Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of dwelling occupants on energy consumption: the case of heat waves in Australia

Climate change predictions indicate more extremes in weather conditions in the coming decades with more frequent and severe heat waves in certain locations including Australia. It is likely that the more vulnerable members of the community will be at risk during heat waves in the future from both health and financial perspectives. The trend towards fully air conditioned larger homes has already seen very large peaks in electricity demand during past heat waves. Electricity prices have increased substantially in recent years and will increase further due to greater peak demand and environmental levies. To manage the risks to dwelling occupants during heat waves, research has been carried out to determine methods of promoting adaptive behaviours through residential design. This paper describes the concept of a “cool retreat” within a dwelling which can provide comfort conditions with minimal cooling costs during heat wave periods. The cool retreat concept is shown to have its origins in the past in a number of locations around the world subject to hot conditions. Various cool retreat configurations have been analysed for thermal performance, demonstrating the viability of this approach for dealing with extreme conditions in the future.
High-voltage amorphous silicon thin-film transistors

High-voltage MOSFETs have been fabricated in a large-area thin-film amorphous silicon technology. The transistors have an offset gate structure which allows them to operate at an excess of 400 V. Basic fabrication steps and structure are discussed. These transistors have a gate-controlled region in series with an offset region where the current is space charge limited. The I/sub D/-V/sub D/ characteristics exhibit a unique instability under drain voltage stress; there is a parallel shift in the I/sub D/ versus V/sub D/ characteristic to a higher V/sub D/. This instability arises from the creation of localized states in the a-Si during depletion. It is analyzed through experiment and two-dimensional simulation. Structural variations are described, including the application of a field plate, to stabilize transistor drive current. Reliability and process uniformity are discussed for arrays of transistors. An appropriate circuit simulation model is discussed. Operation of output drive circuits with high-voltage thin-film transistors (TFTs) is shown, and the application of these to an electrographic plotter is described. >
Analysis of an interventional protein experiment using a vine copula based structural equation model

While there is considerable effort to identify signaling pathways using linear Gaussian Bayesian networks from data, there is less emphasis of understanding and quantifying conditional densities and probabilities of nodes given its parents from the identifed Bayesian network. Most graphical models for continuous data assume a multivariate Gaussian distribution, which might be too restrictive. We re-analyse data from an experimental setting considered in Sachs et al. (2005) to illustrate the effects of such restrictions. For this we propose a novel non Gaussian nonlinear structural equation model based on vine copulas. In particular the D-vine regression approach of Kraus and Czado (2017) is adapted. We show that this model class is more suited to fit the data than the standard linear structural equation model based on the biological consent graph given in Sachs et al. (2005). The modelling approach also allows to study which pathway edges are supported by the data and which can be removed. For data experiment cd3cd28+aktinhib this approach identified three edges, which are no longer supported by the data. For each of these edges a plausible explanation based on underlying the experimental conditions could be found.
Panoptic Segmentation: A Review

Image segmentation for video analysis plays an essential role in different research fields such as smart city, healthcare, computer vision and geoscience, and remote sensing applications. In this regard, a significant effort has been devoted recently to developing novel segmentation strategies; one of the latest outstanding achievements is panoptic segmentation. The latter has resulted from the fusion of semantic and instance segmentation. Explicitly, panoptic segmentation is currently under study to help gain a more nuanced knowledge of the image scenes for video surveillance, crowd counting, self-autonomous driving, medical image analysis, and a deeper understanding of the scenes in general. To that end, we present in this paper the first comprehensive review of existing panoptic segmentation methods to the best of the authors' knowledge. Accordingly, a well-defined taxonomy of existing panoptic techniques is performed based on the nature of the adopted algorithms, application scenarios, and primary objectives. Moreover, the use of panoptic segmentation for annotating new datasets by pseudo-labeling is discussed. Moving on, ablation studies are carried out to understand the panoptic methods from different perspectives. Moreover, evaluation metrics suitable for panoptic segmentation are discussed, and a comparison of the performance of existing solutions is provided to inform the state-of-the-art and identify their limitations and strengths. Lastly, the current challenges the subject technology faces and the future trends attracting considerable interest in the near future are elaborated, which can be a starting point for the upcoming research studies. The papers provided with code are available at: https://github.com/elharroussomar/Awesome-Panoptic-Segmentation
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models. Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds. The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects. In this study, we present a new benchmark called ClevrTex, designed as the next challenge to compare, evaluate and analyze algorithms. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. It includes 50k examples depicting 3-10 objects arranged on a background, created using a catalog of 60 materials, and a further test set featuring 10k images created using 25 different materials. We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data. We also create variants of the ClevrTex dataset, controlling for different aspects of scene complexity, and probe current approaches for individual shortcomings. Dataset and code are available at https://www.robots. ox.ac. uk/~vgg/research/clevrtex.
Three dimensional finite element analysis of N-port waveguide junctions using edge-elements

