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Source author record

Zhuo Wang

Zhuo Wang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
cond-mat.supr-conArtificial IntelligenceComputer Visioncond-mat.mes-hallcond-mat.mtrl-scicond-mat.str-elDistributed, Parallel, and Cluster ComputingMachine Learningquant-phastro-ph.COgr-qchep-phhep-thnlin.PSphysics.chem-phphysics.opticsRobotics

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Published work

17 published item(s)

preprint2026arXiv

Multiple nodal superconducting phases and order-parameter evolution in pressurized UTe$_2$

Spin-triplet superconductivity (SC) offers a unique avenue for realizing non-Abelian Majorana zero modes and thus the fault-tolerant topological quantum computation, and has attracted a broad audience for both fundamental research and potential applications. The recently discovered heavy-fermion spin-triplet superconductor candidate UTe$_2$ has sparked great interest for its ultrahigh upper critical field and reentrant SC phases in the proximity to a field-polarized magnetic state. Despite extensive studies on the phase diagrams and competing orders induced by pressure and magnetic field, limited has been known about its SC order parameters and their evolution with these control parameters, largely due to the lack of appropriate symmetry-sensitive detections. Here, we report comprehensive point-contact spectroscopy measurements of pressurized UTe$_2$ on the (0~0~1) surface. The observation of Andreev bound state strongly suggests the presence of a $p_z$ component in the SC order parameters. Quantitative analysis based on an extended Blonder-Tinkham-Klapwijk model unveils $B_{2u}$ or $B_{3u}$ as the most likely representation for both ambient and pressurized UTe$_2$, and remarkably, the multiple SC phases can be distinguished by a single parameter $\langle Δ_{z}\rangle/\langleΔ_{x(y)}\rangle$, the relative weight between the $p_z$-wave and $p_{x(y)}$-wave pairings. These findings not only impose stringent constraints on the superconducting order parameter in UTe$_2$, but also provide key spectroscopic evidence for the existence of multiple SC phases tuned through pressure.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory

Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, salience, or summary quality. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to support good decisions. We cast this as a decision-centric rate-distortion problem, measuring memory quality by the loss in achievable decision quality induced by compression. This yields an exact forgetting boundary for what can be safely forgotten, and a memory-distortion frontier characterizing the optimal tradeoff between memory budget and decision quality. Motivated by this decision-centric view of memory, we propose DeMem, an online memory learner that refines its partition only when data certify that a shared state would induce decision conflict, and prove near-minimax regret guarantees. On both controlled synthetic diagnostics and long-horizon conversational benchmarks, DeMem yields consistent gains under the same runtime budget, supporting the principle that memory should preserve the distinctions that matter for decisions, not descriptions.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm

The transition from static Large Language Models (LLMs) to self-improving agents is hindered by the lack of structured reasoning in traditional evolutionary approaches. Existing methods often struggle with premature convergence and inefficient exploration in high-dimensional code spaces. To address these challenges, we introduce LoongFlow, a self-evolving agent framework that achieves state-of-the-art solution quality with significantly reduced computational costs. Unlike "blind" mutation operators, LoongFlow integrates LLMs into a cognitive "Plan-Execute-Summarize" (PES) paradigm, effectively mapping the evolutionary search to a reasoning-heavy process. To sustain long-term architectural coherence, we incorporate a hybrid evolutionary memory system. By synergizing Multi-Island models with MAP-Elites and adaptive Boltzmann selection, this system theoretically balances the exploration-exploitation trade-off, maintaining diverse behavioral niches to prevent optimization stagnation. We instantiate LoongFlow with a General Agent for algorithmic discovery and an ML Agent for pipeline optimization. Extensive evaluations on the AlphaEvolve benchmark and Kaggle competitions demonstrate that LoongFlow outperforms leading baselines (e.g., OpenEvolve, ShinkaEvolve) by up to 60% in evolutionary efficiency while discovering superior solutions. LoongFlow marks a substantial step forward in autonomous scientific discovery, enabling the generation of expert-level solutions with reduced computational overhead.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

$^{75}$As NMR study of the antiferromagnetic Kondo lattice compound CeNiAsO

We revisit the magnetic properties of the antiferromagnetic Kondo lattice CeNiAsO by $^{75}$As nuclear magnetic resonance measurements. Our results confirm two successive antiferromagnetic transitions of Ce moments at $T_{N1}=9.0(3)$ K and $T_{N2}=7.0(3)$ K. Incommensurate and commensurate antiferromagnetic orders are suggested for $T_{N2}<T<T_{N1}$ and $T<T_{N2}$ respectively, consistent with previous neutron and muon experiments. A Knight shift anomaly, characterized by the failure of $K(T)-χ(T)$ scaling, is observed below $T^*\sim15$ K, which gives a measure of the onset of coherent $c-f$ correlations. This energy scale is further confirmed by the spin-lattice relaxation rate ($1/T_1$). The analysis of spin dynamics also reveals a quasi-two-dimensional character of spin fluctuations in CeNiAsO. This work paves the way for further $^{75}$As nuclear magnetic resonance studies under pressure.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing

With diverse presentation attacks emerging continually, generalizable face anti-spoofing (FAS) has drawn growing attention. Most existing methods implement domain generalization (DG) on the complete representations. However, different image statistics may have unique properties for the FAS tasks. In this work, we separate the complete representation into content and style ones. A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space. Then, to obtain a generalized representation, a contrastive learning strategy is developed to emphasize liveness-related style information while suppress the domain-specific one. Finally, the representations of the correct assemblies are used to distinguish between living and spoofing during the inferring. On the other hand, despite the decent performance, there still exists a gap between academia and industry, due to the difference in data quantity and distribution. Thus, a new large-scale benchmark for FAS is built up to further evaluate the performance of algorithms in reality. Both qualitative and quantitative results on existing and proposed benchmarks demonstrate the effectiveness of our methods. The codes will be available at https://github.com/wangzhuo2019/SSAN.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. In this work, we provide a two-pronged approach to this challenge: (i) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs. Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning

With the implementation of reinforcement learning (RL) algorithms, current state-of-art autonomous vehicle technology have the potential to get closer to full automation. However, most of the applications have been limited to game domains or discrete action space which are far from the real world driving. Moreover, it is very tough to tune the parameters of reward mechanism since the driving styles vary a lot among the different users. For instance, an aggressive driver may prefer driving with high acceleration whereas some conservative drivers prefer a safer driving style. Therefore, we propose an apprenticeship learning in combination with deep reinforcement learning approach that allows the agent to learn the driving and stopping behaviors with continuous actions. We use gradient inverse reinforcement learning (GIRL) algorithm to recover the unknown reward function and employ REINFORCE as well as Deep Deterministic Policy Gradient algorithm (DDPG) to learn the optimal policy. The performance of our method is evaluated in simulation-based scenario and the results demonstrate that the agent performs human like driving and even better in some aspects after training.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Transparent Classification with Multilayer Logical Perceptrons and Random Binarization

Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance, security, etc. However, existing models are hard to simultaneously satisfy the above two properties. In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure. To address the challenge of efficiently learning the non-differentiable CRS model, we propose a novel neural network architecture, Multilayer Logical Perceptron (MLLP), which is a continuous version of CRS. Using MLLP and the Random Binarization (RB) method we proposed, we can search the discrete solution of CRS in continuous space using gradient descent and ensure the discrete CRS acts almost the same as the corresponding continuous MLLP. Experiments on 12 public data sets show that CRS outperforms the state-of-the-art approaches and the complexity of the learned CRS is close to the simple decision tree. Source code is available at https://github.com/12wang3/mllp.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Comparative study of microwave radiation-induced magnetoresistive oscillations induced by circularly- and linearly- polarized photo-excitation

A comparative study of the radiation-induced magnetoresistance oscillations in the high mobility GaAs/AlGaAs heterostructure two dimensional electron system (2DES) under linearly- and circularlypolarized microwave excitation indicates a profound difference in the response observed upon rotating the microwave launcher for the two cases, although circularly polarized microwave radiation induced magnetoresistance oscillations observed at low magnetic fields are similar to the oscillations observed with linearly polarized radiation. For the linearly polarized radiation, the magnetoresistive response is a strong sinusoidal function of the launcher rotation (or linear polarization) angle, θ. For circularly polarized radiation, the oscillatory magnetoresistive response is hardly sensitive to θ.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Superconducting-contact-induced resistance-anomalies in the 3D topological insulator Bi2Te3

This study examines the magnetotransport response observed in flakes of the 3D topological insulator (TI) Bi2Te3, including indium superconducting electrodes, and demonstrates two critical transitions in the magnetoresistive response with decreasing temperatures below T = 3.4K. The first transition is attributed to superconductivity in the indium electrodes, and the second transition, with a critical field exceeding the transition field of indium, is attributed to a proximity effect at the 2D planar interface of this hybrid TI/superconductor structure.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Observation of Optical Solitons and Abnormal Modulation Instability in Liquid Crystals with Negative Dielectric Anisotropy

We investigate theoretically and experimentally the optical beam propagation in the nematic liquid crystal with negative dielectric anisotropy, which is aligned homeotropically in a $80μm$-thickness planar cell in the presence of an externally voltage. It is predicted that the nonlocal nonlinearity of liquid crystal undergo an oscillatory response function with a negative nonlinear refractive index coefficient. We found that the oscillatory nonlocal nonlinearity can support stable bright solitons, which are observed in experiment. We also found that abnormal modulation instability occurs with infinity gain coefficient at a fixed spatial frequency, which is no depend on the beam intensity. We observed the modulation instability in the liquid crystal at a very low intensity ($0.26W/cm^2$), and the maximum gain frequency were found kept unchange when beam power changes over 2-3 orders of magnitude.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Optimization and analysis of large scale data sorting algorithm based on Hadoop

