pwc-archive/papers-with-abstracts huggingface dataset:
Wasserstein Distributional Learning
Authors: Chengliang Tang, Nathan Lenssen, Ying WEI, Tian Zheng
Date: 2022-09-12 00:00:00
Tasks: regression

Learning conditional densities and identifying factors that influence the entire distribution are vital tasks in data-driven applications. Conventional approaches work mostly with summary statistics, and are hence inadequate for a comprehensive investigation. Recently, there have been developments on functional regression methods to model density curves as functional outcomes. A major challenge for developing such models lies in the inherent constraint of non-negativity and unit integral for the functional space of density outcomes. To overcome this fundamental issue, we propose Wasserstein Distributional Learning (WDL), a flexible density-on-scalar regression modeling framework that starts with the Wasserstein distance $W_2$ as a proper metric for the space of density outcomes. We then introduce a heterogeneous and flexible class of Semi-parametric Conditional Gaussian Mixture Models (SCGMM) as the model class $\mathfrak{F} \otimes \mathcal{T}$. The resulting metric space $(\mathfrak{F} \otimes \mathcal{T}, W_2)$ satisfies the required constraints and offers a dense and closed functional subspace. For fitting the proposed model, we further develop an efficient algorithm based on Majorization-Minimization optimization with boosted trees. Compared with methods in the previous literature, WDL better characterizes and uncovers the nonlinear dependence of the conditional densities, and their derived summary statistics. We demonstrate the effectiveness of the WDL framework through simulations and real-world applications.
Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes
Authors: Song Wei, Yao Xie, Christopher S. Josef, Rishikesan Kamaleswaran
Date: 2022-09-09 00:00:00

Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have elucidated prodromes (i.e., event chains) that can be used to identify and intercept the adverse event early in its course. Clinically relevant and interpretable results require a framework that can (i) infer temporal interactions across multiple patient features found in EMR data (e.g., Labs, vital signs, etc.) and (ii) identify patterns that precede and are specific to an impending adverse event (e.g., sepsis). In this work, we propose a linear multivariate Hawkes process model, coupled with ReLU link function, to recover a Granger Causal (GC) graph with both exciting and inhibiting effects. We develop a scalable two-phase gradient-based method to obtain a maximum surrogate-likelihood estimator, which is shown to be effective via extensive numerical simulation. Our method is subsequently extended to a data set of patients admitted to Grady hospital system in Atlanta, GA, USA, where the estimated GC graph identifies several highly interpretable GC chains that precede sepsis. The code is available at \url{https://github.com/SongWei-GT/two-phase-MHP}.
A challenge-based survey of e-recruitment recommendation systems
Authors: Yoosof Mashayekhi, Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie
Date: 2022-09-12 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Survey

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences. Therefore, e-recruitment recommendation systems could greatly impact job seekers' careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the companies' competitive edge in the market. Hence, the domain of e-recruitment recommendation deserves specific attention. Existing surveys on this topic tend to discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach, which we believe might be more practical to developers facing a concrete e-recruitment design task with a specific set of challenges, as well as to researchers looking for impactful research projects in this domain. We first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider promising in the e-recruitment recommendation domain.
Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models
Authors: Yinqiong Cai, Jiafeng Guo, Yixing Fan, Qingyao Ai, Ruqing Zhang, Xueqi Cheng
Date: 2022-09-12 00:00:00
Tasks: Information Retrieval, Retrieval

Neural ranking models (NRMs) have become one of the most important techniques in information retrieval (IR). Due to the limitation of relevance labels, the training of NRMs heavily relies on negative sampling over unlabeled data. In general machine learning scenarios, it has shown that training with hard negatives (i.e., samples that are close to positives) could lead to better performance. Surprisingly, we find opposite results from our empirical studies in IR. When sampling top-ranked results (excluding the labeled positives) as negatives from a stronger retriever, the performance of the learned NRM becomes even worse. Based on our investigation, the superficial reason is that there are more false negatives (i.e., unlabeled positives) in the top-ranked results with a stronger retriever, which may hurt the training process; The root is the existence of pooling bias in the dataset constructing process, where annotators only judge and label very few samples selected by some basic retrievers. Therefore, in principle, we can formulate the false negative issue in training NRMs as learning from labeled datasets with pooling bias. To solve this problem, we propose a novel Coupled Estimation Technique (CET) that learns both a relevance model and a selection model simultaneously to correct the pooling bias for training NRMs. Empirical results on three retrieval benchmarks show that NRMs trained with our technique can achieve significant gains on ranking effectiveness against other baseline strategies.
Joint Upper & Lower Bound Normalization for IR Evaluation
Authors: Shubhra Kanti Karmaker Santu, Dongji Feng
Date: 2022-09-12 00:00:00
Tasks: Learning-To-Rank

