Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel ReRAM-Based Processing-in-Memory Architecture for Graph Traversal

Graph algorithms such as graph traversal have been gaining ever-increasing importance in the era of big data. However, graph processing on traditional architectures issues many random and irregular memory accesses, leading to a huge number of data movements and the consumption of very large amounts of energy. To minimize the waste of memory bandwidth, we investigate utilizing processing-in-memory (PIM), combined with non-volatile metal-oxide resistive random access memory (ReRAM), to improve both computation and I/O performance. We propose a new ReRAM-based processing-in-memory architecture called RPBFS, in which graph data can be persistently stored and processed in place. We study the problem of graph traversal, and we design an efficient graph traversal algorithm in RPBFS. Benefiting from low data movement overhead and high bank-level parallel computation, RPBFS shows a significant performance improvement compared with both the CPU-based and the GPU-based BFS implementations. On a suite of real-world graphs, our architecture yields a speedup in graph traversal performance of up to 33.8×, and achieves a reduction in energy over conventional systems of up to 142.8×.
Learning Time-Efficient Deep Architectures with Budgeted Super Networks

Learning neural network architectures is a way to discover new highly predictive models. We propose to focus on this problem from a different perspective where the goal is to discover architectures efficient in terms of both prediction quality and computation cost, e.g time in milliseconds, number of operations. For instance, our approach is able to solve the following task: find the best neural network architecture (in a very large set of possible architectures) able to predict well in less than 100 milliseconds on my mobile phone. Our contribution is based on a new family of models called Budgeted Super Networks that are learned using reinforcement-learning inspired techniques applied to a budgeted learning objective function which includes the computation cost during disk/memory operations at inference. We present a set of experiments on computer vision problems and show the ability of our method to discover efficient architectures in terms of both predictive quality and computation time.
Large-Scale Human Activity Mapping using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.
Joint Dynamic MRI Reconstruction and Aggregated Motion Estimation with Optical Flow Constraint

This paper proposes a novel framework to jointly reconstruct the dynamic magnetic resonance images (DMRI) and estimate the motion vectors from the under-sampled measurements. Due to the inherent motion effects in DMRI acquisition, reconstruction of DMRI using motion estimation/compensation has been studied under a compressed sensing (CS) scheme. In this paper, by embedding the intensity based optical flow (OF) constraint into the traditional CS scheme, we are able to couple the DMRI reconstruction with vector motion estimation. The resulting optimization problem is then solved by a primal-dual algorithm with linesearch due to its efficiency when dealing with non-differentiable problems. Moreover, the proposed framework is capable of handling a wide class of prior information (regularizations) for DMRI reconstruction, such as sparsity, low rank, total variation. In order to reduce the computational cost, the OF constraint is employed in a specific coarse scale. Experiments on various DMRI data, ranging from in vivo lung data to simulated phantom, validate the reconstruction quality improvement using the proposed scheme in comparison to several state-of-the-art algorithms.
Model-Based Planning in Discrete Action Spaces

Planning actions using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity over model-free RL methods, reuse of learned models across different tasks, and the ability to perform efficient gradient-based optimization in continuous action spaces. However, this approach does not apply straightforwardly when the action space is discrete, which may have limited its adoption. In this work, we introduce two discrete planning tasks inspired by existing question-answering datasets and show that it is in fact possible to effectively perform planning via backprop in discrete action spaces using two simple yet principled modifications. Our experiments show that this approach can significantly outperform model-free RL based methods and supervised imitation learners.
Convolutional Channel Features For Pedestrian, Face and Edge Detection

