pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Agent Learning for Resilient Distributed Control Systems
Authors: Yuhan Zhao, Craig Rieger, Quanyan Zhu
Date: 2022-08-09 00:00:00

Resilience describes a system's ability to function under disturbances and threats. Many critical infrastructures, including smart grids and transportation networks, are large-scale complex systems consisting of many interdependent subsystems. Decentralized architecture becomes a key resilience design paradigm for large-scale systems. In this book chapter, we present a multi-agent system (MAS) framework for distributed large-scale control systems and discuss the role of MAS learning in resiliency. This chapter introduces the creation of an artificial intelligence (AI) stack in the MAS to provide computational intelligence for subsystems to detect, respond, and recover. We discuss the application of learning methods at the cyber and physical layers of the system. The discussions focus on distributed learning algorithms for subsystems to respond to each other, and game-theoretic learning for them to respond to disturbances and adversarial behaviors. The book chapter presents a case study of distributed renewable energy systems to elaborate on the MAS architecture and its interface with the AI stack.
Testing for homogeneous treatment effects in linear and nonparametric instrumental variable models
Authors: Jad Beyhum, Jean-Pierre Florens, Elia Lapenta, Ingrid Van Keilegom
Date: 2022-08-10 00:00:00

The hypothesis of homogeneous treatment effects is central to the instrumental variables literature. This assumption signifies that treatment effects are constant across all subjects. It allows to interpret instrumental variable estimates as average treatment effects over the whole population of the study. When this assumption does not hold, the bias of instrumental variable estimators can be larger than that of naive estimators ignoring endogeneity. This paper develops two tests for the assumption of homogeneous treatment effects when the treatment is endogenous and an instrumental variable is available. The tests leverage a covariable that is (jointly with the error terms) independent of a coordinate of the instrument. This covariate does not need to be exogenous. The first test assumes that the potential outcomes are linear in the regressors and is computationally simple. The second test is nonparametric and relies on Tikhonov regularization. The treatment can be either discrete or continuous. We show that the tests have asymptotically correct level and asymptotic power equal to one against a range of alternatives. Simulations demonstrate that the proposed tests attain excellent finite sample performances. The methodology is also applied to the evaluation of returns to schooling and the effect of price on demand in a fish market.
Endogeneity in Weakly Separable Models without Monotonicity
Authors: Songnian Chen, Shakeeb Khan, Xun Tang
Date: 2022-08-09 00:00:00

We identify and estimate treatment effects when potential outcomes are weakly separable with a binary endogenous treatment. Vytlacil and Yildiz (2007) proposed an identification strategy that exploits the mean of observed outcomes, but their approach requires a monotonicity condition. In comparison, we exploit full information in the entire outcome distribution, instead of just its mean. As a result, our method does not require monotonicity and is also applicable to general settings with multiple indices. We provide examples where our approach can identify treatment effect parameters of interest whereas existing methods would fail. These include models where potential outcomes depend on multiple unobserved disturbance terms, such as a Roy model, a multinomial choice model, as well as a model with endogenous random coefficients. We establish consistency and asymptotic normality of our estimators.
Measuring Race in US Economic Statistics: What Do We Know?
Authors: Sonya Ravindranath Waddell, John M. Abowd, Camille Busette, Mark Hugo Lopez
Date: 2022-08-10 00:00:00

This article is an edited transcript of the session of the same name at the 38th Annual NABE Economic Policy Conference: Policy Options for Sustainable and Inclusive Growth. The panelists are experts from government and private research organizations.
Patronage and power in rural India: a study based on interaction networks
Authors: Anindya Bhattacharya, Anirban Kar, Sunil Kumar, Alita Nandi
Date: 2022-08-09 00:00:00
Tasks: Survey

