OpenRaiser/Intern-Atlas huggingface dataset:
Deriving a Generalized, Actuator Position-Independent Expression for the Force Output of a Scissor Lift
Venue: arXiv cs.RO 2016
Year: 2016

Scissor lifts, a staple of mechanical design, especially in competitive robotics, are a type of linkage that can be used to raise a load to some height, when acted upon by some force, usually exerted by an actuator. The position of this actuator, however, can affect the mechanical advantage and velocity ratio of the system. Hence, there needs to be a concrete way to analytically compare different actuator positions. However, all current research into the analysis of scissor lifts either focusses only on the screw jack configuration, or derives separate force expressions for different actuator positions. This, once again, leaves the decision between different actuator positions to trial and error, since the expression to test the potency of the position can only be derived once the position is chosen. This paper proposes a derivation for a general force expression, in terms of a few carefully chosen position variables, which can be used to generate the force expression for any actuator position. Hence, this expression illustrates exactly how each of the position variables (called a, b and i in this paper, as defined later) affect the force output, and hence can be used to pick an appropriate actuator position, by choosing values for the position variables that give the desired result.
Bandlimited Field Reconstruction from Samples Obtained at Unknown Random Locations on a Grid
Venue: arXiv cs.IT 2016
Year: 2016

We study the sampling of spatial fields using sensors that are location-unaware but deployed according to a known statistical distribution. It has been shown that uniformly distributed location-unaware sensors cannot infer bandlimited fields due to the symmetry and shift-invariance of the field. This work studies asymmetric (nonuniform) distributions on location-unaware sensors that will enable bandlimited field inference. For the sake of analytical tractability, location-unaware sensors are restricted to a discrete grid. Oversampling followed by clustering of the samples using the probability distribution that governs sensor placement on the grid is used to infer the field . Based on this clustering algorithm, the main result of this work is to find the optimal probability distribution on sensor locations that minimizes the detection error-probability of the underlying spatial field. The proposed clustering algorithm is also extended to include the case of signal reconstruction in the presence of sensor noise by treating the distribution of the noisy samples as a mixture model and using clustering to estimate the mixture model parameters.
An Artificial Agent for Robust Image Registration
Venue: arXiv cs.CV 2016
Year: 2016

3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonly performed by optimizing an image matching metric as a cost function. However, this task is challenging due to the non-convex nature of the matching metric over the plausible registration parameter space and insufficient approaches for a robust optimization. As a result, current approaches are often customized to a specific problem and sensitive to image quality and artifacts. In this paper, we propose a completely different approach to image registration, inspired by how experts perform the task. We first cast the image registration problem as a "strategy learning" process, where the goal is to find the best sequence of motion actions (e.g. up, down, etc.) that yields image alignment. Within this approach, an artificial agent is learned, modeled using deep convolutional neural networks, with 3D raw image data as the input, and the next optimal action as the output. To cope with the dimensionality of the problem, we propose a greedy supervised approach for an end-to-end training, coupled with attention-driven hierarchical strategy. The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy). We demonstrate, on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-art registration methods by a large margin in terms of both accuracy and robustness.
Optimizing Quantiles in Preference-based Markov Decision Processes
Venue: arXiv cs.AI 2016
Year: 2016

In the Markov decision process model, policies are usually evaluated by expected cumulative rewards. As this decision criterion is not always suitable, we propose in this paper an algorithm for computing a policy optimal for the quantile criterion. Both finite and infinite horizons are considered. Finally we experimentally evaluate our approach on random MDPs and on a data center control problem.
When to Reset Your Keys: Optimal Timing of Security Updates via Learning
Venue: arXiv cs.LG 2016
Year: 2016

