pwc-archive/papers-with-abstracts huggingface dataset:
Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression
Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan, Qintai Yang, Xiaoying Wang
Date: 2021-06-21 00:00:00
Proceeding: Pattern Recognition 2021 6
Tasks: Action Assessment, Anomaly Detection

Human action evaluation (HAE) involves judgments about the abnormality and quality of human actions. If performed effectively, HAE based on skeleton data can be used to monitor the outcomes of behavioral therapies for Alzheimer's disease (AD). In this paper, we propose a two-task graph convolutional network (2T-GCN) to represent skeleton data for HAE tasks involving abnormality detection and quality evaluation. The network  is first evaluated using the UI-PRMD dataset and demonstrates accurate abnormality detection. Regarding quality evaluation, in addition to laboratory-collected UI-PRMD data, we test the network on a set of real exercise data collected from patients with AD. A numerical score indicating the degree to which actions deviate from normal is taken to reflect the severity of AD; thus, we apply 2T-GCN to determine such scores. Experimental results show that numerical scores for certain exercises performed by patients with AD are consistent with their AD severity level as identified by clinical staff. This corroboration highlights the potential of our approach for monitoring AD and other neurodegenerative diseases.
A General Approach for Traffic Classification in Wireless Networks Using Deep Learning
Authors: Miguel Camelo
Date: 2022-03-01 00:00:00
Proceeding: 2022-03 2022 3
Tasks: Management, Traffic Classification

Traffic Classification (TC) systems allow inferring the application that is generating the traffic being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have outperformed traditional methods in complex and modern scenarios, even if traffic is encrypted. Most of the works on TC assume the traffic flows on a wired network under the same network management domain. This assumption limits the capabilities of TC systems in wireless networks since users’ traffic on one network domain can be negatively impacted by undetected users’ traffic from other network domains or detected ones but with no traffic context in a shared spectrum. To solve this problem, we introduce a novel framework to achieve TC at any layer on the radio network stack. We propose a spectrum-based procedure that uses a DL-based classifier to realize this framework. We design two DL-based classifiers, a novel Convolutional Neural Network (CNN) spectrum-based TC and a Recurrent Neural Networks (RNN) as baseline architecture, and benchmark their performance on three TC tasks at different radio stack layers. The datasets were generated by combining packet traces from real transmissions with an 802.11 standard-compliant waveform generator. Performance evaluations show that the best model can achieve an accuracy above 92% in the most demanding TC task, a drop of only 4.37% in accuracy compared to a byte-based DL approach, with micro-second per-packet prediction time, which is very promising for delivering real-time spectrum-based traffic analyzers.
Foreign participation in federal biddings: A quantitative approach using the procurement panel
Authors: Carlos Ferreira
Date: 2023-03-25 00:00:00

The bidding is the Public Administration's administrative process and other designated persons by law to select the best proposal, through objective and impersonal criteria, for contracting services and purchasing goods. In times of globalization, it is common for companies seeking to expand their business by participating in biddings. Brazilian legislation allows the participation of foreign suppliers in bids held in the country. Through a quantitative approach, this article discusses the weight of foreign suppliers' involvement in federal bidding between 2011 and 2018. To this end, a literature review was conducted on public procurement and international biddings. Besides, an extensive data search was achieved through the Federal Government Procurement Panel. The results showed that between 2011 and 2018, more than R\$ 422.6 billion was confirmed in public procurement processes, and of this total, about R\$ 28.9 billion was confirmed to foreign suppliers. The Ministry of Health accounted for approximately 88.67% of these confirmations. The Invitation, Competition and International Competition modalities accounted for 0.83% of the amounts confirmed to foreign suppliers. Impossible Bidding, Waived Bidding, and Reverse Auction modalities accounted for 99.17% of the confirmed quantities to foreign suppliers. Based on the discussion of the results and the limitations found, some directions for further studies and measures to increase public resources expenditures' effectiveness and efficiency are suggested.
EGCN: An Ensemble-based Learning Framework for Exploring Effective Skeleton-based Rehabilitation Exercise Assessment
Authors: Bruce X.B. Yu, Yan Liu, Xiang Zhang, Gong Chen, Keith C.C. Chan
Date: 2022-07-01 00:00:00
Proceeding: IJCAI 2022 7
Tasks: Action Assessment

Recently, some skeleton-based physical therapy systems have been attempted to automatically evaluate the correctness or quality of an exercise performed by rehabilitation subjects. However, in terms of algorithms and evaluation criteria, the task remains not fully explored regarding making full use of different skeleton features. To advance the prior work, we propose a learning framework called Ensemble-based Graph Convolutional Network (EGCN) for skeleton-based rehabilitation exercise assessment. As far as we know, this is the first attempt that utilizes both two skeleton feature groups and investigates different ensemble strategies for the task. We also examine the properness of existing evaluation criteria and focus on evaluating the prediction ability of our proposed method. We then conduct extensive cross-validation experiments on two latest public datasets: UI-PRMD and KIMORE. Results indicate that the model-level ensemble scheme of our EGCN achieves better performance than existing methods. Code is available: https://github.com/bruceyo/EGCN.
A New Production Function Approach
Authors: Samidh Pal
Date: 2023-03-25 00:00:00

