We study SLE$_{kappa}$ theory with elements of Quasi-Sure Stochastic Analysis through Aggregation. Specifically, we show how the latter can be used to construct the SLE$_{kappa}$ traces quasi-surely (i.e. simultaneously for a family of probability measures with certain properties) for $kappa in mathcal{K}cap mathbb{R}_+ setminus ([0, epsilon) cup {8})$, for any $epsilon>0$ with $mathcal{K} subset mathbb{R}_{+}$ a nontrivial compact interval, i.e. for all $kappa$ that are not in a neighborhood of zero and are different from $8$. As a by-product of the analysis, we show in this language a version of the continuity in $kappa$ of the SLE$_{kappa}$ traces for all $kappa$ in compact intervals as above.

This paper describes a dataset and protocols for evaluating continuous speech separation algorithms. Most prior studies on speech separation use pre-segmented signals of artificially mixed speech utterances which are mostly emph{fully} overlapped, and the algorithms are evaluated based on signal-to-distortion ratio or similar performance metrics. However, in natural conversations, a speech signal is continuous, containing both overlapped and overlap-free components. In addition, the signal-based metrics have very weak correlations with automatic speech recognition (ASR) accuracy. We think that not only does this make it hard to assess the practical relevance of the tested algorithms, it also hinders researchers from developing systems that can be readily applied to real scenarios. In this paper, we define continuous speech separation (CSS) as a task of generating a set of non-overlapped speech signals from a extit{continuous} audio stream that contains multiple utterances that are emph{partially} overlapped by a varying degree. A new real recorded dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate a conversation and capturing the audio replays with far-field microphones. A Kaldi-based ASR evaluation protocol is also established by using a well-trained multi-conditional acoustic model. By using this dataset, several aspects of a recently proposed speaker-independent CSS algorithm are investigated. The dataset and evaluation scripts are available to facilitate the research in this direction.

In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.

Interaction languages such as MSC are often associated with formal semantics by means of translations into distinct behavioral formalisms such as automatas or Petri nets. In contrast to translational approaches we propose an operational approach. Its principle is to identify which elementary communication actions can be immediately executed, and then to compute, for every such action, a new interaction representing the possible continuations to its execution. We also define an algorithm for checking the validity of execution traces (i.e. whether or not they belong to an interaction's semantics). Algorithms for semantic computation and trace validity are analyzed by means of experiments.

For future Internet of Things (IoT)-based Big Data applications (e.g., smart cities/transportation), wireless data collection from ubiquitous massive smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to interpret the meaning behind the collected data, it is also challenging for edge fusion centers running computing tasks over large data sets with limited computation capacity. To tackle these challenges, by exploiting the superposition property of a multiple-access channel and the functional decomposition properties, the recently proposed technique, over-the-air computation (AirComp), enables an effective joint data collection and computation from concurrent sensor transmissions. In this paper, we focus on a single-antenna AirComp system consisting of $K$ sensors and one receiver (i.e., the fusion center). We consider an optimization problem to minimize the computation mean-squared error (MSE) of the $K$ sensors' signals at the receiver by optimizing the transmitting-receiving (Tx-Rx) policy, under the peak power constraint of each sensor. Although the problem is not convex, we derive the computation-optimal policy in closed form. Also, we comprehensively investigate the ergodic performance of AirComp systems in terms of the average computation MSE and the average power consumption under Rayleigh fading channels with different Tx-Rx policies. For the computation-optimal policy, we prove that its average computation MSE has a decay rate of $O(1/sqrt{K})$, and our numerical results illustrate that the policy also has a vanishing average power consumption with the increasing $K$, which jointly show the computation effectiveness and the energy efficiency of the policy with a large number of sensors.

