pwc-archive/papers-with-abstracts huggingface dataset:
Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocation
Authors: Patrick LeBlanc, Li Ma
Date: 2021-09-11 00:00:00
Tasks: Bayesian Inference

Mixed-membership (MM) models such as Latent Dirichlet Allocation (LDA) have been applied to microbiome compositional data to identify latent subcommunities of microbial species. These subcommunities are informative for understanding the biological interplay of microbes and for predicting health outcomes. However, microbiome compositions typically display substantial cross-sample heterogeneities in subcommunity compositions -- that is, the variability in the proportions of microbes in shared subcommunities across samples -- which is not accounted for in prior analyses. As a result, LDA can produce inference which is highly sensitive to the specification of the number of subcommunities and often divides a single subcommunity into multiple artificial ones. To address this limitation, we incorporate the logistic-tree normal (LTN) model into LDA to form a new MM model. This model allows cross-sample variation in the composition of each subcommunity around some "centroid" composition that defines the subcommunity. Incorporation of auxiliary P\'olya-Gamma variables enables a computationally efficient collapsed blocked Gibbs sampler to carry out Bayesian inference under this model. By accounting for such heterogeneity, our new model restores the robustness of the inference in the specification of the number of subcommunities and allows meaningful subcommunities to be identified.
PAC Mode Estimation using PPR Martingale Confidence Sequences
Authors: Shubham Anand Jain, Rohan Shah, Sanit Gupta, Denil Mehta, Inderjeet Jayakumar Nair, Jian Vora, Sushil Khyalia, Sourav Das, Vinay J. Ribeiro, Shivaram Kalyanakrishnan
Date: 2021-09-10 00:00:00

We consider the problem of correctly identifying the \textit{mode} of a discrete distribution $\mathcal{P}$ with sufficiently high probability by observing a sequence of i.i.d. samples drawn from $\mathcal{P}$. This problem reduces to the estimation of a single parameter when $\mathcal{P}$ has a support set of size $K = 2$. After noting that this special case is tackled very well by prior-posterior-ratio (PPR) martingale confidence sequences \citep{waudby-ramdas-ppr}, we propose a generalisation to mode estimation, in which $\mathcal{P}$ may take $K \geq 2$ values. To begin, we show that the "one-versus-one" principle to generalise from $K = 2$ to $K \geq 2$ classes is more efficient than the "one-versus-rest" alternative. We then prove that our resulting stopping rule, denoted PPR-1v1, is asymptotically optimal (as the mistake probability is taken to $0$). PPR-1v1 is parameter-free and computationally light, and incurs significantly fewer samples than competitors even in the non-asymptotic regime. We demonstrate its gains in two practical applications of sampling: election forecasting and verification of smart contracts in blockchains.
BeautifAI -- A Personalised Occasion-oriented Makeup Recommendation System
Authors: Kshitij Gulati, Gaurav Verma, Mukesh Mohania, Ashish Kundu
Date: 2021-09-13 00:00:00

With the global metamorphosis of the beauty industry and the rising demand for beauty products worldwide, the need for an efficacious makeup recommendation system has never been more. Despite the significant advancements made towards personalised makeup recommendation, the current research still falls short of incorporating the context of occasion in makeup recommendation and integrating feedback for users. In this work, we propose BeautifAI, a novel makeup recommendation system, delivering personalised occasion-oriented makeup recommendations to users while providing real-time previews and continuous feedback. The proposed work's novel contributions, including the incorporation of occasion context, region-wise makeup recommendation, real-time makeup previews and continuous makeup feedback, set our system apart from the current work in makeup recommendation. We also demonstrate our proposed system's efficacy in providing personalised makeup recommendation by conducting a user study.
ARGO: Modeling Heterogeneity in E-commerce Recommendation
Authors: Daqing Wu, Xiao Luo, Zeyu Ma, Chong Chen, Minghua Deng, Jinwen Ma
Date: 2021-09-13 00:00:00
Tasks: Recommendation Systems

