We investigate impacts of massive neutrino on the cosmic velocity fields, employing high-resolution cosmological N-body simulations provided by the information-optimized CUBE code, where cosmic neutrinos are evolved using collisionless hydrodynamics
and their perturbations can be accurately resolved. In this study we focus, for the first time, on the analysis of massive-neutrino induced suppression effects in various cosmic velocity field components of velocity magnitude, divergence, vorticity and dispersion. By varying the neutrino mass sum $M_ u$ from 0 -- 0.4 eV, the simulations show that, the power spectra of vorticity and dispersion -- exclusively sourced by non-linear structure formation that are affected by massive neutrinos significantly -- are very sensitive to the mass sum, which potentially provide novel signatures in detecting massive neutrinos. Furthermore, using the chi-square statistic, we quantitatively test the sensitivity of the density and velocity power spectra to the neutrino mass sum. Indeed, we find that, the vorticity spectrum has the highest sensitivity, and the null hypothesis of massless neutrinos for both vorticity and dispersion spectra from $M_ u=0.1$ eV can be rejected at high significance. These results demonstrate clearly the importance of peculiar velocity field measurements, in particular of vorticity and dispersion components, in determination of neutrino mass and mass hierarchy.
Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such
as CBCT, to meet the online demands of plan evaluation and adaptation. Deep learning (DL)-based automatic segmentation has gained great success in segmenting planning CT, but its applications to CBCT yielded inferior results due to the low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT segmentation, we propose a registration-guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models. The registration algorithm generates initial contours, which were used as guidance by DL model to obtain accurate final segmentations. We had two implementations the proposed framework--Rig-RgDL (Rig for rigid body) and Def-RgDL (Def for deformable)--with rigid body (RB) registration or deformable image registration (DIR) as the registration algorithm respectively and U-Net as DL model architecture. The two implementations of RgDL framework were trained and evaluated on seven OARs in an institutional clinical Head and Neck (HN) dataset. Compared to the baseline approaches using the registration or the DL alone, RgDL achieved more accurate segmentation, as measured by higher mean Dice similarity coefficients (DSC) and other distance-based metrics. Rig-RgDL achieved a DSC of 84.5% on seven OARs on average, higher than RB or DL alone by 4.5% and 4.7%. The DSC of Def-RgDL is 86.5%, higher than DIR or DL alone by 2.4% and 6.7%. The inference time took by the DL model to generate final segmentations of seven OARs is less than one second in RgDL. The resulting segmentation accuracy and efficiency show the promise of applying RgDL framework for online ART.
Stokes Raman scattering is known to be a particularly robust nonlinearity, occurring in virtually every material, with spectra defined by the material and strengths dependent on the material as well as light intensities. This ubiquity has made it an
indispensable tool in spectroscopy, but also presents itself as a stubborn source of noise or parasitic emission in several applications. Here, we show that orbital angular momentum carrying light beams experiencing spin-orbit interactions can fundamentally alter the selection rules for Raman scattering. This enables tailoring its spectral shape (by over half the Raman shift in a given material) as well as strength (by about 100 times) simply by controlling the topological charge of light, which is a capability of utility across the multitude of applications where modulating Raman scattering is desired.
Purpose: To develop a model to generate volumetric dose distribution from two isodose surfaces (iso-surfaces), and to interactively tune dose distribution by iso-surface dragging. Methods: We model volumetric dose distribution as analytical extension
of two iso-surfaces with the extension variables as distances to iso-surfaces. We built a 3D lookup table (LUT) which are generated based on clinical dose distributions. Two LUT tables store the mean and standard deviation of voxel dose values of clinical doses and binned as distance to 100% iso-surface, reference iso-surface and reference dose level. The process of interactive tuning starts from a given base plan. A user drags iso-surface for a desired carving. Our method responds with tuned dose. The derivation of tuned dose follows two steps. Dose is extended from the two user-desired iso-surfaces (eg.100% and 50%) to the whole patient volume by table lookup, using distances to two iso-surfaces and reference dose level as keys. Then we fine tune the extended dose by a correction strategy utilizing the information of base plan. Results: We validated this method on coplanar VMAT doses of post-operative prostate plans. The LUT was populated by dose distributions of 27 clinical plans. We optimized two plans with different rectum sparing for an independent case to mimic the process of dose tuning. The plan with less rectum sparing is set as base plan. The 50% iso-surface of the more-sparing plan is defined as the desired iso-surface input. The dose output by our method (expansion and correction) agrees with the more-sparing plan obtained by optimization, in terms of gamma (97.2%), DVH and profiles. The overall dose reconstruction time is within two seconds. Conclusion: We developed a distance-to-isosurface based volumetric dose reconstruction method, and applied it to interactive tuning with iso-surface dragging.
