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Pair density wave (PDW) is a distinct superconducting state characterized by a periodic modulation of its order parameter in real space. Its intricate interplay with the charge density wave (CDW) state is a continuing topic of interest in condensed matter physics. While PDW states have been discovered in cuprates and other unconventional superconductors, the understanding of diverse PDWs and their interactions with different types of CDWs remains limited. Here, utilizing scanning tunneling microscopy, we unveil the subtle correlations between PDW ground states and two distinct CDW phases -- namely, anion-centered-CDW (AC-CDW) and hollow-centered-CDW (HC-CDW) -- in 2H-NbSe$_2$. In both CDW regions, we observe coexisting PDWs with a commensurate structure that aligns with the underlying CDW phase. The superconducting gap size, $\Delta(r)$, related to the pairing order parameter is in phase with the charge density in both CDW regions. Meanwhile, the coherence peak height, $H(r)$, qualitatively reflecting the electron-pair density, exhibits a phase difference of approximately $2\pi/3$ relative to the CDW. The three-fold rotational symmetry is preserved in the HC-CDW region but is spontaneously broken in the AC-CDW region due to the PDW state, leading to the emergence of nematic superconductivity., Comment: 21 pages, 5 figures

One of the primary challenges in short packet ultra-reliable and low-latency communications (URLLC) is to achieve reliable channel estimation and data detection while minimizing the impact on latency performance. Given the small packet size in mini-slot-assisted URLLC, relying solely on pilot-based coherent detection is almost impossible to meet the seemingly contradictory requirements of high channel estimation accuracy, high reliability, low training overhead, and low latency. In this paper, we explore differential modulation both in the frequency domain and in the time domain, and propose adopting an adaptive approach that integrates both differential and coherent detection to achieve mini-slot-assisted short packet URLLC, striking a balance among training overhead, system performance, and computational complexity. Specifically, differential (especially in the frequency domain) and coherent detection schemes can be dynamically activated based on application scenarios, channel statistics, information payloads, mini-slot deployment options, and service requirements. Furthermore, we derive the block error rate (BLER) for pilot-based, frequency domain, and time domain differential OFDM using non-asymptotic information-theoretic bounds. Simulation results validate the feasibility and effectiveness of adaptive differential and coherent detection., Comment: 14 pages, 8 figures, journal

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Frechet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the following best method. The proposed method successfully generates high-quality energy data through metadata, and its code will be open-sourced, establishing a foundation for a broader array of energy data generation models in the future., Comment: 17 pages, 11 figures, submitted to journal "Energy and Buildings"

We carry out a systematic study of the electronic and magnetic structure of the ambient-pressure bilayer La$_3$Ni$_2$O$_7$. Employing the hybrid exchange-correlation functional, we show that the exchange-correlation pushes the bonding $d_{z^2}$ bands below the Fermi level to be fully occupied. The calculated Fermi surfaces and the correlation normalized band structure match well with the experimental findings at ambient pressure. Moreover, the electronic susceptibility calculated for this new band structure features nesting-induced peaks near the wave vector $Q=(\pi/2, \pi/2)$, suggesting a possible density wave instability in agreement with recent experiments. Through a mean field study and DFT+U calculation introducing a Hubbard U interaction within conventional density functional theory, we confirm the spin-charge intertwined double stripe order is the magnetic ground state. Our results provide a faithful description for the low-pressure La$_3$Ni$_2$O$_7$ electronic structure., Comment: 4 pages, 7 figures and supplemental material

