OpenRaiser/Intern-Atlas huggingface dataset:
Resolving Conflicts in Clinical Guidelines using Argumentation
Venue: arXiv cs.AI 2019
Year: 2019

Automatically reasoning with conflicting generic clinical guidelines is a burning issue in patient-centric medical reasoning where patient-specific conditions and goals need to be taken into account. It is even more challenging in the presence of preferences such as patient's wishes and clinician's priorities over goals. We advance a structured argumentation formalism for reasoning with conflicting clinical guidelines, patient-specific information and preferences. Our formalism integrates assumption-based reasoning and goal-driven selection among reasoning outcomes. Specifically, we assume applicability of guideline recommendations concerning the generic goal of patient well-being, resolve conflicts among recommendations using patient's conditions and preferences, and then consider prioritised patient-centered goals to yield non-conflicting, goal-maximising and preference-respecting recommendations. We rely on the state-of-the-art Transition-based Medical Recommendation model for representing guideline recommendations and augment it with context given by the patient's conditions, goals, as well as preferences over recommendations and goals. We establish desirable properties of our approach in terms of sensitivity to recommendation conflicts and patient context.
Load-Balancing for Parallel Delaunay Triangulations
Venue: arXiv cs.DS 2019
Year: 2019

Computing the Delaunay triangulation (DT) of a given point set in $\mathbb{R}^D$ is one of the fundamental operations in computational geometry. Recently, Funke and Sanders (2017) presented a divide-and-conquer DT algorithm that merges two partial triangulations by re-triangulating a small subset of their vertices - the border vertices - and combining the three triangulations efficiently via parallel hash table lookups. The input point division should therefore yield roughly equal-sized partitions for good load-balancing and also result in a small number of border vertices for fast merging. In this paper, we present a novel divide-step based on partitioning the triangulation of a small sample of the input points. In experiments on synthetic and real-world data sets, we achieve nearly perfectly balanced partitions and small border triangulations. This almost cuts running time in half compared to non-data-sensitive division schemes on inputs exhibiting an exploitable underlying structure.
Where Do Human Heuristics Come From?
Venue: arXiv cs.LG 2019
Year: 2019

Human decision-making deviates from the optimal solution, that maximizes cumulative rewards, in many situations. Here we approach this discrepancy from the perspective of bounded rationality and our goal is to provide a justification for such seemingly sub-optimal strategies. More specifically we investigate the hypothesis, that humans do not know optimal decision-making algorithms in advance, but instead employ a learned, resource-bounded approximation. The idea is formalized through combining a recently proposed meta-learning model based on Recurrent Neural Networks with a resource-bounded objective. The resulting approach is closely connected to variational inference and the Minimum Description Length principle. Empirical evidence is obtained from a two-armed bandit task. Here we observe patterns in our family of models that resemble differences between individual human participants.
A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data
Venue: arXiv cs.SI 2019
Year: 2019

Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.
Analysis of the Wikipedia Network of Mathematicians
Venue: arXiv cs.DL 2019
Year: 2019

We look at the network of mathematicians defined by the hyperlinks between their biographies on Wikipedia. We show how to extract this information using three snapshots of the Wikipedia data, taken in 2013, 2017 and 2018. We illustrate how such Wikipedia data can be used by performing a centrality analysis. These measures show that Hilbert and Newton are the most important mathematicians. We use our example to illustrate the strengths and weakness of centrality measures and to show how to provide estimates of the robustness of centrality measurements. In part, we do this by comparison to results from two other sources: an earlier study of biographies on the MacTutor website and a small informal survey of the opinion of mathematics and physics students at Imperial College London.
Distribution Grid Admittance Estimation with Limited Non-Synchronized Measurements
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we propose a method for estimating radial distribution grid admittance matrix using a limited number of measurement devices. Neither synchronized three-phase measurements nor phasor measurements are required. After making several practical assumptions, the method estimates even impedances of lines which have no local measurement devices installed. The computational complexity of the proposed method is low, and this makes it possible to use for on-line applications. The effectiveness of the proposed method is tested using data from a real-world distribution grid in Vienna, Austria.
How Do the Open Source Communities Address Usability and UX Issues? An Exploratory Study
Venue: arXiv cs.HC 2019
Year: 2019