This work has two intertwined components: first, as part of a research programme it introduces a new methodology for identifying `power-centres' in rural societies of developing countries in general and then applies that in the specific context of contemporary rural India for addressing some debates on the dynamics of power in rural India. We identify the nature of `local' rural institutions based on primary data collected by ourselves (in 2013 and 2014). We took 36 villages in the states of Maharashtra, Odisha and Uttar Pradesh - 12 in each of these states - as the sites for our observation and data collection. We quantify nature of institutions from data on the day-to-day interactions of households in the spheres of economy, society and politics. Our household survey shows that there is substantial variation in power structure across regions. We identified the presence of `local elites' in 22 villages out of 36 surveyed. We conducted a follow-up survey, called `elite survey', to get detailed information about the identified elite households. We observe that landlordism has considerably weakened, land has ceased to be the sole source of power and new power-centres have emerged. Despite these changes, caste, landownership and patron-client relation continue to be three important pillars of rural power structure.
Welfare ordering of voting weight allocations
Authors: Kazuya Kikuchi
Date: 2022-08-10 00:00:00

This paper studies the allocation of voting weights in a committee representing groups of different sizes. We introduce a partial ordering of weight allocations based on stochastic comparison of social welfare. We show that when the number of groups is sufficiently large, this ordering asymptotically coincides with the total ordering induced by the cosine proportionality between the weights and the group sizes. A corollary is that a class of expectation-form objective functions, including expected welfare, the mean majority deficit and the probability of inversions, are asymptotically monotone in the cosine proportionality.
Conditions for none to be whipped by `Rank and Yank' under the majority rule
Authors: Fujun Hou
Date: 2022-08-10 00:00:00

`Rank and Yank' is practiced in many organizations. This paper is concerned with the condtions for none to be whipped by `Rank and Yank' when the evaluation data under each criterion are assumed to be ordinal rankings and the majority rule is used. Two sufficient conditions are set forth of which the first one formulates the alternatives indifference definition in terms of the election matrix, while the second one specifies a certain balance in the probabilities of alternatives being ranked at positions. In a sense, `none to be whipped' means that the organization is of stability. Thus the second sufficient condition indicates an intrinsic relation of balance and organization stability. In addition, directions for future research are put forward.
Pricing Novel Goods
Authors: Francesco Giovannoni, Toomas Hinnosaar
Date: 2022-08-09 00:00:00

We study a bilateral trade problem where a principal has private information that is revealed with delay, such as a seller who does not yet know her production cost. Postponing the contracting process incurs a costly delay, while early contracting with limited information can create incentive issues, as the principal might misrepresent private information that will be revealed later. We show that the optimal mechanism can effectively address these challenges by leveraging the sequential nature of the problem. The optimal mechanism is a menu of two-part tariffs, where the variable part is determined by the principal's incentives and the fixed part by the agent's incentives. As two-part tariffs might be impractical in some applications, we also study price mechanisms. We show that the optimal price mechanism often entails trade at both the ex-ante and ex-post stages. Dynamic price mechanisms can lower the cost of delay by transacting with high-type agents early and relax the incentive constraints by postponing contracts with lower-type agents. We also generalize our analysis to costly learning and study ex-post efficiency in our context.
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech
Authors: Kaitao Song, Teng Wan, Bixia Wang, Huiqiang Jiang, Luna Qiu, Jiahang Xu, Liping Jiang, Qun Lou, Yuqing Yang, Dongsheng Li, Xudong Wang, Lili Qiu
Date: 2022-08-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition, Speech-to-Text

