OpenRaiser/Intern-Atlas huggingface dataset:
A Hassle-Free Machine Learning Method for Cohort Selection of Clinical Trials
Venue: arXiv cs.CL 2018
Year: 2018

Traditional text classification techniques in clinical domain have heavily relied on the manually extracted textual cues. This paper proposes a generally supervised machine learning method that is equally hassle-free and does not use clinical knowledge. The employed methods were simple to implement, fast to run and yet effective. This paper proposes a novel named entity recognition (NER) based an ensemble system capable of learning the keyword features in the document. Instead of merely considering the whole sentence/paragraph for analysis, the NER based keyword features can stress the important clinic relevant phases more. In addition, to capture the semantic information in the documents, the FastText features originating from the document level FastText classification results are exploited.
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos
Venue: arXiv cs.CV 2018
Year: 2018

We propose an end-to-end learning framework for segmenting generic objects in both images and videos. Given a novel image or video, our approach produces a pixel-level mask for all "object-like" regions---even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning an object/background label to each pixel, implemented using a deep fully convolutional network. When applied to a video, our model further incorporates a motion stream, and the network learns to combine both appearance and motion and attempts to extract all prominent objects whether they are moving or not. Beyond the core model, a second contribution of our approach is how it leverages varying strengths of training annotations. Pixel-level annotations are quite difficult to obtain, yet crucial for training a deep network approach for segmentation. Thus we propose ways to exploit weakly labeled data for learning dense foreground segmentation. For images, we show the value in mixing object category examples with image-level labels together with relatively few images with boundary-level annotations. For video, we show how to bootstrap weakly annotated videos together with the network trained for image segmentation. Through experiments on multiple challenging image and video segmentation benchmarks, our method offers consistently strong results and improves the state-of-the-art for fully automatic segmentation of generic (unseen) objects. In addition, we demonstrate how our approach benefits image retrieval and image retargeting, both of which flourish when given our high-quality foreground maps. Code, models, and videos are at:http://vision.cs.utexas.edu/projects/pixelobjectness/
Imagining the Unseen: Learning a Distribution over Incomplete Images with Dense Latent Trees
Venue: arXiv cs.CV 2018
Year: 2018

Images are composed as a hierarchy of object parts. We use this insight to create a generative graphical model that defines a hierarchical distribution over image parts. Typically, this leads to intractable inference due to loops in the graph. We propose an alternative model structure, the Dense Latent Tree (DLT), which avoids loops and allows for efficient exact inference, while maintaining a dense connectivity between parts of the hierarchy. The usefulness of DLTs is shown for the example task of image completion on partially observed MNIST and Fashion-MNIST data. We verify having successfully learned a hierarchical model of images by visualising its latent states.
An Assertion language for slicing Constraint Logic Languages
Venue: arXiv cs.LO 2018
Year: 2018

Constraint Logic Programming (CLP) is a language scheme for combining two declarative paradigms: constraint solving and logic programming. Concurrent Constraint Programming (CCP) is a declarative model for concurrency where agents interact by telling and asking constraints in a shared store. In a previous paper, we developed a framework for dynamic slicing of CCP where the user first identifies that a (partial) computation is wrong. Then, she marks (selects) some parts of the final state corresponding to the data (constraints) and processes that she wants to study more deeply. An automatic process of slicing begins, and the partial computation is "depurated" by removing irrelevant information. In this paper we give two major contributions. First, we extend the framework to CLP, thus generalizing the previous work. Second, we provide an assertion language suitable for both, CCP and CLP, which allows the user to specify some properties of the computations in her program. If a state in a computation does not satisfy an assertion then some "wrong" information is identified and an automatic slicing process can start. This way we make one step further towards automatizing the slicing process. We show that our framework can be integrated with the previous semi-automatic one, giving the user more choices and flexibility. We show by means of examples and experiments the usefulness of our approach.
Timed Network Games with Clocks
Venue: arXiv cs.GT 2018
Year: 2018

