pwc-archive/papers-with-abstracts huggingface dataset:
Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs
Authors: Marc Boubnovski Martell, Mitchell Chen, Kristofer Linton-Reid, Joram M. Posma, Susan J Copley, Eric O. Aboagye
Date: 2021-05-11 00:00:00
Tasks: Multi-Task Learning, Segmentation

Automated lobar segmentation allows regional evaluation of lung disease and is important for diagnosis and therapy planning. Advanced statistical workflows permitting such evaluation is a needed area within respiratory medicine; their adoption remains slow, with poor workflow accuracy. Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes due to oblique or lacking fissures. This impact motivated developing an improved machine learning method to segment lung lobes that utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing multi-task learning (MTL) in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm. In keeping with the model's adeptness for better generalisation, high performance was retained in an external dataset of patients with four distinct diseases: severe lung cancer, COVID-19 pneumonitis, collapsed lungs and Chronic Obstructive Pulmonary Disease (COPD), even though the training data included none of these cases. The benefit of our external validation test is specifically relevant since our choice includes those patients who have diagnosed lung disease with associated radiological abnormalities. To ensure equal rank is given to all segmentations in the main task we report the following performance (Dice score) on a per-segment basis: normal lungs 0.97, COPD 0.94, lung cancer 0.94, COVID-19 pneumonitis 0.94 and collapsed lung 0.92, all at p<0.05. Even segmenting lobes with large deformations on CT images, the model maintained high accuracy. The approach can be readily adopted in the clinical setting as a robust tool for radiologists.
ReflectNet - A Generative Adversarial Method for Single Image Reflection Suppression
Authors: Andreea Birhala, Ionut Mironica
Date: 2021-05-11 00:00:00
Tasks: Reflection Removal, SSIM

Taking pictures through glass windows almost always produces undesired reflections that degrade the quality of the photo. The ill-posed nature of the reflection removal problem reached the attention of many researchers for more than decades. The main challenge of this problem is the lack of real training data and the necessity of generating realistic synthetic data. In this paper, we proposed a single image reflection removal method based on context understanding modules and adversarial training to efficiently restore the transmission layer without reflection. We also propose a complex data generation model in order to create a large training set with various type of reflections. Our proposed reflection removal method outperforms state-of-the-art methods in terms of PSNR and SSIM on the SIR benchmark dataset.
Diffusion Models Beat GANs on Image Synthesis
Authors: Prafulla Dhariwal, Alex Nichol
Date: 2021-05-11 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Conditional Image Generation, Diversity, Image Generation

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\times$128, 4.59 on ImageNet 256$\times$256, and 7.72 on ImageNet 512$\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\times$256 and 3.85 on ImageNet 512$\times$512. We release our code at https://github.com/openai/guided-diffusion
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective
Authors: Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu
Date: 2021-05-11 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can recover the performance of these developments not by changing the objective, but by regularising the value-function estimator. Constraining the Lipschitz constant of a single layer using spectral normalisation is sufficient to elevate the performance of a Categorical-DQN agent to that of a more elaborated \rainbow{} agent on the challenging Atari domain. We conduct ablation studies to disentangle the various effects normalisation has on the learning dynamics and show that is sufficient to modulate the parameter updates to recover most of the performance of spectral normalisation. These findings hint towards the need to also focus on the neural component and its learning dynamics to tackle the peculiarities of Deep Reinforcement Learning.
Doing Natural Language Processing in A Natural Way: An NLP toolkit based on object-oriented knowledge base and multi-level grammar base
Authors: Yu Guo
Date: 2021-05-11 00:00:00
Tasks: Semantic Parsing

We introduce an NLP toolkit based on object-oriented knowledge base and multi-level grammar base. This toolkit focuses on semantic parsing, it also has abilities to discover new knowledge and grammar automatically, new discovered knowledge and grammar will be identified by human, and will be used to update the knowledge base and grammar base. This process can be iterated many times to improve the toolkit continuously.
Including Signed Languages in Natural Language Processing
Authors: Kayo Yin, Amit Moryossef, Julie Hochgesang, Yoav Goldberg, Malihe Alikhani
Date: 2021-05-11 00:00:00
Proceeding: ACL 2021 5

Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in the direction of research.
Performance Comparison of Different Machine Learning Algorithms on the Prediction of Wind Turbine Power Generation
Authors: Onder Eyecioglu, Batuhan Hangun, Korhan Kayisli, Mehmet Yesilbudak
Date: 2021-05-11 00:00:00
Tasks: BIG-bench Machine Learning, regression

Over the past decade, wind energy has gained more attention in the world. However, owing to its indirectness and volatility properties, wind power penetration has increased the difficulty and complexity in dispatching and planning of electric power systems. Therefore, it is needed to make the high-precision wind power prediction in order to balance the electrical power. For this purpose, in this study, the prediction performance of linear regression, k-nearest neighbor regression and decision tree regression algorithms is compared in detail. k-nearest neighbor regression algorithm provides lower coefficient of determination values, while decision tree regression algorithm produces lower mean absolute error values. In addition, the meteorological parameters of wind speed, wind direction, barometric pressure and air temperature are evaluated in terms of their importance on the wind power parameter. The biggest importance factor is achieved by wind speed parameter. In consequence, many useful assessments are made for wind power predictions.
kdehumor at semeval-2020 task 7: a neural network model for detecting funniness in dataset humicroedit
Authors: Rida Miraj, Masaki Aono
Date: 2021-05-11 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Sentence, Sentence Embedding, Sentence-Embedding

This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. Here we present a method based on a deep neural network. In recent years, quite some attention has been devoted to humor production and perception. Our team KdeHumor employs recurrent neural network models including Bi-Directional LSTMs (BiLSTMs). Moreover, we utilize the state-of-the-art pre-trained sentence embedding techniques. We analyze the performance of our method and demonstrate the contribution of each component of our architecture.
Integrating extracted information from bert and multiple embedding methods with the deep neural network for humour detection
Authors: Rida Miraj, Masaki Aono
Date: 2021-05-11 00:00:00
Tasks: Sentence, Sentence Embedding, Sentence-Embedding

Humour detection from sentences has been an interesting and challenging task in the last few years. In attempts to highlight humour detection, most research was conducted using traditional approaches of embedding, e.g., Word2Vec or Glove. Recently BERT sentence embedding has also been used for this task. In this paper, we propose a framework for humour detection in short texts taken from news headlines. Our proposed framework (IBEN) attempts to extract information from written text via the use of different layers of BERT. After several trials, weights were assigned to different layers of the BERT model. The extracted information was then sent to a Bi-GRU neural network as an embedding matrix. We utilized the properties of some external embedding models. A multi-kernel convolution in our neural network was also employed to extract higher-level sentence representations. This framework performed very well on the task of humour detection.
ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS
Authors: Federica Filippini, Danilo Ardagna, Marco Lattuada, Edoardo Amaldi, Michele Ciavotta, Maciek Riedl, Katarzyna Materka, Paweł Skrzypek, Fabrizio Magugliani, Marco Cicala
Date: 2021-05-11 00:00:00
Tasks: GPU, Management, Scheduling

Artificial Intelligence (AI) and Deep Learning (DL) algorithms are currently applied to a wide range of products and solutions. DL training jobs are highly resource demanding and they experience great benefits when exploiting AI accelerators (e.g., GPUs). However, the effective management of GPU-powered clusters comes with great challenges. Among these, efficient scheduling and resource allocation solutions are crucial to maximize performance and minimize Data Centers operational costs. In this paper we propose ANDREAS, an advanced scheduling solution that tackles these problems jointly, aiming at optimizing DL training runtime workloads and their energy consumption in accelerated clusters. Experiments based on simulation demostrate that we can achieve a cost reduction between 30 and 62% on average with respect to first-principle methods while the validation on a real cluster shows a worst case deviation below 13% between actual and predicted costs, proving the effectiveness of ANDREAS solution in practical scenarios.
Zero-Shot Generalization using Intrinsically Motivated Compositional Emergent Protocols
Authors: Rishi Hazra, Sonu Dixit, Sayambhu Sen
Date: 2021-05-11 00:00:00
Tasks: Zero-shot Generalization

