Topological characterization of non-Hermitian band structures demands more than a straightforward generalization of the Hermitian cases. Even for one-dimensional tight binding models with non-reciprocal hopping, the appearance of point gaps and the s
kin effect leads to the breakdown of the usual bulk-boundary correspondence. Luckily, the correspondence can be resurrected by introducing a winding number for the generalized Brillouin zone for systems with even number of bands and chiral symmetry. Here, we analyze the topological phases of a non-reciprocal hopping model on the stub lattice, where one of the three bands remains flat. Due to the lack of chiral symmetry, the bi-orthogonal Zak phase is no longer quantized, invalidating the winding number as a topological index. Instead, we show that a $Z_2$ invariant can be defined from Majoranas stellar representation of the eigenstates on the Bloch sphere. The parity of the total azimuthal winding of the entire Majorana constellation correctly predicts the appearance of edge states between the bulk gaps. We further show that the system is not a square-root topological insulator, despite the fact that its parent Hamiltonian can be block diagonalized and related to a sawtooth lattice model. The analysis presented here may be generalized to understand other non-Hermitian systems with multiple bands.
The soft gamma-ray repeater Swift J1555.2-5402 was discovered by means of a 12-ms duration short burst detected with Swift BAT on 2021 June 3. Then 1.6 hours after the first burst detection, NICER started daily monitoring of this X-ray source for a m
onth. The absorbed 2-10 keV flux stays nearly constant at around 4e-11 erg/s/cm2 during the monitoring timespan, showing only a slight gradual decline. A 3.86-s periodicity is detected, and the time derivative of this period is measured to be 3.05(7)e-11 s/s. The soft X-ray pulse shows a single sinusoidal shape with a root-mean-square pulsed fraction that increases as a function of energy from 15% at 1.5 keV to 39% at 7 keV. The equatorial surface magnetic field, characteristic age, and spin-down luminosity are derived under the dipole field approximation to be 3.5e+14 G, 2.0 kyr, and 2.1e+34 erg/s, respectively. An absorbed blackbody with a temperature of 1.1 keV approximates the soft X-ray spectrum. Assuming a source distance of 10 kpc, the peak X-ray luminosity is ~8.5e+35 erg/s in the 2--10 keV band. During the period of observations, we detect 5 and 37 short bursts with Swift/BAT and NICER, respectively. Based on these observational properties, especially the inferred strong magnetic field, this new source is classified as a magnetar. We also coordinated hard X-ray and radio observations with NuSTAR, DSN, and VERA. A hard X-ray power-law component that extends up to at least 40 keV is detected at 3-sigma significance. The 10-60 keV flux, which is dominated by the power-law component, is ~9e-12 erg/s/cm2 with a photon index of ~1.2. The pulsed fraction has a sharp cutoff above 10 keV, down to ~10% in the hard-tail component band. No radio pulsations are detected during the DSN nor VERA observations. We place 7{sigma} upper limits of 0.043mJy and 0.026 mJy on the flux density at S-band and X-band, respectively.
Weyl semimetal is an archetypical gapless topological phase of matter. Its bulk dispersion contains pairs of band degeneracy points, or Weyl points, that act as magnetic monopoles in momentum space and lead to Fermi arc surface states. It also realiz
es chiral anomaly first discovered in quantum field theory: parallel electric and magnetic fields generate a finite chiral current. Here, we introduce a minimal model for non-Hermitian Weyl semimetal, dubbed point-gap Weyl semimetal, where a pair of Weyl points are located on the imaginary axis of the complex energy plane. We show the generalization triggers a few fundamental changes to the topological characterization and response of Weyl semimetals. The non-Hermitian system is characterized by a new point-gap invariant $W_3$, giving rise to complex Fermi arc surface states that cover the point gap area $W_3$ times. The splitting of Weyl points on the complex energy plane also leads to anisotropic skin effect as well as a novel type of boundary-skin modes in wire geometry. A unique feature of point-gap Weyl semimetal is a time-dependent electric current flowing along the direction of the magnetic field in the absence of electric field, due to the chiral imbalance created by the different lifetime of the Weyl fermions. We discuss the experimental signatures in wave-packet dynamics and possible realizations of point-gap Weyl semimetal in synthetic platforms.
