pwc-archive/papers-with-abstracts huggingface dataset:
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
Authors: Haoxuan Li, Yan Lyu, Chunyuan Zheng, Peng Wu
Date: 2022-03-19 00:00:00
Tasks: Imputation, Recommendation Systems, Selection bias

Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either imputed errors or learned propensities are accurate. However, our theoretical analysis reveals that DR usually has a large variance. Meanwhile, DR would suffer unexpectedly large bias and poor generalization caused by inaccurate imputed errors and learned propensities, which usually occur in practice. In this paper, we propose a principled approach that can effectively reduce bias and variance simultaneously for existing DR approaches when the error imputation model is misspecified. In addition, we further propose a novel semi-parametric collaborative learning approach that decomposes imputed errors into parametric and nonparametric parts and updates them collaboratively, resulting in more accurate predictions. Both theoretical analysis and experiments demonstrate the superiority of the proposed methods compared with existing debiasing methods.
Reasoning over Public and Private Data in Retrieval-Based Systems
Authors: Simran Arora, Patrick Lewis, Angela Fan, Jacob Kahn, Christopher Ré
Date: 2022-03-14 00:00:00
Tasks: Fact Checking, Information Retrieval, Multi-hop Question Answering, Question Answering, Retrieval

Users and organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private data is important to personalize open-domain applications such as question-answering, fact-checking, and personal assistants. State-of-the-art systems for these tasks explicitly retrieve relevant information to a user question from a background corpus before producing an answer. While today's retrieval systems assume the corpus is fully accessible, users are often unable or unwilling to expose their private data to entities hosting public data. We first define the PUBLIC-PRIVATE AUTOREGRESSIVE INFORMATION RETRIEVAL (PAIR) privacy framework for the novel retrieval setting over multiple privacy scopes. We then argue that an adequate benchmark is missing to study PAIR since existing textual benchmarks require retrieving from a single data distribution. However, public and private data intuitively reflect different distributions, motivating us to create ConcurrentQA, the first textual QA benchmark to require concurrent retrieval over multiple data-distributions. Finally, we show that existing systems face large privacy vs. performance tradeoffs when applied to our proposed retrieval setting and investigate how to mitigate these tradeoffs.
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
Authors: Buyun Zhang, Liang Luo, Xi Liu, Jay Li, Zeliang Chen, Weilin Zhang, Xiaohan Wei, Yuchen Hao, Michael Tsang, Wenjun Wang, Yang Liu, Huayu Li, Yasmine Badr, Jongsoo Park, Jiyan Yang, Dheevatsa Mudigere, Ellie Wen
Date: 2022-03-11 00:00:00
Tasks: Click-Through Rate Prediction

Learning feature interactions is important to the model performance of online advertising services. As a result, extensive efforts have been devoted to designing effective architectures to learn feature interactions. However, we observe that the practical performance of those designs can vary from dataset to dataset, even when the order of interactions claimed to be captured is the same. That indicates different designs may have different advantages and the interactions captured by them have non-overlapping information. Motivated by this observation, we propose DHEN - a deep and hierarchical ensemble architecture that can leverage strengths of heterogeneous interaction modules and learn a hierarchy of the interactions under different orders. To overcome the challenge brought by DHEN's deeper and multi-layer structure in training, we propose a novel co-designed training system that can further improve the training efficiency of DHEN. Experiments of DHEN on large-scale dataset from CTR prediction tasks attained 0.27\% improvement on the Normalized Entropy (NE) of prediction and 1.2x better training throughput than state-of-the-art baseline, demonstrating their effectiveness in practice.
Technologies for AI-Driven Fashion Social Networking Service with E-Commerce
Authors: Jinseok Seol, Seongjae Kim, Sungchan Park, Holim Lim, Hyunsoo Na, EunYoung Park, Dohee Jung, Soyoung Park, Kangwoo Lee, Sang-goo Lee
Date: 2022-03-11 00:00:00
Tasks: Deep Learning, Recommendation Systems

The rapid growth of the online fashion market brought demands for innovative fashion services and commerce platforms. With the recent success of deep learning, many applications employ AI technologies such as visual search and recommender systems to provide novel and beneficial services. In this paper, we describe applied technologies for AI-driven fashion social networking service that incorporate fashion e-commerce. In the application, people can share and browse their outfit-of-the-day (OOTD) photos, while AI analyzes them and suggests similar style OOTDs and related products. To this end, we trained deep learning based AI models for fashion and integrated them to build a fashion visual search system and a recommender system for OOTD. With aforementioned technologies, the AI-driven fashion SNS platform, iTOO, has been successfully launched.
AI Annotated Recommendations in an Efficient Visual Learning Environment with Emphasis on YouTube (AI-EVL)
Authors: Faeze Gholamrezaie, Melika Bahman-Abadi, M. B. Ghaznavi-Ghoushchi
Date: 2022-03-10 00:00:00