Hypernasality is an abnormal resonance in human speech production, especially in patients with craniofacial anomalies such as cleft palate. In clinical application, hypernasality estimation is crucial in cleft palate diagnosis, as its results determine the subsequent surgery and additional speech therapy. Therefore, designing an automatic hypernasality assessment method will facilitate speech-language pathologists to make precise diagnoses. Existing methods for hypernasality estimation only conduct acoustic analysis based on low-resource cleft palate dataset, by using statistical or neural network-based features. In this paper, we propose a novel approach that uses automatic speech recognition model to improve hypernasality estimation. Specifically, we first pre-train an encoder-decoder framework in an automatic speech recognition (ASR) objective by using speech-to-text dataset, and then fine-tune ASR encoder on the cleft palate dataset for hypernasality estimation. Benefiting from such design, our model for hypernasality estimation can enjoy the advantages of ASR model: 1) compared with low-resource cleft palate dataset, the ASR task usually includes large-scale speech data in the general domain, which enables better model generalization; 2) the text annotations in ASR dataset guide model to extract better acoustic features. Experimental results on two cleft palate datasets demonstrate that our method achieves superior performance compared with previous approaches.
G-PCC Post-Processing Using Fractional Super-Resolution
Authors: Renan U. B. Ferreira, Tomas M. Borges, Diogo C. Garcia, Ricardo L. De Queiroz
Date: 2022-08-09 00:00:00
Tasks: Super-Resolution

We present a method for post-processing point clouds' geometric information by applying a previously proposed fractional super-resolution technique to clouds compressed and decoded with MPEG's G-PCC codec. In some sense, this is a continuation of that previous work, which requires only a down-scaled point cloud and a scaling factor, both of which are provided by the G-PCC codec. For non-solid point clouds, an a priori down-scaling is required for improved efficiency. The method is compared to the GPCC itself, as well as machine-learning-based techniques. Results show a great improvement in quality over GPCC and comparable performance to the latter techniques, with the
Multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer's Disease Prediction
Authors: Yongcheng Zong, Changhong Jing, Qiankun Zuo
Date: 2022-08-09 00:00:00
Tasks: Disease Prediction, Hippocampus

The application of machine learning algorithms to the diagnosis and analysis of Alzheimer's disease (AD) from multimodal neuroimaging data is a current research hotspot. It remains a formidable challenge to learn brain region information and discover disease mechanisms from various magnetic resonance images (MRI). In this paper, we propose a simple but highly efficient end-to-end model, a multiscale autoencoder with structural-functional attention network (MASAN) to extract disease-related representations using T1-weighted Imaging (T1WI) and functional MRI (fMRI). Based on the attention mechanism, our model effectively learns the fused features of brain structure and function and finally is trained for the classification of Alzheimer's disease. Compared with the fully convolutional network, the proposed method has further improvement in both accuracy and precision, leading by 3% to 5%. By visualizing the extracted embedding, the empirical results show that there are higher weights on putative AD-related brain regions (such as the hippocampus, amygdala, etc.), and these regions are much more informative in anatomical studies. Conversely, the cerebellum, parietal lobe, thalamus, brain stem, and ventral diencephalon have little predictive contribution.
User Association and Hybrid Beamforming Designs for Cooperative mmWave MIMO Systems
Authors: Pengfei Ni, Rang Liu, Ming Li, Qian Liu
Date: 2022-08-10 00:00:00
Tasks: CPU

Hybrid analog and digital beamforming has emerged as a key enabling technology for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems since it can balance the trade-off between system performance and hardware efficiency. Owing to the strong ability of central control, cooperative networks show great potential to enhance the spectral efficiency of mmWave communications. In this paper, we consider cooperative mmWave MIMO systems and propose user association and hybrid beamforming design algorithms for three typical hybrid beamforming architectures. The central processing unit (CPU) of the cooperative networks first matches the service pairs of base stations (BSs) and users. Then, an iterative hybrid beamforming design algorithm is proposed to maximize the weighted achievable sum-rate performance of the mmWave MIMO system with fully connected hybrid beamforming architecture. Moreover, a heuristic analog beamforming design algorithm is introduced for the fixed subarray hybrid beamforming architecture. In an effort to further exploit multiple-antenna diversities, we also consider the dynamic subarray architecture and propose a novel antenna design algorithm for the analog beamforming design. Simulation results illustrate that the proposed hybrid beamforming algorithms achieve a significant performance improvement than other existing approaches and the dynamic subarray architecture has great advantages of improving the energy efficiency (EE) performance.
Partially Distributed Beamforming Design for RIS-Aided Cell-Free Networks
Authors: Pengfei Ni, Ming Li, Rang Liu, Qian Liu
Date: 2022-08-10 00:00:00
Tasks: CPU

