pwc-archive/papers-with-abstracts huggingface dataset:
Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions
Authors: Haitao Lin, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong
Date: 2022-05-26 00:00:00
Proceeding: ACL 2022 5
Tasks: Decoder

Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role's content separately and thus are prone to ignore the information from other roles. However, we believe that other roles' content could benefit the quality of summaries, such as the omitted information mentioned by other roles. Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization. It adopts cross attention and decoder self-attention interactions to interactively acquire other roles' critical information. The cross attention interaction aims to select other roles' critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles' summaries. Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets. Extensive analyses have demonstrated that other roles' content could help generate summaries with more complete semantics and correct topic structures.
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
Authors: Chao Zhao, Faeze Brahman, Tenghao Huang, Snigdha Chaturvedi
Date: 2022-05-26 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Sentence, Text Generation

Pre-trained models (PTMs) have lead to great improvements in natural language generation (NLG). However, it is still unclear how much commonsense knowledge they possess. With the goal of evaluating commonsense knowledge of NLG models, recent work has proposed the problem of generative commonsense reasoning, e.g., to compose a logical sentence given a set of unordered concepts. Existing approaches to this problem hypothesize that PTMs lack sufficient parametric knowledge for this task, which can be overcome by introducing external knowledge or task-specific pre-training objectives. Different from this trend, we argue that PTM's inherent ability for generative commonsense reasoning is underestimated due to the order-agnostic property of its input. In particular, we hypothesize that the order of the input concepts can affect the PTM's ability to utilize its commonsense knowledge. To this end, we propose a pre-ordering approach to elaborately manipulate the order of the given concepts before generation. Experiments show that our approach can outperform the more sophisticated models that have access to a lot of external data and resources.
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling
Authors: Kaitao Song, Yichong Leng, Xu Tan, Yicheng Zou, Tao Qin, Dongsheng Li
Date: 2022-05-25 00:00:00
Tasks: Causal Language Modeling, Language Modeling, Language Modelling, Masked Language Modeling, Reranking, Sentence

Sentence scoring aims at measuring the likelihood score of a sentence and is widely used in many natural language processing scenarios, like reranking, which is to select the best sentence from multiple candidates. Previous works on sentence scoring mainly adopted either causal language modeling (CLM) like GPT or masked language modeling (MLM) like BERT, which have some limitations: 1) CLM only utilizes unidirectional information for the probability estimation of a sentence without considering bidirectional context, which affects the scoring quality; 2) MLM can only estimate the probability of partial tokens at a time and thus requires multiple forward passes to estimate the probability of the whole sentence, which incurs large computation and time cost. In this paper, we propose \textit{Transcormer} -- a Transformer model with a novel \textit{sliding language modeling} (SLM) for sentence scoring. Specifically, our SLM adopts a triple-stream self-attention mechanism to estimate the probability of all tokens in a sentence with bidirectional context and only requires a single forward pass. SLM can avoid the limitations of CLM (only unidirectional context) and MLM (multiple forward passes) and inherit their advantages, and thus achieve high effectiveness and efficiency in scoring. Experimental results on multiple tasks demonstrate that our method achieves better performance than other language modelings.
Benchmarking of Deep Learning models on 2D Laminar Flow behind Cylinder
Authors: Mritunjay Musale, Vaibhav Vasani
Date: 2022-05-26 00:00:00
Tasks: Benchmarking, Deep Learning

The rapidly advancing field of Fluid Mechanics has recently employed Deep Learning to solve various problems within that field. In that same spirit we try to perform Direct Numerical Simulation(DNS) which is one of the tasks in Computational Fluid Dynamics, using three fundamental architectures in the field of Deep Learning that were each used to solve various high dimensional problems. We train these three models in an autoencoder manner, for this the dataset is treated like sequential frames given to the model as input. We observe that recently introduced architecture called Transformer significantly outperforms its counterparts on the selected dataset.Furthermore, we conclude that using Transformers for doing DNS in the field of CFD is an interesting research area worth exploring.
CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization
Authors: Ryoki Hamano, Shota Saito, Masahiro Nomura, Shinichi Shirakawa
Date: 2022-05-26 00:00:00

