Miracle-dz/UniFAR-FTUs huggingface dataset:
Green Building Rating Systems and Whole-Building Life Cycle Assessment: Comparative Study of the Existing Assessment Tools

AbstractThere is a growing interest in integrating life cycle assessment (LCA) into building design decision making due to LCA’s comprehensive and systemic approach to environmental evaluation. Many green building rating systems (GBRSs) use LCA to various degrees. In this paper, a comparative study was performed to evaluate the LCA software tools available to building designers. A whole-building LCA was performed for a large building using three software LCA tools. The software tools vary in key aspects, such as intended users (e.g., LCA experts or novices), design stage in which they can be used, and time. The evaluated LCA tools varied significantly in the possibility of their use in early design and decision making. Some of the applications rely on a bill of materials that changes constantly in design alterations. However, others showed a greater advantage in that they can be integrated from the beginning of the design process. The comparative LCA results indicated that the impact of LCA software is de.
On the long time behaviour of single stochastic Hodgkin-Huxley neurons with constant signal, and a construction of circuits of interacting neurons showing self-organized rhythmic oscillations

The stochastic Hodgkin-Huxley neurons considered in this paper replace time-constant deterministic input $a dt$ of the classical deterministic model by increments $\vartheta dt + dX_t$ of a stochastic process: $X$ is Ornstein-Uhlenbeck with volatility $\sigma>0$ and backdriving force $\tau>0$, and we call $\vartheta>0$ the signal. We have ergodicity and strong laws of large numbers for various functionals of the process, and characterize 'quiet behaviour' and 'regular spiking' as events whose probability depends on the parameters $(\tau,\sigma)$ and on the signal $\vartheta$. The notions of quiet behaviour and regular spiking allow for a construction of circuits of interacting stochastic Hodgkin-Huxley neurons, combining excitation with inhibition according to a bloc structure along the circuit, on which self-organized rhythmic oscillations can be observed.
Mixed state representability of entropy-density pairs

One of the basic questions arising in the variational approach of manybody quantum mechanics, and raised by Coleman [2], is whether some quantities like one-body or two-body density matrices, or other reduced quantities, are the ones of an N -body fermionic wavefunction having finite kinetic energy. Indeed, for instance the ground state of a system can be extracted from the minimization of the energy over the set of two-particle density matrices coming from fermionic wavefunctions [2, 3, 16], and knowing a simple characterization of this set would enable to reduce the exact many-body problem to a low-dimensional problem. Those issues are called the purestate representability problems. For one-body densities it was solved by Harriman in [7], using determinantal functions, and was later put on rigorous grounds by Lieb in [13, Theorem 1.2]. Then the problem of pure-state representability of current densities was addressed by Lieb and Schrader in [14], solved with explicit bounds when the velocity field is curl-free. The case of pure-state representability of magnetizations was solved by Gontier in [5, 6], also with explicit bounds. Finally, see [2] and [15, Theorem 3.2] for a solution of the mixed state representability of one-body density matrices. On the contrary, representing pair densities [1,19,20] and two-body (or q-body) density matrices [2, 9, 17, 23] with N -body fermionic states are important but still open problems, simple necessary and sufficient conditions are not known. In this document, we address the corresponding problem of state representability in the quantum thermodynamic ensembles. More precisely, we build mixed states having prescribed density and entropy (ρ, S) in the canonical case, and having prescribed density, mean number of particles and entropy (p,N, S) in the grand-canonical case, for both bosons and fermions, providing bounds on the kinetic energy of those mixed states. Finally, we can apply those results to define internal Levy-Lieb functionals in the Thermal DFT [21] configuration, and show some basic properties of those functionals.
Indoor SLAM using a foot-mounted IMU and the local magnetic field

In this paper, a simultaneous localization and mapping algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g. corridors and doors. The magnetic map models local magnetic field anomalies in the environment using a Gaussian process model and uses them as position information. These maps are used in a Rao-Blackwellized particle filter to correct the pedestrian position and orientation estimates from the pedestrian dead-reckoning. The pedestrian dead-reckoning is computed using an extended Kalman filter with zero-velocity updates. The algorithm is validated using experimental sequences and the results show the efficacy of the algorithm in localizing pedestrians in indoor environments.
ReplaceBlock: An improved regularization method based on background information

