OpenRaiser/Intern-Atlas huggingface dataset:
Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis
Venue: arXiv cs.LG 2020
Year: 2020

Retrosynthesis is a problem to infer reactant compounds to synthesize a given product compound through chemical reactions. Recent studies on retrosynthesis focus on proposing more sophisticated prediction models, but the dataset to feed the models also plays an essential role in achieving the best generalizing models. Generally, a dataset that is best suited for a specific task tends to be small. In such a case, it is the standard solution to transfer knowledge from a large or clean dataset in the same domain. In this paper, we conduct a systematic and intensive examination of data transfer approaches on end-to-end generative models, in application to retrosynthesis. Experimental results show that typical data transfer methods can improve test prediction scores of an off-the-shelf Transformer baseline model. Especially, the pre-training plus fine-tuning approach boosts the accuracy scores of the baseline, achieving the new state-of-the-art. In addition, we conduct a manual inspection for the erroneous prediction results. The inspection shows that the pre-training plus fine-tuning models can generate chemically appropriate or sensible proposals in almost all cases.
Block-wise Image Transformation with Secret Key for Adversarially Robust Defense
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a novel defensive transformation that enables us to maintain a high classification accuracy under the use of both clean images and adversarial examples for adversarially robust defense. The proposed transformation is a block-wise preprocessing technique with a secret key to input images. We developed three algorithms to realize the proposed transformation: Pixel Shuffling, Bit Flipping, and FFX Encryption. Experiments were carried out on the CIFAR-10 and ImageNet datasets by using both black-box and white-box attacks with various metrics including adaptive ones. The results show that the proposed defense achieves high accuracy close to that of using clean images even under adaptive attacks for the first time. In the best-case scenario, a model trained by using images transformed by FFX Encryption (block size of 4) yielded an accuracy of 92.30% on clean images and 91.48% under PGD attack with a noise distance of 8/255, which is close to the non-robust accuracy (95.45%) for the CIFAR-10 dataset, and it yielded an accuracy of 72.18% on clean images and 71.43% under the same attack, which is also close to the standard accuracy (73.70%) for the ImageNet dataset. Overall, all three proposed algorithms are demonstrated to outperform state-of-the-art defenses including adversarial training whether or not a model is under attack.
Deep Composer Classification Using Symbolic Representation
Venue: arXiv cs.SD 2020
Year: 2020

In this study, we train deep neural networks to classify composer on a symbolic domain. The model takes a two-channel two-dimensional input, i.e., onset and note activations of time-pitch representation, which is converted from MIDI recordings and performs a single-label classification. On the experiments conducted on MAESTRO dataset, we report an F1 value of 0.8333 for the classification of 13~classical composers.
The Complexity of Boolean State Separation (Technical Report)
Venue: arXiv cs.LO 2020
Year: 2020

For a Boolean type of nets $τ$, a transition system $A$ is synthesizeable into a $τ$-net $N$ if and only if distinct states of $A$ correspond to distinct markings of $N$, and $N$ prevents a transition firing if there is no related transition in $A$. The former property is called $τ$-state separation property ($τ$-SSP) while the latter -- $τ$-event/state separation property ($τ$-ESSP). $A$ is embeddable into the reachability graph of a $τ$-net $N$ if and only if $A$ has the $τ$-SSP. This paper presents a complete characterization of the computational complexity of \textsc{$τ$-SSP} for all Boolean Petri net types.
Maximal benefits and possible detrimental effects of binary decision aids
Venue: arXiv cs.HC 2020
Year: 2020