Human language has been described as a system that makes \textit{use of finite means to express an unlimited array of thoughts}. Of particular interest is the aspect of compositionality, whereby, the meaning of a compound language expression can be deduced from the meaning of its constituent parts. If artificial agents can develop compositional communication protocols akin to human language, they can be made to seamlessly generalize to unseen combinations. Studies have recognized the role of curiosity in enabling linguistic development in children. In this paper, we seek to use this intrinsic feedback in inducing a systematic and unambiguous protolanguage. We demonstrate how compositionality can enable agents to not only interact with unseen objects but also transfer skills from one task to another in a zero-shot setting: \textit{Can an agent, trained to `pull' and `push twice', `pull twice'?}.
Robotic Assistant Agent for Student and Machine Co-Learning on AI-FML Practice with AIoT Application
Authors: Chang-Shing Lee, Mei-Hui Wang, Zong-Han Ciou, Rin-Pin Chang, Chun-Hao Tsai, Shen-Chien Chen, Tzong-Xiang Huang, Eri Sato-Shimokawara, Toru Yamaguchi
Date: 2021-05-11 00:00:00

In this paper, the Robotic Assistant Agent for student and machine co-learning on AI-FML practice with AIoT application is presented. The structure of AI-FML contains three parts, including fuzzy logic, neural network, and evolutionary computation. Besides, the Robotic Assistant Agent (RAA) can assist students and machines in co-learning English and AI-FML practice based on the robot Kebbi Air and AIoT-FML learning tool. Since Sept. 2019, we have introduced an Intelligent Speaking English Assistant (ISEA) App and AI-FML platform to English and computer science learning classes at two elementary schools in Taiwan. We use the collected English-learning data to train a predictive regression model based on students' monthly examination scores. In Jan. 2021, we further combined the developed AI-FML platform with a novel AIoT-FML learning tool to enhance students' interests in learning English and AI-FML with basic hands-on practice. The proposed RAA is responsible for reasoning students' learning performance and showing the results on the AIoT-FML learning tool after communicating with the AI-FML platform. The experimental results and the collection of students' feedback show that this kind of learning model is popular with elementary-school and high-school students, and the learning performance of elementary-school students is improved.
A Hybrid Decomposition-based Multi-objective Evolutionary Algorithm for the Multi-Point Dynamic Aggregation Problem
Authors: Guanqiang Gao, Bin Xin, Yi Mei, Shuxin Ding, Juan Li
Date: 2021-05-11 00:00:00

An emerging optimisation problem from the real-world applications, named the multi-point dynamic aggregation (MPDA) problem, has become one of the active research topics of the multi-robot system. This paper focuses on a multi-objective MPDA problem which is to design an execution plan of the robots to minimise the number of robots and the maximal completion time of all the tasks. The strongly-coupled relationships among robots and tasks, the redundancy of the MPDA encoding, and the variable-size decision space of the MO-MPDA problem posed extra challenges for addressing the problem effectively. To address the above issues, we develop a hybrid decomposition-based multi-objective evolutionary algorithm (HDMOEA) using $ \varepsilon $-constraint method. It selects the maximal completion time of all tasks as the main objective, and converted the other objective into constraints. HDMOEA decomposes a MO-MPDA problem into a series of scalar constrained optimization subproblems by assigning each subproblem with an upper bound robot number. All the subproblems are optimized simultaneously with the transferring knowledge from other subproblems. Besides, we develop a hybrid population initialisation mechanism to enhance the quality of initial solutions, and a reproduction mechanism to transmit effective information and tackle the encoding redundancy. Experimental results show that the proposed HDMOEA method significantly outperforms the state-of-the-art methods in terms of several most-used metrics.
Forecast Analysis of the COVID-19 Incidence in Lebanon: Prediction of Future Epidemiological Trends to Plan More Effective Control Programs
Authors: Salah El Falou, Fouad Trad
Date: 2021-05-11 00:00:00

