Novel acceleration approaches of accurate and efficient modeling of high speed interconnects in layered media

Accurate modeling of interconnect structures is an important issue in modern high frequency circuit and chip design; such as the accurate computation of the frequency dependent internal impedance of interconnect structures, like wires and conducting strips, and the accurate and efficient electromagnetic (EM) modeling for shielded microstrip structures, especially in multilayered medium. In the first part of this dissertation, a rigorous volume integral equation (VIE) is developed for the current distributions over two-dimensional conducting cylinders. For very low frequencies, it can be reduced to the widely-used quasi-static approximation. The different VIEs, surface integral equation (SIE), and partial differential equation (PDE) with Dirichlet boundary condition method are used to calculate the current distributions. The VIE with quasi-static approximation for good conductor is not accurate enough for the current distributions as there is a constant ratio between the results calculated from the quasi-static VIE and SIE. Two more leading terms from the Hankel function have been added into the integral kernel to solve this problem. We also calculate the internal impedance by using the different VIEs and the PDE with Dirichlet boundary condition method. The different results between VIE and PDE methods are due to the different boundary conditions. In the second part of this dissertation, the novel acceleration approaches for spectral domain approach (SDA) over single layer substrate and for spectral domain immitance approach (SDIA) over multilayered substrates have been developed using one of the most promising extrapolation method--the Levin\u27s transformation. It avoids the leading term extraction of the Green\u27s functions and the Bessel\u27s functions (basis functions) by recasting the summation kernel to a suitable form which can be applied in the Levin\u27s transformation. The extrapolation delay has been introduced to successfully apply the Levin\u27s transformation. Accurate results have been obtained for the propagation constant by only using twenty to thirty terms. The final accuracy could be further improved if only the first leading term added with the Levin\u27s transformation. The new techniques match with or are even better than other acceleration techniques with high order leading term extraction. The two-dimensional PMCHWT formulation was developed from internal and external equivalent problems, along with the spatial and spectral domain dyadic Green\u27s functions to deal with the arbitrary cross section and finite conductivity of multiple metal lines over multilayered substrates. The pulse and triangular basis were chosen to be applied in the Galerkin method. The matrix elements were calculated from spatial domain integration in internal equivalent problem, while in external equivalent problem we need to transfer the spatial domain integration into spectral domain summation

A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.Comment: 8 pages, 5 figures, 4 table

Electroweak corrections to Higgs boson production via Z Z fusion at the future LHeC

An important mechanism for production of the Higgs boson at the prospective Large Hadron-electron Collider (LHeC) is via neutral current (NC) weak boson fusion (WBF) processes. Aside from its role in measurements of Higgs couplings within the standard model, this production mode is particularly useful in searchings of Higgs decays into invisble particles in various models for the Higg portal dark matter. In this work we compute the electroweak corrections for the NC WBF at the LHeC up to the 1-loop level. For a center-of-mass energy of 1.98 TeV, the magnitudes of the relative corrections for the total cross section at next-to-leading (NLO) order are respectively 8% and 17%, in the two renormalization schemes we use. The NLO terms also distort various distributions (notably, those for Higgs and electron observables) computed at the leading order. Along with our previous treatment of the charge current processes, this paper completes the calulation of the NLO EW effects for the dominant Higgs production modes at the LHeC.Comment: 14 pages, 10 figures. arXiv admin note: text overlap with arXiv:2207.1451

Layout Sequence Prediction From Noisy Mobile Modality

Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. Nevertheless, real-world situations often involve obstructed cameras, missed objects, or objects out of sight due to environmental factors, leading to incomplete or noisy trajectories. To overcome these limitations, we propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories. LTrajDiff utilizes sensor data from mobile phones to surmount out-of-sight constraints, albeit introducing new challenges such as modality fusion, noisy data, and the absence of spatial layout and object size information. We employ a denoising diffusion model to predict precise layout sequences from noisy mobile data using a coarse-to-fine diffusion strategy, incorporating the RMS, Siamese Masked Encoding Module, and MFM. Our model predicts layout sequences by implicitly inferring object size and projection status from a single reference timestamp or significantly obstructed sequences. Achieving SOTA results in randomly obstructed experiments and extremely short input experiments, our model illustrates the effectiveness of leveraging noisy mobile data. In summary, our approach offers a promising solution to the challenges faced by layout sequence and trajectory prediction models in real-world settings, paving the way for utilizing sensor data from mobile phones to accurately predict pedestrian bounding box trajectories. To the best of our knowledge, this is the first work that addresses severely obstructed and extremely short layout sequences by combining vision with noisy mobile modality, making it the pioneering work in the field of layout sequence trajectory prediction.Comment: In Proceedings of the 31st ACM International Conference on Multimedia 2023 (MM 23

Poly[(μ5-5-carboxylatotetrahydrofuran-2,3,4-tricarboxylic acid)sodium]

The search for the novel metal-organic frameworks (MOFs) materials using tetra­hydro­furan-2,3,4,5-tetra­carboxylic acid (THFTCA) as a versatile multi-carboxyl ligand, lead to the synthesis and the structure determination of the title compound, [Na(H3THFTCA)] or [Na(C8H7O9)]n, which was obtained by a solution reaction at room temperature. The ligand is mono-deprotonated, coordinating five sodium ions through one furan oxygen atom and six carboxyl oxygen atoms. The sodium ion exhibits a distorted penta­gonal-bipyramidal NaO7 geometry consisting of seven O atoms derived from five surrounding ligands. Two adjacent pentagonal bipyramids share an O—O edge, forming a dinuclear sodium cluster. Finally, these clusters are effectively linked by the carboxyl groups of THFTCA ligands, forming a firm metal organic framework and O—H⋯O hydrogen bonds contribute to the crystal packing