Miracle-dz/UniFAR-FTUs huggingface dataset:
FAIR4Cov: Fused Audio Instance and Representation for COVID-19 Detection

Audio-based classification techniques on body sounds have long been studied to support diagnostic decisions, particularly in pulmonary diseases. In response to the urgency of the COVID-19 pandemic, a growing number of models are developed to identify COVID-19 patients based on acoustic input. Most models focus on cough because the dry cough is the best-known symptom of COVID-19. However, other body sounds, such as breath and speech, have also been revealed to correlate with COVID-19 as well. In this work, rather than relying on a specific body sound, we propose Fused Audio Instance and Representation for COVID-19 Detection (FAIR4Cov). It relies on constructing a joint feature vector obtained from a plurality of body sounds in waveform and spectrogram representation. The core component of FAIR4Cov is a self-attention fusion unit that is trained to establish the relation of multiple body sounds and audio representations and integrate it into a compact feature vector. We set up our experiments on different combinations of body sounds using only waveform, spectrogram, and a joint representation of waveform and spectrogram. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an Area Under the Receiver Operating Characteristic Curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. This AUC is 0.0227 higher than the one of the models trained on spectrograms only and 0.0847 higher than the one of the models trained on waveforms only. The results demonstrate that the combination of spectrogram with waveform representation helps to enrich the extracted features and outperforms the models with single representation.
Learning Dynamic View Synthesis With Few RGBD Cameras

There have been significant advancements in dynamic novel view synthesis in recent years. However, current deep learning models often require (1) prior models (e.g., SMPL human models), (2) heavy pre-processing, or (3) per-scene optimization. We propose to utilize RGBD cameras to remove these limitations and synthesize free-viewpoint videos of dynamic indoor scenes. We generate feature point clouds from RGBD frames and then render them into free-viewpoint videos via a neural renderer. However, the inaccurate, unstable, and incomplete depth measurements induce severe distortions, flickering, and ghosting artifacts. We enforce spatial-temporal consistency via the proposed Cycle Reconstruction Consistency and Temporal Stabilization module to reduce these artifacts. We introduce a simple Regional Depth-Inpainting module that adaptively inpaints missing depth values to render complete novel views. Additionally, we present a Human-Things Interactions dataset to validate our approach and facilitate future research. The dataset consists of 43 multi-view RGBD video sequences of everyday activities, capturing complex interactions between human subjects and their surroundings. Experiments on the HTI dataset show that our method outperforms the baseline per-frame image fidelity and spatial-temporal consistency. We will release our code, and the dataset on the website soon.
Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-Based Clustering for Many-Objective Optimization

Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show high optimization performance for many-objective optimization problems (MaOPs). Especially, algorithms that employ a clustering algorithm with a topological structure (i.e., a network composed of nodes and edges) show superior optimization performance to other MOEAs for MaOPs with irregular Pareto optimal fronts (PFs). These algorithms, however, do not effectively utilize information of the topological structure in the search process. Moreover, the clustering algorithms typically used in conventional studies have limited clustering performance, inhibiting the ability to extract useful information for the search process. This paper proposes an adaptive reference vector-guided evolutionary algorithm using an adaptive resonance theory-based clustering with a topological structure. The proposed algorithm utilizes the information of the topological structure not only for reference vector adaptation but also for mating selection. The proposed algorithm is compared with eight state-of-the-art MOEAs on 78 test problems. Experimental results reveal the outstanding optimization performance of the proposed algorithm over the others on MaOPs with various properties.
Implicit Object Mapping With Noisy Data

—Modelling individual objects as Neural Radiance Fields (NeRFs) within a robotic context can beneﬁt many downstream tasks such as scene understanding and object manipulation. However, real-world training data collected by a robot deviate from the ideal in several key aspects. (i) The trajectories are constrained and full visual coverage is not guaranteed – especially when obstructions are present. (ii) The poses associated with the images are noisy. (iii) The objects are not easily isolated from the background. This paper addresses the above three points and uses the outputs of an object-based SLAM system to bound objects in the scene with coarse primitives and – in concert with instance masks – identify obstructions in the training images. Objects are therefore automatically bounded, and non-relevant geometry is excluded from the NeRF representation. The method’s performance is benchmarked under ideal conditions and tested against errors in the poses and instance masks. Our results show that object-based NeRFs are robust to pose variations but sensitive to the quality of the instance masks.
Analysis of Temporal Difference Learning: Linear System Approach

