Miracle-dz/UniFAR-FTUs huggingface dataset:
A review on arbitrarily regular conforming virtual element methods for elliptic partial differential equations

The Virtual Element Method is well suited to the formulation of arbitrarily regular Galerkin approximations of elliptic partial differential equations of order 2p1, for any integer p1 ≥ 1. In fact, the virtual element paradigm provides a very effective design framework for conforming, finite dimensional subspaces of H2(Ω), Ω being the computational domain and p2 ≥ p1 another suitable integer number. In this study, we first present an abstract setting for such highly regular approximations and discuss the mathematical details of how we can build conforming approximation spaces with a global high-order continuity on Ω. Then, we illustrate specific examples in the case of secondand fourth-order partial differential equations, that correspond to the cases p1 = 1 and 2, respectively. Finally, we investigate numerically the effect on the approximation properties of the conforming highly-regular method that results from different choices of the degree of continuity of the underlying virtual element spaces and how different stabilization strategies may impact on convergence.
Experiences with User Studies in Augmented Reality

The research field of augmented reality (AR) is of increasing popularity, as seen, among others, in several recently published surveys. To produce further advancements in AR, it is not only necessary to create new systems or applications, but also to evaluate them. One important aspect in regards to the evaluation is the general understanding of how users experience a given AR application, which can also be seen by the increased number of papers focusing on this topic that were published in the last years. With the steadily growing understanding and development of AR in general, it is only a matter of time until AR devices make the leap into the consumer market where such an in-depth user understanding is even more essential. Thus, a better understanding of factors that could influence the design and results of user experience studies can help us to make them more robust and dependable in the future. In this position paper, we describe three challenges which researchers face while designing and conducting AR users studies. We encountered these challenges in our past and current research, including papers that focus on perceptual studies of visualizations, interaction studies, and studies exploring the use of AR applications and their design spaces.
The topologies and the differentiable structures of the images of special generic maps having simple structures

Special generic maps are smooth maps at each singular point of which we can represent as (x1, · · · , xm) 7→ (x1, · · · , xn−1, ∑ m k=n xk ) for suitable coordinates. Morse functions with exactly two singular points on homotopy spheres and canonical projections of unit spheres are special generic. They are known to restrict the topologies and the differentiable structures of the manifolds in various situations. On the other hands, various manifolds admit such maps. This article first presents a special generic map on a 7-dimensional manifold and the image. This results also seems to present a new example of 7-dimensional closed and simply-connected manifolds having non-vanishing triple Massey products and seems to be a new work related to similar works by Dranishnikov and Rudyak. We also review results on vanishing of products of cohomology classes, previously obtained by the author. The images of special generic maps are smoothly immersed manifolds whose dimensions are equal to the dimensions of the targets. The author studied the topologies of these images previously and studies on homology groups, cohomology rings and structures of them for special generic maps having simple structures will be presented as new results.
Uncovering erosion effects on magnetic flux rope twist

Context. Magnetic clouds (MCs) are transient structures containing large-scale magnetic flux ropes from solar eruptions. The twist of magnetic field lines around the rope axis reveals information about flux rope formation processes and geoeffectivity. During propagation MC flux ropes may erode via reconnection with the ambient solar wind. Any erosion reduces the magnetic flux and helicity of the ropes, and changes their cross-sectional twist profiles. Aims. This study relates twist profiles in MC flux ropes observed at 1 AU to the amount of erosion undergone by the MCs in interplanetary space. Methods. The twist profiles of two clearly identified MC flux ropes associated with the clear appearance of post eruption arcades in the solar corona are analyzed. To infer the amount of erosion, the magnetic flux content of the ropes in the solar atmosphere is estimated, and compared to estimates at 1 AU. Results. The first MC shows a monotonically decreasing twist from the axis to the periphery, while the second displays high twist at the axis, rising twist near the edges, and lower twist in between. The first MC displays a larger reduction in magnetic flux between the Sun and 1 AU, suggesting more erosion than that seen in the second MC. Conclusions. In the second cloud the rising twist at the rope edges may have been due to an envelope of overlying coronal field lines with relatively high twist, formed by reconnection beneath the erupting flux rope in the low corona. This high-twist envelope remained almost intact from the Sun to 1 AU due to the low erosion levels. In contrast, the high-twist envelope of the first cloud may have been entirely peeled away via erosion by the time it reaches 1 AU.
On Biasing Transformer Attention Towards Monotonicity