Cybersecurity is increasingly threatened by advanced and persistent attacks. As these attacks are often designed to disable a system (or a critical resource, e.g., a user account) repeatedly, it is crucial for the defender to keep updating its security measures to strike a balance between the risk of being compromised and the cost of security updates. Moreover, these decisions often need to be made with limited and delayed feedback due to the stealthy nature of advanced attacks. In addition to targeted attacks, such an optimal timing policy under incomplete information has broad applications in cybersecurity. Examples include key rotation, password change, application of patches, and virtual machine refreshing. However, rigorous studies of optimal timing are rare. Further, existing solutions typically rely on a pre-defined attack model that is known to the defender, which is often not the case in practice. In this work, we make an initial effort towards achieving optimal timing of security updates in the face of unknown stealthy attacks. We consider a variant of the influential FlipIt game model with asymmetric feedback and unknown attack time distribution, which provides a general model to consecutive security updates. The defender's problem is then modeled as a time associative bandit problem with dependent arms. We derive upper confidence bound based learning policies that achieve low regret compared with optimal periodic defense strategies that can only be derived when attack time distributions are known.
Blind Estimation of Sparse Multi-User Massive MIMO Channels
Venue: arXiv cs.IT 2016
Year: 2016

We provide a maximum likelihood formulation for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure. The main advantage of this approach is the fact that the overhead due to pilot sequences can be reduced dramatically especially when operating at low SNR per antenna. Thereby, the sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. On the other hand, as only the sparsity is assumed, the proposed method is robust with respect to the statistical properties of the channel and data and allows the estimation in rapidly time-varying scenarios and eventually the separation of interfering users from adjacent base stations. Additionally, a performance limit is derived based on the clairvoyant Cramér Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy with reasonable computational complexity and limited model assumptions.
A New Method for Classification of Datasets for Data Mining
Venue: arXiv cs.LG 2016
Year: 2016

Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3. In our proposed algorithm attributes are divided into groups and then we apply the selection measure 5 for these groups. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Training Bit Fully Convolutional Network for Fast Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation. However, a typical FCN usually requires lots of floating point computation and large run-time memory, which effectively limits its usability. We propose a method to train Bit Fully Convolution Network (BFCN), a fully convolutional neural network that has low bit-width weights and activations. Because most of its computation-intensive convolutions are accomplished between low bit-width numbers, a BFCN can be accelerated by an efficient bit-convolution implementation. On CPU, the dot product operation between two bit vectors can be reduced to bitwise operations and popcounts, which can offer much higher throughput than 32-bit multiplications and additions. To validate the effectiveness of BFCN, we conduct experiments on the PASCAL VOC 2012 semantic segmentation task and Cityscapes. Our BFCN with 1-bit weights and 2-bit activations, which runs 7.8x faster on CPU or requires less than 1\% resources on FPGA, can achieve comparable performance as the 32-bit counterpart.
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
Venue: arXiv cs.CV 2016
Year: 2016

Automatic image synthesis research has been rapidly growing with deep networks getting more and more expressive. In the last couple of years, we have observed images of digits, indoor scenes, birds, chairs, etc. being automatically generated. The expressive power of image generators have also been enhanced by introducing several forms of conditioning variables such as object names, sentences, bounding box and key-point locations. In this work, we propose a novel deep conditional generative adversarial network architecture that takes its strength from the semantic layout and scene attributes integrated as conditioning variables. We show that our architecture is able to generate realistic outdoor scene images under different conditions, e.g. day-night, sunny-foggy, with clear object boundaries.
Mitigation of relative intensity noise of Quantum Dash mode-locked lasers for PAM4 based optical interconnects using encoding techniques
Venue: arXiv cs.NI 2016
Year: 2016

Quantum Dash (Q-Dash) Passively Mode-Locked Lasers (PMLLs) exhibit significant low frequency Relative Intensity Noise (RIN), due to the high Mode Partition Noise (MPN), which prevents the implementation of multilevel amplitude modulation formats such as PAM4. The authors demonstrate low frequency RIN mitigation by employing 8B/10B and Manchester encoding with PAM4 modulation format. These encoding techniques reduce the overlap between the modulation spectral content and the low-frequency RIN of the Q-Dash devices, at the expense of increased overhead. The RIN of the 33.6 GHz free spectral range Q-Dash PMLL was characterized, and the results obtained show very high levels of RIN from DC to 4 GHz, but low levels for higher frequencies. The performance improvement for 28 GBaud 8B/10B and Manchester encoded PAM4 signal has been demonstrated compared to the case when no encoding is used. Finally, the effect of RIN on the system performance was demonstrated by comparing the Bit Error Rate (BER) performance of the PAM4 signaling obtained with an External Cavity Laser (ECL) to those obtained with Q-Dash PMLL.
Performance Evaluation of Caching Policies in NDN - an ICN Architecture
Venue: arXiv cs.NI 2016
Year: 2016

