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Kai Wang

Publications and source records attributed to Kai Wang.

At least 559 records · Page 31Linked to original sources
arXiv2023

Generating Driving Scenes with Diffusion

In this paper we describe a learned method of traffic scene generation designed to simulate the output of the perception system of a self-driving car. In our "Scene Diffusion" system, inspired by latent diffusion, we use a novel combination of diffusion and object detection to directly create realistic and physically plausible arrangements of discrete bounding boxes for agents. We show that our scene generation model is able to adapt to different regions in the US, producing scenarios that capture the intricacies of each region.

cs.CV↗
arXiv2023

River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning

We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning. For the first time, we link the path redundancy issue in the state-of-the-art KG reasoning models based on path encoding and message passing to the transformation error in model training, which brings us new theoretical insights into KG reasoning, as well as high efficacy in practice. On the theoretical side, we analyze the entropy of transformation error in KG paths and point out query-specific redundant paths causing entropy increases. These findings guide us to maintain the shortest paths and remove redundant paths for minimized-entropy message passing. To achieve this goal, on the practical side, we propose an efficient Graph Percolation Process motivated by the percolation model in Fluid Mechanics, and design a lightweight GNN-based KG reasoning framework called Graph Percolation Embeddings (GraPE). GraPE outperforms previous state-of-the-art methods in both transductive and inductive reasoning tasks while requiring fewer training parameters and less inference time.

cs.AI↗
arXiv2023

Late-formed halos prefer to host quiescent central galaxies. I. Observational results

The star formation and quenching of central galaxies are regulated by the assembly histories of their host halos. In this work, we use the central stellar mass to halo mass ratio as a proxy of halo formation time, and we devise three different models, from the physical hydrodynamical simulation to the empirical statistical model, to demonstrate its robustness. With this proxy, we inferred the dependence of the central galaxy properties on the formation time of their host halos using the SDSS main galaxy sample, where central galaxies are identified with the halo-based group finder. We found that central galaxies living in late-formed halos have higher quiescent fractions and lower spiral fractions than their early-formed counterparts by $\lesssim$ 8%. Finally, we demonstrate that the group finding algorithm has a negligible impact on our results.

astro-ph.GA↗
arXiv2023

High-energy Neutrino Productions from AGN Disk Transients Impacted by Circum-disk Medium

Various supernovae (SN), compact object coalescences, and tidal disruption events are widely believed to occur embedded in active galactic nuclei (AGN) accretion disks and generate detectable electromagnetic (EM) signals. We collectively refer to them as \emph{AGN disk transients}. The inelastic hadronuclear ($pp$) interactions between shock-accelerated cosmic rays and AGN disk materials shortly after the ejecta shock breaks out of the disk can produce high-energy neutrinos. However, the expected efficiency of neutrino production would decay rapidly by adopting a pure Gaussian density atmosphere profile applicable for stable gas-dominated disks. On the other hand, AGN outflows and disk winds are commonly found around AGN accretion disks. In this paper, we present that the circum-disk medium would further consume the shock kinetic energy to more efficiently produce high-energy neutrinos, especially for $\sim$\,TeV$-$PeV neutrinos that IceCube detects. Thanks to the existence of the circum-disk medium, we find that the neutrino production will be enhanced significantly and make a much higher contribution to the diffuse neutrino background. Optimistically, $\sim20\%$ diffuse neutrino background can be contributed from AGN disk transients.

astro-ph.HE↗
arXiv2023

A Soft Coordination Method of Heterogeneous Devices in Distribution System Voltage Control

With the continuous increase of photovoltaic (PV) penetration, the voltage control interactions between newly installed PV inverters and previously deployed on-load tap-changer (OLTC) transformers become ever more significant. To achieve coordinated voltage regulation, current methods often rely on a decision-making algorithm to fully take over the control of all devices, requiring OLTC to give up its existing tap switching logic and execute corresponding upgrades. Aiming at bridging this gap, a soft coordination framework is proposed in this paper. Specifically, the decision-making commands are only applied on inverters, and OLTC that retains its own operation rule will be indirectly controlled by the changed system voltage, which is a result of appropriately adjusting inverters' Var output. The proposed method achieves the soft coordination by establishing a modified actor-critic algorithm to train a proxy model of inverters. The well-trained proxy model can properly adjust inverters' Var output to "softly" coordinate OLTC's tap operations, which finally attains coordinated voltage regulation and line loss minimization. Simulation results verify the superiority of our proposed method over traditional ones in coordinating heterogeneous devices for voltage control.

eess.SY↗
arXiv2023

Implication of GRB 221009A: Can TeV Emission Come from the GRB Prompt Phase?