The finite element method is formulated in such a way that the electromagnetic solution of an excited cavity formed from an N-port waveguide junction leads directly and naturally to circuit characteristics of this junction. The use of edge elements eliminates nonphysical solutions. The reliability of the solution is assured compared to a penalty method. Accuracy of the method is demonstrated through the presentation of results for a waveguide T-junction while its efficiency is proven through the presentation of results for a case study of a finline step discontinuity. <<ETX>>
More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech

In this paper we present VDTTS, a Visually-Driven Text-to-Speech model. Motivated by dubbing, VDTTS takes ad-vantage of video frames as an additional input alongside text, and generates speech that matches the video signal. We demonstrate how this allows VDTTS to, unlike plain TTS models, generate speech that not only has prosodic variations like natural pauses and pitch, but is also synchronized to the input video. Experimentally, we show our model produces well-synchronized outputs, approaching the video-speech synchronization quality of the ground-truth, on several challenging benchmarks including “in-the-wild” content from VoxCeleb2. Supplementary demo videos demonstrating video-speech synchronization, robustness to speaker ID swapping, and prosody, presented at the project page.11Project page: http://google-research.github. io/lingvo-lab/vdtts
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems

Deep neural networks (DNNs), regardless of their impressive performance, are vulnerable to attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the decision of the model. We expose the existence of an intriguing class of spatially bounded, physically realizable, adversarial examples— Universal NaTuralistic adversarial paTches—we call TnTs, by exploring the super set of the spatially bounded adversarial example space and the natural input space within generative adversarial networks. Now, an adversary can arm themselves with a patch that is naturalistic, less malicious-looking, physically realizable, highly effective—achieving high attack success rates, and universal. A TnT is universal because any input image captured with a TnT in the scene will: i) misguide a network (untargeted attack); or ii) force the network to make a malicious decision (targeted attack). Interestingly, now, an adversarial patch attacker has the potential to exert a greater level of control—the ability to choose a location independent, natural-looking patch as a trigger in contrast to being constrained to noisy perturbations—an ability is thus far shown to be only possible with Trojan attack methods needing to interfere with the model building processes to embed a backdoor at the risk discovery; but, still realize a patch deployable in the physical world. Through extensive experiments on the large-scale visual classification task, ImageNet with evaluations across its entire validation set of 50,000 images, we demonstrate the realistic threat from TnTs and the robustness of the attack. We show a generalization of the attack to create patches achieving higher attack success rates than existing state-of-the-art methods. Our results show the generalizability of the attack to different visual classification tasks (CIFAR-10, GTSRB, PubFig) and multiple state-of-the-art deep neural networks such as WideResnet50, Inception-V3 and VGG-16.
Explainable predictions of different machine learning algorithms used to predict Early Stage diabetes

Machine Learning (ML) and Artificial Intelligence (AI) can be widely used to diagnose chronic diseases so that necessary precautionary treatment can be done in critical time. Diabetes Mellitus which is one of the major diseases can be easily diagnosed by several Machine Learning algorithms. Early stage diagnosis is crucial to prevent dangerous consequences. In this paper we have made a comparative analysis of several machine learning algorithms viz. Random Forest, Decision Tree, Artificial Neural Networks, K – Nearest Neighbor, Support Vector Machine and XGBoost along with feature attribution using SHAP to identify the most important feature in predicting the diabetes on a dataset collected from Sylhet Hospital. As per the experimental results obtained, the Random Forest algorithm has outperformed all the other algorithms with an accuracy of 99% on this particular dataset.
Real-Time Magnetometry Using Dark States of a Nitrogen Vacancy Center