Information Centric Networking (ICN) advocates the philosophy of accessing the content independent of its location. Owing to this location independence in ICN, the routers en-route can be enabled to cache the content to serve the future requests for the same content locally. Several ICN architectures have been proposed in the literature along with various caching algorithms for caching and cache replacement at the routers en-route. The aim of this paper is to critically evaluate various caching policies using Named Data Networking (NDN), an ICN architecture proposed in literature. We have presented the performance comparison of different caching policies naming First In First Out (FIFO), Least Recently Used (LRU), and Universal Caching (UC) in two network models; Watts-Strogatz (WS) model (suitable for dense short link networks such as sensor networks) and Sprint topology (better suited for large Internet Service Provider (ISP) networks) using ndnSIM, an ns3 based discrete event simulator for NDN architecture. Our results indicate that UC outperforms other caching policies such as LRU and FIFO and makes UC a better alternative for both sensor networks and ISP networks.
Neural Document Embeddings for Intensive Care Patient Mortality Prediction
Venue: arXiv cs.CL 2016
Year: 2016

We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes. Proposing a convolutional document embedding approach, our empirical investigation using the MIMIC-III intensive care database shows significant performance gains compared to previously employed methods such as latent topic distributions or generic doc2vec embeddings. These improvements are especially pronounced for the difficult problem of post-discharge mortality prediction.
Extensive Large-Scale Study of Error in Samping-Based Distinct Value Estimators for Databases
Venue: arXiv cs.DB 2016
Year: 2016

The problem of distinct value estimation has many applications. Being a critical component of query optimizers in databases, it also has high commercial impact. Many distinct value estimators have been proposed, using various statistical approaches. However, characterizing the errors incurred by these estimators is an open problem: existing analytical approaches are not powerful enough, and extensive empirical studies at large scale do not exist. We conduct an extensive large-scale empirical study of 11 distinct value estimators from four different approaches to the problem over families of Zipfian distributions whose parameters model real-world applications. Our study is the first that \emph{scales to the size of a billion-rows} that today's large commercial databases have to operate in. This allows us to characterize the error that is encountered in real-world applications of distinct value estimation. By mining the generated data, we show that estimator error depends on a key latent parameter --- the average uniform class size --- that has not been studied previously. This parameter also allows us to unearth error patterns that were previously unknown. Importantly, ours is the first approach that provides a framework for \emph{visualizing the error patterns} in distinct value estimation, facilitating discussion of this problem in enterprise settings. Our characterization of errors can be used for several problems in distinct value estimation, such as the design of hybrid estimators. This work aims at the practitioner and the researcher alike, and addresses questions frequently asked by both audiences.
Object-Centric Representation Learning from Unlabeled Videos
Venue: arXiv cs.CV 2016
Year: 2016

Supervised (pre-)training currently yields state-of-the-art performance for representation learning for visual recognition, yet it comes at the cost of (1) intensive manual annotations and (2) an inherent restriction in the scope of data relevant for learning. In this work, we explore unsupervised feature learning from unlabeled video. We introduce a novel object-centric approach to temporal coherence that encourages similar representations to be learned for object-like regions segmented from nearby frames. Our framework relies on a Siamese-triplet network to train a deep convolutional neural network (CNN) representation. Compared to existing temporal coherence methods, our idea has the advantage of lightweight preprocessing of the unlabeled video (no tracking required) while still being able to extract object-level regions from which to learn invariances. Furthermore, as we show in results on several standard datasets, our method typically achieves substantial accuracy gains over competing unsupervised methods for image classification and retrieval tasks.
Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations
Venue: arXiv cs.IT 2016
Year: 2016