The scalar fields, $U(1)$ gauge vector fields, and Kalb-Ramond fields are the $0$-form, 1-form, and 2-form fields, respectively. In four-dimensional space-time, free $q$-form fields are equivalent to scalar or vector fields by a duality, however, in higher-dimensional space-time, $q-$form fields correspond to new types of particles. In this paper, with considering a coupling mechanism between the kinetic term of the $q-$form fields and the background spacetime, we investigate the localization of Kaluza-Klein modes for the $q-$form fields on a $D-$dimensional Randall-Sundrum-like brane model. We demonstrate that the zero mode can be localized on the brane, and the massive Kaluza-Klein modes could be localized or quasi-localized on the brane when with certain value of parameters. Then, we present the localization of the scalar fields, $U(1)$ gauge vector fields, and 2-form Kalb-Ramond fields within a five-dimensional bulk spacetime. For a massless scalar field, an interesting characteristic emerges: When the parameter $t_2$ is negative, the zero mode is localized on both sides of the brane, whereas the massive Kaluza-Klein modes can be quasi-localized at the center of the brane., Comment: 39 pages, 20figures, 4 tables

The fractional quantum Hall (FQH) states are exotic quantum many-body phases whose elementary charged excitations are neither bosons nor fermions but anyons, obeying fractional braiding statistics. While most FQH states are believed to have Abelian anyons, the Moore-Read type states with even denominators, appearing at half filling of a Landau level (LL), are predicted to possess non-Abelian excitations with appealing potentials in topological quantum computation. These states, however, depend sensitively on the orbital contents of the single-particle LL wavefunction and the mixing between different LLs. Although they have been observed in a few materials, their non-Abelian statistics still awaits experimental confirmation. Here we show magnetotransport measurements on Bernal-stacked trilayer graphene (TLG), whose unique multiband structure facilitates the interlaced LL mixing, which can be controlled by external magnetic and displacement fields. We observe a series of robust FQH states including even-denominator ones at filling factors $\nu=-9/2$, $-3/2$, $3/2$ and $9/2$. In addition, we are able to finetune the LL mixing and crossings to drive quantum phase transitions of these half-filling states and their neighboring odd-denominator ones, exhibiting a related emerging and waning behavior. Our results establish TLG as a controllable system for tuning the weights of LL orbitals and mixing strength, and a fresh platform to seek for non-Abelian quasi-particles.

Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior of these anomalies. As an ongoing research question related to Deep Learning, a model's applicability to limited data settings can be explored by introducing prior knowledge in the network. This same strategy can also lead to more interpretable predictions, hence facilitating the field application of the approach. For that purpose, the aim of this paper is to propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing data imputation in commercial buildings. In particular, the presented method enforces physics-inspired soft constraints to the loss function of a Denoising Autoencoder (DAE). In order to quantify the benefits of the physical component, an ablation study between different DAE configurations is conducted. First, three univariate DAEs are optimized separately on indoor air temperature, heating, and cooling data. Then, two multivariate DAEs are derived from the previous configurations. Eventually, a building thermal balance equation is coupled to the last multivariate configuration to obtain PI-DAE. Additionally, two commonly used benchmarks are employed to support the findings. It is shown how introducing physical knowledge in a multivariate Denoising Autoencoder can enhance the inherent model interpretability through the optimized physics-based coefficients. While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment., Comment: Accepted for publication in Energy and Buildings

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments., Comment: 15 pages, 9 figures

Motivated by the recent discovery of high-temperature superconductivity in bilayer La$_3$Ni$_2$O$_7$ under pressure, we study its electronic properties and superconductivity due to strong electron correlation. Using the inversion symmetry, we decouple the low-energy electronic structure into block-diagonal symmetric and antisymmetric sectors. We find that the antisymmetric sector can be reduced to a one-band system near half filling, while the symmetric bands occupied by about two electrons are heavily overdoped individually. Using the strong coupling mean field theory, we obtain strong superconducting pairing with $B_{1g}$ symmetry in the antisymmetric sector. We propose that due to the spin-orbital exchange coupling between the two sectors, $B_{1g}$ pairing is induced in the symmetric bands, which in-turn boosts the pairing gap in the antisymmetric band and enhances the high-temperature superconductivity with a congruent $d$-wave symmetry in pressurized La$_3$Ni$_2$O$_7$., Comment: 4 pages and 3 figures with supplemental materials

