In this paper, we provide some topological criteria for the Poisson Dixmier-Moeglin equivalence for $A$ in terms of the poset $({ m P. spec A}, subseteq)$ and the symplectic leaf or core stratification on its maximal spectrum. In particular, we prove that the Zariski topology of the Poisson prime spectrum and of each symplectic leaf or core can detect the Poisson Dixmier-Moeglin equivalence for any complex affine Poisson algebra. Moreover, we generalize the weaker version of the Poisson Dixmier-Moeglin equivalence for a complex affine Poisson algebra proved in [J. Bell, S. Launois, O.L. S'anchez, and B. Moosa, Poisson algebras via model theory and differential algebraic geometry, J. Eur. Math. Soc. (JEMS), 19(2017), no. 7, 2019-2049] to the general context of a commutative differential algebra.

We introduce a notion of c-group, which is a group up to congruence relation and consider the corresponding category. Extensions, actions and crossed modules (c-crossed modules) are defined in this category and the semi-direct product is constructed. We prove that each categorical group gives rise to c-groups and to a c-crossed module, which is a connected, special and strict c-crossed module in the sense defined by us. The results obtained here will be applied in the proof of an equivalence of the categories of categorical groups and connected, special and strict c-crossed modules.

The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we develop a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property.

Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks. The basic principle in these works is instance discrimination: learning to differentiate between two augmented versions of the same image and a large batch of unrelated images. Networks learn to ignore the augmentation noise and extract semantically meaningful representations. Prior work uses artificial data augmentation techniques such as cropping, and color jitter which can only affect the image in superficial ways and are not aligned with how objects actually change e.g. occlusion, deformation, viewpoint change. In this paper, we argue that videos offer this natural augmentation for free. Videos can provide entirely new views of objects, show deformation, and even connect semantically similar but visually distinct concepts. We propose Video Noise Contrastive Estimation, a method for using unlabeled video to learn strong, transferable single image representations. We demonstrate improvements over recent unsupervised single image techniques, as well as over fully supervised ImageNet pretraining, across a variety of temporal and non-temporal tasks. Code and the Random Related Video Views dataset are available at https://www.github.com/danielgordon10/vince

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.

Histopathology is a reflection of the molecular changes and provides prognostic phenotypes representing the disease progression. In this study, we introduced feature scores generated from hematoxylin and eosin histology images based on deep learning (DL) models developed for prostate pathology. We demonstrated that these feature scores were significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and had simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that were not previously exposed to the datasets of the current study. Further, we discussed the potential of such feature scores to improve the current tumor grading system and the challenges that are associated with tumor heterogeneity and the development of prognostic models from histology images. Our findings uncover the potential of feature scores from histology images as digital biomarkers in precision medicine and as an expanding utility for digital pathology.

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image.

Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world.

It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction methods because thin structures often lack distinct point features and have severe self-occlusion.

We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera.

Specifically, we present a new curve-based approach to estimate accurate camera poses by establishing correspondences between featureless thin objects in the foreground in consecutive video frames, without requiring visual texture in the background scene to lock on.

Enabled by this effective curve-based camera pose estimation strategy, we develop an iterative optimization method with tailored measures on geometry, topology as well as self-occlusion handling for reconstructing 3D thin structures.

Extensive validations on a variety of thin structures show that our method achieves accurate camera pose estimation and faithful reconstruction of 3D thin structures with complex shape and topology at a level that has not been attained by other existing reconstruction methods.

Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data. This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network. The self-supervised learning is achieved by minimizing a photo-consistency loss, which is evaluated between a video frame and its neighboring frames warped according to the estimated depth and the 3D non-rigid motion of the human body. To solve this non-rigid motion, we first estimate a rough SMPL model at each video frame and compute the non-rigid body motion accordingly, which enables self-supervised learning on estimating the shape details. Experiments demonstrate that our method enjoys better generalization and performs much better on data in the wild.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

We introduce a variant of PCPs, that we refer to as rectangular PCPs, wherein proofs are thought of as square matrices, and the random coins used by the verifier can be partitioned into two disjoint sets, one determining the row of each query and the other determining the *column*.

We construct PCPs that are efficient, short, smooth and (almost-)rectangular. As a key application, we show that proofs for hard languages in NTIME$(2^n)$, when viewed as matrices, are rigid infinitely often. This strengthens and considerably simplifies a recent result of Alman and Chen [FOCS, 2019] constructing explicit rigid matrices in FNP. Namely, we prove the following theorem: - There is a constant $delta in (0,1)$ such that there is an FNP-machine that, for infinitely many $N$, on input $1^N$ outputs $N imes N$ matrices with entries in $mathbb{F}_2$ that are $delta N^2$-far (in Hamming distance) from matrices of rank at most $2^{log N/Omega(log log N)}$.

