Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards continual learning in medical imaging

This work investigates continual learning of two segmentation tasks in brain MRI with neural networks. To explore in this context the capabilities of current methods for countering catastrophic forgetting of the first task when a new one is learned, we investigate elastic weight consolidation, a recently proposed method based on Fisher information, originally evaluated on reinforcement learning of Atari games. We use it to sequentially learn segmentation of normal brain structures and then segmentation of white matter lesions. Our findings show this recent method reduces catastrophic forgetting, while large room for improvement exists in these challenging settings for continual learning.
DIAG-NRE: A Deep Pattern Diagnosis Framework for Distant Supervision Neural Relation Extraction

Modern neural network models have achieved the state-of-the-art performance on relation extraction (RE) tasks. Although distant supervision (DS) can automatically generate training labels for RE, the effectiveness of DS highly depends on datasets and relation types, and sometimes it may introduce large labeling noises. In this paper, we propose a deep pattern diagnosis framework, DIAG-NRE, that aims to diagnose and improve neural relation extraction (NRE) models trained on DS-generated data. DIAG-NRE includes three stages: (1) The deep pattern extraction stage employs reinforcement learning to extract regular-expression-style patterns from NRE models. (2) The pattern refinement stage builds a pattern hierarchy to find the most representative patterns and lets human reviewers evaluate them quantitatively by annotating a certain number of pattern-matched examples. In this way, we minimize both the number of labels to annotate and the difficulty of writing heuristic patterns. (3) The weak label fusion stage fuses multiple weak label sources, including DS and refined patterns, to produce noise-reduced labels that can train a better NRE model. To demonstrate the broad applicability of DIAG-NRE, we use it to diagnose 14 relation types of two public datasets with one simple hyper-parameter configuration. We observe different noise behaviors and obtain significant F1 improvements on all relation types suffering from large labeling noises.
GIDS: GAN based Intrusion Detection System for In-Vehicle Network

A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features. To detect suspicious network connections effectively, the intrusion detection system (IDS) is strongly required. Unlike the traditional IDS for Internet, there are small number of known attack signatures for vehicle networks. Also, IDS for vehicle requires high accuracy because any false-positive error can seriously affect the safety of the driver. To solve this problem, we propose a novel IDS model for in-vehicle networks, GIDS (GAN based Intrusion Detection System) using deep-learning model, Generative Adversarial Nets. GIDS can learn to detect unknown attacks using only normal data. As experiment result, GIDS shows high detection accuracy for four unknown attacks.
Deductive Evaluation: Formal Code Analysis with Low User Burden

We describe a framework for symbolically evaluating iterative C code using a deductive approach that automatically discovers and proves program properties. Although verification is not performed, the method can infer detailed program behavior. Software engineering workflows could be enhanced by this type of analysis. Floyd-Hoare verification principles are applied to synthesize loop invariants, using a library of iteration-specific deductive knowledge. When needed, theorem proving is interleaved with evaluation and performed on the fly. Evaluation results take the form of inferred expressions and type constraints for values of program variables. An implementation using PVS (Prototype Verification System) is presented along with results for sample C functions.
Near-eye light field displays

We propose near-eye light field displays that enable thin, lightweight head-mounted displays (HMDs) capable of presenting nearly correct convergence, accommodation, binocular disparity, and retinal defocus depth cues. Sharp images are depicted by out-of-focus elements by synthesizing light fields corresponding to virtual objects within a viewer's natural accommodation range. We formally assess the capabilities of microlens arrays to achieve practical near-eye light field displays. Building on concepts shared with existing integral imaging displays and light field cameras, we optimize performance in the context of near-eye viewing. We establish fundamental trade-offs between the quantitative parameters of resolution, field of view, and depth of field, as well as the ergonomic parameters of form factor and ranges of allowed eye movement. As with light field cameras, our design supports continuous accommodation of the eye throughout a finite depth of field; as a result, binocular configurations provide a means to address the accommodation-convergence conflict occurring with existing stereoscopic displays. We construct a complete prototype display system, comprising: a custom-fabricated HMD using modified off-the-shelf parts and real-time, GPU-accelerated light field renderers (including a general ray tracing method and a "backward compatible" rasterization method supporting existing stereoscopic content). Through simulations and experiments, we motivate near-eye light field displays as thin, lightweight alternatives to conventional near-eye displays.
In-memory Spatial-Aware Framework for Processing Proximity-Alike Queries in Big Spatial Data

