OpenRaiser/Intern-Atlas huggingface dataset:
Polyhedra Circuits and Their Applications
Venue: arXiv cs.CG 2018
Year: 2018

We introduce polyhedra circuits. Each polyhedra circuit characterizes a geometric region in $\mathbb{R}^d$. They can be applied to represent a rich class of geometric objects, which include all polyhedra and the union of a finite number of polyhedra. They can be used to approximate a large class of $d$-dimensional manifolds in $\mathbb{R}^d$. Barvinok developed polynomial time algorithms to compute the volume of a rational polyhedra, and to count the number of lattice points in a rational polyhedra in a fixed dimensional space $\mathbb{R}^d$ with a fix $d$. Define $T_V(d,\, n)$ be the polynomial time in $n$ to compute the volume of one rational polyhedra, $T_L(d,\, n)$ be the polynomial time in $n$ to count the number of lattice points in one rational polyhedra with $d$ be a fixed dimensional number, $T_I(d,\, n)$ be the polynomial time in $n$ to solve integer linear programming time with $d$ be the fixed dimensional number, where $n$ is the total number of linear inequalities from input polyhedra. We develop algorithms to count the number of lattice points in the geometric region determined by a polyhedra circuit in $O\left(nd\cdot r_d(n)\cdot T_V(d,\, n)\right)$ time and to compute the volume of the geometric region determined by a polyhedra circuit in $O\left(n\cdot r_d(n)\cdot T_I(d,\, n)+r_d(n)T_L(d,\, n)\right)$ time, where $n$ is the number of input linear inequalities, $d$ is number of variables and $r_d(n)$ be the maximal number of regions that $n$ linear inequalities with $d$ variables partition $\mathbb{R}^d$.
Three dimensional Deep Learning approach for remote sensing image classification
Venue: arXiv cs.CV 2018
Year: 2018

Recently, a variety of approaches has been enriching the field of Remote Sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited faced to the rich spatio-spectral content of today's large datasets. It would seem intriguing to resort to Deep Learning (DL) based approaches at this stage with regards to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS datasets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is firstly to explore the performance of DL architectures for the RS hyperspectral dataset classification and secondly to introduce a new three-dimensional DL approach that enables a joint spectral and spatial information process. A set of three-dimensional schemes is proposed and evaluated. Experimental results based on well knownhyperspectral datasets demonstrate that the proposed method is able to achieve a better classification rate than state of the art methods with lower computational costs.
Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm
Venue: arXiv cs.OH 2018
Year: 2018

A new adopted evolutionary algorithm is presented in this paper to solve the non-smooth, non-convex and non-linear multi-area economic dispatch (MAED). MAED includes some areas which contains its own power generation and loads. By transmitting the power from the area with lower cost to the area with higher cost, the total cost function can be minimized greatly. The tie line capacity, multi-fuel generator and the prohibited operating zones are satisfied in this study. In addition, a new algorithm based on electro search optimization algorithm (ESOA) is proposed to solve the MAED optimization problem with considering all the constraints. In ESOA algorithm all probable moving states for individuals to get away from or move towards the worst or best solution needs to be considered. To evaluate the performance of the ESOA algorithm, the algorithm is applied to both the original economic dispatch with 40 generator systems and the multi-area economic dispatch with 3 different systems such as: 6 generators in 2 areas; and 40 generators in 4 areas. It can be concluded that, ESOA algorithm is more accurate and robust in comparison with other methods.
Detecting Dead Weights and Units in Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Deep Neural Networks are highly over-parameterized and the size of the neural networks can be reduced significantly after training without any decrease in performance. One can clearly see this phenomenon in a wide range of architectures trained for various problems. Weight/channel pruning, distillation, quantization, matrix factorization are some of the main methods one can use to remove the redundancy to come up with smaller and faster models. This work starts with a short informative chapter, where we motivate the pruning idea and provide the necessary notation. In the second chapter, we compare various saliency scores in the context of parameter pruning. Using the insights obtained from this comparison and stating the problems it brings we motivate why pruning units instead of the individual parameters might be a better idea. We propose some set of definitions to quantify and analyze units that don't learn and create any useful information. We propose an efficient way for detecting dead units and use it to select which units to prune. We get 5x model size reduction through unit-wise pruning on MNIST.
Formalizing Category Theory and Presheaf Models of Type Theory in Nuprl
Venue: arXiv cs.LO 2018
Year: 2018