Attention mechanism, being frequently used to train networks for better feature representations, can effectively disentangle the target object from irrelevant objects in the background. Given an arbitrary image, we find that the background's irrelevant objects are most likely to occlude/block the target object. We propose, based on this finding, a ReplaceBlock to simulate the situations when the target object is partially occluded by the objects that are deemed as background. Specifically, ReplaceBlock erases the target object in the image, and then generates a feature map with only irrelevant objects and background by the model. Finally, some regions in the background feature map are used to replace some regions of the target object in the original image feature map. In this way, ReplaceBlock can effectively simulate the feature map of the occluded image. The experimental results show that ReplaceBlock works better than DropBlock in regularizing convolutional networks.
Robust, Automated, and Accurate Black-box Variational Inference

: Black-box variational inference (BBVI) now sees widespread use in ma- chine learning and statistics as a fast yet ﬂexible alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, stochastic optimization methods for BBVI remain unreliable and require substantial expertise and hand-tuning to apply eﬀectively. In this paper, we propose Robust, Automated, and Accurate BBVI (RAABBVI), a framework for reliable BBVI optimization. RAABBVI is based on rigorously justiﬁed automation techniques, includes just a small number of intuitive tuning parameters, and detects inaccurate estimates of the optimal variational approximation. RAABBVI adaptively decreases the learning rate by detecting convergence of the ﬁxed–learning-rate iterates, then estimates the symmetrized Kullback–Leiber (KL) divergence between the current variational approximation and the optimal one. It also employs a novel optimization termination criterion that enables the user to balance desired accuracy against computational cost by comparing (i) the predicted relative decrease in the symmetrized KL divergence if a smaller learning were used and (ii) the predicted computation required to converge with the smaller learning rate. We vali- date the robustness and accuracy of RAABBVI through carefully designed simulation studies and on a diverse set of real-world model and data examples.
Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models

We address the problem of detecting speech directed to a device that does not contain a specific wake-word. Specifically, we focus on audio coming from a touch-based invocation. Mitigating virtual assistants (VAs) activation due to accidental button presses is critical for user experience. While the majority of approaches to false trigger mitigation (FTM) are designed to detect the presence of a target keyword, inferring user intent in absence of keyword is difficult. This also poses a challenge when creating the training/evaluation data for such systems due to inherent ambiguity in the user's data. To this end, we propose a novel FTM approach that uses weakly-labeled training data obtained with a newly introduced data sampling strategy. While this sampling strategy reduces data annotation efforts, the data labels are noisy as the data are not annotated manually. We use these data to train an acoustics-only model for the FTM task by regularizing its loss function via knowledge distillation from an ASR-based (LatticeRNN) model. This improves the model decisions, resulting in 66% gain in accuracy, as measured by equal-error-rate (EER), over the base acoustics-only model. We also show that the ensemble of the LatticeRNN and acoustic-distilled models brings further accuracy improvement of 20%.
Investigation of data centric diagnostic techniques for transformer condition assessment

I Declaration by author. III Publications during candidature . IV Publications included in this thesis. VI Contributions by others to the thesis. IX Statement of parts of the thesis submitted to qualify for the award of another degree . IX Acknowledgements . X
Beta-Hydroxybutyrate Suppresses Hepatic Production of the Ghrelin Receptor Antagonist LEAP2

Abstract Introduction Liver-expressed antimicrobial peptide-2 (LEAP2) is an endogenous ghrelin receptor antagonist, which is upregulated in the fed state and downregulated during fasting. We hypothesized that the ketone body beta-hydroxybutyrate (BHB) is involved in the downregulation of LEAP2 during conditions with high circulating levels of BHB. Methods Hepatic and intestinal Leap2 expression were determined in 3 groups of mice with increasing circulating levels of BHB: prolonged fasting, prolonged ketogenic diet, and oral BHB treatment. LEAP2 levels were measured in lean and obese individuals, in human individuals following endurance exercise, and in mice after BHB treatment. Lastly, we investigated Leap2 expression in isolated murine hepatocytes challenged with BHB. Results We confirmed increased circulating LEAP2 levels in individuals with obesity compared to lean individuals. The recovery period after endurance exercise was associated with increased plasma levels of BHB levels and decreased LEAP2 levels in humans. Leap2 expression was selectively decreased in the liver after fasting and after exposure to a ketogenic diet for 3 weeks. Importantly, we found that oral administration of BHB increased circulating levels of BHB in mice and decreased Leap2 expression levels and circulating LEAP2 plasma levels, as did Leap2 expression after direct exposure to BHB in isolated murine hepatocytes. Conclusion From our data, we suggest that LEAP2 is downregulated during different states of energy deprivation in both humans and rodents. Furthermore, we here provide evidence that the ketone body, BHB, which is highly upregulated during fasting metabolism, directly downregulates LEAP2 levels. This may be relevant in ghrelin receptor–induced hunger signaling during energy deprivation.
Dose Finding Studies for Therapies with Late-Onset Safety and Efficacy Outcomes