The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Tradi
tional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.
Differentiable Architecture Search (DARTS) is a recently proposed neural architecture search (NAS) method based on a differentiable relaxation. Due to its success, numerous variants analyzing and improving parts of the DARTS framework have recently b
een proposed. By considering the problem as a constrained bilevel optimization, we propose and analyze three improvements to architectural weight competition, update scheduling, and regularization towards discretization. First, we introduce a new approach to the activation of architecture weights, which prevents confounding competition within an edge and allows for fair comparison across edges to aid in discretization. Next, we propose a dynamic schedule based on per-minibatch network information to make architecture updates more informed. Finally, we consider two regularizations, based on proximity to discretization and the Alternating Directions Method of Multipliers (ADMM) algorithm, to promote early discretization. Our results show that this new activation scheme reduces final architecture size and the regularizations improve reliability in search results while maintaining comparable performance to state-of-the-art in NAS, especially when used with our new dynamic informed schedule.
Dynamic analysis based on the full-system emulator QEMU is widely used for various purposes. However, it is challenging to run firmware images of embedded devices in QEMU, especially theprocess to boot the Linux kernel (we call this process rehosting
the Linux kernel in this paper.) Thats because embedded devices usually use different system-on-chips (SoCs) from multiple vendors andonly a limited number of SoCs are currently supported in QEMU. In this work, we propose a technique calledperipheral transplantation. The main idea is to transplant the device drivers of designated peripherals into the Linux kernel binary. By doing so, it can replace the peripherals in the kernel that are currently unsupported in QEMU with supported ones, thus making the Linux kernel rehostable. After that, various applications can be built upon. We implemented this technique inside a prototype system called ECMO and applied it to 824 firmware images, which consist of 17 kern
Selective data protection is a promising technique to defend against the data leakage attack. In this paper, we revisit technical challenges that were neglected when applying this protection to real applications. These challenges include the secure i
nput channel, granularity conflict, and sensitivity conflict. We summarize the causes of them and propose corresponding solutions. Then we design and implement a prototype system for selective data protection and evaluate the overhead using the RISC-V Spike simulator. The evaluation demonstrates the efficiency (less than 3% runtime overhead with optimizations) and the security guarantees provided by our system.
Deep learning-based fluence map prediction(DL-FMP) method has been reported in the literature, which generated fluence maps for desired dose by deep neural network(DNN)-based inverse mapping. We hypothesized that DL-FMP is similar to general fluence
map optimization(FMO) because its theoretically based on a general inverse mapping. We designed four experiments to validate the generalizability of DL-FMP to other types of plans apart from the training data, which contained only clinical head and neck(HN) full-arc VMAT plans. The first three experiments quantified the generalizability of DL-FMP to multiple anatomical sites, different delivery modalities, and various degree of modulation(DOM), respectively. The fourth experiment explored the generalizability and stability to infeasible dose inputs. Results of the first experiment manifested that DL-FMP can generalize to lung, liver, esophagus and prostate, with gamma passing rates (GPR) higher than 95%(2%/2mm). The second experiment showed that DL-FMP can generalize to partial-arc plans and predict partial-arc fluences. GPR(3mm/3%) ranged from 96% to 99%. DL-FMP cannot generate fluence maps in discrete beam angles for IMRT input. But the predicted dose still agreed with ground truth dose with 93% GPR(5%/5mm). The third experiment demonstrated that DL-FMP can generalize to various DOMs, with GPRs(3%/3mm) ranged in 94%-98%. Moreover, the DOM of predicted fluence maps correlated to the optimality of the input dose accordingly. The fourth experiment exemplified that DL-FMP can make stable predictions for infeasible dose input. In conclusion, we validated that DL-FMP can generalize to plans for multiple anatomical sites, plans of different delivery modalities and plans with various DOM. It can also make stable prediction for infeasible input.
Thermal ammonolysis of quasi-two-dimensional (quasi-2D) CoTa2O6 yields the O2-/N3- and anionic vacancy ordered Co2+Ta5+2O6-xN2x/3$Box$x/3 (x $leq$ 0.15) that exhibits a transition from antiferromagnetism to defect engineered above room-temperature fe
rromagnetism as evidenced by diffraction, spectroscopic and magnetic characterizations. First-principles calculations reveal the origin of ferromagnetism is a particular CoON configuration with N located at Wyckoff position 8j, which breaks mirror symmetry about ab plane. A pressure-induced electronic phase transition is also predicted at around 24.5 GPa, accompanied by insulator-to-metal transition and magnetic moment vanishing.