This paper presents a new nested production function that is specifically designed for analyzing capital and labor intensity of manufacturing industries in developing and developed regions. The paper provides a rigorous theoretical foundation for this production function, as well as an empirical analysis of its performance in a sample of industries. The analysis shows that the production function can be used to accurately estimate the level of capital and labor intensity in industries, as well as to analyze the capacity utilization of these industries.
Quadratic Graph Attention Network (Q-GAT) for Robust Construction of Gene Regulatory Networks
Authors: HUI ZHANG, Xuexin An, Qiang He, YuDong Yao, Yudong Zhang, Feng-Lei Fan, Yueyang Teng
Date: 2023-03-24 00:00:00
Tasks: Graph Attention

Gene regulatory relationships can be abstracted as a gene regulatory network (GRN), which plays a key role in characterizing complex cellular processes and pathways. Recently, graph neural networks (GNNs), as a class of deep learning models, have emerged as a useful tool to infer gene regulatory relationships from gene expression data. However, deep learning models have been found to be vulnerable to noise, which greatly hinders the adoption of deep learning in constructing GRNs, because high noise is often unavoidable in the process of gene expression measurement. Can we preferably prototype a robust GNN for constructing GRNs? In this paper, we give a positive answer by proposing a Quadratic Graph Attention Network (Q-GAT) with a dual attention mechanism. We study the changes in the predictive accuracy of Q-GAT and 9 state-of-the-art baselines by introducing different levels of adversarial perturbations. Experiments in the E. coli and S. cerevisiae datasets suggest that Q-GAT outperforms the state-of-the-art models in robustness. Lastly, we dissect why Q-GAT is robust through the signal-to-noise ratio (SNR) and interpretability analyses. The former informs that nonlinear aggregation of quadratic neurons can amplify useful signals and suppress unwanted noise, thereby facilitating robustness, while the latter reveals that Q-GAT can leverage more features in prediction thanks to the dual attention mechanism, which endows Q-GAT with the ability to confront adversarial perturbation. We have shared our code in https://github.com/Minorway/Q-GAT_for_Robust_Construction_of_GRN for readers' evaluation.
XVoxel-Based Parametric Design Optimization of Feature Models
Authors: Ming Li, Chengfeng Lin, Wei Chen, Yusheng Liu, Shuming Gao, Qiang Zou
Date: 2023-03-17 00:00:00
Tasks: Computational Efficiency

Parametric optimization is an important product design technique, especially in the context of the modern parametric feature-based CAD paradigm. Realizing its full potential, however, requires a closed loop between CAD and CAE (i.e., CAD/CAE integration) with automatic design modifications and simulation updates. Conventionally the approach of model conversion is often employed to form the loop, but this way of working is hard to automate and requires manual inputs. As a result, the overall optimization process is too laborious to be acceptable. To address this issue, a new method for parametric optimization is introduced in this paper, based on a unified model representation scheme called eXtended Voxels (XVoxels). This scheme hybridizes feature models and voxel models into a new concept of semantic voxels, where the voxel part is responsible for FEM solving, and the semantic part is responsible for high-level information to capture both design and simulation intents. As such, it can establish a direct mapping between design models and analysis models, which in turn enables automatic updates on simulation results for design modifications, and vice versa -- effectively a closed loop between CAD and CAE. In addition, robust and efficient geometric algorithms for manipulating XVoxel models and efficient numerical methods (based on the recent finite cell method) for simulating XVoxel models are provided. The presented method has been validated by a series of case studies of increasing complexity to demonstrate its effectiveness. In particular, a computational efficiency improvement of up to 55.8 times the existing FCM method has been seen.
Mitigating Decentralized Finance Liquidations with Reversible Call Options
Authors: Kaihua Qin, Jens Ernstberger, Liyi Zhou, Philipp Jovanovic, Arthur Gervais
Date: 2023-02-10 00:00:00