In this article, we create a system called AI-EVL. This is an annotated-based learning system. We extend AI to learning experience. If a user from the main YouTube page browses YouTube videos and a user from the AI-EVL system does the same, the amount of traffic used will be much less. It is due to ignoring unwanted contents which indicates a reduction in bandwidth usage too. This system is designed to be embedded with online learning tools and platforms to enrich their curriculum. In evaluating the system using Google 2020 trend data, we were able to extract rich ontological information for each data. Of the data collected, 34.86% belong to wolfram, 30.41% to DBpedia, and 34.73% to Wikipedia. The video subtitle information is displayed interactively and functionally to the user over time as the video is played. This effective visual learning system, due to the unique features, prevents the user's distraction and makes learning more focused. The information about the subtitle text is displayed in multiple layers including AI-annotated topics, Wikipedia/DBpedia, and Wolfram enriched texts via interactive and visual widgets.
Filter Drug-induced Liver Injury Literature with Natural Language Processing and Ensemble Learning
Authors: Xianghao Zhan, Fanjin Wang, Olivier Gevaert
Date: 2022-03-09 00:00:00
Tasks: Ensemble Learning

Drug-induced liver injury (DILI) describes the adverse effects of drugs that damage liver. Life-threatening results including liver failure or death were also reported in severe DILI cases. Therefore, DILI-related events are strictly monitored for all approved drugs and the liver toxicity became important assessments for new drug candidates. These DILI-related reports are documented in hospital records, in clinical trial results, and also in research papers that contain preliminary in vitro and in vivo experiments. Conventionally, data extraction from previous publications relies heavily on resource-demanding manual labelling, which considerably decreased the efficiency of the information extraction process. The recent development of artificial intelligence, particularly, the rise of natural language processing (NLP) techniques, enabled the automatic processing of biomedical texts. In this study, based on around 28,000 papers (titles and abstracts) provided by the Critical Assessment of Massive Data Analysis (CAMDA) challenge, we benchmarked model performances on filtering out DILI literature. Among four word vectorization techniques, the model using term frequency-inverse document frequency (TF-IDF) and logistic regression outperformed others with an accuracy of 0.957 with our in-house test set. Furthermore, an ensemble model with similar overall performances was implemented and was fine-tuned to lower the false-negative cases to avoid neglecting potential DILI reports. The ensemble model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the hold-out validation data provided by the CAMDA committee. Moreover, important words in positive/negative predictions were identified via model interpretation. Overall, the ensemble model reached satisfactory classification results, which can be further used by researchers to rapidly filter DILI-related literature.
Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks
Authors: Jibing Gong, Yao Wan, Ye Liu, Xuewen Li, Yi Zhao, Cheng Wang, YuTing Lin, Xiaohan Fang, Wenzheng Feng, Jingyi Zhang, Jie Tang
Date: 2022-03-08 00:00:00
Tasks: Graph Attention, Graph Neural Network, reinforcement-learning, Reinforcement Learning (RL)

Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem of concept recommendation in this paper, which can be viewed as recommending knowledge to users in a fine-grained way. We put forward a novel approach, termed HinCRec-RL, for Concept Recommendation in MOOCs, which is based on Heterogeneous Information Networks and Reinforcement Learning. In particular, we propose to shape the problem of concept recommendation within a reinforcement learning framework to characterize the dynamic interaction between users and knowledge concepts in MOOCs. Furthermore, we propose to form the interactions among users, courses, videos, and concepts into a heterogeneous information network (HIN) to learn the semantic user representations better. We then employ an attentional graph neural network to represent the users in the HIN, based on meta-paths. Extensive experiments are conducted on a real-world dataset collected from a Chinese MOOC platform, XuetangX, to validate the efficacy of our proposed HinCRec-RL. Experimental results and analysis demonstrate that our proposed HinCRec-RL performs well when comparing with several state-of-the-art models.
Consonant-Vowel Transition Models Based on Deep Learning for Objective Evaluation of Articulation
Authors: Vikram C. Mathad, Julie M. Liss, Kathy Chapman, Nancy Scherer, Visar Berisha
Date: 2022-03-18 00:00:00

Spectro-temporal dynamics of consonant-vowel (CV) transition regions are considered to provide robust cues related to articulation. In this work, we propose an objective measure of precise articulation, dubbed the objective articulation measure (OAM), by analyzing the CV transitions segmented around vowel onsets. The OAM is derived based on the posteriors of a convolutional neural network pre-trained to classify between different consonants using CV regions as input. We demonstrate the OAM is correlated with perceptual measures in a variety of contexts including (a) adult dysarthric speech, (b) the speech of children with cleft lip/palate, and (c) a database of accented English speech from native Mandarin and Spanish speakers.
Symbol quantization in interstellar communications: methods and observations
Authors: William J. Crilly Jr
Date: 2022-03-18 00:00:00
Tasks: Quantization