This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in this study, is a population-based stochastic search method that samples solution candidates from a multivariate Gaussian distribution (MGD), which shows excellent performance in continuous BBO. The parameters of MGD, mean and (co)variance, are updated based on the evaluation value of candidate solutions in the CMA-ES. If the CMA-ES is applied to the MI-BBO with straightforward discretization, however, the variance corresponding to the integer variables becomes much smaller than the granularity of the discretization before reaching the optimal solution, which leads to the stagnation of the optimization. In particular, when binary variables are included in the problem, this stagnation more likely occurs because the granularity of the discretization becomes wider, and the existing modification to the CMA-ES does not address this stagnation. To overcome these limitations, we propose a simple modification of the CMA-ES based on lower-bounding the marginal probabilities associated with the generation of integer variables in the MGD. The numerical experiments on the MI-BBO benchmark problems demonstrate the efficiency and robustness of the proposed method.
Neuromorphic Artificial Intelligence Systems
Authors: Dmitry Ivanov, Aleksandr Chezhegov, Andrey Grunin, Mikhail Kiselev, Denis Larionov
Date: 2022-05-25 00:00:00

Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. Next, it presents an overview of currently available neuromorphic AI projects in which these limitations are overcame by bringing some brain features into the functioning and organization of computing systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida). Also, the article presents the principle of classifying neuromorphic AI systems by the brain features they use (neural networks, parallelism and asynchrony, impulse nature of information transfer, local learning, sparsity, analog and in-memory computing). In addition to new architectural approaches used in neuromorphic devices based on existing silicon microelectronics technologies, the article also discusses the prospects of using new memristor element base. Examples of recent advances in the use of memristors in euromorphic applications are also given.
Factor selection in screening experiments by aggregation over random models
Authors: Rakhi Singh, John Stufken
Date: 2022-05-26 00:00:00

Screening experiments are useful for screening out a small number of truly important factors from a large number of potentially important factors. The Gauss-Dantzig Selector (GDS) is often the preferred analysis method for screening experiments. Just considering main-effects models can result in erroneous conclusions, but including interaction terms, even if restricted to two-factor interactions, increases the number of model terms dramatically and challenges the GDS analysis. We propose a new analysis method, called Gauss-Dantzig Selector Aggregation over Random Models (GDS-ARM), which performs a GDS analysis on multiple models that include only some randomly selected interactions. Results from these different analyses are then aggregated to identify the important factors. We discuss the proposed method, suggest choices for the tuning parameters, and study its performance on real and simulated data.
Classification ensembles for multivariate functional data with application to mouse movements in web surveys
Authors: Amanda Fernández-Fontelo, Felix Henninger, Pascal J. Kieslich, Frauke Kreuter, Sonja Greven
Date: 2022-05-26 00:00:00
Tasks: Survey

We propose new ensemble models for multivariate functional data classification as combinations of semi-metric-based weak learners. Our models extend current semi-metric-type methods from the univariate to the multivariate case, propose new semi-metrics to compute distances between functions, and consider more flexible options for combining weak learners using stacked generalisation methods. We apply these ensemble models to identify respondents' difficulty with survey questions, with the aim to improve survey data quality. As predictors of difficulty, we use mouse movement trajectories from the respondents' interaction with a web survey, in which several questions were manipulated to create two scenarios with different levels of difficulty.
LeiBi@COLIEE 2022: Aggregating Tuned Lexical Models with a Cluster-driven BERT-based Model for Case Law Retrieval
Authors: Arian Askari, Georgios Peikos, Gabriella Pasi, Suzan Verberne
Date: 2022-05-26 00:00:00
Tasks: Re-Ranking, Retrieval, Sentence

This paper summarizes our approaches submitted to the case law retrieval task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2022. Our methodology consists of four steps; in detail, given a legal case as a query, we reformulate it by extracting various meaningful sentences or n-grams. Then, we utilize the pre-processed query case to retrieve an initial set of possible relevant legal cases, which we further re-rank. Lastly, we aggregate the relevance scores obtained by the first stage and the re-ranking models to improve retrieval effectiveness. In each step of our methodology, we explore various well-known and novel methods. In particular, to reformulate the query cases aiming to make them shorter, we extract unigrams using three different statistical methods: KLI, PLM, IDF-r, as well as models that leverage embeddings (e.g., KeyBERT). Moreover, we investigate if automatic summarization using Longformer-Encoder-Decoder (LED) can produce an effective query representation for this retrieval task. Furthermore, we propose a novel re-ranking cluster-driven approach, which leverages Sentence-BERT models that are pre-tuned on large amounts of data for embedding sentences from query and candidate documents. Finally, we employ a linear aggregation method to combine the relevance scores obtained by traditional IR models and neural-based models, aiming to incorporate the semantic understanding of neural models and the statistically measured topical relevance. We show that aggregating these relevance scores can improve the overall retrieval effectiveness.
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
Authors: Mingshi Yan, Zhiyong Cheng, Chen Gao, Jing Sun, Fan Liu, Fuming Sun, Haojie Li
Date: 2022-05-26 00:00:00
Tasks: Multi-Task Learning

