Quaking aspen (Populus tremuloides Michx.) is a valuable species that is declining in the Blue Mountains of northeastern Oregon. This publication is a compilation of over 20 years of aspen management experience by USDA Forest Service workers in the Blue Mountains.

Something different for the kids to enjoy

Focused Update Highlights: Newer anticoagulants, known as non-vitamin K oral anticoagulants (NOACs), are recommended over the traditional warfarin to prevent stroke in people with atrial fibrillation (AFib). Weight loss is recommended for overweight or obese people with AFib.

Editor’s note: The Gazette sports staff has compiled lists of its top 15 favorite sports movies. Each day, a different staffer will share some insight into one of their favorites. Some of them...

Multisite is a powerful tool that will help you create a network of sites to fulfill a variety of purposes, and which you can customize to make life easier for your users and help your network run more efficiently and make you money.

In this paper, we mainly establish the existence and uniqueness theorem for solutions of the exterior Dirichlet problem for a class of fully nonlinear second-order elliptic equations related to the eigenvalues of the Hessian, with prescribed generalized symmetric asymptotic behavior at infinity. Moreover, we give some new results for the Hessian equations, Hessian quotient equations and the special Lagrangian equations, which have been studied previously.

In this article, we prove that the completeness of the Hamilton flow and essential self-dajointness are equivalent for real principal type operators on the circle. Moreover, we study spectral properties of these operators.

Let $k ,=, mathbb{Q}(sqrt[5]{n},zeta_5)$, where $n$ is a positive integer, $5^{th}$ power-free, whose $5-$class group is isomorphic to $mathbb{Z}/5mathbb{Z} imesmathbb{Z}/5mathbb{Z}$. Let $k_0,=,mathbb{Q}(zeta_5)$ be the cyclotomic field containing a primitive $5^{th}$ root of unity $zeta_5$. Let $C_{k,5}^{(sigma)}$ the group of the ambiguous classes under the action of $Gal(k/k_0)$ = $<sigma>$. The aim of this paper is to determine all integers $n$ such that the group of ambiguous classes $C_{k,5}^{(sigma)}$ has rank $1$ or $2$.

We study flows on C*-algebras with the Rokhlin property. We show that every Kirchberg algebra carries a unique Rokhlin flow up to cocycle conjugacy, which confirms a long-standing conjecture of Kishimoto. We moreover present a classification theory for Rokhlin flows on C*-algebras satisfying certain technical properties, which hold for many C*-algebras covered by the Elliott program. As a consequence, we obtain the following further classification theorems for Rokhlin flows. Firstly, we extend the statement of Kishimoto's conjecture to the non-simple case: Up to cocycle conjugacy, a Rokhlin flow on a separable, nuclear, strongly purely infinite C*-algebra is uniquely determined by its induced action on the prime ideal space. Secondly, we give a complete classification of Rokhlin flows on simple classifiable $KK$-contractible C*-algebras: Two Rokhlin flows on such a C*-algebra are cocycle conjugate if and only if their induced actions on the cone of lower-semicontinuous traces are affinely conjugate.

We study a class of fourth order nonlinear parabolic equations which include the thin-film equation and the quantum drift-diffusion model as special cases. We investigate these equations by first developing functional inequalities of the type $ int_Omega u^{2gamma-alpha-eta}Delta u^alphaDelta u^eta dx geq cint_Omega|Delta u^gamma |^2dx $, which seem to be of interest on their own right.

In the spirit of very recent articles by J. Bonet, W. Lusky and J. Taskinen we are studying the so-called solid hulls and cores of spaces of weighted entire functions when the weights are given in terms of associated weight functions coming from weight sequences. These sequences are required to satisfy certain (standard) growth and regularity properties which are frequently arising and used in the theory of ultradifferentiable and ultraholomorphic function classes (where also the associated weight function plays a prominent role). Thanks to this additional information we are able to see which growth behavior the so-called "Lusky-numbers", arising in the representations of the solid hulls and cores, have to satisfy resp. if such numbers can exist.

Let $H$ be an infinite dimensional, reflexive, separable Hilbert space and $NA(H)$ the class of all norm-attainble operators on $H.$ In this note, we study an implicit scheme for a canonical representation of nonexpansive contractions in norm-attainable classes.

We introduce natural deformation classes of generalized K"ahler structures using the Courant symmetry group. We show that these yield natural extensions of the notions of K"ahler class and K"ahler cone to generalized K"ahler geometry. Lastly we show that the generalized K"ahler-Ricci flow preserves this generalized K"ahler cone, and the underlying real Poisson tensor.

