pwc-archive/papers-with-abstracts huggingface dataset:
Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?
Authors: Xiaotong Zhao, Siyuan Lu, Qingjiang Shi, Zhi-Quan Luo
Date: 2022-05-12 00:00:00

Precoding design for maximizing weighted sum-rate (WSR) is a fundamental problem for downlink of massive multi-user multiple-input multiple-output (MU-MIMO) systems. It is well-known that this problem is generally NP-hard due to the presence of multi-user interference. The weighted minimum mean-square error (WMMSE) algorithm is a popular approach for WSR maximization. However, its computational complexity is cubic in the number of base station (BS) antennas, which is unaffordable when the BS is equipped with a large antenna array. In this paper, we consider the WSR maximization problem with either a sum-power constraint (SPC) or per-antenna power constraints (PAPCs). For the former, we prove that any nontrivial stationary point must have a low-dimensional subspace structure, and then propose a reduced-WMMSE (R-WMMSE) with linear complexity by exploiting the solution structure. For the latter, we propose a linear-complexity WMMSE approach, named PAPC-WMMSE, by using a novel recursive design of the algorithm. Both R-WMMSE and PAPC-WMMSE have simple closed-form updates and guaranteed convergence to stationary points. Simulation results verify the efficacy of the proposed designs, especially the much lower complexity as compared to the state-of-the-art approaches for massive MU-MIMO systems.
Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
Authors: Takayuki Nagata, Keigo Yamada, Taku Nonomura, Kumi Nakai, Yuji Saito, Shunsuke Ono
Date: 2022-05-12 00:00:00

The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based on proximal optimization and determines sensor locations by minimizing the trace of the inverse of the Fisher information matrix under a block-sparsity hard constraint. The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations. The problem can be efficiently solved by the alternating direction method of multipliers, and the computational complexity of the proposed method is proportional to the number of potential sensor locations when it is used in combination with a low-rank expression of the measurement noise model. The advantage of the proposed method over existing sensor selection methods is demonstrated through experiments using artificial and real datasets.
Annealed Langevin Dynamics for Massive MIMO Detection
Authors: Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
Date: 2022-05-11 00:00:00

Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term -- the score of the likelihood -- by a neural network. Through numerical experiments in both synthetic and real-world data, we show that our proposed detector yields state-of-the-art symbol error rate performance and the robust version becomes noise-variance agnostic.
CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
Authors: The Critical Assessment of Genome Interpretation Consortium
Date: 2022-05-12 00:00:00

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state of the art for computational prediction of genetic variant impact, particularly those relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. Overall, results show that while current methods are imperfect, they have major utility for research and clinical applications. Missense variant interpretation methods are able to estimate biochemical effects with increasing accuracy. Performance is particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Assessment of methods for regulatory variants and complex trait disease risk is less definitive, and indicates performance potentially suitable for auxiliary use in the clinic. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.
Two are not always better than one: Role specialization is an important determinant of collaborative task performance
Authors: Asuka Takai, Qiushi Fu, Yuzuru Doibata, Giuseppe Lisi, Toshiki Tsuchiya, Keivan Mojtahedi, Toshinori Yoshioka, Mitsuo Kawato, Jun Morimoto, Marco Santello
Date: 2022-05-12 00:00:00
Tasks: Decision Making

Collaboration frequently yields better results in decision making, learning, and haptic interactions than when these actions are performed individually. However, is collaboration always superior to solo actions, or do its benefits depend on whether collaborating individuals have different or the same roles? To answer this question, we asked human subjects to perform virtual-reality collaborative and individual beam transportation tasks. These tasks were simulated in real-time by coupling the motion of a pair of hand-held robotic manipulanda to the virtual beam using virtual spring-dampers. For the task to be considered successful, participants had to complete it within temporal and spatial constraints. While the visual feedback remained the same, the underlying dynamics of the beam were altered to create two distinctive task contexts which were determined by a moving pivot constraint. When the pivot was placed at the center of the beam, two hands contribute to the task with symmetric mechanical leverage (symmetric context). When the pivot was placed at the left side of the beam, two hands contribute to the task with asymmetric mechanical leverage (asymmetric context). Participants performed these task contexts either individually with both hands (solo), or collaboratively by pairing one hand with another one (dyads). We found that dyads in the asymmetric context performed better than solos. In contrast, solos performed the symmetric context better than dyads. Importantly, we found that two hands took different roles in the asymmetric context for both solos and dyads. In contrast, the contribution from each hand was statistically indistinguishable in the symmetric context. Our findings suggest that better performance in dyads than solos is not a general phenomenon, but rather that collaboration yields better performance only when role specialization emerges in dyadic interactions.
Hormonal Factors Moderate the Associations Between Vascular Risk Factors and White Matter Hyperintensities
Authors: Abdullah Alqarni, Wei Wen, Ben C. P. Lam, John D. Crawford, Perminder S. Sachdev, Jiyang Jiang
Date: 2022-05-12 00:00:00
Tasks: Management