In this paper, we present a novel perspective towards IR evaluation by proposing a new family of evaluation metrics where the existing popular metrics (e.g., nDCG, MAP) are customized by introducing a query-specific lower-bound (LB) normalization term. While original nDCG, MAP etc. metrics are normalized in terms of their upper bounds based on an ideal ranked list, a corresponding LB normalization for them has not yet been studied. Specifically, we introduce two different variants of the proposed LB normalization, where the lower bound is estimated from a randomized ranking of the corresponding documents present in the evaluation set. We next conducted two case-studies by instantiating the new framework for two popular IR evaluation metric (with two variants, e.g., DCG_UL_V1,2 and MSP_UL_V1,2 ) and then comparing against the traditional metric without the proposed LB normalization. Experiments on two different data-sets with eight Learning-to-Rank (LETOR) methods demonstrate the following properties of the new LB normalized metric: 1) Statistically significant differences (between two methods) in terms of original metric no longer remain statistically significant in terms of Upper Lower (UL) Bound normalized version and vice-versa, especially for uninformative query-sets. 2) When compared against the original metric, our proposed UL normalized metrics demonstrate higher Discriminatory Power and better Consistency across different data-sets. These findings suggest that the IR community should consider UL normalization seriously when computing nDCG and MAP and more in-depth study of UL normalization for general IR evaluation is warranted.
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Authors: Amanda Bower, Kristian Lum, Tomo Lazovich, Kyra Yee, Luca Belli
Date: 2022-09-12 00:00:00
Tasks: Fairness, Recommendation Systems

Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user. In recent years, methods relying on stochastic ordering have been developed to create "fairer" rankings that reduce inequality in who or what is shown to users. Complete randomization -- ordering candidate items randomly, independent of estimated relevance -- is largely considered a baseline procedure that results in the most equal distribution of exposure. In industry settings, recommender systems often operate via a two-step process in which candidate items are first produced using computationally inexpensive methods and then a full ranking model is applied only to those candidates. In this paper, we consider the effects of inequality at the first step and show that, paradoxically, complete randomization at the second step can result in a higher degree of inequality relative to deterministic ordering of items by estimated relevance scores. In light of this observation, we then propose a simple post-processing algorithm in pursuit of reducing exposure inequality that works both when candidate sets have a high level of imbalance and when they do not. The efficacy of our method is illustrated on both simulated data and a common benchmark data set used in studying fairness in recommender systems.
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality
Authors: Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras
Date: 2022-09-11 00:00:00
Tasks: Diversity, Explainable Recommendation, Knowledge Graphs, Recommendation Systems

Numerous Knowledge Graphs (KGs) are being created to make Recommender Systems (RSs) not only intelligent but also knowledgeable. Integrating a KG in the recommendation process allows the underlying model to extract reasoning paths between recommended products and already experienced products from the KG. These paths can be leveraged to generate textual explanations to be provided to the user for a given recommendation. However, the existing explainable recommendation approaches based on KG merely optimize the selected reasoning paths for product relevance, without considering any user-level property of the paths for explanation. In this paper, we propose a series of quantitative properties that monitor the quality of the reasoning paths from an explanation perspective, based on recency, popularity, and diversity. We then combine in- and post-processing approaches to optimize for both recommendation quality and reasoning path quality. Experiments on three public data sets show that our approaches significantly increase reasoning path quality according to the proposed properties, while preserving recommendation quality. Source code, data sets, and KGs are available at https://tinyurl.com/bdbfzr4n.
Towards Diversity-tolerant RDF-stores
Authors: Masoud Salehpour, Joseph G. Davis
Date: 2022-09-10 00:00:00
Tasks: Diversity

We propose a novel approach to designing RDF-stores with the goal of improving the consistency and predictability of query performance. When designing these systems, three properties are commonly desired: support for the full range of SPARQL query features (Q), support for widely varying RDF datasets in terms of structuredness and size (S), and high performance (P). We develop the empirical SPQ conjecture which states that it may be impossible to achieve all the three desiderata simultaneously. We present a strong case for its plausibility based on our experimental results. The tradeoffs among the three and design guidelines based on the SPQ conjecture are also discussed.
DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images
Authors: Vipin Venugopal, Justin Joseph, M Vipin Das, Malaya Kumar Nath
Date: 2022-07-12 00:00:00
Proceeding: Computers in Biology and Medicine 2022 7
Tasks: Lesion Segmentation, Skin Lesion Segmentation

