OpenRaiser/Intern-Atlas huggingface dataset:
A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets
Venue: arXiv cs.CV 2017
Year: 2017

Following the rapidly growing digital image usage, automatic image categorization has become preeminent research area. It has broaden and adopted many algorithms from time to time, whereby multi-feature (generally, hand-engineered features) based image characterization comes handy to improve accuracy. Recently, in machine learning, pre-trained deep convolutional neural networks (DCNNs or ConvNets) have been that the features extracted through such DCNN can improve classification accuracy. Thence, in this paper, we further investigate a feature embedding strategy to exploit cues from multiple DCNNs. We derive a generalized feature space by embedding three different DCNN bottleneck features with weights respect to their Softmax cross-entropy loss. Test outcomes on six different object classification data-sets and an action classification data-set show that regardless of variation in image statistics and tasks the proposed multi-DCNN bottleneck feature fusion is well suited to image classification tasks and an effective complement of DCNN. The comparisons to existing fusion-based image classification approaches prove that the proposed method surmounts the state-of-the-art methods and produces competitive results with fully trained DCNNs as well.
Object-Level Context Modeling For Scene Classification with Context-CNN
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional Neural Networks (CNNs) have been used extensively for computer vision tasks and produce rich feature representation for objects or parts of an image. But reasoning about scenes requires integration between the low-level feature representations and the high-level semantic information. We propose a deep network architecture which models the semantic context of scenes by capturing object-level information. We use Long Short Term Memory(LSTM) units in conjunction with object proposals to incorporate object-object relationship and object-scene relationship in an end-to-end trainable manner. We evaluate our model on the LSUN dataset and achieve results comparable to the state-of-art. We further show visualization of the learned features and analyze the model with experiments to verify our model's ability to model context.
Theseus: Incentivizing Truth Discovery in Mobile Crowd Sensing Systems
Venue: arXiv cs.GT 2017
Year: 2017

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order to identify truthful values from (crowd) workers' noisy or even conflicting sensory data, truth discovery algorithms, which jointly estimate workers' data quality and the underlying truths through quality-aware data aggregation, have drawn significant attention. However, the power of these algorithms could not be fully unleashed in MCS systems, unless workers' strategic reduction of their sensing effort is properly tackled. To address this issue, in this paper, we propose a payment mechanism, named Theseus, that deals with workers' such strategic behavior, and incentivizes high-effort sensing from workers. We ensure that, at the Bayesian Nash Equilibrium of the non-cooperative game induced by Theseus, all participating workers will spend their maximum possible effort on sensing, which improves their data quality. As a result, the aggregated results calculated subsequently by truth discovery algorithms based on workers' data will be highly accurate. Additionally, Theseus bears other desirable properties, including individual rationality and budget feasibility. We validate the desirable properties of Theseus through theoretical analysis, as well as extensive simulations.
A Finitary Analogue of the Downward Löwenheim-Skolem Property
Venue: arXiv cs.LO 2017
Year: 2017

We present a model-theoretic property of finite structures, that can be seen to be a finitary analogue of the well-studied downward Löwenheim-Skolem property from classical model theory. We call this property as the *$\mathcal{L}$-equivalent bounded substructure property*, denoted $\mathcal{L}$-$\mathsf{EBSP}$, where $\mathcal{L}$ is either FO or MSO. Intuitively $\mathcal{L}$-$\mathsf{EBSP}$ states that a large finite structure contains a small "logically similar" substructure, where logical similarity means indistinguishability with respect to sentences of $\mathcal{L}$ having a given quantifier nesting depth. It turns out that this simply stated property is enjoyed by a variety of classes of interest in computer science: examples include various classes of posets, such as regular languages of words, trees (unordered, ordered or ranked) and nested words, and various classes of graphs, such as cographs, graph classes of bounded tree-depth, those of bounded shrub-depth and $n$-partite cographs. Further, $\mathcal{L}$-$\mathsf{EBSP}$ remains preserved in the classes generated from the above by operations that are implementable using quantifier-free translation schemes. We show that for natural tree representations for structures that all the aforementioned classes admit, the small and logically similar substructure of a large structure can be computed in time linear in the size of the representation, giving linear time fixed parameter tractable (f.p.t.) algorithms for checking $\mathcal{L}$ definable properties of the large structure. We conclude by presenting a strengthening of $\mathcal{L}$-$\mathsf{EBSP}$, that asserts "logical self-similarity at all scales" for a suitable notion of scale. We call this the *logical fractal* property and show that most of the classes mentioned above are indeed, logical fractals.
Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine
Venue: arXiv cs.CV 2017
Year: 2017

