Miracle-dz/UniFAR-FTUs huggingface dataset:
Equilibrium response to carbon dioxide and aerosol forcing changes in a 1D air–sea interactive model

A 1D air–sea interactive model that couples an atmospheric column model with a slab ocean model was introduced. The model simulated an exact balance between radiative cooling and convective heating in the free troposphere. Within the planet boundary layer, the turbulent mixing produces cooling in the upper part and warming in the lower part, which is compensated by radiative and convective heating together. The model was then used to explore the equilibrium response of sea surface temperature (SST) and precipitation to carbon dioxide (CO2) and aerosol forcing changes. Results show a warming or cooling signal owning to increased CO2 or aerosols can be well captured by the ocean, leading to an increasing or decreasing of the SST, and hence the precipitation. Cutting off the interactions between atmosphere and ocean however renders different results. By applying the relaxed weak temperature gradient (WTG) approximation, the local response to aerosol forcing perturbations was investigated, which was shown to be largely different from the global response in spite of the same forcing. To understand the nonlinear interactions among different forcings, the equilibrium responses to multiple combinations of CO2 and aerosol forcings are analyzed.
Surface passivation of silicon photonic devices with high surface-to-volume-ratio nanostructures

Recently, a high-speed and high-efficiency silicon photodiode (PD) was demonstrated by enabling light trapping micro/nanostructured holes. While attractive for light manipulation, these high surface-to-volume-ratio nanostructures, which are created by top-down dry etching processes, can also bring other challenges, such as creating silicon surface damage and crystalline defects. To reduce the dark current level and minimize surface recombination, successful passivation is a vital step to achieving the ultimate performance of the silicon PD based on photon-trapping structures. In this paper, we present and compare several effective silicon surface passivation schemes including plasma-enhanced chemical vapor deposited SiO2 and Si3N4, hydrofluoric acid (HF) treatment, thermal oxidation, and low ion energy dry etch. These passivation techniques can reduce the dark current by more than 4 orders of magnitude, bringing it down to the nanoampere level. Among these passivation schemes, HF treatment is relatively simple and cost effective, while other techniques may alter the light trapping characteristics of silicon PDs to some extent, thus affecting their external quantum efficiency. The passivation techniques discussed in this paper are CMOS compatible and can also be applied to other silicon photonic devices, such as photovoltaics.
Attractor dimension of time-averaged climate observables: insights from a low-order ocean-atmosphere model

Abstract The ocean and atmosphere have very different characteristic timescales and display a rich range of interactions. Here, we investigate the sensitivity of the dynamical properties of the coupled atmosphere-ocean system when time-averaging of the trajectories of the original system is performed. We base our analysis on a conceptual model of the atmosphere-ocean dynamics which allows us to compute the attractor properties for different coupling coefficients and averaging periods. When the averaging period is increased, the attractor dimension initially shows a non-monotonic behaviour, but ultimately decreases for windows longer than 1 year. The analysis of daily, monthly and annual instrumental and reconstructed indices of oceanic and atmospheric circulation supports our results. This has important implications for the analysis and interpretation of long climate timeseries with a low temporal resolution, but also for the possible convergence of climate observables subjected to long time-averages towards attractors close to hyperbolicity.
Analysis on English Vocabulary Appearance Pattern in Korean CSAT

—A text-mining-based word class categorization method and LSTM-based vocabulary pattern prediction method are introduced in this paper. A preprocessing method based on simple text appearance frequency analysis is first described. This method was developed as a data screening tool but showed 4.35 ~ 6.21 times higher than previous works. An LSTM deep learning method is also suggested for vocabulary appearance pattern prediction method. AI performs a regression with various size of data window of previous exams to predict the probabilities of word appearance in the next exam. Predicted values of AI over various data windows are processed into a single score as a weighted sum, which we call an "AI-Score", which represents the probability of word appearance in next year's exam. Suggested method showed 100% accuracy at the range 100-score area and showed only 1.7% error of prediction in the section where the scores were over 60 points. All source codes are freely available at the authors' Git Hub repository. (https://github.com/needleworm/bigdata_voca)
NMR and molecular dynamics studies of the interaction of melatonin with calmodulin

