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Researcher profile

Wei Xiong

Wei Xiong contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.mtrl-sciphysics.app-phquant-phMachine Learningeess.IVgr-qcArtificial IntelligenceComputer Science and Game Theorycond-mat.mes-hallecon.EMeess.SPhep-thInformation RetrievalInformation Theorymath.ITmath.NTmath.STNetworking and Internet ArchitectureStatistics Theory

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Published work

28 published item(s)

preprint2026arXiv

Aurora: Unified Video Editing with a Tool-Using Agent

Recent video editing models have converged on a unified conditioning design: a single diffusion transformer jointly consumes text, source video, and reference images, and one set of weights covers replacement, removal, style transfer, and reference-driven insertion. The design is flexible, but it assumes that the user already provides model-ready text, reference images, and spatial grounding for local edits, which real requests often omit. We present Aurora, an agentic video editing framework that pairs a tool-augmented vision-language model (VLM) agent with a unified video diffusion transformer. The VLM agent maps a raw user request to a structured edit plan aligned with the transformer's conditioning channels, thereby resolving textual and visual underspecification before generation. We train the VLM agent with supervised data for complete edit planning and reference-image selection, together with preference pairs for robust tool use and instruction refinement. We introduce AgentEdit-Bench to evaluate agent-enhanced video editing under textual and visual underspecification. Experiments on AgentEdit-Bench and two existing video editing benchmarks show that Aurora improves over instruction-only baselines and that the VLM agent transfers to compatible frozen video editing models. Project page: https://yeates.github.io/Aurora-Page

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Rethinking Importance Sampling in LLM Policy Optimization: A Cumulative Token Perspective

Reinforcement learning, including reinforcement learning with verifiable rewards (RLVR), has emerged as a powerful approach for LLM post-training. Central to these approaches is the design of the importance sampling (IS) ratio used in off-policy policy-gradient estimation. Existing methods face a fundamental bias-variance dilemma: token-level IS ratios, as adopted by PPO (Schulman et al., 2017) and GRPO (Shao et al., 2024), introduce bias by ignoring prefix state distribution mismatch; full sequence ratios provide exact trajectory-level correction but suffer from high variance due to the multiplicative accumulation of per-token ratios, while GSPO (Zheng et al., 2025) improves numerical stability via length normalization at the cost of deviating from the exact full-sequence IS correction. In this work, we identify the cumulative token IS ratio, the product of per-token ratios up to position $t$, as a theoretically principled solution to this dilemma. We prove that, under the token-level policy-gradient formulation, this ratio provides an unbiased prefix correction for each token-level gradient term and has strictly lower variance than the full sequence ratio. Building on this insight, we propose CTPO (Cumulative Token Policy Optimization), which combines the cumulative token IS ratio with position-adaptive clipping that scales log-space clip bounds according to the natural $\sqrt{t}$ growth of the cumulative log-ratio. This yields more consistent regularization across token positions. We implement and evaluate CTPO in the tool-integrated reasoning setting on several challenging mathematical reasoning benchmarks, achieving the best average performance across both model scales compared with strong GRPO and GSPO baselines. Code will be available at https://github.com/horizon-llm/CTPO.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Entangling spins using cubic nonlinear dynamics

Entangled states with a large number of $N$ atomic spins are a key ingredient for quantum information processing and quantum metrology. Nowadays, the preparation of such states has mainly relied on the quadratic nonlinear dynamics. Here, we investigate the preparation of spin-spin multipartite entanglement, witnessed by quantum Fisher information, by using the cubic nonlinear dynamics. We find that, in the regime of weak coupling, the cubic scheme can greatly speed up the rate of entanglement generation as compared to the quadratic scheme (about $N$ times faster). In the strong coupling regime, the cubic nonlinear dynamics enables the periodic in time generation of a broad variety of new-type macroscopic superposition states, which allow us to realize near-Heisenberg-limit phase sensitivity. In addition, we also reveal an interesting feature that the amount of entanglement generated by the cubic scheme has a macroscopic sensitivity to the parity of $N$, which has no counterpart in quadratic nonlinear dynamics and can be exploited for sensing the parity of $N$ at the single-spin level. We also propose a new approach for a fast and high-fidelity generation of maximally entangled Greenberger-Horne-Zeilinger (GHZ) states. By using an alternative cubic-quadratic-admixture type of nonlinear interaction, we show that one may accelerate the procedure of GHZ-state generation. The realization of the cubic nonlinear dynamics is also considered, showing that the cubic nonlinear dynamics can be realized by either repeatedly using linear- and quadratic-nonlinear dynamics or utilizing light-mediated interactions in just one step. Finally, by taking realistic imperfections into account, we find that the cubic scheme is sensitivity to the single-spin decay in the strong coupling regime, while is robust against the collective dephasing.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Generation of long-lived $W$ states via reservoir engineering in dissipatively coupled systems