Multi-behavior recommendation exploits multiple types of user-item interactions to alleviate the data sparsity problem faced by the traditional models that often utilize only one type of interaction for recommendation. In real scenarios, users often take a sequence of actions to interact with an item, in order to get more information about the item and thus accurately evaluate whether an item fits personal preference. Those interaction behaviors often obey a certain order, and different behaviors reveal different information or aspects of user preferences towards the target item. Most existing multi-behavior recommendation methods take the strategy to first extract information from different behaviors separately and then fuse them for final prediction. However, they have not exploited the connections between different behaviors to learn user preferences. Besides, they often introduce complex model structures and more parameters to model multiple behaviors, largely increasing the space and time complexity. In this work, we propose a lightweight multi-behavior recommendation model named Cascading Residual Graph Convolutional Network (CRGCN for short), which can explicitly exploit the connections between different behaviors into the embedding learning process without introducing any additional parameters. In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors. The multi-task learning method is adopted to jointly optimize our model based on different behaviors. Extensive experimental results on two real-world benchmark datasets show that CRGCN can substantially outperform state-of-the-art methods. Further studies also analyze the effects of leveraging multi-behaviors in different numbers and orders on the final performance.
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
Authors: Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain, Venkata Vivek Thallam, Ruizhou Ding, Diana Marculescu
Date: 2022-05-20 00:00:00
Tasks: Quantization

As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantization levels, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements (PE) into the accelerator design space while having accurate and fast power, performance, and area models. In this work, we present QADAM, a highly parameterized quantization-aware power, performance, and area modeling framework for DNN accelerators. Our framework can facilitate future research on design space exploration and Pareto-efficiency of DNN accelerators for various design choices such as bit precision, PE type, scratchpad sizes of PEs, global buffer size, number of total PEs, and DNN configurations. Our results show that different bit precisions and PE types lead to significant differences in terms of performance per area and energy. Specifically, our framework identifies a wide range of design points where performance per area and energy varies more than 5x and 35x, respectively. We also show that the proposed lightweight processing elements (LightPEs) consistently achieve Pareto-optimal results in terms of accuracy and hardware-efficiency. With the proposed framework, we show that LightPEs achieve on par accuracy results and up to 5.7x more performance per area and energy improvement when compared to the best INT16 based design.
Communicating with Anecdotes
Authors: Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius, Divyarthi Mohan
Date: 2022-05-26 00:00:00

We study a communication game between a sender and a receiver. The sender chooses one of her signals about the state of the world (i.e., anecdotes) and communicates to the receiver who takes an action affecting both players. The sender and the receiver both care about the state of the world but are also influenced by personal preferences, so their ideal actions can differ. We characterize perfect Bayesian equilibria. The sender faces a temptation to persuade: she wants to select a biased anecdote to influence the receiver's action. Anecdotes are still informative to the receiver (who will debias at equilibrium) but the attempt to persuade comes at a cost to precision. This gives rise to informational homophily where the receiver prefers to listen to like-minded senders because they provide higher-precision signals. Communication becomes polarized when the sender is an expert with access to many signals, with the sender choosing extreme outlier anecdotes at equilibrium (unless preferences are perfectly aligned). This polarization dissipates all gains from communication with an increasingly well-informed sender when the anecdote distribution is heavy-tailed. Experts can therefore face a curse of informedness: receivers will prefer to listen to less-informed senders who cannot pick biased signals as easily.
Open Arms: Open-Source Arms, Hands & Control
Authors: David Hanson, Alishba Imran, Gerardo Morales, Vytas Krisciunas, Aditya Sagi, Aman Malali, Rushali Mohbe, Raviteja Upadrashta
Date: 2022-05-20 00:00:00
Tasks: Robotic Grasping

Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation tools, and application development tools to build and operate Open Arms. This paper describes these hands controls, sensing, mechanisms, aesthetic design, and manufacturing and their real-world applications with a teleoperated nursing robot. From 2015 to 2022, the authors have designed and established the manufacturing of Open Arms as a low-cost, high functionality robotic arms hardware and software framework to serve both humanoid robot applications and the urgent demand for low-cost prosthetics, as part of the Hanson Robotics Sophia Robot platform. Using the techniques of consumer product manufacturing, we set out to define modular, low-cost techniques for approximating the dexterity and sensitivity of human hands. To demonstrate the dexterity and control of our hands, we present a Generative Grasping Residual CNN (GGR-CNN) model that can generate robust antipodal grasps from input images of various objects in real-time speeds (22ms). We achieved state-of-the-art accuracy of 92.4% using our model architecture on a standard Cornell Grasping Dataset, which contains a diverse set of household objects.
Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR
Authors: Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang, Li-Rong Dai
Date: 2022-05-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Speech enhancement (SE) is usually required as a front end to improve the speech quality in noisy environments, while the enhanced speech might not be optimal for automatic speech recognition (ASR) systems due to speech distortion. On the other hand, it was shown that self-supervised pre-training enables the utilization of a large amount of unlabeled noisy data, which is rather beneficial for the noise robustness of ASR. However, the potential of the (optimal) integration of SE and self-supervised pre-training still remains unclear. In order to find an appropriate combination and reduce the impact of speech distortion caused by SE, in this paper we therefore propose a joint pre-training approach for the SE module and the self-supervised model. First, in the pre-training phase the original noisy waveform or the waveform obtained by SE is fed into the self-supervised model to learn the contextual representation, where the quantified clean speech acts as the target. Second, we propose a dual-attention fusion method to fuse the features of noisy and enhanced speeches, which can compensate the information loss caused by separately using individual modules. Due to the flexible exploitation of clean/noisy/enhanced branches, the proposed method turns out to be a generalization of some existing noise-robust ASR models, e.g., enhanced wav2vec2.0. Finally, experimental results on both synthetic and real noisy datasets show that the proposed joint training approach can improve the ASR performance under various noisy settings, leading to a stronger noise robustness.
Linear Reduced Order Model Predictive Control
Authors: Joseph Lorenzetti, Andrew McClellan, Charbel Farhat, Marco Pavone
Date: 2020-12-06 00:00:00
Tasks: Computational Efficiency, model, Model Predictive Control

Model predictive controllers use dynamics models to solve constrained optimal control problems. However, computational requirements for real-time control have limited their use to systems with low-dimensional models. Nevertheless, high-dimensional models arise in many settings, for example discretization methods for generating finite-dimensional approximations to partial differential equations can result in models with thousands to millions of dimensions. In such cases, reduced order models (ROMs) can significantly reduce computational requirements, but model approximation error must be considered to guarantee controller performance. In this work, a reduced order model predictive control (ROMPC) scheme is proposed to solve robust, output feedback, constrained optimal control problems for high-dimensional linear systems. Computational efficiency is obtained by using projection-based ROMs, and guarantees on robust constraint satisfaction and stability are provided. Performance of the approach is demonstrated in simulation for several examples, including an aircraft control problem leveraging an inviscid computational fluid dynamics model with dimension 998,930.
Minimization of THD in Nine Level Cascaded H-Bridge Inverter Using Artificial Neural Network
Authors: Manoj Mathews, B. Ramesh, T. Sreedhar
Date: 2022-05-25 00:00:00

Multilevel inverter converts different level DC voltage to AC voltage. It has wide interest in power industry especially in high power applications. In power electronic equipment the major drawback is the harmonics. Several control strategies are available to reduce the harmonic content and the most widely used measure of Total Harmonic Distortion (THD). In this project, the comparison has been made for the open loop and closed loop PI controller and neural network that predict the switching angle in order to reduce the harmonics. The mapping between Modulation Index and Switching angles are plotted for the forward neural network. After the prediction of switching angles the neural network topologies are executed for better result. This technique is applied for any type of multilevel inverter, Cascaded H-Bridge multilevel inverter is chosen. A nine level Cascaded H-Bridge multilevel inverter power circuit is simulated in MATLAB 8.3 simulink with sinusoidal PWM technique. The comparison results reveal that the THD is reduced to about 3% with neural network control compared to open loop control. The results are presented and analyzed.