OpenRaiser/Intern-Atlas huggingface dataset:
Meaningless comparisons lead to false optimism in medical machine learning
Venue: arXiv cs.OH 2017
Year: 2017

A new trend in medicine is the use of algorithms to analyze big datasets, e.g. using everything your phone measures about you for diagnostics or monitoring. However, these algorithms are commonly compared against weak baselines, which may contribute to excessive optimism. To assess how well an algorithm works, scientists typically ask how well its output correlates with medically assigned scores. Here we perform a meta-analysis to quantify how the literature evaluates their algorithms for monitoring mental wellbeing. We find that the bulk of the literature ($\sim$77%) uses meaningless comparisons that ignore patient baseline state. For example, having an algorithm that uses phone data to diagnose mood disorders would be useful. However, it is possible to over 80% of the variance of some mood measures in the population by simply guessing that each patient has their own average mood - the patient-specific baseline. Thus, an algorithm that just predicts that our mood is like it usually is can explain the majority of variance, but is, obviously, entirely useless. Comparing to the wrong (population) baseline has a massive effect on the perceived quality of algorithms and produces baseless optimism in the field. To solve this problem we propose "user lift" that reduces these systematic errors in the evaluation of personalized medical monitoring.
Study of Buffer-aided physical-layer network coding with ML and MMSE linear code designs for cooperative DS-CDMA systems
Venue: arXiv cs.IT 2017
Year: 2017

In this work, we propose buffer-aided physical-layer network coding (PLNC) techniques for cooperative direct-sequence code-division multiple access systems. In the proposed buffer-aided PLNC schemes, a relay pair selection algorithm is employed to obtain the relay pair and the packets in the buffer entries with the best performance and the associated link combinations used for data transmission. We also devise PLNC schemes based on the design of linear network coding matrices using maximum likelihood and minimum mean-square error criteria to generate the network coded symbols that are sent to the destination. Simulation results show that the proposed techniques significantly outperform prior art.
An Alon-Boppana Type Bound for Weighted Graphs and Lowerbounds for Spectral Sparsification
Venue: arXiv cs.DM 2017
Year: 2017

We prove the following Alon-Boppana type theorem for general (not necessarily regular) weighted graphs: if $G$ is an $n$-node weighted undirected graph of average combinatorial degree $d$ (that is, $G$ has $dn/2$ edges) and girth $g> 2d^{1/8}+1$, and if $λ_1 \leq λ_2 \leq \cdots λ_n$ are the eigenvalues of the (non-normalized) Laplacian of $G$, then \[ \frac {λ_n}{λ_2} \geq 1 + \frac 4{\sqrt d} - O \left( \frac 1{d^{\frac 58} }\right) \] (The Alon-Boppana theorem implies that if $G$ is unweighted and $d$-regular, then $\frac {λ_n}{λ_2} \geq 1 + \frac 4{\sqrt d} - O\left( \frac 1 d \right)$ if the diameter is at least $d^{1.5}$.) Our result implies a lower bound for spectral sparsifiers. A graph $H$ is a spectral $ε$-sparsifier of a graph $G$ if \[ L(G) \preceq L(H) \preceq (1+ε) L(G) \] where $L(G)$ is the Laplacian matrix of $G$ and $L(H)$ is the Laplacian matrix of $H$. Batson, Spielman and Srivastava proved that for every $G$ there is an $ε$-sparsifier $H$ of average degree $d$ where $ε\approx \frac {4\sqrt 2}{\sqrt d}$ and the edges of $H$ are a (weighted) subset of the edges of $G$. Batson, Spielman and Srivastava also show that the bound on $ε$ cannot be reduced below $\approx \frac 2{\sqrt d}$ when $G$ is a clique; our Alon-Boppana-type result implies that $ε$ cannot be reduced below $\approx \frac 4{\sqrt d}$ when $G$ comes from a family of expanders of super-constant degree and super-constant girth. The method of Batson, Spielman and Srivastava proves a more general result, about sparsifying sums of rank-one matrices, and their method applies to an "online" setting. We show that for the online matrix setting the $4\sqrt 2 / \sqrt d$ bound is tight, up to lower order terms.
A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations
Venue: arXiv cs.DC 2017
Year: 2017

