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Kai Wang

Publications and source records attributed to Kai Wang.

At least 649 records · Page 36Linked to original sources

Takagi topological insulator with odd $\mathcal P\mathcal T$ pairs of corner states

We present a novel class of topological insulators, termed the Takagi topological insulators (TTIs), which is protected by the sublattice symmetry and spacetime inversion ($\mathcal P\mathcal T$) symmetry. The required symmetries for the TTIs can be realized on any bipartite lattice where the inversion exchanges sublattices. The protecting symmetries lead to the classifying space of Hamiltonians being unitary symmetric matrices, and therefore Takagi's factorization can be performed. Particularly, the global Takagi's factorization can (cannot) be done on a $3$D ($2$D) sphere. In 3D, there is a $\mathbb{Z}_2$ topological invariant corresponding to the parity of the winding number of Takagi's unitary-matrix factor over the entire Brillouin zone, where the $\mathbb Z_2$ nature comes from the $O(N)$ gauge degrees of freedom in Takagi's factorization. In 2D, the obstruction for a global Takagi's factorization is characterized by another $\mathbb{Z}_2$ topological invariant, equivalent to the second Stiefel-Whitney number. For the third-order topological phases, the $3$D TTIs feature a parity condition for corner zero-modes, i.e., there always exist odd $\mathcal P\mathcal T$ pairs of corners with zero-modes. Moreover, for any $\mathcal P\mathcal T$ invariant sample geometry, all configurations of corner zero-modes satisfying the parity condition can exist with the same nontrivial bulk topological invariant. Actually, without closing the bulk gap, the boundary phase diagram have a cellular structure, where each topological boundary phase associated with a particular (cross-order) boundary-mode pattern corresponds to a contractible cell with certain dimension in the parameter space.

cond-mat.mes-hall↗

Feudal Reinforcement Learning by Reading Manuals

Reading to act is a prevalent but challenging task which requires the ability to reason from a concise instruction. However, previous works face the semantic mismatch between the low-level actions and the high-level language descriptions and require the human-designed curriculum to work properly. In this paper, we present a Feudal Reinforcement Learning (FRL) model consisting of a manager agent and a worker agent. The manager agent is a multi-hop plan generator dealing with high-level abstract information and generating a series of sub-goals in a backward manner. The worker agent deals with the low-level perceptions and actions to achieve the sub-goals one by one. In comparison, our FRL model effectively alleviate the mismatching between text-level inference and low-level perceptions and actions; and is general to various forms of environments, instructions and manuals; and our multi-hop plan generator can significantly boost for challenging tasks where multi-step reasoning form the texts is critical to resolve the instructed goals. We showcase our approach achieves competitive performance on two challenging tasks, Read to Fight Monsters (RTFM) and Messenger, without human-designed curriculum learning.

cs.AI↗

The Low-Energy Spectral Index of Gamma-Ray Burst Prompt Emission from Internal Shocks

The prompt emission of most gamma-ray bursts (GRBs) typically exhibits a non-thermal Band component. The synchrotron radiation in the popular internal shock model is generally put forward to explain such a non-thermal component. However, the low-energy photon index $α\sim -1.5$ predicted by the synchrotron radiation is inconsistent with the observed value $α\sim -1$. Here, we investigate the evolution of a magnetic field during propagation of internal shocks within an ultrarelativistic outflow, and revisit the fast cooling of shock-accelerated electrons via synchrotron radiation for this evolutional magnetic field. We find that the magnetic field is first nearly constant and then decays as $B'\propto t^{-1}$, which leads to a reasonable range of the low-energy photon index, $-3/2 < α< -2/3$. In addition, if a rising electron injection rate during a GRB is introduced, we find that $α$ reaches $-2/3$ more easily. We thus fit the prompt emission spectra of GRB 080916c and GRB~080825c.

astro-ph.HE↗

Prevalence of Extra Power-Law Spectral Components in Short Gamma-Ray Bursts

Prompt extra power-law (PL) spectral component is discovered in some bright gamma-ray bursts (GRBs), which usually dominates the spectral energy distribution below tens of keV or above about 10 MeV. However, its origin is still unclear. In this paper, we present a systematic analysis 13 Fermi short GRBs as of August 2020, with the contemporaneous keV-MeV and GeV detections at the prompt emission phase. We find that the extra PL component is a ubiquitous spectral feature for short GRBs, showing up in all 13 analyzed GRBs. The PL indices are mostly harder than -2.0, which may be well reproduced by considering the electromagnetic cascade induced by ultra-relativistic protons or electrons accelerated in the prompt emission phase. The average flux of these extra PL components positively correlates with that of the main spectral components, which implies they may share the same physical origin.

