Miracle-dz/UniFAR-FTUs huggingface dataset:
Summary Statistics for Partitionings and Feature Allocations

Infinite mixture models are commonly used for clustering. One can sample from the posterior of mixture assignments by Monte Carlo methods or find its maximum a posteriori solution by optimization. However, in some problems the posterior is diffuse and it is hard to interpret the sampled partitionings. In this paper, we introduce novel statistics based on block sizes for representing sample sets of partitionings and feature allocations. We develop an element-based definition of entropy to quantify segmentation among their elements. Then we propose a simple algorithm called entropy agglomeration (EA) to summarize and visualize this information. Experiments on various infinite mixture posteriors as well as a feature allocation dataset demonstrate that the proposed statistics are useful in practice.
HYPERACCRETING NEUTRON STAR DISKS AND NEUTRINO ANNIHILATION

Newborn neutron stars surrounded by hyperaccreting and neutrino-cooled disks may exist in some gamma-ray bursts and/or supernovae. In this paper, we further study the structure of such a neutron star disk based on the two-region (i.e., inner & outer) disk scenario following our previous work, and calculate the neutrino annihilation luminosity from the disk in various cases. We investigate the effects of the viscosity parameter α, energy parameter ε (measuring the neutrino cooling efficiency of the inner disk), and outflow strength on the structure of the entire disk as well as the effect of emission from the neutron star surface boundary emission on the total neutrino annihilation rate. The inner disk satisfies the entropy-conservation self-similar structure for the energy parameter ε ≃ 1 and the advection-dominated structure for ε < 1. An outflow from the disk decreases the density and pressure but increases the thickness of the disk. Moreover, compared with the black hole disk, the neutrino annihilation luminosity above the neutron star disk is higher, and the neutrino emission from the boundary layer could increase the neutrino annihilation luminosity by about one order of magnitude higher than the disk without boundary emission. The neutron star disk with the advection-dominated inner disk could produce the highest neutrino luminosity while the disk with an outflow has the lowest. Although a heavily mass-loaded outflow from the neutron star surface at early times of neutron star formation prevents the outflow material from being accelerated to a high bulk Lorentz factor, an energetic ultrarelativistic jet via neutrino annihilation can be produced above the stellar polar region at late times if the disk accretion rate and the neutrino emission luminosity from the surface boundary layer are sufficiently high.
Calculation of Quantum Entanglement

Quantum entanglement which is used as the basis for such applications as quantum cryptography, quantum teleportation, and quantum computing, is a multifaceted property that has attracted much attention. The calculation of quantum entanglement therefore has become important. But the calculation of entanglement has become much more complex as systems that use entanglement have evolved. Based on the relative entropy of entanglement, a general method was developed for the calculation of entanglement for n-qubit or n-qudit system, which using an optimization algorithm technique such as genetic algorithm or Quantum-behaved Particle Swarm Optimization. The method using different optimization algorithm was tested on a two qubit system, for which there are some known points, and compared to exact calculations using the entanglement of formation and with each other. Advantages and disadvantages of the method are discussed and directions of future research are exposed.
Article Title: a Self-organizing State-space-model Approach for Parameter Estimation in Hodgkin-huxley-type Models of Single Neurons a Self-organizing State-space-model Approach for Parameter Estimation in Hodgkin-huxley-type Models of Single Neurons

This paper is made available online in accordance with publisher policies. Please scroll down to view the document itself. Please refer to the repository record for this item and our policy information available from the repository home page for further information. To see the final version of this paper please visit the publisher's website. Access to the published version may require a subscription. Abstract Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models. Copyright: ß 2012 Vavoulis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The funders had no role in study design, data collection and analysis, decision to …
Semantic-enriched visual vocabulary construction in a weakly supervised context

One of the prevalent learning tasks involving images is content-based image classification. This is a difficult task especially because the low-level features used to digitally describe images usually capture little information about the semantics of the images. In this paper, we tackle this difficulty by enriching the semantic content of the image representation by using external knowledge. The underlying hypothesis of our work is that creating a more semantically rich representation for images would yield higher machine learning performances, without the need to modify the learning algorithms themselves. The external semantic information is presented under the form of non-positional image labels, therefore positioning our work in a weakly supervised context. Two approaches are proposed: the first one leverages the labels into the visual vocabulary construction algorithm, the result being dedicated visual vocabularies. The second approach adds a filtering phase as a pre-processing of the vocabulary construction. Known positive and known negative sets are constructed and features that are unlikely to be associated with the objects denoted by the labels are filtered. We apply our proposition to the task of content-based image classification and we show that semantically enriching the image representation yields higher classification performances than the baseline representation.
Flexible OFDM Waveform for PLC/RF In-Vehicle Communications