A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF's eRisk open tasks. SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale. However, SS3 processes the input using a bag-of-word model lacking the ability to recognize important word sequences. This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations. In the standard document classification field, it is very common to use word n-grams to try to overcome some of these limitations. Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important "on the fly". This paper introduces t-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically. We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection. Experimental results suggest that t-SS3 is able to improve both current results and the richness of visual explanations.

Traffic signs are essential map features globally in the era of autonomous driving and smart cities. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes. The dataset includes 52K images that are fully annotated and 48K images that are partially annotated. This is the largest and the most diverse traffic sign dataset consisting of images from all over world with fine-grained annotations of traffic sign classes. We have run extensive experiments to establish strong baselines for both the detection and the classification tasks. In addition, we have verified that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research: https://www.mapillary.com/dataset/trafficsign.

We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with in materio computing paradigm. As the Reservoir Computing is a suitable model for describing embedded in materio computation, we propose that this type of presented basic construction unit can be used as a source for "reservoir of states" necessary for simple tuning of the readout layer. In that perspective, buildings constructed from computing concrete could function as a highly parallel information processor for smart architecture. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal.

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.

We establish versions of Conley's (i) fundamental theorem and (ii) decomposition theorem for a broad class of hybrid dynamical systems. The hybrid version of (i) asserts that a globally-defined "hybrid complete Lyapunov function" exists for every hybrid system in this class. Motivated by mechanics and control settings where physical or engineered events cause abrupt changes in a system's governing dynamics, our results apply to a large class of Lagrangian hybrid systems (with impacts) studied extensively in the robotics literature. Viewed formally, these results generalize those of Conley and Franks for continuous-time and discrete-time dynamical systems, respectively, on metric spaces. However, we furnish specific examples illustrating how our statement of sufficient conditions represents merely an early step in the longer project of establishing what formal assumptions can and cannot endow hybrid systems models with the topologically well characterized partitions of limit behavior that make Conley's theory so valuable in those classical settings.

What am I supposed to say here?…

You might not know it from the fancy attire they wear on stage at the Fox Theater, but for the musicians in the Spokane Symphony, it's a part-time gig. It's a prestigious gig, to be sure, but like most artists, for the musicians, it's just one piece of a puzzle full of hustle they have to solve to make a living.…

Here are a few ways to keep yourself entertained, and maybe even educate yourself a bit, while you're stuck at home:…

Techniques for evaluation and/or retraining of a classification model built using labeled training data. In some aspects, a classification model having a first set of weights is retrained by using unlabeled input to reweight the labeled training data to have a second set of weights, and by retraining the classification model using the labeled training data weighted according to the second set of weights. In some aspects, a classification model is evaluated by building a similarity model that represents similarities between unlabeled input and the labeled training data and using the similarity model to evaluate the labeled training data to identify a subset of the plurality of items of labeled training data that is more similar to the unlabeled input than a remainder of the labeled training data.

A system and method for classifying unclassified samples. The method includes detecting a number of classes including training samples in training data sets. The method includes, for each class, determining a vector for each training sample based on a specified number of nearest neighbor distances between the training sample and neighbor training samples, and determining a class distribution based on the vectors. The method also includes detecting an unclassified sample in a data set and, for each class, determining a vector for the unclassified sample based on the specified number of nearest neighbor distances between the unclassified sample and nearest neighbor training samples within the class, and determining a probability that the unclassified sample is a member of the class based on the vector and the class distribution. The method further includes classifying the unclassified sample based on the probabilities.

A system and method for providing more than two levels of classification distinction of a user state are provided. The first and second general states of a user are sensed. The first general state is classified as either a first state or a second state, and the second general state is classified as either a third state or a fourth state. The user state of the user is then classified as one of at least three different classification states.

A computer-implemented method for classifying documents for data loss prevention may include 1) identifying training documents for a machine learning classifier configured for data loss prevention, 2) performing a semantic analysis on training documents to identify topics within the set training documents, 3) applying a similarity metric to the topics to identify at least one unrelated topic with a similarity to the other topics within the plurality of topics, as determined by the similarity metric, that falls below a similarity threshold, 4) identifying, based on the semantic analysis, at least one irrelevant training document within the set of training documents in which a predominance of the unrelated topic is above a predominance threshold, and 5) excluding the irrelevant training document from the set of training documents based on the predominance of the unrelated topic within the irrelevant training document. Various other methods, systems, and computer-readable media are also disclosed.

A computer implemented method for automatic chemical assay classification, the method comprising steps the computer is programmed to perform, the steps comprising: receiving a plurality of sets of parameters, each one of the received sets of parameters characterizing a respective assay of a chemical reaction, calculating a space enhancing proximity among points representative of assays of qualitatively identical chemical reactions, and representing each one of at least two of the received sets of parameters as a respective point in the calculated space, and dividing the points in the calculated space into a number of groups, according to proximity among the points in the calculated space, each group pertaining to a respective chemical reaction, thereby classifying the assays.

