pwc-archive/papers-with-abstracts huggingface dataset:
Activate index: an integrated index to reveal disrupted brain network organizations of major depressive disorder patients
Authors: Yu Fu, Yanyan Huang, Meng Niu, Le Xue, Shunjie Dong, Shunlin Guo, Junqiang Lei, Cheng Zhuo
Date: 2022-02-14 00:00:00

Altered functional brain networks have been a typical manifestation that distinguishes major depressive disorder (MDD) patients from healthy control (HC) subjects in functional magnetic resonance imaging (fMRI) studies. Recently, rich club and diverse club metrics have been proposed for network or network neuroscience analyses. The rich club defines a set of nodes that tend to be the hubs of specific communities, and the diverse club defines the nodes that span more communities and have edges diversely distributed across different communities. Considering the heterogeneity of rich clubs and diverse clubs, combining them and on the basis to derive a novel indicator may reveal new evidence of brain functional integration and separation, which might provide new insights into MDD. This study for the first time discussed the differences between MDD and HC using both rich club and diverse club metrics and found the complementarity of them in analyzing brain networks. Besides, a novel index, termed "active index", has been proposed in this study. The active index defines a group of nodes that tend to be diversely distributed across communities while avoiding being a hub of a community. Experimental results demonstrate the superiority of active index in analyzing MDD brain mechanisms.
Reality: Physics, the Physical Universe Language and Mathematics, Physics Formalisms in Human Brain Machine Language
Authors: Jahan. N. Schad
Date: 2022-02-08 00:00:00

The nature of the existence, revealed through Human cognitive system, has been evolving since the development of the languages. Part of such revelations were the geometrical forms and the numbers, whose beauty and order, wondrous and mysterious, conveyed a sense of unreality beyond the physical reality normally discerned; to Plato they were indications of the other reality of which only those few glimpses had occurred. And full access for the earthlings required breaking out of their shackles of captivity; beautifully portrayed in the Masters cave allegory. The Present era sense of wonder of the effectiveness of the mathematics in the formulations of the discoveries of the fundamental and natural laws of existence, and the advances in computation, and especially in artificial intelligence (AI), have revived the platonic idea with some adherents among the very minds who contribute to furthering of our understanding of our physical universe. The work presented here is an attempt in proving that a physical world precludes the prospects for the mathematics to be its other reality. This end is achieved by bringing to light the processes involved in the discernment of the world by humans. The fundamentals involved make it clear that the events of the physical universe, are played out in our physical brains operational language of physics; and the symbolic language of mathematics is another machine language of our computational brains for the specific expressions of the underlying physics of some of the phenomena it processes. This rapidly developing language, presently much in appearances strange to the physical reality, and taken by many great minds; Plato, Galileo, Kant, Winger and the likes, as indication of an incontrovertible and mysteriousness mathematical world, is in fact inadvertently, blazing the trail for discoveries of more novelties of the physics of the physical universe.
Application and reduction of a nonlinear hyperelastic wall model capturing ex vivo relationships between fluid pressure, area and wall thickness in normal and hypertensive murine left pulmonary arteries
Authors: Mansoor A. Haider, Katherine J. Pearce, Naomi C. Chesler, Nicholas A. Hill, Mette S. Olufsen
Date: 2022-02-11 00:00:00
Tasks: parameter estimation

Pulmonary hypertension is a cardiovascular disorder manifested by elevated arterial blood pressure together with vessel wall stiffening and thickening due to alterations in collagen, elastin and smooth muscle cells. Hypoxia-induced (type 3) pulmonary hypertension can be studied in animals exposed to a low oxygen environment for prolonged time periods leading to biomechanical alterations in vessel wall structure. This study formulates and systematically reduces a nonlinear elastic structural wall model for a large pulmonary artery, generating a novel pressure-area relation capturing remodeling in type 3 pulmonary hypertension. The model is calibrated using {\em ex vivo} measurements of vessel diameter and wall thickness changes, under controlled flow conditions, in left pulmonary arteries isolated from control and hypertensive mice. A two-layer, hyperelastic, anisotropic model incorporating residual stresses is formulated using the Holzapfel-Gasser-Ogden model. Complex relations predicting vessel area and wall thickness with increasing blood pressure are derived and calibrated using the data. Sensitivity analysis, parameter estimation and subset selection are used to systematically reduce the 16-parameter model to one in which a much smaller subset of identifiable parameters is estimated via solution of an inverse problem. Our final reduced model includes a single set of three elastic moduli. Estimated ranges of these parameters demonstrate that nonlinear stiffening is dominated by elastin in the control animals and by collagen in the hypertensive group. The novel pressure-area relation developed in this study has potential impact on one-dimensional fluids network models of vessel wall remodeling in the presence of cardiovascular disease.
The impact of accumulative pension policy on welfare of individuals
Authors: Marika Khozrevanidze
Date: 2022-01-04 00:00:00

