Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiyear Composite View of Ozone Enhancements and Stratosphere‐to‐Troposphere Transport in Dry Intrusions of Northern Hemisphere Extratropical Cyclones

We examine the role of extratropical cyclones in stratosphere‐to‐troposphere (STT) exchange with cyclone‐centric composites of O3 retrievals from the Microwave Limb Sounder (MLS) and the Tropospheric Emission Spectrometer (TES), contrasting them to composites obtained with the Modern‐Era Retrospective‐analysis for Research and Applications (MERRA and MERRA‐2) reanalyses and the GEOS‐Chem chemical transport model. We identify 15,978 extratropical cyclones in the northern hemisphere (NH) for 2005–2012. The lowermost stratosphere (261 hPa) and middle troposphere (424 hPa) composites feature a 1,000 km wide O3 enhancement in the dry intrusion (DI) airstream to the southwest of the cyclone center, coinciding with a lowered tropopause, enhanced potential vorticity, and decreased H2O. MLS composites at 261 hPa show that the DI O3 enhancements reach a 210 ppbv maximum in April. At 424 hPa, TES composites display maximum O3 enhancements of 27 ppbv in May. The magnitude and seasonality of these enhancements are captured by MERRA and MERRA‐2, but GEOS‐Chem is a factor of 2 too low. The MERRA‐2 composites show that the O3‐rich DI forms a vertically aligned structure between 300 and 800 hPa, wrapping cyclonically with the warm conveyor belt. In winter and spring DIs, O3 is enhanced by 100 ppbv or 100–130% at 300 hPa, with significant enhancements below 500 hPa (6–20 ppbv or 15–30%). We estimate that extratropical cyclones result in a STT flux of 119 ± 56 Tg O3 yr−1, accounting for 42 ± 20% of the NH extratropical O3 STT flux. The STT flux in cyclones displays a strong dependence on westerly 300 hPa wind speeds.
Global Sourcing and Foreign Knowledge Seeking: A Formal Model and Empirical Analysis

This study examines the antecedents of firm global sourcing choices, focusing on the role of foreign knowledge seeking in this context. We develop a formal model showing that firms that invest in foreign R&D are driven to increase their levels of offshore integration, especially when they have strong capabilities, and from developed countries. In contrast, firms that do not invest in foreign knowledge seeking are more likely to pursue offshore outsourcing in response to cost pressures, especially when they have weak capabilities, and when they are able to better monitor third party suppliers through investments in information technology. We find support for these insights analyzing a unique and comprehensive firm-level panel of cross-border product transfers by US MNCs from 1989 to 2004. Our paper thus highlights the effect of foreign knowledge seeking on firm global sourcing choices, while distinguishing between the antecedents of offshore integration and offshore outsourcing.
Skin condition detection of smartphone face image using multi-feature decision method

In this paper, we proposed a system which can detect the skin condition such as wrinkles, skin pores and acnes on smartphone using multi-feature decision method. After the user inputs the facial image into our system, the system will conduct the image preprocessing in the first place, then divide the face into 11 regions for doing the next stage of detection. Next, our system will detect the following different kinds of skin conditions: wrinkles, skin pores, and acnes using our multi-feature decision method based on Laws mask, Gabor filter and Kirsch filter. At last, after users receiving their personal results, they will not only find out their skin condition but also get the appropriate and customized suggestions to take care of their skin.
Mechanisms for the Holton‐Tan relationship and its decadal variation

This study provides a mechanistic explanation of why the Holton‐Tan (HT) effect, a phenomenon in which the strength of northern stratospheric winter polar vortex synchronizes with the equatorial quasi‐biennial oscillation (QBO), was disrupted in the middle to late winters of 1978–1997. In line with recent reassessments of the HT effect, we find that an easterly QBO in the lower stratosphere leads to the formation of a midlatitude wave guide that enhances both the upward propagating planetary waves from the troposphere into the lower stratosphere (~35–50°N, 30–200 hPa) and the northward wave propagation in the upper to middle stratosphere (~35–60°N, 20–5 hPa). This enhanced poleward refraction of planetary waves results in a more disturbed polar vortex, causing the HT effect. The weakening of the HT effect in 1978–1997 was associated with a broader and strengthened polar vortex in November to January. The divergence of wave activity generated by eddies growing within the vortex provided the momentum source and allowed wave activity to propagate meridionally away from the vortex; this interfered with the QBO modulation of planetary wave propagation and led to a weakening of the HT effect during this period. The stronger than average polar vortex in 1978–1997 was associated with a vertically coherent cooling signature over northeastern Asia in the stratosphere. We suggest that a change of stratospheric circulation and/or a change of the stratosphere‐troposphere coupling were the main causes for the disrupted HT effect in 1978–1997.
Control of Complex Systems Using Bayesian Networks and Genetic Algorithm