—The goal of this technical note is to introduce a new ﬁnite-time convergence analysis of temporal difference (TD) learning based on stochastic linear system models. TD-learning is a fundamental reinforcement learning (RL) to evaluate a given policy by estimating the corresponding value function for a Markov decision process. While there has been a series of successful works in theoretical analysis of TD-learning, it was not until recently that researchers found some guarantees on its statistical efﬁciency by developing ﬁnite-time error bounds. In this paper, we propose a simple control theoretic ﬁnite-time analysis of TD-learning, which exploits linear system models and standard notions in linear system communities. The proposed work provides new simple templets for RL analysis, and additional insights on TD-learning and RL based on ideas in control theory.
On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design

Classical Bayesian mechanism design relies on the common prior assumption, but the common prior is often not available in practice. We study the design of prior-independent mechanisms that relax this assumption: the seller is selling an indivisible item to n buyers such that the buyers' valuations are drawn from a joint distribution that is unknown to both the buyers and the seller; buyers do not need to form beliefs about competitors, and the seller assumes the distribution is adversarially chosen from a specified class. We measure performance through the worst-caseregret, or the difference between the expected revenue achievable with perfect knowledge of buyers' valuations and the actual mechanism revenue. We study a broad set of classes of valuation distributions that capture a wide spectrum of possible dependencies: independent and identically distributed (i.i.d.) distributions, mixtures of i.i. d. distributions, affiliated and exchangeable distributions, exchangeable distributions, and all joint distributions. We derive in quasi closed form the minimax values and the associated optimal mechanism. In particular, we show that the first three classes admit the same minimax regret value, which is decreasing with the number of competitors, while the last two have the same minimax regret equal to that of the case n = 1. Furthermore, we show that the minimax optimal mechanisms have a simple form across all settings: asecond-price auction with random reserve prices, which shows its robustness in prior-independent mechanism design. En route to our results, we also develop a principled methodology to determine the form of the optimal mechanism and worst-case distribution via first-order conditions that should be of independent interest in other minimax problems. The full paper is available at https://arxiv.org/abs/2204.10478 and https://ssrn.com/abstract=4090071.
Federated Learning Via Inexact ADMM

One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, in this article, we develop an inexact alternating direction method of multipliers (ADMM), which is both computation- and communication-efficient, capable of combating the stragglers’ effect, and convergent under mild conditions. Furthermore, it has high numerical performance compared with several state-of-the-art algorithms for federated learning.
CLOUD COMPUTING IMPACT ON HEATHCARE IN SOUTH AFRICA THEMBAYENA MGOZI

The emergence of cloud computing in the health sector is increasingly gaining acceptance as an effective means of improving healthcare delivery across the globe. In South Africa, the adoption of cloud computing may be hampered by persistent reliance on using traditional, disparate and unreliable Information and Communications Technology (ICT) platforms. This includes the imple mentation of the National Health Insurance (NHI) scheme to provide universal healthcare coverage supported by the use of ICT for health (eHealth) initiative in the country. This research study considered long‐term strategic plans for the development and implementation of a value‐ based healthcare delivery system. This is in line with the National Development Plan (NDP ) for 2030 vision on promoting health in South Africa. Correspondingly, this research study was conducted to leverage eHealth and propose a conceptual cloud computing model to improve healthcare delivery. The aim of this study was to describe the challenges and solutions necessary to enable cloud computing ec osystem within the health sector. The study employed a qualitative research methodology, using a narrative approach to obtain the findings with respect to the research objectives. The findings attest that the health information system is not meeting the requirements to strengthen healthcare system effectiveness. A key concern is the lack of well‐coordinate d regulatory frameworks for proper governance of eHealth standards implementation; including Patient Health Information (PHI) with respect to privacy and security. The recommendation is that the South African government should first address concerns of collaboration within the broader stakeholders to ensure coordinated healthcare delivery. Further i nvestigation on eHealth may well be of value to improve the usefulness of the proposed model. Future research studies on eHealth will be driven by advancements in clinical standard practices, information management and medical technologies. This will lead to the development of new eHealth components essential to strengthen healthcare delivery through effective utilisation of the healt h outcomes in the future.
Unraveling the $0\nu\beta\beta$ Decay Mechanisms