Our construction of rectangular PCPs starts with an analysis of how randomness yields queries in the Reed--Muller-based outer PCP of Ben-Sasson, Goldreich, Harsha, Sudan and Vadhan [SICOMP, 2006; CCC, 2005]. We then show how to preserve rectangularity under PCP composition and a smoothness-inducing transformation. This warrants refined and stronger notions of rectangularity, which we prove for the outer PCP and its transforms.

The interoperability of proof assistants and the integration of their libraries is a highly valued but elusive goal in the field of theorem proving. As a preparatory step, in previous work, we translated the libraries of multiple proof assistants, specifically the ones of Coq, HOL Light, IMPS, Isabelle, Mizar, and PVS into a universal format: OMDoc/MMT.

Each translation presented tremendous theoretical, technical, and social challenges, some universal and some system-specific, some solvable and some still open. In this paper, we survey these challenges and compare and evaluate the solutions we chose.

We believe similar library translations will be an essential part of any future system interoperability solution and our experiences will prove valuable to others undertaking such efforts.

Dialogue engines that incorporate different types of agents to converse with humans are popular.

However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem.

We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a emph{Neural Response Selection} method.

The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism.

Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems.

An interesting and frequent type of multi-word expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named entities ("Wells Fargo") and dates ("July 5, 2020") as well as certain productive constructions ("blow for blow", "day after day"). Despite their special status and prevalence, current dependency-annotation schemes require treating such flat structures as if they had internal syntactic heads, and most current parsers handle them in the same fashion as headed constructions. Meanwhile, outside the context of parsing, taggers are typically used for identifying MWEs, but taggers might benefit from structural information. We empirically compare these two common strategies--parsing and tagging--for predicting flat MWEs. Additionally, we propose an efficient joint decoding algorithm that combines scores from both strategies. Experimental results on the MWE-Aware English Dependency Corpus and on six non-English dependency treebanks with frequent flat structures show that: (1) tagging is more accurate than parsing for identifying flat-structure MWEs, (2) our joint decoder reconciles the two different views and, for non-BERT features, leads to higher accuracies, and (3) most of the gains result from feature sharing between the parsers and taggers.

Lambda functions are quite an intuitive concept of Modern C++ introduced in C++11, so there are already tons of articles on lambda function tutorials over the internet. But still, there are some untold things (like IIFE, types of lambda, etc.) left, which nobody talks about. Therefore, here I am to not only show you lambda function in C++, but we'll also cover how it works internally and other aspects of Lambda.

The title of this article is a bit misleading. Because lambda doesn't always synthesize to function pointer. It's an expression (precisely unique closure). But I have kept it that way for simplicity. So from now on, I will use lambda function and expression interchangeably.

The Cannabis Issue It's been almost a month since the COVID-19 pandemic forced folks inside and made "social distancing" part of our daily lexicons.…

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The Spokane Comedy Club might be quiet right now, but there are still laughs to be had on Zoom, and not just from watching your co-workers try to navigate the online meeting platform. Saturday night, and again next Saturday, the comedy club is hosting Comedians Doing Comedy: A Virtual Comedy Show.…

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At around 5:30 pm on Thursday, there were two camps of people set up in Coeur d'Alene Park in Browne's Addition.…

Disclosed herein is a method for producing an alcohol and an amine from an amide under an atmosphere of hydrogen with the use of, as a catalyst, a ruthenium complex that is easily prepared, easy to handle, and relatively cheaply obtained. Specifically, the method is a method for producing an alcohol and/or an amine from an amide compound under an atmosphere of hydrogen with the use of as a catalyst, a ruthenium carbonyl complex represented by the following general formula (1): RuXY(CO)(L) (1) wherein X and Y may be the same or different from each other and each represents an anionic ligand and L represents a tridentate aminodiphosphine ligand containing two phosphino groups and a —NH— group.

The present disclosure involves systems, software, and computer-implemented methods for providing process intelligence by correlating events from multiple business process systems to a single business scenario using configurable correlation strategies. An example method includes identifying a raw event associated with a sending business process and a receiving business process, identifying a sending business process attribute associated with the sending business process and a receiving business process attribute associated with the receiving business process, determining a correlation strategy for associating the raw event with a business scenario instance, the determination based at least in part on the sending business process attribute and the receiving business process attribute, and generating a visibility scenario event from the raw event according to the correlation strategy, the visibility scenario event associated with the business scenario instance.

