Flood insurance companies play a significant role in society. It is because businesses, people, organizations and other entities benefit from having insurance which covers particular risks related to floods. Even though the most palpable reason for getting insured is to get compensations for losses, there is more to that. The insured does not get a loss from the occurrence of floods. It is a guarantee of security. It depends on whether the plan is meant to cover for you, your business or organization based on the terms of the contract.
The solution to high cost of restoration is getting an insurance cover because you will not have to dig from your pockets to cover for the losses. The insurance company professionally manages your wealth to prevent significant losses in the case of a natural calamity. It comes in handy to reduce the losses which the insurance company has to pay for in case the risk happens. They help people to access credits from the lender.
You do not have to be a pro to get a good one, but you have to understand how to pick the most productive one. For that reason, you have to familiarize with the elements which qualify the best flood insurance company. The benefits stated above will be real when you get a flood policy with the following qualifications. The company needs a legal registration for you to sign with them. For you to have a healthy business, the insurance company has to comply with the law. When for instance, the need for a lawsuit arises, it will be easier to physically locate them unlike an online one.
An insurance company which has a good reputation means that they serve their customers well. Carry out personal investigations to gather information on the flood company before making a deal with them. When you interview them personally, you will know they interact with clients and be able to make informed judgments. Use the internet to gather information like comments given by their previous customers. When you have a prospective ones in your inventory and learn that they have more grievances and criticisms from clients, cut them from the list. You will gain imperative insight on the monetary strength of the insurers.
It is essential to check on the financial stability of a company before trusting them with your property. It is vital to have the amount of money which you can afford to pay after every agreed period and not strain your budget and cater for the property at the same time. You should know how fast they deal with customer issue and whether or not they are focused on such issues. The accountability of the insurance company is a crucial matter when it comes to claims. There are some insurers who find excuses when it is their turn to take responsibility and you have to ensure that this does not happen to you. You should have a guarantee that they will not change the terms when you need their services.