Very recently, dissipative coupling was discovered, which develops and broadens methods for controlling and utilizing light-matter interactions. Here, we propose a scheme to generate the tripartite $W$ state in a dissipatively coupled system, where one qubit and two resonators simultaneously interact with a common reservoir. With appropriate parameters, we find the $W$ state is a dark state of the system. By driving the qubit, the dissipatively coupled system will evolve from the ground state to the tripartite $W$ state. Because the initial state is the ground state of the system and no measurement is required, our scheme is easy to implement in experiments. Moreover, the $W$ state decouples from the common reservoir and thus has a very long lifetime. This scheme is applicable to a wide class of dissipatively coupled systems, and we specifically illustrate how to prepare the $W$ state in a hybrid qubit-photon-magnon system by using this scheme.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders

Objective and Impact Statement. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes osteolytic lesions, and results in skeletal related events (SREs) including severe pain and even fatal fractures. Although current imaging techniques can detect macroscopic bone lesions, predicting the occurrence and progression of bone lesions remains a challenge. Methods. We adopt a temporal variational auto-encoder (T-VAE) model that utilizes a combination of variational auto-encoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn the distribution of their future states from data. Results. We test our model against other deep learning-based prediction models on the bone lesion progression prediction task. Our model produces much more accurate predictions than existing models under various evaluation metrics. Conclusion. We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamical spontaneous scalarization in Einstein-Maxwell-scalar theory

We study the linear instability and the nonlinear dynamical evolution of the Reissner-Nordström (RN) black hole in the Einstein-Maxwell-scalar theory in asymptotic flat spacetime. We focus on the coupling function $f(ϕ)=e^{-bϕ^2}$ which allows both the scalar-free RN solution and scalarized black hole solution. We first present the evolution of system parameters during dynamic scalarization. For parameter regions where spontaneous scalarization occurs, we find that the evolution of the scalar field at the horizon is dominated by the fundamental unstable mode from linear analysis at early times. At late times, the nonlinear evolution can be viewed as the perturbation of scalarized black holes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Entanglement Wedge Minimum Cross-Section for Holographic Aether Gravity

We study the entanglement wedge cross-section (EWCS) in holographic Aether gravity theory, a gravity theory with Lorentz symmetry violation while keeping the general covariance intact. We find that only a limited parameter space is allowed to obtain a black brane with positive Hawking temperature. Subject to these allowed parameter regions, we find that the EWCS could exhibit non-monotonic behaviors with system parameters. Meanwhile, the holographic entanglement entropy (HEE), and the corresponding mutual information (MI), can only exhibit monotonic behaviors. These phenomena suggest that the EWCS could capture much more rich content of the entanglement than that of the HEE and the MI. The role of the Lorentz violation in determining the behaviors of quantum information-related quantities is also analyzed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets

We study episodic two-player zero-sum Markov games (MGs) in the offline setting, where the goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset collected a priori. When the dataset does not have uniform coverage over all policy pairs, finding an approximate NE involves challenges in three aspects: (i) distributional shift between the behavior policy and the optimal policy, (ii) function approximation to handle large state space, and (iii) minimax optimization for equilibrium solving. We propose a pessimism-based algorithm, dubbed as pessimistic minimax value iteration (PMVI), which overcomes the distributional shift by constructing pessimistic estimates of the value functions for both players and outputs a policy pair by solving NEs based on the two value functions. Furthermore, we establish a data-dependent upper bound on the suboptimality which recovers a sublinear rate without the assumption on uniform coverage of the dataset. We also prove an information-theoretical lower bound, which suggests that the data-dependent term in the upper bound is intrinsic. Our theoretical results also highlight a notion of "relative uncertainty", which characterizes the necessary and sufficient condition for achieving sample efficiency in offline MGs. To the best of our knowledge, we provide the first nearly minimax optimal result for offline MGs with function approximation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Phase Transformations During Continuous Cooling in Inconel 718 Alloys Manufactured by Laser Powder Bed Fusion and Suction Casting