Usability and user experience (UX) issues are often not well emphasized and addressed in open source software (OSS) development. There is an imperative need for supporting OSS communities to collaboratively identify, understand, and fix UX design issues in a distributed environment. In this paper, we provide an initial step towards this effort and report on an exploratory study that investigated how the OSS communities currently reported, discussed, negotiated, and eventually addressed usability and UX issues. We conducted in-depth qualitative analysis of selected issue tracking threads from three OSS projects hosted on GitHub. Our findings indicated that discussions about usability and UX issues in OSS communities were largely influenced by the personal opinions and experiences of the participants. Moreover, the characteristics of the community may have greatly affected the focus of such discussion.
An Energy-efficient Time-domain Analog VLSI Neural Network Processor Based on a Pulse-width Modulation Approach
Venue: arXiv cs.ET 2019
Year: 2019

A time-domain analog-weighted-sum calculation model based on a pulse-width modulation (PWM) approach is proposed. The proposed calculation model can be applied to any types of network structure including multi-layer feedforward networks. We also propose very large-scale integrated (VLSI) circuits to implement the proposed model. Unlike the conventional analog voltage or current mode circuits used in computing-in-memory circuits, our time-domain analog circuits use transient operation in charging/discharging processes to capacitors. Since the circuits can be designed without operational amplifiers, they can be operated with extremely low power consumption. However, they have to use very high-resistance devices, on the order of giga-ohms. We designed a CMOS VLSI chip to verify weighted-sum operation based on the proposed model with binary weights, which realizes the BinaryConnect model. In the chip, memory cells of static-random-access memory (SRAM) are used for synaptic connection weights. High-resistance operation was realized by using the subthreshold operation region of MOS transistors unlike the ordinary computing-in-memory circuits. The chip was designed and fabricated using a 250-nm fabrication technology. Measurement results showed that energy efficiency for the weighted-sum calculation was 300~TOPS/W (Tera-Operations Per Second per Watt), which is more than one order of magnitude higher than that in state-of-the-art digital AI processors, even though the minimum width of interconnection used in this chip was several times larger than that in such digital processors. If state-of-the-art VLSI technology is used to implement the proposed model, an energy efficiency of more than 1,000~TOPS/W will be possible. For practical applications, development of emerging analog memory devices such as ferroelectric-gate field effect transistors (FeFETs) is necessary.
Using Ultra-Wideband Technology in Vehicles for Infrastructure-free Localization
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, we investigate using Ultra-Wideband (UWB) technology in vehicles for localization as well as other possible infrastructure-free applications. To that end, we first introduce the on-vehicle UWB anchor layout, then conduct a theoretical analysis to shed light on the capabilities and limitations of using UWB anchors with this layout. To verify the analysis conducted, extensive field trials are performed, which yield results that match the predictions of the conducted analysis. Finally, Virtual Pedestrian Traffic Light (VPTL), an infrastructure-free pedestrian traffic light system is introduced as an example application of the presented approach.
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
Venue: arXiv cs.CV 2019
Year: 2019

We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic videos and a multi-view stereo method, e.g., structure from motion, to supervise learning in a sparse manner. Consequently, our method requires neither manual labeling nor patient computed tomography (CT) scan in the training and application phases. In a cross-patient experiment using CT scans as groundtruth, the proposed method achieved submillimeter mean residual error. In a comparison study to recent self-supervised depth estimation methods designed for natural video on in vivo sinus endoscopy data, we demonstrate that the proposed approach outperforms the previous methods by a large margin. The source code for this work is publicly available online at https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch.
Counting basic-irreducible factors mod $p^k$ in deterministic poly-time and $p$-adic applications
Venue: arXiv cs.SC 2019
Year: 2019

Finding an irreducible factor, of a polynomial $f(x)$ modulo a prime $p$, is not known to be in deterministic polynomial time. Though there is such a classical algorithm that {\em counts} the number of irreducible factors of $f\bmod p$. We can ask the same question modulo prime-powers $p^k$. The irreducible factors of $f\bmod p^k$ blow up exponentially in number; making it hard to describe them. Can we count those irreducible factors $\bmod~p^k$ that remain irreducible mod $p$? These are called {\em basic-irreducible}. A simple example is in $f=x^2+px \bmod p^2$; it has $p$ many basic-irreducible factors. Also note that, $x^2+p \bmod p^2$ is irreducible but not basic-irreducible! We give an algorithm to count the number of basic-irreducible factors of $f\bmod p^k$ in deterministic poly(deg$(f),k\log p$)-time. This solves the open questions posed in (Cheng et al, ANTS'18 \& Kopp et al, Math.Comp.'19). In particular, we are counting roots $\bmod\ p^k$; which gives the first deterministic poly-time algorithm to compute Igusa zeta function of $f$. Also, our algorithm efficiently partitions the set of all basic-irreducible factors (possibly exponential) into merely deg$(f)$-many disjoint sets, using a compact tree data structure and {\em split} ideals.
Simulating Forces - Learning Through Touch, Virtual Laboratories
Venue: arXiv cs.HC 2019
Year: 2019

