OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Image Stylization Using Deep Fully Convolutional Networks
Venue: arXiv cs.CV 2018
Year: 2018

Color and tone stylization strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjustments to various semantic regions. Such an ability enables a broader range of visual styles. In this paper, we propose a novel deep learning architecture for automatic image stylization, which learns local enhancement styles from image pairs. Our deep learning architecture is an end-to-end deep fully convolutional network performing semantics-aware feature extraction as well as automatic image adjustment prediction. Image stylization can be efficiently accomplished with a single forward pass through our deep network. Experiments on existing datasets for image stylization demonstrate the effectiveness of our deep learning architecture.
An Optimal Space Lower Bound for Approximating MAX-CUT
Venue: arXiv cs.DS 2018
Year: 2018

We consider the problem of estimating the value of MAX-CUT in a graph in the streaming model of computation. At one extreme, there is a trivial $2$-approximation for this problem that uses only $O(\log n)$ space, namely, count the number of edges and output half of this value as the estimate for the size of the MAX-CUT. On the other extreme, for any fixed $ε> 0$, if one allows $\tilde{O}(n)$ space, a $(1+ε)$-approximate solution to the MAX-CUT value can be obtained by storing an $\tilde{O}(n)$-size sparsifier that essentially preserves MAX-CUT value. Our main result is that any (randomized) single pass streaming algorithm that breaks the $2$-approximation barrier requires $Ω(n)$-space, thus resolving the space complexity of any non-trivial approximations of the MAX-CUT value to within polylogarithmic factors in the single pass streaming model. We achieve the result by presenting a tight analysis of the Implicit Hidden Partition Problem introduced by Kapralov et al.[SODA'17] for an arbitrarily large number of players. In this problem a number of players receive random matchings of $Ω(n)$ size together with random bits on the edges, and their task is to determine whether the bits correspond to parities of some hidden bipartition, or are just uniformly random. Unlike all previous Fourier analytic communication lower bounds, our analysis does not directly use bounds on the $\ell_2$ norm of Fourier coefficients of a typical message at any given weight level that follow from hypercontractivity. Instead, we use the fact that graphs received by players are sparse (matchings) to obtain strong upper bounds on the $\ell_1$ norm of the Fourier coefficients of the messages of individual players, and then argue, using the convolution theorem, that similar strong bounds on the $\ell_1$ norm are essentially preserved once messages of different players are combined.
Beta Distribution Drift Detection for Adaptive Classifiers
Venue: arXiv cs.LG 2018
Year: 2018

With today's abundant streams of data, the only constant we can rely on is change. For stream classification algorithms, it is necessary to adapt to concept drift. This can be achieved by monitoring the model error, and triggering counter measures as changes occur. In this paper, we propose a drift detection mechanism that fits a beta distribution to the model error, and treats abnormal behavior as drift. It works with any given model, leverages prior knowledge about this model, and allows to set application-specific confidence thresholds. Experiments confirm that it performs well, in particular when drift occurs abruptly.
Hermitian Laplacians and a Cheeger inequality for the Max-2-Lin problem
Venue: arXiv cs.DS 2018
Year: 2018

We study spectral approaches for the MAX-2-LIN(k) problem, in which we are given a system of $m$ linear equations of the form $x_i - x_j \equiv c_{ij}\mod k$, and required to find an assignment to the $n$ variables $\{x_i\}$ that maximises the total number of satisfied equations. We consider Hermitian Laplacians related to this problem, and prove a Cheeger inequality that relates the smallest eigenvalue of a Hermitian Laplacian to the maximum number of satisfied equations of a MAX-2-LIN(k) instance $\mathcal{I}$. We develop an $\widetilde{O}(kn^2)$ time algorithm that, for any $(1-\varepsilon)$-satisfiable instance, produces an assignment satisfying a $\left(1 - O(k)\sqrt{\varepsilon}\right)$-fraction of equations. We also present a subquadratic-time algorithm that, when the graph associated with $\mathcal{I}$ is an expander, produces an assignment satisfying a $\left(1- O(k^2)\varepsilon \right)$-fraction of the equations. Our Cheeger inequality and first algorithm can be seen as generalisations of the Cheeger inequality and algorithm for MAX-CUT developed by Trevisan.
Beyond One Glance: Gated Recurrent Architecture for Hand Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