Network games are widely used as a model for selfish resource-allocation problems. In the classical model, each player selects a path connecting her source and target vertices. The cost of traversing an edge depends on the {\em load}; namely, number of players that traverse it. Thus, it abstracts the fact that different users may use a resource at different times and for different durations, which plays an important role in determining the costs of the users in reality. For example, when transmitting packets in a communication network, routing traffic in a road network, or processing a task in a production system, actual sharing and congestion of resources crucially depends on time. In \cite{AGK17}, we introduced {\em timed network games}, which add a time component to network games. Each vertex $v$ in the network is associated with a cost function, mapping the load on $v$ to the price that a player pays for staying in $v$ for one time unit with this load. Each edge in the network is guarded by the time intervals in which it can be traversed, which forces the players to spend time in the vertices. In this work we significantly extend the way time can be referred to in timed network games. In the model we study, the network is equipped with {\em clocks}, and, as in timed automata, edges are guarded by constraints on the values of the clocks, and their traversal may involve a reset of some clocks. We argue that the stronger model captures many realistic networks. The addition of clocks breaks the techniques we developed in \cite{AGK17} and we develop new techniques in order to show that positive results on classic network games carry over to the stronger timed setting.
Multi-Sector and Multi-Panel Performance in 5G mmWave Cellular Networks
Venue: arXiv cs.NI 2018
Year: 2018

The next generation of cellular networks (5G) will exploit the mmWave spectrum to increase the available capacity. Communication at such high frequencies, however, suffers from high path loss and blockage, therefore directional transmissions using antenna arrays and dense deployments are needed. Thus, when evaluating the performance of mmWave mobile networks, it is necessary to accurately model the complex channel, the directionality of the transmission, but also the interplay that these elements can have with the whole protocol stack, both in the radio access and in the higher layers. In this paper, we improve the channel model abstraction of the mmWave module for ns-3, by introducing the support of a more realistic antenna array model, compliant with 3GPP NR requirements, and of multiple antenna arrays at the base stations and mobile handsets. We then study the end-to-end performance of a mmWave cellular network by varying the channel and antenna array configurations, and show that increasing the number of antenna arrays and, consequently, the number of sectors is beneficial for both throughput and latency.
Deep EHR: Chronic Disease Prediction Using Medical Notes
Venue: arXiv cs.LG 2018
Year: 2018

Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease specificfeature engineering, and can handle negations and numerical values that exist in the text. Ourresults on a cohort of about 1 million patients show that models using text outperform modelsusing just structured data, and that models capable of using numerical values and negations in thetext, in addition to the raw text, further improve performance. Additionally, we compare differentvisualization methods for medical professionals to interpret model predictions.
A theory of finite structures
Venue: arXiv cs.LO 2018
Year: 2018

We develop a novel formal theory of finite structures, based on a view of finite structures as a fundamental artifact of computing and programming, forming a common platform for computing both within particular finite structures, and in the aggregate for computing over infinite data-types construed as families of finite structures. A "finite structure" is here a finite collection of finite partial-functions, over a common universe of atoms. The theory is second-order, as it uses quantification over finite functions. Our formal theory FS uses a small number of fundamental axiom-schemas, with finiteness enforced by a schema of induction on finite partial-functions. We show that computability is definable in the theory by existential formulas, generalizing Kleene's Theorem on the Sigma-1 definability of RE sets, and use that result to prove that FS is mutually interpretable with Peano Arithmetic.
A prototypical Skin Cancer Information System
Venue: arXiv cs.CY 2018
Year: 2018