Pineal hormone melatonin (N‐acetyl‐5‐methoxytryptamine) is thought to modulate the calcium/calmodulin signaling pathway either by changing intracellular Ca2+ concentration via activation of its G‐protein–coupled membrane receptors, or through a direct interaction with calmodulin (CaM). The present work studies the direct interaction of melatonin with intact calcium‐saturated CaM both experimentally, by fluorescence and nuclear magnetic resonance spectroscopies, and theoretically, by molecular dynamics simulations. The analysis of the experimental data shows that the interaction is calcium‐dependent. The affinity, as obtained from monitoring 15N and 1H chemical shift changes for a melatonin titration, is weak (in the millimolar range) and comparable for the N‐ and C‐terminal domains. Partial replacement of diamagnetic Ca2+ by paramagnetic Tb3+ allowed the measurement of interdomain NMR pseudocontact shifts and residual dipolar couplings, indicating that each domain movement in the complex is not correlated with the other one. Molecular dynamics simulations allow us to follow the dynamics of melatonin in the binding pocket of CaM. Overall, this study provides an example of how a combination of experimental and theoretical approaches can shed light on a weakly interacting system of biological and pharmacological significance.
Creating the gene ontology resource: design and implementation.

The exponential growth in the volume of accessible biological information has generated a confusion of voices surrounding the annotation of molecular information about genes and their products. The Gene Ontology (GO) project seeks to provide a set of structured vocabularies for specific biological domains that can be used to describe gene products in any organism. This work includes building three extensive ontologies to describe molecular function, biological process, and cellular component, and providing a community database resource that supports the use of these ontologies. The GO Consortium was initiated by scientists associated with three model organism databases: SGD, the Saccharomyces Genome database; FlyBase, the Drosophila genome database; and MGD/GXD, the Mouse Genome Informatics databases. Additional model organism database groups are joining the project. Each of these model organism information systems is annotating genes and gene products using GO vocabulary terms and incorporating these annotations into their respective model organism databases. Each database contributes its annotation files to a shared GO data resource accessible to the public at http://www.geneontology. org/. The GO site can be used by the community both to recover the GO vocabularies and to access the annotated gene product data sets from the model organism databases. The GO Consortium supports the development of the GO database resource and provides tools enabling curators and researchers to query and manipulate the vocabularies. We believe that the shared development of this molecular annotation resource will contribute to the unification of biological information.
On Efficiently Finding Small Separators in Temporal Graphs

Vertex separators, that is, vertex sets whose deletion disconnects two distinguished vertices in a graph, play a pivotal role in algorithmic graph theory. For many realistic models of the real world it is necessary to consider graphs whose edge set changes with time. More specifically, the edges are labeled with time stamps. In the literature, these graphs are referred to as temporal graphs, temporal networks, time-varying networks, edge-scheduled networks, etc. While there is an extensive literature on separators in “static” graphs, much less is known for the temporal setting. Building on previous work, we study the problem of finding a small vertex set (the separator) in a temporal graph with two designated terminal vertices such that the removal of the set breaks all temporal paths connecting one terminal to the other. Herein, we consider two models of temporal paths: paths that contain arbitrarily many hops per time step (nonstrict) and paths that contain at most one hop per time step (strict). We settle the hardness dichotomy (NP-hardness versus polynomial-time solvability) of both problem variants regarding the number of time steps of a temporal graph. Moreover we prove both problem variants to be NP-complete even on temporal graphs whose underlying graph is planar. We show that on temporal graphs whose underlying graph is planar, if additionally the number of time steps is constant then the problem variant for strict paths is solvable in quasi linear time. Finally, for general temporal graphs we introduce the notion of a temporal core (vertices whose incident edges change over time). We prove that on temporal graphs with constant-sized temporal core, the non-strict variant is solvable in polynomial time, where the degree of the polynomial is independent of the size of the temporal core, while the strict variant remains NP-complete.
Low levels of asparagine deamidation can have a dramatic effect on aggregation of amyloidogenic peptides: Implications for the study of amyloid formation

The polypeptide hormone amylin forms amyloid deposits in Type 2 diabetes mellitus and a 10‐residue fragment of amylin (amylin20–29) is commonly used as a model system to study this process. Studies of amylin20–29 and several variant peptides revealed that low levels of deamidation can have a significant effect on the secondary structure and aggregation behavior of these molecules. Results obtained with a variant of amylin20–29, which has the primary sequence SNNFPAILSS, are highlighted. This peptide is particularly interesting from a technical standpoint. In the absence of impurities the peptide does not spontaneously aggregate and is not amyloidogenic. This peptide can spontaneously deamidate, and the presence of less than 5% of deamidation impurities leads to the formation of aggregates that have the hallmarks of amyloid. In addition, small amounts of deamidated material can induce amyloid formation by the purified peptide. These results have fundamental implications for the definition of an amyloidogenic sequence and for the standards of purity of peptides and proteins used for studies of amyloid formation.
Capacity-achieving decoding by guessing noise