Highly focused images of skin captured with ordinary cameras, called macro-images, are extensively used in dermatology. Being highly focused views, the macro-images contain only lesions and background regions. Hence, the localization of lesions on the macro-images is a simple thresholding problem. However, algorithms that offer an accurate estimate of threshold and retain consistent performance on different dermatological macro-images are rare. A deep learning model, termed ‘Deep Threshold Prediction Network (DTP-Net)’, is proposed in this paper to address this issue. For training the model, grayscale versions of the macro-images are fed as input to the model, and the corresponding gray-level threshold values at which the Dice similarity index (DSI) between the segmented and the ground-truth images are maximized are defined as the targets. The DTP-Net exhibited the least value of root mean square error for the predicted threshold compared with 11 state-of-the-art threshold estimation algorithms (such as Otsu’s thresholding, Valley emphasized otsu’s thresholding, Isodata thresholding, Histogram slope difference distribution-based thresholding, Minimum error thresholding, Poisson’s distribution-based minimum error thresholding, Kapur’s maximum entropy thresholding, Entropy-weighted otsu’s thresholding, Minimum cross-entropy thresholding, Type-2 fuzzy-based thresholding, and Fuzzy entropy thresholding). The DTP-Net could learn the difference between the lesion and background in the intensity space and accurately predict the threshold that separates the lesion from the background. The proposed DTP-Net can be integrated into the segmentation module in automated tools that detect skin cancer from dermatological macro-images.
Alcohol Intake Differentiates AD and LATE: A Telltale Lifestyle from Two Large-Scale Datasets
Authors: Xinxing Wu, Chong Peng, Peter T. Nelson, Qiang Cheng
Date: 2022-08-25 00:00:00
Tasks: feature selection

Alzheimer's disease (AD), as a progressive brain disease, affects cognition, memory, and behavior. Similarly, limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently defined common neurodegenerative disease that mimics the clinical symptoms of AD. At present, the risk factors implicated in LATE and those distinguishing LATE from AD are largely unknown. We leveraged an integrated feature selection-based algorithmic approach, to identify important factors differentiating subjects with LATE and/or AD from Control on significantly imbalanced data. We analyzed two datasets ROSMAP and NACC and discovered that alcohol consumption was a top lifestyle and environmental factor linked with LATE and AD and their associations were differential. In particular, we identified a specific subpopulation consisting of APOE e4 carriers. We found that, for this subpopulation, light-to-moderate alcohol intake was a protective factor against both AD and LATE, but its protective role against AD appeared stronger than LATE. The codes for our algorithms are available at https://github.com/xinxingwu-uk/PFV.
Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty
Authors: Paul Trust, Ahmed Zahran, Rosane Minghim
Date: 2022-08-10 00:00:00
Tasks: Articles

The need for timely data analysis for economic decisions has prompted most economists and policy makers to search for non-traditional supplementary sources of data. In that context, text data is being explored to enrich traditional data sources because it is easy to collect and highly abundant. Our work focuses on studying the potential of textual data, in particular news pieces, for measuring economic policy uncertainty (EPU). Economic policy uncertainty is defined as the public's inability to predict the outcomes of their decisions under new policies and future economic fundamentals. Quantifying EPU is of great importance to policy makers, economists, and investors since it influences their expectations about the future economic fundamentals with an impact on their policy, investment and saving decisions. Most of the previous work using news articles for measuring EPU are either manual or based on a simple keyword search. Our work proposes a machine learning based solution involving weak supervision to classify news articles with regards to economic policy uncertainty. Weak supervision is shown to be an efficient machine learning paradigm for applying machine learning models in low resource settings with no or scarce training sets, leveraging domain knowledge and heuristics. We further generated a weak supervision based EPU index that we used to conduct extensive econometric analysis along with the Irish macroeconomic indicators to validate whether our generated index foreshadows weaker macroeconomic performance
Solidarity in natural gas storage: A potential allocation mechanism of stored quantities among several players during times of crisis
Authors: Dávid Csercsik, Anne Neumann
Date: 2022-09-12 00:00:00

The recently experienced disruptions in the EU's energy supply pointed out that supply crises pose a real thread and the member states must be better prepared do deal with the related challenges. According to the current practice, member states fill their gas storages independently, while it is not clear how solidarity could be put into practice in the future, i.e. how the accumulated reserves of one or more members may be potentially redistributed to help others in need. In this paper we propose some possible guidelines for a potential solidarity framework, and formalize a game-theoretic model in order to capture the basic features of the problem, considering the related uncertainty of the future conditions related to gas storage levels and possible transmission bottlenecks as well. The proposed mechanism of supply-security related cooperation is based on voluntary participation, and may contribute to the more efficient utilization of storage capacities. Via the computational model we demonstrate the operation of the proposed framework on a simple example and show that under the assumption of risk-averse participants, the concept exhibits potential.
Active and Passive IRS Jointly Aided Communication: Deployment Design and Achievable Rate
Authors: Min Fu, Rui Zhang
Date: 2022-09-12 00:00:00