Binary decision aids, such as alerts, are a simple and widely used form of automation. The formal analysis of a user's task performance with an aid sees the process as the combination of information from two detectors who both receive input about an event and evaluate it. The user's decisions are based on the output of the aid and on the information, the user obtains independently. We present a simple method for computing the maximal benefits a user can derive from a binary aid as a function of the user's and the aid's sensitivities. Combining the user and the aid often adds little to the performance the better detector could achieve alone. Also, if users assign non-optimal weights to the aid, performance may drop dramatically. Thus, the introduction of a valid aid can actually lower detection performance, compared to a more sensitive user working alone. Similarly, adding a user to a system with high sensitivity may lower its performance. System designers need to consider the potential adverse effects of introducing users or aids into systems.
Resource Allocation for Large IRS-Assisted SWIPT Systems with Non-linear Energy Harvesting Model
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate resource allocation algorithm design for large intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. To this end, we adopt a physics-based IRS model that, unlike the conventional IRS model, takes into account the impact of the incident and reflection angles of the impinging electromagnetic wave on the reflected signal. To facilitate efficient resource allocation design for large IRSs, we employ a scalable optimization framework, where the IRS is partitioned into several tiles and the phase shift elements of each tile are jointly designed to realize different transmission modes. Then, the beamforming vectors at the base station (BS) and the transmission mode selection of the tiles of the IRS are jointly optimized for minimization of the BS transmit power taking into account the quality-of-service requirements of both non-linear energy harvesting receivers and information decoding receivers. For handling the resulting non-convex optimization problem, we apply a penalty-based method, successive convex approximation, and semidefinite relaxation to develop a computationally efficient algorithm which asymptotically converges to a locally optimal solution of the considered problem. Our simulation results show that the proposed scheme enables considerable power savings compared to two baseline schemes. Moreover, our results also illustrate that the advocated physics-based model and scalable optimization framework for large IRSs allows us to strike a balance between system performance and computational complexity, which is vital for realizing large IRS-assisted communication systems.
Building Large Lexicalized Ontologies from Text: a Use Case in Automatic Indexing of Biotechnology Patents
Venue: arXiv cs.AI 2020
Year: 2020

This paper presents a tool, TyDI, and methods experimented in the building of a termino-ontology, i.e. a lexicalized ontology aimed at fine-grained indexation for semantic search applications. TyDI provides facilities for knowledge engineers and domain experts to efficiently collaborate to validate, organize and conceptualize corpus extracted terms. A use case on biotechnology patent search demonstrates TyDI's potential.
Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples
Venue: arXiv cs.CV 2020
Year: 2020

With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification. However, supervised learning requires a huge number of annotated data for training. When labeled samples are not sufficient, the most common solution is to fine-tune the pre-training models using a large natural image dataset (e.g. ImageNet). However, this learning paradigm is not a panacea, especially when the target remote sensing images (e.g. multispectral and hyperspectral data) have different imaging mechanisms from RGB natural images. To solve this problem, we introduce new self-supervised learning (SSL) mechanism to obtain the high-performance pre-training model for RSIs scene classification from large unlabeled data. Experiments on three commonly used RSIs scene classification datasets demonstrated that this new learning paradigm outperforms the traditional dominant ImageNet pre-trained model. Moreover, we analyze the impacts of several factors in SSL on RSIs scene classification tasks, including the choice of self-supervised signals, the domain difference between the source and target dataset, and the amount of pre-training data. The insights distilled from our studies can help to foster the development of SSL in the remote sensing community. Since SSL could learn from unlabeled massive RSIs which are extremely easy to obtain, it will be a potentially promising way to alleviate dependence on labeled samples and thus efficiently solve many problems, such as global mapping.
OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
Venue: arXiv cs.CV 2020
Year: 2020

Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Proper benchmarking being a key issue for comparing methods, this paper addresses the question of evaluating how complex is a given dataset with respect to the prediction problem. For assessing a dataset complexity, we define a series of indicators around three concepts: Trajectory predictability; Trajectory regularity; Context complexity. We compare the most common datasets used in HTP in the light of these indicators and discuss what this may imply on benchmarking of HTP algorithms. Our source code is released on Github.
Self-Play Reinforcement Learning for Fast Image Retargeting
Venue: arXiv cs.CV 2020
Year: 2020

In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes. In one of the best-performing methods called MULTIOP, multiple retargeting operators were combined and retargeted images at each stage were generated to find the optimal sequence of operators that minimized the distance between original and retargeted images. The limitation of this method is in its tremendous processing time, which severely prohibits its practical use. Therefore, the purpose of this study is to find the optimal combination of operators within a reasonable processing time; we propose a method of predicting the optimal operator for each step using a reinforcement learning agent. The technical contributions of this study are as follows. Firstly, we propose a reward based on self-play, which will be insensitive to the large variance in the content-dependent distance measured in MULTIOP. Secondly, we propose to dynamically change the loss weight for each action to prevent the algorithm from falling into a local optimum and from choosing only the most frequently used operator in its training. Our experiments showed that we achieved multi-operator image retargeting with less processing time by three orders of magnitude and the same quality as the original multi-operator-based method, which was the best-performing algorithm in retargeting tasks.
A straightforward line search approach on the expected empirical loss for stochastic deep learning problems
Venue: arXiv cs.LG 2020
Year: 2020

