Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms

A process of global importance in carbon cycling is the remineralization of algae biomass by heterotrophic bacteria, most notably during massive marine algae blooms. Such blooms can trigger secondary blooms of planktonic bacteria that consist of swift successions of distinct bacterial clades, most prominently members of the Flavobacteriia, Gammaproteobacteria and the alphaproteobacterial Roseobacter clade. We investigated such successions during spring phytoplankton blooms in the southern North Sea (German Bight) for four consecutive years. Dense sampling and high-resolution taxonomic analyses allowed the detection of recurring patterns down to the genus level. Metagenome analyses also revealed recurrent patterns at the functional level, in particular with respect to algal polysaccharide degradation genes. We, therefore, hypothesize that even though there is substantial inter-annual variation between spring phytoplankton blooms, the accompanying succession of bacterial clades is largely governed by deterministic principles such as substrate-induced forcing. DOI: http://dx.doi. org/10.7554/eLife.11888. 001
Dynamic software randomisation: Lessons learnec from an aerospace case study

Timing Validation and Verification (V&V) is an important step in real-time system design, in which a system's timing behaviour is assessed via Worst Case Execution Time (WCET) estimation and scheduling analysis. For WCET estimation, measurement-based timing analysis (MBTA) techniques are widely-used and well-established in industrial environments. However, the advent of complex processors makes it more difficult for the user to provide evidence that the software is tested under stress conditions representative of those at system operation. Measurement-Based Probabilistic Timing Analysis (MBPTA) is a variant of MBTA followed by the PROXIMA European Project that facilitates formulating this representativeness argument. MBPTA requires certain properties to be applicable, which can be obtained by selectively injecting randomisation in platform's timing behaviour via hardware or software means. In this paper, we assess the effectiveness of the PROXIMA's dynamic software randomisation (DSR) with a space industrial case study executed on a real unmodified hardware platform and an industrial operating system. We present the challenges faced in its development, in order to achieve MBPTA compliance and the lessons learned from this process. Our results, obtained using a commercial timing analysis tool, indicate that DSR does not impact the average performance of the application, while it enables the use of MBPTA. This results in tighter pWCET estimates compared to current industrial practice.
A Phase-Coupled Compiler Backend for a New VLIW Processor Architecture Using Two-step Register Allocation

This paper starts with the introduction of the Synchronous Transfer Architecture (STA), a new variant of VLIW architecture which can be viewed as a version of Transport-Triggered Architectures (TTA). Since the quality of assembly code generated by traditional high-level language compiler for such architectures is not satisfactory, we propose a novel phase-coupled compiler, in which the instruction selection and register allocation are solved together by using a two-step register allocator. According to our studies, this approach can reduce both execution time and size of the generated code by about 40%-60% in comparison to code assembled by conventional (phase-separated) compilers. Moreover, assembly code quality achieved by using two-step register allocation is comparable to the code quality obtained by a phase-coupled compiler backend based on Integer Linear Programming which requires much longer compilation time.
Spatio-temporal analysis reveals active control of both task-relevant and task-irrelevant variables

The Uncontrolled Manifold (UCM) hypothesis and Minimal Intervention principle propose that the observed differential variability across task relevant (i.e., task goals) vs. irrelevant (i.e., in the null space of those goals) variables is evidence of a separation of task variables for efficient neural control, ranked by their respective variabilities (sometimes referred to as hierarchy of control). Support for this comes from spatial domain analyses (i.e., structure of) of kinematic, kinetic, and EMG variability. While proponents admit the possibility of preferential as opposed to strictly uncontrolled variables, such distinctions have only begun to be quantified or considered in the temporal domain when inferring control action. Here we extend the study of task variability during tripod static grasp to the temporal domain by applying diffusion analysis. We show that both task-relevant and task-irrelevant parameters show corrective action at some time scales; and conversely, that task-relevant parameters do not show corrective action at other time scales. That is, the spatial fluctuations of fingertip forces show, as expected, greater ranges of variability in task-irrelevant variables (>98% associated with changes in total grasp force; vs. only <2% in task-relevant changes associated with acceleration of the object). But at some time scales, however, temporal fluctuations of task-irrelevant variables exhibit negative correlations clearly indicative of corrective action (scaling exponents <0.5); and temporal fluctuations of task-relevant variables exhibit neutral and positive correlations clearly indicative of absence of corrective action (scaling exponents ≥0.5). In agreement with recent work in other behavioral contexts, these results propose we revise our understanding of variability vis-á-vis task relevance by considering both spatial and temporal features of all task variables when inferring control action and understanding how the CNS confronts task redundancy. Instead of a dichotomy of presence vs. absence of control, we should speak of a continuum of weaker to stronger—and potentially different—control strategies in specific spatiotemporal domains, indicated here by the magnitude of deviation from the 0.5 scaling exponent. Moreover, these results are counter examples to the UCM hypothesis and the Minimal Intervention principle, and the similar nature of control actions across time scales in both task-relevant and task-irrelevant spaces points to a level of modularity not previously recognized.
Network Topologies by Emulating Network Development Processes

