Twistings, crossed coproducts and Hopf-Galois coextensions

Let $H$ be a Hopf algebra. Ju and Cai introduced the notion of twisting of an $H$-module coalgebra. In this note, we study the relationship between twistings, crossed coproducts and Hopf-Galois coextensions. In particular, we show that a twisting of an $H$-Galois coextension remains $H$-Galois if the twisting is invertible.Comment: 20 page

Chemical Modification Methods for Protein Misfolding Studies

Protein misfolding is the basis of various human diseases, including Parkinson’s disease, Alzheimer’s disease and Type 2 diabetes. When a protein misfolds, it adopts the wrong three dimensional structures that are dysfunctional and sometime pathological. Little structural details are known about this misfolding phenomenon due to the lack of characterization tools. Our group previously demonstrated that a thioamide, a single atom substitution of the peptide bond, could serve as a minimalist fluorescence quencher. In the current study, we showed the development of protein semi-synthesis strategies for the incorporation of thioamides into full-length proteins for misfolding studies. We adopted the native chemical ligation (NCL) method between a C-terminal thioester fragment and an N-terminal Cys fragment. We first devised strategies for the synthesis of thioamide-containing peptide thioesters as NCL substrates, and demonstrated their applications in generating a thioamide/Trp-dually labeled α-synuclein (αS), which was subsequently used in a proof-of-concept misfolding study. To remove the constraint of a Cys at the ligation site, we explored traceless ligation methods that desulfurized Cys into Ala, or β- and γ- thiol analogs into native amino acids after ligation in the presence of thioamides. We further demonstrated that selective deselenization could be achieved in the presence of both Cys residues and thioamides, expanding the scope of thioamide incorporation through traceless ligation to proteins with native Cys. Finally, we showed that hemiselenide protected selenocysteines (Sec) can be incorporated onto the protein N-terminus through chemoenzymatic modification by aminoacyl transferase (AaT) as ligation handles. Further developments are underway in our laboratory to expand the AaT substrate scope for β- and γ- thiol amino acid analogs. In summary, we developed a set of methods that allowed the incorporation of thioamide probes into full-length protein, which enabled the application of this minimalist probe in protein misfolding studies

A hybrid intrusion detection system

Anomaly intrusion detection normally has high false alarm rates, and a high volume of false alarms will prevent system administrators identifying the real attacks. Machine learning methods provide an effective way to decrease the false alarm rate and improve the detection rate of anomaly intrusion detection. In this research, we propose a novel approach using kernel methods and Support Vector Machine (SVM) for improving anomaly intrusion detectors\u27 accuracy. Two kernels, STIDE kernel and Markov Chain kernel, are developed specially for intrusion detection applications. The experiments show the STIDE and Markov Chain kernel based two class SVM anomaly detectors have better accuracy rate than the original STIDE and Markov Chain anomaly detectors.;Generally, anomaly intrusion detection approaches build normal profiles from labeled training data. However, labeled training data for intrusion detection is expensive and not easy to obtain. We propose an anomaly detection approach, using STIDE kernel and Markov Chain kernel based one class SVM, that does not need labeled training data. To further increase the detection rate and lower the false alarm rate, an approach of integrating specification based intrusion detection with anomaly intrusion detection is also proposed.;This research also establish a platform which generates automatically both misuse and anomaly intrusion detection software agents. In our method, a SIFT representing an intrusion is automatically converted to a Colored Petri Net (CPNs) representing an intrusion detection template, subsequently, the CPN is compiled into code for misuse intrusion detection software agents using a compiler and dynamically loaded and launched for misuse intrusion detection. On the other hand, a model representing a normal profile is automatically generated from training data, subsequently, an anomaly intrusion detection agent which carries this model is generated and launched for anomaly intrusion detection. By engaging both misuse and anomaly intrusion detection agents, our system can detect known attacks as well as novel unknown attacks

Security enhancement for NOMA-UAV networks

Owing to its distinctive merits, non-orthogonal multiple access (NOMA) techniques have been utilized in unmanned aerial vehicle (UAV) enabled wireless base stations to provide effective coverage for terrestrial users. However, the security of NOMA-UAV systems remains a challenge due to the line-of-sight air-to-ground channels and higher transmission power of weaker users in NOMA. In this paper, we propose two schemes to guarantee the secure transmission in UAV-NOMA networks. When only one user requires secure transmission, we derive the hovering position for the UAV and the power allocation to meet rate threshold of the secure user while maximizing the sum rate of remaining users. This disrupts the eavesdropping towards the secure user effectively. When multiple users require secure transmission, we further take the advantage of beamforming via multiple antennas at the UAV to guarantee their secure transmission. Due to the non-convexity of this problem, we convert it into a convex one for an iterative solution by using the second order cone programming. Finally, simulation results are provided to show the effectiveness of the proposed scheme