With the expansion of e-learning course curricula and the affordability of haptic devices, at-home virtual laboratories are emerging as an increasingly viable option for e-learners. We outline three novel haptic simulations for the introductory physics concepts of friction, the Coriolis Effect, and Precession. These simulations provide force feedback through one or more Novint Falcon devices, allowing students to "feel" the forces at work in a controlled learning environment. This multi-modal approach to education (beyond the audiovisual) may lead to increased interest and immersion for e-learners and appeal to the kinesthetic learners who may struggle in a traditional e-learning course setting.
Class-independent sequential full image segmentation, using a convolutional net that finds a segment within an attention region, given a pointer pixel within this segment
Venue: arXiv cs.CV 2019
Year: 2019

This work examines the use of a fully convolutional net (FCN) to find an image segment, given a pixel within this segment region. The net receives an image, a point in the image and a region of interest (RoI ) mask. The net output is a binary mask of the segment in which the point is located. The region where the segment can be found is contained within the input RoI mask. Full image segmentation can be achieved by running this net sequentially, region-by-region on the image, and stitching the output segments into a single segmentation map. This simple method addresses two major challenges of image segmentation: 1) Segmentation of unknown categories that were not included in the training set. 2) Segmentation of both individual object instances (things) and non-objects (stuff), such as sky and vegetation. Hence, if the pointer pixel is located within a person in a group, the net will output a mask that covers that individual person; if the pointer point is located within the sky region, the net returns the region of the sky in the image. This is true even if no example for sky or person appeared in the training set. The net was tested and trained on the COCO panoptic dataset and achieved 67% IOU for segmentation of familiar classes (that were part of the net training set) and 53% IOU for segmentation of unfamiliar classes (that were not included in the training).
Finding big matchings in planar graphs quickly
Venue: arXiv cs.DS 2019
Year: 2019

It is well-known that every $n$-vertex planar graph with minimum degree 3 has a matching of size at least $\frac{n}{3}$. But all proofs of this use the Tutte-Berge-formula for the size of a maximum matching. Hence these proofs are not directly algorithmic, and to find such a matching one must apply a general-purposes maximum matching algorithm, which has run-time $O(n^{1.5}α(n))$ for planar graphs. In contrast to this, this paper gives a linear-time algorithm that finds a matching of size at least $\frac{n}{3}$ in any planar graph with minimum degree 3.
ERSFQ 8-bit Parallel Binary Shifter for Energy-Efficient Superconducting CPU
Venue: arXiv cs.AR 2019
Year: 2019

We have designed and tested a parallel 8-bit ERSFQ binary shifter that is one of the essential circuits in the design of the energy-efficient superconducting CPU. The binary shifter performs a bi-directional SHIFT instruction of an 8-bit argument. It consists of a bi-direction triple-port shift register controlled by two (left and right) shift pulse generators asynchronously generating a set number of shift pulses. At first clock cycle, an 8-bit word is loaded into the binary shifter and a 3-bit shift argument is loaded into the desired shift-pulse generator. Next, the generator produces the required number of shift SFQ pulses (from 0 to 7) asynchronously, with a repetition rate set by the internal generator delay of ~ 30 ps. These SFQ pulses are applied to the left (positive) or the right (negative) input of the binary shifter. Finally, after the shift operation is completed, the resulting 8-bit word goes to the parallel output. The complete 8-bit ERSFQ binary shifter, consisting of 820 Josephson junctions, was simulated and optimized using PSCAN2. It was fabricated in MIT Lincoln Lab 10-kA/cm2 SFQ5ee fabrication process with a high-kinetic inductance layer. We have successfully tested the binary shifter at both the LSB-to-MSB and MSB-to-LSB propagation regimes for all eight shift arguments. A single shift operation on a single input word demonstrated operational margins of +/-16% of the dc bias current. The correct functionality of the 8-bit ERSFQ binary shifter with the large, exhaustive data pattern was observed within +/-10% margins of the dc bias current. In this paper, we describe the design and present the test results for the ERSFQ 8-bit parallel binary shifter.
Rate-Splitting for Multi-User Multi-Antenna Wireless Information and Power Transfer
Venue: arXiv cs.IT 2019
Year: 2019