* Corresponding author, funded by the Engineering and Physical Sciences Research Council (EPSRC), UK and British Telecommunications PLC Abstract: Recent developments in Internet mapping and metrification as well as research into scale-free networks has given us an insight into modelling inter-connected IP networks. A number of topology generators have emerged which attempt to generate topologies which follow the power laws discovered in Internet topologies but don’t consider the causes of the power laws. They tend to concentrate on creating a topology that follows the power laws on a macroscopic scale. In this paper we present a generalisation of a topology generator which emulates microscopic network growth decisions to generate macro-scale topologies which may follow power-laws. The microscopic decisions are examined further and their effect on the macroscopic result demonstrated. The design of these schemes and the possible impact of the underlying transport network is also briefly considered.
A storage & search efficient representation of medical data

Integrating Large-scale medical data is important to support healthcare research. Medical data have a number of unique characteristics like data sparseness, high dimensionality and rapidly changing set of attributes. To handle the above challenges of medical data, Entity Attribute Value (EAV) is the widely used solution. However, EAV suffers from higher storage requirement and not search efficient. In this paper, we have proposed a storage & search efficient data model: Optimized Column-Oriented Model (OCOM) for the physical representation of Medical data as an alternative of widely used EAV. This model optimizes basic Column-Oriented model. We have demonstrated its effectiveness by testing it in a medical data-warehousing environment.
Structural Regression Trees

In many real-world domains the task of machine learning algorithms is to learn a theory for predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with nondeterminate background knowledge. (The only exception is a covering algorithm called FORS.) In this paper we present Structural Regression Trees (SRT), a new algorithm which can be applied to the above class of problems. SRT integrates the statistical method of regression trees into ILP. It constructs a tree containing a literal (an atomic formula or its negation) or a conjunction of literals in each node, and assigns a numerical value to each leaf. SRT provides more comprehensible results than purely statistical methods, and can be applied to a class of problems most other ILP systems cannot handle. Experiments in several real-world domains demonstrate that the approach is competitive with existing methods, indicating that the advantages are not at the expense of predictive accuracy.
DARPP-32 Is a Robust Integrator of Dopamine and Glutamate Signals

Integration of neurotransmitter and neuromodulator signals in the striatum plays a central role in the functions and dysfunctions of the basal ganglia. DARPP-32 is a key actor of this integration in the GABAergic medium-size spiny neurons, in particular in response to dopamine and glutamate. When phosphorylated by cAMP-dependent protein kinase (PKA), DARPP-32 inhibits protein phosphatase-1 (PP1), whereas when phosphorylated by cyclin-dependent kinase 5 (CDK5) it inhibits PKA. DARPP-32 is also regulated by casein kinases and by several protein phosphatases. These complex and intricate regulations make simple predictions of DARPP-32 dynamic behaviour virtually impossible. We used detailed quantitative modelling of the regulation of DARPP-32 phosphorylation to improve our understanding of its function. The models included all the combinations of the three best-characterized phosphorylation sites of DARPP-32, their regulation by kinases and phosphatases, and the regulation of those enzymes by cAMP and Ca2+ signals. Dynamic simulations allowed us to observe the temporal relationships between cAMP and Ca2+ signals. We confirmed that the proposed regulation of protein phosphatase-2A (PP2A) by calcium can account for the observed decrease of Threonine 75 phosphorylation upon glutamate receptor activation. DARPP-32 is not simply a switch between PP1-inhibiting and PKA-inhibiting states. Sensitivity analysis showed that CDK5 activity is a major regulator of the response, as previously suggested. Conversely, the strength of the regulation of PP2A by PKA or by calcium had little effect on the PP1-inhibiting function of DARPP-32 in these conditions. The simulations showed that DARPP-32 is not only a robust signal integrator, but that its response also depends on the delay between cAMP and calcium signals affecting the response to the latter. This integration did not depend on the concentration of DARPP-32, while the absolute effect on PP1 varied linearly. In silico mutants showed that Ser137 phosphorylation affects the influence of the delay between dopamine and glutamate, and that constitutive phosphorylation in Ser137 transforms DARPP-32 in a quasi-irreversible switch. This work is a first attempt to better understand the complex interactions between cAMP and Ca2+ regulation of DARPP-32. Progressive inclusion of additional components should lead to a realistic model of signalling networks underlying the function of striatal neurons.
Excitation Source Features for Improving the Detection of Vowel Onset and Offset Points in a Speech Sequence