Ever since the COVID-19 pandemic started, all the governments have been trying to limit its effects on their citizens and countries. This pandemic was harsh on different levels for almost all populations worldwide and this is what drove researchers and scientists to get involved and work on several kinds of simulations to get a better insight into this virus and be able to stop it the earliest possible. In this study, we simulate the spread of COVID-19 in Lebanon using an Agent-Based Model where people are modeled as agents that have specific characteristics and behaviors determined from statistical distributions using Monte Carlo Algorithm. These agents can go into the world, interact with each other, and thus, infect each other. This is how the virus spreads. During the simulation, we can introduce different Non-Pharmaceutical Interventions - or more commonly NPIs - that aim to limit the spread of the virus (wearing a mask, closing locations, etc). Our Simulator was first validated on concepts (e.g. Flattening the Curve and Second Wave scenario), and then it was applied on the case of Lebanon. We studied the effect of opening schools and universities on the pandemic situation in the country since the Lebanese Ministry of Education is planning to do so progressively, starting from 21 April 2021. Based on the results we obtained, we conclude that it would be better to delay the school openings while the vaccination campaign is still slow in the country.
On Characterizing GAN Convergence Through Proximal Duality Gap
Authors: Sahil Sidheekh, Aroof Aimen, Narayanan C. Krishnan
Date: 2021-05-11 00:00:00

Despite the accomplishments of Generative Adversarial Networks (GANs) in modeling data distributions, training them remains a challenging task. A contributing factor to this difficulty is the non-intuitive nature of the GAN loss curves, which necessitates a subjective evaluation of the generated output to infer training progress. Recently, motivated by game theory, duality gap has been proposed as a domain agnostic measure to monitor GAN training. However, it is restricted to the setting when the GAN converges to a Nash equilibrium. But GANs need not always converge to a Nash equilibrium to model the data distribution. In this work, we extend the notion of duality gap to proximal duality gap that is applicable to the general context of training GANs where Nash equilibria may not exist. We show theoretically that the proximal duality gap is capable of monitoring the convergence of GANs to a wider spectrum of equilibria that subsumes Nash equilibria. We also theoretically establish the relationship between the proximal duality gap and the divergence between the real and generated data distributions for different GAN formulations. Our results provide new insights into the nature of GAN convergence. Finally, we validate experimentally the usefulness of proximal duality gap for monitoring and influencing GAN training.
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
Authors: Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin, Qing He
Date: 2021-05-11 00:00:00
Tasks: Meta-Learning, Recommendation Systems

Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary (source) domain to improve the performance of recommender system in the target domain. In these CDR approaches, the family of Embedding and Mapping methods for CDR (EMCDR) is very effective, which explicitly learn a mapping function from source embeddings to target embeddings with overlapping users. However, these approaches suffer from one serious problem: the mapping function is only learned on limited overlapping users, and the function would be biased to the limited overlapping users, which leads to unsatisfying generalization ability and degrades the performance on cold-start users in the target domain. With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage. In the transfer (pre-training) stage, a source model and a target model are trained on source and target domains, respectively. In the meta stage, a task-oriented meta network is learned to implicitly transform the user embedding in the source domain to the target feature space. In addition, the TMCDR is a general framework that can be applied upon various base models, e.g., MF, BPR, CML. By utilizing data from Amazon and Douban, we conduct extensive experiments on 6 cross-domain tasks to demonstrate the superior performance and compatibility of TMCDR.
Learning to Ask Appropriate Questions in Conversational Recommendation
Authors: Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, Kai Zheng
Date: 2021-05-11 00:00:00
Tasks: Conversational Recommendation, Question Generation, Question-Generation, Recommendation Systems

Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user preference. In a typical conversational recommendation scenario, a CRS firstly generates questions to let the user clarify her/his demands and then makes suitable recommendations. Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations. However, existing CRSs fall short in asking high-quality questions because: (1) system-generated responses heavily depends on the performance of the dialogue policy agent, which has to be trained with huge conversation corpus to cover all circumstances; and (2) current CRSs cannot fully utilize the learned latent user profiles for generating appropriate and personalized responses. To mitigate these issues, we propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation. Distinct from previous conversational recommender systems, KBQG models a user's preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph (KG). Conditioned on the varied importance of different relations, the generated clarifying questions could perform better in impelling users to provide more details on their preferences. Finially, accurate recommendations can be generated in fewer conversational turns. Furthermore, the proposed KBQG outperforms all baselines in our experiments on two real-world datasets.