Miracle-dz/UniFAR-FTUs huggingface dataset:
Environmental controls on the biogeography of diazotrophy and Trichodesmium in the Atlantic Ocean

The cyanobacterium Trichodesmium is responsible for a significant proportion of the annual “new” nitrogen introduced into the global ocean. Despite being arguably the best studied marine diazotroph, the factors controlling the distribution and growth of Trichodesmium remain a subject of debate, with sea surface temperature, the partial pressure of CO2, and nutrients including iron (Fe) and phosphorus (P), all suggested to be important. Synthesizing data from seven cruises collectively spanning large temporal and spatial scales across the Atlantic Ocean, including two previously unreported studies crossing the largely undersampled South Atlantic gyre, we assessed the relationship between proposed environmental drivers and both community N2 fixation rates and the distribution of Trichodesmium. Simple linear regression analysis would suggest no relationship between any of the sampled environmental variables and N2 fixation rates. However, considering the concentrations of iron and phosphorus together within a simplified resource‐ratio framework, illustrated using an idealized numerical model, indicates the combined effects these nutrients have on Trichodesmium and broader diazotroph biogeography, alongside the reciprocal maintenance of different biogeographic provinces of the (sub)tropical Atlantic in states of Fe or P oligotrophy by diazotrophy. The qualitative principles of the resource‐ratio framework are argued to be consistent with both the previously described North‐South Atlantic contrast in Trichodesmium abundance and the presence and consequence of a substantial non‐Trichodesmium diazotrophic community in the western South Atlantic subtropical gyre. A comprehensive, observation‐based explanation of the interactions between Trichodesmium and the wider diazotrophic community with iron and phosphorus in the Atlantic Ocean is thus revealed.
Adjustment of airborne laser altimetry strips

Whereas the objectives of a strip adjustment procedure are simple to define, namely improving the accuracy of laser data and creating a seamless dataset, achieving them is difficult. Complex data acquisition systems and data that carry only little information are two aspects that make the formulation of a strip adjustment model difficult. Furthermore, difficulties in processing the data manually and the existence of partial data that usually contain only the laser points but not the system measurements, are other aspects that should be handled by a strip adjustment algorithm to make it useful. This paper presents a 3D system-driven strip adjustment algorithm. Based on the properties of the data the error recovery model is surface based. To eliminate manual processing of the data, handling tie and control information is autonomous, and to have the model applicable, the input data are the laser points. The application of the procedure is demonstrated here for the estimation of GPS biases to analyze the magnitude of positional offsets in the data. Results show the existence of significant errors in position.
Determination algorithm of service composition based on QoS relation

In order to represent the relation between different service classes, service requester's preference as well as internal relations among different QoS factors in service composition. We propose Determination Algorithm of Service Composition based on QoS Relation. QoS properties such as price, availability, response time, reliability and reputation are defined and used in the algorithm. Furthermore, we make use of uncertain theory to construct co-QoS which aggregated by availability, reliability and reputation. As a result, in evaluating the quality of service composition, we take relations on QoS properties between different service classes into account to some extent. The result of experiments show the algorithm holds good executive efficiency, stability as well as outstanding outcome.
Speech Perception and Production by a Self-Organizing Neural Network.

Abstract : Considerations of the real-time self-organization of neural networks for speech recognition and production have lead to a new understanding of several key issues in such networks, most notably a definition of new processing units and functions of hierarchical levels in the auditory system. An important function of a particular neural level in the auditory system is to provide a partially-compressed code, mapped to the articulatory system, to permit imitation of novel sounds. Furthermore, top-down priming signals from the articulatory system to the auditory system help to stabilize the emerging auditory code. These structures help explain results from the motor theory, which states the speech is analyzed by how it would be produced. Higher stages of processing require chunking or unitization of the emerging language code, an example of a classical grouping problem. The partially compressed auditory codes are further compressed into item codes (e.g., phonemic segments), which are stored in a working memory representation whose short-term memory pattern is its code. A masking field level receives input from this working memory and encodes this input into list chunks, whose top-down signals organize the items in working memory into coherent groupings with invariant properties. This total architecture sheds new light on key speech issues such as coarticulation, analysis-by-synthesis, motor theory, categorical perception, invariant speech perception, word superiority, and phonemic restoration.
Stages and transformations in parallel programming

An approach, called SAT (Stages And Transformations), is introduced to support the derivation of parallel distributed-memory programs. During the design, a program is viewed as a single thread of stages, with parallelism concentrated within stages; the target program is of the SPMD format. The design process is based on the transformation rules of the Bird-Meertens formalism of higher-order functions over lists. The approach is illustrated by three case studies which include: a systematic method of constructing list homomorphisms, a scalable, load-balanced implementation of divide-and-conquer based on a specialized topology and a formal derivation of a time and cost optimal parallel algorithm for straightforward polynomial multiplication.
On Cooperative Learning Teams for Multiagent Team Formation

