Reviewerly/exHarmony huggingface dataset:
Instance based Transfer Learning for Genetic Programming for Symbolic Regression

Transfer learning aims to utilise knowledge acquired from the source domain to improve the learning performance in the target domain. It attracts increasing interests and many transfer learning approaches have been proposed. However, studies on transfer learning for genetic programming for symbolic regression are still rare, although clearly desired, due to the difficulty to evolve models with a good cross-domain generalisation ability. This work proposes a new instance weighting framework for transfer learning in genetic programming for symbolic regression. The key idea is to utilise a local weight updating scheme to identify and learn from more useful source domain instances and reduce the effort on the source domain instances, which are more different from the target domain data. The experimental results show that the proposed method notably enhances the learning capacity and the generalisation performance of genetic programming on the target domain and also outperforms some state-of-the-art regression methods.
Ensemble MultiBoost Based on RIPPER Classifier for Prediction of Imbalanced Software Defect Data

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR_SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm.
Genetic Programming with Rademacher Complexity for Symbolic Regression

Genetic Programming (GP) for symbolic regression is often prone to overfitting the training data, causing poor performance on unseen data. A number of recent works in the field have been devoted to regulating this problem by investigating both the structural and functional complexity of GP individuals during the evolutionary process. This work uses the Rademacher complexity and incorporates it into the fitness function of GP, utilising it as a means of controlling the functional complexity of GP individuals. The experiment results confirm that the new GP method has a notable generalization gain compared to the standard GP and Support Vector Regression (SVR) in most of the considered problems. Further investigations also show that the new GP method generates symbolic regression models that could not only release the overfitting trend in standard GP but also are significantly smaller in size compared to their counterparts in standard GP.
Mapping State Space using Landmarks for Universal Goal Reaching

An agent that has well understood the environment should be able to apply its skills for any given goals, leading to the fundamental problem of learning the Universal Value Function Approximator (UVFA). A UVFA learns to predict the cumulative rewards between all state-goal pairs. However, empirically, the value function for long-range goals is always hard to estimate and may consequently result in failed policy. This has presented challenges to the learning process and the capability of neural networks. We propose a method to address this issue in large MDPs with sparse rewards, in which exploration and routing across remote states are both extremely challenging. Our method explicitly models the environment in a hierarchical manner, with a high-level dynamic landmark-based map abstracting the visited state space, and a low-level value network to derive precise local decisions. We use farthest point sampling to select landmark states from past experience, which has improved exploration compared with simple uniform sampling. Experimentally we showed that our method enables the agent to reach long-range goals at the early training stage, and achieve better performance than standard RL algorithms for a number of challenging tasks.
A Dataset of General-Purpose Rebuttal

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog.Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it.We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech.The general rebuttals we describe here (written in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics.This allows creating responses beyond the scope of topics for which specific arguments are available.
NICT’s Supervised Neural Machine Translation Systems for the WMT19 News Translation Task

Raj Dabre, Kehai Chen, Benjamin Marie, Rui Wang, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita. Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). 2019.
Sequence-to-Segments Networks for Detecting Segments in Videos

Detecting segments of interest from videos is a common problem for many applications. And yet it is a challenging problem as it often requires not only knowledge of individual target segments, but also contextual understanding of the entire video and the relationships between the target segments. To address this problem, we propose the Sequence-to-Segments Network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N), a novel and general end-to-end sequential encoder-decoder architecture. S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N first encodes the input video into a sequence of hidden states that capture information progressively, as it appears in the video. It then employs the Segment Detection Unit (SDU), a novel decoding architecture, that sequentially detects segments. At each decoding step, the SDU integrates the decoder state and encoder hidden states to detect a target segment. During training, we address the problem of finding the best assignment of predicted segments to ground truth using the Hungarian Matching Algorithm with Lexicographic Cost. Additionally we propose to use the squared Earth Mover's Distance to optimize the localization errors of the segments. We show the state-of-the-art performance of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N across numerous tasks, including video highlighting, video summarization, and human action proposal generation.
Transfer Learning for Health-related Twitter Data

Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data.This paper describes the methods developed by team TMRLeiden for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task.Our methods use state-of-the-art transfer learning methods to classify, extract and normalise adverse drug effects (ADRs) and to classify personal health mentions from health-related tweets.The code and fine-tuned models are publicly available. 1
Stimulated measurement of spontaneous four-wave mixing from a warm atomic ensemble