Understanding alloy phase transformations during continuous cooling is important for post-processing design and optimization. In this work, continuous-cooling-transformation (CCT) diagrams of Inconel 718 alloys manufactured by laser powder bed fusion (LPBF) and suction casting are developed under different homogenization conditions. Unlike the available CCT diagrams in the reported studies, no gamma double prime and gamma prime precipitates can be observed. NbC and delta are determined to be the precipitates after cooling from the gamma matrix. Importantly, homogenization time and manufacturing methods are found to affect the Nb homogeneity in the matrix near NbC particles and thus significantly influence the precipitation process of the delta phase, which has a high content in Nb. In the alloys with high Nb homogeneity, the nucleation process mainly contributes to the precipitation, whereas in the alloys with low Nb homogeneity, the precipitation is primarily associated with the growth process. Subgrains are found to form after cooling at 0.1 K/s and can cause the highest hardness in samples. This work provides a new viewpoint on the study of processing-structure-property relationships during cooling in Inconel 718 and is beneficial to the development of alloy post-processing strategies.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Strong Long-Range Spin-Spin Coupling via a Kerr Magnon Interface

Strong long-range coupling between distant spins is crucial for spin-based quantum information processing. However, achieving such a strong spin-spin coupling remains challenging. Here we propose to realize a strong coupling between two distant spins via the Kerr effect of magnons in a yttrium-iron-garnet nanosphere. By applying a microwave field on this nanosphere, the Kerr effect of magnons can induce the magnon squeezing, so that the coupling between the spin and the squeezed magnons can be exponentially enhanced. This in turn allows the spin-magnon distance to increase from nano- to micrometer scale. By considering the virtual excitation of the squeezed magnons in the dispersive regime, strong spin-spin coupling mediated by the squeezed magnons can be achieved, and a remote quantum-state transfer, as well as the nonlocal two-qubit ISWAP gate with high fidelity becomes implementable. Our approach offers a feasible scheme to perform quantum information processing among distant spins.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Unsupervised Low-light Image Enhancement with Decoupled Networks

In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion. Conventional unsupervised learning-based approaches usually tackle the low-light image enhancement problem using an image-to-image translation model. They focus primarily on illumination or contrast enhancement but fail to suppress the noise that ubiquitously exists in images taken under real-world low-light conditions. To address this issue, we explicitly decouple this task into two sub-tasks: illumination enhancement and noise suppression. We propose to learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. To facilitate the unsupervised training of our model, we construct samples with pseudo labels. Furthermore, we propose an adaptive content loss to suppress real image noise in different regions based on illumination intensity. In addition to conventional benchmark datasets, a new unpaired low-light image enhancement dataset is built and used to thoroughly evaluate the performance of our model. Extensive experiments show that our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Dissipation-induced nonreciprocal magnon blockade in a magnon-based hybrid system

We propose an experimentally realizable nonreciprocal magnonic device at the single-magnon level by exploiting magnon blockade in a magnon-based hybrid system. The coherent qubit-magnon coupling, mediated by virtual photons in a microwave cavity, leads to the energy-level anharmonicity of the composite modes. In contrast, the corresponding dissipative counterpart, induced by traveling microwaves in a waveguide, yields inhomogeneous broadenings of the energy levels. As a result, the cooperative effects of these two kinds of interactions give rise to the emergence of the direction-dependent magnon blockade. We show that this can be demonstrated by studying the equal-time second-order correlation function of the magnon mode. Our study opens an avenue to engineer nonreciprocal magnonic devices in the quantum regime involving only a small number of magnons.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

Result relevance prediction is an essential task of e-commerce search engines to boost the utility of search engines and ensure smooth user experience. The last few years eyewitnessed a flurry of research on the use of Transformer-style models and deep text-match models to improve relevance. However, these two types of models ignored the inherent bipartite network structures that are ubiquitous in e-commerce search logs, making these models ineffective. We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction. We design, for the first time, an end-to-end First-and-Second-order Relevance prediction model for e-commerce item relevance. The model is augmented by the neighborhood structures of bipartite networks that are built using the information of user behavioral feedback, including clicks and purchases. To ensure that edges accurately encode relevance information, we introduce external knowledge generated from BERT to refine the network of user behaviors. This allows the new model to integrate information from neighboring items and queries, which are highly relevant to the focus query-item pair under consideration. Results of offline experiments showed that the new model significantly improved the prediction accuracy in terms of human relevance judgment. An ablation study showed that the First-and-Second-order model gained a 4.3% average gain over the First-order model. Results of an online A/B test revealed that the new model derived more commercial benefits compared to the base model.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Comparative Analysis of Inconel 718 Made by Additive Manufacturing and Suction Casting: Microstructure Evolution in Homogenization