text:
Written by A.V. Thompson September 4, 2019 August 30, 2019
28. Twenty Questions
Another episode of Man Raised by Spiders, the coming-of-age story of Valentine Shelawn.
To while away the time in jail, Valentine and Aramil play a game they call 20 Questions, which really just consists of them asking each other things they wouldn't normally ask. Aramil reclines on his cot; Valentine is kicked back with his feet propped up on the cell bars, the stool balanced on two legs. They study the abstract pattern of the ceiling, with its stains from water leaks, rust and mold growth. It has a kind of irregular, cloud-like beauty, though the colors are different: rust and viridian against a backdrop of pale gray-green institutional paint.
Aramil starts off. "How did you manage to remain a virgin all that time?"
"Lack of opportunity. There are women everywhere — I trained with a lot of them — but Drow women were strictly off limits. Early on, before I was in the military, if I looked any Drow in the face, I'd be flogged. And even later, there were guys who tried, but that seemed to me like a quick way to get your cock cut off. My attitude was, 'My cock and I are playing a long game. I can wait.'"
"But weren't there other prisoners? Servants? Something?"
"Oh, yeah, of course. But slaves are prisoners of war, usually other races. So you'd be in with this jumble of half-orcs, dwarves, humans, maybe a sad halfling or two. I don't know about you, but I don't fancy those dwarf chicks. And, to be fair, it would be a hard sell. They don't go to sleep dreaming of some pale, little elf. 'Mmm, those violet eyes! He's so… refined and tiny…' Prisoners and slaves tend to stick with their own kind, and I was a rare little bird in the Underdark. What about you? How did you manage to lose your virginity?"
"Dad sent me to a whorehouse when I was 15," Aramil says in a matter-of-fact tone.
"What?" Valentine almost tips over backwards, rights the stool with difficulty. "Uncle Marcus sent you to a whorehouse? Really?"
"Oh, yeah. It's a pretty typical arrangement. He gave me a French letter, said, 'You won't use one of these with your wife,' which at the time raised more questions in my mind than it answered, and gave me the address of a classy little establishment. No cheap molls, only young ladies of refinement."
Valentine is appalled. "You just show up and…"
"Well, you know, some of them specialize in that kind of work. They're sweet, jolly girls. It's an easy paycheck for them. And I will say, you don't leave with any questions unanswered. If you're curious about something beyond the curriculum, she'll just say, 'Of course, love — that'll be three gold pieces.' I think I ran a tab for awhile, then Dad told me to make my own arrangements."
"Did Penelope know?"
"I suppose she must have. She does the household accounts. I'm sure there's a line item."
"No wonder she thinks you're a vulgar wretch. Is that usual here?"
"It's not unusual, certainly."
"I don't think I could do that," says Valentine with a shudder.
"I think you'd find that you could. Like I say, they're sweet girls. Not educated — half-elves and wood elves, mostly — a bit down on their luck. Most of them are lovely. I think the first one, Molly, had a crush on me," he says naively. "After all, it's not like I'm some sailor on shore leave. My turn. So, Sir Spider, what's the nicest thing about coming aboveground?"
"Oh, that's easy. Sunlight. Clouds. Rain. The sky. Everything's so beautiful."
Aramil pauses, feels his way. "What's the hardest thing?"
Valentine looks away uncomfortably for a long time — so long that Aramil thinks he won't answer. "I'm not sure I can explain it. Okay. So, after the funeral I was standing in the hall with Sieia. You know how people are always kind of relieved and happy after a funeral? So, people were almost jocular. I felt blank. Numb. Like I'd been left back at the cemetery. Breisis came up to us, and I could see that she felt the same way — just gutted. I shook her hand and said goodbye. Sieia just looked at her in that way she has. She opened her arms, and that terrible old woman snuggled up to her. Breisis cried and cried, and Sieia held her for he longest time. I mean, it went on and on. I felt tense and uncomfortable and like I should sneak away. Finally Sieia let her go, and Briesis put on her coat and left.
"Ever since then, I keep thinking, I have no fucking idea how that works — how Sieia knew to hug her, and could just be so kind and patient and hold her for so long. And how Breisis could seek that comfort and accept it.
"The hardest thing is, I'm really scared something's missing in me." As Valentine says these things, he can't look at Aramil — he's sitting hunched over, arms wrapped around himself. "It's not just being illiterate and not knowing how to ride or dance or use silverware. It's not knowing how to touch people and look at them and talk to them. She was my heart — she connected me to everything. I thought, it's not too late. I'll have a family. We'll have children. We'll figure this out together. I felt like I knew how to talk to her and touch her.
"Then she was dead, and I thought, Fuck it. I can't do this. I lost too much. I need that promise of love and family and I need her. If I can't have her, I quit." He falls silent for a long time. He looks back at Aramil, who's watching him from his little prison cot, under his thin blanket that smells of bleach. "It wasn't just losing her. I felt like I lost everything all over again. Which is fucking childish — the big, bad universe kicked me in the nuts and I'm going to lie here and refuse to get up until the gods see that they were mean to me, and — what? Apologize? Pick me up and dust me off tenderly? Great fucking strategy." He shrugs. "So I cut all my hair off, sliced my arm open, got a tattoo. The Drow aren't stupid — they know how to shift pain into the body, make it concrete. Change the terms of the argument from 'Woe is me' to 'Fuck it. I always feel better after I kill something.'"
He stops. He's staring down at his hands, remembering how he was forced to strip his clothes off and burn them, wash her scent from his hands, put her in the ground. "I'm sorry. All this is so stupid. I don't know what I'm saying. I don't understand anything. I don't know anything."
Aramil says, "Hey, cousin."
Aramil stands up, slips his hand through the cell bars. Valentine looks at it uncomprehendingly. Aramil says, "Come on, man," and Valentine reaches over, grips his cousin's hand. After a moment, he leans against the bars. His movements are graceless and ugly. Aramil says softly, "You don't have to understand." He strokes Valentine's short, fine hair. "You were so good to her." He's searching for words. "Ah, shit — she died knowing you loved her. Not everyone gets that." After a long moment he adds, "And I am so fucking grateful you're here. If it weren't for you, I'd be the prison wife of some half-orc sailor. And I'd have lice."
Valentine gives a bark of laughter, pulls away, reclaims his stool. "Am I standing in the way of love? Shit, man, I hear once you go orc you can't go back."
"You can't go back because no decent elf will have you."
"Thanks for being willing to go to jail. I had this romantic idea about family where we'd all sit around hugging in the ancestral mansion and inheriting money from each other. Turns out it's more like, 'Congratulations! Your uncle committed an unspeakable crime, so you were born into the middle of a blood feud involving your bastard half-cousin. You've got another cousin in jail for crimes he admits he committed, and everyone looks down on you because you don't know which fork to use at dinner. Enjoy!'"
Aramil gives a rude snort of laughter. "You ungrateful little Drow bitch! Before you got here I'd never seen the inside of a tattoo parlor. I learned things in the men's room of Hairy Tattoos that I can't un-know."
Valentine adopts a spurious air of compassion. "Which one was it? The nun with the hot dogs? She seemed like a sweet, jolly creature. She might not be a real nun. It's easy money for them, you know. And it's not like a hot dog is a sailor on shore leave."
"Shut up and bring me my half-orc prison husband, you vulgar wretch."
They lapse into silence. As Valentine sits contemplating his newfound ability to accept comfort and kindness, a guard bawls down the corridor, "Visitor for Caramel Shoelorn!"