We introduce a new algorithm for realizing Maximum Likelihood (ML) decoding in channels with memory. The algorithm is based on the principle that the receiver rank orders noise sequences from most likely to least likely. Subtracting noise from the received signal in that order, the first instance that results in an element of the code-book is the ML decoding. For channels where the noise is independent of the input and determinable from the input and output, we establish that the algorithm is capacity-achieving with uniformly-at-random selected code-books. When the code-book rate is less than capacity, we identify error exponents as the block length becomes large. When the code-book rate is beyond capacity, we identify exponents for the probability that the ML decoding is the transmitted code-word. We determine properties of the complexity of the scheme in terms of the number of computations the receiver must perform. For code rates greater than capacity, this reveals thresholds for the number of guesses in which if an element of the code-book is identified, it is likely to be the transmitted code-word. A sufficient condition for this to occur is that the normalized code-book rate is less than one minus the min-entropy rate of the noise. Based on the analysis of this noise guessing approach to ML decoding, we introduce an Approximate ML decoding scheme (AML) where the receiver abandons the search for an element of the code-book after a fixed number of noise removal queries, giving an upper-bound on complexity. While not an ML decoder, we establish that AML is also capacity-achieving for an appropriate choice of abandonment threshold, and characterize its complexity, error and success exponents. Worked examples are presented for binary memoryless and Markovian noise. These indicate that the decoding scheme provides high rates for small block sizes with low complexity.
An Evaluation of Methods for Assessing the Functional Form of Covariates in the Cox Model

In this thesis, two methods for assessing the functional form of covariates in the Cox proportional hazards model are evaluated. The methods include one graphical check based on martingale residuals and one graphical check, together with a Kolmogorov-type supremum test, based on cumulative sums of martingale residuals. The methods are evaluated in a simulation study under five different covariate misspecifications with varying sample sizes and censoring degrees. The results from both methods indicate that the type of covariate misspecification, sample size and censoring degree affect the ability to detect and identify the misspecification. The procedure based on smoothed scatterplots of martingale residuals reveals difficulties with assessing whether the behaviour of the smoothed curve in the plot is an indication of a misspecification or a phenomenon that can occur in a correctly specified model. The graphical check together with the test procedure based on cumulative sums of martingale residuals is shown to successfully detect and identify three out of five covariate misspecifications for large sample sizes. For small sample sizes, especially combined with a high censoring degree, the power of the supremum test is low for all covariate misspecifications.
Gradient-based structural change detection for nonstationary time series M-estimation

We consider structural change testing for a wide class of time series M-estimation with nonstationary predictors and errors. Flexible predictor-error relationships, including exogenous, state-heteroscedastic and autoregressive regressions and their mixtures, are allowed. New uniform Bahadur representations are established with nearly optimal approximation rates. A CUSUM-type test statistic based on the gradient vectors of the regression is considered. In this paper, a simple bootstrap method is proposed and is proved to be consistent for M-estimation structural change detection under both abrupt and smooth nonstationarity and temporal dependence. Our bootstrap procedure is shown to have certain asymptotically optimal properties in terms of accu-racy and power. A public health time series dataset is used to illustrate our methodology, and asymmetry of structural changes in high and low quantiles is found.
Tracking Network Evolution and Their Applications in Structural Network Analysis

Structural network analysis, including node ranking, community detection, and link prediction, has received a lot of attention lately. In the literature, most works focused on the structural analysis of a single network. In this paper, we are particularly interested in how the network structure evolves over time. For this, we propose a general framework to track, model, and predict the dynamic network structures. Unlike some recent works that directly tracks the adjacency matrices of the networks, our framework utilizes the spectral graph theory to track the latent feature vectors obtained by a low-rank eigendecomposition of the Laplacian matrices of the networks. We then use the Finite Impulse Response (FIR) filter to model the evolution of the latent feature vector of each node. By solving a ridge regression problem, the parameters of the FIR filter can be learned and used for predicting the future network structures, including node ranking, community detection, and link prediction. To test the effectiveness of our framework, we perform various experiments based on our synthetic datasets and three real-world datasets. Our experimental results show that our framework is very effective in tracking latent feature vectors and predicting future network structures.
Velocity measurement using frequency domain interferometer and chirped pulse laser