The widespread adoption of sensor-enabled and mobile ubiquitous devices has caused an avalanche of big data that is mostly geospatially tagged. Most cloud-based big data processing systems are designed for general-purpose workloads, neglecting spatial-characteristics. However, interesting analytics often seek answers for proximity-alike queries. We fill this gap by providing custom geospatial service layer atop of Apache Spark. To be more specific, we leverage Spark to design a custom spatial-aware partitioning method to boost geospatial query performances. Our results show that our patches outperform state-of-the-art implementations by significant fractions.
Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning

Solving composites tasks, which consist of several inherent sub-tasks, remains a challenge in the research area of dialogue. Current studies have tackled this issue by manually decomposing the composite tasks into several sub-domains. However, much human effort is inevitable. This paper proposes a dialogue framework that autonomously models meaningful sub-domains and learns the policy over them. Our experiments show that our framework outperforms the baseline without subdomains by 11% in terms of success rate, and is competitive with that with manually defined sub-domains.
Optimizing Agent-Based Simulations for the GPU

Agent-based simulation (ABS) is a powerful tool to analyze the behavior of agents interacting with each other. Many simulations contain a large number of agents or re¬quire multiple executions with different parameters, making the simulation-based experimentation slow. In recent years, graphics processing units (GPUs) have proven to be a powerful platform for accelerating ABS. In this paper, we propose several novel techniques to further improve the efficiency of GPU-based ABS. Our techniques reduce the total amount of computation in ABS, improve memory access patterns, and exploit temporal locality to minimize redundant calculations. Furthermore, the optimizations can be implemented as a layer on top of existing simulators, as we demonstrate by modifying the popular GPU-based simulator FLAME. We perform experiments using the Boids and Social Force simulations, and show that our techniques improve overall performance by up to 3.2× and 2.1× respectively compared to the original FLAME.
Fairness-Preserving Text Summarzation

Given the rapid growth in online information content, text summarization algorithms are progressively used to provide users a succinct idea about the total information content. Historically, summarization algorithms are evaluated only based on how close their outputs are to human-written gold standard summaries. In this work, we propose to evaluate summarization algorithms from an unprecedented viewpoint. Considering that an extractive summarization algorithm selects a subset of the textual units (tweets / sentences) in the input data for inclusion in the summary, we examine the fairness of this selection. Importantly, if the data to be summarized is generated by different socially salient groups, or different political groups, or different news media sources, then we check whether the generated summaries fairly represent these different groups or sources. In real-world datasets we observe that existing summarization algorithms often represent the groups very differently compared to their distributions in the input data. To alleviate such adverse impacts, we propose a novel fair summarization algorithm 'FairSumm' capable of generating high-quality fair summaries.
Evolution of neural networks

Evolution of artificial neural networks has recently emerged as a powerful technique in two areas. First, while the standard value-function based reinforcement learning works well when the environment is fully observable, neuroevolution makes it possible to disambiguate hidden state through memory. Such memory makes new applications possible in areas such as robotic control, game playing, and artificial life. Second, deep learning performance depends crucially on the network architecture and hyperparameters. While many such architectures are too complex to be optimized by hand, neuroevolution can be used to do so automatically. As a result, deep learning can be scaled to utilize more of the available computing power, and extended to domains combining multiple modalities. In this tutorial, I will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining gradient-based training with evolutionary methods, and (3) applications of these techniques in control, robotics, artificial life, games, image processing, and language.
Dynamic Index Finger Gesture Video Dataset for Mobile Interaction

This paper introduces an original video dataset containing dynamic index finger gestures in a mobile context. The dataset consists of 746 video sequences of 6 index finger gestures: left, right, up, down, tap, circle. The video sequences are obtained from a mobile phone's rear camera equipped with a wide angle lens, thus the dataset features particular challenges due to background motion and the distorted field of view. We present a baseline method that uses optical flow features in the form of a histogram to represent the motion information in the video sequences.
Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation

In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference. We notice that some words are always copied during paraphrasing, which we call copy knowledge. Considering the stability of such knowledge, a good candidate translation should contain all these words appeared in the reference sentence. Therefore, in this participation of the WMT’2018 metrics shared task we introduce a simple statistical method for copy knowledge extraction, and incorporate it into Meteor metric, resulting in a new machine translation metric Meteor++. Our experiments show that Meteor++ can nicely integrate copy knowledge and improve the performance significantly on WMT17 and WMT15 evaluation sets.
Information Needs in Contemporary Code Review