Many countries around the world have had to carry out radical reforms periodically in their pension systems. Global experience shows that it is important to optimize the costs of pension and social security systems in order to ensure a decent old age in addition to reducing the pressure on budgetary resources. By Georgia is changing demographic situation, special attention is paid to proper functioning of the pension policy. The pension reform carried out in Georgia in 2019 caused a difference of opinion among experts. This issue in today is conditions does not lose relevance. The presented thesis discusses the impact of the mandatory funded pension system on the well-being of people. Thesis includes the following issues: peculiarities of the formation of pension systems in Georgia. It is presented a small historical excursion in terms of the development of pension systems. In addition, are discussed the international experience of pension systems and comparative analysis in relation to Georgia. This paper specifically focuses on the essence of the mandatory funded pension system and assesses the current situation in terms of investment potential of the resource accumulated in the pension fund. In conclusion, are presented the challenges of this system and the ways of perfection.
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
Authors: Mikaela Angelina Uy, Yen-Yu Chang, Minhyuk Sung, Purvi Goel, Joseph Lambourne, Tolga Birdal, Leonidas Guibas
Date: 2021-12-17 00:00:00
Proceeding: CVPR 2022 1
Tasks: CAD Reconstruction

We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders. Reverse engineering from a raw geometry to a CAD model is an essential task to enable manipulation of the 3D data in shape editing software and thus expand their usages in many downstream applications. Particularly, the form of CAD models having a sequence of extrusion cylinders -- a 2D sketch plus an extrusion axis and range -- and their boolean combinations is not only widely used in the CAD community/software but also has great expressivity of shapes, compared to having limited types of primitives (e.g., planes, spheres, and cylinders). In this work, we introduce a neural network that solves the extrusion cylinder decomposition problem in a geometry-grounded way by first learning underlying geometric proxies. Precisely, our approach first predicts per-point segmentation, base/barrel labels and normals, then estimates for the underlying extrusion parameters in differentiable and closed-form formulations. Our experiments show that our approach demonstrates the best performance on two recent CAD datasets, Fusion Gallery and DeepCAD, and we further showcase our approach on reverse engineering and editing.
The Wanderings of Odysseus in 3D Scenes
Authors: Yan Zhang, Siyu Tang
Date: 2021-12-16 00:00:00
Proceeding: CVPR 2022 1

Our goal is to populate digital environments, in which digital humans have diverse body shapes, move perpetually, and have plausible body-scene contact. The core challenge is to generate realistic, controllable, and infinitely long motions for diverse 3D bodies. To this end, we propose generative motion primitives via body surface markers, or GAMMA in short. In our solution, we decompose the long-term motion into a time sequence of motion primitives. We exploit body surface markers and conditional variational autoencoder to model each motion primitive, and generate long-term motion by implementing the generative model recursively. To control the motion to reach a goal, we apply a policy network to explore the generative model's latent space and use a tree-based search to preserve the motion quality during testing. Experiments show that our method can produce more realistic and controllable motion than state-of-the-art data-driven methods. With conventional path-finding algorithms, the generated human bodies can realistically move long distances for a long period of time in the scene. Code is released for research purposes at: \url{https://yz-cnsdqz.github.io/eigenmotion/GAMMA/}
A Data-Driven Approach to Improve 3D Head-Pose Estimation
Authors: Nima Aghli, Eraldo Ribeiro
Date: 2022-01-01 00:00:00
Proceeding: ISVC 2022 1
Tasks: Face Recognition, Head Pose Estimation, Image Augmentation, Pose Estimation, Transfer Learning

