Miracle-dz/UniFAR-FTUs huggingface dataset:
Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting

This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse noise contain no information about the true image, how to find this set correctly is a very important problem. We propose two methods based on blind inpainting and $\ell_0$ minimization that can simultaneously find the damaged pixels and restore the image. By iteratively restoring the image and updating the set of damaged pixels, these methods have better performance than other methods, as shown in the experiments. In addition, we provide convergence analysis for these methods, these algorithms will converge to coordinatewise minimum points. In addition, they will converge to local minimum points (or with probability one) with some modifications in the algorithms.
Approximation and inference methods for stochastic biochemical kinetics—a tutorial review

Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the chemical master equation. Despite its simple structure, no analytic solutions to the chemical master equation are known for most systems. Moreover, stochastic simulations are computationally expensive, making systematic analysis and statistical inference a challenging task. Consequently, significant effort has been spent in recent decades on the development of efficient approximation and inference methods. This article gives an introduction to basic modelling concepts as well as an overview of state of the art methods. First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods. Next, we discuss several approximation methods, including the chemical Langevin equation, the system size expansion, moment closure approximations, time-scale separation approximations and hybrid methods. We discuss their various properties and review recent advances and remaining challenges for these methods. We present a comparison of several of these methods by means of a numerical case study and highlight some of their respective advantages and disadvantages. Finally, we discuss the problem of inference from experimental data in the Bayesian framework and review recent methods developed the literature. In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.
Predicate Pushdown in Parquet and Apache Spark Author :

The cost of warehousing data has dropped significantly over the years, and as a result, many organizations now store large amounts of both recent and historical data. Gaining insights from this data involves queries of an analytical nature. Such queries typically access large amounts of the data while selecting only a small fraction of it. In this thesis we research predicate pushdown, an optimization technique for speeding up such selective queries by pushing down filtering operations into the scan operator responsible for reading in the data. Exploiting early filtering opportunities allows us to reduce the amount of data entering the query execution pipeline and thereby increase overall query performance. Our approach goes beyond established early filtering techniques such as min/max skipping, which rely on minimum and maximum value statistics. Even considering clustered indexes and multi-dimensional clustering, there will still be columns that the indexing did not prepare for. We discuss the implementation, applicability and performance aspects of predicate pushdown in an established data processing framework, given the constraints of an established file format. We present a predicate pushdown implementation for the Databricks Runtime (a performance optimized version of Apache Spark) and the Apache Parquet columnar storage format. We explore predicates expressed directly on the scanned columns, as well as predicates that can be derived from filters on other tables involved in a join operation. For the latter, we employ Bloom filters to be able to express non-trivial filtering. Our experiments show that we can effectively speed up the evaluation of predicates up to 8x by exploiting the dictionary compression scheme of the Parquet format. We exemplify the benefit of Bloom filter pushdown on query 17 of the industry standard TPC-H benchmark suite, for which we observe speedup up to 10x. Additionally we demonstrate that we can leverage the selection on the predicate/join key columns to effectively skip I/O for the payload columns. However, we also argue how several aspects of the Parquet format hinder the potential for predicate pushdown.
Oliguria as an early indicator of mortality risk in patients with multiple myeloma and renal impairment

Background A change in urine output has been recently recognized as a valuable biomarker of acute kidney injury that is associated with mortality in critically ill patients. We investigated the prognostic impact of oliguria for survival outcomes in multiple myeloma (MM) patients presenting with renal impairment (RI). Methods Retrospective data on 98 patients with MM and RI, who received initial treatment with novel therapies, were analyzed. Oliguria was defined as a urine output of <0.5 mL/kg/h. Results The baseline median eGFR was 39.7 mL/min (range, 5.1-59.8). Achievement of renal complete response (CR) was observed in 39.8% of patients. Nine patients (9.2%) presented with oliguria at initial diagnosis, and 4 initially required dialysis. Over a median follow-up period of 17.1 months (range, 1.7-100.0), the median overall survival (OS) was 38.7 months (95% CI 25.0-52.5). Multivariate analyses indicated that oliguria at diagnosis [hazard ratio (HR) 3.628, 95% CI 1.366-9.849, P=0.011], and thrombocytopenia <100×109/L at diagnosis (HR 2.534, 95% CI 1.068-6.015, P=0.035), were significantly associated with overall survival. Conclusion Oliguria was significantly associated with higher mortality in MM patients with RI. Therefore, close monitoring of urine output could be important for these patients.
Composition and the Contents of Fatty Acids of Some Organs of Gastropod Mollusk Volutopsius Castaneus