Many sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enforced learning of monotonic attention behavior via specialized attention functions or pretraining. In this work, we introduce a monotonicity loss function that is compatible with standard attention mechanisms and test it on several sequence-to-sequence tasks: grapheme-to-phoneme conversion, morphological inflection, transliteration, and dialect normalization. Experiments show that we can achieve largely monotonic behavior. Performance is mixed, with larger gains on top of RNN baselines. General monotonicity does not benefit transformer multihead attention, however, we see isolated improvements when only a subset of heads is biased towards monotonic behavior.
A transfer-learning approach for lesion detection in endoscopic images from the urinary tract

Ureteroscopy and cystoscopy are the gold standard methods to identify and treat tumors along the urinary tract. It has been reported that during a normal procedure a rate of 10-20 % of the lesions could be missed. In this work we study the implementation of 3 different Convolutional Neural Networks (CNNs), using a 2-steps training strategy, to classify images from the urinary tract with and without lesions. A total of 6,101 images from ureteroscopy and cystoscopy procedures were collected. The CNNs were trained and tested using transfer learning in a two-steps fashion on 3 datasets. The datasets used were: 1) only ureteroscopy images, 2) only cystoscopy images and 3) the combination of both of them. For cystoscopy data, VGG performed better obtaining an Area Under the ROC Curve (AUC) value of 0.846. In the cases of ureteroscopy and the combination of both datasets, ResNet50 achieved the best results with AUC values of 0.987 and 0.940. The use of a training dataset that comprehends both domains results in general better performances, but performing a second stage of transfer learning achieves comparable ones. There is no single model which performs better in all scenarios, but ResNet50 is the network that achieves the best performances in most of them. The obtained results open the opportunity for further investigation with a view for improving lesion detection in endoscopic images of the urinary system.
Implementation of weak measurement amplification with the weak coherent light and optomechanical system

Most studies for postselected weak measurement in optomechanical system focus on using a single photon as a measured system. However, we find that using weak coherent light instead of a single photon can also amplify the mirror’s position displacement of one photon. In the weak value amplification (WVA) regime, the weak value of one photon can lie outside the eigenvalue spectrum, proportional to the difference of the mirror’s position displacement between the successful and failed postselection and its successful postselection probability is dependent of the mean photon number of the coherent light and improved by adjusting it accordingly. Outside the WVA regime, the amplification limit can reach the level of the vacuum fluctuations, and when the mean photon number and the optomechanical coupling parameter are selected appropriately, its successful postselection probability becomes higher, which is beneficial to observe the maximum amplification value under the current experimental conditions. This result breaks the constraint that it is difficult to detect outside the WVA regime. This opens up a new regime for the study of a single photon nonlinearity in optomechanical system.
Learning What To Do by Simulating the Past

Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences, and thus an agent can extract information about what humans want from the state. Such learning is possible in principle, but requires simulating all possible past trajectories that could have led to the observed state. This is feasible in gridworlds, but how do we scale it to complex tasks? In this work, we show that by combining a learned feature encoder with learned inverse models, we can enable agents to simulate human actions backwards in time to infer what they must have done. The resulting algorithm is able to reproduce a specific skill in MuJoCo environments given a single state sampled from the optimal policy for that skill.
Phoneme-Based Distribution Regularization for Speech Enhancement

Existing speech enhancement methods mainly separate speech from noises at the signal level or in the time-frequency domain. They seldom pay attention to the semantic information of a corrupted signal. In this paper, we aim to bridge this gap by extracting phoneme identities to help speech enhancement. Specifically, we propose a phoneme-based distribution regularization (PbDr) for speech enhancement, which incorporates frame-wise phoneme information into speech enhancement network in a conditional manner. As different phonemes always lead to different feature distributions in frequency, we propose to learn a parameter pair, i.e. scale and bias, through a phoneme classification vector to modulate the speech enhancement network. The modulation parameter pair includes not only frame-wise but also frequency-wise conditions, which effectively map features to phoneme-related distributions. In this way, we explicitly regularize speech enhancement features by recognition vectors. Experiments on public datasets demonstrate that the proposed PbDr module can not only boost the perceptual quality for speech enhancement but also the recognition accuracy of an ASR system on the enhanced speech. This PbDr module could be readily incorporated into other speech enhancement networks as well.
Question-Driven Design Process for Explainable AI User Experiences