A method based on Bayesian neural networks and genetic algorithm is proposed to control the fermentation process. The relationship between input and output variables is modelled using Bayesian neural network that is trained using hybrid Monte Carlo method. A feedback loop based on genetic algorithm is used to change input variables so that the output variables are as close to the desired target as possible without the loss of confidence level on the prediction that the neural network gives. The proposed procedure is found to reduce the distance between the desired target and measured outputs significantly.
An improved method for generating multiresolution animation models

In computer graphics, animated models are widely used to represent time-varying data. In this paper, we propose an improved method to generate multiresolution animation models. We use a curvature sensitive quadric error metric (QEM) criterion as our basic measurement, which can preserve local features on the surface. We append a deformation weight to the aggregated edge contraction cost for the whole animation to preserve areas with large deformation. At last, we introduce a mesh optimization method to deal with the animation sequence, which can efficiently improve the temporal coherence and reduce visual artifacts. The results show our approach is efficient, easy to implement, and good quality progressive animation models can be generated at any level of detail.
The Metastasis Suppressor, N-myc Downregulated Gene 1 (NDRG1), Is a Prognostic Biomarker for Human Colorectal Cancer

Metastasis remains to be one of the most prevalent causes leading to poor long-term survival of colorectal cancer (CRC) patients. The clinical significances of tumor metastatic suppressor, N-myc downregulated gene 1 (NDRG1), have been inconsistently reported in a variety of cancerous diseases. In this study with 240 CRC clinical specimens, we showed that NDRG1 expression was significantly decreased in most of CRC tissues compared to the paired non-tumor counterparts. Statistical analysis revealed a significant inverse correlation of NDRG1 expression with tumor stage, differentiation status and metastasis. Compared with NDRG1-negative group, NDRG1-positve group had better disease-free/overall survival (p = 0.000) over 5 years’ follow-up. Furthermore, NDRG1 was considered to be an independent prognostic factor for overall survival (p = 0.001) and recurrence (p = 0.003). Our study concludes that NDRG1 is a novel favorable predictor for the prognosis in CRC patients.
A 3D prototyping chip based on a wafer-level stacking technology

We have developed a new 3-dimensional stacking technology using wafer-to-wafer stacked method and evaluated the connectivity between TSV and micro-bump. The prototype 3-layer stacking device has been tried using our technology and the functional yield reached more than 60%. Using 8-inch wafer. We propose one of the design methodologies for a 3D stacked device.
Green Light Adjusts the Plastid Transcriptome during Early Photomorphogenic Development1[W]

During the transition from darkness to light, a suite of light sensors guides gene expression, biochemistry, and morphology to optimize acclimation to the new environment. Ultraviolet, blue, red, and far-red light all have demonstrated roles in modulating light responses, such as changes in gene expression and suppression of stem growth rate. However, green wavebands induce stem growth elongation, a response not likely mediated by known photosensors. In this study, etiolated Arabidopsis (Arabidopsis thaliana) seedlings were treated with a short, dim, single pulse of green light comparable in fluence and duration to that previously shown to excite robust stem elongation. Genome microarrays were then used to monitor coincident changes in gene expression. As anticipated, phytochrome A-regulated, nuclear-encoded transcripts were induced, confirming proper function of the sensitive phytochrome system. In addition, a suite of plastid-encoded transcripts decreased in abundance, including several typically up-regulated after phytochrome and/or cryptochrome activation. Further analyses using RNA gel-blot experiments demonstrated that the response is specific to green light, fluence dependent, and detectable within 30 min. The response obeys reciprocity and persists in the absence of known photosensors. Plastid transcript down-regulation was also observed in tobacco (Nicotiana tabacum) with similar temporal and fluence-response kinetics. Together, the down-regulation of plastid transcripts and increase in stem growth rate represent a mechanism that tempers progression of early commitment to the light environment, helping tailor seedling development during the critical process of establishment.
Control results for an electromagnetically levitated vehicle prototype

Several magnetically levitated transport systems have been constructed, tested and constantly improved. Various technologies for the magnetic levitation system are also being developed and some of them use electromagnets for the vehicle levitation. In this work, a magnetically levitated vehicle prototype with four electromagnets, located in each one of the extremities of the vehicle, is studied. The vehicle levitation is achieved through the control of the gaps of these electromagnets. The constructed prototype will have independent levitation and propulsion systems; this paper considers only the magnetic levitation system. The resultant system is strongly non-linear, multivariable and open loop unstable. A MIMO controller. based on an analytical model of the vehicle, was designed in the LQG/LTR context. The performance and robustness bounds for this design were based on simulations for a similar SISO system. Although conceived in the MIMO context, the final controller resulted close to a set of decentralized SISO controllers.
An energy-efficient user scheduling scheme for multiuser MIMO systems with RF chain sleeping