This paper deals with the implementation of an embedded communication system for vehicle, based on vehicular power line (VPLC) and/or wireless technologies using the USRP platforms. This platform allows a modular design of OFDM based systems. Many configurations of the PHY and MAC layers are studied on real vehicular PLC/RF environments, without developing a specific board. According to the used channels, the radio frequency daughter cards can be used without changing the digital signal processing.
Effect of Asperity Height on Wear Behavior by Finite Element Method

This paper presents an investigation of contact surface interface, which is of strong interest for wear applications. The prediction of the wear depth in contact surfaces is thus a challenging task. The effect of the asperities height (0.045, 0.055, 0.065, and 0.07 )mm, the applied forces and the shape of the contact surfaces on the wear depth and contact pressure are investigated by finite element method via ANSYS software. Wear simulation approach based on Archard’s model was proposed, in which ANSYS was used to simulate the progressive accumulation of wear between contact surfaces. Results showed that the wear behavior can be simulated with the series of discrete models and the method proposed can be widely used to predict wear problems for engineering. Two cases are studied, the first one is for rectangular contact surfaces and the other for circlerectangle contact surfaces with different height of asperities. It was also concluded that the increased of contact’s time caused the wear depth for different asperities increased and vice versa for contact pressure.
Huntingtin Modulates Transcription, Occupies Gene Promoters In Vivo, and Binds Directly to DNA in a Polyglutamine-Dependent Manner

Transcriptional dysregulation is a central pathogenic mechanism in Huntington's disease, a fatal neurodegenerative disorder associated with polyglutamine (polyQ) expansion in the huntingtin (Htt) protein. In this study, we show that mutant Htt alters the normal expression of specific mRNA species at least partly by disrupting the binding activities of many transcription factors which govern the expression of the dysregulated mRNA species. Chromatin immunoprecipitation (ChIP) demonstrates Htt occupation of gene promoters in vivo in a polyQ-dependent manner, and furthermore, ChIP-on-chip and ChIP subcloning reveal that wild-type and mutant Htt exhibit differential genomic distributions. Exon 1 Htt binds DNA directly in the absence of other proteins and alters DNA conformation. PolyQ expansion increases Htt–DNA interactions, with binding to recognition elements of transcription factors whose function is altered in HD. Together, these findings suggest mutant Htt modulates gene expression through abnormal interactions with genomic DNA, altering DNA conformation and transcription factor binding.
Expectation–Maximization-Driven Geodesic Active Contour With Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology

The presence of lymphocytic infiltration (LI) has been correlated with nodal metastasis and tumor recurrence in HER2+ breast cancer (BC). The ability to automatically detect and quantify extent of LI on histopathology imagery could potentially result in the development of an image based prognostic tool for human epidermal growth factor receptor-2 (HER2+) BC patients. Lymphocyte segmentation in hematoxylin and eosin (H&E) stained BC histopathology images is complicated by the similarity in appearance between lymphocyte nuclei and other structures (e.g., cancer nuclei) in the image. Additional challenges include biological variability, histological artifacts, and high prevalence of overlapping objects. Although active contours are widely employed in image segmentation, they are limited in their ability to segment overlapping objects and are sensitive to initialization. In this paper, we present a new segmentation scheme, expectation-maximization (EM) driven geodesic active contour with overlap resolution (EMaGACOR), which we apply to automatically detecting and segmenting lymphocytes on HER2+ BC histopathology images. EMaGACOR utilizes the expectation-maximization algorithm for automatically initializing a geodesic active contour (GAC) and includes a novel scheme based on heuristic splitting of contours via identification of high concavity points for resolving overlapping structures. EMaGACOR was evaluated on a total of 100 HER2+ breast biopsy histology images and was found to have a detection sensitivity of over 86% and a positive predictive value of over 64%. By comparison, the EMaGAC model (without overlap resolution) and GAC model yielded corresponding detection sensitivities of 42% and 19%, respectively. Furthermore, EMaGACOR was able to correctly resolve over 90% of overlaps between intersecting lymphocytes. Hausdorff distance (HD) and mean absolute distance (MAD) for EMaGACOR were found to be 2.1 and 0.9 pixels, respectively, and significantly better compared to the corresponding performance of the EMaGAC and GAC models. EMaGACOR is an efficient, robust, reproducible, and accurate segmentation technique that could potentially be applied to other biomedical image analysis problems.
Green synthesis of silver nanoparticles using Alysicarpus monilifer leaf extract and its antibacterial activity against MRSA and CoNS isolates in HIV patients