Photon-pair sources from atomic ensembles are considered essential to the development of quantum science and technology based on atom-photon interfaces. The measurement of spectral-temporal biphoton wave forms is important for the characterization of frequency-time entangled photon pairs. Here, we demonstrate a stimulated measurement of the biphoton spectral wave form (BSW) of narrow-band photon pairs emitted from a Doppler-broadened cascade-type atomic ensemble. A BSW with submegahertz resolution is obtained as a function of the absolute optical frequency determined by atomic transitions, which reveals a spectral shift of approximately 510 MHz from the expected resonance frequency of the $5{P}_{3/2}\ensuremath{-}5{D}_{5/2}$ and $5{S}_{1/2}\ensuremath{-}5{P}_{3/2}$ transitions of $^{87}\mathrm{Rb}$ atoms.
Batik image retrieval using convolutional neural network

This paper presents a simple technique for performing Batik image retrieval using the Convolutional Neural Network (CNN) approach. Two CNN models, i.e. supervised and unsupervised learning approach, are considered to perform end-to-end feature extraction in order to describe the content of Batik image. The distance metrics measure the similarity between the query and target images in database based on the feature generated from CNN architecture. As reported in the experimental section, the proposed supervised CNN model achieves better performance compared to unsupervised CNN in the Batik image retrieval system. In addition, image feature composed from the proposed CNN model yields better performance compared to that of the handcrafted feature descriptor. Yet, it demonstrates the superiority performance of deep learning-based approach in the Batik image retrieval system.
A SEMI-SUPERVISED APPROACH TO SAR-OPTICAL IMAGE MATCHING

Abstract. Matching synthetic aperture radar (SAR) and optical remote sensing imagery is a key first step towards exploiting the complementary nature of these data in data fusion frameworks. While numerous signal-based approaches to matching have been proposed, they often fail to perform well in multi-sensor situations. In recent years deep learning has become the go-to approach for solving image matching in computer vision applications, and has also been adapted to the case of SAR-optical image matching. However, the hitherto proposed techniques still fail to match SAR and optical imagery in a generalizable manner. These limitations are largely due to the complexities in creating large-scale datasets of corresponding SAR and optical image patches. In this paper we frame the matching problem within semi-supervised learning, and use this as a proxy for investigating the effects of data scarcity on matching. In doing so we make an initial contribution towards the use of semi-supervised learning for matching SAR and optical imagery. We further gain insight into the non-complementary nature of commonly used supervised and unsupervised loss functions, as well as dataset size requirements for semi-supervised matching.
SP-Net: A Novel Framework to Identify Composite Sketch

Identifying composite sketches with digital face photos is an important and challenging task for law enforcement agencies. It has attracted a wide research interest in the face recognition area. In this paper, we present a novel framework that identifies the photo corresponding to a given composite face sketch. A coupled deep convolutional neural network, named Sketch-Photo Net (SP-Net) is proposed, which is fed with a positive or negative photo-sketch pair. In the proposed SP-Net, the customized VGG-Face network is adopted as base model and is followed by two branches, namely S-Net and P-Net, for sketch and photo, respectively. The S-Net and the P-Net are able to learn discriminative features between the sketches and the photos, regardless of the appearance gap by introducing the concept of elastic learning. In other words, to extract the most important features from the input, the network needs to learn the relevant features along with the irrelevant ones. To do so, higher dimension layers are used after the three 512 layers from VGG-FaceNet. Since the network learns representative features, we decrease the dimension of the layers to produce the most representative features. In addition, contrastive loss is employed to discover the coherent visual structures between sketch and photo. Experimental results on E-PRIP face sketch dataset indicate that the proposed network significantly outperforms the state-of-the-art composite sketch identification methods.
Benchmarking Tether-based UAV Motion Primitives