With increased radio frequency (RF) chains, base station (BS) with multiple antennas consumes more circuit power. Turning off RF chains will help to save energy. However, in turn, it needs more sophisticated user scheduling. Therefore, an energy-efficient scheduling scheme is proposed with which users and RF chains are jointly selected at each frame. Here, Lyapunov driftplus-penalty ratio is used to policy design. If the average data arrival rates locate in the capacity region, it is proved that the proposed policy achieves the maximum energy efficiency than any other stationary, randomized, queue-independent policies, while ensuring the stability of the system. At each frame, the selection of users and RF chains depends on the number of selected users, sum queue length of them and energy efficiency they achieve. A key observation is that the numbers of selected users and RF chains should be equal under zero-forcing beamforming. Simulation results have shown that it even achieves higher energy efficiency than the Maximum Weighted Queue scheduling scheme when average arrival rate vector is relative small.
Target detection for THz radar based on information geometry

Information geometry, a new discipline direction originated from differential geometry in the area of mathematics, studies the probability and information problems, and provides a new solution method to solve the problems in radar signal processing. In the current study, radar echoes were usually modeled as the complex multivariate Gaussian distribution. We propose a kind of CFAR detector for THz radar based on information geometry, and analyze its detection performance. Simulations show that the detection performance of the new geometry approach is better than the conventional detection method in THz band, which is helpful to further research on target recognition and parameter estimation in THz band based on information geometry.
The Matrix Cookbook

Acknowledgements: We would like to thank the following for contributions and suggestions: Bill Baxter, Brian Templeton, Christian Rishoj, Christian Schroppel Douglas L. Theobald, Esben Hoegh-Rasmussen, Glynne Casteel, Jan Larsen, Jun Bin Gao, Jurgen Struckmeier, Kamil Dedecius, Korbinian Strimmer, Lars Christiansen, Lars Kai Hansen, Leland Wilkinson, Liguo He, Loic Thibaut, Miguel Barao, Ole Winther, Pavel Sakov, Stephan Hattinger, Vasile Sima, Vincent Rabaud, Zhaoshui He. We would also like thank The Oticon Foundation for funding our PhD studies.
Macro-Level and Micro-Level Routing (MMR) for Urban Vehicular Ad Hoc Networks

Finding a reliable and efficient routing path in vehicular ad hoc networks (VANETs) is a challenging issue due to high mobility of vehicles and frequent link breakage. Motivated by this, we propose a robust and efficient routing protocol, called MMR. The contribution of this paper is two-fold: two-level routing and a new routing metric. A routing process of MMR consists of the macro level and the micro level. MMR forwards a packet to an approximate location of the destination at the macro level and then forwards a packet to the exact location of the destination at the micro level. This two-level routing reduces the protocol overhead and improves scalability in terms of the number of nodes. MMR also introduces a new routing metric that reduces the protocol overhead and path breakage by considering velocities of vehicles. Through simulations, we show that MMR improves the routing performance by about 30~40% in highly mobile environments, compared to the existing ad hoc routing protocols such as AODV and GPSR.
Restructuring software systems using clustering

In this paper we are focusing on the problem of restructuring object oriented software systems using clustering techniques. Refactoring ([1]) is one major issue to improve the design of software systems, increasing the internal software quality. This paper aims at introducing a new k-medoids based clustering algorithm that can be used for improving the design of software systems, by identifying the needed refactorings. The algorithm uses a measure that evaluates a software system design. Clustering ([2]) is used in order to recondition the class structure of a software system. The proposed approach can be useful for assisting software engineers in their daily works of refactoring software systems. We evaluate our approach using the open source case study JHotDraw ([3]), illustrating the advantages of our approach in comparison with existing approaches.
Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization

This paper describes the synthesis method of linear array geometry with minimum sidelobe level and null control using the particle swarm optimization (PSO) algorithm. The PSO algorithm is a newly discovered, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the PSO algorithm is much easier to understand and implement and requires the least of mathematical preprocessing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression and/or null placement in certain directions, and then solved by the PSO algorithm for the optimum element locations. Three design examples are presented that illustrate the use of the PSO algorithm, and the optimization goal in each example is easily achieved. The results of the PSO algorithm are validated by comparing with results obtained using the quadratic programming method (QPM).
A Description Logic Primer