The recent discovery of high-$T_{c}$ superconductivity in bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$ under high pressure has stimulated great interest concerning its pairing mechanism. We argue that the weak coupling model from the almost fully-filled $d_{z^{2}}$ bonding band cannot give rise to its high $T_{c}$, and thus propose a strong coupling model based on local inter-layer spin singlets of Ni-$d_{z^{2}}$ electrons due to their strong on-site Coulomb repulsion. This leads to a minimal effective model that contains local pairing of $d_{z^{2}}$ electrons and a considerable hybridization with near quarter-filled itinerant $d_{x^{2}-y^{2}}$ electrons on nearest-neighbor sites. Their strong coupling provides a unique two-component scenario to achieve high-$T_{c}$ superconductivity. Our theory highlights the importance of the bilayer structure of superconducting La$_{3}$Ni$_{2}$O$_{7}$ and points out a potential route for the exploration of more high-$T_{c}$ superconductors., Comment: 6 pages, 3 figures

Building energy prediction and management has become increasingly important in recent decades, driven by the growth of Internet of Things (IoT) devices and the availability of more energy data. However, energy data is often collected from multiple sources and can be incomplete or inconsistent, which can hinder accurate predictions and management of energy systems and limit the usefulness of the data for decision-making and research. To address this issue, past studies have focused on imputing missing gaps in energy data, including random and continuous gaps. One of the main challenges in this area is the lack of validation on a benchmark dataset with various building and meter types, making it difficult to accurately evaluate the performance of different imputation methods. Another challenge is the lack of application of state-of-the-art imputation methods for missing gaps in energy data. Contemporary image-inpainting methods, such as Partial Convolution (PConv), have been widely used in the computer vision domain and have demonstrated their effectiveness in dealing with complex missing patterns. To study whether energy data imputation can benefit from the image-based deep learning method, this study compared PConv, Convolutional neural networks (CNNs), and weekly persistence method using one of the biggest publicly available whole building energy datasets, consisting of 1479 power meters worldwide, as the benchmark. The results show that, compared to the CNN with the raw time series (1D-CNN) and the weekly persistence method, neural network models with reshaped energy data with two dimensions reduced the Mean Squared Error (MSE) by 10% to 30%. The advanced deep learning method, Partial convolution (PConv), has further reduced the MSE by 20-30% than 2D-CNN and stands out among all models.

The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the scarcity of open data sources is a continuous challenge for built environment researchers and practitioners. Although several efforts have been made to consolidate existing open datasets, no database currently offers a comprehensive collection of building data types with all subcategories and time granularities (e.g., year, month, and sub-hour). This paper presents the Building Data Genome Directory, an open data-sharing platform serving as a one-stop shop for the data necessary for vital categories of building energy research. The data directory is an online portal (http://buildingdatadirectory.org/) that allows filtering and discovering valuable datasets. The directory covers meter, building-level, and aggregated community-level data at the spatial scale and year-to-minute level at the temporal scale. The datasets were consolidated from a comprehensive exploration of sources, including governments, research institutes, and online energy dashboards. The results of this effort include the aggregation of 60 datasets pertaining to building energy ontologies, building energy models, building energy and water data, electric vehicle data, weather data, building information data, text-mining-based research data, image data of buildings, fault detection diagnosis data and occupant data. A crowdsourcing mechanism in the platform allows users to submit datasets they suggest for inclusion by filling out an online form. This directory can fuel research and applications on building energy efficiency, which is an essential step toward addressing the world's energy and environmental challenges.