Objective: To examine the moderation effects of hormonal factors on the associations between vascular risk factors and white matter hyperintensities (WMH) in men and women, separately. Methods: WMH were automatically segmented and quantified in the UK Biobank dataset (N = 18,294). Generalised linear models were applied to examine 1) the main effects of vascular (body mass index, hip to waist ratio, pulse wave velocity, hypercholesterolemia, diabetes, hypertension, smoking status) and hormonal (testosterone levels, contraceptive pill, hormone replacement therapy, menopause) factors on WMH, and 2) the moderation effects of hormonal factors on the relationship between vascular risk factors and WMH volumes. Results: In men with testosterone levels one standard deviation (SD) higher than the mean value, increased body mass index and pulse wave velocity, and smoking were associated with higher WMH volumes. The association between body mass index and WMH was more significant in the periventricular white matter regions, whilst the relationship between pulse wave velocity and WMH was restricted to deep white matter regions. Men with low testosterone levels (one SD below the mean level) showed a significant association between hypercholesterolemia and higher deep WMH volumes. Hypertensive women showed higher WMH volumes than women without hypertension regardless of whether hormone replacement therapy was used. However, higher WMH volumes, especially in the deep white matter regions, were found in women who did not use hormone replacement therapy or use it for a shorter duration. Conclusion: These findings highlighted the importance of considering hormonal risk factors in the prevention and management of WMH.
The role of investor attention in global asset price variation during the invasion of Ukraine
Authors: Martina Halousková, Daniel Stašek, Matúš Horváth
Date: 2022-05-12 00:00:00

We study the impact of event-specific attention indices -- based on Google Trends -- in predictive price variation models before and during the Russian invasion of Ukraine in February 2022. We extend our analyses to the importance of geographical proximity and economic openness to Russia within 51 global equity markets. Our results demonstrate that 36 countries show significant attention to the conflict at the onset of and during the invasion, which helps predict volatility. We find that the impact of attention is more significant in countries with a higher degree of economic openness to Russia and those nearer to it.
Method of indirect estimation of default probability dynamics for industry-target segments according to the data of Bank of Russia
Authors: Mikhail Pomazanov
Date: 2022-05-12 00:00:00
Tasks: Time Series, Time Series Analysis

A direct method for calculating default rates by industry and target corporate segments is not possible given the lack of statistical data. The proposed paper considers a model for filtering the dynamics of the probability of default of corporate companies and other borrowers based on indirect data on the dynamics of overdue debt supplied by the Bank of Russia. The model is based on the equation of the balance of total and overdue debts, the missing links of the corresponding time series are built using the Hodrick_Prescott filtering method. In retail lending segments (mortgage, consumer lending), default statistics are available and supplied by Credit Bureaus. The presented method is validated on this statistic. Over a historical limited period, validation has shown that the result is trustworthy. The resulting default probability series are exogenous variables for macro_economic modelling of sectoral credit risks.
Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture Approximation
Authors: Hoang Phan, Anh Phan Tuan, Son Nguyen, Ngo Van Linh, Khoat Than
Date: 2022-05-10 00:00:00
Proceeding: Pacific-Asia Conference on Knowledge Discovery and Data Mining 2022 5
Tasks: Computational Efficiency, Continual Learning, Variational Inference