As mixed reality is gaining increased momentum, the development of effective and efficient solutions to egocentric hand segmentation is becoming critical. Traditional segmentation techniques typically follow a one-shot approach, where the image is passed forward only once through a model that produces a segmentation mask. This strategy, however, does not reflect the perception of humans, who continuously refine their representation of the world. In this paper, we therefore introduce a novel gated recurrent architecture. It goes beyond both iteratively passing the predicted segmentation mask through the network and adding a standard recurrent unit to it. Instead, it incorporates multiple encoder-decoder layers of the segmentation network, so as to keep track of its internal state in the refinement process. As evidenced by our results on standard hand segmentation benchmarks and on our own dataset, our approach outperforms these other, simpler recurrent segmentation techniques, as well as the state-of-the-art hand segmentation one. Furthermore, we demonstrate the generality of our approach by applying it to road segmentation, where it also outperforms other baseline methods.
Cross-Technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts
Venue: arXiv cs.NI 2018
Year: 2018

Recent years have seen major innovations in developing energy-efficient wireless technologies such as Bluetooth Low Energy (BLE) for Internet of Things (IoT). Despite demonstrating significant benefits in providing low power transmission and massive connectivity, hardly any of these technologies have made it to directly connect to the Internet. Recent advances demonstrate the viability of direct communication among heterogeneous IoT devices with incompatible physical (PHY) layers. These techniques, however, require modifications in transmission power or time, which may affect the media access control (MAC) layer behaviors in legacy networks. In this paper, we argue that the frequency domain can serve as a free side channel with minimal interruptions to legacy networks. To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying (GFSK) demodulator and Channel State Information (CSI). The artificial Doppler shifts can be compensated by the inherent frequency synchronization module and thus have a negligible impact on legacy communications. Our evaluation using commercial off-the-shelf (COTS) BLE chips and 802.11-compliant testbeds have demonstrated that DopplerFi can achieve throughput up to 6.5~Kbps at the cost of merely less than 0.8% throughput loss.
Predicting the Flu from Instagram
Venue: arXiv cs.LG 2018
Year: 2018

Conventional surveillance systems for monitoring infectious diseases, such as influenza, face challenges due to shortage of skilled healthcare professionals, remoteness of communities and absence of communication infrastructures. Internet-based approaches for surveillance are appealing logistically as well as economically. Search engine queries and Twitter have been the primarily used data sources in such approaches. The aim of this study is to assess the predictive power of an alternative data source, Instagram. By using 317 weeks of publicly available data from Instagram, we trained several machine learning algorithms to both nowcast and forecast the number of official influenza-like illness incidents in Finland where population-wide official statistics about the weekly incidents are available. In addition to date and hashtag count features of online posts, we were able to utilize also the visual content of the posted images with the help of deep convolutional neural networks. Our best nowcasting model reached a mean absolute error of 11.33 incidents per week and a correlation coefficient of 0.963 on the test data. Forecasting models for predicting 1 week and 2 weeks ahead showed statistical significance as well by reaching correlation coefficients of 0.903 and 0.862, respectively. This study demonstrates how social media and in particular, digital photographs shared in them, can be a valuable source of information for the field of infodemiology.
The Fact Extraction and VERification (FEVER) Shared Task
Venue: arXiv cs.CL 2018
Year: 2018

We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be Supported or Refuted using evidence retrieved from Wikipedia. We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline. The best performing system achieved a FEVER score of 64.21%. In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
Venue: arXiv cs.CL 2018
Year: 2018

In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, it is non-trivial to impose constraints on RNNs while maintaining generation quality, since RNNs generate sentences sequentially (or with beam search) from the first word to the last. In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation. CGMH allows complicated constraints such as the occurrence of multiple keywords in the target sentences, which cannot be handled in traditional RNN-based approaches. Moreover, CGMH works in the inference stage, and does not require parallel corpora for training. We evaluate our method on a variety of tasks, including keywords-to-sentence generation, unsupervised sentence paraphrasing, and unsupervised sentence error correction. CGMH achieves high performance compared with previous supervised methods for sentence generation. Our code is released at https://github.com/NingMiao/CGMH
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Venue: arXiv cs.CL 2018
Year: 2018