Cell-free networks are regarded as a promising technology to meet higher rate requirements for beyond fifth-generation (5G) communications. Most works on cell-free networks focus on either fully centralized beamforming to maximally enhance system performance, or fully distributed beamforming to avoid extensive channel state information (CSI) exchange among access points (APs). In order to achieve both network capacity improvement and CSI exchange reduction, we propose a partially distributed beamforming design algorithm for reconfigurable intelligent surface (RIS)-aided cell-free networks. We aim at maximizing the weighted sum-rate of all users by designing active and passive beamforming subject to transmit power constraints of APs and unit-modulus constraints of RIS elements. The weighted sum-rate maximization problem is first transformed into an equivalent weighted sum-mean-square-error (sum-MSE) minimization problem, and then alternating optimization (AO) approach is adopted to iteratively design active and passive beamformer. Specifically, active beamforming vectors are obtained by local APs and passive beamforming vector is optimized by central processing unit (CPU). Numerical results not only illustrate the proposed partially distributed algorithm achieves the remarkable performance improvement compared with conventional local beamforming methods, but also further show the considerable potential of deploying RIS in cell-free networks.
Current and perspective sensing methods for monkeypox virus: a reemerging zoonosis in its infancy
Authors: Ijaz Gul, Changyue Liu, Yuan Xi, Zhicheng Du, Shiyao Zhai, Zhengyang Lei, Chen Qun, Muhammad Akmal Raheem, Qian He, Zhang Haihui, Canyang Zhang, Runming Wang, Sanyang Han, Du Ke, Peiwu Qin
Date: 2022-08-10 00:00:00
Tasks: Articles

Objectives The review is dedicated to evaluate the current monkeypox virus (MPXV) detection methods, discuss their pros and cons, and provide recommended solutions to the problems. Methods The literature for this review is identified through searches in PubMed, Web of Science, Google Scholar, ResearchGate, and Science Direct advanced search for articles published in English without any start date until June, 2022, by use of the terms "monkeypox virus" or "poxvirus" along with "diagnosis"; "PCR"; "real-time PCR"; "LAMP"; "RPA"; "immunoassay"; "reemergence"; "biothreat"; "endemic", and "multi-country outbreak" and also, by tracking citations of the relevant papers. The most relevant articles are included in the review. Results Our literature review shows that PCR is the gold standard method for MPXV detection. In addition, loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA) have been reported as alternatives to PCR. Immunodiagnostics, whole particle detection, and image-based detection are the non-nucleic acid-based MPXV detection modalities. Conclusions PCR is easy to leverage and adapt for a quick response to an outbreak, but the PCR-based MPXV detection approaches may not be suitable for marginalized settings. Limited progress has been made towards innovations in MPXV diagnostics, providing room for the development of novel detection techniques for this virus.
Computational challenges of cell cycle analysis using single cell transcriptomics
Authors: Alexander Chervov, Andrei Zinovyev
Date: 2022-08-10 00:00:00

The cell cycle is one of the most fundamental biological processes important for understanding normal physiology and various pathologies such as cancer. Single cell RNA sequencing technologies give an opportunity to analyse the cell cycle transcriptome dynamics in an unprecedented range of conditions (cell types and perturbations), with thousands of publicly available datasets. Here we review the main computational tasks in such analysis: 1) identification of cell cycle phases, 2) pseudotime inference, 3) identification and profiling of cell cycle-related genes, 4) removing cell cycle effect, 5) identification and analysis of the G0 (quiescent) cells. We review seventeen software packages that are available today for the cell cycle analysis using scRNA-seq data. Despite huge progress achieved, none of the packages can produce complete and reliable results with respect to all aforementioned tasks. One of the major difficulties for existing packages is distinguishing between two patterns of cell cycle transcriptomic dynamics: normal and characteristic for embryonic stem cells (ESC), with the latter one shared by many cancer cell lines. Moreover, some cell lines are characterized by a mixture of two subpopulations, one following the standard and one ESC-like cell cycle, which makes the analysis even more challenging. In conclusion, we discuss the difficulties of the analysis of cell cycle-related single cell transcriptome and provide certain guidelines for the use of the existing methods.
Automated Infectious Disease Forecasting: Use-cases and Practical Considerations for Pipeline Implementation
Authors: VP Nagraj, Chris Hulme-Lowe, Shakeel Jessa, Stephen D. Turner
Date: 2022-08-09 00:00:00
Tasks: Cloud Computing

