Recovering Structured Probability Matrices

We consider the problem of accurately recovering a matrix B of size M by M , which represents a probability distribution over M2 outcomes, given access to an observed matrix of "counts" generated by taking independent samples from the distribution B. How can structural properties of the underlying matrix B be leveraged to yield computationally efficient and information theoretically optimal reconstruction algorithms? When can accurate reconstruction be accomplished in the sparse data regime? This basic problem lies at the core of a number of questions that are currently being considered by different communities, including building recommendation systems and collaborative filtering in the sparse data regime, community detection in sparse random graphs, learning structured models such as topic models or hidden Markov models, and the efforts from the natural language processing community to compute "word embeddings". Our results apply to the setting where B has a low rank structure. For this setting, we propose an efficient algorithm that accurately recovers the underlying M by M matrix using Theta(M) samples. This result easily translates to Theta(M) sample algorithms for learning topic models and learning hidden Markov Models. These linear sample complexities are optimal, up to constant factors, in an extremely strong sense: even testing basic properties of the underlying matrix (such as whether it has rank 1 or 2) requires Omega(M) samples. We provide an even stronger lower bound where distinguishing whether a sequence of observations were drawn from the uniform distribution over M observations versus being generated by an HMM with two hidden states requires Omega(M) observations. This precludes sublinear-sample hypothesis tests for basic properties, such as identity or uniformity, as well as sublinear sample estimators for quantities such as the entropy rate of HMMs

THE EFFECTS OF HYDROGEN SULFIDE (H2S) ON THE RELEASE OF INFLAMMATORY MEDIATORS FROM MOUSE MACROPHAGES

Ph.DDOCTOR OF PHILOSOPH

Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems

Source localization is the inverse problem of graph information dissemination and has broad practical applications. However, the inherent intricacy and uncertainty in information dissemination pose significant challenges, and the ill-posed nature of the source localization problem further exacerbates these challenges. Recently, deep generative models, particularly diffusion models inspired by classical non-equilibrium thermodynamics, have made significant progress. While diffusion models have proven to be powerful in solving inverse problems and producing high-quality reconstructions, applying them directly to the source localization is infeasible for two reasons. Firstly, it is impossible to calculate the posterior disseminated results on a large-scale network for iterative denoising sampling, which would incur enormous computational costs. Secondly, in the existing methods for this field, the training data itself are ill-posed (many-to-one); thus simply transferring the diffusion model would only lead to local optima. To address these challenges, we propose a two-stage optimization framework, the source localization denoising diffusion model (SL-Diff). In the coarse stage, we devise the source proximity degrees as the supervised signals to generate coarse-grained source predictions. This aims to efficiently initialize the next stage, significantly reducing its convergence time and calibrating the convergence process. Furthermore, the introduction of cascade temporal information in this training method transforms the many-to-one mapping relationship into a one-to-one relationship, perfectly addressing the ill-posed problem. In the fine stage, we design a diffusion model for the graph inverse problem that can quantify the uncertainty in the dissemination. The proposed SL-Diff yields excellent prediction results within a reasonable sampling time at extensive experiments

A novel autoregressive rainflow-integrated moving average modeling method for the accurate state of health prediction of lithium-ion batteries.

The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of health in real-time to predict and improve accuracy. This article selects the ternary lithium-ion battery as the research object. Based on the cycle method and data-driven idea, the improved rain flow counting algorithm is combined with the autoregressive integrated moving average model prediction model to propose a new prediction for the battery state of health method. Experiments are carried out with dynamic stress test and cycle conditions, and a confidence interval method is proposed to fit the error range. Compared with the actual value, the method proposed in this paper has a maximum error of 5.3160% under dynamic stress test conditions, a maximum error of 5.4517% when the state of charge of the cyclic conditions is used as a sample, and a maximum error of 0.7949% when the state of health under cyclic conditions is used as a sample

Giant coercivity induced by perpendicular anisotropy in Mn2.42Fe0.58Sn single crystals