In Phase I/II dose-finding trials, the objective is to find the Optimal Biological Dose (OBD), a dose that is both safe and efficacious that maximises some optimality criterion based on safety and efficacy. This is further complicated when the investigated treatment consists of multiple cycles of therapy, and both toxicity and efficacy outcomes may occur at any point throughout the follow up of multiple cycles. In this work we present the Joint TITE-CRM, a model-based design for late onset toxicities and efficacies based on the well-known TITE-CRM. It is found to be superior to both currently available alternative designs that account for late onset bivariate outcomes; a model-assisted method and a bivariate survival design, as well as being both intuitive and computationally feasible.
A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria

In this work, we propose a numerical method for the solution of local generalized Nash equilibria (GNE) for the class of open-loop general-sum dynamic games for agents with nonlinear dynamics and constraints. In particular, we formulate a sequential quadratic programming (SQP) approach which requires only the solution of a single convex quadratic program at each iteration and is locally convergent. Central to the effectiveness of our approach is a non-monotonic line search method and a novel merit function for SQP step acceptance which helps to improve solver convergence beyond the local neighborhood of a GNE. We demonstrate the effectiveness of the algorithm in the context of car racing, where we see up to 32% improvement of success rate when comparing against a recent solution approach for dynamic games. We also make our code available at https://github.com/zhu-edward/DGSQP.
Ferromagnetism and doublon localization in a Wannier-Hubbard chain

We derive a “Wannier-Hubbard” model consisting of an array of overlapping atomic orbitals interacting via a local Coulomb interaction. Transforming to an orthogonal Wannier basis set, the resulting Hamiltonian displays long range hopping and interactions, with new terms such as correlated hopping and ferromagnetic direct exchange, among others. We numerically study the one-dimensional version of the model at half-ﬁlling using the density matrix renormalization group (DMRG) method, unveiling a rich phase diagram as a function of the interaction U and the overlap s with metallic, and ferromagnetic phases, separated by a ferrimagnetic region. Our results indicate a path toward understanding new emergent phases under pressure and beyond standard model Hamiltonians.
Atomistic Modeling of Spin and Electron Dynamics in Two-Dimensional Magnets Switched by Two-Dimensional Topological Insulators

To design fast memory devices, we need material combinations which can facilitate fast read and write operation. We present a heterostructure comprising a two-dimensional (2D) magnet and a 2D topological insulator (TI) as a viable option for designing fast memory devices. We theoretically model spin-charge dynamics between the 2D magnets and 2D TIs. Using the adiabatic approximation, we combine the non-equilibrium Green’s function method for spin-dependent electron transport, and time-quantiﬁed Monte-Carlo for simulating magnetization dynamics. We show that it is possible to switch the magnetic domain of a ferromagnet using spin-torque from spin-polarized edge states of 2D TI. We further show that the switching between TIs and 2D magnets is strongly dependent on the interface exchange ( J int ), and an optimal interface exchange depending on the exchange interaction within the magnet is required for eﬃcient switching. Finally, we compare the experimentally grown Cr-compounds and show that Cr-compounds with higher anisotropy (such as CrI 3 ) results in lower switching speed but more stable magnetic order.
Learning Instance-Specific Adaptation for Cross-Domain Segmentation

We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at test time, we adapt a pre-trained model by conducting instance-specific BatchNorm (statistics) calibration. Our approach has two core components. First, we replace the manually designed BatchNorm calibration rule with a learnable module. Second, we leverage strong data augmentation to simulate random domain shifts for learning the calibration rule. In contrast to existing domain adaptation methods, our method does not require accessing the target domain data at training time or conducting computationally expensive test-time model training/optimization. Equipping our method with models trained by standard recipes achieves significant improvement, comparing favorably with several state-of-the-art domain generalization and one-shot unsupervised domain adaptation approaches. Combining our method with the domain generalization methods further improves performance, reaching a new state of the art.
OPD: Single-view 3D Openable Part Detection

