arXiv subjects

Jiaxing Guo

Publications and source records attributed to Jiaxing Guo.

12 recordsLinked to original sources

arXiv2026

CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules

At a finite public-chance cut, counterfactual regret minimization (CFR) must choose how many outcomes to evaluate before each regret update. Exact evaluation processes the full cut at one strategy profile; persistent partial evaluation processes a fixed without-replacement order across evolving profiles. The latter covers every outcome once per epoch, yet its feedback is generally conditionally biased because earlier batches influence the profiles seen by later batches. We establish a deterministic target-transfer theorem for uniform, nonnested additive public cuts. The theorem bounds full-cut exploitability by regret on the delivered feedback and a public-debit term that couples prefix coverage discrepancy with motion along the realized strategy path. Consecutively balanced schedules consequently converge for additive signed regret matching (RM) and RM+ under predetermined averaging weights, while a fixed RM+ construction proves that the discrepancy--path product is necessary in general. A component-resolved form of the theorem converts an execution trace into a numerical exploitability certificate. On two released heads-up no-limit hold'em turn endgames, persistent order improves substantially over fresh reshuffling despite identical epochwise coverage, and partial coverage wins every registered shallow matched-budget comparison. A depth study locates a crossover between 32 and 64 full-cut outcome budgets, after which complete coverage dominates. These results characterize public-chance width and order as learning variables and provide a deterministic basis for designing and auditing persistent CFR schedules.

cs.GT↗

arXiv2026

Safe Observation Capacity for Opponent Exploitation under Showdown Censoring

In poker-like games, folds hide private cards, so showdown data are missing not at random: per-card estimates converge to behavior conditional on reveal, and shrinking confidence sets can lose coverage. A floor-safe probe carries a line to showdown; sequence-form flow then recovers censored fold mass on reveal-certified histories. We price acquisition through safe observation capacity, the largest target reach attainable by a floor-safe plan at a given value slack. Its frontier is concave and piecewise linear, with initial slope given by the floor's shadow price. When that reach converts fully to reveal and parent flow is non-bottleneck, matching local bounds make the hands required for conditional-probability half-width $\varepsilon$ inversely proportional, up to logarithms, to capacity, opponent continuation mass, and $\varepsilon^2$. Safe Active De-censoring (SAD) combines public screening, an independent reveal batch, and robust deployment; a max-min safe audit gives positive joint reveal rate to every coordinate in a finite library-covered target set, including public-null deviations. With $10^6$ hands, SAD raises the river over-fold certified gain from $0.485$ to $0.692$ and improves both gains over public-only collection on all three deviations (Holm-adjusted paired $p\le0.012$), while selecting no control target. On a fixed-board public twin, a disjoint audit-refit-deploy loop detects all $30$ simulation seeds and no control seed, certifying absolute value $0.655$ (95% confidence-interval half-width $0.008$). Every floor-constrained probe and response passes a floor audit.

cs.GT↗

arXiv2026

Multi-Knob Switchable Chiral Superconductivity Quartet in Rhombohedral Graphene

Chiral superconductors break orbital time-reversal symmetry and may host topological quasiparticles with non-Abelian statistics. In rhombohedral graphene, superconductivity develops from a spin-valley-polarized quarter-metal (QM) parent state and features unique magnetic hysteresis of resistance that indicates orbital time-reversal-symmetry-breaking. Exploring and controlling the full spin-valley flavors of such superconductivity could enable novel superconducting and topological devices, but have remained unexplored. Here we report transport measurements on rhombohedral hexalayer graphene (R6G), which reveal a new superconducting state (SCH) that is induced by an out-of-plane magnetic field, in addition to chiral superconductivity (CSC) similar to those observed in thinner layers. This SCH state emerges above 0.8 T, persists up to 1.6 T and can be switched on/off by magnetic field $H_\perp$, carrier density $n$, and gate displacement field $D$. Quantum oscillations and anomalous Hall measurements show that SCH stems from a field-induced quarter-metal (QM$'$) parent phase, which carries orbital magnetization opposite to that of the zero-field QM. Across the full $(n, D, H_\perp)$ parameter space, superconductivity can be realized from all four spin-valley isospin flavors, establishing a switchable chiral-superconductor quartet in R6G. We interpret the parent-state switching as arising from competition between a Kane-Mele-like spin-valley splitting and magnetic-field coupling to spin-valley-dependent magnetic moments. Our work establishes rhombohedral graphene as a multi-knob platform for different isospin-polarized superconductivities, which enables programmable superconducting networks with possible Majorana modes along domain walls.