Last several years, GPUs are used to accelerate computations in many computer science domains. We focused on GPU accelerated Support Vector Machines (SVM) training with non-linear kernel functions. We had searched for all available GPU accelerated C++ open-source implementations and created an open-source C++ benchmark project. We modifed all the implementations to run on actual hardware and software and in both Windows and Linux operating systems. The benchmark project offers making a fair and direct comparison of the individual implementations under the same conditions, datasets, and hardware. In addition, we selected the most popular datasets in the community and tested them. Finally, based on the evaluation, we recommended the best-performing implementations for dense and sparse datasets.
Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data
Venue: arXiv cs.CL 2017
Year: 2017

Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence-to-sequence Autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as query-by-example Spoken Term Detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch phonetic structure from the audio segments of the target language if the source and target languages are similar. In query-by-example STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.
An All-in-One Network for Dehazing and Beyond
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level task performance on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN and training the joint pipeline from end to end, we witness a large improvement of the object detection performance on hazy images.
A Customisable Underwater Robot
Venue: arXiv cs.RO 2017
Year: 2017

We present a model of a configurable underwater drone, whose parts are optimised for 3D printing processes. We show how - through the use of printable adapters - several thrusters and ballast configurations can be implemented, allowing different maneuvering possibilities. After introducing the model and illustrating a set of possible configurations, we present a functional prototype based on open source hardware and software solutions. The prototype has been successfully tested in several dives in rivers and lakes around Berlin. The reliability of the printed models has been tested only in relatively shallow waters. However, we strongly believe that their availability as freely downloadable models will motivate the general public to build and to test underwater drones, thus speeding up the development of innovative solutions and applications. The models and their documentation will be available for download at the following link: https://adapt.informatik.hu-berlin.de/schillaci/underwater.html.
Performance Analysis of Non-Orthogonal Multiple Access in Free Space Optical Communication System
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, a multipoint-to-point system consisting K users and a central node with non-orthogonal multiple access (NOMA) technique over free space optical (FSO) channel is characterized for two cases. In the case of guaranteed user's quality of service (QoS), the outage probability is derived. Another case is that user's QoS is determined by their own channel conditions. The developed analytical results of ergodic sum data rate show that NOMA outperforms orthogonal multiple access (OMA). Besides, the power control scheme is proposed, and results show that the power control scheme has great influence to the user's QoS. Monto Carlo simulation has been done, and it matches quite well with the theoretical analysis.
Crowdsourcing, Sharing Economies and Development
Venue: arXiv cs.CY 2017
Year: 2017

What are the similarities and differences between crowdsourcing and sharing economy? What factors influence their use in developing countries? In light of recent developments in the use of IT-mediated technologies, such as crowdsourcing and the sharing economy, this manuscript examines their similarities and differences, and the challenges regarding their effective use in developing countries. We first examine each individually and highlight different forms of each IT-mediated technology. Given that crowdsourcing and sharing economy share aspects such as the use of IT, a reliance on crowds, monetary exchange, and the use of reputation systems, we systematically compare the similarities and differences of different types of crowdsourcing with the sharing economy, thus addressing a gap in the current literature. Using this knowledge, we examine the different challenges faced by developing countries when using crowdsourcing and the sharing economy, and highlight the differences in the applicability of these IT-mediated technologies when faced with specific development issues.
Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments
Venue: arXiv cs.AI 2017
Year: 2017

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks.
Tweeting about journal articles: Engagement, marketing or just gibberish?
Venue: arXiv cs.DL 2017
Year: 2017

This paper presents preliminary results on the analysis of tweets to journal articles in the field of Dentistry. We present two case studies in which we critically examine the contents and context that motivate the tweeting of journal articles. We then focus on a specific aspect, the role played by journals on self-promoting their contents and the effect this has on the total number of tweets their papers produce. In a context where many are pushing to the use of altmetrics as an alternative or complement to traditional bibliometric indicators. We find a lack of evidence (and interest) on critically examining the many claims that are being made as to their capability to trace evidences of 'broader forms of impact'. Our first results are not promising and question current approaches being made in the field of altmetrics.
A monadic solution to the Cartwright-Felleisen-Wadler conjecture
Venue: arXiv cs.PL 2017
Year: 2017