Head-pose estimation from images is an important research topic in computer vision. Its many applications include detecting focus of attention, tracking driver behavior, and human-computer interaction. Recent research on head-pose estimation has focused on developing models based on deep convolutional neural networks (CNNs). These models are trained using transfer-learning and image augmentation to achieve better initiation states and robustness against occlusions. However, methods that use transfer-learning networks are usually aimed at general image recognition and offer no in-depth study of transfer learning from more task-related networks. Additionally, for the head-pose estimation, robustness against heavy occlusion, and noise such as motion blur and low-brightness are vital. In this paper, we propose a new image-augmentation approach that significantly improves the estimation accuracy of the head-pose model. We also propose a task-related weight initialization to further improve the estimation accuracy by studying internal activations of models trained for face-related tasks such as face-recognition. We test our head-pose estimation model on three challenging test sets and achieve better results to state-of-the-art methods.
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
Authors: Maria Mahbub, Sudarshan Srinivasan, Edmon Begoli, Gregory D Peterson
Date: 2022-02-26 00:00:00
Tasks: Domain Adaptation, Machine Reading Comprehension, Reading Comprehension, Transfer Learning

Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model's performance. We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets -- BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. Availability: BioADAPT-MRC is freely available as an open-source project at \url{https://github.com/mmahbub/BioADAPT-MRC}.
MSCTD: A Multimodal Sentiment Chat Translation Dataset
Authors: Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie zhou
Date: 2022-02-28 00:00:00
Proceeding: ACL 2022 5
Tasks: Machine Translation, Multimodal Machine Translation, Sentiment Analysis, Translation

Multimodal machine translation and textual chat translation have received considerable attention in recent years. Although the conversation in its natural form is usually multimodal, there still lacks work on multimodal machine translation in conversations. In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context. To this end, we firstly construct a Multimodal Sentiment Chat Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs in 14,762 bilingual dialogues and 30,370 English-German utterance pairs in 3,079 bilingual dialogues. Each utterance pair, corresponding to the visual context that reflects the current conversational scene, is annotated with a sentiment label. Then, we benchmark the task by establishing multiple baseline systems that incorporate multimodal and sentiment features for MCT. Preliminary experiments on four language directions (English-Chinese and English-German) verify the potential of contextual and multimodal information fusion and the positive impact of sentiment on the MCT task. Additionally, as a by-product of the MSCTD, it also provides two new benchmarks on multimodal dialogue sentiment analysis. Our work can facilitate research on both multimodal chat translation and multimodal dialogue sentiment analysis.
Deep learning enhanced Rydberg multifrequency microwave recognition
Authors: Zong-Kai Liu, Li-Hua Zhang, Bang Liu, Zheng-Yuan Zhang, Guang-Can Guo, Dong-Sheng Ding, Bao-Sen Shi
Date: 2022-02-28 00:00:00
Tasks: Deep Learning

Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields is promising in MW radar and MW communications. However, Rydberg atoms are sensitive not only to the MW signal but also to noise from atomic collisions and the environment, meaning that solution of the governing Lindblad master equation of light-atom interactions is complicated by the inclusion of noise and high-order terms. Here, we solve these problems by combining Rydberg atoms with deep learning model, demonstrating that this model uses the sensitivity of the Rydberg atoms while also reducing the impact of noise without solving the master equation. As a proof-of-principle demonstration, the deep learning enhanced Rydberg receiver allows direct decoding of the frequency-division multiplexed (FDM) signal. This type of sensing technology is expected to benefit Rydberg-based MW fields sensing and communication.
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization
Authors: Junren Chen, Cheng-Long Wang, Michael K. Ng, Di Wang
Date: 2022-02-26 00:00:00
Tasks: compressed sensing, Low-Rank Matrix Completion, Matrix Completion, Quantization, Vocal Bursts Intensity Prediction

In this paper, we propose a uniformly dithered 1-bit quantization scheme for high-dimensional statistical estimation. The scheme contains truncation, dithering, and quantization as typical steps. As canonical examples, the quantization scheme is applied to the estimation problems of sparse covariance matrix estimation, sparse linear regression (i.e., compressed sensing), and matrix completion. We study both sub-Gaussian and heavy-tailed regimes, where the underlying distribution of heavy-tailed data is assumed to have bounded moments of some order. We propose new estimators based on 1-bit quantized data. In sub-Gaussian regime, our estimators achieve near minimax rates, indicating that our quantization scheme costs very little. In heavy-tailed regime, while the rates of our estimators become essentially slower, these results are either the first ones in an 1-bit quantized and heavy-tailed setting, or already improve on existing comparable results from some respect. Under the observations in our setting, the rates are almost tight in compressed sensing and matrix completion. Our 1-bit compressed sensing results feature general sensing vector that is sub-Gaussian or even heavy-tailed. We also first investigate a novel setting where both the covariate and response are quantized. In addition, our approach to 1-bit matrix completion does not rely on likelihood and represent the first method robust to pre-quantization noise with unknown distribution. Experimental results on synthetic data are presented to support our theoretical analysis.
Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation
Authors: Song Wang, Jianke Zhu, Ruixiang Zhang
Date: 2022-02-27 00:00:00
Tasks: 3D Semantic Segmentation, Autonomous Vehicles, Image Segmentation, LIDAR Semantic Segmentation, Segmentation, Semantic Segmentation