An ultra-intense short pulse laser induces a shock wave in material. The pressure of shock compression is stronger than a few tens GPa. To characterize shock waves, time-resolved velocity measurement in nano- or pico-second time scale is needed. Frequency domain interferometer and chirped pulse laser provide single-shot time-resolved measurement. We have developed a laser-driven shock compression system and frequency domain interferometer with CPA laser. In this paper, we show the principle of velocity measurement using a frequency domain interferometer and a chirped pulse laser. Next, we numerically calculated spectral interferograms and show the time-resolved velocity measurement can be done from the phase analysis of spectral interferograms. Moreover we conduct the laser driven shock generation and shock velocity measurement. From the spectral fringes, we analyze the velocities of the sample and shockwaves.
Dueling bandits for online ranker evaluation

In every domain where a service or a product is provided, an important question is that of evaluation: given a set of possible choices for deployment, what is the best one? An important example, which is considered in this work, is that of ranker evaluation from the field of information retrieval (IR). The goal of IR is to satisfy the information need of a user in response to a query issued by them, where this information need is typically satisfied by a document (or a small set of documents) contained in what is often a much larger collection. This goal is often attained by ranking the documents according to their usefulness to the issued query using an algorithm, called a ranker, a procedure that takes as input a query and a set of documents and specifies how the documents need to be ordered. This thesis is concerned with ranker evaluation. The goal of ranker evaluation is to determine the quality of the rankers under consideration to allow us to choose the best option: given a finite set of possible rankers, which one of them leads to the highest level of user satisfaction? There are two main methods for carrying this out: absolute metrics and relative comparisons. This thesis is concerned with the second, relative form of ranker evaluation because it is more efficient at distinguishing between rankers of different quality: for instance interleaved comparisons take a fraction of the time required by A/B testing, but they produce the same outcome. More precisely, the problem of online ranker evaluation from relative feedback can be described as follows: given a finite set of rankers, choose the best using only pairwise comparisons between the rankers under consideration, while minimizing the number of comparisons involving sub-optimal rankers. This problem is an instance of what is referred to as the dueling bandit problem in the literature. The main contribution of this thesis is devising a dueling bandit algorithm, called Copeland Confidence Bounds (CCB), that solves this problem under practically general assumptions and providing theoretical guarantees for its proper functioning. In addition to that, the thesis contains a number of other algorithms that are better suited for dueling bandit problems with particular properties.
: A

Although there is a plethora of research articles that study ISO 9000 quality management systems and their association with business success, there is little empirical research that can attribute causality to certification. Contributes to the question of causality, through a comparison against a control group of the actual sales and profitability of 400 certified companies pre and post registration. Using a longitudinal methodology finds that, although the performance of certified companies is superior to that of 400 non-certified firms, there is no evidence of improved performance after registration in the 400 certified firms studied. Concludes, from these findings, that the superior performance of certified firms is due to firms with superior performance having a greater propensity to pursue ISO 9000 registration. Illustrates the potential dangers in inferring that ISO 9000 certification leads to superior business performance. Additionally the findings should give pause for thought for decision-makers. Certification is a major investment yet the findings show that inflated expectations of performance improvement after ISO 9000 accreditation may be unfounded.
Interaction‐Feature Enhanced Multiuser Model Learning for a Home Environment Using Ambient Sensors

Activity recognition (AR) is a key enabler for a context‐aware smart home since knowing what the residents’ current activities helps a smart home provide more desirable services. This is why AR is often used in assistive technologies for cognitively impaired people to evaluate their abilities to undertake activities of daily living. In a real‐life scenario, multiple‐resident AR has been considered as a very challenging problem, primarily due to the complexity of data association. In addition, most prior research has not considered the potential interpersonal interactions among residents to simplify complexity, especially in an environment monitored by ambient sensors. In this study, we propose two types of multiuser activity models, both of which are derived from an interaction‐feature enhanced multiuser model learning framework. These two models consider interpersonal interactions and data association for multiuser AR using ambient sensors. We then compare their performance with the other two baseline models with or without consideration of data association and interpersonal interactions. The experimental results show that the derived models outperform other baseline classifiers. Therefore, the proposed approach can increase the opportunities for providing context‐aware services for a multiresident smart home.