In this paper, we revisit the multiple channel features approach proposed by Dollar et al., which has shown excellent performances in various computer vision tasks. Enlightened by the ConvNets, we introduce an extended version of multiple channel features called Convolutional Channel Features (CCF), which transfers low-level features from off-the-shelf ConvNet models to feed the boosting classifiers based on decision trees. With the combination of CNN features and decision trees, CCF benefits from the rich capacity, robustness and sparsity in feature representation, as well as more efficiency in computation and storage during inference and learning process. Similar to multiple channel features, CCF is capable to solve diverse vision problems in a sliding window manner, and the computation cost in CCF multi-scale feature pyramid construction can be further reduced with power law based approximation in nearby scales and patchwork for shared convolution. We investigate into a large design space of CCF and show with experiments that CCF achieves leading performances in pedestrian detection, face detection, edge detection and object proposal generation. Codes are available at https://bitbucket.org/binyangderek/ccf.
Learning to Detect Red Lesions in Fundus Photographs: An Ensemble Approach based on Deep Learning

Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Moreover, it provides comprehensive feedback that is easy to assess by the physicians. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a convolutional neural network (CNN) are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our system is publicly available at https://github.com/ignaciorlando/red-lesion-detection.
Mnemonic Reader for Machine Comprehension

Recently, several end-to-end neural models have been proposed for machine comprehension tasks. Typically, these models use attention mechanisms to capture the complicated interaction between the context and the query and then point the boundary of answer. To better point the correct answer, we introduce the Mnemonic Reader for machine comprehension tasks, which enhance the attention reader in two aspects. Firstly, we use a self-alignment attention to model the long-distance dependency among context words, and obtain query-aware and selfaware contextual representation for each word in the context. Second, we use a memory-based query-dependent pointer to predict the answer, which integrates both explicit and implicit query information, such as query category. Our experimental evaluations show that our model obtains the state-of-the-art result on the large-scale machine comprehension benchmarks SQuAD.
Noise-adaptive Margin-based Active Learning for Multi-dimensional Data

We present and analyze an adaptive margin-based algorithm that actively learns the optimal linear separator for multi-dimensional data. The algorithm has the capacity of adapting to unknown level of label noise in the underlying distribution, making it suitable for model selection under the active learning setting. Compared to other alternative agnostic active learning algorithms, our proposed method is much simpler and achieves the optimal convergence rate in query budget T and data dimension d, if logarithm factors are ignored. Furthermore, our algorithm can handle classification loss functions other than the 0-1 loss, such as hinge and logistic loss, and hence is computationally feasible.
Online Convolutional Sparse Coding

Convolutional sparse coding (CSC) improves upon sparse coding to learn shift-invariant dictionaries on whole data. Existing methods in CSC are optimized in batch mode and need to store all data and codes in the memory. As CSC uses a large set of codes to represent a single instance in the dataset, the memory cost is intractable in face of large-scale challenge. In this paper, we propose online CSC (OCSC), which processes each sample only once and updates the dictionaries with the help of low-dimensional history matrices recording statistics about past samples. Hence the memory and computational cost per iteration are significantly reduced. And the dynamic evolving content within data is captured through online learning. We show that convergence to some stationary point of the CSC problem is still guaranteed by our OCSC algorithm. Moreover, we extend the proposed method to a even lower computational and memory cost by approximating a large set of dictionaries by linear combinations of a smaller number of base dictionaries. Extensive experiments results validate OCSC’s scalability in terms of memory and time, and the effectiveness of reconstructing better or comparable images.
Failure-based Lifespan Performance Analysis of Network Fabric in Modular Data Centers: Toward Deployment in Canada's North

Data centers have evolved from a passive element of compute infrastructure to become an active and core part of any ICT solution. Modular data centers are a promising design approach to improve resiliency of data centers, and they can play a key role in deploying ICT infrastructure in remote and in- hospitable environments with low temperatures and hydro- and wind-electric capabilities. Modular data centers can also survive even with lack of contin- uous physical maintenance and support. Generally, the most critical part of a data center is its network fabric that could impede the whole system even if all other components are fully functional. In this work, a complete failure analysis of modular data centers using failure models of various components including servers, switches, and links is performed using a proposed Monte- Carlo approach. This approach allows us to calculate the performance of a design along its lifespan even at the terminal stages. A class of modified
Identity Alignment by Noisy Pixel Removal