In this paper, we propose a team formation methodology based on cooperative learning teams, adopted from the area of educational research. Cooperative learning is a type of learning where students work in teams and learn through team-based interactions. In education, research in assigning students to appropriate teams and enforcing fair assessment of student performance in a team have generated useful policies and rules. In our multiagent systems project, we use these policies and rules as the underlying framework to evaluate and form teams. We have built a system called I-MINDS as an infrastructure to support cooperative learning among remote and in-class students.
Analysis of Kernel Mean Matching under Covariate Shift

In real supervised learning scenarios, it is not uncommon that the training and test sample follow different probability distributions, thus rendering the necessity to correct the sampling bias. Focusing on a particular covariate shift problem, we derive high probability confidence bounds for the kernel mean matching (KMM) estimator, whose convergence rate turns out to depend on some regularity measure of the regression function and also on some capacity measure of the kernel. By comparing KMM with the natural plug-in estimator, we establish the superiority of the former hence provide concrete evidence/ understanding to the effectiveness of KMM under covariate shift.
Unhealthy behaviors during pregnancy : who continues to smoke and consume alcohol, and is treatment of anxiety and depressive symptoms effective?

This thesis investigated continued smoking and alcohol consumption during pregnancy, and treatment of anxiety and depressive symptoms during pregnancy using cognitive behavioral therapy. Data from a large prospective population-based cohort study and a randomized controlled trial were used. Regarding continued smoking and alcohol consumption during pregnancy, we studied associations with stressful events during pregnancy and personality. We found that the experienced severity of the following categories of stressful events are associated with continued alcohol consumption: ‘conflict with loved ones’, ‘crime related’, ‘pregnancy-specific’, and the total including all events. As for personality, we found that higher levels of openness to experience and lower levels of conscientiousness are associated with continued alcohol consumption. No associations were found with continued smoking during pregnancy. Furthermore, we studied the association of adverse pregnancy and delivery outcomes with postpartum smoking relapse, and found a positive association. Last, we investigated the effect of individual cognitive behavioral therapy on anxiety and depressive symptoms during pregnancy. We found no evidence for a beneficial effect of cognitive behavioral therapy, when compared to care as usual, among a group of pregnant women with subclinical anxiety and depressive symptoms and disorders that were not active help-seekers. Our findings raise new questions on the treatment of anxiety and depression symptoms during pregnancy. Our results suggest that more evidence is needed for which specific groups screening and treatment may be beneficial during pregnancy, including pregnant women with disorders.
Controlling biofilm formation, prophage excision and cell death by rewiring global regulator H‐NS of Escherichia coli

The global regulator H‐NS of Escherichia coli controls genes related to stress response, biofilm formation and virulence by recognizing curved DNA and by silencing acquired genes. Here, we rewired H‐NS to control biofilm formation using protein engineering; H‐NS variant K57N was obtained that reduces biofilm formation 10‐fold compared with wild‐type H‐NS (wild‐type H‐NS increases biofilm formation whereas H‐NS K57N reduces it). Whole‐transcriptome analysis revealed that H‐NS K57N represses biofilm formation through its interaction with the nucleoid‐associated proteins Cnu and StpA and in the absence of these proteins, H‐NS K57N was unable to reduce biofilm formation. Significantly, H‐NS K57N enhanced the excision of defective prophage Rac while wild‐type H‐NS represses excision, and H‐NS controlled only Rac excision among the nine resident E. coli K‐12 prophages. Rac prophage excision not only led to the change in biofilm formation but also resulted in cell lysis through the expression of toxin HokD. Hence, the H‐NS regulatory system may be evolved through a single‐amino‐acid change in its N‐terminal oligomerization domain to control biofilm formation, prophage excision and apoptosis.
Controlled Positivity under constrained controls of general 2-D systems

The problem of synthesizing stabilizing controllers, when the closed-loop system is required to remain positive, is solved for a class of 2-D systems described by a general model. More precisely, the synthesis of local state-feedback controllers is solved in terms of Linear Programming to deal with the stabilization under constrained controls and nonnegative states. A numerical example is given to illustrate the proposed approach.
06 10 19 5 v 1 1 7 O ct 2 00 6 Moduli Corrections to D-term Inflation

