OpenRaiser/Intern-Atlas huggingface dataset:
DeepPap: Deep Convolutional Networks for Cervical Cell Classification
Venue: arXiv cs.CV 2018
Year: 2018

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells - without prior segmentation - based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pre-trained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively re-sampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (AUC) (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross-validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.
Identifying single influential publications in a research field: New analysis opportunities of the CRExplorer
Venue: arXiv cs.DL 2018
Year: 2018

Reference Publication Year Spectroscopy (RPYS) has been developed for identifying the cited references (CRs) with the greatest influence in a given paper set (mostly sets of papers on certain topics or fields). The program CRExplorer (see www.crexplorer.net) was specifically developed by Thor, Marx, Leydesdorff, and Bornmann (2016a, 2016b) for applying RPYS to publication sets downloaded from Scopus or Web of Science. In this study, we present some advanced methods which have been newly developed for CRExplorer. These methods are able to identify and characterize the CRs which have been influential across a longer period (many citing years). The new methods are demonstrated in this study using all the papers published in Scientometrics between 1978 and 2016. The indicators N_TOP50, N_TOP25, and N_TOP10 can be used to identify those CRs which belong to the 50%, 25%, or 10% most frequently cited publications (CRs) over many citing publication years. In the Scientometrics dataset, for example, Lotka's (1926) paper on the distribution of scientific productivity belongs to the top 10% publications (CRs) in 36 citing years. Furthermore, the new version of CRExplorer analyzes the impact sequence of CRs across citing years. CRs can have below average (-), average (0), or above average (+) impact in citing years (whereby average is meant in the sense of expected values). The sequence (e.g. 00++---0--00) is used by the program to identify papers with typical impact distributions. For example, CRs can have early, but not late impact ("hot papers", e.g. +++---) or vice versa ("sleeping beauties", e.g. ---0000---++).
Satisfiability Modulo Transcendental Functions via Incremental Linearization
Venue: arXiv cs.LO 2018
Year: 2018

In this paper we present an abstraction-refinement approach to Satisfiability Modulo the theory of transcendental functions, such as exponentiation and trigonometric functions. The transcendental functions are represented as uninterpreted in the abstract space, which is described in terms of the combined theory of linear arithmetic on the rationals with uninterpreted functions, and are incrementally axiomatized by means of upper- and lower-bounding piecewise-linear functions. Suitable numerical techniques are used to ensure that the abstractions of the transcendental functions are sound even in presence of irrationals. Our experimental evaluation on benchmarks from verification and mathematics demonstrates the potential of our approach, showing that it compares favorably with delta-satisfiability /interval propagation and methods based on theorem proving.
Safe Exploration in Continuous Action Spaces
Venue: arXiv cs.AI 2018
Year: 2018

We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated. We show how to exploit the typically smooth dynamics of these systems and enable RL algorithms to never violate constraints during learning. Our technique is to directly add to the policy a safety layer that analytically solves an action correction formulation per each state. The novelty of obtaining an elegant closed-form solution is attained due to a linearized model, learned on past trajectories consisting of arbitrary actions. This is to mimic the real-world circumstances where data logs were generated with a behavior policy that is implausible to describe mathematically; such cases render the known safety-aware off-policy methods inapplicable. We demonstrate the efficacy of our approach on new representative physics-based environments, and prevail where reward shaping fails by maintaining zero constraint violations.
Modeling of languages for tensor manipulation
Venue: arXiv cs.PL 2018
Year: 2018

Numerical applications and, more recently, machine learning applications rely on high-dimensional data that is typically organized into multi-dimensional tensors. Many existing frameworks, libraries, and domain-specific languages support the development of efficient code for manipulating tensors and tensor expressions. However, such frameworks and languages that are used in practice often lack formal specifications. The present report formally defines a model language for expressing tensor operations. The model language is simple and yet general enough so that it captures the fundamental tensor operations common to most existing languages and frameworks. It is shown that the given formal semantics are sensible, in the sense that well-typed programs in the model language execute correctly, without error. Moreover, an alternative implementation of the model language is formally defined. The alternative implementation introduces padding into the storage of tensors, which may benefit performance on modern hardware platforms. Based on their formal definitions, the original implementation of the model language and the implementation with padding are proven equivalent. Finally, some possible extensions of the presented model language for tensor manipulation are discussed.
Constructing LDPC Codes from Partition and Latin-Style Splicing
Venue: arXiv cs.IT 2018
Year: 2018