Skin cancer is a common problem in Australia and indeed around the world. Within the domain of e-Health, there appears to be no satisfactory clinical software that follows the flow of a normal skin cancer examination. This paper introduces a system that was specifically designed, coded and implemented to store patient health records as a means of registering the diagnoses of skin cancer along with the treatment. The information system was intended to be web-based, and connect to remote database servers. The implemented system was designed to incorporate features such as inserting procedural details, generating forms and reports with interactive interfaces, yet be relatively unsophisticated to use. We expect the system once fully implemented and on line, will aid in Australia's e-Health industry, delivering more accurate information to doctors and patients in an effort to combat issues involving skin cancer. Other parameters discussed are the need for data encryption of medical records and the role such a system can play in medical information.
C-RAN with Hybrid RF/FSO Fronthaul Links: Joint Optimization of RF Time Allocation and Fronthaul Compression
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers the uplink of a cloud radio access network (C-RAN) comprised of several multi-antenna remote radio units (RUs) which compress the signals that they receive from multiple mobile users (MUs) and forward them to a CU via wireless fronthaul links. To enable reliable high rate fronthaul links, we employ a hybrid radio frequency (RF)/free space optical (FSO) system for fronthauling. To strike a balance between complexity and performance, we consider three different quantization schemes at the RUs, namely per-antenna vector quantization (AVQ), per-RU vector quantization (RVQ), and distributed source coding (DSC), and two different receivers at the CU, namely the linear minimum mean square error receiver and the optimal successive interference cancellation receiver. For this network architecture, we investigate the joint optimization of the quantization noise covariance matrices at the RUs and the RF time allocation to the multiple-access and fronthaul links for rate region maximization. To this end, we formulate a unified weighted sum rate maximization problem valid for each possible pair of the considered quantization and detection schemes. To handle the non-convexity of the unified problem, we transform it into a bi-convex problem which facilitates the derivation of an efficient suboptimal solution using alternating convex optimization and golden section search. Our simulation results show that for each pair of the considered quantization and detection schemes, C-RAN with hybrid RF/FSO fronthauling can achieve a considerable sum rate gain compared to conventional systems employing pure FSO fronthauling, especially under unfavorable atmospheric conditions. Moreover, employing a more sophisticated quantization scheme can significantly improve the system performance under adverse atmospheric conditions.
Statistical Piano Reduction Controlling Performance Difficulty
Venue: arXiv cs.AI 2018
Year: 2018

We present a statistical-modelling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.
Frame-validity games and lower bounds on the complexity of modal axioms
Venue: arXiv cs.LO 2018
Year: 2018

We introduce frame-equivalence games tailored for reasoning about the size, modal depth, number of occurrences of symbols and number of different propositional variables of modal formulae defining a given frame-property. Using these games, we prove lower bounds on the above measures for a number of well-known modal axioms; what is more, for some of the axioms, we show that they are optimal among the formulae defining the respective class of frames.
Temporal Phase Shifts in SCADA Networks
Venue: arXiv cs.CR 2018
Year: 2018

In Industrial Control Systems (ICS/SCADA), machine to machine data traffic is highly periodic. Previous work showed that in many cases, it is possible to create an automata-based model of the traffic between each individual Programmable Logic Controller (PLC) and the SCADA server, and to use the model to detect anomalies in the traffic. When testing the validity of previous models, we noticed that overall, the models have difficulty in dealing with communication patterns that change over time. In this paper we show that in many cases the traffic exhibits phases in time, where each phase has a unique pattern, and the transition between the different phases is rather sharp. We suggest a method to automatically detect traffic phase shifts, and a new anomaly detection model that incorporates multiple phases of the traffic. Furthermore we present a new sampling mechanism for training set assembly, which enables the model to learn all phases during the training stage with lower complexity. The model presented has similar accuracy and much less permissiveness compared to the previous general DFA model. Moreover, the model can provide the operator with information about the state of the controlled process at any given time, as seen in the traffic phases.
Multi-feature Fusion for Image Retrieval Using Constrained Dominant Sets
Venue: arXiv cs.CV 2018
Year: 2018

Aggregating different image features for image retrieval has recently shown its effectiveness. While highly effective, though, the question of how to uplift the impact of the best features for a specific query image persists as an open computer vision problem. In this paper, we propose a computationally efficient approach to fuse several hand-crafted and deep features, based on the probabilistic distribution of a given membership score of a constrained cluster in an unsupervised manner. First, we introduce an incremental nearest neighbor (NN) selection method, whereby we dynamically select k-NN to the query. We then build several graphs from the obtained NN sets and employ constrained dominant sets (CDS) on each graph G to assign edge weights which consider the intrinsic manifold structure of the graph, and detect false matches to the query. Finally, we elaborate the computation of feature positive-impact weight (PIW) based on the dispersive degree of the characteristics vector. To this end, we exploit the entropy of a cluster membership-score distribution. In addition, the final NN set bypasses a heuristic voting scheme. Experiments on several retrieval benchmark datasets show that our method can improve the state-of-the-art result.
On Optimizing VLC Networks for Downlink Multi-User Transmission: A Survey
Venue: arXiv cs.IT 2018
Year: 2018

