Miracle-dz/UniFAR-FTUs huggingface dataset:
Comfort expectations: the impact of demand-management strategies in Australia

Australia's residential peak electricity demand is escalating rapidly and causing major distribution and generation challenges for the industry. This rise is largely attributed to converging and rising expectations of comfort in the home, which are being satisfied by Australians' increasing reliance on air-conditioning. Residential demand-management strategies seek to address this by either actively managing consumer demand, or encouraging consumers to act as co-managers of their demand, during peak times. Drawing on interviews with demand-management practitioners from electricity distributors and retailers, the impact of several residential demand-management techniques is considered in relation to comfort expectations. It is argued that utilities delivering demand-management trials are shaping, and being shaped by, ‘normal’ expectations of comfort. Further social research is called for to assess the impact of demand-management techniques on escalating comfort expectations, and to design and implement strategies that address this fundamental issue. En Australie, le pic de la demande d'électricité résidentielle augmente rapidement, provoque des perturbations importantes et est à l'origine de difficultés pour l'industrie. Cette augmentation est due, en grande partie, à la convergence et à de nouvelles attentes en matière de confort au foyer qui sont satisfaites par une dépendance de plus en plus importante des Australiens vis-à-vis de la climatisation. Les stratégies de gestion de la demande d'électricité résidentielle cherchent à relever ces défis soit en gérant de manière active la demande des consommateurs soit en encourageant ces derniers à agir comme des co-gestionnaires de la demande pendant les heures de pointe. S'appuyant sur des interviews de spécialistes de la gestion de la demande des distributeurs d'électricité et des détaillants, l'auteur analyse l'impact de plusieurs techniques de gestion de la demande d'électricité résidentielle par rapport aux attentes des consommateurs en matière de confort. Il ressort de cette étude que les services proposant des essais de gestion de la demande donnent forme aux attentes normales en matière de confort mais sont aussi formés par ces dernières. Il est nécessaire de mener d'autres recherches sur le plan social pour évaluer l'impact des techniques de gestion de la demande sur l'augmentation des attentes en matière de confort et de concevoir et mettre en œuvre des stratégies pour résoudre cette question fondamentale. comportement adaptatif, climatisation, changement climatique, confort, gestion de la demande, qualité de l'environnement intérieur, habitants, pic de la demande, consommation d'énergie résidentielle, compteurs intelligents
Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements

Effective characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in diffusion MRI (dMRI). Solving the problem of relating the dMRI signal with cytoarchitectural characteristics calls for the definition of a mathematical model that describes brain tissue via a handful of physiologically-relevant parameters and an algorithm for inverting the model. To address this issue, we propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model. As opposed to other approaches from the literature, our algorithm yields not only an estimation of the parameter vector θ that best describes a given observed data point x0 , but also a full posterior distribution p(θ|x0) over the parameter space. This enables a richer description of the model inversion, providing indicators such as credible intervals for the estimated parameters and a complete characterization of the parameter regions where the model may present indeterminacies. We approximate the posterior distribution using deep neural density estimators, known as normalizing flows, and fit them using a set of repeated simulations from the forward model. We validate our approach on simulations using dmipy and then apply the whole pipeline on two publicly available datasets.
Global Sensitivity Analysis of Four Chamber Heart Hemodynamics Using Surrogate Models

Computational Fluid Dynamics (CFD) is used to assist in designing artificial valves and planning procedures, focusing on local flow features. However, assessing the impact on overall cardiovascular function or predicting longer-term outcomes may require more comprehensive whole heart CFD models. Fitting such models to patient data requires numerous computationally expensive simulations, and depends on specific clinical measurements to constrain model parameters, hampering clinical adoption. Surrogate models can help to accelerate the fitting process while accounting for the added uncertainty. We create a validated patient-specific four-chamber heart CFD model based on the Navier-Stokes-Brinkman (NSB) equations and test Gaussian Process Emulators (GPEs) as a surrogate model for performing a variance-based global sensitivity analysis (GSA). GSA identified preload as the dominant driver of flow in both the right and left side of the heart, respectively. Left-right differences were seen in terms of vascular outflow resistances, with pulmonary artery resistance having a much larger impact on flow than aortic resistance. Our results suggest that GPEs can be used to identify parameters in personalized whole heart CFD models and highlight the importance of accurate preload measurements.
Hybrid Reflection Modulation