Nowadays, E-commerce is increasingly integrated into our daily lives. Meanwhile, shopping process has also changed incrementally from one behavior (purchase) to multiple behaviors (such as view, carting and purchase). Therefore, utilizing interaction data of auxiliary behavior data draws a lot of attention in the E-commerce recommender systems. However, all existing models ignore two kinds of intrinsic heterogeneity which are helpful to capture the difference of user preferences and the difference of item attributes. First (intra-heterogeneity), each user has multiple social identities with otherness, and these different identities can result in quite different interaction preferences. Second (inter-heterogeneity), each item can transfer an item-specific percentage of score from low-level behavior to high-level behavior for the gradual relationship among multiple behaviors. Thus, the lack of consideration of these heterogeneities damages recommendation rank performance. To model the above heterogeneities, we propose a novel method named intra- and inter-heterogeneity recommendation model (ARGO). Specifically, we embed each user into multiple vectors representing the user's identities, and the maximum of identity scores indicates the interaction preference. Besides, we regard the item-specific transition percentage as trainable transition probability between different behaviors. Extensive experiments on two real-world datasets show that ARGO performs much better than the state-of-the-art in multi-behavior scenarios.
An Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods
Authors: Meysam Varasteh, Mehdi Soleiman Nejad, Hadi Moradi, Mohammad Amin Sadeghi, Ahmad Kalhor
Date: 2021-09-12 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

One of the main challenges in recommender systems is data sparsity which leads to high variance. Several attempts have been made to improve the bias-variance trade-off using auxiliary information. In particular, document modeling-based methods have improved the model's accuracy by using textual data such as reviews, abstracts, and storylines when the user-to-item rating matrix is sparse. However, such models are insufficient to learn optimal representation for users and items. User-based and item-based collaborative filtering, owing to their efficiency and interpretability, have been long used for building recommender systems. They create a profile for each user and item respectively as their historically interacted items and the users who interacted with the target item. This work combines these two approaches with document context-aware recommender systems by considering users' opinions on these items. Another advantage of our model is that it supports online personalization. If a user has new interactions, it needs to refresh the user and item history representation vectors instead of updating model parameters. The proposed algorithm is implemented and tested on three real-world datasets that demonstrate our model's effectiveness over the baseline methods.
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
Authors: Jingwei Yi, Fangzhao Wu, Chuhan Wu, Ruixuan Liu, Guangzhong Sun, Xing Xie
Date: 2021-09-12 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Federated Learning, News Recommendation, Privacy Preserving

News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data. However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients. In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients. More specifically, the clients request the user model and news representations from the server, and send their locally computed gradients to the server for aggregation. The server updates its global user model with the aggregated gradients, and further updates its news model to infer updated news representations. Since the local gradients may contain private information, we propose a secure aggregation method to aggregate gradients in a privacy-preserving way. Experiments on two real-world datasets show that our method can reduce the computation and communication cost on clients while keep promising model performance.
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation
Authors: Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, Fei Wu
Date: 2021-09-11 00:00:00
Tasks: counterfactual, Recommendation Systems, Representation Learning, Sequential Recommendation

Learning user representations based on historical behaviors lies at the core of modern recommender systems. Recent advances in sequential recommenders have convincingly demonstrated high capability in extracting effective user representations from the given behavior sequences. Despite significant progress, we argue that solely modeling the observational behaviors sequences may end up with a brittle and unstable system due to the noisy and sparse nature of user interactions logged. In this paper, we propose to learn accurate and robust user representations, which are required to be less sensitive to (attack on) noisy behaviors and trust more on the indispensable ones, by modeling counterfactual data distribution. Specifically, given an observed behavior sequence, the proposed CauseRec framework identifies dispensable and indispensable concepts at both the fine-grained item level and the abstract interest level. CauseRec conditionally samples user concept sequences from the counterfactual data distributions by replacing dispensable and indispensable concepts within the original concept sequence. With user representations obtained from the synthesized user sequences, CauseRec performs contrastive user representation learning by contrasting the counterfactual with the observational. We conduct extensive experiments on real-world public recommendation benchmarks and justify the effectiveness of CauseRec with multi-aspects model analysis. The results demonstrate that the proposed CauseRec outperforms state-of-the-art sequential recommenders by learning accurate and robust user representations.
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
Authors: Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie
Date: 2021-09-11 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: News Generation, News Recommendation, Privacy Preserving, Recommendation Systems

News recommendation is important for personalized online news services. Most existing news recommendation methods rely on centrally stored user behavior data to both train models offline and provide online recommendation services. However, user data is usually highly privacy-sensitive, and centrally storing them may raise privacy concerns and risks. In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way. Following a widely used paradigm in real-world recommender systems, our framework contains two stages. The first one is for candidate news generation (i.e., recall) and the second one is for candidate news ranking (i.e., ranking). At the recall stage, each client locally learns multiple interest representations from clicked news to comprehensively model user interests. These representations are uploaded to the server to recall candidate news from a large news pool, which are further distributed to the user client at the ranking stage for personalized news display. In addition, we propose an interest decomposer-aggregator method with perturbation noise to better protect private user information encoded in user interest representations. Besides, we collaboratively train both recall and ranking models on the data decentralized in a large number of user clients in a privacy-preserving way. Experiments on two real-world news datasets show that our method can outperform baseline methods and effectively protect user privacy.
Discovering Technology Gaps using the IntSight Knowledge Navigator
Authors: Aurpon Gupta, Subhasis Dasgupta, Snehasis Sinha, Amarnath Gupta
Date: 2021-09-11 00:00:00
Tasks: Knowledge Graphs, Management