Computer based recognition and detection of abnormalities in ECG signals is proposed. For this purpose, the Support Vector Machines (SVM) are combined with the advantages of Hermite transform representation. SVM represent a special type of classification techniques commonly used in medical applications. Automatic classification of ECG could make the work of cardiologic departments faster and more efficient. It would also reduce the number of false diagnosis and, as a result, save lives. The working principle of the SVM is based on translating the data into a high dimensional feature space and separating it using a linear classificator. In order to provide an optimal representation for SVM application, the Hermite transform domain is used. This domain is proved to be suitable because of the similarity of the QRS complex with Hermite basis functions. The maximal signal information is obtained using a small set of features that are used for detection of irregular QRS complexes. The aim of the paper is to show that these features can be employed for automatic ECG signal analysis.
Obtaining the Current-Flux Relations of the Saturated PMSM by Signal Injection
Venue: arXiv cs.SY 2017
Year: 2017

This paper proposes a method based on signal injection to obtain the saturated current-flux relations of a PMSM from locked-rotor experiments. With respect to the classical method based on time integration, it has the main advantage of being completely independent of the stator resistance; moreover, it is less sensitive to voltage biases due to the power inverter, as the injected signal may be fairly large.
Monaural Audio Speaker Separation with Source Contrastive Estimation
Venue: arXiv cs.SD 2017
Year: 2017

We propose an algorithm to separate simultaneously speaking persons from each other, the "cocktail party problem", using a single microphone. Our approach involves a deep recurrent neural networks regression to a vector space that is descriptive of independent speakers. Such a vector space can embed empirically determined speaker characteristics and is optimized by distinguishing between speaker masks. We call this technique source-contrastive estimation. The methodology is inspired by negative sampling, which has seen success in natural language processing, where an embedding is learned by correlating and de-correlating a given input vector with output weights. Although the matrix determined by the output weights is dependent on a set of known speakers, we only use the input vectors during inference. Doing so will ensure that source separation is explicitly speaker-independent. Our approach is similar to recent deep neural network clustering and permutation-invariant training research; we use weighted spectral features and masks to augment individual speaker frequencies while filtering out other speakers. We avoid, however, the severe computational burden of other approaches with our technique. Furthermore, by training a vector space rather than combinations of different speakers or differences thereof, we avoid the so-called permutation problem during training. Our algorithm offers an intuitive, computationally efficient response to the cocktail party problem, and most importantly boasts better empirical performance than other current techniques.
Exploiting Trust Degree for Multiple-Antenna User Cooperation
Venue: arXiv cs.IT 2017
Year: 2017

For a user cooperation system with multiple antennas, we consider a trust degree based cooperation techniques to explore the influence of the trustworthiness between users on the communication systems. For the system with two communication pairs, when one communication pair achieves its quality of service (QoS) requirement, they can help the transmission of the other communication pair according to the trust degree, which quantifies the trustworthiness between users in the cooperation. For given trust degree, we investigate the user cooperation strategies, which include the power allocation and precoder design for various antenna configurations. For SISO and MISO cases, we provide the optimal power allocation and beamformer design that maximize the expected achievable rates while guaranteeing the QoS requirement. For a SIMO case, we resort to semidefinite relaxation (SDR) technique and block coordinate update (BCU) method to solve the corresponding problem, and guarantee the rank-one solutions at each step. For a MIMO case, as MIMO is the generalization of MISO and SIMO, the similarities among their problem structures inspire us to combine the methods from MISO and SIMO together to efficiently tackle MIMO case. Simulation results show that the trust degree information has a great effect on the performance of the user cooperation in terms of the expected achievable rate, and the proposed user cooperation strategies achieve high achievable rates for given trust degree.
Progression of Decomposed Local-Effect Action Theories
Venue: arXiv cs.AI 2017
Year: 2017

In many tasks related to reasoning about consequences of a logical theory, it is desirable to decompose the theory into a number of weakly-related or independent components. However, a theory may represent knowledge that is subject to change, as a result of executing actions that have effects on some of the initial properties mentioned in the theory. Having once computed a decomposition of a theory, it is advantageous to know whether a decomposition has to be computed again in the newly-changed theory (obtained from taking into account changes resulting from execution of an action). In the paper, we address this problem in the scope of the situation calculus, where a change of an initial theory is related to the notion of progression. Progression provides a form of forward reasoning; it relies on forgetting values of those properties, which are subject to change, and computing new values for them. We consider decomposability and inseparability, two component properties known from the literature, and contribute by 1) studying the conditions when these properties are preserved and 2) when they are lost wrt progression and the related operation of forgetting. To show the latter, we demonstrate the boundaries using a number of negative examples. To show the former, we identify cases when these properties are preserved under forgetting and progression of initial theories in local-effect basic action theories of the situation calculus. Our paper contributes to bridging two different communities in Knowledge Representation, namely research on modularity and research on reasoning about actions.
Fundamental Limits of DNA Storage Systems
Venue: arXiv cs.IT 2017
Year: 2017

