pwc-archive/papers-with-abstracts huggingface dataset:
Short-Term Stock Price-Trend Prediction Using Meta-Learning
Authors: Shin-Hung Chang, Cheng-Wen Hsu, Hsing-Ying Li, Wei-Sheng Zeng, Jan-Ming Ho
Date: 2021-05-28 00:00:00
Tasks: Meta-Learning, Prediction, Stock Price Prediction, Time Series, Time Series Analysis

Although conventional machine learning algorithms have been widely adopted for stock-price predictions in recent years, the massive volume of specific labeled data required are not always available. In contrast, meta-learning technology uses relatively small amounts of training data, called fast learners. Such methods are beneficial under conditions of limited data availability, which often obtain for trend prediction based on time-series data limited by sparse information. In this study, we consider short-term stock price prediction using a meta-learning framework with several convolutional neural networks, including the temporal convolution network, fully convolutional network, and residual neural network. We propose a sliding time horizon to label stocks according to their predicted price trends, referred to as called slope-detection labeling, using prediction labels including "rise plus," "rise," "fall," and "fall plus". The effectiveness of the proposed meta-learning framework was evaluated by application to the S&P500. The experimental results show that the inclusion of the proposed meta-learning framework significantly improved both regular and balanced prediction accuracy and profitability.
Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation
Authors: El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed
Date: 2021-05-28 00:00:00
Proceeding: NAACL (CALCS) 2021 6
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Translation

Recent progress in neural machine translation (NMT) has made it possible to translate successfully between monolingual language pairs where large parallel data exist, with pre-trained models improving performance even further. Although there exists work on translating in code-mixed settings (where one of the pairs includes text from two or more languages), it is still unclear what recent success in NMT and language modeling exactly means for translating code-mixed text. We investigate one such context, namely MT from code-mixed Modern Standard Arabic and Egyptian Arabic (MSAEA) into English. We develop models under different conditions, employing both (i) standard end-to-end sequence-to-sequence (S2S) Transformers trained from scratch and (ii) pre-trained S2S language models (LMs). We are able to acquire reasonable performance using only MSA-EN parallel data with S2S models trained from scratch. We also find LMs fine-tuned on data from various Arabic dialects to help the MSAEA-EN task. Our work is in the context of the Shared Task on Machine Translation in Code-Switching. Our best model achieves $\bf25.72$ BLEU, placing us first on the official shared task evaluation for MSAEA-EN.
Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning
Authors: Jiayu Shang, Yanni Sun
Date: 2021-05-28 00:00:00

Background: Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction. Results: In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (GCN) is adopted to exploit viruses with or without known hosts in training to enhance the learning ability. During the GCN training, we minimize the expected calibrated error (ECE) to ensure the confidence of the predictions. We tested HostG on both simulated and real sequencing data and compared its performance with other state-of-the-art methods specifcally designed for virus host classification (VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion: HostG outperforms other popular methods, demonstrating the efficacy of using a GCN-based semi-supervised learning approach. A particular advantage of HostG is its ability to predict hosts from new taxa.
Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach
Authors: Carlos Carrion, Zenan Wang, Harikesh Nair, Xianghong Luo, Yulin Lei, Xiliang Lin, Wenlong Chen, Qiyu Hu, Changping Peng, Yongjun Bao, Weipeng Yan
Date: 2021-05-28 00:00:00
Tasks: Marketing

In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior. The former content helps advertisers achieve their marketing goals and provides a stream of ad revenue to the platform. The latter content contributes to users' engagement with the platform, which is key to its long-term health. A burning issue for e-commerce platform design is how to blend advertising with content in a way that respects these interactions and balances these multiple business objectives. This paper describes a system developed for this purpose in the context of blending personalized sponsored content with non-sponsored content on the product detail pages of JD.COM, an e-commerce company. This system has three key features: (1) Optimization of multiple competing business objectives through a new virtual bids approach and the expressiveness of the latent, implicit valuation of the platform for the multiple objectives via these virtual bids. (2) Modeling of users' click behavior as a function of their characteristics, the individual characteristics of each sponsored content and the influence exerted by other sponsored and non-sponsored content displayed alongside through a deep learning approach; (3) Consideration of externalities in the allocation of ads, thereby making it directly compatible with a Vickrey-Clarke-Groves (VCG) auction scheme for the computation of payments in the presence of these externalities. The system is currently deployed and serving all traffic through JD.COM's mobile application. Experiments demonstrating the performance and advantages of the system are presented.
A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation
Authors: Alexander E. Siemenn, Evyatar Shaulsky, Matthew Beveridge, Tonio Buonassisi, Sara M. Hashmi, Iddo Drori
Date: 2021-05-28 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