We demonstrate real-time magnetometry by detecting fluorescence from a nitrogen vacancy center in the setting of coherent population trapping and by estimating magnetic field from the time series of the observed photon counts, which are correlated with the underlying field. The proof-of-principle experiment uses an external time-varying magnetic field that follows an Ornstein-Uhlenbeck (OU) process. By taking into consideration the statistical properties of the OU process, a Bayesian inference-based estimator can effectively update dynamical information of the field in real time with the detection of just a single photon.
Building a Question Answering System for the Manufacturing Domain

The design or simulation analysis of special equipment products must follow the national standards, and hence it may be necessary to repeatedly consult the contents of the standards in the design process. However, it is difficult for the traditional question answering system based on keyword retrieval to give accurate answers to technical questions. Therefore, we use natural language processing techniques to design a question answering system for the decision-making process in pressure vessel design. To solve the problem of insufficient training data for the technology question answering system, we propose a method to generate questions according to a declarative sentence from several different dimensions so that multiple question-answer pairs can be obtained from a declarative sentence. In addition, we designed an interactive attention model based on a bidirectional long short-term memory (BiLSTM) network to improve the performance of the similarity comparison of two question sentences. Finally, the performance of the question answering system was tested on public and technical domain datasets.
Joint Delay and Phase Precoding Under True-Time Delay Constraints for THz Massive MIMO

A new approach is presented to the problem of compensating the beam squint effect arising in wideband terahertz (THz) hybrid massive multiple-input multiple-output (MIMO) systems, based on the joint optimization of the phase shifter (PS) and true-time delay (TTD) values under per-TTD device time delay constraints. Unlike the prior approaches, the new approach does not require the unbounded time delay assumption; the range of time delay values that a TTD device can produce is strictly limited in our approach. Instead of focusing on the design of TTD values, we jointly optimize both the TTD and PS values to effectively cope with the practical time delay constraints. Simulation results that illustrate the performance benefits of the new method for the beam squint compensation are presented. Through simulations and analysis, we show that our approach is a generalization of the prior TTD-based precoding approaches.
Superconductivity and bosonic fluid emerging from moiré flat bands

(Dated: Although evidence of inter-valley attraction-mediated by phonon or topological ﬂuctuations is accumulating, the origin of superconductivity in the ﬂat-band quantum moir´e materials remains an open question. Here, instead of attempting to pinpoint the origin of the superconductivity, we aim at identifying universal properties of moir´e ﬂat bands that shall emerge in the presence of inter-valley attractions. We show that by matching the interaction strength of inter-valley attraction with intra-valley repulsion, the ﬂat-band limit becomes exactly solvable. Away from the ﬂat-band limit, the system can be simulated via quantum Monte Carlo (QMC) methods without sign problem for any ﬁllings. Combining analytic solutions with large-scale numerical simulations, we show that upon increasing temperature, the superconducting phase melts into a bosonic ﬂuid of Cooper pairs with large/diverging compressibility. In contrast to ﬂat-band attractive Hubbard models, where similar eﬀects arise only for on-site interactions, our study indicates this physics is a universal property of moir´e ﬂat bands, regardless of microscopic details such as the range of interactions and/or spin-oribt couplings. At higher temperature, the boson ﬂuid phase gives its way to a pseudo gap phase, where some Cooper pairs are torn apart by thermal ﬂuctuations, resulting in fermion density of states inside the gap. Unlike the superconducting transition temperature, which is very sensitive to doping and twisting angles, the gap and the temperature scale of the boson ﬂuid phase and the pseudo gap phase are found to be nearly independent of doping level and/or ﬂat-band bandwidth. The relevance of these phases with experimental discoveries in the ﬂat band quantum moir´e materials is discussed.
Quantifying EPR: the resource theory of nonclassicality of common-cause assemblages

Einstein-Podolsky-Rosen (EPR) steering is often (implicitly or explicitly) taken to be evidence for spooky action-at-a-distance. An alternative perspective on steering is that Alice has no causal influence on the physical state of Bob's system; rather, Alice merely updates her knowledge of the state of Bob's system by performing a measurement on a system correlated with his. In this work, we elaborate on this perspective (from which the very term 'steering' is seen to be inappropriate), and we are led to a resource-theoretic treatment of correlations in EPR scenarios. For both bipartite and multipartite scenarios, we develop the resulting resource theory, wherein the free operations are local operations and shared randomness (LOSR). We show that resource conversion under free operations in this paradigm can be evaluated with a single instance of a semidefinite program, making the problem numerically tractable. Moreover, we find that the structure of the pre-order of resources features interesting properties, such as infinite families of incomparable resources. In showing this, we derive new EPR resource monotones. We also discuss advantages of our approach over a pre-existing proposal for a resource theory of 'steering', and discuss how our approach sheds light on basic questions, such as which multipartite assemblages are classically explainable.
Forward and inverse scattering for discrete layered lossy and absorbing media

