arXiv subjects

Yinan Hu

Publications and source records attributed to Yinan Hu.

15 recordsLinked to original sources

arXiv2026

Constrained Density Estimation via Optimal Transport

A novel framework for density estimation under expectation constraints is proposed. The framework minimizes the Wasserstein distance between the estimated density and a prior, subject to the constraints that the expected value of a set of functions adopts or exceeds given values. The framework is generalized to include regularization inequalities to mitigate the artifacts in the target measure. An annealing-like algorithm is developed to address non-smooth constraints, with its effectiveness demonstrated through both synthetic and proof-of-concept real world examples in finance.

stat.ML↗

arXiv2024

Understanding Police Force Resource Allocation using Adversarial Optimal Transport with Incomplete Information

Adversarial optimal transport has been proven useful as a mathematical formulation to model resource allocation problems to maximize the efficiency of transportation with an adversary, who modifies the data. It is often the case, however, that only the adversary knows which nodes are malicious and which are not. In this paper we formulate the problem of seeking adversarial optimal transport into Bayesian games. We construct the concept of Bayesian equilibrium and design a distributed algorithm that achieve those equilibria, making our model applicable to large-scale networks. Keywords: game theory, crime control, Markov games

cs.GT↗

arXiv2023

Detection in Human-sensor Systems under Quantum Prospect Theory using Bayesian Persuasion Frameworks

Human-sensor systems have a wide range of applications in fields such as robotics, healthcare, and finance. These systems utilize sensors to observe the true state of nature and generate strategically designed signals, aiding humans in making more accurate decisions regarding the state of nature. We adopt a Bayesian persuasion framework that is integrated with quantum prospect theories. In this framework, we develop a detection scheme where humans aim to determine the true state by observing the realization of quantum states from the sensor. We derive the optimal signaling rule for the sensor and the optimal decision rule for humans. We discover that this scenario violates the total law of probability. Furthermore, we examine how the concepts of rationality can influence the human detection performance and the signaling rules employed by the sensor.

eess.SP↗

arXiv2023

Game of Travesty: Decoy-based Psychological Cyber Deception for Proactive Human Agents

The concept of cyber deception has been receiving emerging attention. The development of cyber defensive deception techniques requires interdisciplinary work, among which cognitive science plays an important role. In this work, we adopt a signaling game framework between a defender and a human agent to develop a cyber defensive deception protocol that takes advantage of the cognitive biases of human decision-making using quantum decision theory to combat insider attacks (IA). The defender deceives an inside human attacker by luring him to access decoy sensors via generators producing perceptions of classical signals to manipulate the human attacker's psychological state of mind. Our results reveal that even without changing the classical traffic data, strategically designed generators can result in a worse performance for defending against insider attackers in identifying decoys than the ones in the deceptive scheme without generators, which generate random information based on input signals. The proposed framework leads to fundamental theories in designing more effective signaling schemes.

cs.CR↗

arXiv2023

A Real-time Non-contact Localization Method for Faulty Electric Energy Storage Components using Highly Sensitive Magnetometers

With the wide application of electric energy storage component arrays, such as battery arrays, capacitor arrays, inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot meet the needs of non-contact and real-time diagnosis for faulty components inside these massive arrays. To solve this problem, this paper proposes a new method based on the beamforming spatial filtering algorithm to precisely locate the faulty components within the arrays in real-time. The method uses highly sensitive magnetometers to collect the magnetic signals from energy storage component arrays, without damaging or even contacting any component. The experimental results demonstrate the potential of the proposed method in securing energy storage component arrays. Within an imaging area of 80 mm $\times$ 80 mm, the one faulty component out of nine total components can be localized with an accuracy of 0.72 mm for capacitor arrays and 1.60 mm for battery arrays.

physics.app-ph↗

arXiv2023

Quantum Man-in-the-middle Attacks: a Game-theoretic Approach with Applications to Radars

The detection and discrimination of quantum states serve a crucial role in quantum signal processing, a discipline that studies methods and techniques to process signals that obey the quantum mechanics frameworks. However, just like classical detection, evasive behaviors also exist in quantum detection. In this paper, we formulate an adversarial quantum detection scenario where the detector is passive and does not know the quantum states have been distorted by an attacker. We compare the performance of a passive detector with the one of a non-adversarial detector to demonstrate how evasive behaviors can undermine the performance of quantum detection. We use a case study of target detection with quantum radars to corroborate our analytical results.