A novel method guaranteeing nondecreasing girth is presented for constructing longer low-density parity-check (LDPC) codes from shorter ones. The parity-check matrix of a shorter base code is decomposed into N (N>=2) non-overlapping components with the same size. Then, these components are combined together to form the parity-check matrix of a longer code, according to a given N*N Latin square. To illustrate this method, longer quasi-cyclic (QC) LDPC codes are obtained with girth at least eight and satisfactory performance, via shorter QC-LDPC codes with girth eight but poor performance. The proposed method naturally includes several well-known methods as special cases, but is much more general compared with these existing approaches.
Mirrored and Hybrid Disk Arrays: Organization, Scheduling, Reliability, and Performance
Venue: arXiv cs.DC 2018
Year: 2018

Basic mirroring (BM) classified as RAID level 1 replicates data on two disks, thus doubling disk access bandwidth for read requests. RAID1/0 is an array of BM pairs with balanced loads due to striping. When a disk fails the read load on its pair is doubled, which results in halving the maximum attainable bandwidth. We review RAID1 organizations which attain a balanced load upon disk failure, but as shown by reliability analysis tend to be less reliable than RAID1/0. Hybrid disk arrays which store XORed instead of replicated data tend to have a higher reliability than mirrored disks, but incur a higher overhead in updating data. Read request response time can be improved by processing them at a higher priority than writes, since they have a direct effect on application response time. Shortest seek distance and affinity based routing both shorten seek time. Anticipatory arm placement places arms optimally to minimize the seek distance. The analysis of RAID1 in normal, degraded, and rebuild mode is provided to quantify RAID1/0 performance. We compare the reliability of mirrored disk organizations against each other and hybrid disks and erasure coded disk arrays.
Automatically Extracting Web API Specifications from HTML Documentation
Venue: arXiv cs.SE 2018
Year: 2018

Web API specifications are machine-readable descriptions of APIs. These specifications, in combination with related tooling, simplify and support the consumption of APIs. However, despite the increased distribution of web APIs, specifications are rare and their creation and maintenance heavily relies on manual efforts by third parties. In this paper, we propose an automatic approach and an associated tool called D2Spec for extracting specifications from web API documentation pages. Given a seed online documentation page on an API, D2Spec first crawls all documentation pages on the API, and then uses a set of machine learning techniques to extract the base URL, path templates, and HTTP methods, which collectively describe the endpoints of an API. We evaluated whether D2Spec can accurately extract endpoints from documentation on 120 web APIs. The results showed that D2Spec achieved a precision of 87.5% in identifying base URLs, a precision of 81.3% and a recall of 80.6% in generating path templates, and a precision of 84.4% and a recall of 76.2% in extracting HTTP methods. In addition, we found that D2Spec was useful when applied to APIs with pre-existing API specifications: D2Spec revealed many inconsistencies between web API documentation and their corresponding publicly available specifications. Thus, D2Spec can be used by web API providers to keep documentation and specifications in synchronization.
Trajectory Generation using Sharpness Continuous Dubins-like Paths with Applications in Control of Heavy Duty Vehicles
Venue: arXiv cs.SY 2018
Year: 2018

We present a trajectory generation framework for control of wheeled vehicles under steering actuator constraints. The motivation is smooth autonomous driving of heavy vehicles. The key idea is to take into account rate, and additionally, torque limitations of the steering actuator directly. Previous methods only take into account curvature rate limitations, which deal indirectly with steering rate limitations. We propose the new concept of Sharpness Continuous curves, which uses cubic and sigmoid curvature trajectories together with circular arcs to steer the vehicle. The obtained trajectories are characterized by a smooth and continuously differentiable steering angle profile. These trajectories provide low-level controllers with reference signals which are easier to track, resulting in improved performance. The smoothness of the obtained steering profiles also results in increased passenger comfort. The method is characterized by a fast computation time, which can be further speeded up through the use of simple pre-computations. We detail possible path planning applications of the method, and conduct simulations that show its advantages and real time capabilities.
Tools for online tutorials: comparing capture devices, tutorial representations, and access devices
Venue: arXiv cs.HC 2018
Year: 2018