Generating droplets from a continuous stream of fluid requires precise tuning of a device to find optimized control parameter conditions. It is analytically intractable to compute the necessary control parameter values of a droplet-generating device that produces optimized droplets. Furthermore, as the length scale of the fluid flow changes, the formation physics and optimized conditions that induce flow decomposition into droplets also change. Hence, a single proportional integral derivative controller is too inflexible to optimize devices of different length scales or different control parameters, while classification machine learning techniques take days to train and require millions of droplet images. Therefore, the question is posed, can a single method be created that universally optimizes multiple length-scale droplets using only a few data points and is faster than previous approaches? In this paper, a Bayesian optimization and computer vision feedback loop is designed to quickly and reliably discover the control parameter values that generate optimized droplets within different length-scale devices. This method is demonstrated to converge on optimum parameter values using 60 images in only 2.3 hours, 30x faster than previous approaches. Model implementation is demonstrated for two different length-scale devices: a milliscale inkjet device and a microfluidics device.
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers
Authors: Xi Li, David J. Miller, Zhen Xiang, George Kesidis
Date: 2021-05-28 00:00:00
Tasks: Data Poisoning

Data Poisoning (DP) is an effective attack that causes trained classifiers to misclassify their inputs. DP attacks significantly degrade a classifier's accuracy by covertly injecting attack samples into the training set. Broadly applicable to different classifier structures, without strong assumptions about the attacker, an {\it unsupervised} Bayesian Information Criterion (BIC)-based mixture model defense against "error generic" DP attacks is herein proposed that: 1) addresses the most challenging {\it embedded} DP scenario wherein, if DP is present, the poisoned samples are an {\it a priori} unknown subset of the training set, and with no clean validation set available; 2) applies a mixture model both to well-fit potentially multi-modal class distributions and to capture poisoned samples within a small subset of the mixture components; 3) jointly identifies poisoned components and samples by minimizing the BIC cost defined over the whole training set, with the identified poisoned data removed prior to classifier training. Our experimental results, for various classifier structures and benchmark datasets, demonstrate the effectiveness and universality of our defense under strong DP attacks, as well as its superiority over other works.
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture
Authors: Suyoung Lee, Sae-Young Chung
Date: 2021-05-28 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Meta Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The generalization ability of most meta-reinforcement learning (meta-RL) methods is largely limited to test tasks that are sampled from the same distribution used to sample training tasks. To overcome the limitation, we propose Latent Dynamics Mixture (LDM) that trains a reinforcement learning agent with imaginary tasks generated from mixtures of learned latent dynamics. By training a policy on mixture tasks along with original training tasks, LDM allows the agent to prepare for unseen test tasks during training and prevents the agent from overfitting the training tasks. LDM significantly outperforms standard meta-RL methods in test returns on the gridworld navigation and MuJoCo tasks where we strictly separate the training task distribution and the test task distribution.
Lattice partition recovery with dyadic CART
Authors: Oscar Hernan Madrid Padilla, Yi Yu, Alessandro Rinaldo
Date: 2021-05-27 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: regression

We study piece-wise constant signals corrupted by additive Gaussian noise over a $d$-dimensional lattice. Data of this form naturally arise in a host of applications, and the tasks of signal detection or testing, de-noising and estimation have been studied extensively in the statistical and signal processing literature. In this paper we consider instead the problem of partition recovery, i.e.~of estimating the partition of the lattice induced by the constancy regions of the unknown signal, using the computationally-efficient dyadic classification and regression tree (DCART) methodology proposed by \citep{donoho1997cart}. We prove that, under appropriate regularity conditions on the shape of the partition elements, a DCART-based procedure consistently estimates the underlying partition at a rate of order $\sigma^2 k^* \log (N)/\kappa^2$, where $k^*$ is the minimal number of rectangular sub-graphs obtained using recursive dyadic partitions supporting the signal partition, $\sigma^2$ is the noise variance, $\kappa$ is the minimal magnitude of the signal difference among contiguous elements of the partition and $N$ is the size of the lattice. Furthermore, under stronger assumptions, our method attains a sharper estimation error of order $\sigma^2\log(N)/\kappa^2$, independent of $k^*$, which we show to be minimax rate optimal. Our theoretical guarantees further extend to the partition estimator based on the optimal regression tree estimator (ORT) of \cite{chatterjee2019adaptive} and to the one obtained through an NP-hard exhaustive search method. We corroborate our theoretical findings and the effectiveness of DCART for partition recovery in simulations.
Avancee-1 Mission and SaDoD Method: LiDAR-based stimulated atomic disintegration of space debris (SaDoD) using Optical Neural Networks
Authors: Manuel Ntumba, Saurabh Gore
Date: 2021-05-27 00:00:00
Tasks: object-detection, Object Detection