Given a programming language, can we give a monadic denotational semantics that is stable under language extension? Models containing only a single monad are not stable. Models based on type-and-effect systems, in which there is a monad for every set of operations in the language, are. Cartwright and Felleisen, and Wadler, conjectured such monadic semantics can be generated. We describe a new general method of constructing stable models from standard monadic models, based on factorizations of monad morphisms. We show that under certain conditions factorizations induce a monad for every set of operations, and explain why the conditions usually hold. We also describe preliminary work using fibrations for logical relations generated from these factorization systems for proving the correctness of the resulting model.
Head Detection with Depth Images in the Wild
Venue: arXiv cs.CV 2017
Year: 2017

Head detection and localization is a demanding task and a key element for many computer vision applications, like video surveillance, Human Computer Interaction and face analysis. The stunning amount of work done for detecting faces on RGB images, together with the availability of huge face datasets, allowed to setup very effective systems on that domain. However, due to illumination issues, infrared or depth cameras may be required in real applications. In this paper, we introduce a novel method for head detection on depth images that exploits the classification ability of deep learning approaches. In addition to reduce the dependency on the external illumination, depth images implicitly embed useful information to deal with the scale of the target objects. Two public datasets have been exploited: the first one, called Pandora, is used to train a deep binary classifier with face and non-face images. The second one, collected by Cornell University, is used to perform a cross-dataset test during daily activities in unconstrained environments. Experimental results show that the proposed method overcomes the performance of state-of-art methods working on depth images.
Energy-Performance Trade-offs in Mobile Data Transfers
Venue: arXiv cs.NI 2017
Year: 2017

By year 2020, the number of smartphone users globally will reach 3 Billion and the mobile data traffic (cellular + WiFi) will exceed PC internet traffic the first time. As the number of smartphone users and the amount of data transferred per smartphone grow exponentially, limited battery power is becoming an increasingly critical problem for mobile devices which increasingly depend on network I/O. Despite the growing body of research in power management techniques for the mobile devices at the hardware layer as well as the lower layers of the networking stack, there has been little work focusing on saving energy at the application layer for the mobile systems during network I/O. In this paper, to the best of our knowledge, we are first to provide an in depth analysis of the effects of application layer data transfer protocol parameters on the energy consumption of mobile phones. We show that significant energy savings can be achieved with application layer solutions at the mobile systems during data transfer with no or minimal performance penalty. In many cases, performance increase and energy savings can be achieved simultaneously.
HMM-based Writer Identification in Music Score Documents without Staff-Line Removal
Venue: arXiv cs.CV 2017
Year: 2017

Writer identification from musical score documents is a challenging task due to its inherent problem of overlapping of musical symbols with staff lines. Most of the existing works in the literature of writer identification in musical score documents were performed after a preprocessing stage of staff lines removal. In this paper we propose a novel writer identification framework in musical documents without removing staff lines from documents. In our approach, Hidden Markov Model has been used to model the writing style of the writers without removing staff lines. The sliding window features are extracted from musical score lines and they are used to build writer specific HMM models. Given a query musical sheet, writer specific confidence for each musical line is returned by each writer specific model using a loglikelihood score. Next, a loglikelihood score in page level is computed by weighted combination of these scores from the corresponding line images of the page. A novel Factor Analysis based feature selection technique is applied in sliding window features to reduce the noise appearing from staff lines which proves efficiency in writer identification performance.In our framework we have also proposed a novel score line detection approach in musical sheet using HMM. The experiment has been performed in CVC-MUSCIMA dataset and the results obtained that the proposed approach is efficient for score line detection and writer identification without removing staff lines. To get the idea of computation time of our method, detail analysis of execution time is also provided.
LtFi: Cross-technology Communication for RRM between LTE-U and IEEE 802.11
Venue: arXiv cs.NI 2017
Year: 2017

Cross-technology communication (CTC) was proposed in recent literature as a way to exploit the opportunities of collaboration between heterogeneous wireless technologies. This paper presents LtFi, a system which enables to set-up a CTC between nodes of co-located LTE-U and WiFi networks. LtFi follows a two-step approach: using the air-interface LTE-U BSs are broadcasting connection and identification data to adjacent WiFi nodes, which is used to create a bi-directional control channel over the wired Internet. This way LtFi enables the development of advanced cross-technology interference and radio resource management schemes between heterogeneous WiFi and LTE-U networks. LtFi is of low complexity and fully compliant with LTE-U technology and works on WiFi side with COTS hardware. It was prototypically implemented and evaluated. Experimental results reveal that LtFi is able to reliably decoded the data transmitted over the LtFi air-interface in a crowded wireless environment at even very low LTE-U receive power levels of -92dBm. Moreover, results from system-level simulations show that LtFi is able to accurately estimate the set of interfering LTE-U BSs in a typical LTE-U multi-cell environment.
Multi-kernel learning of deep convolutional features for action recognition
Venue: arXiv cs.CV 2017
Year: 2017