astro-ph.HE↗

Deformations and homotopy theory of Rota-Baxter algebras of any weight

This paper studies formal deformations and homotopy theory of Rota-Baxter algebras of any weight. We define an $L_\infty$-algebra, which controls simultaneous deformations of associative products and Rota-Baxter operators. As a consequence, we develop a cohomology theory of Rota-Baxter algebras of any weight and justify it by interpreting lower degree cohomology groups as formal deformations and abelian extensions. The notion of homotopy Rota-Baxter algebras is introduced and it is shown that the operad governing homotopy Rota-Baxter algebras is a minimal model of the operad of Rota-Baxter algebras.

math.RA↗

Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms

With AlphaGo defeats top human players, reinforcement learning(RL) algorithms have gradually become the code-base of building stronger artificial intelligence(AI). The RL algorithm design firstly needs to adapt to the specific environment, so the designed environment guides the rapid and profound development of RL algorithms. However, the existing environments, which can be divided into real world games and customized toy environments, have obvious shortcomings. For real world games, it is designed for human entertainment, and too much difficult for most of RL researchers. For customized toy environments, there is no widely accepted unified evaluation standard for all RL algorithms. Therefore, we introduce the first virtual user-friendly environment framework for RL. In this framework, the environment can be easily configured to realize all kinds of RL tasks in the mainstream research. Then all the mainstream state-of-the-art(SOTA) RL algorithms can be conveniently evaluated and compared. Therefore, our contributions mainly includes the following aspects: 1.single configured environment for all classification of SOTA RL algorithms; 2.combined environment of more than one classification RL algorithms; 3.the evaluation standard for all kinds of RL algorithms. With all these efforts, a possibility for breeding an AI with capability of general competency in a variety of tasks is provided, and maybe it will open up a new chapter for AI.

cs.LG↗

Multi-UAV Cooperative Trajectory for Servicing Dynamic Demands and Charging Battery

Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing their demands locally. How to dynamically determine a UAV swarm's cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question but is unaddressed in the literature. To our best knowledge, this paper is the first to design and analyze cooperative path planning algorithms of a large UAV swarm for optimally servicing many spatial locations, where ground users' demands are released dynamically in the long time horizon. Regarding a single UAV's path planning design, we manage to substantially simplify the traditional dynamic program and propose an optimal algorithm of low computation complexity, which is only polynomial with respect to both the numbers of spatial locations and user demands. After coordinating a large number $K$ of UAVs, this simplified dynamic optimization problem becomes intractable and we alternatively present a fast iterative cooperation algorithm with provable approximation ratio $1-(1-\frac{1}{K})^{K}$ in the worst case, which is proved to obviously outperform the traditional approach of partitioning UAVs to serve different location clusters separately. To relax UAVs' battery capacity limit for sustainable service provisioning, we further allow UAVs to travel to charging stations in the mean time and thus jointly design UAVs' path planning over users' locations and charging stations. Despite of the problem difficulty, for the optimal solution, we successfully transform the problem to an integer linear program by creating novel directed acyclic graph of the UAV-state transition diagram, and propose an iterative algorithm with constant approximation ratio.

cs.NI↗

Interpretable Visual Reasoning via Induced Symbolic Space

We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images; and achieve an interpretable model via working on the induced symbolic concept space. To this end, we first design a new framework named object-centric compositional attention model (OCCAM) to perform the visual reasoning task with object-level visual features. Then, we come up with a method to induce concepts of objects and relations using clues from the attention patterns between objects' visual features and question words. Finally, we achieve a higher level of interpretability by imposing OCCAM on the objects represented in the induced symbolic concept space. Our model design makes this an easy adaption via first predicting the concepts of objects and relations and then projecting the predicted concepts back to the visual feature space so the compositional reasoning module can process normally. Experiments on the CLEVR and GQA datasets demonstrate: 1) our OCCAM achieves a new state of the art without human-annotated functional programs; 2) our induced concepts are both accurate and sufficient as OCCAM achieves an on-par performance on objects represented either in visual features or in the induced symbolic concept space.

