Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed dynamic elastic nets: A scalable approach for regularization in dynamic manufacturing environments

In this paper, we focus on the task of learning influential parameters under unsteady and dynamic environments. Such unsteady and dynamic environments often occur in the ramp-up phase of manufacturing. We propose a novel regularization-based framework, called Distributed Dynamic Elastic Nets (DDEN), for this problem and formulate it as a convex optimization objective. Our approach solves the optimization problem using a distributed framework. Consequently it is highly scalable and can easily be applied to very large datasets. We implement a L-BFGS based solver in the Apache Spark framework. For validating our algorithm, we consider the issue of scrap reduction at an assembly line during the ramp-up phase of a manufacturing plant. By considering the logistic regression as a sample model, we evaluate the performance of our approach although extensions of the proposed regularizer to other classification and regression techniques is straightforward. Through experiments on data collected at a functioning manufacturing plant, we show that the proposed method not only reduces model variance but also helps preserve the relative importance of features in dynamic conditions compared to standard approaches. The experiments further show that the classification performance of DDEN if often better than logistic regression with standard elastic nets for datasets from dynamic and unsteady environments. We are collaborating with manufacturing units to use this process for improving production yields during the ramp-up phase. This work serves as a demonstration of how data mining can be used to solve problem in manufacturing.
Markov Chain Monte Carlo for Arrangement of Hyperplanes in Locality-Sensitive Hashing

Since Hamming distances can be calculated by bitwise computations, they can be calculated with less computational load than L2 distances. Similarity searches can therefore be performed faster in Hamming distance space. The elements of Hamming distance space are bit strings. On the other hand, the arrangement of hyperplanes induce the transformation from the feature vectors into feature bit strings. This transformation method is a type of locality-sensitive hashing that has been attracting attention as a way of performing approximate similarity searches at high speed. Supervised learning of hyperplane arrangements allows us to obtain a method that transforms them into feature bit strings reflecting the information of labels applied to higher-dimensional feature vectors. In this p aper, we propose a supervised learning method for hyperplane arrangements in feature space that uses a Markov chain Monte Carlo (MCMC) method. We consider the probability density functions used during learning, and evaluate their performance. We also consider the sampling method for learning data pairs needed in learning, and we evaluate its performance. We confirm that the accuracy of this learning method when using a suitable probability density function and sampling method is greater than the accuracy of existing learning methods.
Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity

The state of the sensory cortical network can have a profound impact on neural responses and perception. In rodent auditory cortex, sensory responses are reported to occur in the context of network events, similar to brief UP states, that produce “packets” of spikes and are associated with synchronized synaptic input (Bathellier et al., 2012; Hromadka et al., 2013; Luczak et al., 2013). However, traditional models based on data from visual and somatosensory cortex predict that ascending sensory thalamocortical (TC) pathways sequentially activate cells in layers 4 (L4), L2/3, and L5. The relationship between these two spatio-temporal activity patterns is unclear. Here, we used calcium imaging and electrophysiological recordings in murine auditory TC brain slices to investigate the laminar response pattern to stimulation of TC afferents. We show that although monosynaptically driven spiking in response to TC afferents occurs, the vast majority of spikes fired following TC stimulation occurs during brief UP states and outside the context of the L4>L2/3>L5 activation sequence. Specifically, monosynaptic subthreshold TC responses with similar latencies were observed throughout layers 2–6, presumably via synapses onto dendritic processes located in L3 and L4. However, monosynaptic spiking was rare, and occurred primarily in L4 and L5 non-pyramidal cells. By contrast, during brief, TC-induced UP states, spiking was dense and occurred primarily in pyramidal cells. These network events always involved infragranular layers, whereas involvement of supragranular layers was variable. During UP states, spike latencies were comparable between infragranular and supragranular cells. These data are consistent with a model in which activation of auditory cortex, especially supragranular layers, depends on internally generated network events that represent a non-linear amplification process, are initiated by infragranular cells and tightly regulated by feed-forward inhibitory cells.
Performance Comparison and Analysis of Yarn's Schedulers with Stress Cases

Hadoop, as a popular distributed storage and computing platform, has been widely used in many companies. Yarn is the resource management platform in Hadoop and plays an important role in the resource managing, because it can affect the cluster's energy efficiency and the usability for applications. The schedulers are the brain of Yarn, which manage and schedule resources from cluster to applications. In this paper, we conduct experiments to compare and analyze the performance of Yarn's schedulers. We use various scenarios to demonstrate the strengths and weaknesses of each scheduler from the perspective of response speed, cluster's efficiency, scheduler's speciality etc. Experimental results demonstrate that the FIFO Scheduler has a better performance and data locality sense for batch jobs processing than the other schedulers, but the Capacity Scheduler and the FIFO Scheduler have better response speed and cluster's usability than the FIFO Scheduler which has a hunger problem in mixed scenario.
Autonomous driving in dynamic environments