Homogenization is one of the critical stages in the post-heat treatment of additive manufacturing (AM) component to achieve uniform microstructure. During homogenization, grain coarsening could be an issue to reserve strength, which requires careful design of both time and temperature. Therefore, a proper design of homogenization becomes particularly important for AM design, for which work hardening is usually no longer an option. In this work, we discovered an intriguing phenomenon during homogenization of suction-cast and AM Inconel 718 superalloys. Through both short and long-term isothermal heat treatments at 1180°C, we observed an abnormal grain growth in the suction-cast alloy but continuous recrystallization in the alloy made by laser powder bed fusion (LPBF). The grain size of AM samples keeps as small as 130 μm and is even slightly reduced after homogenization for 12 hours. The homogeneity of Nb in the AM alloys is identified as the critical factor for NbC formation, which further influences the recrystallization kinetics at 1180°C. Multi-type dislocation behaviors are studied to elucidate the grain refinement observed in homogenized alloys after LPBF. This work provides a new pathway on microstructure engineering of AM alloys for improved mechanical performance superior to traditionally manufactured ones.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A new high-throughput method using additive manufacturing for alloy design and heat treatment optimization

Many alloys made by Additive Manufacturing (AM) require careful design of post-heat treatment as an indispensable step of microstructure engineering to further enhance the performance. We developed a high-throughput approach by fabricating a long-bar sample heat-treated under a monitored gradient temperature zone for phase transformation study to accelerate the post-heat treatment development of AM alloys. This approach has been proven efficient in determining the aging temperature with peak hardness. We observed that the precipitation strengthening is predominant for the studied superalloy by laser powder bed fusion, and the grain size variation is insensitive on temperature between 605 and 825 Celcius. This new approach can be applied to post-heat treatment optimization of other materials made by AM, and further assist new alloy development.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Coherent perfect absorption in a weakly coupled atom-cavity system

We study coherent perfect absorption (CPA) theoretically based on a weakly coupled atom-cavity system with an optically pumped second-order nonlinear crystal (SOC) embedded in the cavity. Our system does not require a strong coupling, which is often needed for CPA in previous studies but is challenging to implement experimentally in some systems. The role of the SOC is to introduce a tunable effective decay rate of the cavity, which can lead to CPA in the weak coupling regime. The proposed system exhibits bistable behaviors, with bistable patterns switchable between conventional and unconventional shapes. By varying the properties of the SOC, the operation point of CPA can be tuned to be inside or outside the bistable regime. It can also be located at the upper or the lower stable branch or even the unstable branch of the bistable hysteresis loop. It is however robust against the parameters of the SOC for any fixed effective decay rate. Our system can potentially be applied to realize optical devices such as optical switches in the weakly coupled regime.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cyclic Re-austenitization of Copper-bearing High-Strength Low-Alloy Steels Fabricated by Laser Powder Bed Fusion

For the first time, cyclic re-austenitization is carried out for additively manufactured high-strength low-alloy (HSLA) steels in order to refine the microstructure by reducing the prior austenite grain (PAG) size. In this work, HSLA-100 steels processed using laser powder bed fusion (LPBF) technique are subjected to several cycles of re-austenitization using quenching dilatometry. Microstructure characterization for every cycle revealed the presence of bainite, martensite and martensite/austenite (M/A) islands. From the analysis of the dilatometry curves and extensive microstructure characterization, it was found that till the 2nd cycle of re-austenitization, both PAG size and martensite start (Ms) temperature get reduced, while the amount of bainite transformed decreased and the retained austenite content increased. Concomitantly, the highest microhardness along with peak nanohardness of the constituent phases was achieved at the 2nd cycle. Conversely, from the 3rd cycle, the microhardness, as well as the nanohardness of the constituent phases, are found to decrease due to an increase in the PAG size. This behavior is in contrast to the general tendency where a saturation limit is reached after the peak refinement is achieved. It is found that retained austenite can act as a pinning particle to obstruct the PAG boundary movement and its fraction is found to decrease from the 3rd cycle. Hence, the increase in PAG size after the 3rd cycle can be attributed to the destabilization of effective pinning particles to hinder the PAG boundary movement during the re-austenitization.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Decentralized Multi-player Multi-armed Bandits with No Collision Information