We address the task of predicting what parts of an object can open and how they move when they do so. The input is a single image of an object, and as output we detect what parts of the object can open, and the motion parameters describing the articulation of each openable part. To tackle this task, we create two datasets of 3D objects: OPDSynth based on existing synthetic objects, and OPDReal based on RGBD reconstructions of real objects. We then design OPDRCNN, a neural architecture that detects openable parts and predicts their motion parameters. Our experiments show that this is a challenging task especially when considering generalization across object categories, and the limited amount of information in a single image. Our architecture outperforms baselines and prior work especially for RGB image inputs. Short video summary at https://www.youtube. com/watch? v=P85iCaD0rfc
A continuum of physics-based lithium-ion battery models reviewed

Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the single particle model, deriving in the process other common models, such as the Doyle–Fuller–Newman model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models.
Altered axion abundance from a dynamical Peccei-Quinn scale

We build a model in which the relic abundance of axions is altered from the standard misalignment mechanism, either increased or decreased, due to the presence of a new light scalar that couples to the radial part of the Peccei-Quinn (PQ) ﬁeld. The light scalar makes the eﬀective PQ symmetry-breaking scale dynamical, altering the early-time dynamics for the axion and aﬀecting its late-time dark matter abundance. We analyze this new mechanism semianalytically and numerically, showing that we can accommodate both lighter or heavier axion dark matter, compared to the standard treatments. We discuss implications of the model for axion searches and fundamental physics.
Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee

The objective of this study is to address the problem of background/foreground separation with missing pixels by combining the video acquisition, video recovery, background/foreground separation into a single framework. To achieve this, a smooth robust tensor completion (SRTC) model is proposed to recover the data and decompose it into the static background and smooth foreground, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition and the smooth foreground (moving objects) is modeled by the spatiotemporal continuity, which is enforced by the total variation regularization. An efficient algorithm based on tensor proximal alternating minimization (tenPAM) is implemented to solve the proposed model with global convergence guarantee under very mild conditions. Extensive experiments on real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation with missing pixels.
The relation between accretion rate and jet power in early-type galaxies with thermally unstable hot atmospheres

We use Chandra X-ray data and Very Large Array radio observations for a sample of 20 nearby, massive, X-ray bright, early-type galaxies to investigate the relation between the Bondi accretion rates and the mechanical jet powers. We find a strong correlation ($\rho = 0.96^{+0.03}_{-0.09}$; BF10 > 100) between the Bondi accretion power, PBondi, and the mechanical jet power, Pjet, for a subsample of 14 galaxies, which also host cool Hα+[N ii] line emitting gas and thus likely have thermally unstable atmospheres. The relation between the Bondi accretion power and the mechanical jet power for this subsample is well described by a power-law model $\log \frac{P_{\mathrm{Bondi}}}{{10^{43} \, \mathrm{erg \, s^{-1}}}} = \alpha + \beta \log \frac{P_{\mathrm{jet}}}{{10^{43} \, \mathrm{erg \, s^{-1}}}}$, where α = 1.10 ± 0.25 and β = 1.10 ± 0.24 with an intrinsic scatter $\sigma = 0.08^{+0.14}_{-0.06}$ dex. The results indicate that in all galaxies with thermally unstable atmospheres the cooling atmospheric gas feeds the central black holes at a similar jet-to-Bondi power ratio. For the full sample of 20 galaxies, the correlation is weaker and in a subset of galaxies with no signs of Hα+[N ii] emission, we see a hint for a systematically lower jet-to-Bondi power ratio. We also investigate the dependence of jet power on individual quantities in the Bondi formula such as the supermassive black hole mass (M•) and the specific entropy of the gas (K) at the Bondi radius. For the subsample of Hα+[N ii] emitting galaxies, we find a very tight correlation of Pjet with M• ($\rho = 0.91^{+0.06}_{-0.11}$; BF10 > 100) and, although poorly constrained, a hint of an anti-correlation for Pjet and K ($\rho = -0.47^{+0.60}_{-0.37}$; BF10 = 1.1).