A small, bookish-looking elf with red hair and sallow skin comes around the corner. He looks to be an Arahir, and sure enough, introduces himself by saying, "Aramil Shelawn? I'm Sulla Arahir — a second cousin through your grandmother, Lavinia. Xardic Ceralac asked me to represent you in the matter you have pending before the criminal court." He and Aramil exchange bows, and Sulla casts an inquiring look at Valentine.
"My cousin, Valentine Shelawn," Aramil says.
Valentine steps forward to shake hands, and Sulla says, "Valentine. An unusual name. It sounds familiar."
"We're related in some fashion," Valentine offers. "My father, Lucius, was married to Valeria Arahir."
Sulla makes the connection, apparently finds it awkward. "Of course, yes, I've heard of you. Your child name was Claudius." He turns back to Aramil. "I've met with the prosecutor and the magistrate at the mayor's request. I've come to explain the charges they're prepared to bring, and the evidence they have." He's no older than Valentine, but Sulla's manner is pompous and formal. Aramil stiffens visibly. Valentine stood to greet him, so Sulla gestures towards the stool and says, "May I?"
Aramil replies mockingly, "Yes, by all means, have a seat. I apologize that I can't offer you refreshment." He perches on the edge of his unmade jail cot. Valentine crosses his arms and leans against the corridor wall. Though he doesn't realize it, he's a forbidding figure: tall, tattoos visible around his collar and cuffs, wearing boots, spurs, and an expression best described as Resting Badass Face. Sulla reaches into a legal file, starts arranging papers on his lap. Once they're laid out to his satisfaction, he glances at Valentine, then at Aramil. "Would your cousin care to leave us?"
"No. He wouldn't," says Valentine.
"All right, then," says Sulla. In a surprisingly sonorous voice, he begins to read a list of charges stemming from the most recent robbery. Their number and variety is surprising, especially to two young men unacquainted with the gray elven legal system. After several moments Sulla pauses to flip to a second sheet, clears his throat, and reads off a more detailed list of charges connected with the stolen goods found in Aramil's lodgings, and the robberies associated with them. Throughout the performance, Valentine deliberately focuses his gaze on Sulla, and ignores Aramil's attempts to get his attention. Sulla pauses once more, then, unbelievably, locates and starts to read from a third list, a troublingly thorough summary of evidence connected with the first set of charges.
Aramil cuts him off. "So what you're saying is, I'm fucked."
Sulla titters at Aramil's language. "Your legal position is extremely difficult, yes."
"Most of us would call that 'fucked.'"
"Yes, if you like. The evidence I've read to you is only the beginning, you know." As he studies Aramil, the lenses of Sulla's glasses catch the light, obscuring his dark brown eyes. Aramil is frightened, feels that Sulla is goading him. Sulla senses that Aramil would have casually bullied him in school. Both of them are young enough to react instinctively from their respective social positions.
Valentine breaks in, his voice quiet and calm. "Sulla, does the prosecutor seem inclined to offer a plea bargain?"
"We only had a very preliminary discussion, but he's inclined to proceed with the case." Aramil slumps back like a marionette whose strings have been cut.
"What about the magistrate?" Valentine asks.
Sulla taps his pen thoughtfully. "Right now, his role in the case is informal. He's inclined to help, but politically his situation is difficult. The same is true for Xardic. Neither of them can afford to be seen to excuse a series of violent felonies because the perpetrator happens to belong to a prominent family."
Aramil seems inclined to take issue with Sulla's words. Valentine quells him with a look and says, "Sulla, we're very much in your hands. If you see a solution that would be agreeable to all parties… Xardic mentioned a suspended sentence in exchange for banishment. In your opinion, is that a real possibility?"
Sulla presses his fingertips together, considers. "I can open a discussion around the possibility."
"You'd be doing the family a great service. It's a difficult situation." Sulla nods, notes something in the margin of one of his lists. Valentine continues, "Do you get the sense that there's any enmity against the Shelawns or against Aramil in particular? Or are the parties looking to reach an agreement?"
"The latter," says Sulla quickly. "They just need to come up with a solution that allows everyone to save face."
Valentine's severe demeanor breaks into a smile. "I'm sure Xardic has confidence in your ability to find that solution."
Sulla says, "I believe so, yes. And I don't think his confidence is misplaced." He directs a small, cold smile at Valentine, then looks back at Aramil. "You're aware of the charges and evidence. I'll open a discussion with the prosecutor about a guilty plea. You have no evidence to offer against anyone else?"
Aramil says, "There's no one else to blame. I was the ringleader for the original robbery, and I worked alone after that."
Sulla restores his papers to their folders. "I'll get back to you as soon as I have further news. I can find my own way out."
As soon as Sulla has turned the corner and is out of earshot, Aramil groans, "What a little prick! I can't believe that's who's going to be representing me."
"You could do much worse," says Valentine. "Being cool, or even kind, is probably a liability in his line of work." He reclaims the stool, continues in a cold, quiet voice. "I'm going to give you some advice, cousin. When you're dealing with someone who has that much power over you, be deferential, be polite, use some flattery."
"That little prick has power over me?" says Aramil contemptuously.
Valentine's tone shifts to a sound like the crack of a bullwhip. "He absolutely does, and you know it. Don't fuck that up. You're used to people thinking you're hot shit because you're cute and you're a Shelawn. Put that out of your head now. You're behind bars. You have no bargaining power — none. No one here or in the court system owes you shit. When you play bullshit games like that, even in your own head, you just makes things harder on Xardic and your father and mother. And on me, for that matter." He cuts himself off, realizing that Aramil's face is white with shock and anger. Valentine sighs, passes a hand over his face. "I'm sorry, Aramil. I'll stop being a dick now. I'm worried, just like everyone else."
"Valentine, do you think I'm screwed?"
"Honestly, I don't know. It feels to me like it could go really wrong. Your connections and so on — that kind of thing is totally out of my experience. I'm sure it means more than I know. But, God, Aramil, why take any fucking chances? What makes you think this will all work itself out? I'm asking the question seriously — I don't understand how you see it."
"I didn't think it would ever get this far. I guess I thought that if I got into trouble, Xardic would settle everything."
Valentine shrugs, "Maybe he will. But if I were you, I would start to think seriously about what happens if he can't."
Posted in liamelia, man raised by spiders.Tagged drow, elves, family drama, fantasy fiction, gray elves, highway robbery, tattooing, tattoos, thug life.
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text:
\section{Introduction}
\vspace{-2.5 mm}
Spatial perception has undergone a true step-change during the past few years, partly thanks to the Deep Learning revolution in Computer Vision, but not only. Spatial perception -- core to e.g.\ a robotic system -- involves a plethora or elements that should be addressed jointly: these include the understanding of camera pose relative to the environment, i.e.\ Simultaneous Localization and Mapping (SLAM), which ideally includes a very dense reconstruction of at least the immediate surrounding environment, allowing the robot to interact with it. As purely geometric understanding is still limiting, the research community has been shifting towards tightly integrating it with semantic understanding, object-level mapping, estimating dynamic content, etc.

