arXiv subjects

Jiawen Jiang

Publications and source records attributed to Jiawen Jiang.

5 recordsLinked to original sources

arXiv2026

Stochasticity Is Not the Hard Part: Reduction and Complexity in Instructional Sequencing over Prerequisite DAGs

When a student must learn concepts connected by prerequisite dependencies, when does the order of instruction matter, and what does it cost to find the best one? We study instructional sequencing as a stochastic shortest-path problem in which attempting a concept succeeds with a state-dependent probability and failure leaves the learner state unchanged. We first prove that this stochasticity can be eliminated exactly: the problem collapses to a deterministic shortest-path problem on the lattice of prerequisite order ideals, preserving optimal values and actions. The collapse removes stochastic complexity but not combinatorial complexity: optimal sequencing remains NP-hard -- via reduction from feedback arc set in tournaments -- even with no prerequisite edges, unit costs, uniform binary nonnegative transfer, and success probabilities at least $1/2$. Hardness is not uniform: when realizable transfer preferences remain jointly acyclic with the prerequisites, any topological order of the residual joint graph is optimal, and fixed prerequisite width yields polynomial-time exact dynamic programming. A computable diagnostic, $mΔ$, bounds the value of sequencing before optimization. On 70,893 interactions from an introductory CS course, the diagnostic certifies a doubly easy regime -- little value to optimize and little space to search -- while constructed transfer instances realize the challenging regime, where myopic sequencing suffers large regret yet exact A* with a consistent heuristic expands only linearly many states on that family.

cs.AI↗

arXiv2024

Universal and robust dynamic decoupling controls for zero-field magnetometry by using molecular clock sensors

Color centers in diamond and silicon carbide (SiC), and molecular spins through a host matrix control are promising for nanoscale quantum sensing because they can be optically addressable, coherently controllable, and placed proximate to the targets. However, large transverse zero-field splitting (ZFS) is often inevitable due to their intrinsic symmetry and/or the high local strains of the host matrix. Although spin coherence can be extended due to magnetic noise-insensitive clock transitions at a vanishing magnetic field, the eigenstates of these sensors are not sensitive to weak magnetic signals in the linear order. We address this challenge by employing a combination of radio-frequency (RF) field driving along the NV orientation and microwave (MW) dynamic decoupling pulse sequences. RF driving can effectively mitigate the transverse ZFS effect and enhance the NV center's sensitivity to AC magnetic field signals. This combination not only suppresses environmental noise but also enables quantum frequency mixing between the transverse ZFS and the signal. It also offers the potential to detect weak AC signals at intermediate and high frequencies with high resolution, a task difficult to achieve using conventional methods.

quant-ph↗

arXiv2024

Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba

Traditional survival analysis methods often struggle with complex time-dependent data,failing to capture and interpret dynamic characteristics adequately.This study aims to evaluate the performance of three long-sequence models,LSTM,Transformer,and Mamba,in analyzing recurrence event data and integrating them with the Cox proportional hazards model.This study integrates the advantages of deep learning models for handling long-sequence data with the Cox proportional hazards model to enhance the performance in analyzing recurrent events with dynamic time information.Additionally,this study compares the ability of different models to extract and utilize features from time-dependent clinical recurrence data.The LSTM-Cox model outperformed both the Transformer-Cox and Mamba-Cox models in prediction accuracy and model fit,achieving a Concordance index of up to 0.90 on the test set.Significant predictors of bladder cancer recurrence,such as treatment stop time,maximum tumor size at recurrence and recurrence frequency,were identified.The LSTM-Cox model aligned well with clinical outcomes,effectively distinguishing between high-risk and low-risk patient groups.This study demonstrates that the LSTM-Cox model is a robust and efficient method for recurrent data analysis and feature extraction,surpassing newer models like Transformer and Mamba.It offers a practical approach for integrating deep learning technologies into clinical risk prediction systems,thereby improving patient management and treatment outcomes.

cs.LG↗

arXiv2024

Robust external spin hyperpolarization of quadrupolar nuclei enabled by strain

In a theoretical study, we investigate the spin dynamics of interacting nitrogen-vacancy (NV) centers and quadrupolar I = 3/2 nuclear spins, specifically 11B spins in hexagonal boron nitride (h-BN) nanosheets located near the microdiamond surface. We demonstrate the possibility of obtaining external spin-polarization by magnetic-field sweeps across the level anticrossings around zero-field. To achieve this, we leverage crystal strains to establish a polarization transfer mechanism that remains robust against variations in NV orientation, crystal strain inhomogeneity, and electron-nuclear effective couplings. These results pave the way for hyperpolarization of spins in nanomaterials near the diamond surface without experiencing polarization loss to intrinsic nuclear spin-1/2 species, such as 13C and 1H nuclear spins in diamond. The 11B spins in h-BN nanosheets, with their extended relaxation time and large surface area, present a promising alternative for relayed nuclear polarization to the liquid phase and for the development of quantum simulators based on surface nuclear spins.

quant-ph↗

arXiv2022

Selective nuclear-spin interaction based on a dissipatively stabilized nitrogen-vacancy center

Current typical methods to realize nuclear-nuclear quantum gates require a sequence of electronnuclear quantum gates by using dynamical decoupling techniques, which are implemented at low temperature because of short decoherence and relaxation time of the NV spin at room temperature. This limitation could be overcome by using periodical resets of an NV spin as a mediator of interaction between two nuclear spins [Chen, Schwarz, and Plenio, 119, 010801 (2017)]. However, this method works under stringent coupling strengths condition, which makes it not applicable to heteronuclear quantum gate operations. Here we develop this scheme by using radio-frequency (RF) fields to control different nuclear spin species. Periodical resets of the NV center protect the nuclear spins from decoherence and relaxation of the NV spin. RF control provides probability to have highly selective and high fidelity quantum gates between heteronuclear spins as well as detecting nuclear spins by using a nuclear spin sensor under ambient conditions.

quant-ph↗