Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy efficiency refurbishment of UK owner-occupied homes : the householders' perspective

Owner-occupiers represent 63.1% of UK households (ONS, 2016); a key group to focus attention on as a means to increase domestic energy efficiency rates (Risholt et al, 2013). The experiences of early-adopters of measures such as external wall insulation within this group may affect the future adoption rates, through word-of mouth referrals (McMichael & Shipworth, 2013). Householders’ are often willing to tolerate disruption, but unexpected delays or complications can cause considerable stress (Vadodaria et al., 2010). Negative attitudes can develop towards a refurbishment experience due to technological faults or internal disputes leading to problems with workmanship (Crosbie and Baker, 2010). These experiences can lead to dissatisfaction with the experience and potentially the refurbishment outcome in terms of energy demand and living environment comfort. This study has followed ten owner-occupied case study households through an energy efficiency refurbishment process. Householders’ were interviewed pre, during and post refurbishment over a period of one year, with a focus on the expectations and experiences of the refurbishment in terms of process and potential changes to thermal environment comfort and space heating energy demand. The interview data were triangulated with measurements of internal air temperature, air leakage tests and space heating energy use in addition to space heating energy modelling, using a version of the Standard Assessment Procedure (2009). The findings show that in terms of refurbishment process, measures which installers’ have more apparent experience in, such as combination boiler or window replacements have few unexpected events and outcomes, whereas measures such as external wall insulation and air leakage sealing can lead to refurbishment delays or dissatisfaction with workmanship. Two households exceeded the 2050 target for an 80% reduction in CO2 emissions from space heating. Nine improved thermal environment comfort levels, as supported by measurements. However the impact on householder perceptions of disruption and workmanship may affect future uptake. Policy and installer recommendations are made including the need for consistent policy and urgent improvements to installer customer service, energy efficiency expertise and skills.
Writing History Backwards: Meiji Japan Revisited

In following the paths of historical development … the analyst finds himself … engaged in the rather thankless task of trying to derive known from unknown or, at least, better-known from less well-known facts. Would it not be much more efficient to reverse this procedure? By establishing the base of his operations, that is, the principal store of primary factual information in the present or a very recent past, and then moving on backward with the help of theoretical weapons step by step toward the more and more distant past, the analytical historian could make most effective use of the limited amount of direct factual information to which he usually has access.
A Simple Parameter Estimation Method for Periodic Signals Applicable to Vital Sensing Using Doppler Sensors

A Doppler sensor enables us to detect the movement of objects. It transmits an electro-magnetic wave and receives signals yielded by the reflection at the objects so that we can detect the movement of the objects by measuring the frequency deviation between the transmittedand the received signals. Recently, it is also drawing attention as a tool of non-contact vital sensing such as heartbeats and breathing. This is particularly interesting as a means for monitoring of patients in hospitals or residents in nursing homes in order to make such facilities more efficient. In this paper, aiming at such application, we propose a new very simple signal processing scheme in order to estimate (1) the number of signals, (2) the period of each signal, and (3) the fundamental waveform. It is also emphasized that the estimations (1)–(3) are available for two signals. The performance is verified through computer simulations.
Lieber die Taube auf dem Dach! Eine Skala zur Erfassung interindividueller Unterschiede in der Maximierungstendenz

The current paper provides a German translation of the maximizing scale, originally presented by Schwartz et al. (2002). The maximizing scale assesses the individual tendency to strive for optimal versus satisfactory decisions. A translation of the regret scale is also provided, as this scale was initially introduced along with the maximizing scale. Two studies (N = 317, 219) investigated the factor structure, reliability and validity of the German translation of the maximizing scale. For the first time, it is shown that maximizers compared to satisficers strive more for optimal decisions and exhibit a more intensive search for alternatives.
Decomposition in Chern-Simons theories in three dimensions

In this paper we discuss decomposition in the context of three-dimensional Chern-Simons theories. Specifically, we argue that a Chern-Simons theory with a gauged noneffectively-acting one-form symmetry is equivalent to a disjoint union of Chern-Simons theories, with discrete theta angles couplings to the image under a Bockstein homomorphism of a canonical degree-two characteristic class. On three-manifolds with boundary, we show that the bulk discrete theta angles (coupling to bundle characteristic classes) are mapped to choices of discrete torsion in boundary orbifolds. We use this to verify that the bulk three-dimensional Chern-Simons decomposition reduces on the boundary to known decompositions of two-dimensional (WZW) orbifolds, providing a strong consistency test of our proposal.
GitHub Copilot AI pair programmer: Asset or Liability?

Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.
High-Performance Broad-Band Photodetection Based on Graphene-MoS2xSe2(1-x) Alloy Engineered Phototransistors.

The concept of alloy engineering has emerged as a viable technique toward tuning the band gap as well as engineering the defect levels in two-dimensional transition-metal dichalcognides (TMDCs). The possibility of synthesizing these ultrathin TMDC materials through a chemical route has opened up realistic possibilities to fabricate hybrid multifunctional devices. By synthesizing nanosheets with different composites of MoS2xSe2(1-x) (x = 0 - 1) using simple chemical methods, we systematically investigate the photoresponse properties of three terminal hybrid devices by decorating large-area graphene with these nanosheets (x = 0, 0.5, 1) in 2D-2D configurations. Among them, the graphene-MoSSe hybrid phototransistor exhibits optoelectronic properties superior to those of its binary counterparts. The device exhibits extremely high photoresponsivity (>104 A/W), low noise equivalent power (∼10-14 W/Hz0.5), and higher specific detectivity (∼1011 jones) in the wide UV-NIR (365-810 nm) range with excellent gate tunability. The broad-band light absorption of MoSSe, ultrafast charge transport in graphene, and controllable defect engineering in MoSSe makes this device extremely attractive. Our work demonstrates the large-area scalability with the wafer-scale production of MoS2xSe2(1-x) alloys, having important implications toward the facile and scalable fabrication of high-performance optoelectronic devices and providing important insights into the fundamental interactions between van der Waals materials.
Investigating nuclear structure near 
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Nuclear mass measurements of isotopes are key to improving our understanding of nuclear structure across the chart of nuclides, in particular for the determination of the appearance or disappearance of nuclear shell closures. We present high-precision mass measurements of neutron-rich Ca, Ti and V isotopes performed at the TITAN and LEBIT facilities. These measurements were made using the TITAN multiple-reflection time-of-flight mass spectrometer (MR-ToF-MS) and the LEBIT 9.4T Penning trap mass spectrometer. In total, 13 masses were measured, eight of which represent increases in precision over previous measurements. These measurements refine trends in the mass surface around $N = 32$ and $N = 34$, and support the disappearance of the $N = 32$ shell closure with increasing proton number. Additionally, our data does not support the presence of a shell closure at $N = 34$.
Rejuvenated Accretors Have Less Bound Envelopes: Impact of Roche Lobe Overflow on Subsequent Common Envelope Events

Common envelope (CE) evolution is an outstanding open problem in stellar evolution, critical to the formation of compact binaries including gravitational-wave sources. In the “classical” isolated binary evolution scenario for double compact objects, the CE is usually the second mass transfer phase. Thus, the donor star of the CE is the product of a previous binary interaction, often stable Roche lobe overflow (RLOF). Because of the accretion of mass during the first RLOF, the main-sequence core of the accretor star grows and is “rejuvenated. ” This modifies the core-envelope boundary region and decreases significantly the envelope binding energy for the remaining evolution. Comparing accretor stars from self-consistent binary models to stars evolved as single, we demonstrate that the rejuvenation can lower the energy required to eject a CE by ∼42%–96% for both black hole and neutron star progenitors, depending on the evolutionary stage and final orbital separation. Therefore, binaries experiencing first stable mass transfer may more easily survive subsequent CE events and result in possibly wider final separations compared to current predictions. Despite their high mass, our accretors also experience extended “blue loops,” which may have observational consequences for low-metallicity stellar populations and asteroseismology.
Neural Network Assisted Depth Map Packing for Compression Using Standard Hardware Video Codecs