Image understanding using deep convolutional network has reached human-level performance, yet a closely related problem of video understanding especially, action recognition has not reached the requisite level of maturity. We combine multi-kernels based support-vector-machines (SVM) with a multi-stream deep convolutional neural network to achieve close to state-of-the-art performance on a 51-class activity recognition problem (HMDB-51 dataset); this specific dataset has proved to be particularly challenging for deep neural networks due to the heterogeneity in camera viewpoints, video quality, etc. The resulting architecture is named pillar networks as each (very) deep neural network acts as a pillar for the hierarchical classifiers. In addition, we illustrate that hand-crafted features such as improved dense trajectories (iDT) and Multi-skip Feature Stacking (MIFS), as additional pillars, can further supplement the performance.
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition
Venue: arXiv cs.CL 2017
Year: 2017

Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling problem. We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion. The modular structure splits the problem into three sub-tasks: frame-wise interpreting, utterance-level speaker tracing, and speech recognition. The pretraining regimen uses these modules to solve progressively harder tasks. Transfer learning leverages parallel clean speech to improve the training targets for the network. Our discriminative training formulation is a modification of standard formulations, that also penalizes competing outputs of the system. Experiments are conducted on the artificial overlapped Switchboard and hub5e-swb dataset. The proposed framework achieves over 30% relative improvement of WER over both a strong jointly trained system, PIT for ASR, and a separately optimized system, PIT for speech separation with clean speech ASR model. The improvement comes from better model generalization, training efficiency and the sequence level linguistic knowledge integration.
Bilinear Assignment Problem: Large Neighborhoods and Experimental Analysis of Algorithms
Venue: arXiv cs.DS 2017
Year: 2017

The bilinear assignment problem (BAP) is a generalization of the well-known quadratic assignment problem (QAP). In this paper, we study the problem from the computational analysis point of view. Several classes of neigborhood structures are introduced for the problem along with some theoretical analysis. These neighborhoods are then explored within a local search and a variable neighborhood search frameworks with multistart to generate robust heuristic algorithms. Results of systematic experimental analysis have been presented which divulge the effectiveness of our algorithms. In addition, we present several very fast construction heuristics. Our experimental results disclosed some interesting properties of the BAP model, different from those of comparable models. This is the first thorough experimental analysis of algorithms on BAP. We have also introduced benchmark test instances that can be used for future experiments on exact and heuristic algorithms for the problem.
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
Venue: arXiv cs.CV 2017
Year: 2017

Matching pedestrians across disjoint camera views, known as person re-identification (re-id), is a challenging problem that is of importance to visual recognition and surveillance. Most existing methods exploit local regions within spatial manipulation to perform matching in local correspondence. However, they essentially extract \emph{fixed} representations from pre-divided regions for each image and perform matching based on the extracted representation subsequently. For models in this pipeline, local finer patterns that are crucial to distinguish positive pairs from negative ones cannot be captured, and thus making them underperformed. In this paper, we propose a novel deep multiplicative integration gating function, which answers the question of \emph{what-and-where to match} for effective person re-id. To address \emph{what} to match, our deep network emphasizes common local patterns by learning joint representations in a multiplicative way. The network comprises two Convolutional Neural Networks (CNNs) to extract convolutional activations, and generates relevant descriptors for pedestrian matching. This thus, leads to flexible representations for pair-wise images. To address \emph{where} to match, we combat the spatial misalignment by performing spatially recurrent pooling via a four-directional recurrent neural network to impose spatial dependency over all positions with respect to the entire image. The proposed network is designed to be end-to-end trainable to characterize local pairwise feature interactions in a spatially aligned manner. To demonstrate the superiority of our method, extensive experiments are conducted over three benchmark data sets: VIPeR, CUHK03 and Market-1501.