pwc-archive/papers-with-abstracts huggingface dataset:
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis
Authors: Rong Zhang, Qifei Zhou, Bo Wu, Weiping Li, Tong Mo
Date: 2020-05-07 00:00:00
Proceeding: SIAM International Conference on Data Mining (SDM20) 2020 5
Tasks: Community Question Answering, Natural Language Inference, Paraphrase Identification, Question Answering, Word Embeddings

Duplicate Question Identification (DQI) improves the processing efficiency and accuracy of large-scale community question answering and automatic QA system. The purpose of DQI task is to identify whether the paired questions are semantically equivalent. However, how to distinguish the synonyms or homonyms in paired questions is still challenging. Most previous works focus on the word-level or phrase-level semantic differences. We firstly propose to explore the asking emphasis of a question as a key factor in DQI. Asking emphasis bridges semantic equivalence between two questions. In this paper, we propose an attention model with multi-fusion asking emphasis (MFAE) for DQI. At first, BERT is used to obtain the dynamic pre-trained word embeddings. Then we get inter- and intra-asking emphasis by summing inter-attention and self-attention, respectively; the idea is that, the more a word interacts with others, the more important the word is. Finally, we use eight-way combinations to generate multi-fusion asking emphasis and multi-fusion word representation. Experimental results demonstrate that our model achieves state-of-the-art performance on both Quora Question Pairs and CQADupStack data. In addition, our model can also improve the results for natural language inference task on SNLI and MultiNLI datasets. The code is available at https://github.com/rzhangpku/MFAE.
Monocular Arbitrary Moving Object Discovery and Segmentation
Authors: Michal Neoral, Jan Šochman, Jiří Matas
Date: 2021-11-22 00:00:00
Proceeding: The 32nd British Machine Vision Conference 2021 11
Tasks: Motion Segmentation, Object, Object Discovery, Optical Flow Estimation, Segmentation

We propose a method for discovery and segmentation of objects that are, or their parts are, independently moving in the scene. Given three monocular video frames, the method outputs semantically meaningful regions, i.e. regions corresponding to the whole object, even when only a part of it moves.
The architecture of the CNN-based end-to-end method, called Raptor, combines semantic and motion backbones, which pass their outputs to a final region segmentation network. The semantic backbone is trained in a class-agnostic manner in order to generalise to object classes beyond the training data. The core of the motion branch is a geometrical cost volume computed from optical flow, optical expansion, mono-depth and the estimated camera motion.
Evaluation of the proposed architecture on the instance motion segmentation and binary moving-static segmentation problems on KITTI, DAVIS-Moving and YTVOS-Moving datasets shows that the proposed method achieves state-of-the-art results on all the datasets and is able to generalise well to various environments. For the KITTI dataset, we provide an upgraded instance motion segmentation annotation which covers all moving objects. Dataset, code and models are available on the github project page github.com/michalneoral/Raptor.
Functional Connectivity Based Classification of ADHD Using Different Atlases
Authors: Sartaj Ahmed Salman, Zhichao Lian, Marva Saleem, Yuduo Zhang
Date: 2022-02-18 00:00:00
Tasks: Classification, Functional Connectivity

These days, computational diagnosis strategies of neuropsychiatric disorders are gaining attention day by day. It's critical to determine the brain's functional connectivity based on Functional-Magnetic-Resonance-Imaging(fMRI) to diagnose the disorder. It's known as a chronic disease, and millions of children amass the symptoms of this disease, so there is much vacuum for the researcher to formulate a model to improve the accuracy to diagnose ADHD accurately. In this paper, we consider the functional connectivity of a brain extracted using various time templates/Atlases. Local-Binary Encoding-Method (LBEM) algorithm is utilized for feature extraction, while Hierarchical- Extreme-Learning-Machine (HELM) is used to classify the extracted features. To validate our approach, fMRI data of 143 normal and 100 ADHD affected children is used for experimental purpose. Our experimental results are based on comparing various Atlases given as CC400, CC200, and AAL. Our model achieves high performance with CC400 as compared to other Atlases
TernGEMM: GEneral Matrix Multiply Library with Ternary Weights for Fast DNN Inference
Authors: Seokhyeon Choi, Kyuhong Shim, Jungwook Choi, Wonyong Sung, Byonghyo Shim
Date: 2021-11-13 00:00:00
Proceeding: 2021 IEEE Workshop on Signal Processing Systems (SiPS) 2021 11
Tasks: CPU