The surface degradation of satellites in Low Earth Orbit (LEO) is affected by Atomic Oxygen (AO) and varies depending on the spacecraft orbital parameters. Atomic oxygen initiates several chemical and physical reactions with materials and produces erosion and self-disintegration of the debris at high energy. This paper discusses Avancee-1 Mission, LiDAR-based space debris removal using Optical Neural Networks (ONN) to optimize debris detection and mission accuracy. The SaDoD Method is a Stimulated Atomic Disintegration of Orbital Debris, which in this case has been achieved using LiDAR technology and Optical Neural Networks. We propose Optical Neural Network algorithms with a high ability of image detection and classification. The results show that orbital debris has a higher chance of disintegration when the laser beam is coming from Geostationary Orbit (GEO) satellites and in the presence of high solar activities. This paper proposes a LiDAR-based space debris removal method depending on the variation of atomic oxygen erosion with orbital parameters and solar energy levels. The results obtained show that orbital debris undergoes the most intense degradation at low altitudes and higher temperatures. The satellites in GEO use Optical Neural Network algorithms for object detection before sending the laser beams to achieve self-disintegration. The SaDoD Method can be implemented with other techniques, but especially for the Avancee-1 Mission, the SaDoD was implemented with LiDAR technologies and Optical Neural Network algorithms.
On Linear Stability of SGD and Input-Smoothness of Neural Networks
Authors: Chao Ma, Lexing Ying
Date: 2021-05-27 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Adversarial Robustness

The multiplicative structure of parameters and input data in the first layer of neural networks is explored to build connection between the landscape of the loss function with respect to parameters and the landscape of the model function with respect to input data. By this connection, it is shown that flat minima regularize the gradient of the model function, which explains the good generalization performance of flat minima. Then, we go beyond the flatness and consider high-order moments of the gradient noise, and show that Stochastic Gradient Descent (SGD) tends to impose constraints on these moments by a linear stability analysis of SGD around global minima. Together with the multiplicative structure, we identify the Sobolev regularization effect of SGD, i.e. SGD regularizes the Sobolev seminorms of the model function with respect to the input data. Finally, bounds for generalization error and adversarial robustness are provided for solutions found by SGD under assumptions of the data distribution.
Non-negative matrix factorization algorithms greatly improve topic model fits
Authors: Peter Carbonetto, Abhishek Sarkar, ZiHao Wang, Matthew Stephens
Date: 2021-05-27 00:00:00
Tasks: parameter estimation, Topic Models, Variational Inference

We report on the potential for using algorithms for non-negative matrix factorization (NMF) to improve parameter estimation in topic models. While several papers have studied connections between NMF and topic models, none have suggested leveraging these connections to develop new algorithms for fitting topic models. NMF avoids the "sum-to-one" constraints on the topic model parameters, resulting in an optimization problem with simpler structure and more efficient computations. Building on recent advances in optimization algorithms for NMF, we show that first solving the NMF problem then recovering the topic model fit can produce remarkably better fits, and in less time, than standard algorithms for topic models. While we focus primarily on maximum likelihood estimation, we show that this approach also has the potential to improve variational inference for topic models. Our methods are implemented in the R package fastTopics.
FuSeConv: Fully Separable Convolutions for Fast Inference on Systolic Arrays
Authors: Surya Selvam, Vinod Ganesan, Pratyush Kumar
Date: 2021-05-27 00:00:00
Tasks: Neural Architecture Search