With the bigger number of cars on our roads every year, also the potential danger to die or get injured in road traffic increases. Most of these accidents are due to human error, while driver assistant systems like ESP help making traffic more safe. To increase this effect, advanced driver assistant systems are under development and a whole body of research is performed all over the world to enable autonomous driving on our roads. In this context in 2004 the work for this thesis was started focusing on the question on how to enable autonomous driving in dynamic traffic scenes, especially considering dynamic obstacles. An autonomous driving demonstrator was built that was a platform for research in the field of autonomous navigation in both structured on road environments and unstructured environments like parking lots. Driving among other traffic participants needs a deep understanding of their behaviors to predict their possible behaviors and act accordingly to ensure safe driving. The algorithms presented in this thesis where widely tested on the SmartTer vehicle at EPFL and ETH and during the preparation of the DARPA Urban Challenge entry of the Tartanracing Team at Carnegie Mellon University in Pittsburgh, Pennsylvania, USA. In the SPARC project (Secure Propulsion using Advanced Redundant Control) the experiences gained from autonomous driving research where also applied to advanced driver assistant systems and showed the strong link between autonomous driving and driver assistant systems.
Aggregating preferences in multi-issue domains by using maximum likelihood estimators

In this paper, we study a maximum likelihood estimation (MLE) approach to voting when the set of alternatives has a multi-issue structure, and the voters' preferences are represented by CP-nets. We first consider general multi-issue domains, and study whether and how issue-by-issue voting rules and sequential voting rules can be represented by MLEs. We first show that issue-by-issue voting rules in which each local rule is itself an MLE (resp. a candidate scoring rule) can be represented by MLEs with a weak (resp. strong) decomposability property. Then, we prove two theorems that state that if the noise model satisfies a very weak decomposability property, then no sequential voting rule that satisfies unanimity can be represented by an MLE, unless the number of voters is bounded. We then consider multi-issue domains in which each issue is binary; for these, we propose a general family of distance-based noise models, of which give an axiomatic characterization. We then propose a more specific family of natural distance-based noise models that are parameterized by a threshold. We identify the complexity of winner determination for the corresponding MLE voting rule in the two most important subcases of this framework.
Toward a theory of biological‐physical control of harmful algal bloom dynamics and impacts

A growing body of laboratory, field, and theoretical work suggests that the dynamics of harmful algal blooms and their impacts on other organisms are frequently controlled not only by physiological responses to local environmental conditions as modified by trophic interactions, but also by a series of interactions between biological and physical processes occurring over an extremely broad range of temporal and spatial scales. All too frequently, major gaps in our ability to identify, measure, and model the underlying biological and physical processes and their interactions over the appropriate temporal and spatial scales have prevented the quantitative assessment of the importance of these factors in causing past blooms and the development of predictive models of bloom dynamics and impacts. For these reasons, we have combined fluid continuity equations with a conservation equation for population dynamics to quantify how biological and physical processes and their interactions affect the population dynamics of harmful algae and their potential impact on other organisms. Applications of the resulting numerical and conceptual models to toxic algal blooms in upwelling systems and pycnocline layers suggest not only that bloom dynamics and impacts are sensitive to biological‐physical interactions occurring at multiple scales, but also that such interactions may be critical components of the life‐history strategies of these organisms.
Heat-inducible DNA Binding of Purified Heat Shock Transcription Factor 1 (*)

The heat-induced expression of heat shock proteins, called the cellular stress response, is mediated by heat shock transcription factor 1 (HSF1). HSF1 exists in unstressed cells in an inactive form, which is converted to the DNA binding form upon exposure of cells to elevated temperature. We have developed a protocol for isolation of the non-DNA binding form of recombinant mouse HSF1, involving expression and affinity purification of HSF1 as a fusion with the glutathione S-transferase protein in Escherichia coli, followed by specific protease cleavage to release pure HSF1 protein. We report here that the purified inactive HSF1 can be converted to the DNA binding form by heat treatment in vitro. Chemical cross-linking analysis demonstrates that this conversion is accompanied by oligomerization of HSF1 from a monomeric to a trimeric native structure, similar to that observed for HSF1 in heat-shocked cells. These results indicate that elements residing in the HSF1 polypeptide are sufficient both for maintenance of this factor in the non-DNA binding form and for its heat-induced conversion to the DNA binding form and support a role for HSF1 as the “molecular thermostat” in eukaryotic cells, which senses adverse environmental conditions and activates the cellular stress response.
Evolving Synaptic Plasticity with an Evolutionary Cellular Development Model