cond-mat.supr-con↗

arXiv2026

TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices

Under the trend of multi-waveform coexistence in 6G IoT, intelligent receivers must first identify physical-layer waveform types before performing correct demodulation and resource scheduling. However, existing signal identification research largely focuses on symbol-level modulation classification. Research directly targeting physical-layer waveform types (e.g., OFDM, OTFS, LoRa) is not only extremely scarce but also heavily reliant on deep neural networks and complex time-frequency transforms, making deployment on resource-constrained terminals difficult. Symbol modulation classification methods themselves cannot circumvent the prerequisite of ``waveform identification first.'' To address this dual gap, we propose an ultra-lightweight waveform classification framework based on time-frequency multidimensional features with a cooperative Z-test tree (ZTree). The framework employs low-complexity time-domain feature extraction, and the classification backend adopts a ZTree optimized by Z-statistical testing, which uses hypothesis testing confidence to automatically control decision tree splitting and size, ensuring efficient execution on resource-limited processors. Tested on ten 6G candidate waveforms including OFDM, OTFS, DSSS, LoRa, and NB-IoT, the method achieves 99.5\% average accuracy under AWGN and 87.4\% under TDL-C multipath channels, with main confusion between OTFS and LoRa. Implemented in C on an x86 platform, single inference latency is under 4~ms. To the best of our knowledge, this is the first work achieving real-time recognition of ten IoT waveform types. Future work will target deployment acceleration on embedded MCUs. Code and dataset are open-sourced at: https://github.com/Einstein-sworder/IoT-wave.

eess.SP↗

arXiv2026

Tunable interplay of orbital and spin magnetization in trigonal tellurium

Orbital effects, despite their fundamental significance and potential to engender novel physical phenomena and enable new applications, have long been underexplored compared to their spin counterparts. Recently, surging interest in the orbital degree of freedom has led to the discovery of a plethora of orbital-related effects, underscoring the need for a deeper understanding of their roles in quantum materials. Here, we report systematic experimental evidence consistent with orbital magnetization and spontaneous rotational symmetry breaking in trigonal Tellurium, an elemental semiconductor with a unique helical crystal structure that serves as a natural platform for investigating orbital effects. Detailed angular dependent linear and nonlinear magnetotransport measurements, supported by symmetry-guided Boltzmann transport analysis, support the interpretation of coexistence of current-induced spin polarization and orbital magnetization. With the goal of disentangling the interplay between spin and orbital degrees of freedom through electrostatic gating, this work establishes a general framework for understanding orbital magnetization in chiral crystals and beyond, paving the way for its utilization in orbitronics and spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning

Outcome-rewarded Large Language Models (LLMs) have demonstrated remarkable success in mathematical problem-solving. However, this success often masks a critical issue: models frequently achieve correct answers through fundamentally unsound reasoning processes, a phenomenon indicative of reward hacking. We introduce MathOlympiadEval, a new dataset with fine-grained annotations, which reveals a significant gap between LLMs' answer correctness and their low process correctness. Existing automated methods like LLM-as-a-judge struggle to reliably detect these reasoning flaws. To address this, we propose ParaStepVerifier, a novel methodology for meticulous, step-by-step verification of mathematical solutions. ParaStepVerifier identifies incorrect reasoning steps. Empirical results demonstrate that ParaStepVerifier substantially improves the accuracy of identifying flawed solutions compared to baselines, especially for complex, multi-step problems. This offers a more robust path towards evaluating and training LLMs with genuine mathematical reasoning.

cs.CL↗

arXiv2024

Your Graph Recommender is Provably a Single-view Graph Contrastive Learning

Graph recommender (GR) is a type of graph neural network (GNNs) encoder that is customized for extracting information from the user-item interaction graph. Due to its strong performance on the recommendation task, GR has gained significant attention recently. Graph contrastive learning (GCL) is also a popular research direction that aims to learn, often unsupervised, GNNs with certain contrastive objectives. As a general graph representation learning method, GCLs have been widely adopted with the supervised recommendation loss for joint training of GRs. Despite the intersection of GR and GCL research, theoretical understanding of the relationship between the two fields is surprisingly sparse. This vacancy inevitably leads to inefficient scientific research. In this paper, we aim to bridge the gap between the field of GR and GCL from the perspective of encoders and loss functions. With mild assumptions, we theoretically show an astonishing fact that graph recommender is equivalent to a commonly-used single-view graph contrastive model. Specifically, we find that (1) the classic encoder in GR is essentially a linear graph convolutional network with one-hot inputs, and (2) the loss function in GR is well bounded by a single-view GCL loss with certain hyperparameters. The first observation enables us to explain crucial designs of GR models, e.g., the removal of self-loop and nonlinearity. And the second finding can easily prompt many cross-field research directions. We empirically show a remarkable result that the recommendation loss and the GCL loss can be used interchangeably. The fact that we can train GR models solely with the GCL loss is particularly insightful, since before this work, GCLs were typically viewed as unsupervised methods that need fine-tuning. We also discuss some potential future works inspired by our theory.