eess.SP↗

arXiv2022

Finitely Repeated Adversarial Quantum Hypothesis Testing

We formulate a passive quantum detector based on a quantum hypothesis testing framework under the setting of finite sample size. In particular, we exploit the fundamental limits of performance of the passive quantum detector asymptotically. Under the assumption that the attacker adopts separable optimal strategies, we derive that the worst-case average error bound converges to zero exponentially in terms of the number of repeated observations, which serves as a variation of quantum Sanov's theorem. We illustrate the general decaying results of miss rate numerically, depicting that the `naive' detector manages to achieve a miss rate and a false alarm rate both exponentially decaying to zero given infinitely many quantum states, although the miss rate decays to zero at a much slower rate than a quantum non-adversarial counterpart. Finally we adopt our formulations upon a case study of detection with quantum radars.

quant-ph↗

arXiv2022

PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization

Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we designed a novel soft prompts architecture coupled with a prompt pre-training plus fine-tuning paradigm that is effective and tunes only extremely light parameters. The soft prompts include continuous input embeddings across an encoder and a decoder to fit the structure of the generation models. Importantly, a novel inner-prompt placed in the text is introduced to capture document-level information. The aim is to devote attention to understanding the document that better prompts the model to generate document-related content. The first step in the summarization procedure is to conduct prompt pre-training with self-supervised pseudo-data. This teaches the model basic summarizing capabilities. The model is then fine-tuned with few-shot examples. Experimental results on the CNN/DailyMail and XSum datasets show that our method, with only 0.1% of the parameters, outperforms full-model tuning where all model parameters are tuned. It also surpasses Prompt Tuning by a large margin and delivers competitive results against Prefix-Tuning with 3% of the parameters.

cs.CL↗

arXiv2022

Game-Theoretic Neyman-Pearson Detection to Combat Strategic Evasion

The security in networked systems depends greatly on recognizing and identifying adversarial behaviors. Traditional detection methods focus on specific categories of attacks and have become inadequate for increasingly stealthy and deceptive attacks that are designed to bypass detection strategically. This work aims to develop a holistic theory to countermeasure such evasive attacks. We focus on extending a fundamental class of statistical-based detection methods based on Neyman-Pearson's (NP) hypothesis testing formulation. We propose game-theoretic frameworks to capture the conflicting relationship between a strategic evasive attacker and an evasion-aware NP detector. By analyzing both the equilibrium behaviors of the attacker and the NP detector, we characterize their performance using Equilibrium Receiver-Operational-Characteristic (EROC) curves. We show that the evasion-aware NP detectors outperform the passive ones in the way that the former can act strategically against the attacker's behavior and adaptively modify their decision rules based on the received messages. In addition, we extend our framework to a sequential setting where the user sends out identically distributed messages. We corroborate the analytical results with a case study of anomaly detection.

cs.CR↗

arXiv2021

Battery characterization via eddy-current imaging with nitrogen-vacancy centers in diamond

Sensitive and accurate diagnostic technologies with magnetic sensors are of great importance for identifying and localizing defects of rechargeable solid batteries in a noninvasive detection. We demonstrate a microwave-free AC magnetometry method with negatively charged NV centers in diamond based on a cross-relaxation feature between NV centers and individual substitutional nitrogen (P1) centers occurring at 51.2 mT. We apply the technique to non-destructive solid-state battery imaging. By detecting the eddy-current-induced magnetic field of the battery, we distinguish a defect on the external electrode and identify structural anomalies within the battery body. The achieved spatial resolution is $360\,μ\rm m$. The maximum magnetic field and phase shift generated by the battery at the modulation frequency of 5 kHz are estimated as 0.04 mT and 0.03 rad respectively.

physics.app-ph↗

arXiv2021

Photochemically induced dynamic nuclear polarization of heteronuclear singlet order