Identity alignment models assume precisely annotated images manually. Human labelling is unrealistic on large sized imagery data. Detection models introduce varying amount of noise and hamper identity alignment performance. In this work, we propose to refine images by removing the undesired pixels. This is achieved by learning to eliminate less informative pixels in identity alignment. To this end, we formulate a method of automatically detecting and removing identity class irrelevant pixels in auto-detected bounding boxes. Experiments validate the benefits of our model in improving identity alignment.
Deep Neural Networks for Sketch Recognition

Deep Neural Networks (DNNs) have recently outperformed traditional object recognition algorithms on multiple large-scale datasets, such as ImageNet. However, the model trained on ImageNet fails on recognising the sketches, because the data source is dominated by photos and all kinds of sketches are roughly labelled as 'cartoon' rather than their own categorises (e.g., 'cat'). Most of sketch recognition methods still heavily rely on the hand-craft feature extraction techniques, thus it is interesting to know whether DNNs can eliminate the needs of specific feature engineering in this area, and how the architecture is designed for sketch recognition purpose. To the best of our knowledge, it is the first deep neural network model for sketch classification, and it has outperformed state-of-the-art results in the TU-Berlin sketch benchmark. Based on that, we outline a sketch image retrieval system in a unified neural network framework.
Practical Differential Privacy for SQL Queries Using Elastic Sensitivity

Differential privacy promises to enable general data analytics while protecting individual privacy, but existing differential privacy mechanisms do not support the wide variety of features and databases used in real-world SQL-based analytics systems. This paper presents the first practical approach for differential privacy of SQL queries. Using 8.1 million real-world queries, we conduct an empirical study to determine the requirements for practical differential privacy, and discuss limitations of previous approaches in light of these requirements. To meet these requirements we propose elastic sensitivity, a novel method for approximating the local sensitivity of queries with general equijoins. We prove that elastic sensitivity is an upper bound on local sensitivity and can therefore be used to enforce differential privacy using any local sensitivity-based mechanism. We build FLEX, a practical end-to-end system to enforce differential privacy for SQL queries using elastic sensitivity. We demonstrate that FLEX is compatible with any existing database, can enforce differential privacy for real-world SQL queries, and incurs negligible (0.03%) performance overhead.
Direction-Constrained Rectangle Escape Routing

Given a set of buses with available escape directions inside a chip, a two-phase algorithm is proposed to assign one feasible escape direction onto any bus such that the number of used layers is minimized and to allocate the pin rectangle and the projection rectangle of any escape bus onto the minimized layers in direction-constrained rectangle escape routing. In our proposed algorithm, based on the concept of two-dimensional maximum density inside a chip, the escape directions of the buses can be first assigned to minimize the number of the used layers by iteratively eliminating unnecessary escape directions for any bus inside a chip. Furthermore, based on the construction of the represented intervals and the assignment constraints for the escape buses, a modified left-edge algorithm can be used to allocate all the escape buses onto the minimized layers. Compared with Ma’s integer linear program (ILP)-based algorithm [10] using lp_solve and Gurobi in rectangle escape routing, the experimental results show that our proposed algorithm obtains the same results but reduces CPU time by 94.2% and 35.7% when using lp_solve and Gurobi for 16 tested examples with no direction constraint on average, respectively. Compared with the modified algorithm from Ma's ILP-based algorithm [10] using lp_solve and Gurobi in direction-constrained rectangle escape routing, the experimental results show that our proposed algorithm obtains the same results but reduces CPU time by 94.3% and 37.7% when using lp_solve and Gurobi for 16 tested examples with direction constraints on average, respectively. Besides that, compared with Yan’s iterative algorithm, the experimental results show that our proposed algorithm increases CPU time by 1.0% to reduce the number of used layers 11.1% for 16 tested examples on average.
Utopias of Participation