The Hierarchical Equal Area isoLatitude Pixelation (HEALPix) scheme is used extensively in astrophysics for data collection and analysis on the sphere. The scheme was originally designed for studying the Cosmic Microwave Background (CMB) radiation, which represents the first light to travel during the early stages of the universe's development and gives the strongest evidence for the Big Bang theory to date. Refined analysis of the CMB angular power spectrum can lead to revolutionary developments in understanding the nature of dark matter and dark energy. In this paper, we present a new method for performing spherical harmonic analysis for HEALPix data, which is a central component to computing and analyzing the angular power spectrum of the massive CMB data sets. The method uses a novel combination of a non-uniform fast Fourier transform, the double Fourier sphere method, and Slevinsky's fast spherical harmonic transform (Slevinsky, 2019). For a HEALPix grid with $N$ pixels (points), the computational complexity of the method is $mathcal{O}(Nlog^2 N)$, with an initial set-up cost of $mathcal{O}(N^{3/2}log N)$. This compares favorably with $mathcal{O}(N^{3/2})$ runtime complexity of the current methods available in the HEALPix software when multiple maps need to be analyzed at the same time. Using numerical experiments, we demonstrate that the new method also appears to provide better accuracy over the entire angular power spectrum of synthetic data when compared to the current methods, with a convergence rate at least two times higher.

Compromised accounts on social networks are regular user accounts that have been taken over by an entity with malicious intent. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel general framework for discovering compromised accounts by semantic analysis of text messages coming out from an account. Our framework is built on the observation that normal users will use language that is measurably different from the language that an adversary would use when the account is compromised. We use our framework to develop specific algorithms that use the difference of language models of users and adversaries as features in a supervised learning setup. Evaluation results show that the proposed framework is effective for discovering compromised accounts on social networks and a KL-divergence-based language model feature works best.

Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.

The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer. Nevertheless, little empirical work has been done on quantifying the prevalence of different syntactic divergences across language pairs. We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies. We show that our framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study. We demonstrate the usefulness of the resulting analysis by showing that it can help account for performance patterns of a cross-lingual parser.

Most currently available schemes for performance based ranking of Universities or Research organizations, such as, Quacarelli Symonds (QS), Times Higher Education (THE), Shanghai University based All Research of World Universities (ARWU) use a variety of criteria that include productivity, citations, awards, reputation, etc., while Leiden and Scimago use only bibliometric indicators. The research performance evaluation in the aforesaid cases is based on bibliometric data from Web of Science or Scopus, which are commercially available priced databases. The coverage includes peer reviewed journals and conference proceedings. Google Scholar (GS) on the other hand, provides a free and open alternative to obtaining citations of papers available on the net, (though it is not clear exactly which journals are covered.) Citations are collected automatically from the net and also added to self created individual author profiles under Google Scholar Citations (GSC). This data was used by Webometrics Lab, Spain to create a ranked list of 4000+ institutions in 2016, based on citations from only the top 10 individual GSC profiles in each organization. (GSC excludes the top paper for reasons explained in the text; the simple selection procedure makes the ranked list size-independent as claimed by the Cybermetrics Lab). Using this data (Transparent Ranking TR, 2016), we find the regional and country wise distribution of GS-TR Citations. The size independent ranked list is subdivided into deciles of 400 institutions each and the number of institutions and citations of each country obtained for each decile. We test for correlation between institutional ranks between GS TR and the other ranking schemes for the top 20 institutions.

Web services often impose inter-parameter dependencies that restrict the way in which two or more input parameters can be combined to form valid calls to the service. Unfortunately, current specification languages for web services like the OpenAPI Specification (OAS) provide no support for the formal description of such dependencies, which makes it hardly possible to automatically discover and interact with services without human intervention. In this article, we present an approach for the specification and automated analysis of inter-parameter dependencies in web APIs. We first present a domain-specific language, called Inter-parameter Dependency Language (IDL), for the specification of dependencies among input parameters in web services. Then, we propose a mapping to translate an IDL document into a constraint satisfaction problem (CSP), enabling the automated analysis of IDL specifications using standard CSP-based reasoning operations. Specifically, we present a catalogue of nine analysis operations on IDL documents allowing to compute, for example, whether a given request satisfies all the dependencies of the service. Finally, we present a tool suite including an editor, a parser, an OAS extension, a constraint programming-aided library, and a test suite supporting IDL specifications and their analyses. Together, these contributions pave the way for a new range of specification-driven applications in areas such as code generation and testing.