Since synaptic plasticity is regarded as a potential mechanism for memory formation and learning, there is growing interest in the study of its underlying mechanisms. Recently several evolutionary models of cellular development have been presented, but none have been shown to be able to evolve a range of biological synaptic plasticity regimes. In this paper we present a biologically plausible evolutionary cellular development model and test its ability to evolve different biological synaptic plasticity regimes. The core of the model is a genomic and proteomic regulation network which controls cells and their neurites in a 2D environment. The model has previously been shown to successfully evolve behaving organisms, enable gene related phenomena, and produce biological neural mechanisms such as temporal representations. Several experiments are described in which the model evolves different synaptic plasticity regimes using a direct fitness function. Other experiments examine the ability of the model to evolve simple plasticity regimes in a task -based fitness function environment. These results suggest that such evolutionary cellular development models have the potential to be used as a research tool for investigating the evolutionary aspects of synaptic plasticity and at the same time can serve as the basis for novel artificial computational systems.
Inkjet filling of TSVs with silver nanoparticle ink

Through silicon vias (TSVs) have been used in 3D packaging of microelectronic devices and MEMS devices, where they provide electrical interconnections through the stacked wafers and devices. Currently, chemical vapor deposition (CVD) or electroless deposition are used to partially or fulfill the vias. However, these methods are time consuming. Thus, the potential of inkjet printing to linearly fill the TSVs with silver nanoparticle ink, as an additive digital fabrication technique, will be reviewed. This technique could make the via metallization process much faster, agile, and cost-efficient. In this study, vias with the outer diameter of 80μm and depth of around 115μm fabricated with dry-reactive ion etching (DRIE) are filled with a silver nano-particle ink NPS-J from Harima Chemicals using Dimatix inkjet printer (DMP-2800) with 10pl cartridge. Substrate temperature was found to be potentially more affective to print more droplets rather than increasing waiting time. Moreover, printing on the 60 °C substrate with no delay was optimum considering the uniformity, thickness and quality of the coverage.
A hybrid system to stimulate selfish nodes to cooperate in vehicular Delay-Tolerant Networks

In the last decade vehicular communications have been the focus of research, not only by the related scientific community but also by the automotive industry. Several architectures were proposed in order to overcome some issues found in such networks. Vehicular Delay-Tolerant Networks (VDTNs) try to improve vehicular communications by deploying the Delay-Tolerant Networks (DTNs) store-carry-and-forward model and assuming the bundle layer placement under the network layer. Although all the improvements already achieved by VDTNs, there are still several challenges that must be overcome. One of these challenges is how to stimulate nodes to cooperate and minimize the impact of misbehavior nodes on the network performance. A node may be unwilling to cooperate due to a selfish behavior or to save its own resources from being compromised. To detect, isolate, and exclude this type of nodes a reputation system with different mechanisms (allowing to punish selfish nodes in different ways) was implemented in VDTNs. This paper adapts the already proposed reputation system to perform together with a hybrid system, which main goal is to incentive selfish nodes to share their resources with others, instead of immediately excluding them from the network. With the proposal of this hybrid system, two incentive mechanisms were also created. Across all the experiments, it was shown that by incentive selfish nodes to cooperate contributes to an increase of the overall network performance, when compared to an approach that excludes them immediately from the network.
Point-of-care ultrasound for EPTB – focused assessment with sonography for HIV

181 March 2014, Vol. 104, No. 3 In South Africa (SA), childhood tuberculosis (TB) still accounts for considerable morbidity and mortality. The incidence of TB disease and risk of progression to severe or disseminated forms are especially high in young children or those with HIV infection. Childhood TB presents most commonly as primary TB, often with non specific signs and symptoms; TB may also present as acute pneumonia. The clinical diagnosis can therefore be challenging. [1] Furthermore, due to difficulty in obtaining goodquality specimens and the paucibacillary nature of childhood TB, microbiological confirmation is only achieved in a minority of children, especially in settings where there is limited capacity for microbiological confirmation. Imaging is a major part of the diagnostic work-up for childhood TB. Chest X-rays are relatively inexpensive and widely available. However, detection of mediastinal and hilar lymphadenopathy – cardinal signs of primary pulmonary TB – is often limited, there is wide inter-observer variability in detection of nodes and pulmonary findings may be non specific. Other imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are capable of demonstrating comprehensively the lungs and the mediastinum, but the radiation dose in CT, the need for sedation in MRI, the costs and limited availability all currently prevent their use in routine management. The merit of ultrasound for detecting features of abdominal, pleural and pericardial TB has long been acknowledged and its application in paediatrics is especially attractive as it does not involve radiation nor require sedation. Furthermore, ultrasound of the mediastinum has been shown to detect lymphadenopathy in TB patients who have a normal chest X-ray. One strategy for improving the diagnosis of childhood TB is the refinement of currently available imaging tools and use of simplified protocols tailored to the detection of TB. Point-of-care ultrasound for extrapulmonary TB (EPTB), mediastinal ultrasound and limited chest MRI are all promising new imaging approaches presently being evaluated in paediatric studies. The increasing availability of digital imaging enables telemedicine, where radiological images can be transmitted anywhere in the world allowing for expert interpretation and opinion.
Progress Curve Mechanistic Modeling Approach for Assessing Time-Dependent Inhibition of CYP3A4