Recently, the B.O.A.T. ("brightest of all time") gamma-ray burst, dubbed GRB 221009A, was detected by various instruments. Unprecedentedly, the GRB presented very-high-energy (VHE, energy above 0.1 TeV) gamma-ray emission with energy extending above 10 TeV, as reported by the Large High Altitude Air Shower Observatory (LHAASO). We here demonstrate that the VHE and especially >10 TeV emission may originate from the internal hadronic dissipation of the GRB, without the need of invoking any exotic processes as suggested by some previous studies. We also discuss the constraints on the properties of the GRB ejecta from multiwavelength and multi-messenger observations, which favors a magnetically dominated GRB ejecta. The suggested Poynting-flux-dominated GRB ejecta in this work supports the Blandford & Znajek (BZ) mechanism as the possible central engine model of GRB.

astro-ph.HE↗
arXiv2023

RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System

Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large reality gap. In this paper, we introduce the first open-source real-world dataset, RL4RS, hoping to replace the artificial datasets and semi-simulated RS datasets previous studies used due to the resource limitation of the RL-based RS domain. Unlike academic RL research, RL-based RS suffers from the difficulties of being well-validated before deployment. We attempt to propose a new systematic evaluation framework, including evaluation of environment simulation, evaluation on environments, counterfactual policy evaluation, and evaluation on environments built from test set. In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms. The RL4RS suite can be found at https://github.com/fuxiAIlab/RL4RS. In addition to the RL-based recommender systems, we expect the resource to contribute to research in applied reinforcement learning.

cs.IR↗
arXiv2023

Deformations and cohomology theory of $Ω$-family Rota-Baxter algebras of arbitrary weight

In this paper, we firstly construct an $L_\infty[1]$-algebra via the method of higher derived brackets, whose Maurer-Cartan elements correspond to relative $Ω$-family Rota-Baxter algebras structures of weight $λ$. For a relative $Ω$-family Rota-Baxter algebra of weight $λ$, the corresponding twisted $ L_{\infty}[1] $-algebra controls its deformations, which leads to the cohomology theory of relative $Ω$-family Rota-Baxter algebras of weight $λ$. Moreover, we also obtain the corresponding results for absolute $Ω$-family Rota-Baxter algebras of weight $λ$ from the relative version. At last, we study formal deformations of relative (resp. absolute) $Ω$-family Rota-Baxter algebras of weight $λ$, which can be explained by the lower degree cohomology groups.

math.RA↗
arXiv2023

Zero-shot Generative Model Adaptation via Image-specific Prompt Learning

Recently, CLIP-guided image synthesis has shown appealing performance on adapting a pre-trained source-domain generator to an unseen target domain. It does not require any target-domain samples but only the textual domain labels. The training is highly efficient, e.g., a few minutes. However, existing methods still have some limitations in the quality of generated images and may suffer from the mode collapse issue. A key reason is that a fixed adaptation direction is applied for all cross-domain image pairs, which leads to identical supervision signals. To address this issue, we propose an Image-specific Prompt Learning (IPL) method, which learns specific prompt vectors for each source-domain image. This produces a more precise adaptation direction for every cross-domain image pair, endowing the target-domain generator with greatly enhanced flexibility. Qualitative and quantitative evaluations on various domains demonstrate that IPL effectively improves the quality and diversity of synthesized images and alleviates the mode collapse. Moreover, IPL is independent of the structure of the generative model, such as generative adversarial networks or diffusion models. Code is available at https://github.com/Picsart-AI-Research/IPL-Zero-Shot-Generative-Model-Adaptation.

cs.CV↗
arXiv2023

Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking

In this paper, we follow a data-centric philosophy and propose a novel motion annotation method based on the inherent representativeness of motion data in a given dataset. Specifically, we propose a Representation-based Representativeness Ranking R3 method that ranks all motion data in a given dataset according to their representativeness in a learned motion representation space. We further propose a novel dual-level motion constrastive learning method to learn the motion representation space in a more informative way. Thanks to its high efficiency, our method is particularly responsive to frequent requirements change and enables agile development of motion annotation models. Experimental results on the HDM05 dataset against state-of-the-art methods demonstrate the superiority of our method.