Monitoring users on large computing platforms such as high performance computing (HPC) and cloud computing systems is non-trivial. Utilities such as process viewers provide limited insight into what users are running, due to granularity limitation, and other sources of data, such as system call tracing, can impose significant operational overhead. However, despite technical and procedural measures, instances of users abusing valuable HPC resources for personal gains have been documented in the past cite{hpcbitmine}, and systems that are open to large numbers of loosely-verified users from around the world are at risk of abuse. In this paper, we show how electrical power consumption data from an HPC platform can be used to identify what programs are executed. The intuition is that during execution, programs exhibit various patterns of CPU and memory activity. These patterns are reflected in the power consumption of the system and can be used to identify programs running. We test our approach on an HPC rack at Lawrence Berkeley National Laboratory using a variety of scientific benchmarks. Among other interesting observations, our results show that by monitoring the power consumption of an HPC rack, it is possible to identify if particular programs are running with precision up to and recall of 95\% even in noisy scenarios.

In this paper, we analyze the symbol error rate (SER) performance of the simultaneous wireless information and power transfer (SWIPT) enabled three-node differential decode-and-forward (DDF) relay networks, which adopt the power splitting (PS) protocol at the relay. The use of non-coherent differential modulation eliminates the need for sending training symbols to estimate the instantaneous channel state informations (CSIs) at all network nodes, and therefore improves the power efficiency, as compared with the coherent modulation. However, performance analysis results are not yet available for the state-of-the-art detectors such as the approximate maximum-likelihood detector. Existing works rely on Monte-Carlo simulation to show that there exists an optimal PS ratio that minimizes the overall SER. In this work, we propose a near-optimal detector with linear complexity with respect to the modulation size. We derive an accurate approximate SER expression, based on which the optimal PS ratio can be accurately estimated without requiring any Monte-Carlo simulation.

This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.

In this paper, we present a novel MaxSAT-based technique to compute Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We model the MPMCS problem as a Weighted Partial MaxSAT problem and solve it using a parallel SAT-solving architecture. The results obtained with our open source tool indicate that the approach is effective and efficient.

An apparatus and method are disclosed for analysing a process. An exemplary method includes: generating a process template; and determining a probabilistic model specifying the process template. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, wherein observables may further be caused by background activities of the background node. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, observables may further be caused by background activities of the background node. The method can include measuring values of an observable corresponding to one of the observables nodes; and updating a probabilistic estimate of the process state using the measured values.

Systems and methods are disclosed that may be implemented for network management system (NMS) configuration management support for network devices using a learning and natural language processing application to capture the usage and behavior of the Command Line Interface (CLI) of a network device with the aid of a CLI knowledge model, which in one example may be ontology-based.

In one embodiment, a plurality of objects associated with a dataset and a specified number of nearest objects to be identified are received. The received objects are sorted in a structured format. Further, a key object and a number of adjacent objects corresponding to the key object are selected from the sorted plurality of objects, wherein the number of adjacent objects is selected based on the specified number of nearest objects to be identified. Furthermore, distances between the key object and the number of adjacent objects are determined to identify the specified number of nearest objects, wherein the distances are determined until the specified number of nearest objects is identified. Based on the determined distances, the specified number of nearest objects in the dataset is identified for data analysis.

The invention provides a process for monitoring and/or adjusting a dispersion polymerization of an olefin-based polymer, the process comprising monitoring the concentration of the carbon-carbon unsaturations in the dispersion using Raman Spectroscopy. The invention also provides a process for polymerizing an olefin-based polymer, the process comprising polymerizing one or more monomer types, in the presence of at least one catalyst and at least one solvent, to form the polymer as a dispersed phase in the solvent; and monitoring the concentration of the carbon-carbon unsaturations in the dispersion using Raman Spectroscopy.

Computer-implemented methods for analyzing computer programs written in semi-structured languages are disclosed. The method is based on unification of the two classic forms of program flow analysis, control flow and data flow analysis. As such, it is capable of substantially increased precision, which increases the effectiveness of applications such as automated parallelization and software testing. Certain implementations of the method are based on a process of converting source code to a decision graph and transforming that into one or more alpha graphs which support various applications in software development. The method is designed for a wide variety of digital processing platforms, including highly parallel machines. The method may also be adapted to the analysis of (semi-structured) flows in other contexts including water systems and electrical grids.

One implementation of the disclosure provides a crosstalk analysis method executed by a computer. The method includes steps of: executing a layout program; executing a crosstalk analysis program; acquiring, by the crosstalk analysis program, a plurality of parameters from a layout result generated by the layout program; estimating a crosstalk value according to the parameters; determining whether the crosstalk value is larger than a predetermined value; providing a layout suggestion table when the crosstalk value is larger than the predetermined value.