In this letter, we study the wireless point-to-point communication from a transmitter (Tx) to a receiver (Rx), which is jointly aided by an active intelligent reflecting surface (AIRS) and a passive IRS (PIRS). We consider two practical transmission schemes by deploying the two IRSs in different orders, namely, Tx$\rightarrow$PIRS$\rightarrow$AIRS$\rightarrow$Rx (TPAR) and Tx$\rightarrow$AIRS$\rightarrow$PIRS$\rightarrow$Rx (TAPR). Assuming line-of-sight channels, we derive the achievable rates for the two schemes by optimizing the placement of the AIRS with the location of the PIRS fixed. Our analysis shows that when the number of PIRS elements and/or the AIRS amplification power is small, the AIRS should be deployed closer to the Rx in both schemes, and TAPR outperforms TPAR with their respective optimized AIRS/PIRS placement. Simulation results validate our analysis and show the considerable performance gain achieved by the jointly optimized AIRS/PIRS deployment over the baseline double-PIRS system under the same power and IRS element budgets.
Online Beam Learning with Interference Nulling for Millimeter Wave MIMO Systems
Authors: Yu Zhang, Tawfik Osman, Ahmed Alkhateeb
Date: 2022-09-09 00:00:00

Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. Due to the hardware constraints and the lack of channel knowledge, codebook based beamforming/combining is normally adopted to achieve the desired array gain. However, most of the existing codebooks focus only on improving the gain of their target user, without taking interference into account. This can incur critical performance degradation in dense networks. In this paper, we propose a sample-efficient online reinforcement learning based beam pattern design algorithm that learns how to shape the beam pattern to null the interfering directions. The proposed approach does not require any explicit channel knowledge or any coordination with the interferers. Simulation results show that the developed solution is capable of learning well-shaped beam patterns that significantly suppress the interference while sacrificing tolerable beamforming/combing gain from the desired user. Furthermore, a hardware proof-of-concept prototype based on mmWave phased arrays is built and used to implement and evaluate the developed online beam learning solutions in realistic scenarios. The learned beam patterns, measured in an anechoic chamber, show the performance gains of the developed framework and highlight a promising machine learning based beam/codebook optimization direction for mmWave and terahertz systems.
Structured Optimization-Based Model Order Reduction for Parametric Systems
Authors: Paul Schwerdtner, Manuel Schaller
Date: 2022-09-12 00:00:00

We develop an optimization-based algorithm for parametric model order reduction (PMOR) of linear time-invariant dynamical systems. Our method aims at minimizing the $\mathcal{H}_\infty \otimes \mathcal{L}_\infty$ approximation error in the frequency and parameter domain by an optimization of the reduced order model (ROM) matrices. State-of-the-art PMOR methods often compute several nonparametric ROMs for different parameter samples, which are then combined to a single parametric ROM. However, these parametric ROMs can have a low accuracy between the utilized sample points. In contrast, our optimization-based PMOR method minimizes the approximation error across the entire parameter domain. Moreover, due to our flexible approach of optimizing the system matrices directly, we can enforce favorable features such as a port-Hamiltonian structure in our ROMs across the entire parameter domain. Our method is an extension of the recently developed SOBMOR-algorithm to parametric systems. We extend both the ROM parameterization and the adaptive sampling procedure to the parametric case. Several numerical examples demonstrate the effectiveness and high accuracy of our method in a comparison with other PMOR methods.
Continuous head-related transfer function representation based on hyperspherical harmonics
Authors: Adam Szwajcowski
Date: 2022-09-12 00:00:00

Expressing head-related transfer functions (HRTFs) in spherical harmonic (SH) domain has been thoroughly studied as a method of obtaining continuity over space. However, HRTFs are functions not only of direction but also of frequency. This paper presents an extension of the SH-based method, utilizing hyperspherical harmonics (HSHs) to obtain an HRTF representation that is continuous over both space and frequency. The application of the HSH approximation results in a relatively small set of coefficients which can be decoded into HRTF values at any direction and frequency. The paper discusses results obtained by applying the method to magnitude spectra extracted from exemplary HRTF measurements. The HRTF representations based on SHs and HSHs exhibit similar reproduction accuracy, with the latter one featuring continuity over both space and frequency and requiring much lower number of coefficients. The developed HSH-based continuous functional model can serve multiple purposes, such as interpolation, compression or parametrization for machine-learning applications.