We present a D-term hybrid inflation model, embedded in supergravity with moduli stabilisation. Its novel features allow us to overcome the serious challenges of combining D-term inflation and moduli fields within the same string-motivated theory. One salient point of the model is the positive definite uplifting D-term arising from the moduli stabilisation sector. By coupling this D-term to the inflationary sector, we generate an effective Fayet-Iliopoulos term. Moduli corrections to the inflationary dynamics are also obtained. Successful inflation is achieved for a range of parameter values with spectral index compatible with the WMAP3 data. Cosmic D-term strings are also formed at the end of inflation; these are no longer BPS objects. The properties of the strings are studied.
Performance analysis of opportunistic cooperative communication over Nakagami-m fading channels

In this paper, a dual-hop wireless cooperative network with opportunistic amplify-and-forward (AF) relaying is investigated over independent and non-identically distributed Nakagami-m fading channels. Due to the complicated form of the probability density function of the instantaneous dual-hop link signal-to-noise ratio (SNR), the symbol error rate and outage probability expressions are difficult to obtain in closed form. Taking advantage of Maclaurin series expansion to simplify the probability density function of the output SNR, we present the tight approximate symbol error rate and outage probability expressions at medium and high SNR regions for opportunistic AF cooperative network. Computer simulation results reveal that the analysis expressions about symbol error rate and outage probability are accurate at medium and high SNR regions.
AML: A Manufacturing Language

AML, A Maufacturing Language, was designed to be a well-structured, semantically powerful interactive language for robot programming. In this paper, we identify the de sign objectives for such a language and give a technical description of AML. Important features are described and illustrated through representative examples of robot appli cations programming.
Diffusion maps for dimensionality reduction with partially labeled samples

In this paper, we present a novel diffusion maps based semi-supervised algorithm for dimensionality reduction and data parameterization. Unlike previous works which use only geometric information for similarity metric construction, a distribution similarity metric is introduced to boost the classification accuracy in our algorithm. The metric is related to the posterior probability of the labels of each sample, which is learned through expectation maximization algorithm. The algorithm preserves the local manifold structure in addition to separating samples in different classes. Encouraging experimental results on Hand-written digits, Yale faces and UCI data sets show that the algorithm can improve the classification accuracy significantly.
Towards Image Retrieval by Texture Segmentation with Genetic Programming

This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n times n windows using genetic programming. The classifier is then used to segment the images in the collection. If there is a significant contiguous area of T in an image, it is considered to contain that texture for retrieval purposes. We have experimented with sky and grass textures in the Corel Volume 12 image set. Experiments with a single image indicate that classifiers for the two textures can be learned to a high accuracy. Experiments with a test set of 714 Corel images gave a retrieval accuracy of 84% for both sky and grass textures. These results suggest that the use of texture could enhance retrieval accuracy in content based image retrieval systems
An Example-Driven Approach for Automatic Extension of Monitoring Model

Digital home refers to the use of computer and information technology to control home appliances and features. Management applications of home devices are building based on runtime information of the devices. The runtime model can be built using data from monitoring tools and analysis tools for specific platform. In order to construct runtime model from data provided from both monitoring tools and analysis tools, we propose an example-driven approach for automatic extension of monitoring model. Because analysis results conform to the same meta-model, the meta-model can be inferred from enough examples. Our approach first generates a meta-model of analysis result examples, and then generates the model transformation for synthesis of existing model and analysis results. A validation algorithm is proposed for automatically updating of meta-model based on analysis result.
Synthesis for testability: circuits derived from ordered Kronecker functional decision diagrams

Summary form only given. Testability properties of circuits derived from Ordered Kronecker Functional Decision Diagrams (OKFDDs) are studied with respect to the Stuck-At Fault Model (SAFM) and the Cellular Fault Model (CFM). The computation of complete test sets and of all occurring redundancies can be done easily and efficiently and circuits with high testability can be obtained. <<ETX>>
Multifrequency Jump-Diffusions: An Equilibrium Approach

This paper proposes that equilibrium valuation is a powerful method to generate endogenous jumps in asset prices. We specify an economy with continuous consumption and dividend paths, in which endogenous price jumps originate from the market impact of regime-switches in the drifts and volatilities of fundamentals. We parsimoniously incorporate regimes of heterogeneous durations and verify that the persistence of a shock endogenously increases the magnitude of the induced price jump. As the number of frequencies driving fundamentals goes to infinity, the price process converges to a novel stochastic process, which we call a multifractal jump-diffusion.
Integration of security in network routing protocols

There are two sources of threats to secure operation of routing protocols in networks. The first source of threats is subverted routers that legitimately participate in a routing protocol. The second source of threats is intruders which may illegally attempt to interfere in routing protocols by masquerading as routers. In this paper, we first analyse the security requirements of network routing protocols and then discuss the necessary measures which can be adopted to make the operation of these protocols secure.
Optimized Feature Selection for Initial Launch in Dynamic Software Product Lines

