pwc-archive/papers-with-abstracts huggingface dataset:
MedDialog: Large-scale Medical Dialogue Datasets
Authors: Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Dialogue Generation, Transfer Learning

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build large-scale medical dialogue datasets {--} MedDialog, which contain 1) a Chinese dataset with 3.4 million conversations between patients and doctors, 11.3 million utterances, 660.2 million tokens, covering 172 specialties of diseases, and 2) an English dataset with 0.26 million conversations, 0.51 million utterances, 44.53 million tokens, covering 96 specialties of diseases. To our best knowledge, MedDialog is the largest medical dialogue dataset to date. We pretrain several dialogue generation models on the Chinese MedDialog dataset, including Transformer, GPT, BERT-GPT, and compare their performance. It is shown that models trained on MedDialog are able to generate clinically correct and doctor-like medical dialogues. We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks. It is shown that via transfer learning which finetunes the models pretrained on MedDialog, the performance on medical dialogue generation tasks with small datasets can be greatly improved, as shown in human evaluation and automatic evaluation. The datasets and code are available at https://github.com/UCSD-AI4H/Medical-Dialogue-System
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval
Authors: Chieh-Yang Chen, Pei-Hsin Wang, Shih-Chieh Chang, Da-Cheng Juan, Wei Wei, Jia-Yu Pan
Date: 2020-11-20 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Retrieval, Task-Oriented Dialogue Systems, Text to SQL, Text-To-SQL

Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms. To alleviate the above problem, we propose , an end-to-end trainable text-to-SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries. Specifically, the neural agent first learns to ask and confirm the customer’s intent during the multi-turn interactions, then dynamically determining when to ground the user constraints into executable SQL queries so as to fetch relevant information from KBs. With the help of our method, the agent can use less but more accurate fetched results to generate useful responses efficiently, instead of incorporating the entire KBs. We evaluate the proposed method on the AirDialogue dataset, a large corpus released by Google, containing the conversations of customers booking flight tickets from the agent. The experimental results show that significantly improves over previous work in terms of accuracy and the BLEU score, which demonstrates not only the ability to achieve the given task but also the good quality of the generated dialogues.
FLC tuned with Gravitational Search Algorithm for Nonlinear Pose Filter
Authors: Trenton S Sieb, Ajay Singh, Lorelei Guidos, Hashim A Hashim
Date: 2020-09-25 00:00:00
Tasks: Pose Estimation, Position

Nonlinear pose (\textit{i.e,} attitude and position) filters are characterized with simpler structure and better tracking performance in comparison with other methods of pose estimation. A critical factor when designing a nonlinear pose filter is the selection of the error function. Conventional design of nonlinear pose filter design trade-off between fast adaptation and robustness. This paper introduces a new practical approach based on fuzzy rules for on-line continuous tuning of the nonlinear pose filter. Each of input and output membership functions are optimally tuned using graphical search algorithm optimization considering both pose error and its rate of change. The proposed approach is characterized with high adaptation features and strong level of robustness. Therefore, the proposed approach results of robust and fast convergence properties. The simulation results show the effectiveness of the proposed approach considering uncertain measurements and large error in initialization.
FKPP dynamics mediated by a parent field with a delay
Authors: Steffanie Stanley, Oleg Kogan
Date: 2020-10-30 00:00:00

We examine a modification of the Fisher-Kolmogorov-Petrovskiy-Piskunov (FKPP) process in which the diffusing substance requires a parent density field for reproduction. A biological example would be the density of diffusing spores (propagules) and the density of a stationary fungus (parent). The parent produces propagules at a certain rate, and the propagules turn into the parent substance at another rate. We model this evolution by the FKPP process with delay, which reflects a finite time typically required for a new parent to mature before it begins to produce propagules. While the FKPP process with other types of delays have been considered in the past as a pure mathematical construct, in our work a delay in the FKPP model arises in a natural science setting. The speed of the resulting density fronts is shown to decrease with increasing delay time, and has a non-trivial dependence on the rate of conversion of propagules into the parent substance. Remarkably, the fronts in this model are always slower than Fisher waves of the classical FKPP model. The largest speed is half of the classical value, and it is achieved at zero delay and when the two rates are matched.
Quality Selection in Two-Sided Markets: A Constrained Price Discrimination Approach
Authors: Bar Light, Ramesh Johari, Gabriel Weintraub
Date: 2019-12-04 00:00:00
Tasks: Vocal Bursts Valence Prediction