Both efficient neural networks and hardware accelerators are being explored to speed up DNN inference on edge devices. For example, MobileNet uses depthwise separable convolution to achieve much lower latency, while systolic arrays provide much higher performance per watt. Interestingly however, the combination of these two ideas is inefficient: The computational patterns of depth-wise separable convolution are not systolic and lack data reuse to saturate the systolic array's constrained dataflow. In this paper, we propose FuSeConv (Fully-Separable Convolution) as a drop-in replacement for depth-wise separable convolution. FuSeConv generalizes the decomposition of convolutions fully to separable 1D convolutions along spatial and depth dimensions. The resultant computation is systolic and efficiently utilizes the systolic array with a slightly modified dataflow. With FuSeConv, we achieve a significant speed-up of 3x-7x with the MobileNet family of networks on a systolic array of size 64x64, with comparable accuracy on the ImageNet dataset. The high speed-up motivates exploration of hardware-aware Neural Operator Search (NOS) in complement to ongoing efforts on Neural Architecture Search (NAS).
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data
Authors: YuanZheng Hu, Marina Sokolova
Date: 2021-05-27 00:00:00
Tasks: BIG-bench Machine Learning, Multi-class Classification

Application of Machine Learning algorithms to the medical domain is an emerging trend that helps to advance medical knowledge. At the same time, there is a significant a lack of explainable studies that promote informed, transparent, and interpretable use of Machine Learning algorithms. In this paper, we present explainable multi-class classification of the Covid-19 mental health data. In Machine Learning study, we aim to find the potential factors to influence a personal mental health during the Covid-19 pandemic. We found that Random Forest (RF) and Gradient Boosting (GB) have scored the highest accuracy of 68.08% and 68.19% respectively, with LIME prediction accuracy 65.5% for RF and 61.8% for GB. We then compare a Post-hoc system (Local Interpretable Model-Agnostic Explanations, or LIME) and an Ante-hoc system (Gini Importance) in their ability to explain the obtained Machine Learning results. To the best of these authors knowledge, our study is the first explainable Machine Learning study of the mental health data collected during Covid-19 pandemics.
Flow based features and validation metric for machine learning reconstruction of PIV data
Authors: Ghasem Akbari, Nader Montazerin
Date: 2021-05-27 00:00:00
Tasks: BIG-bench Machine Learning

Reconstruction of flow field from real sparse data by a physics-oriented approach is a current challenge for fluid scientists in the AI community. The problem includes feature recognition and implementation of AI algorithms that link data to a physical feature space in order to produce reconstructed data. The present article applies machine learning approach to study contribution of different flow-based features with practical fluid mechanics applications for reconstruction of the missing data of turbomachinery PIV measurements. Support vector regression (SVR) and multi-layer perceptron (MLP) are selected as two robust regressors capable of modelling non-linear fluid flow phenomena. The proposed flow-based features are optimally scaled and filtered to extract the best configuration. In addition to conventional data-based validation of the regressors, a metric is proposed that reflects mass conservation law as an important requirement for a physical flow reproduction. For a velocity field including 25% of clustered missing data, the reconstruction accuracy achieved by SVR in terms of R2-score is as high as 0.993 for the in-plane velocity vectors in comparison with that obtained by MLP which is up to 0.981. In terms of mass conservation metric, the SVR model by R2-score up to 0.96 is considerably more accurate than the MLP estimator. For extremely sparse data with a gappiness of 75%, vector and contour plots from SVR and MLP were consistent with those of the original field.
Sinan: Data-Driven, QoS-Aware Cluster Management for Microservices
Authors: Yanqi Zhang, Weizhe Hua, Zhuangzhuang Zhou, Edward Suh, Christina Delimitrou
Date: 2021-05-27 00:00:00
Tasks: Management

Cloud applications are increasingly shifting from large monolithic services, to large numbers of loosely-coupled, specialized microservices. Despite their advantages in terms of facilitating development, deployment, modularity, and isolation, microservices complicate resource management, as dependencies between them introduce backpressure effects and cascading QoS violations. We present Sinan, a data-driven cluster manager for interactive cloud microservices that is online and QoS-aware. Sinan leverages a set of scalable and validated machine learning models to determine the performance impact of dependencies between microservices, and allocate appropriate resources per tier in a way that preserves the end-to-end tail latency target. We evaluate Sinan both on dedicated local clusters and large-scale deployments on Google Compute Engine (GCE) across representative end-to-end applications built with microservices, such as social networks and hotel reservation sites. We show that Sinan always meets QoS, while also maintaining cluster utilization high, in contrast to prior work which leads to unpredictable performance or sacrifices resource efficiency. Furthermore, the techniques in Sinan are explainable, meaning that cloud operators can yield insights from the ML models on how to better deploy and design their applications to reduce unpredictable performance.