This paper proposes and benchmarks two tether-based motion primitives for tethered UAVs to execute autonomous flight with proprioception only. Tethered UAVs have been studied mainly due to power and safety considerations. Tether is either not included in the UAV motion (treated same as free-flying UAV) or only in terms of station-keeping and highspeed steady flight. However, feedback from and control over the tether configuration could be utilized as a set of navigational tools for autonomous flight, especially in GPS-denied environments and without vision-based exteroception. In this work, two tether-based motion primitives are proposed, which can enable autonomous flight of a tethered UAV. The proposed motion primitives are implemented on a physical tethered UAV for autonomous path execution with motion capture ground truth. The navigational performance is quantified and compared. The proposed motion primitives make tethered UAV a mobile and safe autonomous robot platform. The benchmarking results suggest appropriate usage of the two motion primitives for tethered UAVs with different path plans.
Simultaneous Tissue Classification and Lateral Ventricle Segmentation via a 2D U-net Driven by a 3D Fully Convolutional Neural Network

In this paper, we proposed and validated a novel and fully automatic pipeline for simultaneous tissue classification and lateral ventricle segmentation via a 2D U-net. The 2D U-net was driven by a 3D fully convolutional neural network (FCN). Multiple T1-weighted atlases which had been pre-segmented into six whole-brain regions including the gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), lateral ventricles (LVs), skull, and the background of the entire image were used. In the proposed pipeline, probability maps of the six whole-brain regions of interest (ROIs) were obtained after a pre-segmentation through a trained 3D patch-based FCN. To further capture the global context of the entire image, the to-be-segmented image and the corresponding six probability maps were input to a trained 2D U-net in a 2D slice fashion to obtain the final segmentation map. Experiments were performed on a dataset consisting of 18 T1-weighted images. Compared to the 3D patch-based FCN on segmenting five ROIs (GM, WM, CSF, LVs, skull) and another two classical methods (SPM and FSL) on segmenting GM and WM, the proposed pipeline showed a superior segmentation performance. The proposed segmentation architecture can also be extended to other medical image segmentation tasks.
SensorSnaps

Adding electronics to textiles can be time-consuming and requires technical expertise. We introduce SensorSnaps, low-power wireless sensor nodes that seamlessly integrate into caps of fabric snap fasteners. SensorSnaps provide a new technique to quickly and intuitively augment any location on the clothing with sensing capabilities. SensorSnaps securely attach and detach from ubiquitous commercial snap fasteners. Using inertial measurement units, the SensorSnaps detect tap and rotation gestures, as well as track body motion. We optimized the power consumption for SensorSnaps to work continuously for 45 minutes and up to 4 hours in capacitive touch standby mode. We present applications in which the SensorSnaps are used as gestural interfaces for a music player controller, cursor control, and motion tracking suit. The user study showed that SensorSnap could be attached in around 71 seconds, similar to attaching off-the-shelf snaps, and participants found the gestures easy to learn and perform. SensorSnaps could allow anyone to effortlessly add sophisticated sensing capacities to ubiquitous snap fasteners.
Drivable Path Planning Using Hybrid Search Algorithm Based on E* and Bernstein–Bézier Motion Primitives

This article proposes a hybrid path-planning algorithm, the HE* algorithm, which combines the discrete grid-based E* search and continuous Bernstein-Bézier (BB) motion primitives. Several researchers have addressed the smooth path planning problem and the sample-based integrated path planning techniques. We believe that the main benefits of the proposed approach are: directly drivable path, no additional post-optimization tasks, reduced search branching, low computational complexity, and completeness guarantee. Several examples and comparisons with the state-of-the-art planners are provided to illustrate and evaluate the main advantages of the HE* algorithm. HE* yields a collision-safe and smooth path that is close to spatially optimal (the Euclidean shortest path) with a guaranteed continuity of curvature. Therefore, the path is easily drivable for a wheeled robot without any additional post-optimization and smoothing required. HE* is a two-stage algorithm which uses a direction-guiding heuristics computed by the E* search in the first stage, which improves the quality and reduces the complexity of the hybrid search in the second stage. In each iteration, the search is expanded by a set of BBs, the parameters of which adapt continuously according to the guiding heuristics. Completeness is guaranteed by relying on a complete node mechanism, which also provides an upper bound for the calculated path cost. A remarkable feature of HE* is that it produces good results even at coarse resolutions.
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator

MR images scanned at low magnetic field ($${<}1$$T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those from high field (typically 1.5T and 3T). We adapt the recent idea of Image Quality Transfer (IQT) to enhance very low-field structural images aiming to estimate the resolution, spatial coverage, and contrast of high-field images. Analogous to many learning-based image enhancement techniques, IQT generates training data from high-field scans alone by simulating low-field images through a pre-defined decimation model. However, the ground truth decimation model is not well-known in practice, and lack of its specification can bias the trained model, aggravating performance on the real low-field scans. In this paper we propose a probabilistic decimation simulator to improve robustness of model training. It is used to generate and augment various low-field images whose parameters are random variables and sampled from an empirical distribution related to tissue-specific SNR on a 0.36T scanner. The probabilistic decimation simulator is model-agnostic, that is, it can be used with any super-resolution networks. Furthermore we propose a variant of U-Net architecture to improve its learning performance. We show promising qualitative results from clinical low-field images confirming the strong efficacy of IQT in an important new application area: epilepsy diagnosis in sub-Saharan Africa where only low-field scanners are normally available.
Zero-Knowledge for QMA from Locally Simulatable Proofs.

We provide several advances to the understanding of the class of Quantum Merlin-Arthur proof systems (QMA), the quantum analogue of NP. First, we answer a longstanding open question by showing that the Consistency of Local Density Matrices problem is QMA-complete under Karp reductions. We also show for the first time a commit-and-open computational zero-knowledge proof system for all of QMA as a quantum analogue of a sigma protocol. We then define a Proof of Quantum Knowledge, which guarantees that a prover is effectively in possession of a quantum witness in an interactive proof, and show that our zero-knowledge proof system satisfies this definition. Finally, we show that our proof system can be used to establish that QMA has a quantum non-interactive zero-knowledge proof system in the secret parameters setting. Our main technique consists in developing locally simulatable proofs for all of QMA: this is an encoding of a QMA witness such that it can be efficiently verified by probing only five qubits and, furthermore, the reduced density matrix of any five-qubit subsystem can be computed in polynomial time and is independent of the witness. This construction follows the techniques of Grilo, Slofstra, and Yuen [FOCS 2019].
Deep Learning on Electronic Health Records to Improve Disease Coding Accuracy.

Characterization of a patient's clinical phenotype is central to biomedical informatics. ICD codes, assigned to inpatient encounters by coders, is important for population health and cohort discovery when clinical information is limited. While ICD codes are assigned to patients by professionals trained and certified in coding there is substantial variability in coding. We present a methodology that uses deep learning methods to model coder decision making and that predicts ICD codes. Our approach predicts codes based on demographics, lab results, and medications, as well as codes from previous encounters. We are able to predict existing codes with high accuracy for all three of the test cases we investigated: diabetes, acute renal failure, and chronic kidney disease. We employed a panel of clinicians, in a blinded manner, to assess ground truth and compared the predictions of coders, model and clinicians. When disparities between the model prediction and coder assigned codes were reviewed, our model outperformed coder assigned ICD codes.
WAT2019: English-Hindi Translation on Hindi Visual Genome Dataset

A multimodal translation is a task of translating a source language to a target language with the help of a parallel text corpus paired with images that represent the contextual details of the text.In this paper, we carried out an extensive comparison to evaluate the benefits of using a multimodal approach on translating text in English to a low resource language, Hindi as a part of WAT2019 (Nakazawa et al., 2019) shared task.We carried out the translation of English to Hindi in three separate tasks with both the evaluation and challenge dataset.First, by using only the parallel text corpora, then through an image caption generation approach and, finally with the multimodal approach.Our experiment shows a significant improvement in the translation with the multimodal approach than the other approach.
Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture

This paper presents our participation in the AGAC Track from the 2019 BioNLP Open Shared Tasks.We provide a solution for Task 3, which aims to extract "gene -function change -disease" triples, where "gene" and "disease" are mentions of particular genes and diseases respectively and "function change" is one of four pre-defined relationship types.Our system extends BERT (Devlin et al., 2018), a state-of-the-art language model, which learns contextual language representations from a large unlabelled corpus and whose parameters can be fine-tuned to solve specific tasks with minimal additional architecture.We encode the pair of mentions and their textual context as two consecutive sequences in BERT, separated by a special symbol.We then use a single linear layer to classify their relationship into five classes (four pre-defined, as well as 'no relation').Despite considerable class imbalance, our system significantly outperforms a random baseline while relying on an extremely simple setup with no specially engineered features.