The Internet of Things (IoT) promises ubiquitous connectivity of everything everywhere, which represents the biggest technology trend in the years to come. It is expected that by 2020 over 25 billion devices will be connected to cellular networks; far beyond the number of devices in current wireless networks. Machine-to-Machine (M2M) communications aims at providing the communication infrastructure for enabling IoT by facilitating the billions of multi-role devices to communicate with each other and with the underlying data transport infrastructure without, or with little, human intervention. Providing this infrastructure will require a dramatic shift from the current protocols mostly designed for human-to-human (H2H) applications. This article reviews recent 3GPP solutions for enabling massive cellular IoT and investigates the random access strategies for M2M communications, which shows that cellular networks must evolve to handle the new ways in which devices will connect and communicate with the system. A massive non-orthogonal multiple access (NOMA) technique is then presented as a promising solution to support a massive number of IoT devices in cellular networks, where we also identify its practical challenges and future research directions.
Application of Microgrids in Supporting Distribution Grid Flexibility
Venue: arXiv cs.SY 2016
Year: 2016

Distributed renewable energy resources have attracted significant attention in recent years due to the falling cost of the renewable energy technology, extensive federal and state incentives, and the application in improving load-point reliability. This growing proliferation, however, is changing the traditional consumption load curves by adding considerable levels of variability and further challenging the electricity supply-demand balance. In this paper, the application of microgrids in effectively capturing the distribution network net load variability, caused primarily by the prosumers, is investigated. Microgrids provide a viable and localized solution to this challenge while removing the need for costly investments by the electric utility on reinforcing the existing electricity infrastructure. A flexibility-oriented microgrid optimal scheduling model is proposed and developed to coordinate the microgrid net load with the aggregated consumers/prosumers net load in the distribution network with a focus on ramping issues. The proposed coordination is performed to capture both inter-hour and intra-hour net load variabilities. Numerical simulations on a test distribution feeder with one microgrid and several consumers and prosumers exhibit the effectiveness of the proposed model.
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects
Venue: arXiv cs.CV 2016
Year: 2016

This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images. A novel structured prediction approach is considered, where 3D poses are represented as skeletal models that naturally operate on manifolds. Given an input depth image, the problem of predicting the most proper articulation of underlying skeletal model is thus formulated as sequentially searching for the optimal skeletal configuration. This is subsequently addressed by convolutional neural nets trained end-to-end to render sequential prediction of the joint locations as regressing a set of tangent vectors of the underlying manifolds. Our approach is examined on various articulated objects including human hand, mouse, and fish benchmark datasets. Empirically it is shown to deliver highly competitive performance with respect to the state-of-the-arts, while operating in real-time (over 30 FPS).
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we study the problem of semantic annotation on 3D models that are represented as shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parameterizing kernels in the spectral domain spanned by graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strive to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single shape, and how to share information across related but different shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parameterization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested our SyncSpecCNN on various tasks, including 3D shape part segmentation and 3D keypoint prediction. State-of-the-art performance has been achieved on all benchmark datasets.
Storage Management in Modern Electricity Power Grids
Venue: arXiv cs.SY 2016
Year: 2016

This letter introduces a method to manage energy storage in electricity grids. Starting from the stochastic characterization of electricity generation and demand, we propose an equation that relates the storage level for every time-step as a function of its previous state and the realized surplus/deficit of electricity. Therefrom, we can obtain the probability that, in the next time-step: (i) there is a generation surplus that cannot be stored, or (ii) there is a demand need that cannot be supplied by the available storage. We expect this simple procedure can be used as the basis of electricity self-management algorithms in micro-level (e.g. individual households) or in meso-level (e.g. groups of houses).
How Are Programs Found? Speculating About Language Ergonomics With Curry-Howard
Venue: arXiv cs.PL 2016
Year: 2016

Functional languages with strong static type systems have beneficial properties to help ensure program correctness and reliability. Surprisingly, their practical significance in applications is low relative to other languages lacking in those dimensions. In this paper, the programs-as-proofs analogy is taken seriously to gain speculative insights by analysis of creation habits in the proof-centric discipline of mathematics. Viewed in light of this analogy, a sampling of mathematicians' attitudes towards formal proof suggests that the crucial role of intuition and experimentation in programming tasks may be under appreciated, hinting at a possible explanation of the challenges rigorously disciplined languages face in practical applications.