A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and accordingly, propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions. We provide a mapping guide between prototypical user questions and exemplars of XAI techniques to reframe the technical space of XAI, also serving as boundary objects to support collaboration between designers and AI engineers. We demonstrate it with a use case of designing XAI for healthcare adverse events prediction, and discuss lessons learned for tackling design challenges of AI systems.
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC

We present a framework for approximate Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained due to computational constraints, which is becoming increasingly common for applications of complex models. We model the log-likelihood function using a Gaussian process (GP) and the main methodological innovation is to apply this model to emulate the progression that an exact Metropolis-Hastings (MH) sampler would take if it was applicable. Informative log-likelihood evaluation locations are selected using a sequential experimental design strategy until the MH accept/reject decision is done accurately enough according to the GP model. The resulting approximate sampler is conceptually simple and sample-efficient. It is also more robust to violations of GP modelling assumptions compared with earlier, related"Bayesian optimisation-like"methods tailored for Bayesian inference. We discuss some theoretical aspects and various interpretations of the resulting approximate MH sampler, and demonstrate its benefits in the context of Bayesian and generalised Bayesian likelihood-free inference for simulator-based statistical models.
A Centralized Optimization Approach for Bidirectional PEV Impacts Analysis in a Commercial Building-Integrated Microgrid

-Building sector is the largest energy user in the United States. Conventional building energy studies mostly involve Heating, Ventilation, and Air Conditioning (HVAC), and lighting energy consumptions. Recent additions of solar Photovoltaics (PV) along with other Distributed Energy Resources (DER), particularly Plug-in Electric Vehicles (PEV) have added a new dimension to this problem and made it more complex. This paper presents an avant-garde framework for selecting the best charging/discharging level of PEV for a commercial building-integrated microgrid. A typical commercial building is used as a microgrid testbed incorporating all the DERs presented in a smart building. A Mixed Integer Linear Programming (MILP) problem is formulated to optimize the energy and demand cost associated with this building operation. The cost function is solved in conjunction with real data and modified to assess the bidirectional PEV impacts on the flexible building loads that are contributing factors in making energy usage decisions. Finally, the impacts of optimized DERs are investigated on a Distribution System (DS) to show the necessity of a holistic approach for selecting the suitable PEV strategies. The results show that bidirectional fast PEV activities can provide higher cost reduction and less voltage deviation in comparison to slow PEV activities. Keywords-building energy cost reduction; DER & PEV integration; distribution system; G2V & V2G; microgrid; MILP optimization.
PrivateSNN: Fully Privacy-Preserving Spiking Neural Networks

How can we bring both privacy and energy-efficiency to a neural system on edge devices? In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pre-trained ANN model without leaking sensitive information contained in a dataset. Here, we tackle two types of leakage problems: 1) Data leakage caused when the networks access real training data during an ANN-SNN conversion process. 2) Class leakage is the concept of leakage caused when class-related features can be reconstructed from network parameters. In order to address the data leakage issue, we generate synthetic images from the pre-trained ANNs and convert ANNs to SNNs using generated images. However, converted SNNs are still vulnerable with respect to the class leakage since the weight parameters have the same (or scaled) value with respect to ANN parameters. Therefore, we encrypt SNN weights by training SNNs with a temporal spike-based learning rule. Updating weight parameters with temporal data makes networks difficult to be interpreted in the spatial domain. We observe that the encrypted PrivateSNN can be implemented not only without the huge performance drop (less than ~5%) but also with significant energy-efficiency gain (about x60 compared to the standard ANN). We conduct extensive experiments on various datasets including CIFAR10, CIFAR100, and TinyImageNet, highlighting the importance of privacy-preserving SNN training.
On regular algebraic hypersurfaces with non-zero constant mean curvature in Euclidean spaces

We prove that there are no regular algebraic hypersurfaces with non-zero constant mean curvature in the Euclidean space $\mathbb {R}^{n+1},\,\;n\geq 2,$ defined by polynomials of odd degree. Also we prove that the hyperspheres and the round cylinders are the only regular algebraic hypersurfaces with non-zero constant mean curvature in $\mathbb {R}^{n+1}, n\geq 2,$ defined by polynomials of degree less than or equal to three. These results give partial answers to a question raised by Barbosa and do Carmo.
Reconstructing Recognizable 3D Face Shapes based on 3D Morphable Models