Real-time forecasting of disease outbreaks requires standardized outputs generated in a timely manner. Development of pipelines to automate infectious disease forecasts can ensure that parameterization and software dependencies are common to any execution of the forecasting code. Here we present our implementation of an automated cloud computing pipeline to forecast infectious disease outcomes, with examples of usage to forecast COVID-19 and influenza targets. We also offer our perspective on the limits of automation and importance of human-in-the-loop automated infectious disease forecasting.
From Omicron to Its BA.5 Subvariant: A New Twist in Coronavirus Contagiousness Evolution
Authors: J. C. Phillips
Date: 2022-08-09 00:00:00

In 2022 new subvariants of Coronavirus appeared in South Africa, and spread rapidly to other parts of the world. The effects of selective evolution on increasing the already high contagiousness of CoV19 up to Omicron were previously discussed with high accuracy using concepts new to molecular biology (from mathematics and physics) that have been developed over the last few decades. Here we discuss the new subvariants, and find novel features successfully combining both improved flexibility and improved stability.
Quantification of metabolic niche occupancy dynamics in a Baltic Sea bacterial community
Authors: Jana C. Massing, Ashkaan Fahimipour, Carina Bunse, Jarone Pinhassi, Thilo Gross
Date: 2022-08-10 00:00:00
Tasks: Time Series, Time Series Analysis

Progress in molecular methods has enabled the monitoring of bacterial populations in time. Nevertheless, understanding community dynamics and its links with ecosystem functioning remains challenging due to the tremendous diversity of microorganisms. Conceptual frameworks that make sense of time-series of taxonomically-rich bacterial communities, regarding their potential ecological function, are needed. A key concept for organizing ecological functions is the niche, the set of strategies that enable a population to persist and define its impacts on the surroundings. Here we present a framework based on manifold learning, to organize genomic information into potentially occupied bacterial metabolic niches over time. We apply the method to re-construct the dynamics of putatively occupied metabolic niches using a long-term bacterial time-series from the Baltic Sea, the Linnaeus Microbial Observatory (LMO). The results reveal a relatively low-dimensional space of occupied metabolic niches comprising groups of taxa with similar functional capabilities. Time patterns of occupied niches were strongly driven by seasonality. Some metabolic niches were dominated by one bacterial taxon whereas others were occupied by multiple taxa, and this depended on season. These results illustrate the power of manifold learning approaches to advance our understanding of the links between community composition and functioning in microbial systems.
Scavengers in the human-dominated landscape: an experimental study
Authors: Sourabh Biswas, Tathagata Bhowmik, Kalyan Ghosh, Anamitra Roy, Aesha Lahiri, Sampita Sarkar, Anindita Bhadra
Date: 2022-08-09 00:00:00

Rapid urbanization is a major cause of habitat and biodiversity loss and human-animal conflict. While urbanization is inevitable, we need to develop a good understanding of the urban ecosystem and the urban-adapted species in order to ensure sustainable cities for our future. Scavengers play a major role in urban ecosystems, and often, urban adaptation involves a shift towards scavenging behaviour in wild animals. We carried out an experiment at different sites in the state of West Bengal, India, to identify the scavenging guild within urban habitats, in response to human provided food. Our study revealed a total of 17 different vertebrate species were identified across sites over 498 sessions of observations. We carried out network analysis to understand the dynamics of the system, and found that the free-ranging dog and common mynah were key species within the scavenging networks. This study revealed the complexity of scavenging networks within human-dominated habitats.