Low bit rate audio coding such as BWE algorithm often encounters conflict goal of achieving high time resolution and high frequency resolution at the same time. In order to achieve best possible quality, input signal can be first classified into fast signal and slow signal. This invention focuses on classifying signal into fast signal and slow signal, based on at least one of the following parameters or a combination of the following parameters: spectral sharpness, temporal sharpness, pitch correlation (pitch gain), and/or spectral envelope variation. This classification information can help to choose different BWE algorithms, different coding algorithms, and different postprocessing algorithms respectively for fast signal and slow signal.

A sequence of storage devices of a data store may include one or more stripesets for storing data stripes of different lengths and of different types. Each data stripe may be stored in a prefix or other portion of a stripeset. Each data stripe may be identified by an array of addresses that identify each page of the data stripe on each included storage device. When a first storage device of a stripeset becomes full, the stripeset may be shifted by removing the full storage device from the stripeset, and adding a next storage device of the data store to the stripeset. A class variable may be associated with storage devices of a stripeset to identify the type of data that the stripeset can store. The class variable may be increased (or otherwise modified) when a computer stores data of a different class in the stripeset.

An abstraction for storage class memory is provided that hides the details of the implementation of storage class memory from a program, and provides a standard channel programming interface for performing certain actions, such as controlling movement of data between main storage and storage class memory or managing storage class memory.

Within machine vision, object movement is often estimated by applying image evaluation techniques to visible light images, utilizing techniques such as perspective and parallax. However, the precision of such techniques may be limited due to visual distortions in the images, such as glare and shadows. Instead, lidar data may be available (e.g., for object avoidance in automated navigation), and may serve as a high-precision data source for such determinations. Respective lidar points of a lidar point cloud may be mapped to voxels of a three-dimensional voxel space, and voxel clusters may be identified as objects. The movement of the lidar points may be classified over time, and the respective objects may be classified as moving or stationary based on the classification of the lidar points associated with the object. This classification may yield precise results, because voxels in three-dimensional voxel space present clearly differentiable statuses when evaluated over time.

Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

A video-on-demand (VOD) content delivery system has a VOD Application Server which manages a database of templates ordered in a hierarchy for presentation of video content elements of different selected types categorized in hierarchical order. The templates include those for higher-order displays which have one or more links to lower-order displays of specific content. The VOD Application Server, in response to viewer request, displays a high-order templatized display, and in response to viewer selection of a link, displays the lower-order display of specific content. The hierarchical templatized displays enable viewers to navigate to an end subject of interest while having a unique visual experience of moving through a series of displays to the end subject of interest.

A premium class passenger suite that includes a main seat positioned in the suite together with separate bed. The bed has a flexible mattress of predetermined dimensions and is movable between a stowed position to one side of the main seat and a deployed position above and separate from the main seat. A drive apparatus is provided for driving the bed between the stowed and deployed positions. The main seat is configured to be movable between a seating position when the bed is stowed, and a stowed position with a lowered seat back when the bed is deployed for use.

A client-server computing system includes a server that has a virtual display driver that classifies and, if necessary, translates graphics application programming interface (API) functions to a cross-platform format. Classification involves determining whether the graphics command(s) are platform-specific and/or client-supported functions. After classification and translation, the graphics command(s) are marshaled and transmitted to a client via a network. The client includes a client display driver that executes the functions using a client 3D library and renders the resulting image data to a display.

A modulation apparatus for a class D switching amplifier is capable of reducing power consumption of an Electro-Migration Interface (EMI) of an output end and a gate driver end in a zero input signal. The modulation apparatus for a class D switching amplifier includes a control unit for detecting and outputting a control signal which is a common signal component of a first modulation signal modulated by using a first input signal and a second modulation signal modulated by using a second input signal; and is characterized by feedback of a first output signal, a second output signal and a common output signal outputted by using the first modulation signal, the second modulation signal and the control signal through an input of the modulation apparatus.

Embodiments of the present disclosure relate to an overlay class F choke of a radio frequency (RF) power amplifier (PA) stage and an RF PA amplifying transistor of the RF PA stage. The overlay class F choke includes a pair of mutually coupled class F inductive elements, which are coupled in series between a PA envelope power supply and a collector of the RF PA amplifying transistor. In one embodiment of the RF PA stage, the RF PA stage receives and amplifies an RF stage input signal to provide an RF stage output signal using the RF PA amplifying transistor. The collector of the RF PA amplifying transistor provides the RF stage output signal. The PA envelope power supply provides an envelope power supply signal to the overlay class F choke. The envelope power supply signal provides power for amplification.