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT). However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are all relatively small. Meanwhile, most of the previous methods rely on complicated structures with given scarce data, which largely limits the efficacy of the neural models. In this paper, we exploit a new direction named coarse-to-fine task transfer, which aims to leverage knowledge learned from a rich-resource source domain of the coarse-grained AC task, which is more easily accessible, to improve the learning in a low-resource target domain of the fine-grained AT task. To resolve both the aspect granularity inconsistency and feature mismatch between domains, we propose a Multi-Granularity Alignment Network (MGAN). In MGAN, a novel Coarse2Fine attention guided by an auxiliary task can help the AC task modeling at the same fine-grained level with the AT task. To alleviate the feature false alignment, a contrastive feature alignment method is adopted to align aspect-specific feature representations semantically. In addition, a large-scale multi-domain dataset for the AC task is provided. Empirically, extensive experiments demonstrate the effectiveness of the MGAN.
Unsupervised Post-processing of Word Vectors via Conceptor Negation
Venue: arXiv cs.CL 2018
Year: 2018

Word vectors are at the core of many natural language processing tasks. Recently, there has been interest in post-processing word vectors to enrich their semantic information. In this paper, we introduce a novel word vector post-processing technique based on matrix conceptors (Jaeger2014), a family of regularized identity maps. More concretely, we propose to use conceptors to suppress those latent features of word vectors having high variances. The proposed method is purely unsupervised: it does not rely on any corpus or external linguistic database. We evaluate the post-processed word vectors on a battery of intrinsic lexical evaluation tasks, showing that the proposed method consistently outperforms existing state-of-the-art alternatives. We also show that post-processed word vectors can be used for the downstream natural language processing task of dialogue state tracking, yielding improved results in different dialogue domains.
Scene Recognition Through Visual and Acoustic Cues Using K-Means
Venue: arXiv cs.RO 2018
Year: 2018

We propose a K-Means based prediction system, nicknamed SERVANT (Scene Recognition Through Visual and Acoustic Cues), that is capable of recognizing environmental scenes through analysis of ambient sound and color cues. The concept and implementation originated within the Learning branch of the Intelligent Wearable Robotics Project (also known as the Third Arm project) at the Stanford Artificial Intelligence Lab-Toyota Center (SAIL-TC). The Third Arm Project focuses on the development and conceptualization of a robotic arm that can aid users in a whole array of situations: i.e. carrying a cup of coffee, holding a flashlight. Servant uses a K-Means fit-and-predict architecture to classify environmental scenes, such as that of a coffee shop or a basketball gym, using visual and auditory cues. Following such classification, Servant can recommend contextual actions based on prior training.
Latent Dirichlet Allocation with Residual Convolutional Neural Network Applied in Evaluating Credibility of Chinese Listed Companies
Venue: arXiv cs.CL 2018
Year: 2018

This project demonstrated a methodology to estimating cooperate credibility with a Natural Language Processing approach. As cooperate transparency impacts both the credibility and possible future earnings of the firm, it is an important factor to be considered by banks and investors on risk assessments of listed firms. This approach of estimating cooperate credibility can bypass human bias and inconsistency in the risk assessment, the use of large quantitative data and neural network models provides more accurate estimation in a more efficient manner compare to manual assessment. At the beginning, the model will employs Latent Dirichlet Allocation and THU Open Chinese Lexicon from Tsinghua University to classify topics in articles which are potentially related to corporate credibility. Then with the keywords related to each topics, we trained a residual convolutional neural network with data labeled according to surveys of fund manager and accountant's opinion on corporate credibility. After the training, we run the model with preprocessed news reports regarding to all of the 3065 listed companies, the model is supposed to give back companies ranking based on the level of their transparency.
Dense xUnit Networks
Venue: arXiv cs.CV 2018
Year: 2018

Deep net architectures have constantly evolved over the past few years, leading to significant advancements in a wide array of computer vision tasks. However, besides high accuracy, many applications also require a low computational load and limited memory footprint. To date, efficiency has typically been achieved either by architectural choices at the macro level (e.g. using skip connections or pruning techniques) or modifications at the level of the individual layers (e.g. using depth-wise convolutions or channel shuffle operations). Interestingly, much less attention has been devoted to the role of the activation functions in constructing efficient nets. Recently, Kligvasser et al. showed that incorporating spatial connections within the activation functions, enables a significant boost in performance in image restoration tasks, at any given budget of parameters. However, the effectiveness of their xUnit module has only been tested on simple small models, which are not characteristic of those used in high-level vision tasks. In this paper, we adopt and improve the xUnit activation, show how it can be incorporated into the DenseNet architecture, and illustrate its high effectiveness for classification and image restoration tasks alike. While the DenseNet architecture is extremely efficient to begin with, our dense xUnit net (DxNet) can typically achieve the same performance with far fewer parameters. For example, on ImageNet, our DxNet outperforms a ReLU-based DenseNet having 30% more parameters and achieves state-of-the-art results for this budget of parameters. Furthermore, in denoising and super-resolution, DxNet significantly improves upon all existing lightweight solutions, including the xUnit-based nets of Kligvasser et al.
An Analytical Approach to Improving Time Warping on Multidimensional Time Series
Venue: arXiv cs.DS 2018
Year: 2018