Online platforms collect rich information about participants and then share some of this information back with them to improve market outcomes. In this paper we study the following information disclosure problem in two-sided markets: If a platform wants to maximize revenue, which sellers should the platform allow to participate, and how much of its available information about participating sellers' quality should the platform share with buyers? We study this information disclosure problem in the context of two distinct two-sided market models: one in which the platform chooses prices and the sellers choose quantities (similar to ride-sharing), and one in which the sellers choose prices (similar to e-commerce). Our main results provide conditions under which simple information structures commonly observed in practice, such as banning certain sellers from the platform while not distinguishing between participating sellers, maximize the platform's revenue. The platform's information disclosure problem naturally transforms into a constrained price discrimination problem where the constraints are determined by the equilibrium outcomes of the specific two-sided market model being studied. We analyze this constrained price discrimination problem to obtain our structural results.
N 2 C : Neural Network Controller Design Using Behavioral Cloning
Authors: Shoaib Azam, Farzeen Munir, Muhammad Aasim Rafique, Ahmad Muqeem Sheri, Muhammad Ishfaq Hussain, Moongu Jeon
Date: 2020-06-01 00:00:00
Tasks: Model Predictive Control, Motion Planning

Modern vehicles communicate data to and from sensors, actuators, and electronic control units (ECUs) using Controller Area Network (CAN) bus, which operates on differential signaling. An autonomous ECU responsible for the execution of decision commands to an autonomous vehicle is developed by assimilating the information from the CAN bus. The conventional way of parsing the decision commands is motion planning, which uses a path tracking algorithm to evaluate the decision commands. This study focuses on designing a robust controller using behavioral cloning and motion planning of autonomous vehicle using a deep learning framework. In the first part of this study, we explore the pipeline of parsing decision commands from the path tracking algorithm to the controller and proposed a neural network-based controller ($N^2C$) using behavioral cloning. The proposed network predicts throttle, brake, and torque when trained with the manual driving data acquired from the CAN bus. The efficacy of the proposed method is demonstrated by comparing the accuracy with the Proportional-Derivative-Integral (PID) controller in conjunction with the path tracking algorithm (pure pursuit and model predictive control based path follower). The second part of this study complements $N^2C$, in which an end-to-end neural network for predicting the speed and steering angle is proposed with image data as an input. The performance of the proposed frameworks are evaluated in real-time and on the Udacity dataset, showing better metric scores in the former and reliable prediction in the later case when compared with the state-of-the-art methods.
Reconfigurable Intelligent Surface-Empowered MIMO Systems
Authors: Aymen Khaleel, Ertugrul Basar
Date: 2020-04-05 00:00:00

Reconfigurable intelligent surface (RIS)-assisted communications appear as a promising candidate for future wireless systems due to its attractive advantages in terms of implementation cost and end-to-end system performance. In this paper, two new multiple-input multiple-output (MIMO) system designs using RISs are presented to enhance the performance and boost the spectral efficiency of state-of-the-art MIMO communication systems. Vertical Bell Labs layered space-time (VBLAST) and Alamouti's schemes have been considered in this study and RIS-based simple transceiver architectures are proposed. For the VBLAST-based new system, an RIS is used to enhance the performance of the nulling and canceling-based sub-optimal detection procedure as well as to noticeably boost the spectral efficiency by conveying extra bits through the adjustment of the phases of the RIS elements. In addition, RIS elements have been utilized in order to redesign Alamouti's scheme with a single radio frequency (RF) signal generator at the transmitter side and to enhance its bit error rate (BER) performance. Monte Carlo simulations are provided to show the effectiveness of our system designs and it has been shown that they outperform the reference schemes in terms of BER performance and spectral efficiency.
导师毁人指南：如何毁掉你的研究生？
Authors: 中国导师
Date: 2020-04-30 00:00:00

摘录：要想毁掉自己的研究生，首先应该做的是摧毁研究生的自尊， 将研究生贬到卑微的地位上，具体方法有这样几条: 1、让研究生觉得自己什么都不行，没人赏识他。2、经常拿比他“行”的人刺激他。3、导师把自己塑造成为课题组牺牲者的形象 这样会使研究生产生罪恶感。4、和研究生说话一定要命令式的口吻。5、切不可给研究生自由。6、要学会迁怒的本事单位上遇到不顺心的事。7、当众出研究生的丑。
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
Authors: Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi
Date: 2019-06-01 00:00:00
Tasks: Imputation, Prediction, Recommendation Systems

In recommender systems, usually the ratings of a user to most items are missing and a critical problem is that the missing ratings are often missing not at random (MNAR) in reality. It is widely acknowledged that MNAR ratings make it difficult to accurately predict the ratings and unbiasedly estimate the performance of rating prediction. Recent approaches use imputed errors to recover the prediction errors for missing ratings, or weight observed ratings with the propensities of being observed. These approaches can still be severely biased in performance estimation or suffer from the variance of the propensities. To overcome these limitations, we first propose an estimator that integrates the imputed errors and propensities in a doubly robust way to obtain unbiased performance estimation and alleviate the effect of the propensity variance. To achieve good performance guarantees, based on this estimator, we propose joint learning of rating prediction and error imputation, which outperforms the state-of-the-art approaches on four real-world datasets.
Improving RNN transducer with normalized jointer network
Authors: Mingkun Huang, Jun Zhang, Meng Cai, Yang Zhang, Jiali Yao, Yongbin You, Yi He, Zejun Ma
Date: 2020-11-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modelling, speech-recognition, Speech Recognition