: We discuss the possibilities of distinguishing among diﬀerent mechanisms of neutrinoless double beta decay arising in the eﬀective ﬁeld theory framework. Following the review and detailed investigation of the particular ways of discrimination, we conclude that the 32 diﬀerent low-energy eﬀective operators can be split into multiple groups that are in principle distinguishable from each other by measurements of the phase-space observables and by comparison of the decay rates obtained using diﬀerent isotopes. This would require not only a substantial experimental precision but necessarily also a considerable improve-ment of the current theoretical knowledge of the underlying nuclear physics. Speciﬁcally, the limiting aspect in our approach turns out to be the currently unknown or uncertain values of low-energy constants. Besides the study adopting the eﬀective ﬁeld theory language we also look into several typical UV models.
Convergence of the Riemannian Langevin Algorithm

We study the Riemannian Langevin Algorithm for the problem of sampling from a distribution with density ν with respect to the natural measure on a manifold with metric g. We assume that the target density satisfies a log-Sobolev inequality with respect to the metric and prove that the manifold generalization of the Unadjusted Langevin Algorithm converges rapidly to ν for Hessian manifolds. This allows us to reduce the problem of sampling non-smooth (constrained) densities in Rn to sampling smooth densities over appropriate manifolds, while needing access only to the gradient of the log-density, and this, in turn, to sampling from the natural Brownian motion on the manifold. Our main analytic tools are (1) an extension of self-concordance to manifolds, and (2) a stochastic approach to bounding smoothness on manifolds. A special case of our approach is sampling isoperimetric densities restricted to polytopes by using the metric defined by the logarithmic barrier.
Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios

The high prevalence of depression in society has given rise to the need for new digital tools to assist in its early detection. To this end, existing research has mainly focused on detecting depression in the domain of social media, where there is a suﬃcient amount of data. How-ever, with the rise of conversational agents like Siri or Alexa, the conversational domain is becoming more critical. Unfortunately, there is a lack of data in the conversational domain. We perform a study focusing on domain adaptation from social media to the conversational domain. Our approach mainly exploits the linguistic information preserved in the vector representation of text. We describe transfer learning techniques to classify users who suﬀer from early signs of depression with high recall. We achieve state-of-the-art results on a commonly used conversational dataset, and we highlight how the method can easily be used in conversational agents. We publicly release all source code 5 .
Nuclear Modification Factor of Neutral Pions in the Forward and Backward Regions in p-Pb Collisions.

The nuclear modification factor of neutral pions is measured in proton-lead collisions collected at a center-of-mass energy per nucleon of 8.16 TeV with the LHCb detector. The π^{0} production cross section is measured differentially in transverse momentum (p_{T}) for 1.5<p_{T}<10.0 GeV and in center-of-mass pseudorapidity (η_{c.m.}) regions 2.5<η_{c.m. }<3.5 (forward) and -4.0<η_{c.m. }<-3.0 (backward) defined relative to the proton beam direction. The forward measurement shows a sizable suppression of π^{0} production, while the backward measurement shows the first evidence of π^{0} enhancement in proton-lead collisions at the LHC. Together, these measurements provide precise constraints on models of nuclear structure and particle production in high-energy nuclear collisions.
Experimental Investigation On The Use Of Natural Waste Fibres As Acoustic Material Of Noise Silencer

The use of synthetic materials as an acoustic material of absorptive silencer is still dominant in the industry. These materials not only have an impact on human health, but also contribute to environmental pollution. This study presents an experimental investigation on the use of natural fibres i.e. coconut and paddy straw fibres as a substitution for the synthetic materials. The samples were fabricated with several different densities of the fibres to identify the most effective composition to absorb sound. The experiments were conducted using the dynamic insertion loss technique as the most applied and simplest method to observe silencer performance. It is found that the natural fibres perform well as absorptive materials and prospective to be used in the future.
ViCE: Improving Visual Concept Embedding Discovery in Unsupervised Semantic Segmentation by Superpixelization