cs.LG↗

arXiv2023

A Surface Acoustic Wave based Single Photon Shifter for Solid-state Sources

Controlling the frequency of nonclassical light is indispensable for implementing quantum computation, communication and bridging various quantum systems. However, frequency-shift devices for solid state single-photon sources that are easy to integrate are practically absent. Here, we propose an integrated single-photon frequency shifter based on acousto-optic modulation. The device consists of two Interdigital Transducers (IDTs) for surface acoustic wave (SAW) generation and a silicon waveguide periodically placed at the nodes the SAW to increase the interaction length. The Vπ*L of the device is 1.2v.cm. Under 133.2MHz driving frequency and 10 volt driving voltage, a shift up to 65.7GHz is achieved with near unity conversion efficiency. Our results demonstrate the feasibility of on-chip deterministic quantum spectral control in constructing hybrid quantum networks.

physics.optics↗

arXiv2023

CoGANPPIS: A Coevolution-enhanced Global Attention Neural Network for Protein-Protein Interaction Site Prediction

Protein-protein interactions are of great importance in biochemical processes. Accurate prediction of protein-protein interaction sites (PPIs) is crucial for our understanding of biological mechanism. Although numerous approaches have been developed recently and achieved gratifying results, there are still two limitations: (1) Most existing models have excavated a number of useful input features, but failed to take coevolutionary features into account, which could provide clues for inter-residue relationships; (2) The attention-based models only allocate attention weights for neighboring residues, instead of doing it globally, which may limit the model's prediction performance since some residues being far away from the target residues might also matter. We propose a coevolution-enhanced global attention neural network, a sequence-based deep learning model for PPIs prediction, called CoGANPPIS. Specifically, CoGANPPIS utilizes three layers in parallel for feature extraction: (1) Local-level representation aggregation layer, which aggregates the neighboring residues' features as the local feature representation; (2) Global-level representation learning layer, which employs a novel coevolution-enhanced global attention mechanism to allocate attention weights to all residues on the same protein sequences; (3) Coevolutionary information learning layer, which applies CNN & pooling to coevolutionary information to obtain the coevolutionary profile representation. Then, the three outputs are concatenated and passed into several fully connected layers for the final prediction. Extensive experiments on two benchmark datasets have been conducted, demonstrating that our proposed model achieves the state-of-the-art performance.

q-bio.QM↗

arXiv2021

Long-lived electron spin coherence in Ga-doped crystals at room temperature

Electron spin dynamics are studied in Ga-doped ZnO single crystals by time-resolved Faraday and Kerr rotation spectroscopies. Long-lived spin coherence with two dephasing processes is discovered where the characteristic time is up to 5.2 ns at room temperature. Through the dependence measurements of laser wavelength and temperature, the room-temperature long-lived spin signal is attributed to localized electrons. The spin dephasing (relaxation) processes are independent of transverse (longitudinal) magnetic fields, indicating the spin dephasing not resulting from the g-factor inhomogeneity and electron-nuclear hyperfine interaction. It reveals that the two spin dephasing processes originate from two types of localized electrons, both of which are dominated by the anisotropic exchange Dzyaloshinskii-Moriya interaction between adjacent localized electrons.

physics.chem-ph↗

arXiv2019

Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition

It is an effective way that improves the performance of the existing Automatic Speech Recognition (ASR) systems by retraining with more and more new training data in the target domain. Recently, Deep Neural Network (DNN) has become a successful model in the ASR field. In the training process of the DNN based methods, a back propagation of error between the transcription and the corresponding annotated text is used to update and optimize the parameters. Thus, the parameters are more influenced by the training samples with a big propagation error than the samples with a small one. In this paper, we define the samples with significant error as the hard samples and try to improve the performance of the ASR system by adding many of them. Unfortunately, the hard samples are sparse in the training data of the target domain, and manually label them is expensive. Therefore, we propose a hard samples mining method based on an enhanced deep multiple instance learning, which can find the hard samples from unlabeled training data by using a small subset of the dataset with manual labeling in the target domain. We applied our method to an End2End ASR task and obtained the best performance.

cs.SD↗

arXiv2019

A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition

Recently, data-driven based Automatic Speech Recognition (ASR) systems have achieved state-of-the-art results. And transfer learning is often used when those existing systems are adapted to the target domain, e.g., fine-tuning, retraining. However, in the processes, the system parameters may well deviate too much from the previously learned parameters. Thus, it is difficult for the system training process to learn knowledge from target domains meanwhile not forgetting knowledge from the previous learning process, which is called as catastrophic forgetting (CF). In this paper, we attempt to solve the CF problem with the lifelong learning and propose a novel multi-task learning (MTL) training framework for ASR. It considers reserving original knowledge and learning new knowledge as two independent tasks, respectively. On the one hand, we constrain the new parameters not to deviate too far from the original parameters and punish the new system when forgetting original knowledge. On the other hand, we force the new system to solve new knowledge quickly. Then, a MTL mechanism is employed to get the balance between the two tasks. We applied our method to an End2End ASR task and obtained the best performance in both target and original datasets.

cs.SD↗