Photochemically induced dynamic nuclear polarization (photo-CIDNP) is a method to hyperpolarize nuclear spins using light. In most cases, CIDNP experiments are performed at a high magnetic field and the sample is irradiated by the light inside a nuclear magnetic resonance (NMR) spectrometer. Here we demonstrate photo-CIDNP hyperpolarization generated in the Earth magnetic field and under zero- to ultralow field (ZULF) conditions. Irradiating a sample for several seconds with inexpensive light-emitting diodes produces a strong hyperpolarization of 1H and 13C nuclear spins enhancing the NMR signals several hundred times. The hyperpolarized spin states at the Earth field and in ZULF are different. In the latter case, the state corresponds to the singlet order between scalar-coupled 1H-13C nuclear spins. This state has a longer lifetime than the state hyperpolarized at Earth field. The method is simple and cost-efficient and should be applicable to many molecular systems known to exhibit photo-CIDNP, including amino acids and nucleotides.

physics.chem-ph↗

arXiv2020

Rapid online solid-state battery diagnostics with optically pumped magnetometers

Solid state battery technology is motivated by the desire to deliver flexible power storage in a safe and efficient manner. The increasingly widespread use of batteries from mass-production facilities highlights the need for a rapid and sensitive diagnostic for identifying battery defects. We demonstrate the use of atomic magnetometry to measure the magnetic fields around miniature solid-state battery cells. These fields encode information about battery manufacturing defects, state of charge, impurities, or can provide important insights into ageing processes. Compared with SQUID-based magnetometry, the availability of atomic magnetometers, however, highlights the possibility for a low-cost, portable, and flexible implementation of battery quality-control and characterization technology.

physics.app-ph↗

arXiv2020

Biomagnetic signals recorded during transcranial magnetic stimulation (TMS)-evoked peripheral muscular activity

Objective: We present magnetomyograms (MMG) of TMS-evoked movement in a human hand, together with a simultaneous surface electromyograph (EMG) and electroencephalograph (EEG) data. Approach: We combined TMS with non-contact magnetic detection of TMS-evoked muscle activity in peripheral limbs to explore a new diagnostic modality that enhances the utility of TMS as a clinical tool by leveraging technological advances in magnetometry. We recorded measurements in a regular hospital room using an array of optically pumped magnetometers (OPM) inside a portable shield that encompasses only the forearm and hand of the subject. Main Results: The biomagnetic signals recorded in the MMG provide detailed spatial and temporal information that is complementary to that of the electric signal channels. Moreover, we identify features in the magnetic recording beyond those of the EMG. Significance: These results validate the viability of MMG recording with a compact OPM based setup in small-sized magnetic shielding, and provide proof-of-principle for a non-contact data channel for detection and analysis of TMS-evoked muscle activity from peripheral limbs.

q-bio.NC↗

arXiv2019

Zero- to Ultralow-Field Nuclear Magnetic Resonance $J$-Spectroscopy with Commercial Atomic Magnetometers

Zero- to ultralow-field nuclear magnetic resonance (ZULF NMR) is an alternative spectroscopic method to high-field NMR, in which samples are studied in the absence of a large magnetic field. Unfortunately, there is a large barrier to entry for many groups, because operating the optical magnetometers needed for signal detection requires some expertise in atomic physics and optics. Commercially available magnetometers offer a solution to this problem. Here we describe a simple ZULF NMR configuration employing commercial magnetometers, and demonstrate sufficient functionality to measure samples with nuclear spins prepolarized in a permanent magnet or initialized using parahydrogen. This opens the possibility for other groups to use ZULF NMR, which provides a means to study complex materials without magnetic susceptibility-induced line broadening, and to observe samples through conductive materials.

physics.chem-ph↗

arXiv2019

Battery Diagnostics with Sensitive Magnetometry

The ever-increasing demand for high-capacity rechargeable batteries highlights the need for sensitive and accurate diagnostic technology for determining the state of a cell, for identifying and localizing defects, or for sensing capacity loss mechanisms. Here, we demonstrate the use of atomic magnetometry to map the weak induced magnetic fields around a Li-ion battery cell as a function of state of charge and upon introducing mechanical defects. These measurements provide maps of the magnetic susceptibility of the cell, which follow trends characteristic for the battery materials under study upon discharge. In addition, the measurements reveal hitherto unknown long time-scale transient internal current effects, which were particularly pronounced in the overdischarged regime. The diagnostic power of this technique is promising for the assessment of cells in research, quality control, or during operation, and could help uncover details of charge storage and failure processes in cells.

physics.app-ph↗