A method and apparatus for partitioning of the input design into repeating patterns called template cores for the application of reticle enhancement methods, design verification for manufacturability and design corrections for optical and process effects is accomplished by hierarchy analysis to extract cell overlap information. Also hierarchy analysis is performed to extract hierarchy statistics. Finally template core candidates are identified. This allows to the design to be made amenable for design corrections or other analyses or modifications that are able to leverage the hierarchy of the design since the cell hierarchy could otherwise be very deep or cells could have significant overlap with each other.

A mechanism for generating gate-level activity data for use in clock gating efficiency analysis of an integrated circuit (IC) design is provided. Generating the gate-level activity data includes generating a signal behaviour description for inter-register signals, generating a gate-level netlist for the IC design, generating gate-level stimuli based at least partly on the generated signal behaviour description, and performing gate-level simulation using the generated gate-level stimuli to generate gate-level activity data for the IC design. In one embodiment, generating the signal behaviour description includes performing Register Transfer Level (RTL) simulation of the IC design, and generating the gate-level netlist includes performing RTL synthesis. The RTL simulation and RTL synthesis are performed on RTL data for the IC design.

An embodiment of an analysis filterbank for filtering a plurality of time domain input frames, wherein an input frame comprises a number of ordered input samples, comprises a windower configured to generate a plurality of windowed frames, wherein a windowed frame comprises a plurality of windowed samples, wherein the windower is configured to process the plurality of input frames in an overlapping manner using a sample advance value, wherein the sample advance value is less than the number of ordered input samples of an input frame divided by two, and a time/frequency converter configured to provide an output frame comprising a number of output values, wherein an output frame is a spectral representation of a windowed frame.

Images are analyzed within a 3D environment that is generated based on spatial relationships of the images and that allows users to experience the images in the 3D environment. Image analysis may include ranking images based on user viewing information, such as the number of users who have viewed an image and how long an image was viewed. Image analysis may further include analyzing the spatial density of images within a 3D environment to determine points of user interest.

Methods are provided herein for: calculating cell growth rates in various environments and genetic backgrounds; calculating the order of substrate utilization from a defined growth medium; calculating metabolic flux reorganization in various environments and at various growth rates; and calculating the maximum metabolic rate and optimal metabolite concentrations and enzyme activities by applying a computational optimization method to a kinetic model of a metabolic pathway. The optimization methods use intracellular molecular crowding parameters and/or well as kinetic rates to assist in modeling metabolic activity.

Methods and devices for cardiac signal analysis in implantable cardiac therapy systems. Several signal processing and/or conditioning methods are shown including R-wave detection embodiments including the use of thresholds related to previous peak amplitudes. Also, some embodiments include sample thresholding to remove extraneous data from sampled signals. Some embodiments include weighting certain samples more heavily than other samples within a sampled cardiac signal for analysis.

A system for imposing and analyzing surface acoustic waves in a substrate to determine characteristics of the substrate is disclosed. Optical elements and arrangements for imposing and analyzing surface acoustic waves in a substrate are also disclosed. NSOM's, gratings, and nanolight elements may be used to impose surface acoustic waves in a substrate and may also be used to measure transient changes in the substrate due to the passage of surface acoustic waves therethrough.

A cryptanalysis method comprising: (A) Performing a ciphertext-only direct cryptanalysis of A5/1 and(B) Using results of Step (A) to facilitate the decryption and/or encryption of further communications that are consistent with encryption using the session key and/or decryption using the session key, wherein the cryptanalysis considers part of the bits of the session key to have a known fixed value, and wherein the cryptanalysis finds the session key. An efficient known plaintext attack on AS/2 comprises trying all the possible values for R4, and for each such value solving the linearized system of equations that describe the output;The solution of the equations gives the internal state of RI, R2, and R3; Together with R4, this gives the full internal state which gives a suggestion for the key.