Knowledge analysis is an important application of knowledge graphs. In this paper, we present a complex knowledge analysis problem that discovers the gaps in the technology areas of interest to an organization. Our knowledge graph is developed on a heterogeneous data management platform. The analysis combines semantic search, graph analytics, and polystore query optimization.
FUNKI: Interactive functional footprint-based analysis of omics data
Authors: Rosa Hernansaiz-Ballesteros, Christian H. Holland, Aurelien Dugourd, Julio Saez-Rodriguez
Date: 2021-09-13 00:00:00

Motivation: Omics data, such as transcriptomics or phosphoproteomics, are broadly used to get a snap-shot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signaling networks. Results: We have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of several omics data, either from bulk or single-cell experiments. FUNKI also features different options to visualise the results and run post-analyses, and is mirrored as a scripted version in R. Availability: FUNKI is a free and open-source application built on R and Shiny, available in GitHub at https://github.com/saezlab/ShinyFUNKI under GNU v3.0 license and accessible also in https://saezlab.shinyapps.io/funki/ Contact: pub.saez@uni-heidelberg.de Supplementary information: We provide data examples within the app, as well as extensive information about the different variables to select, the results, and the different plots in the help page.
Thematic analysis of multiple sclerosis research by enhanced strategic diagram
Authors: Nazlahshaniza Shafina, Che Aishah Nazariah Ismaila, Mohd Zulkifli Mustafa, Nurhafizah Ghani, Asma Hayati Ahmad, Zahiruddin Othman, Adi Wijaya, Rahimah Zakaria
Date: 2021-09-13 00:00:00
Tasks: Articles, Functional Connectivity

This bibliometric review summarised the research trends and analysed research areas in multiple sclerosis (MS) over the last decade. The documents containing the term "multiple sclerosis" in the article title were retrieved from the Scopus database. We found a total of 18003 articles published in journals in the English language between 2012 and 2021. The emerging keywords identified utilising the enhanced strategic diagram were "covid-19", "teriflunomide", "clinical trial", "microglia", "b cells", "myelin", "brain", "white matter", "functional connectivity", "pain", "employment", "health-related quality of life", "meta-analysis" and "comorbidity". In conclusion, this study demonstrates the tremendous growth of MS literature worldwide, which is expected to grow more than double during the next decade especially in the identified emerging topics.
Matching markets with middlemen under transferable utility
Authors: Ata Atay, Eric Bahel, Tamás Solymosi
Date: 2021-09-12 00:00:00

This paper studies matching markets in the presence of middlemen. In our framework, a buyer-seller pair may either trade directly or use the services of a middleman; and a middleman may serve multiple buyer-seller pairs. Direct trade between a buyer and a seller is costlier than a trade mediated by a middleman. For each such market, we examine an associated cooperative game with transferable utility. First, we show that an optimal matching for a matching market with middlemen can be obtained by considering the two-sided assignment market where each buyer-seller pair is allowed to use the mediation service of the middlemen free of charge and attain the maximum surplus. Second, we prove that the core of a matching market with middlemen is always non-empty. Third, we show the existence of a buyer-optimal core allocation and a seller-optimal core allocation. In general, the core does not exhibit a middleman-optimal matching. Finally, we establish the coincidence between the core and the set of competitive equilibrium payoff vectors.
Multi-User Scheduling in Hybrid Millimeter Wave Massive MIMO Systems
Authors: Seyedeh Maryam Hosseini, Shahram Shahsavari, Catherine Rosenberg
Date: 2021-09-13 00:00:00
Tasks: Scheduling

While mmWave bands provide a large bandwidth for mobile broadband services, they suffer from severe path loss and shadowing. Multiple-antenna techniques such as beamforming (BF) can be applied to compensate the signal attenuation. We consider a special case of hybrid BF called per-stream hybrid BF (PSHBF) which is easier to implement than the general hybrid BF because it circumvents the need for joint analog-digital beamformer optimization. Employing BF at the base station enables the transmission of multiple data streams to several users in the same resource block. In this paper, we provide an offline study of proportional fair multi-user scheduling in a mmWave system with PSHBF to understand the impact of various system parameters on the performance. We formulate multi-user scheduling as an optimization problem. To tackle the non-convexity, we provide a feasible solution and show through numerical examples that the performance of the provided solution is very close to an upper-bound. Using this framework, we provide extensive numerical investigations revealing several engineering insights.
Modelling the Utility of Group Testing for Public Health Surveillance
Authors: Günther Koliander, Georg Pichler
Date: 2021-09-11 00:00:00