The composition and the contents of Fatty Acids (FAs) some organs (a foot, gonads, a liver) edible gastropod mollusk Volutopsius castaneus are investigated. Dependence of FA content on time of gathering of a mollusk is shown. Sixty three FAs are identified. The contribution of saturated FAs in lipids of the investigated organs includes 14:0, 16:0, 17:0, 18:0. The contribution of polyunsaturated FAs (40-60 %) is especially considerable. That fact is remarkable, that total content polyunsaturated FAs (PUFAs) during all season changed slightly. The highest level PUFA was observed in a foot (55-60 %). The main С20 PUFAs were arachidonic (20:4n6) and eicosapentaenoic (20:5n3) acids. At tissues investigated were present characteristic for mollusks non-methylene interrupted FAs: 20:2∆5, 11 and 20:2∆5, 13 (to 4.5 %).
Intravaginal ring delivery of tenofovir disoproxil fumarate for prevention of HIV and herpes simplex virus infection.

OBJECTIVES A safe and effective topical prevention strategy will likely require sustained delivery of potent antiviral drugs and a delivery system that simultaneously maximizes drug distribution and overcomes the behavioural challenges related to adherence. Activity against HIV and herpes simplex virus (HSV) would be advantageous, given the epidemiological link between the two pathogens. We hypothesize that tenofovir disoproxil fumarate (tenofovir DF), a prodrug of tenofovir, may be more potent than tenofovir and ideal for sustained intravaginal ring (IVR) delivery. METHODS The anti-HIV and anti-HSV activity of tenofovir and tenofovir DF were assessed in cell and explant models. Cumulative tenofovir DF release and stability from polyether urethane (PEU), ethylene-co-vinyl acetate (EVA) and silicone IVRs were compared, and the activity and safety of drug released were evaluated in cervical explants and in a polarized dual-chamber model. RESULTS Tenofovir DF inhibited HIV and HSV at ≈ 100-fold lower concentrations than tenofovir and retained activity in the presence of semen. PEU rings delivered >1 mg/day of tenofovir DF for 30 days. Pre-treatment of cervical explants with 10 μg/mL tenofovir DF or eluants from PEU minirings resulted in >90% inhibition of HIV and reduced HSV-2 yields by 2.5 log. Tenofovir DF and eluants did not prevent cell growth or polarization, or have any deleterious effects on an epithelial barrier. CONCLUSIONS The findings support the development of a PEU tenofovir DF ring, which may provide potent and sustained protection against HIV and HSV.
A DNA aptamer recognizing MMP14 for in vivo and in vitro imaging identified by cell-SELEX

A key challenge for the management of various types of cancer, including pancreatic cancer and hepatocellular carcinoma, is accurate diagnosis at an early stage. Matrix metalloproteinase 14 (MMP14) is overexpressed in numerous types of cancer and is associated with poor prognosis. Therefore, MMP14-specific imaging probes have potential use in the diagnosis of MMP14-positive cancer. Aptamers are short oligonucleotide sequences that can bind to molecular targets with a high specificity and affinity. Aptamers are typically obtained from an in vitro library; this process is usually termed systematic evolution of ligands by exponential enrichment (SELEX). In the present study, a DNA aptamer targeting MMP14 was obtained by cell-SELEX and termed M17, which specifically recognizes MMP14-positive cells. Aptamer M17 selectively binds to membrane proteins of MMP14-transfected 293T cells (Kd, 4.98±1.26 nM). Pancreatic cancer cell imaging suggested that aptamer M17 can bind to the cell membranes of two pancreatic cancer cell lines (MIA PaCa-2 and PANC-1). In vivo tumor imaging demonstrated that the targeting recognition of MIA PaCa-2 tumor cells in mice could be visualized using Cy5-labeled aptamer M17. Aptamer M17-conjugated polyethylene glycol-Fe3O4 can specifically bind to MIA PaCa-2 and PANC-1 cells, and reduce MRI T2-weighted imaging signal intensity. The DNA aptamer M17 has the advantages of simplicity of synthesis, small size, low immunogenicity, high penetrability and high affinity. Therefore, aptamer M17 is a potential molecular probe for the diagnosis and treatment of MMP14-positive cancer.
INTERFERENCE OF MORNING GLORY IN SOYBEAN YIELD