A method for performing relational analysis of parsed input is employed to create a visual map of knowledge information. A title, header or subject line for an input item of information is parsed into syntactical components of at least a subject component and any predicate component(s) relationally linked as topic and subtopics. A search of topics and subtopics is carried out for each parsed component. If a match is found, then the parsed component is taken as a chosen topic/subtopic label. If no match is found, then the parsed component is formatted as a new entry in the knowledge map. A translation function for translating topics and subtopics from an original language into one or more target languages is enabled by user request or indicated user preference for display on a generated visual map of knowledge information.

In embodiments of the present invention improved capabilities are described for a computer program product embodied in a computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of (1) ascertaining preferences of a plurality of users who are part of an internet based social interactive construct, wherein the plurality of users become a plurality of known users; (2) determining the internet based social interactive graphical representation for the plurality of known users; and (3) inferring the preferences of an unknown user present in the internet based social interactive graphical representation of the plurality of known users based on the interrelationships between the unknown user and the plurality of known users within the graphical representation.

A method of identifying one or more particular images from an image collection, includes indexing the image collection to provide image descriptors for each image in the image collection such that each image is described by one or more of the image descriptors; receiving a query from a user specifying at least one keyword for an image search; and using the keyword(s) to search a second collection of tagged images to identify co-occurrence keywords. The method further includes using the identified co-occurrence keywords to provide an expanded list of keywords; using the expanded list of keywords to search the image descriptors to identify a set of candidate images satisfying the keywords; grouping the set of candidate images according to at least one of the image descriptors, and selecting one or more representative images from each grouping; and displaying the representative images to the user.

A computer implemented method of constructing formal definitions in intelligent glossaries for interpreting text, comprising the steps of: providing at least one Distinction having a Boundary, an Indication, a Counter-indication and a Frame; modeling each Distinction as a diagram to provide a Distinction Model; verifying each distinction model as being an instantiation of a generic Distinction Pattern; providing at least one Arrangement made of nonintersecting Marks of Distinction containing Indications from the verified Distinction Model; writing at least one Formulation for each Indication appearing in verified Distinction model and Arrangement, providing well-founded Indications; calculating precise Formulations in Natural Language from well-founded Indications by substituting Variables symbols and/or replacing Constants symbols to transform imprecise Formulations into precise Formulations; selecting a Definition type and embedding at least one precise Formulation and Definition type as a formal Definition in an Intelligent Glossary to provide computerized Semantic Systems of Intelligent Glossaries.

A process for producing bio-oil from municipal solid waste, the process including: a) liquifying municipal solid waste, to obtain a mixture containing an oily phase containing bio-oil, a solid phase, and a first aqueous phase; b) treating the first aqueous phase from a) with an adsorbing material, to obtain a second aqueous phase; c) fermenting the second aqueous phase from b), to obtain a biomass; d) subjecting the biomass obtained in c) to the liquification a). The bio-oil obtained is advantageously used in the production of biofuels for motor vehicles or for the generation of electric energy or heat.

Methods of separating renewable materials, such as lipids, from microorganisms, such as oleaginous yeasts, may include conditioning cell walls of the microorganisms to form, open or enlarge pores, and removing at least a portion of the renewable material through the pores. These methods may result in delipidated microorganisms with cell walls that are substantially intact and with mesopores. These delipidated microorganisms may be used to produce biofuels.

The present invention relates to a method for removing phosphorus-containing compounds from triglyceride-containing compositions.

Methods are provided for refining natural oil feedstocks and producing dibasic esters and/or dibasic acids. The methods comprise reacting a terminal olefin with an internal olefin in the presence of a metathesis catalyst to form a dibasic ester and/or dibasic acid. In certain embodiments, the olefin esters are formed by reacting the feedstock in the presence of a metathesis catalyst under conditions sufficient to form a metathesized product comprising olefins and esters, separating the olefins from the esters in the metathesized product, and transesterifying the esters in the presence of an alcohol to form a transesterified product having olefin esters.

A process for the production of bio-oil from solid urban waste, comprising the following steps: a) subjecting said solid urban waste to liquefaction, obtaining a mixture including an oily phase consisting of bio-oil, a solid phase and an aqueous phase; b) subjecting the aqueous phase obtained in the liquefaction step a) to fermentation, obtaining a fermented biomass; c) feeding the fermented biomass obtained in the fermentation step b) to the liquefaction step a). The bio-oil (or bio-crude) thus obtained can be advantageously used in the production of biofuels which can be used as such or mixed with other motor vehicle fuels. Alternatively, this bio-oil (or bio-crude) can be used as such (biocombustible) or mixed with fossil combustibles (combustible oil, coal, etc.) for the generation of electric energy or heat.