Liquidations in Decentralized Finance (DeFi) are both a blessing and a curse -- whereas liquidations prevent lenders from capital loss, they simultaneously lead to liquidation spirals and system-wide failures. Since most lending and borrowing protocols assume liquidations are indispensable, there is an increased interest in alternative constructions that prevent immediate systemic-failure under uncertain circumstances. In this work, we introduce reversible call options, a novel financial primitive that enables the seller of a call option to terminate it before maturity. We apply reversible call options to lending in DeFi and devise Miqado, a protocol for lending platforms to replace the liquidation mechanisms. To the best of our knowledge, Miqado is the first protocol that actively mitigates liquidations to reduce the risk of liquidation spirals. Instead of selling collateral, Miqado incentivizes external entities, so-called supporters, to top-up a borrowing position and grant the borrower additional time to rescue the debt. Our simulation shows that Miqado reduces the amount of liquidated collateral by 89.82% in a worst-case scenario.
How creative versus technical constraints affect individual learning in an online innovation community
Authors: Victor P. Seidel, Christoph Riedl
Date: 2023-03-27 00:00:00
Tasks: Management

Online innovation communities allow for a search for novel solutions within a design space bounded by constraints. Past research has focused on the effect of creative constraints on individual projects, but less is known about how constraints affect learning from repeated design submissions and the effect of the technical constraints that are integral to online platforms. How do creative versus technical constraints affect individual learning in exploring a design space in online communities? We analyzed ten years of data from an online innovation community that crowdsourced 136,989 design submissions from 33,813 individuals. We leveraged data from two types of design contests-creatively constrained and unconstrained-running in parallel on the platform, and we evaluated a natural experiment where a platform change reduced technical constraints. We find that creative constraints lead to high rates of learning only if technical constraints are sufficiently relaxed. Our findings have implications for the management of creative design work and the downstream effects of the technical constraints of the information systems that support online innovation communities.
Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machine
Authors: Hiroshi Yamashita, Ken-ichi Okubo, Suguru Shimomura, Yusuke Ogura, Jun Tanida, Hideyuki Suzuki
Date: 2023-03-27 00:00:00
Tasks: Combinatorial Optimization

The spatial photonic Ising machine (SPIM) [D. Pierangeli et al., Phys. Rev. Lett. 122, 213902 (2019)] is a promising optical architecture utilizing spatial light modulation for solving large-scale combinatorial optimization problems efficiently. The primitive version of the SPIM, however, can accommodate Ising problems with only rank-one interaction matrices. In this Letter, we propose a new computing model for the SPIM that can accommodate any Ising problem without changing its optical implementation. The proposed model is particularly efficient for Ising problems with low-rank interaction matrices, such as knapsack problems. Moreover, it acquires the learning ability of Boltzmann machines. We demonstrate that learning, classification, and sampling of the MNIST handwritten digit images are achieved efficiently using the model with low-rank interactions. Thus, the proposed model exhibits higher practical applicability to various problems of combinatorial optimization and statistical learning, without losing the scalability inherent in the SPIM architecture.
Weighted Fair Division with Matroid-Rank Valuations: Monotonicity and Strategyproofness
Authors: Warut Suksompong, Nicholas Teh
Date: 2023-03-25 00:00:00

We study the problem of fairly allocating indivisible goods to agents with weights corresponding to their entitlements. Previous work has shown that, when agents have binary additive valuations, the maximum weighted Nash welfare rule is resource-, population-, and weight-monotone, satisfies group-strategyproofness, and can be implemented in polynomial time. We generalize these results to the class of weighted additive welfarist rules with concave functions and agents with matroid-rank (also known as binary submodular) valuations.
Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications
Authors: Sixian Wang, Jincheng Dai, Xiaoqi Qin, Zhongwei Si, Kai Niu, Ping Zhang
Date: 2023-03-26 00:00:00
Tasks: Data Interaction

Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel coding (NTSCC), extract the semantic latent features of source signal, and learn entropy model to guide the joint source-channel coding with variable rate to transmit latent features over wireless channels. In this paper, we propose a comprehensive framework for improving NTSCC, thereby higher system coding gain, better model versatility, and more flexible adaptation strategy aligned with semantic guidance are all achieved. This new sophisticated NTSCC model is now ready to support large-size data interaction in emerging XR, which catalyzes the application of semantic communications. Specifically, we propose three useful improvement approaches. First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain. On that basis, we further propose response network architectures to formulate versatile NTSCC, i.e., once-trained model supports various rates and channel states that benefits the practical deployment. Following this, we propose an online latent feature editing method to enable more flexible coding rate control aligned with some specific semantic guidance. By comprehensively applying the above three improvement methods for NTSCC, a deployment-friendly semantic coded transmission system stands out finally. Our improved NTSCC system has been experimentally verified to achieve considerable bandwidth saving versus the state-of-the-art engineered VTM + 5G LDPC coded transmission system with lower processing latency.
Modified Implicit Discretization of the Super-Twisting Controller
Authors: Benedikt Andritsch, Lars Watermann, Stefan Koch, Markus Reichhartinger, Johann Reger, Martin Horn
Date: 2023-03-27 00:00:00