In a multi-user multi-antenna Simultaneous Wireless Information and Power Transfer (SWIPT) network, the transmitter sends information to the Information Receivers (IRs) and energy to Energy Receivers (ERs) concurrently. A conventional approach is based on Multi-User Linear Precoding (MU--LP) where each IR directly decodes the intended stream by fully treating the interference from other IRs and ERs as noise. In this paper, we investigate the application of linearly-precoded Rate-Splitting (RS) in Multiple Input Single Output (MISO) SWIPT Broadcast Channel (BC). By splitting the messages of IRs into private and common parts and encoding the common parts into a common stream decoded by all IRs, RS manages the interference dynamically. The precoders are designed such that the Weighted Sum Rate (WSR) of IRs is maximized under the total transmit power constraint and the sum energy constraint for ERs. Numerical results show that the proposed RS-assisted strategy provides a better rate-energy tradeoff in MISO SWIPT BC. Under a sum energy constraint of ERs, RS-assisted strategy achieves better WSR performance of IRs than MU--LP and NOMA in a wide range of IR and ER deployments. Hence, we draw the conclusion that RS is superior for downlink SWIPT networks.
Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices
Venue: arXiv cs.CV 2019
Year: 2019

Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc. Although numerous of efforts have already been paid, most of them focus on addressing the eyeblink detection problem under the constrained indoor conditions with the relative consistent subject and environment setup. Nevertheless, towards the practical applications eyeblink detection in the wild is more required, and of greater challenges. However, to our knowledge this has not been well studied before. In this paper, we shed the light to this research topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established by us. These samples are captured from the unconstrained movies, with the dramatic variation on human attribute, human pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a spatial-temporal pattern recognition problem. After locating and tracking human eye using SeetaFace engine and KCF tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to execute eyeblink verification. A feature extraction approach that reveals appearance and motion characteristics simultaneously is also proposed. The experiments on HUST-LEBW reveal the superiority and efficiency of our approach. It also verifies that, the existing eyeblink detection methods cannot achieve satisfactory performance in the wild.
Long-Bone Fracture Detection using Artificial Neural Networks based on Contour Features of X-ray Images
Venue: arXiv cs.CV 2019
Year: 2019

The following paper proposes two contour-based fracture detection schemes. The development of the contour-based fracture is based on the line-based fracture detection schemes proposed in arXiv:1902.07458. Existing Computer Aided Diagnosis (CAD) systems commonly employs Convolutional Neural Networks (CNN), although the cost to obtain a high accuracy is the amount of training data required. The purpose of the proposed schemes is to obtain a high classification accuracy with a reduced number of training data through the use of detected contours in X-ray images. There are two contour-based fracture detection schemes. The first is the Standard Contour Histogram Feature-Based (CHFB) and the second is the improved CHFB scheme. The difference between the two schemes is the removal of the surrounding detected flesh contours from the leg region in the improved CHFB scheme. The flesh contours are automatically classified as non-fractures. The contours are further refined to give a precise representation of the image edge objects. A total of 19 features are extracted from each refined contour. 8 out of the 19 features are based on the number of occurrences for particular detected gradients in the contour. Moreover, the occurrence of the 0-degree gradient in the contours are employed for the separation of the knee, leg and foot region. The features are a summary representation of the contour, in which it is used as inputs into the Artificial Neural Network (ANN). Both Standard CHFB and improved CHFB schemes are evaluated with the same experimental set-ups. The average system accuracy for the Standard CHFB scheme is 80.7%, whilst the improved CHFB scheme has an average accuracy of 82.98%. Additionally, the hierarchical clustering technique is adopted to highlight the fractured region within the X-ray image, using extracted 0-degree gradients from fractured contours.
Almost exact analysis of soft covering lemma via large deviation
Venue: arXiv cs.IT 2019
Year: 2019

This paper investigates the soft covering lemma under both the relative entropy and the total variation distance as the measures of deviation. The exact order of the expected deviation of the random i.i.d. code for the soft covering problem problem, is determined. The proof technique used in this paper significantly differs from the previous techniques for deriving exact exponent of the soft covering lemma. The achievability of the exact order follows from applying the change of measure trick (which has been broadly used in the large deviation) to the known one-shot bounds in the literature. For the ensemble converse, some new inequalities of independent interest derived and then the change of measure trick is applied again. The exact order of the total variation distance is similar to the exact order of the error probability, thus it adds another duality between the channel coding and soft covering. Finally, The results of this paper are valid for any memoryless channels, not only channels with finite alphabets.