The ancient Chinese notes on hydrogeology

The ancient Chinese notes on hydrogeology are summarized and interpreted, along with records of some related matters, like groundwater exploration and utilization, karst springs, water circulation, water conservation and saline-land transformation, mine drainage, and environmental hydrogeology. The report focuses only on the earliest recorded notes, mostly up until the Han Dynasty (206 BC - AD 25). Besides the references cited, the discussion in this report is based mainly on archaeological material, the preserved written classic literature, and some assumptions and/or conclusions that have been handed down in legends to later ages. Although most material relates to ancient China, the lessons learned may have practical significance worldwide. Compared to other contemporary parts of the world, ancient China, without doubt, took the lead in the field of groundwater hydrology. The great achievements and experience of the Chinese ancestors should provide motivation and inspiration for hydrogeologists to carry out their scientific research and exploration passionately and activel

Valid Randomization Tests in Inexactly Matched Observational Studies via Iterative Convex Programming

In causal inference, matching is one of the most widely used methods to mimic a randomized experiment using observational (non-experimental) data. Ideally, treated units are exactly matched with control units for the covariates so that the treatments are as-if randomly assigned within each matched set, and valid randomization tests for treatment effects can then be conducted as in a randomized experiment. However, inexact matching typically exists, especially when there are continuous or many observed covariates or when unobserved covariates exist. Previous matched observational studies routinely conducted downstream randomization tests as if matching was exact, as long as the matched datasets satisfied some prespecified balance criteria or passed some balance tests. Some recent studies showed that this routine practice could render a highly inflated type-I error rate of randomization tests, especially when the sample size is large. To handle this problem, we propose an iterative convex programming framework for randomization tests with inexactly matched datasets. Under some commonly used regularity conditions, we show that our approach can produce valid randomization tests (i.e., robustly controlling the type-I error rate) for any inexactly matched datasets, even when unobserved covariates exist. Our framework allows the incorporation of flexible machine learning models to better extract information from covariate imbalance while robustly controlling the type-I error rate

Valley Carrier Dynamics in Monolayer Molybdenum Disulphide from Helicity Resolved Ultrafast Pump-probe Spectroscopy

We investigate the valley related carrier dynamics in monolayer MoS2 using helicity resolved non-degenerate ultrafast pump-probe spectroscopy at the vicinity of the high-symmetry K point under the temperature down to 78 K. Monolayer MoS2 shows remarkable transient reflection signals, in stark contrast to bilayer and bulk MoS2 due to the enhancement of many-body effect at reduced dimensionality. The helicity resolved ultrafast time-resolved result shows that the valley polarization is preserved for only several ps before scattering process makes it undistinguishable. We suggest that the dynamical degradation of valley polarization is attributable primarily to the exciton trapping by defect states in the exfoliated MoS2 samples. Our experiment and a tight-binding model analysis also show that the perfect valley CD selectivity is fairly robust against disorder at the K point, but quickly decays from the high-symmetry point in the momentum space in the presence of disorder.Comment: 15 pages,Accepted by ACS Nan

Seasonal cycle of sea surface water characteristics in climate models

The seasonal cycle of sea surface water characteristics is important for the global climate system. Seasonal extrema of sea surface temperature (SST) and sea surface salinity (SSS) determine water mass properties below the surface. Evaluation of climate models typically focuses on annual or long-term mean state, not on seasonal extrema. In this thesis, the seasonal cycles of SST and SSS in HiGEM and SST seasonal extrema in 20 CMIP6 models are assessed globally. Sparse sampling leads to large differences between observational climatologies in both SST and SSS in polar regions. There are also large SST differences in regions with strong SST horizontal gradient, likely because gridding on coarse resolution can smooth the gradient. To exclude regions with large differences between climatologies, masks are proposed for global model assessments. The results demonstrate the importance of evaluating model performance not simply against annual mean properties. Although the biases in SST and SSS seasonal extrema are largely consistent with their annual means, the amplitude of SST and SSS biases has large seasonal variations in specific regions. Large seasonal variations of SST bias in CMIP6 models occur in eastern boundary upwelling regions, polar regions, the North Pacific and eastern equatorial Atlantic. Large seasonal variations of SSS bias in HiGEM occur in equatorial and polar regions. SST biases in some CMIP6 models have seasonal spatial patterns. Models with greater vertical resolution in the ocean typically demonstrate better representation of SST extrema, particularly seasonal maximum SST. However, no significant relationship is found with ocean model horizontal resolution