LiDAR sensor is essential to the perception system in autonomous vehicles and intelligent robots. To fulfill the real-time requirements in real-world applications, it is necessary to efficiently segment the LiDAR scans. Most of previous approaches directly project 3D point cloud onto the 2D spherical range image so that they can make use of the efficient 2D convolutional operations for image segmentation. Although having achieved the encouraging results, the neighborhood information is not well-preserved in the spherical projection. Moreover, the temporal information is not taken into consideration in the single scan segmentation task. To tackle these problems, we propose a novel approach to semantic segmentation for LiDAR sequences named Meta-RangeSeg, where a new range residual image representation is introduced to capture the spatial-temporal information. Specifically, Meta-Kernel is employed to extract the meta features, which reduces the inconsistency between the 2D range image coordinates input and 3D Cartesian coordinates output. An efficient U-Net backbone is used to obtain the multi-scale features. Furthermore, Feature Aggregation Module (FAM) strengthens the role of range channel and aggregates features at different levels. We have conducted extensive experiments for performance evaluation on SemanticKITTI and SemanticPOSS. The promising results show that our proposed Meta-RangeSeg method is more efficient and effective than the existing approaches. Our full implementation is publicly available at https://github.com/songw-zju/Meta-RangeSeg .
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
Authors: MohammadJavad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh
Date: 2022-02-25 00:00:00
Tasks: Meta-Learning

We study a sequential decision problem where the learner faces a sequence of $K$-armed bandit tasks. The task boundaries might be known (the bandit meta-learning setting), or unknown (the non-stationary bandit setting). For a given integer $M\le K$, the learner aims to compete with the best subset of arms of size $M$. We design an algorithm based on a reduction to bandit submodular maximization, and show that, for $T$ rounds comprised of $N$ tasks, in the regime of large number of tasks and small number of optimal arms $M$, its regret in both settings is smaller than the simple baseline of $\tilde{O}(\sqrt{KNT})$ that can be obtained by using standard algorithms designed for non-stationary bandit problems. For the bandit meta-learning problem with fixed task length $\tau$, we show that the regret of the algorithm is bounded as $\tilde{O}(NM\sqrt{M \tau}+N^{2/3}M\tau)$. Under additional assumptions on the identifiability of the optimal arms in each task, we show a bandit meta-learning algorithm with an improved $\tilde{O}(N\sqrt{M \tau}+N^{1/2}\sqrt{M K \tau})$ regret.
An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture Search
Authors: Yongdong Huang, Yuanzhan Li, Xulong Cao, Siyu Zhang, Shen Cai, Ting Lu, Jie Wang, Yuqi Liu
Date: 2022-02-27 00:00:00
Tasks: Binary Classification, Neural Architecture Search, Surface Reconstruction

Using neural networks to represent 3D objects has become popular. However, many previous works employ neural networks with fixed architecture and size to represent different 3D objects, which lead to excessive network parameters for simple objects and limited reconstruction accuracy for complex objects. For each 3D model, it is desirable to have an end-to-end neural network with as few parameters as possible to achieve high-fidelity reconstruction. In this paper, we propose an efficient voxel reconstruction method utilizing neural architecture search (NAS) and binary classification. Taking the number of layers, the number of nodes in each layer, and the activation function of each layer as the search space, a specific network architecture can be obtained based on reinforcement learning technology. Furthermore, to get rid of the traditional surface reconstruction algorithms (e.g., marching cube) used after network inference, we complete the end-to-end network by classifying binary voxels. Compared to other signed distance field (SDF) prediction or binary classification networks, our method achieves significantly higher reconstruction accuracy using fewer network parameters.