Gauge fields provide the fundamental interactions in the Standard Model of particle physics. Gauge field configurations with nontrivial topological windings are known to play crucial roles in many important phenomena, from matter-anti-matter asymmetr
y of todays universe to the permanent quark confinement. Their presence is however elusive for direct detection in experiments. Here we show that measurements of the chiral magnetic effect (CME) in heavy ion collisions can be used for counting the topological windings of the non-Abelian gauge fields in the Quantum Chromodynamics (QCD). To achieve this, we implemented a key ingredient, the stochastic dynamics of gauge field topological fluctuations, into a state-of-the-art framework for simulating the CME in these collisions. This tool has allowed us to quantitatively extract, for the first time, the initial topological windings $Q_w$ from the CME experimental data, revealing a universal scaling relation between $Q_w$ and the particle multiplicity produced in the corresponding collision events.
zero-shot learning is an essential part of computer vision. As a classical downstream task, zero-shot semantic segmentation has been studied because of its applicant value. One of the popular zero-shot semantic segmentation methods is based on the ge
nerative model Most new proposed works added structures on the same architecture to enhance this model. However, we found that, from the view of causal inference, the result of the original model has been influenced by spurious statistical relationships. Thus the performance of the prediction shows severe bias. In this work, we consider counterfactual methods to avoid the confounder in the original model. Based on this method, we proposed a new framework for zero-shot semantic segmentation. Our model is compared with baseline models on two real-world datasets, Pascal-VOC and Pascal-Context. The experiment results show proposed models can surpass previous confounded models and can still make use of additional structures to improve the performance. We also design a simple structure based on Graph Convolutional Networks (GCN) in this work.
Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matchin
g between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism. In this work, we investigate the robustness of text-to-SQL models to synonym substitution. In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases. We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks. Finally, we present two categories of approaches to improve the model robustness. The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training. We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective.
Computer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human translators. Autocompletion, which suggests translation results accordi
ng to the text pieces provided by human translators, is a core function of CAT. There are two limitations in previous research in this line. First, most research works on this topic focus on sentence-level autocompletion (i.e., generating the whole translation as a sentence based on human input), but word-level autocompletion is under-explored so far. Second, almost no public benchmarks are available for the autocompletion task of CAT. This might be among the reasons why research progress in CAT is much slower compared to automatic MT. In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic. In addition, we propose an effective method for GWLAN and compare it with several strong baselines. Experiments demonstrate that our proposed method can give significantly more accurate predictions than the baseline methods on our benchmark datasets.
We tackle a 3D scene stylization problem - generating stylized images of a scene from arbitrary novel views given a set of images of the same scene and a reference image of the desired style as inputs. Direct solution of combining novel view synthesi
s and stylization approaches lead to results that are blurry or not consistent across different views. We propose a point cloud-based method for consistent 3D scene stylization. First, we construct the point cloud by back-projecting the image features to the 3D space. Second, we develop point cloud aggregation modules to gather the style information of the 3D scene, and then modulate the features in the point cloud with a linear transformation matrix. Finally, we project the transformed features to 2D space to obtain the novel views. Experimental results on two diverse datasets of real-world scenes validate that our method generates consistent stylized novel view synthesis results against other alternative approaches.
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine interactive translation system that is able to trade off translation q
uality and efficiency. Compared to existing publicly available interactive translation systems, TranSmart supports three key features, word-level autocompletion, sentence-level autocompletion and translation memory. By word-level and sentence-level autocompletion, TranSmart allows users to interactively translate words in their own manners rather than the strict manner from left to right. In addition, TranSmart has the potential to avoid similar translation mistakes by using translated sentences in history as its memory. This report presents major functions of TranSmart, algorithms for achieving these functions, how to use the TranSmart APIs, and evaluation results of some key functions. TranSmart is publicly available at its homepage (https://transmart.qq.com).
Hydrodynamics is a general theoretical framework for describing the long-time large-distance behaviors of various macroscopic physical systems, with its equations based on conservation laws such as energy-momentum conservation and charge conservation
. Recently there has been significant interest in understanding the implications of angular momentum conservation for a corresponding hydrodynamic theory. In this work, we examine the key conceptual issues for such a theory in the relativistic regime where the orbital and spin components get entangled. We derive the equations for relativistic viscous hydrodynamics with angular momentum through Navier-Stokes type of gradient expansion analysis.