Methods and related systems efficiently and effectively recover a significant amount of valuable, useable oil from byproducts formed during a dry milling process used for producing ethanol. The method may include forming a concentrate from the byproduct and recovering oil from the concentrate. The step of forming the concentrate may comprise evaporating the byproduct using a multi-stage evaporator, as well as recovering the oil before the final stage of the evaporator. Further, the step of recovering oil from the concentrate may comprise using a centrifuge and, in particular, a disk stack centrifuge. Other aspects include related methods and subsystems for recovering oil.

This invention relates to an improved process for the preparation of green fatty acid methyl esters (FAME; commonly called as biodiesel) from different triglyceride oils using mixed metal oxides derived from layered double hydroxides (referred here as LDHs) as reusable solid heterogeneous base catalysts. This process uses very low alcohohoil molar ratio and catalyst and/or products are easily separable after the reaction through simple physical processes. The properties of thus obtained biodiesel meet the standard biodiesel values and can directly be used as transport fuel.

Disclosed is a process for reducing the amount of sterol in a sterol-containing microbial oil composition, including distilling, under short path distillation conditions, a sterol-containing microbial oil wherein said distillation produces a distillate fraction containing the sterol and a triacylglycerol-containing fraction having a reduced amount of the sterol when compared to the amount of sterol in the sterol-containing microbial oil composition that has not been subjected to short path distillation.

A process for producing an alkali metal cyclopentadienylide is disclosed which comprises reacting in a solvent an alkali metal hydride with a disubstituted or trisubstituted 1,3-cyclopentadiene. Further, a process for producing a dihalobis(η-substituted-cyclopentadienyl)zirconium is disclosed which comprises reacting a zirconium halide with the above alkali metal cyclopentadienylide. The former process enables performing the reaction between the disubstituted or trisubstituted 1,3-cyclopentadiene and the alkali metal hydride at an easily controllable temperature of room temperature to about 150° C. and also enables obtaining the alkali metal cyclopentadienylide in high yield. The latter process enables obtaining the dihalobis(η-substituted-cyclopentadienyl)zirconium in high yield.

Polysubstituted cyclopentadiene compound wherein at least two different substituents are present from the group consisting of linear, branched and cyclic alkyls, aralkyls and alkenyls, and a process for the preparation of a cyclopentadiene compound substituted with at least two different groups chosen from the group consisting of linear, branched, cyclic and aromatic alkyls and alkenyls, characterized in that it comprises the reacting of a halide of a first substituting group in a mixture of the cyclopentadiene compound and an aqueous solution of a base, in which the quantity of the base relative to the cyclopentadiene compound is between 5 and 30 mol/mol, in the presence of a phase transfer catalyst, followed by the addition of a halide of a second or optionally a third substituting group to the reaction mixture.

The present invention provides a process for removing oxygenate from an olefin stream comprising oxygenate, comprising providing to an oxygenate recovery zone the olefin stream comprising oxygenate and a solvent comprising ethanol, treating the olefin stream comprising oxygenate with the solvent, and retrieving from the oxygenate recovery zone at least one oxygenate-depleted olefinic product stream comprising olefin and a spent solvent comprising at least part of the oxygenate.

A process to separate a multi-component hydrocarbon stream which includes ethylene and other components with at least some of the components being present in a number of phases, is provided. The process includes in a first flash stage, flashing the multi-component hydrocarbon stream, from an elevated pressure and temperature to a pressure in the range of 10-18 bar(a), producing a first ethylene-containing vapor stream at a pressure in the range of 10-18 bar(a) and a multi-phase stream which includes some ethylene. In a second flash stage, the multi-phase stream is flashed to a pressure of less than 6 bar(a), producing a second vapor stream at a pressure of less than 6 bar(a) and a bottoms stream. The first ethylene-containing vapor stream is removed from the first flash stage, the second vapor stream is removed from the second flash stage and the bottoms stream is removed from the second flash stage.

A method for preparing fuel components from crude tall oil. Feedstock containing tall oil including unsaturated fatty acids is introduced to a catalytic hydrodeoxygenation to convert unsaturated fatty acids, rosin acids and sterols to fuel components. Crude tall oil is purified in a purification by washing the crude tall oil with washing liquid and separating the purified crude tall oil from the washing liquid. The purified crude tall oil is introduced directly to the catalytic hydrodeoxygenation as a purified crude tall oil feedstock. An additional feedstock may be supplied to the catalytic hydrodeoxygenation.

Disclosed is a method for removing weakly basic nitrogen compounds from a hydrocarbon feed stream by contacting the hydrocarbon feed stream with a basic catalyst to convert a portion of the weakly basic nitrogen compounds to basic nitrogen compounds. The method also includes contacting the hydrocarbon feed stream with an acidic adsorbent to adsorb the basic nitrogen compounds from the stream. The hydrocarbon feed stream comprises an aromatic compound and a weakly basic nitrogen compound.