In order to benchmark SLAM, as well as to both train and evaluate semantic understanding, datasets of increasing scale have been playing a central role. While we argue that real recorded datasets will remain crucial for the foreseeable future, they suffer from scalability limitations: for instance preparing ground truth scene models is a challenging task, with methods such as laser scanning being a costly and time-consuming process. Thanks to advances in computer graphics and computational power, the use of rendered synthetic models is an obvious choice and recent works such as ICL-NUIM dataset~\cite{Handa_ICRA_2014} for SLAM and SceneNet RGB-D dataset~\cite{Mccormac:ICCV:2017} for semantic labeling are examples of such works. In this paper, we have improved upon current rendering methods and datasets first of all by proposing a versatile and fast rendering framework for a high degree of photo-realism which leverages the availability of \textit{millions of realistic indoor designs} composed of \textit{millions of detailed digital object models}. Importantly, we model realistic lighting and scene \textit{change over time}. Aside from RGB rendering, we can opt for depth images and semantics. Second, we use and synthesize realistic trajectories as ground truth to render at \textit{video frame rate} from various classes of typical motion patterns.
Fig.~\ref{fig:decoration} shows samples from a real-world environment and synthesized images generated from models. In summary, our contributions are:

\begin{itemize}\vspace{-2.5 mm}
\item  Our scene database contains around 1M furniture CAD models and 22M interior layouts (Sec.~\ref{sec:overview}), which have been created for real-world production and decoration.
\vspace{-3 mm}
\item Our dataset contains two parts: (1) 15k sequences rendered from 10k randomly selected layouts, with 1k images for each sequence; (2) 5M images are rendered from 1.7M randomly selected layouts, with 3 images per layout.\vspace{-3 mm}
\item To simulate realistic scenes that change over time (Sec.~\ref{sec:scene}), we interfaced a physics engine for object movement and provide flexibility to manipulate lighting conditions. \vspace{-3 mm}
\item We introduce a learned algorithm to realistically style random  trajectories (Sec.~\ref{sec:traj}). \vspace{-3 mm}
\item We implemented a fast, photo-realistic renderer, called \textit{ExaRenderer} (Sec.~\ref{sec:renderer}).\vspace{-3 mm}
\item We implemented a user friendly simulator, called \textit{ViSim} (Sec.~\ref{sec:visim}), to assist creating monocular or stereo camera trajectories and synthesize related ground truth.
\vspace{-3 mm}
\item We show the usefulness of the dataset by providing SLAM evaluation results
(Sec~\ref{sec:evaluation}). \vspace{-3 mm}
\item We will release our dataset (consisting of the rendered sequences and images) as well as \textit{ExaRenderer}, \textit{ViSim}, and a subset of the 3D models and layouts used for evaluations
\end{itemize}

\begin{figure*}[t]
\centering
\includegraphics[width=.97\linewidth]{teaser.pdf}
\vspace{-3mm}
\caption{System Overview: an end-to-end pipeline to render an RGB-D-inertial benchmark for large scale interior scene understanding and mapping. (\textbf{A}) We collect around 1 million CAD furniture models from world-leading furniture manufacturers. These models have been used in the real-world production. (\textbf{B}) Based on those models, around 1,100 professional designers/companies create around 22 million interior layouts. Most of such layouts have been used in real-world decorations. (\textbf{C}) For each layout, we generate a number of configurations to represent different lightings and simulate scene change over time in daily life. (\textbf{D}) We provide an interactive simulator (\textit{ViSim}) to create the ground truth monocular/stereo trajectories, as well as IMU and event camera data. Trajectories can be set manually, or using random walk and neural network based generation. (\textbf{E}) All supported image sequences and ground truth data.}
\label{fig:teaser}
\vspace{-4mm}
\end{figure*}