Tutorials are one of the most fundamental means of conveying knowledge. Ideally when the task involves physical or digital objects, tutorials not only describe each step with text or via audio narration but show it as well using photos or animation. In most cases, online tutorial authors capture media from handheld mobile devices to compose these documents, but increasingly they use wearable devices as well. In this work, we explore the full life-cycle of online tutorial creation and viewing using head-mounted capture and displays. We developed a media-capture tool for Google Glass that requires minimal attention to the capture device and instead allows the author to focus on creating the tutorial's content rather than its capture. The capture tool is coupled with web-based authoring tools for creating annotatable videos and multimedia documents. In a study comparing standalone (camera on tripod) versus wearable capture (Google Glass) as well as two types of multimedia representation for authoring tutorials, we show that tutorial authors have a preference for wearable capture devices, especially when recording activities involving larger objects in non-desktop environments. Authors preferred document-based multimedia tutorials because they are more straightforward to compose and the step-based structure translates more directly to explaining a procedure. In addition, we explored using head-mounted displays for accessing tutorials in comparison to lightweight computing devices such as tablets. Our study included tutorials recorded with the same capture methods as in our access study. We found that although authors preferred head-mounted capture, tutorial consumers preferred video recorded by a camera on tripod that provides a more stable image of the workspace.
Adaptive Hybrid Beamforming with Massive Phased Arrays in Macro-Cellular Networks
Venue: arXiv cs.IT 2018
Year: 2018

Hybrid beamforming via large antenna arrays has shown a great potential for increasing data rate in cellular networks by delivering multiple data streams simultaneously. In this paper, several beamforming design algorithms are proposed based on the long-term channel information for macro-cellular environments where the base station is equipped with a massive phased array under per-antenna power constraint. Using an adaptive scheme, beamforming vectors are updated whenever the long-term channel information changes. First, the problem is studied when the base station has a single RF chain (single-beam scenario). Semi-definite relaxation (SDR) with randomization is used to solve the problem. As a second approach, a low-complexity heuristic beam composition algorithm is proposed which performs very close to the upper-bound obtained by SDR. Next, the problem is studied for a generic number of RF chains (multi-beam scenario) where the Gradient Projection method is used to obtain local solutions. Numerical results reveal that using massive antenna arrays with optimized beamforming vectors can lead to 5X network throughput improvement over systems with conventional antennas.
Image2GIF: Generating Cinemagraphs using Recurrent Deep Q-Networks
Venue: arXiv cs.CV 2018
Year: 2018

Given a still photograph, one can imagine how dynamic objects might move against a static background. This idea has been actualized in the form of cinemagraphs, where the motion of particular objects within a still image is repeated, giving the viewer a sense of animation. In this paper, we learn computational models that can generate cinemagraph sequences automatically given a single image. To generate cinemagraphs, we explore combining generative models with a recurrent neural network and deep Q-networks to enhance the power of sequence generation. To enable and evaluate these models we make use of two datasets, one synthetically generated and the other containing real video generated cinemagraphs. Both qualitative and quantitative evaluations demonstrate the effectiveness of our models on the synthetic and real datasets.
Greedy Algorithms for Maximizing Nash Social Welfare
Venue: arXiv cs.GT 2018
Year: 2018

We study the problem of fairly allocating a set of indivisible goods among agents with additive valuations. The extent of fairness of an allocation is measured by its Nash social welfare, which is the geometric mean of the valuations of the agents for their bundles. While the problem of maximizing Nash social welfare is known to be APX-hard in general, we study the effectiveness of simple, greedy algorithms in solving this problem in two interesting special cases. First, we show that a simple, greedy algorithm provides a 1.061-approximation guarantee when agents have identical valuations, even though the problem of maximizing Nash social welfare remains NP-hard for this setting. Second, we show that when agents have binary valuations over the goods, an exact solution (i.e., a Nash optimal allocation) can be found in polynomial time via a greedy algorithm. Our results in the binary setting extend to provide novel, exact algorithms for optimizing Nash social welfare under concave valuations. Notably, for the above mentioned scenarios, our techniques provide a simple alternative to several of the existing, more sophisticated techniques for this problem such as constructing equilibria of Fisher markets or using real stable polynomials.
Combining Convolution and Recursive Neural Networks for Sentiment Analysis
Venue: arXiv cs.CL 2018
Year: 2018