Due to its longevity and enormous information density, DNA is an attractive medium for archival storage. In this work, we study the fundamental limits and tradeoffs of DNA-based storage systems under a simple model, motivated by current technological constraints on DNA synthesis and sequencing. Our model captures two key distinctive aspects of DNA storage systems: (1) the data is written onto many short DNA molecules that are stored in an unordered way and (2) the data is read by randomly sampling from this DNA pool. Under this model, we characterize the storage capacity, and show that a simple index-based coding scheme is optimal.
Riddim: A Rhythm Analysis and Decomposition Tool Based On Independent Subspace Analysis
Venue: arXiv cs.SD 2017
Year: 2017

The goal of this thesis was to implement a tool that, given a digital audio input, can extract and represent rhythm and musical time. The purpose of the tool is to help develop better models of rhythm for real-time computer based performance and composition. This analysis tool, Riddim, uses Independent Subspace Analysis (ISA) and a robust onset detection scheme to separate and detect salient rhythmic and timing information from different sonic sources within the input. This information is then represented in a format that can be used by a variety of algorithms that interpret timing information to infer rhythmic and musical structure. A secondary objective of this work is a "proof of concept" as a non-real-time rhythm analysis system based on ISA. This is a necessary step since ultimately it is desirable to incorporate this functionality in a real-time plug-in for live performance and improvisation.
Efficient Parallel Methods for Deep Reinforcement Learning
Venue: arXiv cs.LG 2017
Year: 2017

We propose a novel framework for efficient parallelization of deep reinforcement learning algorithms, enabling these algorithms to learn from multiple actors on a single machine. The framework is algorithm agnostic and can be applied to on-policy, off-policy, value based and policy gradient based algorithms. Given its inherent parallelism, the framework can be efficiently implemented on a GPU, allowing the usage of powerful models while significantly reducing training time. We demonstrate the effectiveness of our framework by implementing an advantage actor-critic algorithm on a GPU, using on-policy experiences and employing synchronous updates. Our algorithm achieves state-of-the-art performance on the Atari domain after only a few hours of training. Our framework thus opens the door for much faster experimentation on demanding problem domains. Our implementation is open-source and is made public at https://github.com/alfredvc/paac
Faster and Simpler Distributed Algorithms for Testing and Correcting Graph Properties in the CONGEST-Model
Venue: arXiv cs.DC 2017
Year: 2017

In this paper we present distributed testing algorithms of graph properties in the CONGEST-model [Censor-Hillel et al. 2016]. We present one-sided error testing algorithms in the general graph model. We first describe a general procedure for converting $ε$-testers with a number of rounds $f(D)$, where $D$ denotes the diameter of the graph, to $O((\log n)/ε)+f((\log n)/ε)$ rounds, where $n$ is the number of processors of the network. We then apply this procedure to obtain an optimal tester, in terms of $n$, for testing bipartiteness, whose round complexity is $O(ε^{-1}\log n)$, which improves over the $poly(ε^{-1} \log n)$-round algorithm by Censor-Hillel et al. (DISC 2016). Moreover, for cycle-freeness, we obtain a \emph{corrector} of the graph that locally corrects the graph so that the corrected graph is acyclic. Note that, unlike a tester, a corrector needs to mend the graph in many places in the case that the graph is far from having the property. In the second part of the paper we design algorithms for testing whether the network is $H$-free for any connected $H$ of size up to four with round complexity of $O(ε^{-1})$. This improves over the $O(ε^{-2})$-round algorithms for testing triangle freeness by Censor-Hillel et al. (DISC 2016) and for testing excluded graphs of size $4$ by Fraigniaud et al. (DISC 2016). In the last part we generalize the global tester by Iwama and Yoshida (ITCS 2014) of testing $k$-path freeness to testing the exclusion of any tree of order $k$. We then show how to simulate this algorithm in the CONGEST-model in $O(k^{k^2+1}\cdotε^{-k})$ rounds.
Achieving and Managing Availability SLAs with ITIL Driven Processes, DevOps, and Workflow Tools
Venue: arXiv cs.SE 2017
Year: 2017

System and application availability continues to be a fundamental characteristic of IT services. In recent years the IT Operations team at Wolters Kluwer CT Corporation has placed special focus on this area. Using a combination of goals, metrics, processes, organizational models, communication methods, corrective maintenance, root cause analysis, preventative engineering, automated alerting, and workflow automation significant progress has been made in meeting availability SLAs or Service Level Agreements. This paper presents the background of this work, approach, details of its implementation, and results. A special focus is provided on the use of a classical ITIL view as operationalized in an Agile and DevOps environment. Keywords: System Availability, Software Reliability, ITIL, Workflow Automation, Process Engineering, Production Support, Customer Support, Product Support, Change Management, Release Management, Incident Management, Problem Management, Organizational Design, Scrum, Agile, DevOps, Service Level Agreements, Software Measurement, Microsoft SharePoint.
Discovering Multiple Truths with a Hybrid Model
Venue: arXiv cs.DB 2017
Year: 2017