\vspace{-2 mm}
\section{Background}\label{sec:background}
\vspace{-2 mm}
Benchmarking algorithms with real-world and synthetic datasets for various computer vision tasks has been playing an important role, especially in the field of robotics.
In this section, we review popular datasets used in semantic labeling and SLAM.
In semantic labeling, the two most closely related synthetic datasets are the SUN-CG~\cite{song2016ssc} with photo-realistic rendering~\cite{Zhang:etal:CVPR2017}, and SceneNet RGB-D~\cite{Mccormac:ICCV:2017}.
SUN-CG also provides realistic hand-designed layouts similar to ours but from Planner 5D~\cite{planner5d}, a layout tool for amateur interior design.
The number of layouts (45K), unique object meshes  (2.6K), and rendered images (\cite{Zhang:etal:CVPR2017} 500K) are orders of magnitude smaller and without the daily life noise added, when compared to our proposed dataset -- and importantly, we focus on rendered full trajectories rather than still images.
SceneNet RGB-D~\cite{Mccormac:ICCV:2017} provides full trajectories, but at much lower sample rate and of only one type, whereas here we provide a number of base random trajectories, as well as a learned style for realistic jitter.
SceneNet RGB-D also contains fewer images (5M) which are of a lower resolution (320$\times$240) and quality, containing circular artefacts from the photon mapping process.
The object models in SceneNet RGB-D are from ShapeNet~\cite{Shapenet:ARXIV2015} which contains 51K models from public online repositories.
Although an excellent resource, the quality of the models and textures from these repositories can be quite variable and missing ground truth metric scales requires automated prediction~\cite{Savva:2014:RSS:2670291.2670295,DBLP:conf/cvpr/SavvaCH15}.
Additionally, Zhang \emph{et al}\bmvaOneDot~\cite{zhang:arXiv:2016} show that pretraining with a synthetic dataset improves the results of computer vision tasks such as surface normal prediction, semantic segmentation, and object boundary detection.
Qi \emph{et al}\bmvaOneDot~\cite{qi2018human} use human context to simulate layout of the indoor environment.
Here we use only assets of commercial production quality, with accurate metric scales and high quality textures at a much larger scale, all semantically labelled by the manufacturer.
The closest related work with real-world data is the ScanNet dataset~\cite{dai2017scannet}.  It is a dataset consisting of 2.5M frames, containing trajectories of 1.5K indoor scenes. The scenes are manually annotated with a tool designed for use in Amazon Mechanical Turk.

There are many real-world visual localization and mapping datasets.
Examples are the TUM RGB-D dataset with 19 different trajectories~\cite{Sturm_IROS_2012}, the EuRoC dataset with 11 visual and inertial sequences~\cite{Burri:IJRR_2016}, the KITTI dataset with large-scale 2D outdoor trajectories~\cite{Geiger_CVPR_2012}, and New College dataset with medium scale trajectories. Ground truth for these trajectories is either obtained from GPS or motion capture systems, and for the map from laser scanners (in KITTI and EuRoC only). In the active vision dataset~\cite{Ammirato_ICRA_2017},  limited control options are provided to control the trajectory in 2D while navigating through the images. These datasets are very useful, but either the number of trajectories or the environments are limited.

Among synthetic visual SLAM datasets,
UnrealCV~\cite{Qiu_ArXive_2016}, \cite{zhang:arXiv:2016}, uses the Unreal Engine to render realistic images for labelling and SLAM.
The ICL-NUIM dataset~\cite{Handa_ICRA_2014} provides eight sequences from two models using POV-Ray renderer~\cite{povray}.
These methods require manual work to setup the rendering environment and also the number of the models is limited.

\vspace{-2 mm}
\section{Dataset Overview}\label{sec:overview}
\vspace{-2 mm}
The proposed pipeline (Fig.~\ref{fig:teaser}) provides a large scale furniture model database and interior layouts, plus an end-to-end rendering pipeline to create the proposed RGB-D-inertial data.
\vspace{-2 mm}
\subsection{Furniture Models and Interior Layouts}
\vspace{-2mm}

We collected 1,042,632 computer-aided design (CAD) models of furnitures from 42 world-leading manufacturers. Those models have been categorized into 158 main classes and are
manually mapped to NYU40 categories \cite{Silberman:ECCV12}. Fig.~\ref{fig:object} shows the statistics and sample images of the top 50 categories in our database. Our furniture model database provides several unique features: (1) all object meshes are measured in real-world dimensions and feature the exact same measurements as their real-world counterparts; (2) all object meshes are high resolution (triangles and vertex) and associated with hierarchical semantic labeling information. For instance, a specific sofa model contains 734,198 triangles, every vertex of which is  labeled in terms of parts i.e.\ arms, legs, seat cushion, back foam and frame; (3) all objects are used in real production and will be easily spotted in real-world homes; (4) every object is associated with multiple textures and materials. For example, every chair has different color schemes, covers and accessories. The material information of the models is provided by manufacturers, which is compatible to \textit{VRayStyle}~\cite{vray}. Material representation is guided by compositing 4 Bidirectional Reflectance Distribution Function (\textit{BRDF}) properties: lambertian, microfacet, dielectric and transmission.