Dynamic time warping ($\texttt{DTW}$) is one of the most used distance functions to compare time series, e.$\,$g. in nearest neighbor classifiers. Yet, fast state of the art algorithms only compare 1-dimensional time series efficiently. One of these state of the art algorithms uses a lower bound ($\texttt{LB}_\texttt{Keogh}$) introduced by E. Keogh to prune $\texttt{DTW}$ computations. We introduce $\texttt{LB}_\texttt{Box}$ as a canonical extension to $\texttt{LB}_\texttt{Keogh}$ on multi-dimensional time series. We evaluate its performance conceptually and experimentally and show that an alternative to $\texttt{LB}_\texttt{Box}$ is necessary for multi-dimensional time series. We also propose a new algorithm for the dog-keeper distance ($\texttt{DK}$) which is an alternative distance function to $\texttt{DTW}$ and show that it outperforms $\texttt{DTW}$ with $\texttt{LB}_\texttt{Box}$ by more than one order of magnitude on multi-dimensional time series.
Counting Polynomial Roots in Isabelle/HOL: A Formal Proof of the Budan-Fourier Theorem
Venue: arXiv cs.LO 2018
Year: 2018

Many problems in computer algebra and numerical analysis can be reduced to counting or approximating the real roots of a polynomial within an interval. Existing verified root-counting procedures in major proof assistants are mainly based on the classical Sturm theorem, which only counts distinct roots. In this paper, we have strengthened the root-counting ability in Isabelle/HOL by first formally proving the Budan-Fourier theorem. Subsequently, based on Descartes' rule of signs and Taylor shift, we have provided a verified procedure to efficiently over-approximate the number of real roots within an interval, counting multiplicity. For counting multiple roots exactly, we have extended our previous formalisation of Sturm's theorem. Finally, we combine verified components in the developments above to improve our previous certified complex-root-counting procedures based on Cauchy indices. We believe those verified routines will be crucial for certifying programs and building tactics.
Unprocessing Images for Learned Raw Denoising
Venue: arXiv cs.CV 2018
Year: 2018

Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which are applied to real raw camera sensor readings but, due to practical constraints, are often trained on synthetic image data. Though it is understood that generalizing from synthetic to real data requires careful consideration of the noise properties of image sensors, the other aspects of a camera's image processing pipeline (gain, color correction, tone mapping, etc) are often overlooked, despite their significant effect on how raw measurements are transformed into finished images. To address this, we present a technique to "unprocess" images by inverting each step of an image processing pipeline, thereby allowing us to synthesize realistic raw sensor measurements from commonly available internet photos. We additionally model the relevant components of an image processing pipeline when evaluating our loss function, which allows training to be aware of all relevant photometric processing that will occur after denoising. By processing and unprocessing model outputs and training data in this way, we are able to train a simple convolutional neural network that has 14%-38% lower error rates and is 9x-18x faster than the previous state of the art on the Darmstadt Noise Dataset, and generalizes to sensors outside of that dataset as well.
A Concept-Centered Hypertext Approach to Case-Based Retrieval
Venue: arXiv cs.IR 2018
Year: 2018

The goal of case-based retrieval is to assist physicians in the clinical decision making process, by finding relevant medical literature in large archives. We propose a research that aims at improving the effectiveness of case-based retrieval systems through the use of automatically created document-level semantic networks. The proposed research tackles different aspects of information systems and leverages the recent advancements in information extraction and relational learning to revisit and advance the core ideas of concept-centered hypertext models. We propose a two-step methodology that in the first step addresses the automatic creation of document-level semantic networks, then in the second step it designs methods that exploit such document representations to retrieve relevant cases from medical literature. For the automatic creation of documents' semantic networks, we design a combination of information extraction techniques and relational learning models. Mining concepts and relations from text, information extraction techniques represent the core of the document-level semantic networks' building process. On the other hand, relational learning models have the task of enriching the graph with additional connections that have not been detected by information extraction algorithms and strengthening the confidence score of extracted relations. For the retrieval of relevant medical literature, we investigate methods that are capable of comparing the documents' semantic networks in terms of structure and semantics. The automatic extraction of semantic relations from documents, and their centrality in the creation of the documents' semantic networks, represent our attempt to go one step further than previous graph-based approaches.