A progress curve method for assessing time-dependent inhibition of CYP3A4 is based on simultaneous quantification of probe substrate metabolite and inhibitor concentrations during the experiment. Therefore, it may overcome some of the issues associated with the traditional two-step method and estimation of inactivation rate (kinact) and irreversible inhibition (KI) constants. In the current study, seven time-dependent inhibitors were investigated using a progress curve method and recombinant CYP3A4. A novel mechanistic modeling approach was applied to determine inhibition parameters using both inhibitor and probe metabolite data. Progress curves generated for clarithromycin, erythromycin, diltiazem, and N-desmethyldiltiazem were described well by the mechanistic mechanism-based inhibition (MBI) model. In contrast, mibefradil, ritonavir, and verapamil required extension of the model and inclusion of competitive inhibition term for the metabolite. In addition, this analysis indicated that verapamil itself causes minimal MBI, and the formation of inhibitory metabolites was responsible for the irreversible loss of CYP3A4 activity. The kinact and KI estimates determined in the current study were compared with literature data generated using the conventional two-step method. In the current study, the inactivation efficiency (kinact/KI) for clarithromycin, ritonavir, and erythromycin were up to 7-fold higher, whereas kinact/KI for mibefradil, N-desmethyldiltiazem, and diltiazem were, on average, 2- to 4.8-fold lower than previously reported estimates. Use of human liver microsomes instead of recombinant CYP3A4 resulted in 5-fold lower kinact/KI for erythromycin. In conclusion, the progress curve method has shown a greater mechanistic insight when determining kinetic parameters for MBI in addition to providing a more comprehensive experimental protocol.
Verbs and Adverbs: Multidimensional Motion Interpolation

The article describes a system for real-time interpolated animation that addresses some of these problems. Through creating parameterized motions-which the authors call verbs parameterized by adverbs-a single authored verb produces a continuous range of subtle variations of a given motion at real-time rates. As a result, simulated figures alter their actions based on their momentary mood or in response to changes in their goals or environmental stimuli. For example, they demonstrate a walk verb that can show emotions such as happiness and sadness, and demonstrate subtle variations due to walking up or down hill while turning to the left and right. They also describe verb graphs, which act as the glue to assemble verbs and their adverbs into a runtime data structure. Verb graphs provide the means for seamless transition from verb to verb for the simulated figures within an interactive runtime system. Finally they briefly discuss the discrete event simulator that handles the runtime main loop.
The methanol dehydrogenase structural gene mxaF and its use as a functional gene probe for methanotrophs and methylotrophs

The methanol dehydrogenase gene mxaF, encoding the large subunit of the enzyme, was amplified from the DNA of a number of representative methanotrophs, methyletrophs, and environmental samples by PCR using primers designed from regions of conserved amino acid sequence identified by comparison of three known sequences of the large subunit of methanol dehydrogenase. The resulting 550-bp PCR products were cloned and sequenced. Analysis of the predicted amino acid sequences corresponding to these mxaF genes revealed strong sequence conservation. Of the 172 amino acid residues, 47% were conserved among all 22 sequences obtained in this study. Phylogenetic analysis of these MxaF sequences showed that those from type I and type II methanotrophs form two distinct clusters and are separate from MxaF sequences of other gram-negative methylotrophs. MxaF sequences retrieved by PCR from DNA isolated from a blanket bog peat core sample formed a distinct phylogenetic cluster within the MxaF sequences of type II methanotrophs and may originate from a novel group of acidophilic methanotrophs which have yet to be cultured from this environment.
MS plaque segmentation using masking clustering and level set method

In this study, a series of methods with some contributions have been proposed in order to segment brain tissues affected by the multiple sclerosis (MS). Since MS disease usually occurs in white matter of the brain and noting that corpus callosum is the largest white matter structure in the brain, filtering out the parts of corpus callosum that are not affected by disease helps improve segmentation. Magnetic resonance dual T1 - T2 images of the brain is used for masking corpus callosum. Since the pixel by pixel multiplication of T1 - T2 images is possible with dual MR Imaging and the outcome serves for the pupose of masking, the multiplication is used as preprocessing tool. Standart fuzzy clustering algorithm is used to find out the cluster centers. The speed term for the level set method is defined based on the cluster centers. Thus any part of affected tissue that is pointed by radiologist is segmented up to the connected borders by use of Level Set Method. The outcome of this study may help radiologist to track and record the progress of the disease as it might be more time consuming otherwise.