Efficient implementation of deep neural networks on CPU-based systems is very critical because applications proliferate to embedded and Internet of Things (IoT) systems. Many CPUs for personal computers and embedded systems equip Single Instruction Multiple Data (SIMD) instructions, which can be utilized to implement an efficient GEneral Matrix Multiply (GEMM) library that is very necessary for efficient deep neural network implementation. While many deep neural networks show quite good performance even at 1-bit or 2-bit precision, the current CPU instruction and library do not efficiently support arithmetic operations below 8-bit. We propose TernGEMM, a special GEMM library using SIMD instructions for Deep Neural Network (DNN) inference with ternary weights and activations under 8-bit. TernGEMM improves the speed by replacing slow multiply-add with logical operations and also accumulating a number of multiplications without bit expansion operations. We compared the speedup of TernGEMM with tiling optimization and GEMMLowp, an 8-bit precision GEMM library. For Intel CPU, the speedup of ×2.052, ×2.973, and ×2.986 is achieved on ResNet-50, MobileNet-V2, EfficientNet-B0, respectively. For ARM CPU, TernGEMM’s speedup is ×2.143, ×1.765, and ×1.856, respectively.
Thermal Face Detection for High-Speed AI Thermometer
Authors: Woongkyu Lee, Hyucksung Kwon, Jungwook Choi
Date: 2022-01-04 00:00:00
Proceeding: 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) 2022 1
Tasks: Face Detection, object-detection, Object Detection, Vocal Bursts Intensity Prediction

In the era of COVID-19, temperature measurement becomes a crucial procedure for protecting public spaces against the virus. Artificial intelligence techniques such as object detection deep neural networks (DNNs) have been adopted to enhance the accuracy of contactless temperature measurement. However, the computation-demanding nature of DNNs, along with the time-consuming fusion of video and thermal camera frames, raises hurdles for the cost-effective deployment of such AI thermometer systems. In this work, we propose a high-speed and cost-effective implementation of an AI thermometer. We develop a thermal face detection network to detect faces for temperature measurement without a video camera. We optimize the proposed network's precision and structure to exploit high-throughput reduced-precision computations available in the embedded AI platforms. The resulting AI thermometer system demonstrates a live temperature measurement with a speed of 160 frames per second.
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning
Authors: Jiawei Yang, Hanbo Chen, Jiangpeng Yan, Xiaoyu Chen, Jianhua Yao
Date: 2022-02-18 00:00:00
Tasks: Contrastive Learning, Few-Shot Learning, Representation Learning, Self-Supervised Learning

Few-shot learning is an established topic in natural images for years, but few work is attended to histology images, which is of high clinical value since well-labeled datasets and rare abnormal samples are expensive to collect. Here, we facilitate the study of few-shot learning in histology images by setting up three cross-domain tasks that simulate real clinics problems. To enable label-efficient learning and better generalizability, we propose to incorporate contrastive learning (CL) with latent augmentation (LA) to build a few-shot system. CL learns useful representations without manual labels, while LA transfers semantic variations of the base dataset in an unsupervised way. These two components fully exploit unlabeled training data and can scale gracefully to other label-hungry problems. In experiments, we find i) models learned by CL generalize better than supervised learning for histology images in unseen classes, and ii) LA brings consistent gains over baselines. Prior studies of self-supervised learning mainly focus on ImageNet-like images, which only present a dominant object in their centers. Recent attention has been paid to images with multi-objects and multi-textures. Histology images are a natural choice for such a study. We show the superiority of CL over supervised learning in terms of generalization for such data and provide our empirical understanding for this observation. The findings in this work could contribute to understanding how the model generalizes in the context of both representation learning and histological image analysis. Code is available.
A Machine Learning Paradigm for Studying Pictorial Realism: Are Constable's Clouds More Real than His Contemporaries?
Authors: Zhuomin Zhang, Elizabeth C. Mansfield, Jia Li, John Russell, George S. Young, Catherine Adams, James Z. Wang
Date: 2022-02-18 00:00:00
Tasks: BIG-bench Machine Learning

The British landscape painter John Constable is considered foundational for the Realist movement in 19th-century European painting. Constable's painted skies, in particular, were seen as remarkably accurate by his contemporaries, an impression shared by many viewers today. Yet, assessing the accuracy of realist paintings like Constable's is subjective or intuitive, even for professional art historians, making it difficult to say with certainty what set Constable's skies apart from those of his contemporaries. Our goal is to contribute to a more objective understanding of Constable's realism. We propose a new machine-learning-based paradigm for studying pictorial realism in an explainable way. Our framework assesses realism by measuring the similarity between clouds painted by artists noted for their skies, like Constable, and photographs of clouds. The experimental results of cloud classification show that Constable approximates more consistently than his contemporaries the formal features of actual clouds in his paintings. The study, as a novel interdisciplinary approach that combines computer vision and machine learning, meteorology, and art history, is a springboard for broader and deeper analyses of pictorial realism.
DataMUX: Data Multiplexing for Neural Networks
Authors: Vishvak Murahari, Carlos E. Jimenez, Runzhe Yang, Karthik Narasimhan
Date: 2022-02-18 00:00:00
Tasks: image-classification, Image Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, Sentence, Sentence Classification

