Grid Infrastructure <=SEP=> The introduction of “smart grid” solutions imposes that cyber security and power system communication systems must be dealt with extensively. These parts together are essential for proper electricity transmission, where the information infrastructure is critical. The development of communication capabilities, moving power control systems from “islands of automation” to totally integrated computer environments, have opened up new possibilities and vulnerabilities. Since several power control systems have been procured with “openness” requirements, cyber security threats become evident. For refurbishment of a SCADA/EMS system, a separation of the operational and administrative computer systems must be obtained. The paper treats cyber security issues, and it highlights access points in a substation. Also, information security domain modeling is treated. Cyber security issues are important for “smart grid” solutions. Broadband communications open up for smart meters, and the increasing use of wind power requires a “smart grid system”.

TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency <=SEP=> In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence – both semantic and syntactic – but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned endto-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of 6.28%. This is comparable to the state-of-the-art 5.91% resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.

Treatment of keloids and hypertrophic scars. <=SEP=> Clinicians always find it difficult to treat hypertrophic scars and keloids. Various treatment modalities are available. Intralesional corticosteroids, topical applications, cryotherapy, surgery, laser therapy, and silicone sheeting are the widely used options. Radiation therapy can also help in cases of recalcitrant keloids. Most recently, pulsed-dye laser has been successfully used to treat keloids and hypertrophic scars. There are no set guidelines for the treatment of keloids. Treatment has to be individualized depending upon the distribution, size, thickness, and consistency of the lesions and association of inflammation. A combination approach to therapy seems to be the best option.

Advances in direct transesterification of algal oils from wet biomass. <=SEP=> An interest in biodiesel as an alternative fuel for diesel engines has been increasing because of the issue of petroleum depletion and environmental concerns related to massive carbon dioxide emissions. Researchers are strongly driven to pursue the next generation of vegetable oil-based biodiesel. Oleaginous microalgae are considered to be a promising alternative oil source. To commercialize microalgal biodiesel, cost reductions in oil extraction and downstream biodiesel conversion are stressed. Herein, starting from an investigation of oil extraction from wet microalgae, a review is conducted of transesterification using enzymes, homogeneous and heterogeneous catalysts, and yield enhancement by ultrasound, microwave, and supercritical process. In particular, there is a focus on direct transesterification as a simple and energy efficient process that omits a separate oil extraction step and utilizes wet microalgal biomass; however, it is still necessary to consider issues such as the purification of microalgal oils and upgrading of biodiesel properties.

An Atomatic Fundus Image Analysis System for Clinical Diagnosis of Glaucoma <=SEP=> Glaucoma is a serious ocular disease and leads blindness if it couldn¡¦t be detected and treated in proper way. The diagnostic criteria for glaucoma include intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defect. The observation of optic nerve head, cup to disc ratio and neural rim configuration are important for early detecting glaucoma in clinical practice. However, the broad range of cup to disc ratio is difficult to identify early changes of optic nerve head, and different ethnic groups possess various features in optic nerve head structures. Hence, it is still important to develop various detection techniques to assist clinicians to diagnose glaucoma at early stages. In this study, we developed an automatic detection system which contains two major phases: the first phase performs a series modules of digital fundus retinal image analysis including vessel detection, vessel in painting, cup to disc ratio calculation, and neuro-retinal rim for ISNT rule, the second phase determines the abnormal status of retinal blood vessels from different aspect of view. In this study, the novel idea of integrating image in painting and active contour model techniques successfully assisted the correct identification of cup and disk regions. Several clinical fundus retinal images containing normal and glaucoma images were applied to the proposed system for demonstration.

A Practical Approach to Recognizing Physical Activities <=SEP=> We are developing a personal activity recognition system that is practical, reliable, and can be incorporated into a variety of health-care related applications ranging from personal fitness to elder care. To make our system appealing and useful, we require it to have the following properties: (i) data only from a single body location needed, and it is not required to be from the same point for every user; (ii) should work out of the box across individuals, with personalization only enhancing its recognition abilities; and (iii) should be effective even with a cost-sensitive subset of the sensors and data features. In this paper, we present an approach to building a system that exhibits these properties and provide evidence based on data for 8 different activities collected from 12 different subjects. Our results indicate that the system has an accuracy rate of approximately 90% while meeting our requirements. We are now developing a fully embedded version of our system based on a cell-phone platform augmented with a Bluetooth-connected sensor board.

Agronomy for sustainable agriculture. A review <=SEP=> Sustainability rests on the principle that we must meet the needs of the present without compromising the ability of future generations to meet their own needs. Starving people in poor nations, obesity in rich nations, increasing food prices, on-going climate changes, increasing fuel and transportation costs, flaws of the global market, worldwide pesticide pollution, pest adaptation and resistance, loss of soil fertility and organic carbon, soil erosion, decreasing biodiversity, desertification, and so on. Despite unprecedented advances in sciences allowing us to visit planets and disclose subatomic particles, serious terrestrial issues about food show clearly that conventional agriculture is no longer suited to feeding humans and preserving ecosystems. Sustainable agriculture is an alternative for solving fundamental and applied issues related to food production in an ecological way (Lal (2008) Agron. Sustain. Dev. 28, 57–64.). While conventional agriculture is driven almost solely by productivity and profit, sustainable agriculture integrates biological, chemical, physical, ecological, economic and social sciences in a comprehensive way to develop new farming practices that are safe and do not degrade our environment. To address current agronomical issues and to promote worldwide discussions and cooperation we implemented sharp changes at the journal Agronomy for Sustainable Development from 2003 to 2006. Here we report (1) the results of the renovation of the journal and (2) a short overview of current concepts of agronomical research for sustainable agriculture. Considered for a long time as a soft, side science, agronomy is rising fast as a central science because current issues are about food, and humans eat food. This report is the introductory article of the book Sustainable Agriculture, volume 1, published by EDP Sciences and Springer (Lichtfouse et al. (2009) Sustainable Agriculture, Vol. 1, Springer, EDP Sciences, in press).

Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization <=SEP=> Recently, there has been considerable progress towards algorithms for approximating Nash equilibrium strategies in extensive games. One such algorithm, Counterfactual Regret Minimization (CFR), has proven to be effective in two-player zero-sum poker domains. While the basic algorithm is iterative and performs a full game traversal on each iteration, sampling based approaches are possible. For instance, chance-sampled CFR considers just a single chance outcome per traversal, resulting in faster but less precise iterations. While more iterations are required, chance-sampled CFR requires less time overall to converge. In this work, we present new sampling techniques that consider sets of chance outcomes during each traversal to produce slower, more accurate iterations. By sampling only the public chance outcomes seen by all players, we take advantage of the imperfect information structure of the game to (i) avoid recomputation of strategy probabilities, and (ii) achieve an algorithmic speed improvement, performing O(n) work at terminal nodes in O(n) time. We demonstrate that this new CFR update converges more quickly than chance-sampled CFR in the large domains of poker and Bluff.

Learning Content-rich Diffusion Network Embedding 1 <=SEP=> Information networks are ubiquitous in the real world, while embedding, as a kind of network representation, has received attention from many researchers because of its effectiveness in preserving the semantics of the network and its broad application including classification, link prediction etc. Previously, many methods have been proposed to learn network embedding from non-attributed and static networks. Networks are treated simply as nodes and links. However, information is not merely encoded in the structure, nodes itself may have their intrinsic attributes and little research have been done to incorporate this information. Furthermore, outside information will also diffuse on the network over the time and for a specific time we can have a different diffusion structure. In this paper, we will be introducing the idea of diffusion network, and we propose two models for embedding learning that aim to efficiently capture the rich content of nodes as well as that of the diffusion. We then evaluate the quality of our embedding by conducting node classification experiments, the result of which shows that our method for generating embedding outperforms other baselines.

Automatic Shoeprint Retrieval Algorithm for Real Crime Scenes <=SEP=> This study is to propose a fully automatic crime scene shoeprint retrieval algorithm that can be used to link scenes of crime or determine the brand of a shoe. A shoeprint contour model is proposed to roughly correct the geometry distortions. To simulate the character of the forensic experts, a region priority match and similarity estimation strategy is also proposed. The shoeprint is divided into two semantic regions, and their confidence values are computed based on the priority in the forensic practice and the quantity of reliable information. Similarities of each region are computed respectively, and the matching score between the reference image and an image in the database is the weighted sum. For regions with higher confidence value, the similarities are computed based on the proposed coarse-to-fine global invariant descriptors, which are based on Wavelet-Fourier transform and are invariant under slight geometry distortions and interference such as breaks and small holes, etc. For regions with lower confidence value, similarities are estimated based on computed similarities of regions with higher confidence value. Parameters of the proposed algorithm have learned from huge quantity of crime scene shoeprints and standard shoeprints which can cover most practical cases, and the algorithm can have better performance with minimum user intervention. The proposed algorithm has been tested on the crime scene shoeprint database composed of 210,000 shoeprints provided by the third party, and the cumulative matching score of the top 2 percent is 90.87.

An Approximate Analysis of the LRU and FIFO Buffer Replacement Schemes <=SEP=> In this paper, we develop approximate analytical models for predicting the buffer hit probability under the Least Recently Used (LRU) and First In First Out (FIFO) buffer replacement policies under the independent reference model. In the case of the analysis of the LRU policy, the computational complexity for estimating the buffer hit probability is O(KB) where B is the size of the buffer and K denotes the number of items having distinct access probabilities. In the case of the FIFO policy, the solution algorithm is iterative and the computational complexity of each iteration is O(K). Results from these models are compared to exact results for models originally developed by King [KING71] for small values of the buffer size, B, and the total number of items sharing the buffer, D. Results are also compared with results from a simulation for large values of B and D. In most cases, the error is extremely small (less than 0.1%) for both LRU and FIFO, and a maximum error of 3% is observed for very small buffer size (less than 5) when the access probabilities are extremely skewed. To demonstrate the usefulness of the model, we consider two applications. In our first application, we compare the LRU and FIFO policies to an optimal static buffer allocation policy for a database consisting of two classes of data items. We observe that the performance of LRU is close to that of the optimal allocation. As the optimal allocation requires knowledge of the access probabilities, the LRU policy is preferred when this information is unavailable. We also observe that the LRU policy always performs better than the FIFO policy in our experiments. In our second application, we show that if multiple independent reference streams on mutually disjoint sets of data compete for the same buffer, it is better to partition the buffer using an optimal allocation policy than to share a common buffer.

Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology <=SEP=> Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-theart techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series are available, conditioning the forecast of an individual time series on past behaviour of similar, related time series can be beneficial. Given that the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model. We achieve this by globally training a Long Short-Term Memory network (LSTM) that exploits the nonlinear demand relationships available in an E-commerce product assortment hierarchy. Aside from the forecasting engine, we propose a systematic pre-processing framework to overcome the challenges in an E-commerce setting. We also introduce several product grouping strategies to supplement the LSTM learning schemes, in situations where sales patterns in a product portfolio are disparate. We empirically evaluate the proposed forecasting framework on a real-world online marketplace dataset from Walmart.com. Our method achieves competitive results on category level and super-departmental level datasets, outperforming stateof-the-art techniques.