cs.CV↗

MAHGIC: A Model Adapter for the Halo-Galaxy Inter-Connection

We develop a model to establish the interconnection between galaxies and their dark matter halos. We use Principal Component Analysis (PCA) to reduce the dimensionality of both the mass assembly histories of halos/subhalos and the star formation histories of galaxies, and Gradient Boosted Decision Trees (GBDT) to transform halo/subhalo properties into galaxy properties. We use two sets of hydrodynamic simulations to motivate our model architecture and to train the transformation. We then apply the two sets of trained models to dark matter only (DMO) simulations to show that the transformation is reliable and statistically accurate. The model trained by a high-resolution hydrodynamic simulation, or by a set of such simulations implementing the same physics of galaxy formation, can thus be applied to large DMO simulations to make "mock" copies of the hydrodynamic simulation. The model is both flexible and interpretable, which paves the way for future applications in which we will constrain the model using observations at different redshifts simultaneously and explore how galaxies form and evolve in dark matter halos empirically.

astro-ph.GA↗

Free objects and Gröbner-Shirshov bases in operated contexts

This paper investigates algebraic objects equipped with an operator, such as operated monoids, operated algebras etc. Various free object functors in these operated contexts are explicitly constructed. For operated algebras whose operator satisfies a set $Φ$ of relations (usually called operated polynomial identities (aka. OPIs)), Guo defined free objects, called free $Φ$-algebras, via universal algebra. Free $Φ$-algebras over algebras are studied in details. A mild sufficient condition is found such that $Φ$ together with a Gröbner-Shirshov basis of an algebra $A$ form a Gröbner-Shirshov basis of the free $Φ$-algebra over algebra $A$ in the sense of Guo et al.. Ample examples for which this condition holds are provided, such as all Rota-Baxter type OPIs, a class of differential type OPIs, averaging OPIs and Reynolds OPI.

math.RA↗

High-energy Neutrinos from Stellar Explosions in Active Galactic Nuclei Accretion Disks

Some catastrophic stellar explosions, such as supernovae (SNe), compact binary coalescences, and micro-tidal disruption events, are believed to be embedded in the accretion disks of active galactic nuclei (AGN). We show high-energy neutrinos can be produced efficiently through $pp$-interactions between shock-accelerated cosmic rays and AGN disk materials shortly after the explosion ejecta shock breaks out of the disk. AGN stellar explosions are ideal targets for joint neutrino and electromagnetic (EM) multimessenger observations. Future EM follow-up observations of neutrino bursts can help us search for yet-discovered AGN stellar explosions. We suggest that AGN stellar explosions could potentially be important astrophysical neutrino sources. The contribution from AGN stellar explosions to the observed diffuse neutrino background depends on the uncertain local event rate densities of these events in AGN disks. By considering thermonuclear SNe, core-collaspe SNe, gamma-ray burst associated SNe, kilonovae, and choked GRBs in AGN disks with known theoretical local event rate densities, we show that these events may contribute to $\lesssim10\%$ of the observed diffuse neutrino background.

astro-ph.HE↗

RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials

The prospects of using a Reconfigurable Intelligent Surface (RIS) to aid wireless communication systems have recently received much attention from academia and industry. Most papers make theoretical studies based on elementary models, while the prototyping of RIS-aided wireless communication and real-world field trials are scarce. In this paper, we describe a new RIS prototype consisting of 1100 controllable elements working at 5.8 GHz band. We propose an efficient algorithm for configuring the RIS over the air by exploiting the geometrical array properties and a practical receiver-RIS feedback link. In our indoor test, where the transmitter and receiver are separated by a 30 cm thick concrete wall, our RIS prototype provides a 26 dB power gain compared to the baseline case where the RIS is replaced by a copper plate. A 27 dB power gain was observed in the short-distance outdoor measurement. We also carried out long-distance measurements and successfully transmitted a 32 Mbps data stream over 500 m. A 1080p video was live-streamed and it only played smoothly when the RIS was utilized. The power consumption of the RIS is around 1 W. Our paper is vivid proof that the RIS is a very promising technology for future wireless communications.

cs.IT↗

Non-trivial point-gap topology and non-Hermitian skin effect in photonic crystals