This article is the first in a series of articles that explain the formalization of a constructive model of cubical type theory in Nuprl. In this document we discuss only the parts of the formalization that do not depend on the choice of base category. So, it spells out how we make the first steps of our formalization of cubical type theory.
Fairness Under Composition
Venue: arXiv cs.LG 2018
Year: 2018

Algorithmic fairness, and in particular the fairness of scoring and classification algorithms, has become a topic of increasing social concern and has recently witnessed an explosion of research in theoretical computer science, machine learning, statistics, the social sciences, and law. Much of the literature considers the case of a single classifier (or scoring function) used once, in isolation. In this work, we initiate the study of the fairness properties of systems composed of algorithms that are fair in isolation; that is, we study fairness under composition. We identify pitfalls of naive composition and give general constructions for fair composition, demonstrating both that classifiers that are fair in isolation do not necessarily compose into fair systems and also that seemingly unfair components may be carefully combined to construct fair systems. We focus primarily on the individual fairness setting proposed in [Dwork, Hardt, Pitassi, Reingold, Zemel, 2011], but also extend our results to a large class of group fairness definitions popular in the recent literature, exhibiting several cases in which group fairness definitions give misleading signals under composition.
Impact of Channel Models on the End-to-End Performance of mmWave Cellular Networks
Venue: arXiv cs.NI 2018
Year: 2018

Communication at mmWave frequencies is one of the major innovations of the fifth generation of cellular networks, because of the potential multi-gigabit data rate given by the large amounts of available bandwidth. The mmWave channel, however, makes reliable communications particularly challenging, given the harsh propagation environment and the sensitivity to blockage. Therefore, proper modeling of the mmWave channel is fundamental for accurate results in system simulations of mmWave cellular networks. Nonetheless, complex models, such as the 3GPP channel model for frequencies above 6 GHz, may introduce a significant overhead in terms of computational complexity. In this paper we investigate the trade offs related to the accuracy and the simplicity of the channel model in end-to-end network simulations, and the impact on the performance evaluation of transport protocols.
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning
Venue: arXiv cs.RO 2018
Year: 2018

Model-free Reinforcement Learning (RL) offers an attractive approach to learn control policies for high-dimensional systems, but its relatively poor sample complexity often forces training in simulated environments. Even in simulation, goal-directed tasks whose natural reward function is sparse remain intractable for state-of-the-art model-free algorithms for continuous control. The bottleneck in these tasks is the prohibitive amount of exploration required to obtain a learning signal from the initial state of the system. In this work, we leverage physical priors in the form of an approximate system dynamics model to design a curriculum scheme for a model-free policy optimization algorithm. Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance. BaRC is general, in that it can accelerate training of any model-free RL algorithm on a broad class of goal-directed continuous control MDPs. Its curriculum strategy is physically intuitive, easy-to-tune, and allows incorporating physical priors to accelerate training without hindering the performance, flexibility, and applicability of the model-free RL algorithm. We evaluate our approach on two representative dynamic robotic learning problems and find substantial performance improvement relative to previous curriculum generation techniques and naive exploration strategies.
Power-Temperature Stability and Safety Analysis for Multiprocessor Systems
Venue: arXiv cs.SY 2018
Year: 2018

Modern multiprocessor system-on-chips (SoCs) integrate multiple heterogeneous cores to achieve high energy efficiency. The power consumption of each core contributes to an increase in the temperature across the chip floorplan. In turn, higher temperature increases the leakage power exponentially, and leads to a positive feedback with nonlinear dynamics. This paper presents a power-temperature stability and safety analysis technique for multiprocessor systems. This analysis reveals the conditions under which the power-temperature trajectory converges to a stable fixed point. We also present a simple formula to compute the stable fixed point and maximum thermally-safe power consumption at runtime. Hardware measurements on a state-of-the-art mobile processor show that our analytical formulation can predict the stable fixed point with an average error of 2.6%. Hence, our approach can be used at runtime to ensure thermally safe operation and guard against thermal threats.
Offline Extraction of Indic Regional Language from Natural Scene Image using Text Segmentation and Deep Convolutional Sequence
Venue: arXiv cs.CV 2018
Year: 2018