In epidemic or pandemic situations, resources for testing the infection status of individuals may be scarce. Although group testing can help to significantly increase testing capabilities, the (repeated) testing of entire populations can exceed the resources of any country. We thus propose an extension of the theory of group testing that takes into account the fact that definitely specifying the infection status of each individual is impossible. Our theory builds on assigning to each individual an infection status (healthy/infected), as well as an associated cost function for erroneous assignments. This cost function is versatile, e.g., it could take into account that false negative assignments are worse than false positive assignments and that false assignments in critical areas, such as health care workers, are more severe than in the general population. Based on this model, we study the optimal use of a limited number of tests to minimize the expected cost. More specifically, we utilize information-theoretic methods to give a lower bound on the expected cost and describe simple strategies that can significantly reduce the expected cost over currently known strategies. A detailed example is provided to illustrate our theory.
Interactive multi-modal motion planning with Branch Model Predictive Control
Authors: Yuxiao Chen, Ugo Rosolia, Wyatt Ubellacker, Noel Csomay-Shanklin, Aaron D. Ames
Date: 2021-09-10 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control, Motion Planning

Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered. Since the uncontrolled agents usually demonstrate multimodal reactive behavior, the motion planner needs to solve a continuous motion planning problem under these behaviors, which contains a discrete element. We propose a branch Model Predictive Control (MPC) framework that plans over feedback policies to leverage the reactive behavior of the uncontrolled agent. In particular, a scenario tree is constructed from a finite set of policies of the uncontrolled agent, and the branch MPC solves for a feedback policy in the form of a trajectory tree, which shares the same topology as the scenario tree. Moreover, coherent risk measures such as the Conditional Value at Risk (CVaR) are used as a tuning knob to adjust the tradeoff between performance and robustness. The proposed branch MPC framework is tested on an overtake and lane change task and a merging task for autonomous vehicles in simulation, and on the motion planning of an autonomous quadruped robot alongside an uncontrolled quadruped in experiments. The result demonstrates interesting human-like behaviors, achieving a balance between safety and performance.
Studying squeeze-and-excitation used in CNN for speaker verification
Authors: Mickael Rouvier, Pierre-Michel Bousquet
Date: 2021-09-13 00:00:00
Tasks: Speaker Verification

In speaker verification, the extraction of voice representations is mainly based on the Residual Neural Network (ResNet) architecture. ResNet is built upon convolution layers which learn filters to capture local spatial patterns along all the input, then generate feature maps that jointly encode the spatial and channel information. Unfortunately, all feature maps in a convolution layer are learnt independently (the convolution layer does not exploit the dependencies between feature maps) and locally. This problem has first been tackled in image processing. A channel attention mechanism, called squeeze-and-excitation (SE), has recently been proposed in convolution layers and applied to speaker verification. This mechanism re-weights the information extracted across features maps. In this paper, we first propose an original qualitative study about the influence and the role of the SE mechanism applied to the speaker verification task at different stages of the ResNet, and then evaluate several SE architectures. We finally propose to improve the SE approach with a new pool- ing variant based on the concatenation of mean- and standard- deviation-pooling. Results showed that applying SE only on the first stages of the ResNet allows to better capture speaker information for the verification task, and that significant discrimination gains on Voxceleb1-E, Voxceleb1-H and SITW evaluation tasks have been noted using the proposed pooling variant.
Incorporating Real-world Noisy Speech in Neural-network-based Speech Enhancement Systems
Authors: Yangyang Xia, Buye Xu, Anurag Kumar
Date: 2021-09-11 00:00:00
Tasks: Speech Enhancement, Triplet

Supervised speech enhancement relies on parallel databases of degraded speech signals and their clean reference signals during training. This setting prohibits the use of real-world degraded speech data that may better represent the scenarios where such systems are used. In this paper, we explore methods that enable supervised speech enhancement systems to train on real-world degraded speech data. Specifically, we propose a semi-supervised approach for speech enhancement in which we first train a modified vector-quantized variational autoencoder that solves a source separation task. We then use this trained autoencoder to further train an enhancement network using real-world noisy speech data by computing a triplet-based unsupervised loss function. Experiments show promising results for incorporating real-world data in training speech enhancement systems.