Hexagonal boron nitride (h-BN) has been recently found to host a variety of quantum point defects, which are promising candidates as single-photon sources for solid-state quantum nanophotonics applications. Most recently, optically addressable spin q
ubits in h-BN have been the focus of intensive research due to their unique potential in quantum computing, communication, and sensing. However, the number of high-symmetry high-spin defects that are desirable for developing spin qubits in h-BN is highly limited. Here, we combine density functional theory (DFT) and quantum embedding theories to show that out-of-plane XY dimer defects (X, Y = C, N, P, Si) form a new class of stable C3v spin-triplet defects in h-BN. We find that the dimer defects have a robust 3A2 ground state and 3E excited state, both of which are isolated from the h-BN bulk states. We show that 1E and 1A shelving states exist and they are positioned between the 3E and 3A2 states for all the dimer defects considered in this study. To support future experimental identification of the XY dimer defects, we provide an extensive characterization of the defects in terms of their spin and optical properties. We predict that the zero-phonon line of the spin-triplet XY defects lies in the visible range (800 nm - 500 nm). We compute the zero-field splitting of the dimers to range from 1.79 GHz (SiP) to 29.5 GHz (CN). Our results broaden the scope of high-spin defect candidates that would be useful for the development of spin-based solid-state quantum technologies in two-dimensional hexagonal boron nitride.
Hawking radiation from primordial black holes (PBH) can ionize and heat up neutral gas during the cosmic dark ages, leaving imprints on the global 21cm signal of neutral hydrogen. We use the global 21cm signal to constrain the abundance of spinning P
BHs in mass range of $[2 times 10^{13}, 10^{18}]$ grams. We consider several extended PBH distribution models. Our results show that 21cm can set the most stringent PBH bounds in our mass window. Compared with constraints set by {it{Planck}} cosmic microwave background (CMB) data, 21-cm limits are more stringent by about two orders of magnitudes. PBHs with higher spin are typically more strongly constrained. Our 21cm constraints for the monochromatic mass distribution rule out spinless PBHs with initial mass below $1.4 times 10^{17} {rm{g}}$, whereas extreme Kerr PBHs with reduced initial spin of $a_0=0.999$ are excluded as the dominant dark matter component for masses below $6 times 10^{17} {rm{g}}$. We also derived limits for the log-normal, power-law and critical collapse distributions.
A central goal of machine learning is to learn robust representations that capture the causal relationship between inputs features and output labels. However, minimizing empirical risk over finite or biased datasets often results in models latching o
n to spurious correlations between the training input/output pairs that are not fundamental to the problem at hand. In this paper, we define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics. We prove that even when there is only bias of the input distribution (i.e. covariate shift), models can still pick up spurious features from their training data. Group distributionally robust optimization (DRO) provides an effective tool to alleviate covariate shift by minimizing the worst-case training loss over a set of pre-defined groups. Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations that occur in the data. To address this, we further propose to minimize the worst-case losses over a more flexible set of distributions that are defined on the joint distribution of groups and instances, instead of treating each group as a whole at optimization time. Through extensive experiments on one image and two language tasks, we show that our model is significantly more robust than comparable baselines under various partitions. Our code is available at https://github.com/violet-zct/group-conditional-DRO.
In this paper, we are interested in building a domain knowledge based deep learning framework to solve the chiller plants energy optimization problems. Compared to the hotspot applications of deep learning (e.g. image classification and NLP), it is d
ifficult to collect enormous data for deep network training in real-world physical systems. Most existing methods reduce the complex systems into linear model to facilitate the training on small samples. To tackle the small sample size problem, this paper considers domain knowledge in the structure and loss design of deep network to build a nonlinear model with lower redundancy function space. Specifically, the energy consumption estimation of most chillers can be physically viewed as an input-output monotonic problem. Thus, we can design a Neural Network with monotonic constraints to mimic the physical behavior of the system. We verify the proposed method in a cooling system of a data center, experimental results show the superiority of our framework in energy optimization compared to the existing ones.