Reconfigurable intelligent surface (RIS)-empowered communication has emerged as a novel concept for customizing future wireless environments in a cost- and energy-efficient way. However, due to double path loss, existing fully passive RIS systems that purely reflect the incident signals into preferred directions attain an unsatisfactory performance improvement over the traditional wireless networks in certain conditions. To overcome this bottleneck, we propose a novel transmission scheme, named hybrid reflection modulation (HRM), exploiting both active and passive reflecting elements at the RIS and their combinations, which enables to convey information without using any radio frequency (RF) chains. In the HRM scheme, the active reflecting elements using additional power amplifiers are able to amplify and reflect the incoming signal, while the remaining passive elements can simply reflect the signals with appropriate phase shifts. Based on this novel transmission model, we obtain an upper bound for the average bit error probability (ABEP), and derive achievable rate of the system using an information theoretic approach. Moreover, comprehensive computer simulations are performed to prove the superiority of the proposed HRM scheme over existing fully passive, fully active and reflection modulation (RM) systems.
S-DCCRN: Super Wide Band DCCRN with Learnable Complex Feature for Speech Enhancement

In speech enhancement, complex neural network has shown promising performance due to their effectiveness in processing complex-valued spectrum. Most of the recent speech enhancement approaches mainly focus on wide-band signal with a sampling rate of 16K Hz. However, research on super wide band (e.g., 32K Hz) or even full-band (48K) denoising using deep learning is still in its infancy due to the difficulty of modeling more frequency bands and particularly high frequency components. In this paper, we extend our previous deep complex convolution recurrent neural network (DCCRN) substantially to a super wide band version–S-DCCRN, to perform speech denoising on speech of 32K Hz sampling rate. We first employ a cascaded sub-band and full-band processing module, which consists of two small-footprint DCCRNs–one operates on sub-band signal and one operates on full-band signal, aiming at benefiting from both local and global frequency information. Moreover, instead of simply adopting the STFT feature as input, we use a complex feature encoder trained in an end-to-end manner to refine the information of different frequency bands. We also use a complex feature decoder to revert the feature to time-frequency domain. Finally, a learnable spectrum compression method is adopted to adjust the energy of different frequency bands, which is beneficial for neural network learning. The proposed model, S-DCCRN, has surpassed PercepNet as well as several competitive models and achieves state-of-the-art performance in terms of speech quality and intelligibility. Ablation studies also demonstrate the effectiveness of different contributions.
Anisotropic and isotropic persistent singularities of solutions of the fast diffusion equation

Abstract. The aim of this paper is to study a class of positive solutions of the fast diffusion equation with specific persistent singular behavior. First, we construct new types of solutions with anisotropic singularities. Depending on parameters, either these solutions solve the original equation in the distributional sense, or they are not locally integrable in space-time. We show that the latter also holds for solutions with snaking singularities, whose existence has been proved recently by M. Fila, J.R. King, J. Takahashi, and E. Yanagida. Moreover, we establish that in the distributional sense, isotropic solutions whose existence was proved by M. Fila, J. Takahashi, and E. Yanagida in 2019, actually solve the corresponding problem with a moving Dirac source term. Last, we discuss the existence of solutions with anisotropic singularities in a critical case.
On the Importance of Difficulty Calibration in Membership Inference Attacks

The vulnerability of machine learning models to membership inference attacks has received much attention in recent years. However, existing attacks mostly remain impractical due to having high false positive rates, where non-member samples are often erroneously predicted as members. This type of error makes the predicted membership signal unreliable, especially since most samples are non-members in real world applications. In this work, we argue that membership inference attacks can benefit drastically from \emph{difficulty calibration}, where an attack's predicted membership score is adjusted to the difficulty of correctly classifying the target sample. We show that difficulty calibration can significantly reduce the false positive rate of a variety of existing attacks without a loss in accuracy.
Mathematical models to explain the origin of urban scaling laws: a synthetic review