The task of detecting the vowel regions in a given speech signal is a challenging problem. Over the years, several works on accurate detection of vowel regions and the corresponding vowel onset points (VOPs) and vowel end points (VEPs) have been reported. A novel front-end feature extraction technique exploiting the temporal and spectral characteristics of the excitation source information in the speech signal is proposed in this paper to improve the detection of vowel regions, VOPs and VEPs. To do the same, a three-class classifiers (vowel, nonvowel and silence) is developed on the TIMIT database using the proposed features as well as mel-frequency cepstral coefficients (MFCC). Statistical modeling based on deep neural network has been employed for learning the parameters. Using the developed three-class classifier, a given speech sample is then forced aligned against the trained acoustic models to detect the vowel regions. The use of proposed feature results in detection of vowel regions quite different from those obtained through the MFCC. Exploiting the differences in the evidences obtained by using the two kinds of features, a technique to combine the evidences is also proposed in order to get a better estimate of the VOPs and VEPs.
Non‐verbal Assessment of Piagetian Concepts in Preschool Children with Impaired Language Development

ABSTRACT A series of non‐verbal problem solving tasks was administered to a group of 26 preschool children with impaired language development and a group of 26 children with normal language development. The impaired language and normal language groups were matched on chronological age, sex, social class, and nonverbal intelligence test (Leiter) scores. The impaired language group had Leiter scores in the normal range but Reynell language comprehension and/or expression scores at 75% or less than their chronological age. The children were tested with non‐verbal analogues to Piagetian concepts such as equivalence, conservation, seriation, and horizontality. While there were no differences between the impaired language and the normal language groups on the simpler concepts (e.g. comparisons of relative quantity), the impaired language group had significantly more difficulty with seriation, conservation, spatial order, and topological concepts. This suggests that language may play an important role in more co.
Does impaired O2 delivery during exercise accentuate central and peripheral fatigue in patients with coexistent COPD-CHF?

Impairment in oxygen (O2) delivery to the central nervous system (“brain”) and skeletal locomotor muscle during exercise has been associated with central and peripheral neuromuscular fatigue in healthy humans. From a clinical perspective, impaired tissue O2 transport is a key pathophysiological mechanism shared by cardiopulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and chronic heart failure (CHF). In addition to arterial hypoxemic conditions in COPD, there is growing evidence that cerebral and muscle blood flow and oxygenation can be reduced during exercise in both isolated COPD and CHF. Compromised cardiac output due to impaired cardiopulmonary function/interactions and blood flow redistribution to the overloaded respiratory muscles (i.e., ↑work of breathing) may underpin these abnormalities. Unfortunately, COPD and CHF coexist in almost a third of elderly patients making these mechanisms potentially more relevant to exercise intolerance. In this context, it remains unknown whether decreased O2 delivery accentuates neuromuscular manifestations of central and peripheral fatigue in coexistent COPD-CHF. If this holds true, it is conceivable that delivering a low-density gas mixture (heliox) through non-invasive positive pressure ventilation could ameliorate cardiopulmonary function/interactions and reduce the work of breathing during exercise in these patients. The major consequence would be increased O2 delivery to the brain and active muscles with potential benefits to exercise capacity (i.e., ↓central and peripheral neuromuscular fatigue, respectively). We therefore hypothesize that patients with coexistent COPD-CHF stop exercising prematurely due to impaired central motor drive and muscle contractility as the cardiorespiratory system fails to deliver sufficient O2 to simultaneously attend the metabolic demands of the brain and the active limb muscles.
Witnessing workplace bullying and the Chinese manufacturing employee