Weed control using herbicides with the same mechanism of action can encourage the emergence of resistant biotypes or tolerant plants. An example of this is the occurrence of morning-glory (Ipomoea sp.) in soybean crops in southern Brazil. In this sense, the aim of this study was to quantify the losses arising from two species of morning-glory weeding coexisting with soybean crop. A field study was conducted in the 2013/2014 season. The experimental design was a randomized block with four replications, with treatments being distributed in a factorial arrangement (2x2x5), which assessed the effect of competition on two soybean cultivars (TEC6029 and TEC7849) of morning-glory species (I. triloba and I. purpurea) in different population (0, 4, 8, 16 and 32 plants m-2). The interaction of soybean with different densities of morning-glory affects yield components. The first eight morning glory plants have a more intense competition, reducing productivity by 45% and 27% for TEC6029 and TEC7849 cultivars, respectively.
A parallel graph partitioning algorithm to speed up the large-scale distributed graph mining

For the large-scale distributed graph mining, the graph is distributed over a cluster of nodes, thus performing computations on the distributed graph is expensive when large amount of data have to be moved between different computers. A good partitioning of distributed graph is needed to reduce the communication between computers and scale a system up. Existing graph partitioning algorithms incur high computation and communication cost when applied on large distributed graphs. A efficient and scalable partitioning algorithm is crucial for large-scale distributed graph mining. In this paper, we propose a novel parallel multi-level stepwise partitioning algorithm. The algorithm first efficiently aggregates the large graph into a small weighted graph, and then makes a balance partitioning on the weighted graph based on a stepwise minimizing RatioCut Algorithm. The experimental results show that our algorithm generally outperforms the existing algorithms and has a high efficiency and scalability for large-scale graph partitioning. Using our partitioning method, we are able to greatly speed up PageRank computation.
Enabling the User to Setup Ubiquitous Computing Applications Based on Intelligent Context

Context-aware ubiquitous computing applications thrive in extremely dynamic and heterogeneous environments. When they are delivered to the user preconfigured and without the capability to be adapted, they may fail to satisfy the user’s changing needs and may give the user the feeling that control is taken away from him. In this paper we present how context can be exploited in ubiquitous computing environments in order to enable the user to setup his own application. We will focus on how context can be modelled using ontologies, on the management of accumulated context data and on the reasoning process based on intelligent context and rules defined by the user.
How brain type influences online safety WORKING PAPER

Many information security mechanisms fail in practice through poor usability. For example, phishing is a rapidly-growing form of online crime; both real-world experience and academic research demonstrate that many users do not heed the available security indicators. Various explanations have been given, from a lack of training to poor indicator design. In this paper, we offer a more fundamental explanation, namely that people’s brain type significantly influences their success or failure at recognizing online scams. We describe a survey which tested ability to recognize phishing scams, as well as brain type. Our results emphasise that security mechanisms should be designed to be usable by the widest possible range of people.
Semantic-based context-aware alert fusion for distributed Intrusion Detection Systems

One of the fundamental challenges in real-world Intrusion Detection Systems (IDS) is the large number of redundant, non-relevant false positive alerts that they generate. In this paper, we propose an alert fusion approach that incorporates contextual information with the goal of leveraging the benefits of multi-sensor detection while reducing false positives. In order to allow for automated reasoning on the information resources available for the fusion process, we design a set of comprehensive and extensible ontologies, and implemented fusion and detection algorithms as simple rules in Ontologic Web Language Description Logic (OWL-DL), using the Semantic Query-Enhance Web Rule Language (SQWRL). To illustrate and evaluate our approach, we use one of the attack scenarios of the DARPA 2000 dataset. The results obtained show that our approach can reduce false positives, while achieving the same detection rates achieved by using the Snort and ISS RealSecure.
Reverse engineering of polymeric multilayers using AFM-based nanoscale IR spectroscopy and thermal analysis

a nominal spatial resolution of about 3–30 μm in the mid-IR spectral region, depending on wavenumber, but this resolution is insufficient for modern nanoscopic material analysis. SPM can spatially resolve the topography of materials at the nanometer scale. However, despite the abundance of SPM-based surface characterisation techniques, chemical information is not directly available without making assumptions or inferences. With the availability of nanoscale (atomic force microscopy coupled) infrared spectroscopy (AFMIR) and nanoscale thermal analysis (nanoTA), it is possible to obtain both infrared spectra and transition temperatures of each layer in a cross-sectioned multilayer film. Since both techniques are AFM-based, the topographical features can be readily linked to the spectroscopic and thermal data at a much higher spatial resolution than previously achievable.
A Report on the Third VarDial Evaluation Campaign