Our paper studies the continual learning (CL) problems in which data comes in sequence and the trained models are expected to be capable of utilizing existing knowledge to solve new tasks without losing performance on previous ones. This also poses a central difficulty in the field of CL, termed as Catastrophic Forgetting (CF). In an attempt to address this problem, Bayesian methods provide a powerful principle, focusing on the inference scheme to estimate the importance of weights. Variational inference (VI), one of the most widely used methods within this vein, approximates the intractable posterior by a factorized distribution, thus offering computational efficiency. Notwithstanding many state-of-the-art performances in practice, this simple assumption about the posterior distribution typically limits the model capacity to some extent. In this paper, we introduce a novel approach to mitigate forgetting in the Bayesian approach via enriching the posterior distribution with mixture models, which intuitively promotes neural networks to acquire knowledge from multiple tasks at a time. Moreover, in order to reduce the model’s complexity growth when the number of components increases, we propose a solution that conducts low-rank decomposition on the variance of each component based on neural matrix factorization. Extensive experiments show that our method yields significant improvements compared to prior works on different benchmarks
Contrastive learning for natural language-based vehicle retrieval
Authors: Tam Minh Nguyen, Quang Huu Pham, Linh Bao Doan, Hoang Viet Trinh, Viet-Anh Nguyen, Viet-Hoang Phan
Date: 2021-06-20 00:00:00
Proceeding: IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 6
Tasks: Contrastive Learning, Retrieval

AI City Challenge 2021 Task 5: The Natural Language-Based Vehicle Tracking is a Natural Language-based Vehicle Retrieval task, which requires retrieving a single-camera track using a set of three natural language descriptions of the specific targets. In this paper, we present our methods to tackle the difficulties of the provided task. Experiments with our approaches on the competitive dataset from AICity Challenge 2021 show that our techniques achieve Mean Reciprocal Rank score of 0.1701 on the public test dataset and 0.1571 on the private test dataset.
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts
Authors: Yaron Geffen, Yanay Ofran, Ron Unger
Date: 2022-05-10 00:00:00
Proceeding: bioRxiv 2022 5
Tasks: Dimensionality Reduction, Knowledge Distillation, Language Modeling, Language Modelling, Protein Language Model, Protein Secondary Structure Prediction, Triplet

Recently, Deep Learning models, initially developed in the field of Natural Language Processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, "distilled" models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet, and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91, and 0.87 respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e., shuffled sequences classified as proteins by DistilProtBert) we may be able to identify de-novo potential natural-like proteins based on random shuffling of amino acid sequences.
Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and Improved Model Fitting
Authors: Bufan Zhao
Date: 2020-04-16 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2020 4
Tasks: Point Cloud Segmentation, Scene Segmentation, Segmentation

Abstract—Indoor scene segmentation based on 3-D laser point
cloud is important for rebuilding and classification, especially for
permanent building structure. However, the existing segmentation
methods mainly focus on the large-scale planar structures but
ignore the other sharp structures and details, which would
cause accuracy degradation in scene reconstruction. To handle
this issue, an iterative Gaussian mapping-based segmentation
strategy has been proposed in this article, which goes from
rough segmentation to refined one iteratively to decompose
the indoor scene into detectable point cloud clusters layer
by layer. An improved model fitting algorithm based on the
maximum likelihood estimation sampling consensus (MLESAC)
algorithm is proposed for refined segmentation, which is called
the Prior-MLESAC algorithm, to deal with the extraction of
both vertical and nonvertical planar and cylindrical structures.
The experimental results demonstrate that planar and cylindrical
structures are segmented more completely by the proposed
strategy, and more details of the indoor structure are restored
than other existing methods.
Exploring the Temporal Cues to Enhance Video Retrieval on Standardized CDVA
Authors: Won Jo, Guentaek Lim, Joonsoo Kim, Joungil Yun, Yukyung Choi
Date: 2022-04-11 00:00:00
Proceeding: IEEE Access 2022 4
Tasks: Retrieval, Video Retrieval