We show that two-dimensional non-Hermitian photonic crystals made of lossy material can exhibit non-trivial point gap topology in terms of topological winding in its complex frequency band structure. Such crystals can be either made of lossy isotropic media which is reciprocal, or lossy magneto-optical media which is non-reciprocal. We discuss the effects of reciprocity on the properties of the point gap topology. We also show that such a non-trivial point gap topology leads to non-Hermitian skin effect when the photonic crystal is truncated. In contrast to most previous studies on point gap topology which used tight-binding models, our work indicates that such non-Hermitian topology can studied in photonic crystals, which represent a more realistic system that has technological significance.

cond-mat.mes-hall↗

ACAE-REMIND for Online Continual Learning with Compressed Feature Replay

Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset.

cs.CV↗

Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling

There is significant interest in learning and optimizing a complex system composed of multiple sub-components, where these components may be agents or autonomous sensors. Among the rich literature on this topic, agent-based and domain-specific simulations can capture complex dynamics and subgroup interaction, but optimizing over such simulations can be computationally and algorithmically challenging. Bayesian approaches, such as Gaussian processes (GPs), can be used to learn a computationally tractable approximation to the underlying dynamics but typically neglect the detailed information about subgroups in the complicated system. We attempt to find the best of both worlds by proposing the idea of decomposed feedback, which captures group-based heterogeneity and dynamics. We introduce a novel decomposed GP regression to incorporate the subgroup decomposed feedback. Our modified regression has provably lower variance -- and thus a more accurate posterior -- compared to previous approaches; it also allows us to introduce a decomposed GP-UCB optimization algorithm that leverages subgroup feedback. The Bayesian nature of our method makes the optimization algorithm trackable with a theoretical guarantee on convergence and no-regret property. To demonstrate the wide applicability of this work, we execute our algorithm on two disparate social problems: infectious disease control in a heterogeneous population and allocation of distributed weather sensors. Experimental results show that our new method provides significant improvement compared to the state-of-the-art.

cs.LG↗

The clustering of galaxies in the DESI imaging legacy surveys DR8: I. the luminosity and color dependent intrinsic clustering

In a recent study, we developed a method to model the impact of photometric redshift uncertainty on the two-point correlation function (2PCF). In this method, we can obtain both the intrinsic clustering strength and the photometric redshift errors simultaneously by fitting the projected 2PCF with two integration depths along the line-of-sight. Here we apply this method to the DESI Legacy Imaging Surveys Data Release 8 (LS DR8), the largest galaxy sample currently available. We separate galaxies into 20 samples in 8 redshift bins from $z=0.1$ to $z=1.0$, and a few $\rm z$-band absolute magnitude bins, with $M_{\rm z} \le -20$. These galaxies are further separated into red and blue sub-samples according to their $M^{0.5}_{\rm r}-M^{0.5}_{\rm z}$ colors. We measure the projected 2PCFs for all these galaxy (sub-)samples, and fit them using our photometric redshift 2PCF model. We find that the photometric redshift errors are smaller in red sub-samples than the overall population. On the other hand, there might be some systematic photometric redshift errors in the blue sub-samples, so that some of the sub-samples show significantly enhanced 2PCF at large scales. Therefore, focusing only on the red and all (sub-)samples, we find that the biases of galaxies in these (sub-)samples show clear color, redshift and luminosity dependencies, in that red brighter galaxies at higher redshift are more biased than their bluer and low redshift counterparts. Apart from the best fit set of parameters, $σ_{z}$ and $b$, from this state-of-the-art photometric redshift survey, we obtain high precision intrinsic clustering measurements for these 40 red and all galaxy (sub-)samples. These measurements on large and small scales hold important information regarding the cosmology and galaxy formation, which will be used in our subsequent probes in this series.

astro-ph.GA↗

Finding proto-clusters to trace galaxy evolution: I. The finder and its performance

We develop a method to identify proto-clusters based on dark matter halos represented by galaxy groups selected from surveys of galaxies at high redshift. We test the performance of this method on halos in N-body simulations, and find it can correctly identify more than 85% of the true proto-clusters with $\gtrsim$ 95% purity and with mass estimates typical within 0.25 dex from their true values. We show how the information provided by the proto-clusters can be used to link galaxies in present-day clusters of galaxies with their high redshift progenitors. Our tests show that the proto-clusters identified by our method can recover reliably the progenitor stellar mass distribution of galaxies, thereby providing an avenue to investigate the formation and evolution of present-day galaxy clusters and their member galaxies.

astro-ph.GA↗