cs.CV↗
arXiv2023

Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models

The unlearning problem of deep learning models, once primarily an academic concern, has become a prevalent issue in the industry. The significant advances in text-to-image generation techniques have prompted global discussions on privacy, copyright, and safety, as numerous unauthorized personal IDs, content, artistic creations, and potentially harmful materials have been learned by these models and later utilized to generate and distribute uncontrolled content. To address this challenge, we propose \textbf{Forget-Me-Not}, an efficient and low-cost solution designed to safely remove specified IDs, objects, or styles from a well-configured text-to-image model in as little as 30 seconds, without impairing its ability to generate other content. Alongside our method, we introduce the \textbf{Memorization Score (M-Score)} and \textbf{ConceptBench} to measure the models' capacity to generate general concepts, grouped into three primary categories: ID, object, and style. Using M-Score and ConceptBench, we demonstrate that Forget-Me-Not can effectively eliminate targeted concepts while maintaining the model's performance on other concepts. Furthermore, Forget-Me-Not offers two practical extensions: a) removal of potentially harmful or NSFW content, and b) enhancement of model accuracy, inclusion and diversity through \textbf{concept correction and disentanglement}. It can also be adapted as a lightweight model patch for Stable Diffusion, allowing for concept manipulation and convenient distribution. To encourage future research in this critical area and promote the development of safe and inclusive generative models, we will open-source our code and ConceptBench at \href{https://github.com/SHI-Labs/Forget-Me-Not}{https://github.com/SHI-Labs/Forget-Me-Not}.

cs.CV↗
arXiv2023

Series-Parallel Mechanical Circuit Synthesis of a Positive-Real Third-Order Admittance Using at Most Six Passive Elements for Inerter-Based Control

This paper investigates the circuit synthesis problem for a certain positive-real bicubic (third-order) admittance with a simple pole at the origin (s = 0) to be realizable as a one-port series-parallel damper-spring-inerter circuit consisting of at most six elements, where the results can be directly applied to the design and physical realization of inerter-based control systems. Necessary and sufficient conditions for such a specific bicubic admittance to be realizable by a one-port passive series-parallel mechanical circuit containing at most six elements are derived, and a group of mechanical circuit configurations covering the whole set of realizability conditions are presented together with element value expressions. The conditions and element value expressions are related to the admittance coefficients and the roots of certain algebraic equations. The circuit synthesis results of this paper are illustrated by several numerical examples including the control system design of a train suspension system. Any realization circuit in this paper contains much fewer passive elements than the ten-element realization circuit by the well-known Bott-Duffin circuit synthesis approach. The investigations of this paper can contribute to the theory of circuit synthesis and many other related fields.

math.OC↗
arXiv2023

Multiphoton non-local quantum interference controlled by an undetected photon

The interference of quanta lies at the heart of quantum physics. The multipartite generalization of single-quanta interference creates entanglement, the coherent superposition of states shared by several quanta. Entanglement allows non-local correlations between many quanta and hence is a key resource for quantum information technology. Entanglement is typically considered to be essential for creating non-local correlations, manifested by multipartite interference. Here, we show that this is not the case and demonstrate multiphoton non-local quantum interference without entanglement of any intrinsic properties of the photons. We harness the superposition of the physical origin of a four-photon product state, which leads to constructive and destructive interference of the photons' mere existence. With the intrinsic indistinguishability in the generation process of photons, we realize four-photon frustrated quantum interference. We furthermore establish non-local control of multipartite quantum interference, in which we tune the phase of one undetected photon and observe the interference of the other three photons. Our work paves the way for fundamental studies of non-locality and potential applications in quantum technologies.

quant-ph↗
arXiv2023

Multi-dimensional band structure spectroscopy in the synthetic frequency dimension

The concept of synthetic dimensions in photonics provides a versatile platform in exploring multi-dimensional physics. Many of these physics are characterized by band structures in more than one dimensions. Existing efforts on band structure measurements in the photonic synthetic frequency dimension however are limited to either one-dimensional Brillouin zones or one-dimensional subsets of multi-dimensional Brillouin zones. Here we theoretically propose and experimentally demonstrate a method to fully measure multi-dimensional band structures in the synthetic frequency dimension. We use a single photonic resonator under dynamical modulation to create a multi-dimensional synthetic frequency lattice. We show that the band structure of such a lattice over the entire multi-dimensional Brillouin zone can be measured by introducing a gauge potential into the lattice Hamiltonian. Using this method, we perform experimental measurements of two-dimensional band structures of a Hermitian and a non-Hermitian Hamiltonian. The measurements reveal some of the general properties of point-gap topology of the non-Hermitian Hamiltonian in more than one dimensions. Our results demonstrate experimental capabilities to fully characterize high-dimensional physical phenomena in the photonic synthetic frequency dimension.