Contemporary code review is a widespread practice used by software engineers to maintain high software quality and share project knowledge. However, conducting proper code review takes time and developers often have limited time for review. In this paper, we aim at investigating the information that reviewers need to conduct a proper code review, to better understand this process and how research and tool support can make developers become more effective and efficient reviewers. Previous work has provided evidence that a successful code review process is one in which reviewers and authors actively participate and collaborate. In these cases, the threads of discussions that are saved by code review tools are a precious source of information that can be later exploited for research and practice. In this paper, we focus on this source of information as a way to gather reliable data on the aforementioned reviewers' needs. We manually analyze 900 code review comments from three large open-source projects and organize them in categories by means of a card sort. Our results highlight the presence of seven high-level information needs, such as knowing the uses of methods and variables declared/modified in the code under review. Based on these results we suggest ways in which future code review tools can better support collaboration and the reviewing task.
On-the-fly Uplink Training and Pilot Code Sequence Design for Cellular Networks

Cellular networks of massive MIMO base-stations employing TDD/OFDM and relying on uplink training for both downlink and uplink transmission are viewed as an attractive candidate for 5G deployments, as they promise high area spectral and energy efficiencies with relatively simple low-latency operation. We investigate the use of non-orthogonal uplink pilot designs as a means for improving the area spectral efficiency in the downlink of such massive MIMO cellular networks. We develop a class of pilot designs that are locally orthogonal within each cell, while maintaining low inner-product properties between codes in different cells. Using channel estimates provided by observations on these codes, each cell independently serves its locally active users with MU-MIMO transmission that is also designed to mitigate interference to a subset of `strongly interfered' out-of-cell users. As our simulation-based analysis shows, such cellular operation based on the proposed codes yields user-rate CDF improvement with respect to conventional operation, which can be exploited to improve cell and/or cell-throughput performance.
ON-LINE BAYESIAN PARAMETER ESTIMATION IN ELECTROCARDIOGRAM STATE SPACE MODELS

This paper proposes a Bayesian approach to parameter estimation in electrocardiogram state space models. The on-line nature of the proposed method allows it to be applied to real-world electrocardiogram recordings with varying beat morphology, heart rate, and noise; it thereby provides clear advantages over the conventional Gaussian kernel approach. The applicability of the proposed method is demonstrated on benchmark electrocardiogram data. The results indicate that the method provides a promising framework for noise reduction and wave delineation in electrocardiograms.
AlphaSum: size-constrained table summarization using value lattices

Consider a scientist who wants to explore multiple data sets to select the relevant ones for further analysis. Since the visualization real estate may put a stringent constraint on how much detail can be presented to this user in a single page, effective table summarization techniques are needed to create summaries that are both sufficiently small and effective in communicating the available content. In this paper, we first argue that table summarization can benefit from knowledge about acceptable value clustering alternatives for clustering the values in the database. We formulate the problem of table summarization with the help of value lattices. We then provide a framework to express alternative clustering strategies and to account for various utility measures (such as information loss) in assessing different summarization alternatives. Based on this interpretation, we introduce three preference criteria, max-min-util (cautious), max-sum-util (cumulative), and pareto-util, for the problem of table summarization. To tackle with the inherent complexity, we rely on the properties of the fuzzy interpretation to further develop a novel ranked set cover based evaluation mechanism (RSC). These are brought together in an AlphaSum, table summarization system. Experimental evaluations showed that RSC improves both execution times and the summary qualities in AlphaSum, by pruning the search space more effectively than the existing solutions.
STACC, OOV Density and N-gram Saturation: Vicomtech’s Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering

We describe Vicomtech’s participation in the WMT 2018 Shared Task on parallel corpus filtering. We aimed to evaluate a simple approach to the task, which can efficiently process large volumes of data and can be easily deployed for new datasets in different language pairs and domains. We based our approach on STACC, an efficient and portable method for parallel sentence identification in comparable corpora. To address the specifics of the corpus filtering task, which features significant volumes of noisy data, the core method was expanded with a penalty based on the amount of unknown words in sentence pairs. Additionally, we experimented with a complementary data saturation method based on source sentence n-grams, with the goal of demoting parallel sentence pairs that do not contribute significant amounts of yet unobserved n-grams. Our approach requires no prior training and is highly efficient on the type of large datasets featured in the corpus filtering task. We achieved competitive results with this simple and portable method, ranking in the top half among competing systems overall.
Loss of Gene Body Methylation in Eutrema salsugineum Is Associated with Reduced Gene Expression