A fundamental challenge in deep learning is that the optimal step sizes for update steps of stochastic gradient descent are unknown. In traditional optimization, line searches are used to determine good step sizes, however, in deep learning, it is too costly to search for good step sizes on the expected empirical loss due to noisy losses. This empirical work shows that it is possible to approximate the expected empirical loss on vertical cross sections for common deep learning tasks considerably cheaply. This is achieved by applying traditional one-dimensional function fitting to measured noisy losses of such cross sections. The step to a minimum of the resulting approximation is then used as step size for the optimization. This approach leads to a robust and straightforward optimization method which performs well across datasets and architectures without the need of hyperparameter tuning.
Image-based underwater 3D reconstruction for Cultural Heritage: from image collection to 3D. Critical steps and considerations
Venue: arXiv cs.CV 2020
Year: 2020

Underwater Cultural Heritage (CH) sites are widely spread; from ruins in coastlines up to shipwrecks in deep. The documentation and preservation of this heritage is an obligation of the mankind, dictated also by the international treaties like the Convention on the Protection of the Underwater Cultural Her-itage which fosters the use of "non-destructive techniques and survey meth-ods in preference over the recovery of objects". However, submerged CH lacks in protection and monitoring in regards to the land CH and nowadays recording and documenting, for digital preservation as well as dissemination through VR to wide public, is of most importance. At the same time, it is most difficult to document it, due to inherent restrictions posed by the environ-ment. In order to create high detailed textured 3D models, optical sensors and photogrammetric techniques seems to be the best solution. This chapter dis-cusses critical aspects of all phases of image based underwater 3D reconstruc-tion process, from data acquisition and data preparation using colour restora-tion and colour enhancement algorithms to Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques to produce an accurate, precise and complete 3D model for a number of applications.
RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval
Venue: arXiv cs.CV 2020
Year: 2020

Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures. Moreover, 3D objects can be placed with arbitrary position and orientation in real-world applications, which further requires shape descriptors to be robust to rigid transformations. The shape descriptions used in existing 3D shape retrieval systems fail to meet the above two criteria. In this paper, we introduce a novel deep architecture, RISA-Net, which learns rotation invariant 3D shape descriptors that are capable of encoding fine-grained geometric information and structural information, and thus achieve accurate results on the task of fine-grained 3D object retrieval. RISA-Net extracts a set of compact and detailed geometric features part-wisely and discriminatively estimates the contribution of each semantic part to shape representation. Furthermore, our method is able to learn the importance of geometric and structural information of all the parts when generating the final compact latent feature of a 3D shape for fine-grained retrieval. We also build and publish a new 3D shape dataset with sub-class labels for validating the performance of fine-grained 3D shape retrieval methods. Qualitative and quantitative experiments show that our RISA-Net outperforms state-of-the-art methods on the fine-grained object retrieval task, demonstrating its capability in geometric detail extraction. The code and dataset are available at: https://github.com/IGLICT/RisaNET.
A local geometry of hyperedges in hypergraphs, and its applications to social networks
Venue: arXiv cs.LG 2020
Year: 2020

In many real world datasets arising from social networks, there are hidden higher order relations among data points which cannot be captured using graph modeling. It is natural to use a more general notion of hypergraphs to model such social networks. In this paper, we introduce a new local geometry of hyperdges in hypergraphs which allows to capture higher order relations among data points. Furthermore based on this new geometry, we also introduce new methodology--the nearest neighbors method in hypergraphs--for analyzing datasets arising from sociology.
Real-time Collaboration Between Mixed Reality Users in Geo-referenced Virtual Environment
Venue: arXiv cs.HC 2020
Year: 2020