In this paper, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation. DataMUX demonstrates that neural networks are capable of generating accurate predictions over mixtures of inputs, resulting in increased throughput with minimal extra memory requirements. Our approach uses two key components -- 1) a multiplexing layer that performs a fixed linear transformation to each input before combining them to create a mixed representation of the same size as a single input, which is then processed by the base network, and 2) a demultiplexing layer that converts the base network's output back into independent representations before producing predictions for each input. We show the viability of DataMUX for different architectures (Transformers, and to a lesser extent MLPs and CNNs) across six different tasks spanning sentence classification, named entity recognition and image classification. For instance, DataMUX for Transformers can multiplex up to $20$x/$40$x inputs, achieving $11$x/$18$x increase in throughput with minimal absolute performance drops of $<2\%$ and $<4\%$ respectively on MNLI, a natural language inference task. We also provide a theoretical construction for multiplexing in self-attention networks and analyze the effect of various design elements in DataMUX.
Learning Predictions for Algorithms with Predictions
Authors: Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii
Date: 2022-02-18 00:00:00
Tasks: Scheduling

A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in which algorithms can take advantage of a possibly-imperfect prediction of some aspect of the problem. While much work has focused on using predictions to improve competitive ratios, running times, or other performance measures, less effort has been devoted to the question of how to obtain the predictions themselves, especially in the critical online setting. We introduce a general design approach for algorithms that learn predictors: (1) identify a functional dependence of the performance measure on the prediction quality and (2) apply techniques from online learning to learn predictors, tune robustness-consistency trade-offs, and bound the sample complexity. We demonstrate the effectiveness of our approach by applying it to bipartite matching, ski-rental, page migration, and job scheduling. In several settings we improve upon multiple existing results while utilizing a much simpler analysis, while in the others we provide the first learning-theoretic guarantees.
Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy Placement Through MAP-Elites
Authors: Breno M. F. Viana, Leonardo T. Pereira, Claudio F. M. Toledo
Date: 2022-02-18 00:00:00

Procedural Content Generation (PCG) methods are valuable tools to speed up the game development process. Moreover, PCG may also present in games as features, such as the procedural dungeon generation (PDG) in Moonlighter (Digital Sun, 2018). This paper introduces an extended version of an evolutionary dungeon generator by incorporating a MAP-Elites population. Our dungeon levels are discretized with rooms that may have locked-door missions and enemies within them. We encoded the dungeons through a tree structure to ensure the feasibility of missions. We performed computational and user feedback experiments to evaluate our PDG approach. They show that our approach accurately converges almost the whole MAP-Elite population for most executions. Finally, players' feedback indicates that they enjoyed the generated levels, and they could not indicate an algorithm as a level generator.
System Safety and Artificial Intelligence
Authors: Roel I. J. Dobbe
Date: 2022-02-18 00:00:00
Tasks: Decision Making, Management

This chapter formulates seven lessons for preventing harm in artificial intelligence (AI) systems based on insights from the field of system safety for software-based automation in safety-critical domains. New applications of AI across societal domains and public organizations and infrastructures come with new hazards, which lead to new forms of harm, both grave and pernicious. The text addresses the lack of consensus for diagnosing and eliminating new AI system hazards. For decades, the field of system safety has dealt with accidents and harm in safety-critical systems governed by varying degrees of software-based automation and decision-making. This field embraces the core assumption of systems and control that AI systems cannot be safeguarded by technical design choices on the model or algorithm alone, instead requiring an end-to-end hazard analysis and design frame that includes the context of use, impacted stakeholders and the formal and informal institutional environment in which the system operates. Safety and other values are then inherently socio-technical and emergent system properties that require design and control measures to instantiate these across the technical, social and institutional components of a system. This chapter honors system safety pioneer Nancy Leveson, by situating her core lessons for today's AI system safety challenges. For every lesson, concrete tools are offered for rethinking and reorganizing the safety management of AI systems, both in design and governance. This history tells us that effective AI safety management requires transdisciplinary approaches and a shared language that allows involvement of all levels of society.
Surf or sleep? Understanding the influence of bedtime patterns on campus
Authors: Teng Guo, Linhong Li, Dongyu Zhang, Feng Xia
Date: 2022-02-18 00:00:00
Tasks: Management