Recurrent neural transducer (RNN-T) is a promising end-to-end (E2E) model in automatic speech recognition (ASR). It has shown superior performance compared to traditional hybrid ASR systems. However, training RNN-T from scratch is still challenging. We observe a huge gradient variance during RNN-T training and suspect it hurts the performance. In this work, we analyze the cause of the huge gradient variance in RNN-T training and proposed a new \textit{normalized jointer network} to overcome it. We also propose to enhance the RNN-T network with a modified conformer encoder network and transformer-XL predictor networks to achieve the best performance. Experiments are conducted on the open 170-hour AISHELL-1 and industrial-level 30000-hour mandarin speech dataset. On the AISHELL-1 dataset, our RNN-T system gets state-of-the-art results on AISHELL-1's streaming and non-streaming benchmark with CER 6.15\% and 5.37\% respectively. We further compare our RNN-T system with our well trained commercial hybrid system on 30000-hour-industry audio data and get 9\% relative improvement without pre-training or external language model.
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR
Authors: Xiaohui Zhang, Frank Zhang, Chunxi Liu, Kjell Schubert, Julian Chan, Pradyot Prakash, Jun Liu, Ching-Feng Yeh, Fuchun Peng, Yatharth Saraf, Geoffrey Zweig
Date: 2020-11-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Benchmarking, speech-recognition, Speech Recognition

In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on three popular training criteria: LF-MMI, CTC and RNN-T. In transcribing social media videos of 7 languages with training data 3K-14K hours, we conduct large-scale controlled experimentation across each criterion using identical datasets and encoder model architecture. We find that RNN-T has consistent wins in ASR accuracy, while CTC models excel at inference efficiency. Moreover, we selectively examine various modeling strategies for different training criteria, including modeling units, encoder architectures, pre-training, etc. Given such large-scale real-world streaming ASR application, to our best knowledge, we present the first comprehensive benchmark on these three widely used training criteria across a great many languages.
Dynamic latency speech recognition with asynchronous revision
Authors: Mingkun Huang, Meng Cai, Jun Zhang, Yang Zhang, Yongbin You, Yi He, Zejun Ma
Date: 2020-11-03 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition

In this work we propose an inference technique, asynchronous revision, to unify streaming and non-streaming speech recognition models. Specifically, we achieve dynamic latency with only one model by using arbitrary right context during inference. The model is composed of a stack of convolutional layers for audio encoding. In inference stage, the history states of encoder and decoder can be asynchronously revised to trade off between the latency and the accuracy of the model. To alleviate training and inference mismatch, we propose a training technique, segment cropping, which randomly splits input utterances into several segments with forward connections. This allows us to have dynamic latency speech recognition results with large improvements in accuracy. Experiments show that our dynamic latency model with asynchronous revision gives 8\%-14\% relative improvements over the streaming models.
Multi-objective optimal placement of protective devices on microgrid using improved binary multi-objective PSO
Authors: Witoon Prommee，Weerakorn Ongsakul
Date: 2014-08-11 00:00:00

This paper proposes an improved binary multi-objectives particle swarm optimization (IB-MOPSO) for solving optimal multiple protective devices placement using improved reliability model in a microgrid (MG) system. The multi-objectives are to minimize system average interruption frequency index, system average interruption duration index, and total cost including investment and interruption cost. Binary multi-objectives PSO (B-MOPSO) is enhanced by using a bell shape function and three particle movement strategies including global guidance located in the least crowded areas, perturbation with different evolution method, and coverage of unexplored search space in the non-dominated front. For MG planning, IB-MOPSO can find better locations and number of protective devices including reclosers, switches, and fuses than B-MOPSO and ant colony optimization on the 51 section Provincial Electricity Authority of Thailand MG distribution system. Copyright © 2014 John Wiley & Sons, Ltd.
Assessing out-of-domain generalization for robust building damage detection
Authors: Vitus Benson, Alexander Ecker
Date: 2020-11-20 00:00:00
Tasks: Domain Generalization