A pop-free single-ended output class-D amplifier includes: an input signal generator for generating an input signal; a power supply for supplying input power; a reference voltage generator for generating a reference voltage; a gain-adjustable stage for generating an amplified signal according to the reference voltage and adjusting a gain of the single-ended output class-D amplifier; a pulse width modulation module for outputting a pulse width modulation signal according to the reference voltage, the amplified signal, and the input power; a low-pass filter for low-pass filtering the pulse width modulation signal to generate an output voltage; and a logic controller for generating at least one control signal to control the reference voltage generator, the gain-adjustable stage, and the pulse width modulation module according to the input power, the reference voltage, and the pulse width modulation signal.

It is proposed a method for delay spread classification of an orthogonal frequency-division multiplexing signal (multiplexing signal), and a receiving device and a telecommunication device connected thereto, the multiplexing signal comprising at least a first multiplexing symbol comprising at least two first reference symbols in the frequency domain, the method comprising: receiving at least the first multiplexing symbol; demodulating at least the first reference symbols of the first multiplexing symbol; determining at least a first autocorrelation value by autocorrelating the demodulated first reference symbols in the frequency domain; computing the filtered output energy of the autocorrelation and classifying the delay spread by mapping the ratio of the output energy for the filters.

An apparatus includes a substrate, a classical computing processor formed on the substrate, a quantum computing processor formed on the substrate, and one or more coupling components between the classical computing processor and the quantum computing processor, the one or more coupling components being formed on the substrate and being configured to allow data exchange between the classical computing processor and the quantum computing processor.

A method for quantifying classification confidence of obstructions applied to a perception mergence system of a vehicular computer in a vehicle. The method includes steps of: the vehicular computer receiving obstruction information of at least obstruction, image information corresponding to the obstruction information and vehicle body signals, and using a classifier to classify them; calculating a detection result of each range sensor to calculate a existence confidence; using the existence confidences and precision of the classifier to calculate a classification belief assignment of each range sensor corresponding to each obstruction; performing mergence calculation on the classification belief assignments to respectively quantify an obstruction classification confidence of all the range sensor corresponding to each obstruction; and performing a classification ineffectiveness filtering mechanism to exclude the obstruction whose obstruction classification confidence less than a predetermined value. The present invention quantifies the obstruction classification confidence to improve the classification precision.

An apparatus includes a storage medium operable to store a number of data tracks, a read channel circuit operable to process the data tracks read from the storage medium, and a track quality classifier circuit operable to determine a track quality metric for the data tracks read from the storage medium. The track quality metric indicates whether a corresponding one of the data tracks that has failed to successfully process in the read channel circuit can be reprocessed within a track gap period.

Particular embodiments described herein provide for an electronic device that can be configured to analyze data using an ensemble and assign a classification to the data based, at least in part, on the results of the analyses using the ensemble. The ensemble can include one or more multinomial classifiers and each multinomial classifier can assign two or more classifications to the 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.

The present invention refers to a process for classifying tumor samples of unknown and/or uncertain primary origin, specifically including the steps of obtaining patterns of biological activity modulation of tumor of unknown and/or uncertain primary origin and comparing them to an specific and unique group of biomarkers which determine the profiles of biological activity modulation of known origin tumors. The present invention belongs to the molecular biology and genetics field.

A system for adaptive cooling and energy harvesting comprising at least one thermoelectric device capable of acting as a thermoelectric cooler and as a thermoelectric generator, a hierarchical multiple-level control system, and electronics controlled by the control system and connected to the thermoelectric device. The electronics selectively configure the thermoelectric device in at least in a thermoelectric cooler operating mode and in a thermoelectric generation operating mode. The thermoelectric device can incorporate quantum-process and quantum-well materials for higher heat transfer and thermoelectric generation efficiencies. The invention provides for thermoelectric devices to additionally operate in temperature sensing mode. The hierarchical control system can comprise a plurality of control system, each of which can operate in isolation and can be interconnected with additional subsystems associated with other hierarchical levels. The hierarchical control system can comprise linear (additive) control, bilinear (additive and multiplicative) control, nonlinear control, and hysteresis.

Active Nation fitness coaches at Chamberlayne Leisure Centre are supporting the Daily Echo’s Staying Alive campaign, urging Sotonians to burn off those extra calories in a bid to battle killer diseases and illnesses.

School is full of conflict. This week we explore three conflicts in the classroom. Students and teachers use poetry and stories to reflect on moments of friction at school and help us understand why they matter. Stories with a Heartbeat is a new podcast from WUNC hosted by poet Will McIneney that uses poetry and storytelling to explore the complexity of conflict.

Czech warms up for Wimbledon with Edgbaston triumph.