In this paper a novel discrete-time realization of the super-twisting controller is proposed. The closed-loop system is proven to converge to an invariant set around the origin in finite time. Furthermore, the steady-state error is shown to be independent of the controller gains. It only depends on the sampling time and the unknown disturbance. The proposed discrete-time controller is evaluated comparative to previously published discrete-time super-twisting controllers by means of the controller structure and in extensive simulation studies. The continuous-time super-twisting controller is capable of rejecting any unknown Lipschitz-continuous perturbation and converges in finite time. Furthermore, the convergence time decreases, if any of the gains is increased. The simulations demonstrate that the closed-loop systems with each of the known controllers lose one of these properties, introduce discretization-chattering, or do not yield the same accuracy level as with the proposed controller. The proposed controller, in contrast, is beneficial in terms of the above described properties.
Sparse Feedback Controller: From Open-loop Solution to Closed-loop Realization
Authors: Zhicheng Zhang, Yasumasa Fujisaki
Date: 2023-03-27 00:00:00

In this paper, we explore the discrete time sparse feedback control for a linear invariant system, where the proposed optimal feedback controller enjoys input sparsity by using a dynamic linear compensator, i.e., the components of feedback control signal having the smallest possible nonzero values. The resulting augmented dynamics ensures closed-loop stability, which infers sparse feedback controller from open-loop solution to closed-loop realization. In particular, we show that the implemented sparse feedback (closed-loop) control solution is equivalent to the original sparse (open-loop) control solution under a specified basis. We then extend the dynamic compensator to a feedforward tracking control problem. Finally, numerical examples demonstrate the effectiveness of proposed control approach.
LQG Control Over SWIPT-enabled Wireless Communication Network
Authors: Huiwen Yang, Lingying Huang, Yuzhe Li, Subhrakanti Dey, Ling Shi
Date: 2023-03-27 00:00:00

In this paper, we consider using simultaneous wireless information and power transfer (SWIPT) to recharge the sensor in the LQG control, which provides a new approach to prolonging the network lifetime. We analyze the stability of the proposed system model and show that there exist two critical values for the power splitting ratio {\alpha}. Then, we propose an optimization problem to derive the optimal value of {\alpha}. This problem is non-convex but its numerical solution can be derived by our proposed algorithm efficiently. Moreover, we provide the feasible condition of the proposed optimization problem. Finally, simulation results are presented to verify and illustrate the main theoretical results.
Control-oriented modeling of a LiBr/H2O absorption heat pumping device and experimental validation
Authors: Sandra Staudt, Viktor Unterberger, Markus Gölles, Michael Wernhart, René Rieberer, Martin Horn
Date: 2023-03-27 00:00:00
Tasks: Model Predictive Control

Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) are devices that use thermal energy instead of electricity to generate heating and cooling, thereby facilitating the use of waste heat and renewable energy sources such as solar or geothermal energy. Despite this benefit, widespread use of AHPDs is still limited. One reason for this is partly unsatisfactory control performance under varying operating conditions, which can result in poor modulation and part load capability. A promising approach to tackle this issue is using dynamic, model-based control strategies, whose effectiveness, however, strongly depend on the model being used. This paper therefore focuses on the derivation of a viable dynamic model to be used for such model-based control strategies for AHPDs such as state feedback or model-predictive control. The derived model is experimentally validated, showing good modeling accuracy. Its modeling accuracy is also compared to alternative model versions, that contain other heat transfer correlations, as a benchmark. Although the derived model is mathematically simple, it does have the structure of a nonlinear differential-algebraic system of equations. To obtain an even simpler model structure, linearization at an operating point is discussed to derive a model in linear state space representation. The experimental validation shows that the linear model does have slightly worse steady-state accuracy, but that the dynamic accuracy seems to be almost unaffected by the linearization. The presented new modeling approach is considered suitable to be used as a basis for the design of advanced, model-based control strategies, ultimately aiming to improve the modulation and part load capability of AHPDs.
Matrix-Scaled Consensus over Undirected Networks
Authors: Minh Hoang Trinh, Hoang Huy Vu, Nhat-Minh Le-Phan, Quyen Ngoc Nguyen
Date: 2023-03-26 00:00:00

In this paper, we propose matrix-scaled consensus algorithms for linear dynamical agents interacting over an undirected network. Under the proposed algorithms, the state vectors of all agents to asymptotically agree up to some matrix scaling weights. First, the algebraic properties of the matrix-scaled Laplacian and the geometry of the matrix-scaled consensus space are studied. Second, we examine matrix-scaled consensus algorithms for networks of single-integrators with or without constant parametric uncertainties. Nonlinear and finite-time matrix-scaled consensus algorithms are also proposed. Third, observer-based matrix-scaled consensus algorithms for homogeneous or heterogeneous linear-time invariant agents are designed. The convergence of the proposed algorithms is asserted by rigorous mathematical analysis and supported by numerical simulations.