This essay addresses the question of how participatory design (PD) researchers and practitioners can pursue commitments to social justice and democracy while retaining commitments to reflective practice, the voices of the marginal, and design experiments “in the small. ” I argue that contemporary feminist utopianism has, on its own terms, confronted similar issues, and I observe that it and PD pursue similar agendas, but with complementary strengths. I thus propose a cooperative engagement between feminist utopianism and PD at the levels of theory, methodology, and on-the-ground practice. I offer an analysis of a case—an urban renewal project in Taipei, Taiwan—as a means of exploring what such a cooperative engagement might entail. I argue that feminist utopianism and PD have complementary strengths that could be united to develop and to propose alternative futures that reflect democratic values and procedures, emerging technologies and infrastructures as design materials, a commitment to marginalized voices (and the bodies that speak them), and an ambitious, even literary, imagination.
SAM: Semantic Attribute Modulated Language Modeling

As a fundamental task in natural language processing, language modeling aims to estimate the distribution of the word sequences. However, most existing algorithms have focused on the main text while often ignoring the vastly-accessible semantic attributes, e.g., titles, authors, sentiments and tags. To address this issue, we build three text datasets with a diversity of semantic attributes, and propose Semantic Attribute Modulated (SAM) language modeling, a novel language modeling framework that incorporates various attributes. Attributes are selected automatically with an attribute attention mechanism. We empirically examine the language model perplexities of several typical corpora, and demonstrate the superiority of our model with different combinations of the attributes. Lyric generation by taking both the title and author attributes into account further demonstrates the effectiveness of our method.
HYDRA/C.Mmp, An Experimental Computer System

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Provenance Analytics for Workflow-Based Computational Experiments

Until not long ago, manually capturing and storing provenance from scientific experiments were constant concerns for scientists. With the advent of computational experiments (modeled as scientific workflows) and Scientific Workflow Management Systems, produced and consumed data, as well as the provenance of a given experiment, are automatically managed, so provenance capturing and storing in such a context is no longer a major concern. Similarly to several existing big data problems, the bottom line is now on how to analyze the large amounts of provenance data generated by workflow executions and how to be able to extract useful knowledge of this data. In this context, this article surveys the current state of the art on provenance analytics by presenting the key initiatives that have been taken to support provenance data analysis. We also contribute by proposing a taxonomy to classify elements related to provenance analytics.
Know-Evolve: Deep Reasoning in Temporal Knowledge Graphs

Knowledge Graphs are important tools to model multi-relational data that serves as information pool for various applications. Traditionally, these graphs are considered to be static in nature. However, recent availability of large scale event-based interaction data has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such graphs is not yet well understood. In this paper, we present a novel deep evolutionary knowledge network architecture to learn entity embeddings that can dynamically and non-linearly evolve over time. We further propose a multivariate point process framework to model the occurrence of a fact (edge) in continuous time. To facilitate temporal reasoning, the learned embeddings are used to compute relationship score that further parametrizes intensity function of the point process. We demonstrate improved performance over various existing relational learning models on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to any prior reasoning approaches in multi-relational setting.
Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network

Since its development, ingestible wireless endoscopy is considered to be a painless diagnostic method to detect a number of diseases inside GI tract. Medical related engineering companies have made significant improvements in this technology in last decade; however, some major limitations still residue. Localization of the next generation steerable endoscopic capsule robot in six-degree-of freedom (6 DoF) and active motion control are some of these limitations. The significance of localization capability concerns with the correct diagnosis of the disease area. This paper presents a very robust 6-DoF localization method based on supervised training of an architecture consisting of recurrent networks (RNN) embedded into a convolutional neural network (CNN) to make use of both just-in-moment information obtained by CNN and correlative information across frames obtained by RNN. To our knowledge, the idea of embedding RNNs into a CNN architecture is for the first time proposed in literature. The experimental results show that the proposed RNN-in-CNN architecture performs very well for endoscopic capsule robot localization in cases reflection distortions, noise, sudden camera movements and lack of distinguishable features.