As the demand for large-scale video analysis increases, video retrieval research is also becoming more active. In 2014, ISO/IEC MPEG began standardizing compact descriptors for video analysis, known as CDVA, and it is now adopted as a standard. However, the standardized CDVA is not easily compared to other methods because the MPEG-CDVA dataset used for performance verification is not disclosed, despite the fact that follow-up studies are underway with multiple versions of the CDVA experimental model. In addition, analyses of modules constituting the CDVA framework are insufficient in previous studies. Therefore, we conduct self-evaluations of CDVA to analyze the impact of each module on the retrieval task. Furthermore, to overcome the obstacles identified through these self-evaluations, we suggest temporal nested invariance pooling, abbreviated as TNIP, which implies temporal robustness realized by improving nested invariance pooling, abbreviated as NIP, one of the features in CDVA. Finally, benchmarks of the existing CDVA and the proposed approach are provided on several public datasets. Through this, we show that the CDVA framework is capable of boosting the retrieval performance if utilizing the proposed approach.
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
Authors: Jinliang Lin, Zhedong Zheng, Zhun Zhong, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe
Date: 2022-05-15 00:00:00
Proceeding: IEEE Transactions on Image Processing (TIP) 2022 5
Tasks: Drone navigation, Drone-view target localization, geo-localization, Image-Based Localization, Keypoint Detection, Representation Learning

In this paper, we study the cross-view geo-localization problem to match images from different viewpoints. The key motivation underpinning this task is to learn a discriminative viewpoint-invariant visual representation. Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network. Specifically, we introduce a Unit Subtraction Attention Module (USAM) that can automatically discover representative keypoints from feature maps and draw attention to the salient regions. USAM contains very few learning parameters but yields significant performance improvement and can be easily plugged into different networks. We demonstrate through extensive experiments that (1) by incorporating USAM, RK-Net facilitates end-to-end joint learning without the prerequisite of extra annotations. Representation learning and keypoint detection are two highly-related tasks. Representation learning aids keypoint detection. Keypoint detection, in turn, enriches the model capability against large appearance changes caused by viewpoint variants.  (2) USAM is easy to implement and can be integrated with existing methods, further improving the state-of-the-art performance. We achieve competitive geo-localization accuracy on three challenging datasets, i.e., University-1652, CVUSA and CVACT. Our code is available at https://github.com/AggMan96/RK-Net.
Raison d’être of the benchmark dataset: A Survey of Current Practices of Benchmark Dataset Sharing Platforms
Authors: Jaihyun Park, Sullam Jeoung
Date: 2022-05-01 00:00:00
Proceeding: nlppower (ACL) 2022 5

This paper critically examines the current practices of benchmark dataset sharing in NLP and suggests a better way to inform reusers of the benchmark dataset. As the dataset sharing platform plays a key role not only in distributing the dataset but also in informing the potential reusers about the dataset, we believe data-sharing platforms should provide a comprehensive context of the datasets. We survey four benchmark dataset sharing platforms: HuggingFace, PaperswithCode, Tensorflow, and Pytorch to diagnose the current practices of how the dataset is shared which metadata is shared and omitted. To be specific, drawing on the concept of data curation which considers the future reuse when the data is made public, we advance the direction that benchmark dataset sharing platforms should take into consideration. We identify that four benchmark platforms have different practices of using metadata and there is a lack of consensus on what social impact metadata is. We believe the problem of missing a discussion around social impact in the dataset sharing platforms has to do with the failed agreement on who should be in charge. We propose that the benchmark dataset should develop social impact metadata and data curator should take a role in managing the social impact metadata.
Towards Stronger Adversarial Baselines Through Human-AI Collaboration
Authors: Wencong You, Daniel Lowd
Date: 2022-05-01 00:00:00
Proceeding: nlppower (ACL) 2022 5

Natural language processing (NLP) systems are often used for adversarial tasks such as detecting spam, abuse, hate speech, and fake news. Properly evaluating such systems requires dynamic evaluation that searches for weaknesses in the model, rather than a static test set. Prior work has evaluated such models on both manually and automatically generated examples, but both approaches have limitations: manually constructed examples are time-consuming to create and are limited by the imagination and intuition of the creators, while automatically constructed examples are often ungrammatical or labeled inconsistently. We propose to combine human and AI expertise in generating adversarial examples, benefiting from humans’ expertise in language and automated attacks’ ability to probe the target system more quickly and thoroughly. We present a system that facilitates attack construction, combining human judgment with automated attacks to create better attacks more efficiently. Preliminary results from our own experimentation suggest that human-AI hybrid attacks are more effective than either human-only or AI-only attacks. A complete user study to validate these hypotheses is still pending.