Regional language extraction from a natural scene image is always a challenging proposition due to its dependence on the text information extracted from Image. Text Extraction on the other hand varies on different lighting condition, arbitrary orientation, inadequate text information, heavy background influence over text and change of text appearance. This paper presents a novel unified method for tackling the above challenges. The proposed work uses an image correction and segmentation technique on the existing Text Detection Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses standard PVAnet architecture to select features and non maximal suppression to detect text from image. Text recognition is done using combined architecture of MaxOut convolution neural network (CNN) and Bidirectional long short term memory (LSTM) network. After recognizing text using the Deep Learning based approach, the native Languages are translated to English and tokenized using standard Text Tokenizers. The tokens that very likely represent a location is used to find the Global Positioning System (GPS) coordinates of the location and subsequently the regional languages spoken in that location is extracted. The proposed method is tested on a self generated dataset collected from Government of India dataset and experimented on Standard Dataset to evaluate the performance of the proposed technique. Comparative study with a few state-of-the-art methods on text detection, recognition and extraction of regional language from images shows that the proposed method outperforms the existing methods.
Latent Convolutional Models
Venue: arXiv cs.CV 2018
Year: 2018

We present a new latent model of natural images that can be learned on large-scale datasets. The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space. After training, the new model provides a strong and universal image prior for a variety of image restoration tasks such as large-hole inpainting, superresolution, and colorization. To model high-resolution natural images, our approach uses latent spaces of very high dimensionality (one to two orders of magnitude higher than previous latent image models). To tackle this high dimensionality, we use latent spaces with a special manifold structure (convolutional manifolds) parameterized by a ConvNet of a certain architecture. In the experiments, we compare the learned latent models with latent models learned by autoencoders, advanced variants of generative adversarial networks, and a strong baseline system using simpler parameterization of the latent space. Our model outperforms the competing approaches over a range of restoration tasks.
An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation
Venue: arXiv cs.CL 2018
Year: 2018

With the rapid growth of Text sentiment analysis, the demand for automatic classification of electronic documents has increased by leaps and bound. The paradigm of text classification or text mining has been the subject of many research works in recent time. In this paper we propose a technique for text sentiment classification using term frequency- inverse document frequency (TF-IDF) along with Next Word Negation (NWN). We have also compared the performances of binary bag of words model, TF-IDF model and TF-IDF with next word negation (TF-IDF-NWN) model for text classification. Our proposed model is then applied on three different text mining algorithms and we found the Linear Support vector machine (LSVM) is the most appropriate to work with our proposed model. The achieved results show significant increase in accuracy compared to earlier methods.
Study of BEM-Type Channel Estimation Techniques for 5G Multicarrier Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate channel estimation techniques for 5G multicarrier systems. Due to the characteristics of the 5G application scenarios, channel estimation techniques have been tested in Orthogonal Frequency Division Multiplexing (OFDM) and Generalized Frequency Division Multiplexing (GFDM) systems. The orthogonality between subcarriers in OFDM systems permits inserting and extracting pilots without interference. However, due to pulse shaping, subcarriers in GFDM are no longer orthogonal and interfere with each other. Due to such interference, the channel estimation for GFDM is not trivial. A robust and low-complexity channel estimator can be obtained by combining a minimum mean-square error (MMSE) regularization and the basis expansion model (BEM) approach. In this work, we develop a BEM-type channel estimator along with a strategy to obtain the covariance matrix of the BEM coefficients. Simulations show that the BEM-type channel estimation shows performance close to that of the linear MMSE (LMMSE), even though there is no need to know the channel power delay profile, and its complexity is low.
Partitioning Compute Units in CNN Acceleration for Statistical Memory Traffic Shaping
Venue: arXiv cs.DC 2018
Year: 2018

The design complexity of CNNs has been steadily increasing to improve accuracy. To cope with the massive amount of computation needed for such complex CNNs, the latest solutions utilize blocking of an image over the available dimensions and batching of multiple input images to improve data reuse in the memory hierarchy. While there has been numerous works on maximizing data reuse, only a few studies have focused on the memory bottleneck caused by limited bandwidth. Bandwidth bottleneck can easily occur in CNN acceleration as CNN layers have different sizes with varying computation needs and as batching is typically performed over each CNN layer for an ideal data reuse. In this case, the data transfer demand for a layer can be relatively low or high compared to the computation requirement of the layer, and hence temporal fluctuations in memory access can be induced eventually causing bandwidth problems. In this paper, we first show that there exists a high degree of fluctuation in memory access to computation ratio depending on CNN layers and functions in the layer being processed by the compute units (cores), where the units are tightly synchronized to maximize data reuse. Then we propose a strategy of partitioning the compute units where the cores within each partition process a batch of input data synchronously to maximize data reuse but different partitions run asynchronously. As the partitions stay asynchronous and typically process different CNN layers at any given moment, the memory access traffic sizes of the partitions become statistically shuffled. Thus, the partitioning of compute units and asynchronous use of them make the total memory access traffic size be smoothened over time. We call this smoothing statistical memory traffic shaping, and we show that it can lead to 8.0 percent of performance gain on a commercial 64-core processor when running ResNet-50.
SubGram: Extending Skip-gram Word Representation with Substrings
Venue: arXiv cs.CL 2018
Year: 2018