When dealing with massive data sorting, we usually use Hadoop which is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. A common approach in implement of big data sorting is to use shuffle and sort phase in MapReduce based on Hadoop. However, if we use it directly, the efficiency could be very low and the load imbalance can be a big problem. In this paper we carry out an experimental study of an optimization and analysis of large scale data sorting algorithm based on hadoop. In order to reach optimization, we use more than 2 rounds MapReduce. In the first round, we use a MapReduce to take sample randomly. Then we use another MapReduce to order the data uniformly, according to the results of the first round. If the data is also too big, it will turn back to the first round and keep on. The experiments show that, it is better to use the optimized algorithm than shuffle of MapReduce to sort large scale data.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

1/2+1/2>1 for quantum error correction

Quantum error correction assisted by entanglement helps to transmit the encoded qudits through quantum channels with some of them being noiseless. Here we consider a more realistic scheme for experiments what we called as partial-noisy quantum channels in which, instead of completely free of noise, only part of the qudit suffers no noise. In this scenario we show by an explicit example that two half-noisy channels work better than one noiseless channel, a queer phenomenon showing 1/2+1/2>1. Our example also saturates a unified quantum Singleton bound, valid for the standard and entanglement-assisted codes as well. Furthermore, as illustrated by a mixed-alphabet code with half-noisy channels, a higher dimensional physical qudit can so improve the performance of a partial-noisy channel that it even outperforms a noiseless channel.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Quantum Error-Correcting Codes over Mixed Alphabets

Errors are inevitable during all kinds quantum informational tasks and quantum error-correcting codes (QECCs) are powerful tools to fight various quantum noises. For standard QECCs physical systems have the same number of energy levels. Here we shall propose QECCs over mixed alphabets, i.e., physical systems of different dimensions, and investigate their constructions as well as their quantum Singleton bound. We propose two kinds of constructions: a graphical construction based a graph-theoretical object composite coding clique and a projection-based construction. We illustrate our ideas using two alphabets by finding out some 1-error correcting or detecting codes over mixed alphabets, e.g., optimal $((6,8,3))_{4^52^1}$, $((6,4,3))_{4^42^2}$ and $((5,16,2))_{4^32^2}$ code and suboptimal $((5,9,2))_{3^42^1}$ code. Our methods also shed light to the constructions of standard QECCs, e.g., the construction of the optimal $((6,16,3))_4$ code as well as the optimal $((2n+3,p^{2n+1},2))_{p}$ codes with $p=4k$.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

CO2 dissociation activated through electron attachment on reduced rutile TiO2(110)-1x1 surface

Converting CO$_2$ to useful compounds through the solar photocatalytic reduction has been one of the most promising strategies for artificial carbon recycling. The highly relevant photocatalytic substrate for CO$_2$ conversion has been the popular TiO$_2$ surfaces. However, the lack of accurate fundamental parameters that determine the CO$_2$ reduction on TiO$_2$ has limited our ability to control these complicated photocatalysis processes. We have systematically studied the reduction of CO2 at specific sites of the rutile TiO$_2$(110)-1x1 surface using scanning tunneling microscopy at 80 K. The dissociation of CO2 molecules is found to be activated by one electron attachment process and its energy threshold, corresponding to the CO$_2^{\dot-}$/CO$_2$ redox potential, is unambiguously determined to be 2.3 eV higher than the onset of the TiO$_2$ conduction band. The dissociation rate as a function of electron injection energy is also provided. Such information can be used as practical guidelines for the design of effective catalysts for CO$_2$ photoreduction.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Interacting model of new agegraphic dark energy: observational constraints and age problem

Many dark energy models fail to pass the cosmic age test because of the old quasar APM 08279+5255 at redshift $z=3.91$, the $Λ$CDM model and holographic dark energy models being no exception. In this paper, we focus on the topic of age problem in the new agegraphic dark energy (NADE) model. We determine the age of the universe in the NADE model by fitting the observational data, including type Ia supernovae (SNIa), baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB). We find that the NADE model also faces the challenge of the age problem caused by the old quasar APM 08279+5255. In order to overcome such a difficulty, we consider the possible interaction between dark energy and dark matter. We show that this quasar can be successfully accommodated in the interacting new agegraphic dark energy (INADE) model at the $2σ$ level under the current observational constraints.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Single Crystal Growth of Magnesium Diboride by Using Low Melting Point Alloy Flux

Magnesium diboride crystals were grown with low melting alloy flux. The sample with superconductivity at 38.3 K was prepared in the zinc-magnesium flux and another sample with superconductivity at 34.11 K was prepared in the cadmium-magnesium flux. MgB4 was found in both samples, and MgB4's magnetization curve being above zero line had not superconductivity. Magnesium diboride prepared by these two methods is all flaky and irregular shape and low quality of single crystal. However, these two metals should be the optional flux for magnesium diboride crystal growth. The study of single crystal growth is very helpful for the future applications.

0 citations0 reviewsNo code linkedOpen access