Abstract Gene body methylation (gbM) is typically characterized by DNA methylation in the CG context within coding regions and is associated with constitutive genes that have moderate to high expression levels. A recent study discovered the loss of gbM in two plant species (Eutrema salsugineum and Conringia planisiliqua), illustrating that gbM is not necessary for survival and reproduction. The same paper stated there was no detectable effect of gbM loss on gene expression (GE). Here, we reanalyzed the GE data and accounted for experimental variability in expression level estimates. We show that the loss of gbM in E. salsugineum is associated with a small but highly significant decrease in GE relative to the closely related species Arabidospis thaliana. Our results are consistent with various evolutionary analyses that suggest gbM has a function, perhaps as a homeostatic effect on GE.
Finding k-Dominant G-Skyline Groups on High Dimensional Data

Skyline query retrieves a set of skyline points which are not dominated by any other point and has attracted wide attention in database community. Recently, an important variant G-Skyline is developed. It aims to return optimal groups of points. However, when data dimensionality is high, G-Skyline result has too many groups, which makes that users cannot determine which groups are satisfactory. To find less but more representative groups of points, in this paper, we propose a novel concept of <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dominant G-Skyline, which first adopts <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dominance to retrieve more representative points and then computes the groups not <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dominated by others. In addition, we present a two-phase algorithm to efficiently compute <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dominant G-Skyline groups. In the first phase, we construct a <inline-formula> <tex-math notation="LaTeX">$lk$ </tex-math></inline-formula>DG structure while pruning the points never included in any <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dominant G-Skyline group as much as possible. In the second phase, using <inline-formula> <tex-math notation="LaTeX">$lk$ </tex-math></inline-formula>DG, we propose two efficient <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dominant G-Skyline searching methods SM-P and SM-G, which generate new candidate groups from single points and ancestor groups, respectively. Our experimental results indicate that our proposed algorithms are more efficient than the baseline methods on real and synthetic data sets.
Tumor suppression by the EGR1, DMP1, ARF, p53, and PTEN Network

Abstract Recent studies have indicated that EGR1 is a direct regulator of tumor suppressors including TGFβ1, PTEN, and p53. The Myb-like transcription factor Dmp1 is a physiological regulator of the Arf-p53 pathway through transactivation of the Arf promoter and physical interaction of p53. The Dmp1 promoter has binding sites for Egr proteins, and Egr1 is a target for Dmp1. Crosstalks between p53 and PTEN have been reported. The Egr1-Dmp1-Arf-p53-Pten pathway displays multiple modes of interaction with each other, suggesting the existence of a functional network of tumor suppressors that maintain normal cell growth and prevent the emergence of incipient cancer cells.
Hybrid Feature Extraction for Palmprint-Based User Authentication

Biometry is often used as a part of the multi-factor authentication in order to improve the security of IT systems. In this paper, we propose the palmprint-based solution for user identity verification. In particular, we present a new approach to feature extraction. The proposed method is based both on texture and color information. Our experiments show that using the proposed hybrid features allows for achieving satisfactory accuracy without increasing requirements for additional computational resources. It is important from our perspective since the proposed method is dedicated to smartphones and other handhelds in mobile verification scenarios.
Sketchsegnet: A Rnn Model for Labeling Sketch Strokes

We investigate the problem of stroke-level sketch segmentation, which is to train machines to assign strokes with semantic part labels given a input sketch. Solving the problem of sketch segmentation opens the door for fine-grained sketch interpretation, which can benefit many novel sketch-based applications, including sketch recognition and sketch-based image retrieval. In this paper, we treat the problem of stroke-level sketch segmentation as a seqence-to-sequence generation problem, and a reccurent nueral networks (RNN)-based model SketchSegNet is presented to translate sequence of strokes into thier semantic part labels. In addition, for the first time a large-scale stroke-level sketch segmentation dataset is proposed, which is composed of 57K annotated free-hand human sketch selected from QuickDraw. Experimental results of stroke-level sketch segmentation on this novel dataset shows that our approach offers an average accuracy over 90% for stroke labeling.