The emergence of multi‐drug‐resistant microorganisms in hospital environments is a global public health problem and threat to everyone, especially HIV‐infected patients. Methicillin‐resistant Staphylococcus aureus (MRSA) and coagulase‐negative Staphylococci (CoNS) are the major causative agents associated with morbidity and mortality in HIV patients. Therefore, control of MRSA and CoNS‐related infections in HIV patients is a worldwide concern. To investigate novel, potent, and cost‐effective therapeutic approaches, the current study reports a simple and rapid synthesis of silver nanoparticles (AgNPs) using aqueous leaf extract of Alysicarpus monilifer and its antibacterial efficacy against multi‐drug‐resistant MRSA and CoNS isolates from HIV patients. The green‐synthesized AgNPs were characterized using ultraviolet‐visible spectroscopy, transmission electron microscopy, energy dispersive X‐ray analysis, selected area electron diffraction pattern, X‐ray diffraction patterns, and Fourier transform infrared spectroscopy. Stable, well‐defined AgNPs, mostly spherical in shape with mean size of 15 ± 2 nm, were obtained within an hour. Moreover, green synthesized AgNPs revealed significant dose‐dependent antibacterial action against MRSA and CoNS isolates. This study concludes that biogenic AgNPs have demonstrated to be potent antibacterial agents in comparison with conventional antibiotics.
On Querying Incomplete Information in Databases under Bag Semantics

Querying incomplete data is an important task both in data management, and in many AI applications that use query rewriting to take advantage of relational database technology. Usually one looks for answers that are certain, i.e., true in every possible world represented by an incomplete database. For positive queries – expressed either in positive relational algebra or as unions of conjunctive queries – ﬁnding such answers can be done efﬁciently when databases and query answers are sets. Real-life databases however use bag, rather than set, semantics. For bags, instead of saying that a tuple is certainly in the answer, we have more detailed information in terms of the range of the numbers of occurrences of the tuple in query answers. We show that the behavior of positive queries is different under bag semantics: ﬁnding the minimum number of occurrences can still be done efﬁciently, but for maximum it becomes intractable. We use these results to investigate approximation schemes for computing certain answers to arbitrary ﬁrst-order queries that have been proposed for set semantics. One of them cannot be adapted to bags, as it relies on the intractable maxima of occurrences, but another scheme only deals with minima, and we show how to adapt it to bag semantics without losing efﬁciency.
Differential effects of DNA gyrase inhibitors on the genetic transformation of Neisseria gonorrhoeae

Inhibitors of DNA gyrase in Escherichia coli exerted differential effects on the genetic transformation of Neisseria gonorrhoeae. When competent cells of the gonococcus were exposed to novobiocin before the uptake of transforming antibiotic resistance DNA, there was a 50 to 60% reduction in the number of transformants compared with the number of control untreated cells. Norfloxacin, a more potent inhibitor of DNA gyrase and an analog of nalidixic acid, nearly abolished the production of transformants by recipient cells. On the contrary, exposure of competent cells to nalidixic acid had no effect on transformant yield. The target of these inhibitors appears to be at the level of recombination. Possible mechanisms are discussed.
Solid-state tunable deep-ultraviolet laser system from 198 to 300 nm.