We investigate the gauge invariance of massive vector Kaluza-Klein (KK) modes in different brane models with two extra dimensions. We demonstrate that some additional constraint conditions on the geometry of branes are needed in order to get the gauge invariant effective action of the KK modes. Nevertheless, not all the conditions can be satisfied by the solutions of the branes, which will break the gauge invariance. Moreover, in the branes where the conditions can meet the brane solutions, we calculate mass spectra of vector and scalar KK modes, and the results show that when only one kind of scalar KK modes is found to be massive, the gauge invariance will also be broken., Comment: 14pages, 2 figures

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fulfill the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism., Comment: 16 pages, 11 figures

One fundamental difficulty in realizing Majorana zero modes (MZMs) is the required high magnetic field, which causes serious issues, e.g., shrinks the superconducting gap, reduces topological region, and weakens their robustness against disorder. In this work, we propose that the Meissner effect can bring the topological superconducting phase to a superconductor/topological-insulator/superconductor (SC/TI/SC) hybrid system. Remarkably, the required magnetic field strength ($<$10 mT) to support MZMs has been reduced by several orders of magnitude compared to that ($>$0.5 T) in the previous schemes. Tuning the phase difference between the top and bottom superconductors can control the number and position of the MZMs. In addition, we account for the electrostatic potential in the superconductor/topological-insulator (SC/TI) interface through the self-consistent Schr\"odinger-Poisson calculation, which shows the experimental accessibility of our proposal. Our proposal only needs a small magnetic eld of less than 10 mT and is robust against the chemical potential fluctuation, which makes SC/TI/SC hybrid an ideal Majorana platform., Comment: 7 pages, 4 figures, and Supplemental material

Van Hove singularities (VHSs) in proximity to the Fermi level promote electronic interactions and generate diverse competing instabilities. It is also known that a nontrivial Berry phase derived from spin-orbit coupling (SOC) can introduce an intriguing decoration into the interactions and thus alter correlated phenomena. However, it is unclear how and what type of new physics can emerge in a system featured by the interplay between VHSs and the Berry phase. Here, based on a general Rashba model on the square lattice, we comprehensively explore such an interplay and its significant influence on the competing electronic instabilities by performing a parquet renormalization group analysis. Despite the existence of a variety of comparable fluctuations in the particle-particle and particle-hole channels associated with higher-order VHSs, we find that the chiral $p \pm ip$ pairings emerge as two stable fixed trajectories within the generic interaction parameter space, namely the system becomes a robust topological superconductor. The chiral pairings stem from the hopping interaction induced by the nontrivial Berry phase. The possible experimental realization and implications are discussed. Our work sheds new light on the correlated states in quantum materials with strong SOC and offers fresh insights into the exploration of topological superconductivity., Comment: 6 pages, 4 figures

We investigate the effect of spin orbit coupling on proximity induced superconductivity in a normal metal attached to a superconductor. Specifically, we consider a heterostructure where the presence of interfaces gives rise to a Rashba spin orbit coupling. The properties of the induced superconductivity in these systems are addressed within the tunneling Hamiltonian formalism. We find that the spin orbit coupling induces a mixture of singlet and triplet pairing and, under specific circumstances, an odd frequency, even parity, spin triplet pairs can arise. We also address the effect of impurity scattering on the induced pairs, and discuss our results in context of heterostructures consisting of materials with spin-momentum locking., Comment: 10 pages, 8 figures

In this paper, we investigate the localization of the $U(1)$ gauge field on Randall-Sundrum-like braneworld models. The localization of the $U(1)$ gauge field is important because it plays a fundamental role in the branworld theories. To achieve the localization, we propose a novel action with a non-minimal coupling between the $U(1)$ gauge field and gravity. We find that the mass spectrum of the gauge field is continuous, without any gap between the zero-mass mode and the massive modes, and except for the zero-mass mode all the massive modes are not localized on the brane. Furthermore, the massive modes have negative squared masses, indicating they are tachyonic. Our analysis can be applied to a wide range of thin and thick braneworld scenarios, provided that the five-dimensional spacetime is asymptotically anti-de Sitter., Comment: 10 pages, 2 figures, Accepted by JCAP