This paper provides a self-contained first introduction to d escription log- ics (DLs). The main concepts and features are explained with examples before syn- tax and semantics of the DLSROIQ are defined in detail. Additional sections review light-weight DL languages, discuss the relationship to the Web Ontology Language OWL and give pointers to further reading.
Minimizing Deployment Cost of Cloud-Based Web Application with Guaranteed QoS

Cloud computing provides a reliable and cost- effective setting for deploying large-scale web applications. However, choosing and configuring an appropriate cloud Infrastructure-as-a-Service (IaaS), e.g., the appropriate database and computing instances and acceptable service rates, is a daunting task. The task is also challenging when trying to optimize the IaaS for conflicting objectives such as performance and cost. Furthermore, due to lack of understanding of the pricing model and the cloud IaaS, a cloud consumer may pay more than necessary or may not fully utilize the purchased resources. For this reason, we propose an algorithm that suggests the most cost-effective configuration meeting the QoS requirements and budget constraint. In contrast to existing cost optimization proposals, our proposed algorithm maps the minimum requirements of the to- be-deployed web application to deployment costs according to the price model set by cloud providers. The algorithm also considers QoS requirements for different resource types in the cloud, namely, database servers, computing servers, storage, and service rate. The proposed algorithm is evaluated by a series of experiments on a web application with seven different workload scenarios. The experimental results show the effectiveness of the proposed algorithm in achieving a solution with the minimum deployment cost for each scenario while satisfying all customer's requirements.
Automating Mimicry Attacks Using Static Binary Analysis

Intrusion detection systems that monitor sequences of system calls have recently become more sophisticated in defining legitimate application behavior. In particular, additional information, such as the value of the program counter and the configuration of the program's call stack at each system call, has been used to achieve better characterization of program behavior. While there is common agreement that this additional information complicates the task for the attacker, it is less clear to which extent an intruder is constrained. In this paper, we present a novel technique to evade the extended detection features of state-of-the-art intrusion detection systems and reduce the task of the intruder to a traditional mimicry attack. Given a legitimate sequence of system calls, our technique allows the attacker to execute each system call in the correct execution context by obtaining and relinquishing the control of the application's execution flow through manipulation of code pointers. We have developed a static analysis tool for Intel x86 binaries that uses symbolic execution to automatically identify instructions that can be used to redirect control flow and to compute the necessary modifications to the environment of the process. We used our tool to successfully exploit three vulnerable programs and evade detection by existing state-of-the-art system call monitors. In addition, we analyzed three real-world applications to verify the general applicability of our techniques.
A Workload-Aware, Eco-Friendly Daemon for Cluster Computing

This paper presents an eco-friendly daemon that reduces power consumption while better maintaining high performance via a novel behavioral quantification of workload. Specifically, our behavioral quantification achieves a more accurate workload characterization than previous approaches by inferring "processor stall cycles due to off-chip activities. " This quantification, in turn, provides a foundation upon which we construct an interval-based, power-aware, run-time algorithm that is implemented within a system-wide daemon. We then evaluate our power-aware daemon in a cluster-computing environment with the NAS Parallel Benchmarks. The results indicate that our novel behavioral quantification of workload allows our power-aware daemon to more tightly control performance while delivering substantial energy savings.
ORTHONORMAL BASIS FILTERS FOR GAS TURBINE FAULT DIAGNOSTICS SYTEM DESIGN : A REVIEW

Gas turbines have become the dominant technology for power generation. They can be quickly assembled and put to service. They are convenient for engine exchange during system overhaul. The emission of NOx, SOx, CO, and particulates are also significantly law as compared to coal fired power plants. However, their maintenance cost is relatively high. The perceived best approach to reduce the cost is by using a proactive maintenance strategy in which a real-time diagnostics system plays a key role. The purpose of this paper is to review application of Orthonormal Basis Filters (OBFs) to fault detection and diagnostic systems design. The types of OBFs studied include Laguerre filters, Meixner filters, Kaurtz filters, Generalized OBF, and Markov-OBF. The combination of OBFs and computational intelligence methods (artificial neural network, fuzzy systems, and evolutionary optimization) are also highlighted. The review shows that, even though OBFs have been around for more than a decade, their application is limited to model identification only. As such, the only diagnostic problem revealed so far is that concentrating on stirred tank reactor. Therefore, to extend the use of OBFs to power plants, there needs to be further study in the context of power plants or specifically gas turbines.
Tensor LRR and Sparse Coding-Based Subspace Clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established stateof- the-art methods.