We report the discovery of a giant out-of-plane coercivity in the Fe-doped Mn3Sn single crystals. The compound of Mn2.42Fe0.58Sn exhibits a series of magnetic transitions accompanying with large magnetic anisotropy and electric transport properties. Compared with the ab-plane easy axis in Mn3Sn, it switches to the c-axis in Mn2.42Fe0.58Sn, producing a sufficiently large uniaxial anisotropy. At 2 K, a giant out-of-plane coercivity (Hc) up to 3 T was observed, which originates from the large uniaxial magnetocrystalline anisotropy. The modified Sucksmith-Thompson method was used to determine the values of the second-order and the fourth-order magnetocrystalline anisotropy constants K1 and K2, resulting in values of 6.0 * 104 J/m3 and 4.1 * 105 J/m3 at 2 K, respectively. Even though the Curie temperature (TC) of 200 K for Mn2.42Fe0.58Sn is not high enough for direct application, our research presents a valuable case study of a typical uniaxial anisotropy material

Novel adaptive stability enhancement strategy for power systems based on deep reinforcement learning

As the access rate of wind energy in a power system has significantly increased, stabilizing the power system has become challenging. Among these challenges, low-frequency oscillation is one of the most harmful problems, effectively resolved by adding a damping controller according to the relevant properties of the low-frequency oscillation. However, the controller often fails to adapt to the constantly changing wind energy system owing to the lack of a targeted dynamic change strategy. Thus, to address this issue, an adaptive stabilization strategy that uses a static var compensator with an additional damping controller structure is proposed. Specifically, the entire power system is equivalently represented as a generalized regression neural network, with a deep reinforcement learning algorithm called soft actor-critic introduced to train the agent based on the generalized regression neural network model. After the training process, the agent can provide additional efficient static var compensator damping controller parameters under different operating conditions, vastly improving the system stability. Simulation results verify the improved performance using the proposed strategy compared to other optimization methods, regardless of whether the low-frequency oscillations were suppressed in the time or frequency domains

One-pot fabrication of magnetic fluorinated carbon nanotubes adsorbent for efficient extraction of perfluoroalkyl carboxylic acids and perfluoroalkyl sulfonic acids in environmental water samples

Abstract(#br)Efficient extraction of perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs) is challenging due to their highly fluorinated property. Based on the particular characters of PFCAs and PFSAs, a new type of magnetic fluorinated carbon nanotubes adsorbent (MFCA) for magnetic solid phase extraction (MSPE) was fabricated facilely using one-pot hydrothermal approach. The morphology, structure and magnetic properties of the prepared MFCA were investigated by Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy and vibrating sample magnetometry. It was observed that the resultant adsorbent possessed satisfactory superparamagnetism and saturation magnetism. Furthermore, the MFCA exhibited excellent enrichment performance for target PFCAs and PFSAs by means of fluorous-fluorous, hydrophobic and hydrogen bonding interactions. Under the most favorable preparation and extraction conditions, the proposed MFCA/MSPE was combined with high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) to quantify ultra trace target analytes in environmental water samples. The limits of detection (S/N = 3) of PFCAs and PFSAs were 0.010–0.036 ng/L and 0.024–0.50 ng/L, respectively. In addition, the introduced approach also displayed other features such as quick extraction procedure, wide linear dynamic ranges, excellent method precision and eco-friendliness. Finally, the concentrations of PFCAs and PFSAs in tap, river, lake and waste water samples were successfully measured by isotope internal standard calibration curve method

Optimized Placement of Onshore Wind Farms Considering Topography

As the scale of onshore wind farms are increasing, the influence of wake behavior on power production becomes increasingly significant. Wind turbines sittings in onshore wind farms should take terrain into consideration including height change and slope curvature. However, optimized wind turbine (WT) placement for onshore wind farms considering both topographic amplitude and wake interaction is realistic. In this paper, an approach for optimized placement of onshore wind farms considering the topography as well as the wake effect is proposed. Based on minimizing the levelized production cost (LPC), the placement of WTs was optimized considering topography and the effect of this on WTs interactions. The results indicated that the proposed method was effective for finding the optimized layout for uneven onshore wind farms. The optimization method is applicable for optimized placement of onshore wind farms and can be extended to different topographic conditions