An all-solid-state laser system continuously tunable from 198 to 300 nm is developed for the first time to our knowledge. This tunable UV laser system consists of a Ti:sapphire laser, a Nd:YAG laser, and the nonlinear mixing enclosures and has relatively high average output of more than 8 mW and a narrow bandwidth of the order of 0.1 nm over the entire range. Typical characteristics of this laser are presented.
Inkjet Printing of Graphene Inks for Wearable Electronic Applications

Inkjet printing of graphene-based conductive inks is an encouraging research approach in the field of printed electronics as both the benefits of inkjet printing and extra-ordinary electronic, optical and mechanical properties of graphene can be exploited [1]. Inkjet printing is one of the most promising techniques for the fabrication of wearable electronics due to number of advantages over conventional manufacturing techniques such as digital and additive patterning, reduction in material waste, deposition of controlled amount of materials and compatibility with various substrates [2]. In addition, graphene is a single atom thick two-dimensional closely packed honeycomb lattice of sp carbon allotropes, which has been focus of mass investigations in recent years because of its unique physical and chemical properties [3].
Efficient 3D high-frequency impedance extraction for general interconnects and inductors above a layered substrate

We present an efficient and highly accurate approach to high-frequency impedance extraction for VLSI interconnects and intentional on-chip inductors. The approach is based on a three-dimensional (3D) loop formalism that uses discrete complex images approximations applied to a quasi-magnetostatic treatment of the vector potential, resulting in closed-form expressions for the impedance matrix of current filaments in the presence of a multi-layer substrate. Populating the impedance (Z) matrix for 3D configurations of finite transverse dimensions (including non-Manhattan wires and inductors) is computationally inexpensive, and includes substrate eddy current effects that become quantitatively important in the frequency regime beyond 20 GHz which is imminent at the 45 nm technology node onwards. The accuracy, as exemplified by the magnitude of inductor impedance |Z|, is within 5% of a full-wave electromagnetic field solver for frequencies up to 100 GHz, with an order of magnitude lower computation cost. The proposed method represents a core technology for incorporation into system level extraction of analog systems consisting of multiple inductors and nearby interconnects, for CMOS on-chip circuits in the nanometer era.
A Quasi-Sliding Mode Approach for Robust Control and Speed Estimation of PM Synchronous Motors

This paper presents a discrete-time variable-structure-based control and a speed estimator designed for a permanent-magnet synchronous motor (PMSM). A cascade control scheme is proposed which provides accurate speed tracking performance. In this control scheme the speed estimator is a robust digital differentiator that provides the first derivative of the encoder position measurement. The analysis of the control stability is given and the ultimate boundedness of the speed tracking error is proved. The control scheme is experimentally tested on a commercial PMSM drive. Reported experimental evidence shows that the proposed solution produces good speed trajectory tracking performance and it is robust in the presence of disturbances affecting the system.
A Benchtop Fractionation Procedure for Subcellular Analysis of the Plant Metabolome

Although compartmentation is a key feature of eukaryotic cells, biological research is frequently limited by methods allowing for the comprehensive subcellular resolution of the metabolome. It has been widely accepted that such a resolution would be necessary in order to approximate cellular biochemistry and metabolic regulation, yet technical challenges still limit both the reproducible subcellular fractionation and the sample throughput being necessary for a statistically robust analysis. Here, we present a method and a detailed protocol which is based on the non-aqueous fractionation technique enabling the assignment of metabolites to their subcellular localization. The presented benchtop method aims at unraveling subcellular metabolome dynamics in a precise and statistically robust manner using a relatively small amount of tissue material. The method is based on the separation of cellular fractions via density gradients consisting of organic, non-aqueous solvents. By determining the relative distribution of compartment-specific marker enzymes together with metabolite profiles over the density gradient it is possible to estimate compartment-specific metabolite concentrations by correlation. To support this correlation analysis, a spreadsheet is provided executing a calculation algorithm to determine the distribution of metabolites over subcellular compartments. The calculation algorithm performs correlation of marker enzyme activity and metabolite abundance accounting for technical errors, reproducibility and the resulting error propagation. The method was developed, tested and validated in three natural accessions of Arabidopsis thaliana showing different ability to acclimate to low temperature. Particularly, amino acids were strongly shuffled between subcellular compartments in a cold-sensitive accession while a cold-tolerant accession was characterized by a stable subcellular metabolic homeostasis. Finally, we conclude that subcellular metabolome analysis is essential to unambiguously unravel regulatory strategies being involved in plant-environment interactions.