The quest for a theory of cities that could offer a quantitative and systematic approach to manage cities is at the top priority, given the challenges humanity faces due to the increasing urbanization and densification of cities. If such a theory is feasible, then its formulation must be in a mathematical way. As a contribution to organizing the mathematical ideas that deal with such a systematic way of understanding urban phenomena, we present this material, concentrating on one important aspect of what recently has been called the new science of cities. In this paper, we review the main mathematical models present in the literature that aim at explaining the origin and emergence of urban scaling. We intend to present the models, identify similarities and connections between them, and find situations in which different models lead to the same output. In addition, we report situations in which some ideas initially introduced in a particular model can also be introduced in another model, generating more diversification and increasing the scope of the models. The models treated in this paper explain urban scaling from different premises: from gravity ideas, passing through densification ideas and cites' geometry, to a hierarchical organization and socio-network properties. We also investigate scenarios in which these different fundamental ideas could be interpreted as similar -- where the similarity is likely but not obvious. Furthermore, in what concerns the gravity idea, we propose a general framework that includes all gravity models analyzed as a particular case.
TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Recent work in synthetic data generation in the time-series domain has focused on the use of Generative Adversarial Networks. We propose a novel architecture for synthetically generating time-series data with the use of Variational Auto-Encoders (VAEs). The proposed architecture has several distinct properties: interpretability, ability to encode domain knowledge, and reduced training times. We evaluate data generation quality by similarity and predictability against four multivariate datasets. We experiment with varying sizes of training data to measure the impact of data availability on generation quality for our VAE method as well as several state-of-the-art data generation methods. Our results on similarity tests show that the VAE approach is able to accurately represent the temporal attributes of the original data. On next-step prediction tasks using generated data, the proposed VAE architecture consistently meets or exceeds performance of state-of-the-art data generation methods. While noise reduction may cause the generated data to deviate from original data, we demonstrate the resulting de-noised data can significantly improve performance for next-step prediction using generated data. Finally, the proposed architecture can incorporate domain-specific time-patterns such as polynomial trends and seasonalities to provide interpretable outputs. Such interpretability can be highly advantageous in applications requiring transparency of model outputs or where users desire to inject prior knowledge of time-series patterns into the generative model.
Joint Estimation of Extreme Precipitation at Diﬀerent Spatial Scales through Mixture Modelling

Parsimonious and eﬀective models for the extremes of precipitation aggregates that can capture their joint behaviour at diﬀerent spatial resolutions must be built with knowledge of the underlying spatial process. Precipitation is driven by a mixture of processes acting at diﬀerent scales and intensities. The speciﬁc process that drives the extremal behaviour of the aggregate will be dependent on the aggregate resolution; whilst high-intensity, spatially-localised convective events cause extreme high-resolution spatial aggregates, the contribution of low-intensity, large-scale fronts is likely to increase with the scale of the aggregate. Thus, to jointly model low-and high-resolution spatial aggregates, we require a model that can capture both convective and frontal events. We propose a novel spatial extreme values model which is a mixture of two components with diﬀerent marginal and dependence models that are able to capture the extremal behaviour of convective and
Optimal Output Consensus of Heterogeneous Linear Multiagent Systems Over Weight-Unbalanced Directed Networks

This article investigates the distributed optimal output consensus problem of heterogeneous linear multiagent systems over weight-unbalanced directed networks. A novel distributed continuous-time state feedback controller is proposed to steer the outputs of all the agents to converge to the optimal solution of the global cost function. Under the standard condition that the unbalanced digraph is strongly connected and the local cost functions are strongly convex with global Lipschitz gradients, the exponential convergence of the closed-loop multiagent system is established. Then, the proposed state feedback control law is extended to an observer-based output feedback setting. Two examples are finally provided to illustrate the effectiveness of the proposed control schemes.
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning

Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstrate that information exchanged during FL is subject to gradient-based privacy attacks and, consequently, a variety of privacy-preserving methods have been adopted to thwart such attacks. However, these defensive methods either introduce orders of magnitudes more computational and communication overheads (e.g., with homomorphic encryption) or incur substantial model performance losses in terms of prediction accuracy (e.g., with differential privacy). In this work, we propose FEDCG, a novel federated learning method that leverages conditional generative adversarial networks to achieve high-level privacy protection while still maintaining competitive model performance. FEDCG decomposes each client's local network into a private extractor and a public classifier and keeps the extractor local to protect privacy. Instead of exposing extractors, FEDCG shares clients' generators with the server for aggregating clients' shared knowledge aiming to enhance the performance of each client's local networks. Extensive experiments demonstrate that FEDCG can achieve competitive model performance compared with FL baselines, and privacy analysis shows that FEDCG has a high-level privacy-preserving capability.
Learning MPC for Interaction-Aware Autonomous Driving: A Game-Theoretic Approach

We consider the problem of interaction-aware motion planning for automated vehicles in general traffic situations. We model the interaction between the controlled vehicle and surrounding road users using a generalized potential game, in which each road user is assumed to minimize a common cost function subject to shared (collision avoidance) constraints. We propose a quadratic penalty method to deal with the shared constraints and solve the resulting optimal control problem online using an Augmented Lagrangian method based on PANOC. Secondly, we present a simple methodology for learning preferences and constraints of other road users online, based on observed behavior. Through extensive simulations in a highway merging scenario, we demonstrate the practical efficacy of the overall approach as well as the benefits of the proposed online learning scheme.
From Convolutions towards Spikes: The Environmental Metric that the Community currently Misses

Today, the AI community is obsessed with ‘state-of-the-art’ scores ( 80% papers in NeurIPS) as the primarily performance metrics, due to which an important parameter, i.e., the environmental metric, remains unreported. Computational capabilities were a limiting factor a decade ago; however, in foreseeable future circumstances, the challenge will be to develop environment-friendly and power-efﬁcient algorithms. The human brain, which has been optimizing itself for almost a million years, consumes the same amount of power as a typical laptop. Therefore, developing nature-inspired algorithms is one solution to it. In this study, we show that currently used ANNs are not what we ﬁnd in nature, and why, although having lower performance, spiking neural networks, which mirror the mammalian visual cortex, have attracted much interest. We further highlight the hardware gaps restricting the researchers from using spike-based computation for developing neuromorphic energy-efﬁcient microchips on a large scale. Using neuromorphic processors instead of traditional GPUs might be more environment friendly and efﬁcient. These processors will turn SNNs into an ideal solution for the problem. This paper presents in-depth attention highlighting the current gaps, the lack of comparative research, while proposing new research directions at the intersection of two ﬁelds- neuroscience and deep learning. Further, we deﬁne a new evaluation metric ‘ NATURE ’ for reporting the carbon footprint of AI models. The
Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection

In this work, we propose GLUE (Graph Deviation Network with Local Uncertainty Estimation), building on the recently proposed Graph Deviation Network (GDN). GLUE not only automatically learns complex dependencies between variables and uses them to better identify anomalous behavior, but also quantifies its predictive uncertainty, allowing us to account for the variation in the data as well to have more interpretable anomaly detection thresholds. Results on two real world datasets tell us that optimizing the negative Gaussian log likelihood is reasonable because GLUE's forecasting results are at par with GDN and in fact better than the vector autoregressor baseline, which is significant given that GDN directly optimizes the MSE loss. In summary, our experiments demonstrate that GLUE is competitive with GDN at anomaly detection, with the added benefit of uncertainty estimations. We also show that GLUE learns meaningful sensor embeddings which clusters similar sensors together.
Automatic Sleep Staging: Recent Development, Challenges, and Future Directions

Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represent a further serious threat undermining the dependability of AI techniques. In backdoor attacks, the attacker corrupts the training data to induce an erroneous behaviour at test time. Test-time errors, however, are activated only in the presence of a triggering event. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. Recently, backdoor attacks have been an intense research domain focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. Hence, the proposed analysis is suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in.