We develop the Gutzwiller approximation method to obtain the renormalized Hamiltonian of the SU(4) $t$-$J$ model, with the corresponding renormalization factors. Subsequently, a mean-field theory is employed on the renormalized Hamiltonian of the mod
el on the honeycomb lattice under the scenario of a cooperative condensation of carriers moving in the resonating valence bond state of flavors. In particular, we find the extended $s$-wave superconductivity is much more favorable than the $d+id$ superconductivity in the doping range close to quarter filling. The pairing states of the SU(4) case reveal the property that the spin-singlet pairing and the spin-triplet pairing can exist simultaneously. Our results might provide new insights into the twisted bilayer graphene system.
A theoretical formalism of Andreev reflection is proposed to provide a theoretical support for distinguishing the singlet pairing and the triplet pairing by the point contact Andreev reflection (PCAR) experiments, in contrast to the previous models d
esigned only for the singlet pairing case. We utilize our theoretical curves to fit the data of the PCAR experiment on the unconventional superconductivity in the $Bi/Ni$ bilayer [arXiv:1810.10403], and find the Anderson-Brinkman-Morel state satisfies the main characteristics of the experimental data. Moreover, the Andreev reflection spectra of the Balian-Werthamer state and the chiral $p$-wave are also presented.
Error entropy is a important nonlinear similarity measure, and it has received increasing attention in many practical applications. The default kernel function of error entropy criterion is Gaussian kernel function, however, which is not always the b
est choice. In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed. We further derivate the generalized minimum error entropy (GMEE) criterion, and a novel adaptive filtering called GMEE algorithm is derived by utilizing GMEE criterion. The stability, steady-state performance, and computational complexity of the proposed algorithm are investigated. Some simulation indicate that the GMEE algorithm performs well in Gaussian, sub-Gaussian, and super-Gaussian noises environment, respectively. Finally, the GMEE algorithm is applied to acoustic echo cancelation and performs well.
Accurate breast lesion risk estimation can significantly reduce unnecessary biopsies and help doctors decide optimal treatment plans. Most existing computer-aided systems rely solely on mammogram features to classify breast lesions. While this approa
ch is convenient, it does not fully exploit useful information in clinical reports to achieve the optimal performance. Would clinical features significantly improve breast lesion classification compared to using mammograms alone? How to handle missing clinical information caused by variation in medical practice? What is the best way to combine mammograms and clinical features? There is a compelling need for a systematic study to address these fundamental questions. This paper investigates several multimodal deep networks based on feature concatenation, cross-attention, and co-attention to combine mammograms and categorical clinical variables. We show that the proposed architectures significantly increase the lesion classification performance (average area under ROC curves from 0.89 to 0.94). We also evaluate the model when clinical variables are missing.
In cloud computing, it is desirable if suspicious activities can be detected by automatic anomaly detection systems. Although anomaly detection has been investigated in the past, it remains unsolved in cloud computing. Challenges are: characterizing
the normal behavior of a cloud server, distinguishing between benign and malicious anomalies (attacks), and preventing alert fatigue due to false alarms. We propose CloudShield, a practical and generalizable real-time anomaly and attack detection system for cloud computing. Cloudshield uses a general, pretrained deep learning model with different cloud workloads, to predict the normal behavior and provide real-time and continuous detection by examining the model reconstruction error distributions. Once an anomaly is detected, to reduce alert fatigue, CloudShield automatically distinguishes between benign programs, known attacks, and zero-day attacks, by examining the prediction error distributions. We evaluate the proposed CloudShield on representative cloud benchmarks. Our evaluation shows that CloudShield, using model pretraining, can apply to a wide scope of cloud workloads. Especially, we observe that CloudShield can detect the recently proposed speculative execution attacks, e.g., Spectre and Meltdown attacks, in milliseconds. Furthermore, we show that CloudShield accurately differentiates and prioritizes known attacks, and potential zero-day attacks, from benign programs. Thus, it significantly reduces false alarms by up to 99.0%.
In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, this paper aims to
design new operators automatically, which are expected to be search space independent and thus exhibit robust performance on different problems. For this purpose, this work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. Then, we deduce the generic form of translation, scale, and rotation invariant operators. Afterwards, a principled approach is proposed for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000 decision variables.
The combination of the linear size from reverberation mapping (RM) and the angular distance of the broad line region (BLR) from spectroastrometry (SA) in active galactic nuclei (AGNs) can be used to measure the Hubble constant $H_0$. Recently, Wang e
t al. (2020) successfully employed this approach and estimated $H_0$ from 3C 273. However, there may be a systematic deviation between the response-weighted radius (RM measurement) and luminosity-weighted radius (SA measurement), especially when different broad lines are adopted for size indicators (e.g., hb for RM and pa for SA). Here we evaluate the size deviations measured by six pairs of hydrogen lines (e.g., hb, ha and pa) via the locally optimally emitting cloud (LOC) models of BLR. We find that the radius ratios $K$(=$R_{rm SA}$/$R_{rm RM}$) of the same line deviated systematically from 1 (0.85-0.88) with dispersions between 0.063-0.083. Surprisingly, the $K$ values from the pa(SA)/hb(RM) and ha(SA)/hb(RM) pairs not only are closest to 1 but also have considerably smaller uncertainty. Considering the current infrared interferometry technology, the pa(SA)/hb(RM) pair is the ideal choice for the low redshift objects in the SARM project. In the future, the ha(SA)/hb(RM) pair could be used for the high redshift luminous quasars. These theoretical estimations of the SA/RM radius pave the way for the future SARM measurements to further constrain the standard cosmological model.
Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer. In this paper, we propose a simple
and effective passage reranking method, named Reader-guIDEd Reranker (RIDER), which does not involve training and reranks the retrieved passages solely based on the top predictions of the reader before reranking. We show that RIDER, despite its simplicity, achieves 10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) gains without refining the retriever or reader. In addition, RIDER, without any training, outperforms state-of-the-art transformer-based supervised rerankers. Remarkably, RIDER achieves 48.3 EM on the Natural Questions dataset and 66.4 EM on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input after passage reranking.
We present an analysis of the variability of broad absorption lines (BALs) in a quasar SDSS J141955.26+522741.1 at $z=2.145$ with 72 observations from the Sloan Digital Sky Survey Data Release 16 (SDSS DR16). The strong correlation between the equiva
lent widths of BAL and the continuum luminosity, reveals that the variation of BAL trough is dominated by the photoionization. The photoionization model predicts that when the time interval $Delta T$ between two observations is longer than the recombination timescale $t_{rm rec}$, the BAL variations can be detected. This can be characterized as a sharp rise in the detection rate of BAL variation at $Delta T=t_{rm rec}$. For the first time, we detect such a sharp rise signature in the detection rate of BAL variations. As a result, we propose that the $t_{rm rec}$ can be obtained from the sharp rise of the detection rate of BAL variation. It is worth mentioning that the BAL variations are detected at the time-intervals less than the $t_{rm rec}$ for half an order of magnitude in two individual troughs. This result indicates that there may be multiple components with different $t_{rm rec}$ but the same velocity in an individual trough.
We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the gener
ated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