Recent self-supervised computer vision methods have demonstrated equal or better performance to supervised methods, opening for AI systems to learn visual representations from practically unlimited data. However, these methods are classification-based and thus ineffective for learning dense feature maps required for unsupervised semantic segmentation. This work presents a method to effectively learn dense semantically rich visual concept embeddings applicable to high-resolution images. We introduce superpixelization as a means to decompose images into a small set of visually coherent regions, allowing efficient learning of dense semantics by swapped prediction. The expressiveness of our dense embeddings is demonstrated by significantly improving the SOTA representation quality benchmarks on COCO (+16.27 mIoU) and Cityscapes (+19.24 mIoU) for both low-and high-resolution images. Code made available upon publication.
ASSESSING GLOBAL FINANCIAL RISKS

Developments since the September 2006 Global Financial Stability Report (GFSR) have been broadly in line with the baseline scenario of solid economic growth, while near-term economic risks have eased. However, changes in underlying financial risks and conditions in some areas require heightened surveillance. This chapter discusses those changes in risks and conditions, and introduces the global financial stability map, a tool for assessing and summarizing how financial risks have evolved. The map shows that financial stability risks have increased modestly in some areas. While none of the individual areas of risk identified constitutes a direct threat to financial stability, an adverse event affecting any one of those areas could lead to a reappraisal of risks in the others. This possibility is reinforced by low nominal and real interest rates and the environment of low volatility that has continued to encourage risktaking and leverage, suggesting that the markets’ adjustment to a higher level of volatility may not be smooth. A box at the end of the chapter assesses the implications of the February–March 2007 correction. The risks identified as the main spokes of the global financial stability map are examined by exploring several topics. For instance, credit risk is examined by way of a deeper look into the U.S. mortgage market and the current wave of leveraged buyouts (LBOs) and their implications for corporate credit. The chapter then examines the financing of the U.S. current account in light of still-high global imbalances, which has implications for the spokes identified as macroeconomic and market risks. The assessment then turns to emerging market (EM) risks. While those risks have diminished somewhat given the positive global economic backdrop and improvements in fundamentals, the chapter notes that increased risk appetite, which is a financial condition in the stability map, has played a role in the rapid pace and changing composition of capital inflows to EMs—a situation that has been challenging for the officials in these countries. Finally, several risks identified in the spokes are pulled together in a discussion of the low level of volatility and how this may be affecting various trading strategies, including the carry trade, and the possibility of its disorderly unwinding. The chapter concludes with the implications for policy and financial surveillance. The challenge is to ensure that the financial system remains resilient should current benign financial conditions change. Thus, policymakers should use the current “good times” to prepare for a period when conditions are less favorable. Four annexes complete the chapter. Annex 1.1 details the methodology and analytical underpinning of the global financial stability map. Annex 1.2 assesses the credit quality of banking systems in mature and emerging markets. Annex 1.3 assesses recent developments in credit derivatives and structured credit markets. Annex 1.4 provides an update on developments in the hedge fund industry and its oversight.
Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission

Background: The speed of vaccine development has been a singular achievement during the COVID-19 pandemic, although uptake has not been universal. Vaccine opponents often frame their opposition in terms of the rights of the unvaccinated. We sought to explore the impact of mixing of vaccinated and unvaccinated populations on risk of SARS-CoV-2 infection among vaccinated people. Methods: We constructed a simple susceptible–infectious–recovered compartmental model of a respiratory infectious disease with 2 connected subpopulations: people who were vaccinated and those who were unvaccinated. We simulated a spectrum of patterns of mixing between vaccinated and unvaccinated groups that ranged from random mixing to complete like-with-like mixing (complete assortativity), in which people have contact exclusively with others with the same vaccination status. We evaluated the dynamics of an epidemic within each subgroup and in the population as a whole. Results: We found that the risk of infection was markedly higher among unvaccinated people than among vaccinated people under all mixing assumptions. The contact-adjusted contribution of unvaccinated people to infection risk was disproportionate, with unvaccinated people contributing to infections among those who were vaccinated at a rate higher than would have been expected based on contact numbers alone. We found that as like-with-like mixing increased, attack rates among vaccinated people decreased from 15% to 10% (and increased from 62% to 79% among unvaccinated people), but the contact-adjusted contribution to risk among vaccinated people derived from contact with unvaccinated people increased. Interpretation: Although risk associated with avoiding vaccination during a virulent pandemic accrues chiefly to people who are unvaccinated, their choices affect risk of viral infection among those who are vaccinated in a manner that is disproportionate to the portion of unvaccinated people in the population.