Collaboration using mixed reality technology is an active area of research, where significant research is done to virtually bridge physical distances. There exist a diverse set of platforms and devices that can be used for a mixed-reality collaboration, and is largely focused for indoor scenarios, where, a stable tracking can be assumed. We focus on supporting collaboration between VR and AR users, where AR user is mobile outdoors, and VR user is immersed in true-sized digital twin. This cross-platform solution requires new user experiences for interaction, accurate modelling of the real-world, and working with noisy outdoor tracking sensor such as GPS. In this paper, we present our results and observations of real-time collaboration between cross-platform users, in the context of a geo-referenced virtual environment. We propose a solution for using GPS measurement in VSLAM to localize the AR user in an outdoor environment. The client applications enable VR and AR user to collaborate across the heterogeneous platforms seamlessly. The user can place or load dynamic contents tagged to a geolocation and share their experience with remote users in real-time.
Open Set Domain Adaptation using Optimal Transport
Venue: arXiv cs.LG 2020
Year: 2020

We present a 2-step optimal transport approach that performs a mapping from a source distribution to a target distribution. Here, the target has the particularity to present new classes not present in the source domain. The first step of the approach aims at rejecting the samples issued from these new classes using an optimal transport plan. The second step solves the target (class ratio) shift still as an optimal transport problem. We develop a dual approach to solve the optimization problem involved at each step and we prove that our results outperform recent state-of-the-art performances. We further apply the approach to the setting where the source and target distributions present both a label-shift and an increasing covariate (features) shift to show its robustness.
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets
Venue: arXiv cs.LG 2020
Year: 2020

The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, neuroimaging datasets do not provide a sufficient representation of heart features for the modeling of cardiovascular factors in brain disorders. To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features, along with a Gaussian Process emulator that can faithfully reproduce personalised cardiovascular dynamics. Experimental results on UK Biobank show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information, e.g. systolic and diastolic blood pressures only, while jointly estimating the emulated parameters of the lumped model. This allows a novel exploration of the heart-brain joint relationship through simulation of realistic cardiac dynamics corresponding to different conditions of brain anatomy.
Efficient Colon Cancer Grading with Graph Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Dealing with the application of grading colorectal cancer images, this work proposes a 3 step pipeline for prediction of cancer levels from a histopathology image. The overall model performs better compared to other state of the art methods on the colorectal cancer grading data set and shows excellent performance for the extended colorectal cancer grading set. The performance improvements can be attributed to two main factors: The feature selection and graph augmentation method described here are spatially aware, but overall pixel position independent. Further, the graph size in terms of nodes becomes stable with respect to the model's prediction and accuracy for sufficiently large models. The graph neural network itself consists of three convolutional blocks and linear layers, which is a rather simple design compared to other networks for this application.
Gaussian Process Molecule Property Prediction with FlowMO
Venue: arXiv cs.LG 2020
Year: 2020

We present FlowMO: an open-source Python library for molecular property prediction with Gaussian Processes. Built upon GPflow and RDKit, FlowMO enables the user to make predictions with well-calibrated uncertainty estimates, an output central to active learning and molecular design applications. Gaussian Processes are particularly attractive for modelling small molecular datasets, a characteristic of many real-world virtual screening campaigns where high-quality experimental data is scarce. Computational experiments across three small datasets demonstrate comparable predictive performance to deep learning methods but with superior uncertainty calibration.
Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media
Venue: arXiv cs.CL 2020
Year: 2020

Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based on their subject matter, e.g., biology or computer science. Given the range of applications using social media text, and its unique language variety, we pretrain two models on tweets and forum text respectively, and empirically demonstrate the effectiveness of these two resources. In addition, we investigate how similarity measures can be used to nominate in-domain pretraining data. We publicly release our pretrained models at https://bit.ly/35RpTf0.
Efficient Multi-Contact Pattern Generation with Sequential Convex Approximations of the Centroidal Dynamics
Venue: arXiv cs.RO 2020
Year: 2020

This paper investigates the problem of efficient computation of physically consistent multi-contact behaviors. Recent work showed that under mild assumptions, the problem could be decomposed into simpler kinematic and centroidal dynamic optimization problems. Based on this approach, we propose a general convex relaxation of the centroidal dynamics leading to two computationally efficient algorithms based on iterative resolutions of second order cone programs. They optimize centroidal trajectories, contact forces and, importantly, the timing of the motions. We include the approach in a kino-dynamic optimization method to generate full-body movements. Finally, the approach is embedded in a mixed-integer solver to further find dynamically consistent contact sequences. Extensive numerical experiments demonstrate the computational efficiency of the approach, suggesting that it could be used in a fast receding horizon control loop. Executions of the planned motions on simulated humanoids and quadrupeds and on a real quadruped robot further show the quality of the optimized motions.