Given such a large-scale object database, 1,078 professional designers working with end customers have created 22,652,123 interior layouts for different scenarios since Oct.\ 2014. Fig.~\ref{fig:object} illustrates the statistics of our layouts which contain 16 types of rooms adopted from the interior design industry. The most representative room types are bedroom, guest room, bathroom, living room and kitchen. Our layouts are diverse in range from studio to 42-room apartments.
Most of the layouts are being used in real-world decoration.
\vspace{-2 mm}
\subsection{Configurations}
\vspace{-2 mm}
For each layout, we generated a number of different configurations to either rearrange the furniture or randomized the lighting conditions (Fig.~\ref{fig:lighting}). For the former (Sec.~\ref{sec:rearrange}), we implemented a physics based automatic framework to slightly rearrange the movable objects. For the latter (Sec.~\ref{sec:sim_lighting}), we support both manual or automatic changes on color and intensity of the default lighting. These setups diversify our layouts to simulate traces of daily life and the changes of natural lightings through a day.

\begin{figure*}[t]
\centering
\includegraphics[width=.97\linewidth]{obj_state.pdf}
\vspace{-4mm}
\caption{Statistics on object models, rooms and layouts. (\textbf{Top}): occurrences of the 50 most common categories of objects from the layouts (\textit{blue}), and number of objects in our database (\textit{red}). (\textbf{Bottom Left}): distribution of number of rooms per layout. (\textbf{Bottom Middle}): distribution of number of object per layout. (\textbf{Bottom Right}): Distribution of room type.}
\label{fig:object}
\vspace{-3mm}
\end{figure*}

\vspace{-2 mm}
\subsection{Rendered Image Sequences and Ground Truth}
\vspace{-2 mm}
We propose an end-to-end pipeline to render photo-realistic images and associated ground truth data (Fig.~\ref{fig:teaser} (E)). We created multiple trajectories (Sec.~\ref{sec:traj}) for each layout by varying the combination of linear velocity, angular velocity and trajectory types. Each view of a trajectory consists of both a shutter open and shutter close camera pose (many renderings from poses in-between are averaged to obtain motion blurred renderings). Our images support 640$\times$480 resolution, at 25 Hz, resulting in 1,000 images per trajectory. By using an optimal parameter setup for best image quality, each render takes less than 2 ms on our cluster of 1,300 Nvidia GTX TiTanX GPUs and 2,000 Intel Xeon Phi CPUs. Our pipeline supports different RGB variations (pure texture, illumination and normals), and different types of lens models (panorama, fisheye and depth of field). The rendering speed may vary for rendering variations (a 5,000$\times$2,500 panorama image takes 0.5-1 sec on our cluster to render).

Our rendering pipeline is able to create various per-frame ground truth data. Per-pixel semantic labels e.g. NYU40 (Fig.~\ref{fig:nyu}) can be obtained from model settings (provided by manufacturers) and a rendering pass. Per-object semantic context (3D bounding box) is provided to indicate the 3D extent of an object in the scene.
Depth is obtained as the Euclidean distance of the ray intersection; and noisy depth is generated by simulating a real \textit{Kinect} mechanism~\cite{choo2014statistical}. We also generate instance segmentations and optical flow following~\cite{Mccormac:ICCV:2017}.

\begin{figure*}[t]
\centering
\includegraphics[width=1\linewidth]{nyu.pdf}
\vspace{-5mm}
\caption{Our rendered images and the associated NYU40 labels.}
\label{fig:nyu}
\vspace{-6mm}
\end{figure*}

\vspace{-2 mm}
\subsection{Dataset Structure and Hosting}
\vspace{-2 mm}
Our dataset contains 20M photo-realistic images and many forms of ground truth data on around 1.7M layouts randomly selected from our database.
To organize this wealth of data, we separated sequences into 20 different subsets in terms of number of rooms. We also provide a sparse subset that contains a smaller number of images (5M) but at highest diversity. We randomly selected 1.7M layouts, for each of which a random view was rendered in three different configurations, i.e.\ original layout, random lighting and random rearrangement. The parameters for randomly choosing lighting and rearrangement remained the same. The images of each subset mentioned above were randomly divided into 80\%, 10\% and 10\% splits for training, validation and test sets respectively. We also release a subset of CAD models and layouts, as well as the rendering pipeline and the simulator (\textit{ViSim}) for public.

\vspace{-2 mm}
\section{Simulating Realistic Scenes}\label{sec:scene}
\vspace{-2 mm}
To further bring real-life challenges to the dataset, we added two new features to the models: simulating moving objects as in daily life and simulating variations in lighting.

\vspace{-2 mm}
\subsection{Simulating Scene Change Over Time}\label{sec:rearrange}
\vspace{-2 mm}

As one of the main contributions, we provide certain level of flexibility to automatically configure the arrangement of the furniture objects in order to simulate the traces of daily life. Each object in our database is assigned a convex collision hull, as well as a mass (depending on different objects, in range from 0.05 to 43.3 kg) and a friction coefficient (depending on different surface materials, in range from 0.08 to 0.27) provided by the manufacturers. All those objects are then manually labeled as either \textit{Movable} or \textit{Unmovable}. The former may contain some objects moved on a daily basis, such as mugs, chairs, decorations and shoes etc. The latter may be some big furnitures barely moved, such as fridge, TV, bed and bookshelf etc.
We further use an open-source physics engine, Project Chrono~\cite{chrono}
to dynamically rearrange the furniture objects within a layout. For each configuration, we randomly select 5\% to 45\% movable objects within a layout. We offset the center of gravity slightly to the bottom of the object mesh, in order to avoid tipping over. For each of selected object, we apply a random acceleration (in range from 0.5 to 2 ms$^{-2}$) onto the geometric/mass center of the mesh along a random direction but parallel to the ground. The system takes 10 seconds to allow objects to settle to a physically realistic configuration. In most of the cases, the objects would move less than 2 meter from the original position.