A method for detecting electromagnetic waves derived from bacterial DNA, comprising extracting and purifying nucleic acids from a sample; diluting the extracted purified nucleic acids in an aqueous solvent; measuring a low frequency electromagnetic emission over time from the diluted extracted purified nucleic acids in an aqueous solvent; performing a signal analysis of the low frequency electromagnetic emission over time; and producing an output, based on the signal analysis, in dependence on the DNA in the sample. The DNA may be extracted from at least one of blood, feces, urine, saliva, tears, seminal fluid, sweat, seminal and vaginal fluids of a patient, or water to determine, e.g., potability. The samples may be frozen. The extracting and purifying may comprise diluting the sample with an aqueous buffer and mixing; degrading proteins in the diluted sample; precipitating DNA from the buffer solution; and resuspending the precipitated DNA in an aqueous solution.

A sample is cleaned up for spectroscopic analysis by receiving a slide substrate having the sample thereon, fixing the sample to a substrate surface of the slide substrate by applying heat to the slide substrate for a predetermined heating time and incubating the sample on the slide substrate for a predetermined incubation time after fixing the sample to the slide substrate. The sample is further cleaned by washing the sample on the slide substrate after the sample has been incubated and drying the sample by applying heat to the slide substrate for a predetermined drying time, wherein the sample on the slide substrate after drying has retained particles of interest and interferant particles are removed from the substrate. A substrate is also provided for sample collection, which is culturable and Raman silent.

The invention encompasses analyzers and analyzer systems that include a single particle analyzer, methods of using the analyzers and analyzers systems to analyze samples, either for single particles, e.g., protein molecules, or for multiple particles (multiplexing), methods of doing business based on the use of the analyzers or analyzer systems of the system, and electronic media for storing parameters useful in the analyzers and analyzer systems of the invention.

Management of the health status of an animal colony using a plurality of blood collection cards and the analysis of dried blood from members of the colony that has been collected on the cards. Members of the colony may be removed from the colony as a result of the analysis.

View definitions are created for deterministic performance analysis in real-time computing systems, and can then be used to present views for analyzing outliers that occur during run-time execution. Trace data created by a real-time application is compared to a set of view definitions to determine whether the trace data matches the view definition. If so, then related records from the trace are gathered according to specifications in the matched view definition, and calculations (such as elapsed time) can then be performed using the related records. A view definition may be created by prompting a user for selection of parameters to be programmatically inserted into a markup language document. A capability may be provided whereby a user can receive additional information (which is extracted from the trace data, according to specifications in the matched view definition) upon a user gesture such as hovering a selection means over a displayed view.

In a catenary-based transportation system which is provided with integrated power supply equipment having an electricity storage unit which stores electricity regenerated by vehicles traveling by electricity received from a catenary and supplies electricity to the catenary and the other power supply system which is a power supply system different from the electricity storage unit concerned, the performance of a rectifier of the other power supply system is determined based on a power-supplying contribution ratio γ of the other power supply system so that the cost value of the integrated power supply equipment becomes lower than a target cost value.

Technologies for native code invocation using binary analysis are described. A computing device for invoking native code from managed code using binary analysis receives a call from a thread executing a managed code segment to execute a native code segment. The computing device performs a binary analysis of the native code segment and generates, from the binary analysis, a complexity indicator that indicates a level of complexity of the native code segment by comparing the native code segment to at least one predefined complexity rule. Additionally, the computing device stores a status of the thread based on the complexity indicator and executes the native code segment. Other embodiments are described and claimed.

A method for analyzing software with pointer analysis may include obtaining a software program, and determining a first independent program slice of the software program describing a first code segment of the software program. The method may further include determining, using a first pointer analysis objective, a first result from performing a first pointer analysis on the first independent program slice, and determining, using the first result, a first dependent program slice of the software program. The method may further include determining, using a second pointer analysis objective, a second result from performing a second pointer analysis on the first dependent program slice. The method may further include generating a report, using these results, indicating whether the software program satisfies a predetermined criterion.

Embodiments of extraction unit and an analysis method are provided. In one embodiment, the analysis method includes the steps of providing a feed stream and a species-selective solvent to the distillation column, drawing a vapor sample from the distillation column, condensing the vapor sample, and analyzing at least a portion of the condensed vapor sample.

The present invention is a method and apparatus for acoustically manipulating one or more particles.

An exercise machine includes a motor that drives a belt to move, and an analysis system to collect a peak energy from the motor and to convert the peak energy into different user exercising data.