Purpose – The purpose of this study was to investigate the relationship between witnessing workplace bullying behavior and employee attitudes and intentions to turnover.Design/methodology/approach – A sample of 150 full‐time employees from two mid‐sized indigenous private manufacturing plants from the province of Henan, China were surveyed at a single point in time.Findings – Findings indicate a negative relationship between witnessing workplace bullying and employee satisfaction and commitment. In addition, strain and satisfaction fully mediate the relationship between witnessing workplace bullying and employee intention to turnover.Research limitations/implications – Given the link between witnessing workplace bullying and employee attitudes, it is important for Chinese managers to ensure that interactions with employees maintain a positive and professional tone.Originality/value – The paper's findings suggest that it is not necessary for an employee to be the personal victim of bullying behavior, rathe.
Links between Human LINE-1 Retrotransposons and Hepatitis Virus-Related Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for approximately 80% of liver cancers, the third most frequent cause of cancer mortality. The most prevalent risk factors for HCC are infections by hepatitis B or hepatitis C virus. Findings suggest that hepatitis virus-related HCC might be a cancer in which LINE-1 retrotransposon, often termed L1, activity plays a potential role. Firstly, hepatitis viruses can suppress host defense factors that also control L1 mobilization. Secondly, many recent studies also have indicated that hypomethylation of L1 affects the prognosis of HCC patients. Thirdly, endogenous L1 retrotransposition was demonstrated to activate oncogenic pathways in HCC. Fourthly, several L1 chimeric transcripts with host or viral genes are found in hepatitis virus-related HCC. Such lines of evidence suggest a linkage between L1 retrotransposons and hepatitis virus-related HCC. Here, I briefly summarize current understandings of the association between hepatitis virus-related HCC and L1. Then, I discuss potential mechanisms of how hepatitis viruses drive the development of HCC via L1 retrotransposons. An increased understanding of the contribution of L1 to hepatitis virus-related HCC may provide unique insights related to the development of novel therapeutics for this disease.
A hardware implementation of a radial basis function neural network using stochastic logic

Hardware implementations of artificial neural networks typically require significant amounts of hardware resources. This paper proposes a novel radial basis function artificial neural network using stochastic computing elements, which greatly reduces the required hardware. The Gaussian function used for the radial basis function is implemented with a two-dimensional finite state machine. The norm between the input data and the center point is optimized using simple logic gates. Results from two pattern recognition case studies, the standard Iris flower and the MICR font benchmarks, show that the difference of the average mean squared error between the proposed stochastic network and the corresponding traditional deterministic network is only 1.3% when the stochastic stream length is 10kbits. The accuracy of the recognition rate varies depending on the stream length, which gives the designer tremendous flexibility to tradeoff speed, power, and accuracy. From the FPGA implementation results, the hardware resource requirement of the proposed stochastic hidden neuron is only a few percent of the hardware requirement of the corresponding deterministic hidden neuron. The proposed stochastic network can be expanded to larger scale networks for complex tasks with simple hardware architectures.
Cost-Aware Cloud Bursting for Enterprise Applications

The high cost of provisioning resources to meet peak application demands has led to the widespread adoption of pay-as-you-go cloud computing services to handle workload fluctuations. Some enterprises with existing IT infrastructure employ a hybrid cloud model where the enterprise uses its own private resources for the majority of its computing, but then “bursts” into the cloud when local resources are insufficient. However, current commercial tools rely heavily on the system administrator’s knowledge to answer key questions such as when a cloud burst is needed and which applications must be moved to the cloud. In this article, we describe Seagull, a system designed to facilitate cloud bursting by determining which applications should be transitioned into the cloud and automating the movement process at the proper time. Seagull optimizes the bursting of applications using an optimization algorithm as well as a more efficient but approximate greedy heuristic. Seagull also optimizes the overhead of deploying applications into the cloud using an intelligent precopying mechanism that proactively replicates virtualized applications, lowering the bursting time from hours to minutes. Our evaluation shows over 100% improvement compared to naïve solutions but produces more expensive solutions compared to ILP. However, the scalability of our greedy algorithm is dramatically better as the number of VMs increase. Our evaluation illustrates scenarios where our prototype can reduce cloud costs by more than 45% when bursting to the cloud, and that the incremental cost added by precopying applications is offset by a burst time reduction of nearly 95%.
Characterization, modelling, and parameter sensitivity study on electronic packaging polymers

Thermosetting polymers are widely used in electronic packaging. For instance, epoxy molding compound is extensively used as an encapsulant for electronic packages to protect the IC chips from mechanical and chemical hazards. It is well known that molding compounds show not only strong temperature dependent but also time dependent behavior. The thermo-mechanical behavior of these polymer constituents determines the performance, such as functionality and reliability, of the final products. In this paper, experimental characterization was carried out to investigate the time and temperature dependent properties of the selected molding compound. Thermal Mechanical Analysis (TMA) was applied to measure the CTE of the materials. Dynamical Mechanical Analysis (DMA) measurement was performed using temperature and frequency sweep modes. Finite element modeling was conducted on typical QFN (Quad Flat Non-lead) package device. Three material models, i.e., full viscoelastic model, temperature-dependent elastic model (1Hz DMA data) and constant elastic model, are used respectively to describe the behavior of the molding compound. The output responses of the simulations are von Mises stress distribution, package warpage and interlaminar stresses. The results show that the von Mises stress and package warpage are significantly different when considering the EMC as full viscoealstic, temperature-dependent elastic and constant elastic.