The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper. Building on the seminal work of Boursier and Perchet (2019), we propose error correction synchronization involving communication (EC-SIC), whose regret is shown to approach that of the centralized stochastic MP-MAB with collision information. By recognizing that the communication phase without collision information corresponds to the Z-channel model in information theory, the proposed EC-SIC algorithm applies optimal error correction coding for the communication of reward statistics. A fixed message length, as opposed to the logarithmically growing one in Boursier and Perchet (2019), also plays a crucial role in controlling the communication loss. Experiments with practical Z-channel codes, such as repetition code, flip code and modified Hamming code, demonstrate the superiority of EC-SIC in both synthetic and real-world datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Example-Guided Image Synthesis across Arbitrary Scenes using Masked Spatial-Channel Attention and Self-Supervision

Example-guided image synthesis has recently been attempted to synthesize an image from a semantic label map and an exemplary image. In the task, the additional exemplar image provides the style guidance that controls the appearance of the synthesized output. Despite the controllability advantage, the existing models are designed on datasets with specific and roughly aligned objects. In this paper, we tackle a more challenging and general task, where the exemplar is an arbitrary scene image that is semantically different from the given label map. To this end, we first propose a Masked Spatial-Channel Attention (MSCA) module which models the correspondence between two arbitrary scenes via efficient decoupled attention. Next, we propose an end-to-end network for joint global and local feature alignment and synthesis. Finally, we propose a novel self-supervision task to enable training. Experiments on the large-scale and more diverse COCO-stuff dataset show significant improvements over the existing methods. Moreover, our approach provides interpretability and can be readily extended to other content manipulation tasks including style and spatial interpolation or extrapolation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fine-grained Image-to-Image Transformation towards Visual Recognition

Existing image-to-image transformation approaches primarily focus on synthesizing visually pleasing data. Generating images with correct identity labels is challenging yet much less explored. It is even more challenging to deal with image transformation tasks with large deformation in poses, viewpoints, or scales while preserving the identity, such as face rotation and object viewpoint morphing. In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks. The generated images, transformed with large geometric deformation, do not necessarily need to be of high visual quality but are required to maintain as much identity information as possible. To this end, we adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image. In order to preserve the fine-grained contextual details of the input image during the deformable transformation, a constrained nonalignment connection method is proposed to construct learnable highways between intermediate convolution blocks in the generator. Moreover, an adaptive identity modulation mechanism is proposed to transfer the identity information into the output image effectively. Extensive experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models, and as a result significantly boosts the visual recognition performance in fine-grained few-shot learning.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Image Sentiment Transfer

In this work, we introduce an important but still unexplored research task -- image sentiment transfer. Compared with other related tasks that have been well-studied, such as image-to-image translation and image style transfer, transferring the sentiment of an image is more challenging. Given an input image, the rule to transfer the sentiment of each contained object can be completely different, making existing approaches that perform global image transfer by a single reference image inadequate to achieve satisfactory performance. In this paper, we propose an effective and flexible framework that performs image sentiment transfer at the object level. It first detects the objects and extracts their pixel-level masks, and then performs object-level sentiment transfer guided by multiple reference images for the corresponding objects. For the core object-level sentiment transfer, we propose a novel Sentiment-aware GAN (SentiGAN). Both global image-level and local object-level supervisions are imposed to train SentiGAN. More importantly, an effective content disentanglement loss cooperating with a content alignment step is applied to better disentangle the residual sentiment-related information of the input image. Extensive quantitative and qualitative experiments are performed on the object-oriented VSO dataset we create, demonstrating the effectiveness of the proposed framework.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LinksIQ: Robust and Efficient Modulation Recognition with Imperfect Spectrum Scans

While critical for the practical progress of spectrum sharing, modulation recognition has so far been investigated under unrealistic assumptions: (i) a transmitter's bandwidth must be scanned alone and in full, (ii) prior knowledge of the technology must be available and (iii) a transmitter must be trustworthy. In reality these assumptions cannot be readily met, as a transmitter's bandwidth may only be scanned intermittently, partially, or alongside other transmitters, and modulation obfuscation may be introduced by short-lived scans or malicious activity. This paper presents LinksIQ, which bridges the gap between real-world spectrum sensing and the growing body of modrec methods designed under simplifying assumptions. Our key insight is that ordered IQ samples form distinctive patterns across modulations, which persist even with scan deficiencies. We mine these patterns through a Fisher Kernel framework and employ lightweight linear support vector machine for modulation classification. LinksIQ is robust to noise, scan partiality and data biases without utilizing prior knowledge of transmitter technology. Its accuracy consistently outperforms baselines in both simulated and real traces. We evaluate LinksIQ performance in a testbed using two popular SDR platforms, RTL-SDR and USRP. We demonstrate high detection accuracy (i.e. 0.74) even with a $20 RTL-SDR scanning at 50% transmitter overlap. This constitutes an average of 43% improvement over existing counterparts employed on RTL-SDR scans. We also explore the effects of platform-aware classifier training and discuss implications on real-world modrec system design. Our results demonstrate the feasibility of low-cost transmitter fingerprinting at scale.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Max-sum tests for cross-sectional dependence of high-demensional panel data