Many recent works have reconstructed distinctive 3D face shapes by aggregating shape parameters of the same identity and separating those of different people based on parametric models (e.g. 3D morphable models (3DMMs)). However, despite the high accuracy in the face recognition task using these shape parameters, the visual discrimination of face shapes reconstructed from those parameters remains unsatisfactory. Previous works have not answered the following research question: Do discriminative shape parameters guarantee visual discrimination in represented 3D face shapes? This paper analyses the relationship between shape parameters and reconstructed shape geometry, and proposes a novel shape identity‐aware regularization (SIR) loss for shape parameters, aiming at increasing discriminability in both the shape parameter and shape geometry domains. Moreover, to cope with the lack of training data containing both landmark and identity annotations, we propose a network structure and an associated training strategy to leverage mixed data containing either identity or landmark labels. In addition, since face recognition accuracy does not mean the recognizability of reconstructed face shapes from the shape parameters, we propose the SIR metric to measure the discriminability of face shapes. We compare our method with existing methods in terms of the reconstruction error, visual discriminability, and face recognition accuracy of the shape parameters and SIR metric. Experimental results show that our method outperforms the state‐of‐the‐art methods. The code will be released at https://github.com/a686432/SIR.
Optimal ventilation rate for effective displacement ventilation

Indoor ventilation is essential for a healthy and comfortable living environment. A key issue is to discharge anthropogenic air contamination such as CO2 gas or, more seriously, airborne respiratory droplets. Here, by employing direct numerical simulations, we study the mechanical displacement ventilation with the realistic range of air changes per hour (ACH) from 1 to 10. For this ventilation scheme, a cool lower zone is established beneath the warm upper zone with the interface height h depending on ACH. For weak ventilation, we find the scalings relation of the interface height h ~ ACH^{3/5}, as suggested by Hunt&Linden (Build. Environ., vol. 34, 1999, pp. 707-720). Also, the CO2 concentration decreases with ACH within this regime. However, for too strong ventilation, the interface height h becomes insensitive to ACH, and the CO2 concentration remains unchanged. Our results are in contrast to the general belief that stronger flow is more helpful to remove contaminants. We work out the physical mechanism governing the transition between the low ACH and the high ACH regimes. It is determined by the relative strength of the kinetic energy from the inflow, potential energy from the stably-stratified layers, and energy loss due to drag. Our findings provide a physics-based guideline to optimize displacement ventilation.
Uncertainty-aware Remaining Useful Life predictor

Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset can be expected to operate within its defined specifications. Deploying successful RUL prediction methods in real-life applications is a prerequisite for the design of intelligent maintenance strategies with the potential of drastically reducing maintenance costs and machine downtimes. In light of their superior performance in a wide range of engineering fields, Machine Learning (ML) algorithms are natural candidates to tackle the challenges involved in the design of intelligent maintenance systems. In particular, given the potentially catastrophic consequences or substantial costs associated with maintenance decisions that are either too late or too early, it is desirable that ML algorithms provide uncertainty estimates alongside their predictions. However, standard data-driven methods used for uncertainty estimation in RUL problems do not scale well to large datasets or are not sufficiently expressive to model the high-dimensional mapping from raw sensor data to RUL estimates. In this work, we consider Deep Gaussian Processes (DGPs) as possible solutions to the aforementioned limitations. We perform a thorough evaluation and comparison of several variants of DGPs applied to RUL predictions. The performance of the algorithms is evaluated on the N-CMAPSS (New Commercial Modular Aero-Propulsion System Simulation) dataset from NASA for aircraft engines. The results show that the proposed methods are able to provide very accurate RUL predictions along with sensible uncertainty estimates, providing more reliable solutions for (safety-critical) real-life industrial applications.
Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators

We propose a framework for Bayesian Likelihood-Free Inference (LFI) based on Generalized Bayesian Inference using scoring rules (SRs). SRs are used to evaluate probabilistic models given an observation; a proper SR is minimised in expectation when the model corresponds to the data generating process for the observations. Using a strictly proper SR, for which the above minimum is unique, ensures posterior consistency of our method. Further, we prove finite sample posterior consistency and outlier robustness of our posterior for the Kernel and Energy Scores. As the likelihood function is intractable for LFI, we employ consistent estimators of SRs using model simulations in a pseudo-marginal MCMC; we show the target of such chain converges to the exact SR posterior by increasing the number of simulations. Furthermore, we note popular LFI techniques such as Bayesian Synthetic Likelihood (BSL) can be seen as special cases of our framework using only proper (but not strictly so) SR. We empirically validate our consistency and outlier robustness results and show how related approaches do not enjoy these properties. Practically, we use the Energy and Kernel Scores, but our general framework sets the stage for extensions with other scoring rules.