The evolving explosion in high data rate services and applications will soon require the use of untapped, abundant unregulated spectrum of the visible light for communications to adequately meet the demands of the fifth-generation (5G) mobile technologies. Radio-frequency (RF) networks are proving to be scarce to cover the escalation in data rate services. Visible light communication (VLC) has emerged as a great potential solution, either in replacement of, or complement to, existing RF networks, to support the projected traffic demands. Despite of the prolific advantages of VLC networks, VLC faces many challenges that must be resolved in the near future to achieve a full standardization and to be integrated to future wireless systems. Here, we review the new, emerging research in the field of VLC networks and lay out the challenges, technological solutions, and future work predictions. Specifically, we first review the VLC channel capacity derivation, discuss the performance metrics and the associated variables; the optimization of VLC networks are also discussed, including resources and power allocation techniques, user-to-access point (AP) association and APs-to-clustered-users-association, APs coordination techniques, non-orthogonal multiple access (NOMA) VLC networks, simultaneous energy harvesting and information transmission using the visible light, and the security issue in VLC networks. Finally, we propose several open research problems to optimize the various VLC networks by maximizing either the sum rate, fairness, energy efficiency, secrecy rate, or harvested energy.
A Simple Convolutional Generative Network for Next Item Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we introduce a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range item dependencies. The network architecture of the proposed model is formed of a stack of \emph{holed} convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the next item recommendation task. It accordingly can be used as a powerful recommendation baseline to beat in future, especially when there are long sequences of user feedback.
NetScatter: Enabling Large-Scale Backscatter Networks
Venue: arXiv cs.NI 2018
Year: 2018

We present the first wireless protocol that scales to hundreds of concurrent transmissions from backscatter devices. Our key innovation is a distributed coding mechanism that works below the noise floor, operates on backscatter devices and can decode all the concurrent transmissions at the receiver using a single FFT operation. Our design addresses practical issues such as timing and frequency synchronization as well as the near-far problem. We deploy our design using a testbed of backscatter hardware and show that our protocol scales to concurrent transmissions from 256 devices using a bandwidth of only 500 kHz. Our results show throughput and latency improvements of 14--62x and 15--67x over existing approaches and 1--2 orders of magnitude higher transmission concurrency.
Multivariant Assertion-based Guidance in Abstract Interpretation
Venue: arXiv cs.PL 2018
Year: 2018

Approximations during program analysis are a necessary evil, as they ensure essential properties, such as soundness and termination of the analysis, but they also imply not always producing useful results. Automatic techniques have been studied to prevent precision loss, typically at the expense of larger resource consumption. In both cases (i.e., when analysis produces inaccurate results and when resource consumption is too high), it is necessary to have some means for users to provide information to guide analysis and thus improve precision and/or performance. We present techniques for supporting within an abstract interpretation framework a rich set of assertions that can deal with multivariance/context-sensitivity, and can handle different run-time semantics for those assertions that cannot be discharged at compile time. We show how the proposed approach can be applied to both improving precision and accelerating analysis. We also provide some formal results on the effects of such assertions on the analysis results.
Non-Interfering Concurrent Exchange (NICE) Networks
Venue: arXiv cs.DC 2018
Year: 2018

In studying the statistical frequency of exchange in comparison-exchange (CE) networks we discover a new elementary form of comparison-exchange which we name the "2-op". The operation supports concurrent and non-interfering operations of two traditional CEs upon one shared element. More than merely improving overall statistical performance, the introduction of NICE (non-interfering CE) networks lowers long-held bounds in the number of stages required for sorting tasks. Code-based CEs also benefit from improved average/worst case run time costs.