Depth maps are needed by various graphics rendering and processing operations. Depth map streaming is often necessary when such operations are performed in a distributed system and it requires in most cases fast performing compression, which is why video codecs are often used. Hardware implementations of standard video codecs enable relatively high resolution and frame rate combinations, even on resource constrained devices, but unfortunately those implementations do not currently support RGB+depth extensions. However, they can be used for depth compression by first packing the depth maps into RGB or YUV frames. We investigate depth map compression using a combination of depth map packing followed by encoding with a standard video codec. We show that the precision at which depth maps are packed has a large and nontrivial impact on the resulting error caused by the combination of the packing scheme and lossy compression when the bitrate is constrained. Consequently, we propose a variable precision packing scheme assisted by a neural network model that predicts the optimal precision for each depth map given a bitrate constraint. We demonstrate that the model yields near optimal predictions and that it can be integrated into a game engine with very low overhead using modern hardware.
Laplacian Autoencoders for Learning Stochastic Representations

Established methods for unsupervised representation learning such as variational autoencoders produce none or poorly calibrated uncertainty estimates making it difficult to evaluate if learned representations are stable and reliable. In this work, we present a Bayesian autoencoder for unsupervised representation learning, which is trained using a novel variational lower-bound of the autoencoder evidence. This is maximized using Monte Carlo EM with a variational distribution that takes the shape of a Laplace approximation. We develop a new Hessian approximation that scales linearly with data size allowing us to model high-dimensional data. Empirically, we show that our Laplacian autoencoder estimates well-calibrated uncertainties in both latent and output space. We demonstrate that this results in improved performance across a multitude of downstream tasks.
Borsuk's partition problem in four-dimensional $\ell_{p}$ space

In 1933, Borsuk made a conjecture that every n -dimensional bounded set can be divided into n + 1 subsets of smaller diameter. Up to now, the problem is still open for 4 ≤ n ≤ 63. In this paper, we ﬁrstly discuss the Banach-Mazur distance between the n -dimensional cube and the ℓ p ball (1 ≤ p < 2), then we study the generalized Borsuk’s partition problem in metric spaces and prove that all bounded sets X in every four-dimensional ℓ p space can be divided into 2 4 subsets of smaller diameter.
Can the Replication Rate Tell Us About Publication Bias?

A leading explanation for widespread replication failures is publication bias. I show in a simple model of selective publication that, contrary to common per-ceptions, the replication rate is unaﬀected by the suppression of insigniﬁcant results in the publication process. I show further that the expected replication rate falls below intended power owing to issues with common power calculations. I empirically calibrate a model of selective publication and ﬁnd that power issues alone can explain the entirety of the gap between the replication rate and intended power in experimental economics. In psychology, these issues explain two-thirds of the gap.
The maximum capability of a topological feature in link prediction

Abstract Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance. The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links. Because a family of indexes based on the same feature shares the same upper bound, the potential of all others can be estimated from one single index. Furthermore, a feature’s capability is lifted in the supervised prediction, which can be mathematically quantified, allowing us to estimate the benefit of applying machine learning algorithms. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks. The findings have applications in feature and method selection, and shed light on network characteristics that make a topological feature effective in link prediction.
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale

The success of DNNs is driven by the counter-intuitive ability of over-parameterized networks to generalize, even when they perfectly fit the training data. In practice, test error often continues to decrease with increasing over-parameterization, referred to as double descent. This allows practitioners to instantiate large models without having to worry about over-fitting. Despite its benefits, however, prior work has shown that over-parameterization can exacerbate bias against minority subgroups. Several fairness-constrained DNN training methods have been proposed to address this concern. Here, we critically examine MinDiff, a fairness-constrained training procedure implemented within TensorFlow's Responsible AI Toolkit, that aims to achieve Equality of Opportunity. We show that although MinDiff improves fairness for under-parameterized models, it is likely to be ineffective in the over-parameterized regime. This is because an overfit model with zero training loss is trivially group-wise fair on training data, creating an"illusion of fairness,"thus turning off the MinDiff optimization (this will apply to any disparity-based measures which care about errors or accuracy. It won't apply to demographic parity). Within specified fairness constraints, under-parameterized MinDiff models can even have lower error compared to their over-parameterized counterparts (despite baseline over-parameterized models having lower error). We further show that MinDiff optimization is very sensitive to choice of batch size in the under-parameterized regime. Thus, fair model training using MinDiff requires time-consuming hyper-parameter searches. Finally, we suggest using previously proposed regularization techniques, viz. L2, early stopping and flooding in conjunction with MinDiff to train fair over-parameterized models.
On Non-Random Missing Labels in Semi-Supervised Learning

Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in which the label Missing Not At Random (MNAR) problem is more realistic and challenging, compared to the widely-adopted yet naive Missing Completely At Random assumption where both labeled and unlabeled data share the same class distribution. Different from existing SSL solutions that overlook the role of"class"in causing the non-randomness, e.g., users are more likely to label popular classes, we explicitly incorporate"class"into SSL. Our method is three-fold: 1) We propose Class-Aware Propensity (CAP) that exploits the unlabeled data to train an improved classifier using the biased labeled data. 2) To encourage rare class training, whose model is low-recall but high-precision that discards too many pseudo-labeled data, we propose Class-Aware Imputation (CAI) that dynamically decreases (or increases) the pseudo-label assignment threshold for rare (or frequent) classes. 3) Overall, we integrate CAP and CAI into a Class-Aware Doubly Robust (CADR) estimator for training an unbiased SSL model. Under various MNAR settings and ablations, our method not only significantly outperforms existing baselines but also surpasses other label bias removal SSL methods. Please check our code at: https://github.com/JoyHuYY1412/CADR-FixMatch.
Antipolar transitions in GaNb$_4$Se$_8$ and GaTa$_4$Se$_8$

We present dielectric, polarization, resistivity, specific heat, and magnetic susceptibility data on single crystals of the lacunar spinels GaNb 4 Se 8 and GaTa 4 Se 8 , tetrahedral cluster-based materials with substantial spin-orbit coupling. We concentrate on the possible occurrence of antipolar order in these compounds, as previously reported for the isoelectronic GaNb 4 S 8 , where spin-orbit coupling plays a less important role. Our broadband dielectric-spectroscopy investigations reveal clear anomalies of the intrinsic dielectric constant at the magneto-structural transitions in both systems that are in accord with the expectations for antipolar transitions. A similar anomaly is also observed at the cubic-cubic transition of the Nb compound leading to an intermediate phase. Similar to other polar and antipolar lacunar spinels, we find indications for dipolar relaxation dynamics at low temperatures. Polarization measurements on GaNb 4 Se 8 reveal weak ferroelectric ordering below the magneto-structural transition, either superimposed to antipolar order or emerging at structural domain walls. The temperature-dependent dc resistivity evidences essentially thermally-activated charge transport with different activation energies in the different phases. A huge step-like increase of the resistivity at the magneto-structural transition of the Ta compound points to a fundamental change in the electronic structure or the mechanism of the charge transport. At low temperatures, charge transport is governed by in-gap impurity states, as also invoked to explain the resistive switching in these compounds.
Byzantine Agreement with Optimal Resilience via Statistical Fraud Detection

Since the mid-1980s it has been known that Byzantine Agreement can be solved with probability 1 asynchronously, even against an omniscient, computationally unbounded adversary that can adaptively corrupt up to f < n/3 parties. Moreover, the problem is insoluble with f ≥ n/3 corruptions. However, Bracha’s [13] 1984 protocol (see also Ben-Or [8]) achieved f < n/3 resilience at the cost of exponential expected latency 2Θ(n), a bound that has never been improved in this model with f = ⌊(n − 1)/3⌋ corruptions. In this paper, we prove that Byzantine Agreement in the asynchronous, full information model can be solved with probability 1 against an adaptive adversary that can corrupt f < n/3 parties, while incurring only polynomial latency with high probability. Our protocol follows an earlier polynomial latency protocol of King and Saia [33,34], which had suboptimal resilience, namely f ≈ n/109 [33,34]. Resilience f = (n − 1)/3 is uniquely difficult, as this is the point at which the influence of the Byzantine and honest players are of roughly equal strength. The core technical problem we solve is to design a collective coin-flipping protocol that eventually lets us flip a coin with an unambiguous outcome. In the beginning, the influence of the Byzantine players is too powerful to overcome, and they can essentially fix the coin’s behavior at will. We guarantee that after just a polynomial number of executions of the coin-flipping protocol, either (a) the Byzantine players fail to fix the behavior of the coin (thereby ending the game) or (b) we can “blacklist” players such that the blacklisting rate for Byzantine players is at least as large as the blacklisting rate for good players. The blacklisting criterion is based on a simple statistical test of fraud detection.