This paper addresses the problem of sentence-level sentiment analysis. In recent years, Convolution and Recursive Neural Networks have been proven to be effective network architecture for sentence-level sentiment analysis. Nevertheless, each of them has their own potential drawbacks. For alleviating their weaknesses, we combined Convolution and Recursive Neural Networks into a new network architecture. In addition, we employed transfer learning from a large document-level labeled sentiment dataset to improve the word embedding in our models. The resulting models outperform all recent Convolution and Recursive Neural Networks. Beyond that, our models achieve comparable performance with state-of-the-art systems on Stanford Sentiment Treebank.
Ear Recognition With Score-Level Fusion Based On CMC In Long-Wave Infrared Spectrum
Venue: arXiv cs.CV 2018
Year: 2018

Only a few studies have been reported regarding human ear recognition in long wave infrared band. Thus, we have created ear database based on long wave infrared band. We have called that the database is long wave infrared band MIDAS consisting of 2430 records of 81 subjects. Thermal band provides seamless operation both night and day, robust against spoofing with understanding live ear and invariant to illumination conditions for human ear recognition. We have proposed to use different algorithms to reveal the distinctive features. Then, we have reduced the number of dimensions using subspace methods. Finally, the dimension of data is reduced in accordance with the classifier methods. After this, the decision is determined by the best sores or combining some of the best scores with matching fusion. The results have showed that the fusion technique was successful. We have reached 97.71% for rank-1 with 567 test probes. Furthermore, we have defined the perfect rank which is rank number when recognition rate reaches 100% in cumulative matching curve. This evaluation is important for especially forensics, for example corpse identification, criminal investigation etc.
Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Dramatic appearance variation due to pose constitutes a great challenge in fine-grained recognition, one which recent methods using attention mechanisms or second-order statistics fail to adequately address. Modern CNNs typically lack an explicit understanding of object pose and are instead confused by entangled pose and appearance. In this paper, we propose a unified object representation built from a hierarchy of pose-aligned regions. Rather than representing an object by regions aligned to image axes, the proposed representation characterizes appearance relative to the object's pose using pose-aligned patches whose features are robust to variations in pose, scale and rotation. We propose an algorithm that performs pose estimation and forms the unified object representation as the concatenation of hierarchical pose-aligned regions features, which is then fed into a classification network. The proposed algorithm surpasses the performance of other approaches, increasing the state-of-the-art by nearly 2% on the widely-used CUB-200 dataset and by more than 8% on the much larger NABirds dataset. The effectiveness of this paradigm relative to competing methods suggests the critical importance of disentangling pose and appearance for continued progress in fine-grained recognition.
Toward Finding Latent Cities with Non-Negative Matrix Factorization
Venue: arXiv cs.SI 2018
Year: 2018

In the last decade, digital footprints have been used to cluster population activity into functional areas of cities. However, a key aspect has been overlooked: we experience our cities not only by performing activities at specific destinations, but also by moving from one place to another. In this paper, we propose to analyze and cluster the city based on how people move through it. Particularly, we introduce Mobilicities, automatically generated travel patterns inferred from mobile phone network data using NMF, a matrix factorization model. We evaluate our method in a large city and we find that mobilicities reveal latent but at the same time interpretable mobility structures of the city. Our results provide evidence on how clustering and visualization of aggregated phone logs could be used in planning systems to interactively analyze city structure and population activity.
Faster Approximate(d) Text-to-Pattern L1 Distance
Venue: arXiv cs.DS 2018
Year: 2018

The problem of finding \emph{distance} between \emph{pattern} of length $m$ and \emph{text} of length $n$ is a typical way of generalizing pattern matching to incorporate dissimilarity score. For both Hamming and $L_1$ distances only a super linear upper bound $\widetilde{O}(n\sqrt{m})$ are known, which prompts the question of relaxing the problem: either by asking for $(1 \pm \varepsilon)$ approximate distance (every distance is reported up to a multiplicative factor), or $k$-approximated distance (distances exceeding $k$ are reported as $\infty$). We focus on $L_1$ distance, for which we show new algorithms achieving complexities respectively $\widetilde{O}(\varepsilon^{-1} n)$ and $\widetilde{O}((m+k\sqrt{m}) \cdot n/m)$. This is a significant improvement upon previous algorithms with runtime $\widetilde{O}(\varepsilon^{-2} n)$ of Lipsky and Porat [Algorithmica 2011] and $\widetilde{O}(n\sqrt{k})$ of Amir, Lipsky, Porat and Umanski [CPM 2005].