\vspace{-2 mm}
\subsection{Simulating Lighting}
\label{sec:sim_lighting}
\vspace{-2 mm}

As another main contribution, we provide a flexibility to manipulate the lighting. A default lighting setup (\textit{light.xml}) is provided for each layout. Those lighting setups include information of lighting type (SunLight, SpotLight and AreaLight), energy, maximum distance (like \textit{PBRT}-v3~\cite{pharr2016physically}), position, direction and brightness, which are provided by manufacturers and have been aligned and fixed to specific objects with lighting sources e.g.\ lamps. Our simulator is able to assist configuring actual RGB value, temperature and brightness of a specific light, or turning it on/off.
We also support automatically generating different combinations of lighting setups (Fig.~\ref{fig:lighting}).

\vspace{-2 mm}
\section{Trajectory Generation}
\label{sec:traj}
\vspace{-2 mm}

Our dataset provides random trajectories through the scenes. We generated three different types of trajectories, (\textbf{type-1}): two-body random~\cite{Mccormac:ICCV:2017} with height constraints; (\textbf{type-2}): hand-held; and (\textbf{type-3}): look-forward. For each one we have two parameters which can be selected to change the overall speed and angular velocity of the motion. To prevent overly smooth unrealistic trajectories, we augment each of these base trajectories with a learned style model which produces realistic appearing camera jitter.

\begin{figure}[t]
\centering
\includegraphics[width=1\linewidth]{lightingsetup.pdf}
\vspace{-3mm}
\caption{Lighting setup: natural and random color collection, brightness and temperature.}
\label{fig:lighting}
\vspace{-3mm}
\end{figure}

\vspace{-2 mm}
\subsection{Base Random Trajectories}
\vspace{-2 mm}
Our random trajectories follow the basic two-body random trajectory system described in~\cite{Mccormac:ICCV:2017}.  It simulates two physical bodies randomly moving around and colliding within the indoor free-space.  One defines the camera position, and the other the look-at point. To produce more realistic views (as from a person), the minimum and maximum height is limited to be between 1 m and 2 m.  We also used a slightly randomized up direction with a range from 0$^{\circ}$ to 5$^{\circ}$ difference out of gravity direction.
We improve upon this model by creating two additional modes of trajectory generation.

The first one is designed to simulate a bias in hand-held cameras that tend to look downwards, by restricting the look-at point to be lower than the camera position. The second is designed to provide translational motion along the view direction, a motion type not frequently encountered in the basic two-body system. To achieve this, and give a smooth trajectory, a target point is set in the direction of the velocity from the camera motion, and a proportional force is applied to the current look-at point to smoothly correct the displacement to the current velocity vector.  We also bias the random forces to be in the hemisphere of the velocity vector, to create a smoother look-at target.
We also systematically varied the trajectory parameters.  Unlike in~\cite{Mccormac:ICCV:2017}, where a single set of parameters governed every trajectory, we picked random uniform velocity multiplier from 0.5-5.0 and a random angular velocity scalar from 0.5-3.0 for each trajectory. This feature provides more variety amongst the trajectories.

\vspace{-2 mm}
\subsection{Realistic Trajectories}
\vspace{-2 mm}
An important but neglected factor in existing synthetic datasets for scene understanding is the realism of the trajectory models. Previous work on automated trajectories have used simple and smooth trajectories \cite{Mccormac:ICCV:2017}. Although these trajectories adequately explore the extents of the scene, they do not capture the nuances of real camera motion which can have a significant effect on the visual data. For example, the repetitive impulses in acceleration incurred by the steps of a walking cameraman inevitably adds a significant amount of motion blur to the sequence -- especially in low light indoor conditions. In other papers \cite{Handa:ICRA:2016}, motion-capture trajectories have been captured to solve this -- but such an approach is limited to small datasets where it is feasible to manually capture such trajectories. In this paper, we therefore use a data-driven method to automatically synthesize millions of realistic camera trajectories which satisfy the constraints of the scenes. In particular we harness the recently proposed \textit{Wavenet} architecture~\cite{oord2016wavenet}. The model takes as input a window of previous velocities and outputs the next velocity -- in this manner, synthesizing a trajectory of arbitrary length.
To ensure that the synthesized trajectory does not collide with objects in the scene, we apply the force model from the preceding Section to the velocities to avoid collisions, and to constrain the overall motion to that of the base random trajectory. We trained the generative model on trajectories from \cite{song2016ssc} and \cite{Sturm_IROS_2012} and synthesized trajectories on the fly during rendering.