Interstellar communication transmitters, intended to be discovered and decoded to information bits, are expected to transmit signals that contain message symbols quantized in at least one of the degrees of freedom of the transmitted signal. A hypothesis is proposed that signal quantization, in the form of multiplicative values of one or more signal measurements, may be observable during the reception of hypothetical discoverable interstellar communication signals. In previous work, using single and multiple synchronized radio telescopes, candidate hypothetical interstellar communication signals comprising delta-t delta-f opposite circular polarized pulse pairs have been reported and analyzed (ref. arXiv:2105.03727, arXiv:2106.10168, arXiv:2202.12791). In the latter report, an apparent quantization of delta-f at multiples of 58.575 Hz was observed. In the current work, a machine process has been implemented to further examine anomalous delta-f and delta-t quantization, with results reported in this paper. As in some past work, a 26 foot diameter radio telescope with fixed azimuth and elevation pointing is used to enable a Right Ascension filter to measure signals associated with a celestial direction of interest, relative to other directions, over a 6.3 hour range of Right Ascension. The 5.25 plus or minus 0.15 hour Right Ascension, -7.6 degrees plus or minus 1 degree Declination celestial direction presents repetition and quantization anomalies, during an experiment lasting 157 days, with the first 143 days overlapping the previous experiment.
A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods
Authors: Pakshal Bohra, Pol del Aguila Pla, Jean-François Giovannelli, Michael Unser
Date: 2022-03-18 00:00:00
Tasks: Benchmarking

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizations of sparse stochastic processes, which makes them ideally matched to variational sparsity-promoting techniques. We derive Gibbs sampling schemes to compute the minimum mean-square error estimators for processes with Laplace, Student's t, and Bernoulli-Laplace innovations. These allow our framework to provide quantitative measures of the degree of optimality (in the mean-square-error sense) for any given reconstruction method. We showcase our framework by benchmarking the performance of some well-known variational methods and convolutional neural network architectures that perform direct nonlinear reconstructions in the context of deconvolution and Fourier sampling. Our experimental results support the understanding that, while these neural networks outperform the variational methods and achieve near-optimal results in many settings, their performance deteriorates severely for signals associated with heavy-tailed distributions.
High-Density Coding Scheme for SWIPT Systems
Authors: Dongheon Lee, Gyuyeol Kong, Jang-Won Lee, Sooyong Choi
Date: 2022-03-18 00:00:00
Tasks: 2k, Vocal Bursts Intensity Prediction

In this study, a novel coding scheme called highdensity coding based on high-density codebooks using a genetic local search algorithm is proposed. The high-density codebook maximizes the energy transfer capability by maximizing the ratio of 1 in the codebook while satisfying the conditions of a codeword with length n, a codebook with 2k codewords, and a minimum Hamming distance of the codebook of d. Furthermore, the proposed high-density codebook provides a trade-off between the throughput and harvested energy with respect to n, k, and d. The block error rate performances of the designed highdensity codebooks are derived theoretically and compared with the simulation results. The simulation results indicate that as d and k decrease, the throughput decreases by a maximum of 10% and 40%, whereas the harvested energy per time increases by a maximum of 40% and 100%, respectively. When n increases, the throughput decreases by a maximum of 30%, while the harvested energy per time increases by a maximum of 110%. With the proposed high-density coding scheme, the throughput and harvested energy at the user can be controlled adaptively according to the system requirements.
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing
Authors: Zhiyuan Zha, Bihan Wen, Xin Yuan, Saiprasad Ravishankar, Jiantao Zhou, Ce Zhu
Date: 2022-03-17 00:00:00
Tasks: Compressive Sensing

The compressive sensing (CS) scheme exploits much fewer measurements than suggested by the Nyquist-Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. While classic image CS schemes employed sparsity using analytical transforms or bases, the learning-based approaches have become increasingly popular in recent years. Such methods can effectively model the structures of image patches by optimizing their sparse representations or learning deep neural networks, while preserving the known or modeled sensing process. Beyond exploiting local image properties, advanced CS schemes adopt nonlocal image modeling, by extracting similar or highly correlated patches at different locations of an image to form a group to process jointly. More recent learning-based CS schemes apply nonlocal structured sparsity prior using group sparse representation (GSR) and/or low-rank (LR) modeling, which have demonstrated promising performance in various computational imaging and image processing applications. This article reviews some recent works in image CS tasks with a focus on the advanced GSR and LR based methods. Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models. Finally, we discuss the open problems and future directions in the field.
Orientation Estimation using Wireless Device Radiation Patterns
Authors: Thomas Burton, Kasper Rasmussen
Date: 2022-03-18 00:00:00