Poor sleep habits may cause serious problems of mind and body, and it is a commonly observed issue for college students due to study workload as well as peer and social influence. Understanding its impact and identifying students with poor sleep habits matters a lot in educational management. Most of the current research is either based on self-reports and questionnaires, suffering from a small sample size and social desirability bias, or the methods used are not suitable for the education system. In this paper, we develop a general data-driven method for identifying students' sleep patterns according to their Internet access pattern stored in the education management system and explore its influence from various aspects. First, we design a Possion-based probabilistic mixture model to cluster students according to the distribution of bedtime and identify students who are used to staying up late. Second, we profile students from five aspects (including eight dimensions) based on campus-behavior data and build Bayesian networks to explore the relationship between behavioral characteristics and sleeping habits. Finally, we test the predictability of sleeping habits. This paper not only contributes to the understanding of student sleep from a cognitive and behavioral perspective but also presents a new approach that provides an effective framework for various educational institutions to detect the sleeping patterns of students.
(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering
Authors: Anoop Cherian, Chiori Hori, Tim K. Marks, Jonathan Le Roux
Date: 2022-02-18 00:00:00
Tasks: Question Answering, Spatio-temporal Scene Graphs, Video Question Answering

Spatio-temporal scene-graph approaches to video-based reasoning tasks, such as video question-answering (QA), typically construct such graphs for every video frame. These approaches often ignore the fact that videos are essentially sequences of 2D "views" of events happening in a 3D space, and that the semantics of the 3D scene can thus be carried over from frame to frame. Leveraging this insight, we propose a (2.5+1)D scene graph representation to better capture the spatio-temporal information flows inside the videos. Specifically, we first create a 2.5D (pseudo-3D) scene graph by transforming every 2D frame to have an inferred 3D structure using an off-the-shelf 2D-to-3D transformation module, following which we register the video frames into a shared (2.5+1)D spatio-temporal space and ground each 2D scene graph within it. Such a (2.5+1)D graph is then segregated into a static sub-graph and a dynamic sub-graph, corresponding to whether the objects within them usually move in the world. The nodes in the dynamic graph are enriched with motion features capturing their interactions with other graph nodes. Next, for the video QA task, we present a novel transformer-based reasoning pipeline that embeds the (2.5+1)D graph into a spatio-temporal hierarchical latent space, where the sub-graphs and their interactions are captured at varied granularity. To demonstrate the effectiveness of our approach, we present experiments on the NExT-QA and AVSD-QA datasets. Our results show that our proposed (2.5+1)D representation leads to faster training and inference, while our hierarchical model showcases superior performance on the video QA task versus the state of the art.
Molecule Generation for Drug Design: a Graph Learning Perspective
Authors: Nianzu Yang, Huaijin Wu, Kaipeng Zeng, Yang Li, Junchi Yan
Date: 2022-02-18 00:00:00
Tasks: Drug Design, Graph Learning

Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical industry. Our survey offers a comprehensive overview of state-of-the-art methods in molecule design, particularly focusing on \emph{de novo} drug design, which incorporates (deep) graph learning techniques. We categorize these methods into three distinct groups: \emph{i)} \emph{all-at-once}, \emph{ii)} \emph{fragment-based}, and \emph{iii)} \emph{node-by-node}. Additionally, we introduce some key public datasets and outline the commonly used evaluation metrics for both the generation and optimization of molecules. In the end, we discuss the existing challenges in this field and suggest potential directions for future research.
An Integrated Optimization and Machine Learning Models to Predict the Admission Status of Emergency Patients
Authors: Abdulaziz Ahmed, Omar Ashour, Haneen Ali, Mohammad Firouz
Date: 2022-02-18 00:00:00
Tasks: feature selection, Sensitivity, Specificity

This work proposes a framework for optimizing machine learning algorithms. The practicality of the framework is illustrated using an important case study from the healthcare domain, which is predicting the admission status of emergency department (ED) patients (e.g., admitted vs. discharged) using patient data at the time of triage. The proposed framework can mitigate the crowding problem by proactively planning the patient boarding process. A large retrospective dataset of patient records is obtained from the electronic health record database of all ED visits over three years from three major locations of a healthcare provider in the Midwest of the US. Three machine learning algorithms are proposed: T-XGB, T-ADAB, and T-MLP. T-XGB integrates extreme gradient boosting (XGB) and Tabu Search (TS), T-ADAB integrates Adaboost and TS, and T-MLP integrates multi-layer perceptron (MLP) and TS. The proposed algorithms are compared with the traditional algorithms: XGB, ADAB, and MLP, in which their parameters are tunned using grid search. The three proposed algorithms and the original ones are trained and tested using nine data groups that are obtained from different feature selection methods. In other words, 54 models are developed. Performance was evaluated using five measures: Area under the curve (AUC), sensitivity, specificity, F1, and accuracy. The results show that the newly proposed algorithms resulted in high AUC and outperformed the traditional algorithms. The T-ADAB performs the best among the newly developed algorithms. The AUC, sensitivity, specificity, F1, and accuracy of the best model are 95.4%, 99.3%, 91.4%, 95.2%, 97.2%, respectively.