In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019. This year, the campaign included five shared tasks, including one task re-run – German Dialect Identification (GDI) – and four new tasks – Cross-lingual Morphological Analysis (CMA), Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT), Moldavian vs. Romanian Cross-dialect Topic identification (MRC), and Cuneiform Language Identification (CLI). A total of 22 teams submitted runs across the five shared tasks. After the end of the competition, we received 14 system description papers, which are published in the VarDial workshop proceedings and referred to in this report.
How to Study Placebo Responses in Motion Sickness with a Rotation Chair Paradigm in Healthy Participants

Placebo responses occur in every medical intervention when patients or participants expect to receive an effective treatment to relieve symptoms. However, underlying mechanisms of placebo responses are not fully understood. It has repeatedly been shown that placebo responses are associated with changes in neural activity but for many conditions it is unclear whether they also affect the target organ, such as the stomach in motion sickness. Therefore, we present a methodology for the multivariate assessment of placebo responses by subjective, behavioral and objective measures in motion sickness with a rotation chair paradigm. The physiological correlate of motion sickness is a shift in gastric myoelectrical activity towards tachygastria that can be recorded with electrogastrography. The presented study applied the so-called balanced placebo design (BPD) to investigate the effects of ginger compared to placebo and the effects of expectations by verbal information. However, the study revealed no significant main or interactional effects of ginger (as a drug) or information on outcome measures but showed interactions when sex of participants and experimenters are taken into considerations. We discuss limitations of the presented study and report modifications that were used in subsequent studies demonstrating placebo responses when rotation speed was lowered. In general, future placebo studies have to identify the appropriate target organ for the studied placebo responses and to apply the specific methods to assess the physiological correlates.
Relational observables in 2 d quantum gravity

Local observation is an important problem both for the foundations of a quantum theory of gravity and for applications to quantum-cosmological problems such as eternal inflation. While gauge invariant local observables can’t be defined, it has been argued that appropriate relational observables approximately reduce to local observables in certain states. However, quantum mechanics and gravity together imply limitations on the precision of their localization. Such a relational framework is studied in the context of two-dimensional gravity, where there is a high degree of analytic control. This example furnishes a concrete example of some of the essential features of relational observables. ⋆ Email address: giddings@physics.ucsb. edu † Long-term address.
RONTO: relational to ontology schema matching

The population of ontologies with real instances still remains a major practical issue for the Semantic Web community. Significant progress towards a solution for this issue can be achieved through the migration of data stored in existing relational databases. In this paper we present a schema matching methodology and its implementation, necessary for further data migration. AIS SIGSEMIS BULLETIN 3 (3&4) 2006 Copyright: © AIS SIGSEMIS, 2005 33
A Self-Paced Brain-Computer Interface Speller by Combining Motor Imagery and P300 Potential

This study presents a self-paced hybrid brain-computer interface (BCI) speller, which can be voluntarily turn on/off, by merging a motor imagery (MI)-based brain switch into a P300-based BCI. In this paradigm, the participants tried to perform the two different cognitive tasks sequentially, rather than simultaneously, and the multiple EEG components were processed sequentially. The practicability and effectiveness of the proposed approach are validated by eleven participants and all of them achieved satisfactory performance. For the P300 speller, they achieved an average classification accuracy and ITR of 92.93%, and 41.23 bits/min, respectively. The preliminary results indicated that the proposed hybrid BCI system operated with different mental strategies sequentially is feasible and has potential capacities in practical self-paced control applications.
Waveguide-Based Phononic Crystal Micro/Nanomechanical High-$Q$  Resonators

In this paper, we report the design, analysis, fabrication, and characterization of a very high frequency phononic crystal (PnC) micro/nanomechanical resonator architecture based on silicon PnC slab waveguides. The PnC structure completely surrounds the resonant area, and the resonator is excited by a thin aluminum nitride-based piezoelectric transducer stack directly fabricated on top of the resonator. This architecture highly suppresses the support loss of the resonator to the surroundings while providing mechanical support and electrical signal delivery to the resonator. Qs as high as 13 500 in air at a frequency of ~134 MHz with a motional resistance of ~600 Ω and 35-dB spurious-free range of ~20 MHz are obtained. Comparing the Q of this resonator with the previously reported lateral bulk acoustic wave resonators with a similar stack of layers confirms the support loss suppression in this architecture.
Synthetic Adversaries for Urban Combat Training

This paper describes requirements for synthetic adversaries for urban combat training and MOUTBots, a prototype application. The MOUTBots use a commercial computer game to define, implement and test basic behavior representation requirements and the Soar architecture as the engine for knowledge representation and execution. We describe how these components aided the development of the prototype and present an initial evaluation against competence, taskability, fidelity, variability, transparency, and efficiency requirements.