physics.optics↗
arXiv2023

MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID

Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance. Previous works explore new optimizing targets and search spaces for NAS ReID, yet they neglect the difference of training schemes between image classification and ReID. In this work, we propose a novel Twins Contrastive Mechanism (TCM) to provide more appropriate supervision for ReID architecture search. TCM reduces the category overlaps between the training and validation data, and assists NAS in simulating real-world ReID training schemes. We then design a Multi-Scale Interaction (MSI) search space to search for rational interaction operations between multi-scale features. In addition, we introduce a Spatial Alignment Module (SAM) to further enhance the attention consistency confronted with images from different sources. Under the proposed NAS scheme, a specific architecture is automatically searched, named as MSINet. Extensive experiments demonstrate that our method surpasses state-of-the-art ReID methods on both in-domain and cross-domain scenarios. Source code available in https://github.com/vimar-gu/MSINet.

cs.CV↗
arXiv2023

Optimistic Whittle Index Policy: Online Learning for Restless Bandits

Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on whether that arm is pulled. Solving RMABs requires information on transition dynamics, which are often unknown upfront. To plan in RMAB settings with unknown transitions, we propose the first online learning algorithm based on the Whittle index policy, using an upper confidence bound (UCB) approach to learn transition dynamics. Specifically, we estimate confidence bounds of the transition probabilities and formulate a bilinear program to compute optimistic Whittle indices using these estimates. Our algorithm, UCWhittle, achieves sublinear $O(H \sqrt{T \log T})$ frequentist regret to solve RMABs with unknown transitions in $T$ episodes with a constant horizon $H$. Empirically, we demonstrate that UCWhittle leverages the structure of RMABs and the Whittle index policy solution to achieve better performance than existing online learning baselines across three domains, including one constructed from a real-world maternal and childcare dataset.

cs.LG↗
arXiv2023

Electroweak corrections to Higgs boson production via W W fusion at the future LHeC

Precision measurement of quark Yukawa couplings is a crucial aspect of Higgs property study. Proposed as a future upgrade of the Large Hadron Collider (LHC), the Large Hadron electron Collider (LHeC) provides opportunities to probe quark Yukawa couplings with a high precision because of relatively low rate from QCD background as compared with that of the Higgs processes at the LHC. For this purpose, it is important to have a precision prediction of the Higgs production rate at the LHeC. The leading production channel of the Higgs boson at the LHeC is via weak boson fusion (WBF) which has the unique kinematic feature of forward tagging jets. Since there is no color exchange between the forward-backward objects in WBF processes, electroweak radiative corrections become particularly important. In this paper, we focus on the electroweak radiative corrections at next-to-leading order for Higgs boson production in the WBF channel at the LHeC. In the two renormalization schemes we use, the loop corrections are respectively at the level of 9% and 18% relative to the leading order result, and the next-to-leading calculation in both schemes agree up to a truncation error expected from the perturbative method. The size of the EW correction exceeds that from QCD radiations and is shown to be important for the study of Higgs phenomenology.

hep-ph↗
arXiv2023

Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering

The development of clustering heuristics has demonstrated that Bitcoin is not completely anonymous. Currently, existing clustering heuristics only consider confirmed transactions recorded in the Bitcoin blockchain. However, unconfirmed transactions in the mempool have yet to be utilized to improve the performance of the clustering heuristics. In this paper, we bridge this gap by combining unconfirmed and confirmed transactions for clustering Bitcoin addresses effectively. First, we present a data collection system for capturing unconfirmed transactions. Two case studies are performed to show the presence of user behaviors in unconfirmed transactions not present in confirmed transactions. Next, we apply the state-of-the-art clustering heuristics to unconfirmed transactions, and the clustering results can reduce the number of entities after applying, for example, the co-spend heuristics in confirmed transactions by 2.3%. Finally, we propose three novel clustering heuristics to capture specific behavior patterns in unconfirmed transactions, which further reduce the number of entities after the application of the co-spend heuristics by 9.8%. Our results demonstrate the utility of unconfirmed transactions in address clustering and further shed light on the limitations of anonymity in cryptocurrencies. To the best of our knowledge, this paper is the first to apply the unconfirmed transactions in Bitcoin to cluster addresses.

cs.CR↗