Skip-gram (word2vec) is a recent method for creating vector representations of words ("distributed word representations") using a neural network. The representation gained popularity in various areas of natural language processing, because it seems to capture syntactic and semantic information about words without any explicit supervision in this respect. We propose SubGram, a refinement of the Skip-gram model to consider also the word structure during the training process, achieving large gains on the Skip-gram original test set.
Resetting Disturbance Observers with application in Compensation of bounded nonlinearities like Hysteresis in Piezo-Actuators
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents a novel nonlinear (reset) disturbance observer for dynamic compensation of bounded nonlinearities like hysteresis in piezoelectric actuators. Proposed Resetting Disturbance Observer (RDOB) utilizes a novel Constant-gain Lead-phase (CgLp) element based on the concept of reset control. The fundamental limitations of linear DOB which results in contradictory requirements and in a dependent design between DOB and feedback controller are analysed. Two different configurations of RDOB which attempt to alleviate these problems from different perspectives are presented and an example plant is used to highlight the improvement. Stability criteria are presented for both configurations. Performance improvement seen with both RDOB configurations compared to linear DOB is also verified on a practical piezoelectric setup for hysteresis compensation and results analysed.
Mining frequent items in unstructured P2P networks
Venue: arXiv cs.DS 2018
Year: 2018

Large scale decentralized systems, such as P2P, sensor or IoT device networks are becoming increasingly common, and require robust protocols to address the challenges posed by the distribution of data and the large number of peers belonging to the network. In this paper, we deal with the problem of mining frequent items in unstructured P2P networks. This problem, of practical importance, has many useful applications. We design P2PSS, a fully decentralized, gossip--based protocol for frequent items discovery, leveraging the Space-Saving algorithm. We formally prove the correctness and theoretical error bound. Extensive experimental results clearly show that P2PSS provides very good accuracy and scalability, also in the presence of highly dynamic P2P networks with churning. To the best of our knowledge, this is the first gossip--based distributed algorithm providing strong theoretical guarantees for both the Approximate Frequent Items Problem in Unstructured P2P Networks and for the frequency estimation of discovered frequent items.
Evaluating and Characterizing Incremental Learning from Non-Stationary Data
Venue: arXiv cs.LG 2018
Year: 2018

Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffective in this context. Overall, there is a lack of common testing practices. This paper thus presents a testbed for incremental non-stationary learning algorithms, based on specially designed synthetic datasets. Also, test results are reported for some well-known algorithms to show that the proposed methodology is effective at characterizing their strengths and weaknesses. It is expected that this methodology will provide a common basis for evaluating future contributions in the field.
Coupled Fluid Density and Motion from Single Views
Venue: arXiv cs.GR 2018
Year: 2018

We present a novel method to reconstruct a fluid's 3D density and motion based on just a single sequence of images. This is rendered possible by using powerful physical priors for this strongly under-determined problem. More specifically, we propose a novel strategy to infer density updates strongly coupled to previous and current estimates of the flow motion. Additionally, we employ an accurate discretization and depth-based regularizers to compute stable solutions. Using only one view for the reconstruction reduces the complexity of the capturing setup drastically and could even allow for online video databases or smart-phone videos as inputs. The reconstructed 3D velocity can then be flexibly utilized, e.g., for re-simulation, domain modification or guiding purposes. We will demonstrate the capacity of our method with a series of synthetic test cases and the reconstruction of real smoke plumes captured with a Raspberry Pi camera.
A Flow Formulation for Horizontal Coordinate Assignment with Prescribed Width
Venue: arXiv cs.DS 2018
Year: 2018

We consider the coordinate assignment phase of the well known Sugiyama framework for drawing directed graphs in a hierarchical style. The extensive literature in this area has given comparatively little attention to a prescribed width of the drawing. We present a minimum cost flow formulation that supports prescribed width and optionally other criteria like lower and upper bounds on the distance of neighbouring nodes in a layer or enforced vertical edges segments. In our experiments we demonstrate that our approach can compete with state-of-the-art algorithms.