A Dynamic Software Product Line (DSPL) allows the generation of products that can adapt dynamically according to changes in requirements or environment at runtime. This runtime adaptation is often made by the activation and deactivation of features, introducing a cost (e.g., an overhead regarding resource consumption). To reduce this cost, a solution is the partial product configuration at the static binding time. Thus, in DSPLs, one challenge is the feature selection to define which features should be bound permanently before the initial launch and which features should be bound at runtime. In this paper, we address this challenge presenting a graph model formulation to the feature selection problem for the initial launch in DSPLs that considers both static and dynamic binding. This model allows the application of efficient optimization algorithms to solve the problem. We also present a proof of concept showing that the model can be used to generate optimized solutions to the feature selection problem for initial launch in DSPLs.
An Energy Efficient Load Balancing Selection Strategy for Adaptive NoC Routers

Modern chip multi core systems are using Network on Chip (NoC) as the communication infrastructure. Effective output channel selection techniques are used in adaptive routers, which form the back bone of NoC systems to reduce the average packet latency of inter-core communications in multi-core systems. We propose a selection strategy, Cool Centers, for output port selection that ensures load balancing on a mesh NoC system. Cool centers reduces the possibility of traffic hot-spot formation in the network and can be applied on any minimal adaptive routing algorithm for improving the system performance. The proposed system equally distributes the traffic load among the available minimal paths without any significant architectural overhead. This reduces the rate of non-uniform wear and tear of routers and links, and prevent early aging of chips.
An Efficient APSP Algorithm

In many cases, recursion removal improves the efficiency of recursive algorithms, especially algorithms with large formal parameters, such as All Pair Shortest path (APSP) algorithms. In this article, a recursion removal of the Seidel’s APSP [14] is presented, and a general method of recursion removal, called stack indexation is introduced.
Antitumor Activity IL-12 Bioactivity and Demonstrates Protein Retains Antibody Specificity and A Single-Chain IL-12 IgG3 Antibody Fusion

IL-12 is a heterodimeric cytokine with many actions on innate and cellular immunity that may have antitumor and antimetastatic effects. However, systemic administration of IL-12 can be toxic. Tumor-speciﬁc Abs provide a means to selectively target a metastatic/residual nodule and deliver therapeutic quantities of an immunostimulatory molecule like IL-12 with lower systemic levels and ideally, toxicity. We report the construction and characterization of an Ab fusion protein in which single-chain murine IL-12 is fused to an anti-Her2/ neu Ab at the amino terminus (mscIL-12.her2.IgG3). The use of single-chain IL-12 in the fusion protein simpliﬁes vector construction, ensures equimolar concentrations of the two IL-12 subunits, and may confer greater stability to the fusion protein. SDS-PAGE analysis shows this 320-kDa protein is secreted and correctly assembled. FACS analysis demonstrates that this fusion protein binds to cells transfected with the Her2/ neu Ag, thus retaining Ab speciﬁcity; this fusion protein also binds to a cell line and to PHA-activated PBMC that express the IL-12R, thus demonstrating cytokine receptor speciﬁcity. T cell proliferation assays and NK cytotoxicity assays demonstrate that this fusion protein exhibits IL-12 bioactivity comparable to recombinant murine IL-12. In vivo studies demonstrate that this fusion protein has antitumor activity. These results are signiﬁcant and suggest that this IL-12 Ab fusion protein can effectively combine the therapeutic potential of IL-12 with the tumor-targeting ability of the Ab and may provide a viable alternative to systemic administration of IL-12. The Journal of Immunology, 1999, 163: 250–258.
Circuit-level modeling of FinFet sub-threshold slope and DIBL mismatch beyond 22nm

We propose a way of modeling device variability in sub-threshold slope and DIBL at circuit-level using dependent voltage sources. The usual way of modeling variability using threshold voltage shift and drain current amplification is becoming inaccurate as new sources of variability appear in sub-22nm devices. Benchmark experiments on circuit level, using a set of 1000 TCAD-based 10nm-FinFet device models with mismatch as a reference, show systematic accuracy improvements on mean and standard deviation of 6T-SRAM cell stability metrics of up to 30 and 10 percentage scores, respectively.
Reliability and Validity of the Diabetes Distress Scale

Objective To evaluate the reliability and validity of the Chinese version of the Diabetes Distress Scale and to provide an effective and convenient way to evaluate diabetes-related emotional distress in Chinese diabetic patients.Methods The translated version of the Diabetes Distress Scale was applied among 99 diabetic patients and the reliability and validity of the scale was evaluated.Results The Cronbach’s coefficients of the scale and 4 sub-scales were 0.842 ~0.951 and factor loading was approximately consistent with the structure and content of the three sub-scales.Conclusion Chinese version of the Diabetes Distress Scale is reliable and valid to determine diabetes-related emotional distress in Chinese diabetic patients.