An important step for limiting the negative impact of natural disasters is rapid damage assessment after a disaster occurred. For instance, building damage detection can be automated by applying computer vision techniques to satellite imagery. Such models operate in a multi-domain setting: every disaster is inherently different (new geolocation, unique circumstances), and models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event. Accordingly, estimating real-world performance requires an out-of-domain (OOD) test set. However, building damage detection models have so far been evaluated mostly in the simpler yet unrealistic in-distribution (IID) test setting. Here we argue that future work should focus on the OOD regime instead. We assess OOD performance of two competitive damage detection models and find that existing state-of-the-art models show a substantial generalization gap: their performance drops when evaluated OOD on new disasters not used during training. Moreover, IID performance is not predictive of OOD performance, rendering current benchmarks uninformative about real-world performance. Code and model weights are available at https://github.com/ecker-lab/robust-bdd.
Transcriptome profiling research in urothelial cell carcinoma
Authors: Umar Ahmad, Buhari Ibrahim, Mustapha Mohammed, Ahmed Faris Aldoghachi, Mahmood Usman, Abdulbasit Haliru Yakubu, Abubakar Sadiq Tanko, Khadijat Abubakar Bobbo, Usman Adamu Garkuwa, Abdullahi Adamu Faggo, Sagir Mustapha, Mahmoud Al-Masaeed, Syahril Abdullah, Yong Yoke Keong, Abhi Veerakumarasivam
Date: 2020-11-20 00:00:00
Tasks: Prognosis

Urothelial cell carcinoma (UCC) is the ninth most common cancer that accounts for 4.7% of all the new cancer cases globally. UCC development and progression are due to complex and stochastic genetic programmes. To study the cascades of molecular events underlying the poor prognosis that may lead to limited treatment options for advanced disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using modern high-throughput sequencing platforms has been employed. Here we review the principles of RNA-Seq technology and summarize recent studies on human bladder cancer that employed this technique to unravel the pathogenesis of the disease, identify biomarkers, discover pathways and classify the disease state. We list the commonly used computational platforms and software that are publicly available for RNA-Seq analysis. Moreover, we discussed the future perspectives for RNA-Seq studies on bladder cancer and recommend the application of new technology called single cell sequencing (scRNA-Seq) to further understand the disease. Keywords: Transcriptome profiling, RNA-sequencing, genomics, bioinformatics, bladder cancer
Filtering Rules for Flow Time Minimization in a Parallel Machine Scheduling Problem
Authors: Margaux Nattaf, Arnaud Malapert
Date: 2020-11-20 00:00:00
Tasks: Scheduling

This paper studies the scheduling of jobs of different families on parallel machines with qualification constraints. Originating from semiconductor manufacturing, this constraint imposes a time threshold between the execution of two jobs of the same family. Otherwise, the machine becomes disqualified for this family. The goal is to minimize both the flow time and the number of disqualifications. Recently, an efficient constraint programming model has been proposed. However, when priority is given to the flow time objective, the efficiency of the model can be improved. This paper uses a polynomial-time algorithm which minimize the flow time for a single machine relaxation where disqualifications are not considered. Using this algorithm one can derived filtering rules on different variables of the model. Experimental results are presented showing the effectiveness of these rules. They improve the competitiveness with the mixed integer linear program of the literature.
SophiaPop: Experiments in Human-AI Collaboration on Popular Music
Authors: David Hanson, Frankie Storm, Wenwei Huang, Vytas Krisciunas, Tiger Darrow, Audrey Brown, Mengna Lei, Matthew Aylett, Adam Pickrell, Sophia the Robot
Date: 2020-11-20 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

A diverse team of engineers, artists, and algorithms, collaborated to create songs for SophiaPop, via various neural networks, robotics technologies, and artistic tools, and animated the results on Sophia the Robot, a robotic celebrity and animated character. Sophia is a platform for arts, research, and other uses. To advance the art and technology of Sophia, we combine various AI with a fictional narrative of her burgeoning career as a popstar. Her actual AI-generated pop lyrics, music, and paintings, and animated conversations wherein she interacts with humans real-time in narratives that discuss her experiences. To compose the music, SophiaPop team built corpora from human and AI-generated Sophia character personality content, along with pop music song forms, to train and provide seeds for a number of AI algorithms including expert models, and custom-trained transformer neural networks, which then generated original pop-song lyrics and melodies. Our musicians including Frankie Storm, Adam Pickrell, and Tiger Darrow, then performed interpretations of the AI-generated musical content, including singing and instrumentation. The human-performed singing data then was processed by a neural-network-based Sophia voice, which was custom-trained from human performances by Cereproc. This AI then generated the unique Sophia voice singing of the songs. Then we animated Sophia to sing the songs in music videos, using a variety of animation generators and human-generated animations. Being algorithms and humans, working together, SophiaPop represents a human-AI collaboration, aspiring toward human AI symbiosis. We believe that such a creative convergence of multiple disciplines with humans and AI working together, can make AI relevant to human culture in new and exciting ways, and lead to a hopeful vision for the future of human-AI relations.