Disclosed are a Chinese website classification method and system based on characteristic analysis of a website homepage. The method specifically comprises the following steps: S1, crawling website content; S2, labeling a website type; S3, extracting website information; S4, calculating a weight and representing the weight in the form of a characteristic vector; and S5, classifying the website by comparing the characteristic vector. By utilizing the above Chinese website classification method and system, the noise interference can be alleviated to the greatest extent by only extracting a title and meta-information of the website; by means of pre-processing and characteristic vector expression, the characteristics of the website are accurately expressed with the vector, so that the accuracy of classification is increased; and since only the title and meta-information of the website need to be processed, the quantity of data to be processed is small, and the processing speed is high.

Systems and methods for selectively extracting hydrogen gas dissolved in oil are provided. In one embodiment, a system includes a selectively permeable membrane provided at a point of contact between oil and a sensor chamber. The selectively permeable membrane has a hydrogen specificity and a thickness selected to minimize detection of further gasses dissolved in the oil by a hydrogen gas sensor cross-sensitive to the further gasses. The selectively permeable membrane can include polyimide. The further gasses include carbon monoxide, acetylene, and ethylene. The system can include a further membrane and a porous metal disc. The porous metal disc is bound to the selectively permeable membrane by using the further membrane as an adhesive layer and by applying pressure and temperature. The porous metal disc supports the selectively permeable membrane and the further membrane against pressure of the oil when exposed to a vacuum. The further membrane includes fluorohydrocarbons.

A detection section is used in such a manner that it is inserted into a living body by being guided by an insertion needle to be stuck and inserted into the living body. The detection section includes a first region, a second region, and a third region. The first region is provided in a tip end portion of the detection section and includes an electrode layer (detection electrode). The third region includes a wiring section and has a smaller width than the width of a slit of the insertion needle. The second region is provided between the first region and the third region and has the same width as the width of the third region by gradually decreasing from the width of the first region.

A method of detecting an electric arc in an electrical system from a signal originating from at least one sensor detecting acoustic waves in the system, including: a) calculating by means of a processing device, over a sliding window of signal samples, at least one statistical parameter selected from the skewness and the kurtosis of the signal; b) detecting a possible occurrence of an event by taking into account said at least one statistical parameter; and c) performing a frequency analysis of the signal enabling to identify an electric arc when an event is detected at step b).

A multi-dimensional liquid analysis system includes a first dimension system and a second dimension system, wherein outflow from the first dimension system is separated at a flow splitter under controlled conditions. The flow splitter separates the first dimension outflow into first and second split outlet flows, with one of the split outlet flows being metered to a designated flow rate with a flow metering device disposed downstream from the flow splitter. The flow metering device selectively closes or opens an outlet flow path to define a volumetric flow rate along that outlet flow path, so that the other split outlet flow is correspondingly controlled.

Aspects of the present disclosure include methods for processing a biological sample. Methods according to certain embodiments include contacting a biological sample having cells with an assay reagent that includes one or more analyte-specific binding members to produce a biological sample assay composition and introducing the biological assay composition into an inlet of a flow cytometer having an integrated acoustic separator. Systems, including a flow cytometer with integrated acoustic separator having one or more acoustic concentrator devices suitable for practicing the subject methods are also described.

News you don't want to miss for April 7:

1. A nursing home in Orinda is being hit hard by a covid-19 outbreak that has infected 49 people, including 27 residents and 22 staff members, the East Bay Times reports.…

Blue Cat Audio has released a free update for its multiple tracks audio analyzers Blue Cat’s FreqAnalyst Multi, Oscilloscope Multi, and StereoScope Multi. These special audio analysis plugins can communicate between instances: It is thus possible to visualize the spectrum, stereo image and waveform of multiple audio tracks on a single graph simultaneously. These tools […]

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Plugin Boutique has announced a sale on Signum Audio, offering discounts of up to 60% off for a limited time. DSP engineering experts Signum Audio develop a range of high-end and highly intuitive plugins for advanced loudness analysis, limiting and normalisation. Save up to 60% off their entire range, exclusively at Plugin Boutique. The sale […]

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Twelve candidates take the stage for October's Democratic primary debate, which begins at 8 p.m. ET. NPR reporters are posting context and analysis in real time. Loading...