Wireless devices inherently have a non-uniform distribution of energy from their antenna or antennas. The shape that this forms is commonly called a radiation pattern or antenna pattern. We demonstrate that orientation can be estimated without the cooperation of the target device despite only having a small number of RSS measurements per packet. We do this by applying bounds to the amount of rotation in the time interval between packets. Using simulations, we show that this method can achieve a mean orientation error as low as 7.6{\deg}. We then perform a security analysis to demonstrate the method's resistance to spoofing. This paper focuses on consumer wireless devices where patterns are not deliberately highly directional and scenarios that cannot rely on contrived movement patterns of the entities involved, which is unrealistic or impractical in many settings. Our work concentrates on existing wireless systems and infrastructure common in domestic, office, and commercial.
The morphodynamics of 3D migrating cancer cells
Authors: Christopher Z. Eddy, Xinyao Wang, Fuxin Li, Bo Sun
Date: 2018-07-27 00:00:00

Cell shape is an important biomarker that is directly linked to cell function. However, cell morphodynamics, namely the temporal fluctuation of cell shape is much less understood. We study the morphodynamics of MDA-MB-231 cells in type I collagen extracellular matrix (ECM). We find ECM mechanics, as tuned by collagen concentration, controls the morphodynamics but not the static cell morphology. We employ machine learning to classify cell shape into five different morphological phenotypes corresponding to different migration modes. As a result, cell morphodynamics is mapped into temporal evolution of morphological phenotypes. We systematically characterize the phenotype evolutions including occurrence probability, dwell time, transition flux, and 3D migrational characteristics. We find that manipulating Rho-signaling enhances the morphodynamics and phenotype transitions. Using a tumor organoid model, we show that the distinct invasion potentials of each phenotype modulate the phenotype homeostasis. Overall invasion of a tumor organoid is facilitated by individual cells searching for and committing to phenotypes of higher invasive potential. In conclusion, we show that 3D migrating cancer cells exhibit rich morphodynamics that is regulated by ECM mechanics, Rho-signaling, and is closely related with cell motility. Our results pave the way to the systematic characterization and functional understanding of cell morphodynamics as a new biomarker for normal and malignant cells.
AUTOMATED AUDIO CAPTIONING BY FINE-TUNING BART WITH AUDIOSET TAGS
Authors: F ́elix Gontier, Romain Serizel, Christophe Cerisara
Date: 2021-11-15 00:00:00
Proceeding: DCASE workshop 2021 11
Tasks: AudioCaps, Audio captioning, Caption Generation, Language Modeling, Language Modelling, Retrieval-augmented Few-shot In-context Audio Captioning

utomated audio captioning is the multimodal task of describing
environmental audio recordings with fluent natural language. Most
current methods utilize pre-trained analysis models to extract rele-
vant semantic content from the audio input. However, prior infor-
mation on language modeling is rarely introduced, and correspond-
ing architectures are limited in capacity due to data scarcity. In
this paper, we present a method leveraging the linguistic informa-
tion contained in BART, a large-scale conditional language model
with general purpose pre-training. The caption generation is condi-
tioned on sequences of textual AudioSet tags. This input is enriched
with temporally aligned audio embeddings that allows the model to
improve the sound event recognition. The full BART architecture
is fine-tuned with few additional parameters. Experimental results
demonstrate that, beyond the scaling properties of the architecture,
language-only pre-training improves the text quality in the multi-
modal setting of audio captioning. The best model achieves state-
of-the-art performance on AudioCaps with 46.5 SPIDEr.
Combined Optimal Routing and Coordination of Connected and Automated Vehicles
Authors: Heeseung Bang, Behdad Chalaki, Andreas A. Malikopoulos
Date: 2022-03-22 00:00:00

In this letter, we consider a transportation network with a 100\% penetration rate of connected and automated vehicles (CAVs) and present an optimal routing approach that takes into account the efficiency achieved in the network by coordinating the CAVs at specific traffic scenarios, e.g., intersections, merging roadways, and roundabouts. To derive the optimal route of a travel request, we use the information of the CAVs that have already received a routing solution. This enables each CAV to consider the traffic conditions on the roads. The solution of any new travel request determines the optimal travel time at each traffic scenario while satisfying all state, control, and safety constraints. We validate the performance of our framework through numerical simulations. To the best of our knowledge, this is the first attempt to consider the coordination of CAVs in a routing problem.