The quadratic computational and memory complexities of the Transformers attention mechanism have limited its scalability for modeling long sequences. In this paper, we propose Luna, a linear unified nested attention mechanism that approximates softma
x attention with two nested linear attention functions, yielding only linear (as opposed to quadratic) time and space complexity. Specifically, with the first attention function, Luna packs the input sequence into a sequence of fixed length. Then, the packed sequence is unpacked using the second attention function. As compared to a more traditional attention mechanism, Luna introduces an additional sequence with a fixed length as input and an additional corresponding output, which allows Luna to perform attention operation linearly, while also storing adequate contextual information. We perform extensive evaluations on three benchmarks of sequence modeling tasks: long-context sequence modeling, neural machine translation and masked language modeling for large-scale pretraining. Competitive or even better experimental results demonstrate both the effectiveness and efficiency of Luna compared to a variety
The 21-cm intensity mapping (IM) of neutral hydrogen (HI) is a promising tool to probe the large-scale structures. Sky maps of 21-cm intensities can be highly contaminated by different foregrounds, such as Galactic synchrotron radiation, free-free em
ission, extragalactic point sources, and atmospheric noise. We here present a model of foreground components and a method of removal, especially to quantify the potential of Five-hundred-meter Aperture Spherical radio Telescope (FAST) for measuring HI IM. We consider 1-year observational time with the survey area of $20,000,{rm deg}^{2}$ to capture significant variations of the foregrounds across both the sky position and angular scales relative to the HI signal. We first simulate the observational sky and then employ the Principal Component Analysis (PCA) foreground separation technique. We show that by including different foregrounds, thermal and $1/f$ noises, the value of the standard deviation between reconstructed 21-cm IM map and the input pure 21-cm signal is $Delta T = 0.034,{rm mK}$, which is well under control. The eigenmode-based analysis shows that the underlying HI eigenmode is just less than $1$ per cent level of the total sky components. By subtracting the PCA cleaned foreground+noise map from the total map, we show that PCA method can recover HI power spectra for FAST with high accuracy.
This paper strives to predict fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute between fashion items. For example, whether the collar designs of the
two clothes are similar. It has potential value in many fashion related applications, such as fashion copyright protection. To this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly learn multiple attribute-specific embeddings, thus measure the fine-grained similarity in the corresponding space. The proposed ASEN is comprised of a global branch and a local branch. The global branch takes the whole image as input to extract features from a global perspective, while the local branch takes as input the zoomed-in region-of-interest (RoI) w.r.t. the specified attribute thus able to extract more fine-grained features. As the global branch and the local branch extract the features from different perspectives, they are complementary to each other. Additionally, in each branch, two attention modules, i.e., Attribute-aware Spatial Attention and Attribute-aware Channel Attention, are integrated to make ASEN be able to locate the related regions and capture the essential patterns under the guidance of the specified attribute, thus make the learned attribute-specific embeddings better reflect the fine-grained similarity. Extensive experiments on three fashion-related datasets, i.e., FashionAI, DARN, and DeepFashion, show the effectiveness of ASEN for fine-grained fashion similarity prediction and its potential for fashion reranking. Code and data are available at https://github.com/maryeon/asenpp .
Quantum embedding theories are promising approaches to investigate strongly-correlated electronic states of active regions of large-scale molecular or condensed systems. Notable examples are spin defects in semiconductors and insulators. We present a
detailed derivation of a quantum embedding theory recently introduced, which is based on the definition of effective Hamiltonians. The effect of the environment on a chosen active space is accounted for through screened Coulomb interactions evaluated using density functional theory. Importantly, the random phase approximation is not required and the evaluation of virtual electronic orbitals is circumvented with algorithms previously developed in the context of calculations based on many-body perturbation theory. In addition, we generalize the quantum embedding theory to active spaces composed of orbitals that are not eigenstates of Kohn-Sham Hamiltonians. Finally, we report results for spin defects in semiconductors.
Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Co
llision Warning (FCW). It tracks the states (distance, velocity etc.) of relevant traffic objects based on sensor measurements. The tracking output of KF is often fed into downstream logic to produce alerts, which will then be used by human drivers to make driving decisions in near-collision scenarios. In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning. Our attack goal is to negatively affect human braking decisions by causing KF to output incorrect state estimations that lead to false or delayed alerts. We accomplish this by sequentially manipulating measure ments fed into the KF, and propose a novel Model Predictive Control (MPC) approach to compute the optimal manipulation. Via experiments conducted in a simulated driving environment, we show that the attacker is able to successfully change FCW alert signals through planned manipulation over measurements prior to the desired target time. These results demonstrate that our attack can stealthily mislead a distracted human driver and cause vehicle collisions.
Cascade of particles injected as Hawking Radiation from Primordial Black Holes (PBH) can potentially change the cosmic recombination history by ionizing and heating the intergalactic medium, which results in altering the anisotropy spectra of the Cos
mic Microwave Background (CMB). In this paper, we study the expected sensitivity of several future CMB experiments in constraining the abundance of PBHs distributed in $10^{15}sim10^{17}$ g mass window according to four mass functions: the monochromatic, log-normal, power-law and critical collapse models. Our result shows that future experiments, such as CMB-S4 and PICO, can improve current {it{Planck}} bounds by about two orders of magnitudes. All regions in PBH parameter space that are allowed by current CMB data, including monochromatically distributed PBHs with mass heavier than $4 times 10^{16}$ grams, can be excluded by upcoming missions with high significance.