Many data management applications require integrating information from multiple sources. The sources may not be accurate and provide erroneous values. We thus have to identify the true values from conflicting observations made by the sources. The problem is further complicated when there may exist multiple truths (e.g., a book written by several authors). In this paper we propose a model called Hybrid that jointly makes two decisions: how many truths there are, and what they are. It considers the conflicts between values as important evidence for ruling out wrong values, while keeps the flexibility of allowing multiple truths. In this way, Hybrid is able to achieve both high precision and high recall.
A Closed-Form Model for Image-Based Distant Lighting
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present a new mathematical foundation for image-based lighting. Using a simple manipulation of the local coordinate system, we derive a closed-form solution to the light integral equation under distant environment illumination. We derive our solution for different BRDF's such as lambertian and Phong-like. The method is free of noise, and provides the possibility of using the full spectrum of frequencies captured by images taken from the environment. This allows for the color of the rendered object to be toned according to the color of the light in the environment. Experimental results also show that one can gain an order of magnitude or higher in rendering time compared to Monte Carlo quadrature methods and spherical harmonics.
A Proposed Architecture for Big Data Driven Supply Chain Analytics
Venue: arXiv cs.OH 2017
Year: 2017

Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support decision making is one of the sources of competitive advantage for organizations today. Enterprises are leveraging the power of analytics in formulating business strategy in every facet of their operations to mitigate business risk. Volatile global market scenario has compelled the organizations to redefine their supply chain management (SCM). In this paper, we have delineated the relevance of Big Data and its importance in managing end to end supply chains for achieving business excellence. A Big Data-centric architecture for SCM has been proposed that exploits the current state of the art technology of data management, analytics and visualization. The security and privacy requirements of a Big Data system have also been highlighted and several mechanisms have been discussed to implement these features in a real world Big Data system deployment in the context of SCM. Some future scope of work has also been pointed out. Keyword: Big Data, Analytics, Cloud, Architecture, Protocols, Supply Chain Management, Security, Privacy.
Fast Statistical Timing Analysis for Circuits with Post-Silicon Tunable Clock Buffers
Venue: arXiv cs.AR 2017
Year: 2017

Post-Silicon Tunable (PST) clock buffers are widely used in high performance designs to counter process variations. By allowing delay compensation between consecutive register stages, PST buffers can effectively improve the yield of digital circuits. To date, the evaluation of manufacturing yield in the presence of PST buffers is only possible using Monte Carlo simulation. In this paper, we propose an alternative method based on graph transformations, which is much faster, more than 1000 times, and computes a parametric minimum clock period. It also identifies the gates which are most critical to the circuit performance, therefore enabling a fast analysis-optimization flow.
Post-Route Refinement for High-Frequency PCBs Considering Meander Segment Alleviation
Venue: arXiv cs.AR 2017
Year: 2017

In this paper, we propose a post-processing framework which iteratively refines the routing results from an existing PCB router by removing dense meander segments. By swapping and detouring dense meander segments the proposed method can effectively alleviate accumulating crosstalk noise, while respecting pre-defined area constraints. Experimental results show more than 85% reduction of the meander segments and hence the noise cost.
Storage and Caching: Synthesis of Flow-based Microfluidic Biochips
Venue: arXiv cs.ET 2017
Year: 2017

Flow-based microfluidic biochips are widely used in lab- on-a-chip experiments. In these chips, devices such as mixers and detectors connected by micro-channels execute specific operations. Intermediate fluid samples are saved in storage temporarily until target devices become avail- able. However, if the storage unit does not have enough capacity, fluid samples must wait in devices, reducing their efficiency and thus increasing the overall execution time. Consequently, storage and caching of fluid samples in such microfluidic chips must be considered during synthesis to balance execution efficiency and chip area.
Sampling-based Buffer Insertion for Post-Silicon Yield Improvement under Process Variability
Venue: arXiv cs.AR 2017
Year: 2017

At submicron manufacturing technology nodes process variations affect circuit performance significantly. This trend leads to a large timing margin and thus overdesign to maintain yield. To combat this pessimism, post-silicon clock tuning buffers can be inserted into circuits to balance timing budgets of critical paths with their neighbors. After manufacturing, these clock buffers can be configured for each chip individually so that chips with timing failures may be rescued to improve yield. In this paper, we propose a sampling-based method to determine the proper locations of these buffers. The goal of this buffer insertion is to reduce the number of buffers and their ranges, while still maintaining a good yield improvement. Experimental results demonstrate that our algorithm can achieve a significant yield improvement (up to 35%) with only a small number of buffers.