We consider a testing problem for cross-sectional dependence for high-dimensional panel data, where the number of cross-sectional units is potentially much larger than the number of observations. The cross-sectional dependence is described through a linear regression model. We study three tests named the sum test, the max test and the max-sum test, where the latter two are new. The sum test is initially proposed by Breusch and Pagan (1980). We design the max and sum tests for sparse and non-sparse residuals in the linear regressions, respectively.And the max-sum test is devised to compromise both situations on the residuals. Indeed, our simulation shows that the max-sum test outperforms the previous two tests. This makes the max-sum test very useful in practice where sparsity or not for a set of data is usually vague. Towards the theoretical analysis of the three tests, we have settled two conjectures regarding the sum of squares of sample correlation coefficients asked by Pesaran (2004 and 2008). In addition, we establish the asymptotic theory for maxima of sample correlations coefficients appeared in the linear regression model for panel data, which is also the first successful attempt to our knowledge. To study the max-sum test, we create a novel method to show asymptotic independence between maxima and sums of dependent random variables. We expect the method itself is useful for other problems of this nature. Finally, an extensive simulation study as well as a case study are carried out. They demonstrate advantages of our proposed methods in terms of both empirical powers and robustness for residuals regardless of sparsity or not.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Siegel-Weil formula for classical groups over function fields

We establish a Siegel-Weil formula for classical groups over a function field with odd characteristic, which asserts in many cases that the Siegel Eisenstein series is equal to an integral of a theta function. This is a function-field analogue of the classical result proved by A. Weil in his 1965 Acta Math. paper. We also give a convergence criterion for the theta integral by using Harder's reduction theory over function fields.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Post-Heat Treatment Design of High-Strength Low-Alloy Steels Processed by Laser Powder Bed Fusion

In this study, a post-heat treatment design for additively manufactured copper-bearing high-strength low-alloy (HSLA)-100 steel is performed by understanding the process-structure-property relationships. Hot isostatic pressing (HIP) is designed to reduce the porosity from 3% to less than 1% for the HSLA-100 steel processed by laser powder bed fusion (LPBF). Quenching dilatometry is employed to design the HIP parameters with the optimized cooling rate for the maximum amount of martensite transformed after HIP. Afterward, a post-heat treatment step with cyclic re-austenitization is introduced for an effective grain refinement to compensate the coarsened microstructure after HIP. Finally, tempering is optimized through microstructure characterization and microhardness. A two-fold increase in the yield strength of the HSLA with tailored microstructure during post-heat treatment is achieved in comparison with the as-built HSLA.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantum Zeno dynamics induced atomic entanglement in a hybrid atom-cavity-fiber system

Quantum entanglement is important quanum resources in quantum information sicence. Here we propose an approach to {preparing} atomic quantum entanglement in a hybrid atom-cavity-fiber system. Using quantum Zeno dynamics method, the system evolution states are always split into a series of Zeno invariant {subspaces} consisting of dark and bright states. By choosing the initial state of the system, the bright states are all neglected and only dark states are kept to build the effective Hamiltonian. By tuning the system parameters, two-atom multiple-dimensional entanglement, two-atom Bell state, and three-atom GHZ state can be realized by one step. Our proposal provides a way to perform quantum information processing with dark states.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning

Stacking fault energy (SFE) is of the most critical microstructure attribute for controlling the deformation mechanism and optimizing mechanical properties of austenitic steels, while there are no accurate and straightforward computational tools for modeling it. In this work, we applied both thermodynamic modeling and machine learning to predict the stacking fault energy (SFE) for more than 300 austenitic steels. The comparison indicates a high need of improving low-temperature CALPHAD (CALculation of PHAse Diagrams) databases and interfacial energy prediction to enhance thermodynamic model reliability. The ensembled machine learning algorithms provide a more reliable prediction compared with thermodynamic and empirical models. Based on the statistical analysis of experimental results, only Ni and Fe have a moderate monotonic influence on SFE, while many other elements exhibit a complex effect that their influence on SFE may change with the alloy composition.

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