A complete digital signal processing (DSP) theory is developed for forward and inverse scattering in discrete (piecewise-constant) layered lossy systems, generalizing previous work for discrete lossless systems and continuous lossy systems. This paper is motivated by radar reflections from stratified dielectrics and interchip communication modeled by lossy transmission lines. The DSP formulation allows exact solutions, without discretization approximations, and includes all multiple reflections, transmission scattering losses, and absorption effects. For the forward problem, discrete matrix Green's functions are derived for the lossy medium, as are fast algorithms for computing impulse reflection and transmission responses for the medium. For the inverse problem, asymmetric Toeplitz systems of equations which function as discrete counterparts to integral equations are derived, as are fast algorithms for reconstructing the medium from its impulse transmission and reflection responses. Data sufficiency and feasibility are discussed. Finally, these results are applied to the LCRG transmission line and layered dielectric medium problems, dispersion effects are discussed, and a numerical example is presented.
An analysis of midpoint balance for the neutral-point-clamped three-level VSI

The three level voltage source inverter (VSI) is a good topology for high voltage and high power applications where no semiconductor devices are available. However, it has an inherent problem of midpoint charge balance. The problem becomes critical when the load is nonlinear and/or has large phase angle and harmonics. If it exceeds the critical points, there is no way to control the midpoint voltage by itself. The midpoint balancing problem is analyzed with respect to the reactive loads with harmonic currents and a way to solve the problem is suggested.
New biosensors made of specially designed transparent chips with nano-optical tags

Transparent chips with regular micrometer posts were fabricated from glass slides, glass wafers and silicon wafers via the available photolithography and pattern transfer techniques. The fabrication conditions, e.g. the substrate type, the photomask type, the etchant composition and the etching time, have been adjusted to pattern chips with post height of 6–70 µm. On the other hand, colloidal Au particles with size of 12 nm were synthesized by the reduction of tetrachloroauric acid and further modified by mercaptoundecanoic acid into nano-optical tags. The functionalization of transparent chips with 3-aminopropyltrimethoxysilane brings about the attachment of modified Au nanoparticles onto the surface, which shows characteristic UV–vis absorbance and indicates the feasibility as a new type of biosensor based on a specially patterned chip with high sensitivity.
A compromise equalizer design incorporating performance invariance

We give a solution to the problem of designing a fixed compromise equalizer for use in transmission systems involving an ensemble of random channels. The signal and noise spectra, along with the second-order statistics of the channel ensemble, are used to find the equalizer characteristic that minimizes the mean-square distortion between the equalizer output and a scaled version of the transmitter output. The key departure from previous work is that the criterion better captures practical performance invariance; specifically, the cost function incorporates the insensitivity of a well-designed demodulator to any amplitude scaling or time delay introduced by a particular channel. After demonstrating that the optimum equalizer shape is related to the principal eigenfunction of a normalized channel correlation function, we consider several special cases that give further insight into the properties of the solution. We find that the equalizer amplitude is attenuated over those frequencies where the signal-to-noise or signal-to-channel-variance ratios are small. The analysis confirms the standard engineering practice of inverting the average channel in the absence of noise and when the variance of the channel characteristics is small.
Does the shadow economy explain household saving ?

We analyze the question of how indicators of shadow economy correspond to the National Accounts values starting with the notion that shadow economy should show up in the difference between household income and consumption in the sense that household (disposable) income is underreported. By contrast, household consumption is more accurate because most of shadow economy income eventually ends up to consumption. Thus, household saving figures should be negatively related to the values of shadow economy: if these values are large, savings should be small, or even negative and vice versa. We test this hypothesis with European cross-country data for 1991-2017 using the so-called MIMIC model calculations as point of reference. Estimation results lend very little support to this hypothesis even though we can otherwise explain pretty well the cross-country variation in household saving and consumption growth rates. SUERF Policy Briefs No 224, November 2021