\vspace{-2 mm}
\section{\textit{ExaRenderer}: Photo-realistic RGB Renderer}
\label{sec:renderer}
\vspace{-2 mm}
We have developed a fast photo-realistic renderer on top of \textit{Embree}~\cite{Wald:TOG:2014}, an open-source collection of ray-tracing kernels for x86 CPUs. We also have an equivalent variation supporting GPU acceleration to make  full use of our GPU and CPU clusters. Our renderer, coined \textit{ExaRenderer}, supports a common subset of \textit{Path Tracing} operations of leading commercial renderers, and provides flexible APIs to extensively customize the workflow.

\vspace{-2 mm}
\subsection{Path Tracing}
\vspace{-2 mm}
We use the well-known \textit{Path Tracing}~\cite{purcell2002ray} approach to deliver high quality image rendering. Path tracing is a \textit{Monte Carlo} method that approximates realistic Global Illumination (\textit{GI}) for rendering images. It simulates many real-world effects such as soft shadows, depth of field, motion blur and indirect lighting.
We also support common color bleeding from diffuse surfaces and caustics. Comparing to other popular \textit{GI} approaches, e.g.\ \textit{Photon Mapping}~\cite{jensen2002practical}, \textit{Path Tracing} provides a more realistic caustic effect, requires less memory footprint for large-scale rendering, and is efficient to support dynamic scenes.

\vspace{-2 mm}
\subsection{Sensors Simulation: RGB-D Camera, IMU and Event Camera}
\vspace{-2 mm}

Our renderer supports the pinhole camera model and several lens models such as perspective, depth of field, fisheye and panorama. We used a fixed resolution at 640$\times$480 pixels, and a fixed focus lens at 600 pixels for all the RGB images. We used 5,000$\times$2,500 resolution for panorama and 600$\times$600 for fisheye images. Our renderer simulates camera motion blur by following the method from~\cite{Mccormac:ICCV:2017}; the system takes into account all incoming rays throughout the shutter opening time interval, then integrates the irradiance during rendering.

For inertial measurements,
we fit a cubic B-spline to given control poses (described above), to represent position and orientation, the latter parameterized as a rotation vector. The continuous-time representation lends itself ideally to obtain IMU readings by computing time derivatives at an arbitrary sample rate. Specifically, position (expressed in the coordinates of the static world) is differentiated twice w.r.t.\ time, then acceleration due to gravity is added, and finally the vector is rotated into the frame of reference of the IMU to generate ground truth accelerometer measurements; ground truth rotation rate measurements are obtained from the rotation vector time derivative, where the relationship between the two is given e.g.\ in~\cite{kinder}.
We provide IMU readings at 800 Hz (ground truth, or optionally noisy).

Our rendering framework can also be used to generate output of novel camera designs such as event cameras, e.g.~\cite{lichtsteiner2008128}. We can achieve this by rendering at lower resolution but very high frame rate; then, we assess per-pixel brightness change over time and output events with interpolated timestamps whenever the user-settable intensity threshold is crossed.

Our dataset contains around 20M images which required significant computational power for rendering. To achieve this, our renderer is implemented to be compatible for both CPU and GPU platforms, as well as to support dynamic render distribution onto multiple servers. In this context, we rendered the images on a cluster of 1,300 Nvidia GTX TitanX GPUs and 2,000 Intel Xeon Phi CPUs for around 4 days. We applied 256 samples per pixel (\textit{SPP}) -- the most important parameter to trade-off between image quality and rendering speed.

\vspace{-2 mm}
\subsection{\textit{ViSim}: An Interactive Simulator}
\label{sec:visim}
\vspace{-2 mm}
We implemented an interactive simulator, coined \textit{ViSim}, (Fig.~\ref{fig:teaser}~(D)) to assist creating monocular or stereo camera trajectories, ground truth IMU readings and events given a layout from our database or from any other 3D scenes with \textit{obj} format e.g.\ SUNCG~\cite{song2016ssc} and SceneNet RGB-D~\cite{Mccormac:ICCV:2017} etc. The simulator provides a user-friendly interface while adding our simulation functionality behind. It conntains a \textit{Design View} and a collection of \textit{Parameter Options} to configure camera calibration, distortion coefficients, stereo view, frame rate and travel time, etc. We also support exporting the camera trajectory into other formats e.g.\ SLAMBench~\cite{SLAMBench2015}, SLAMBench2~\cite{Bodin2018ICRA}, EuRoC~\cite{Burri:IJRR_2016}, Freiburg~\cite{Sturm_IROS_2012} and ICL-NUIM~\cite{Handa_ICRA_2014}, and also renderer formats such as \textit{OppositeRenderer} (SceneNet RGB-D)~\cite{Mccormac:ICCV:2017}

\vspace{-2 mm}
\section{Evaluation on Simultaneous Localization and Mapping}
\label{sec:evaluation}
\vspace{-2 mm}

To verify the quality of the dataset, we have selected several sequences of images of different trajectory types to run \textit{ORBSLAM2.0}~\cite{ORBSLAM2} and \textit{ElasticFusion}~\cite{Whelan2015RSS}.
The verification test uses the RGB-D mode of \textit{ORBSLAM2.0}, using the default configuration parameters.
For each sequence, the absolute trajectory error (\textit{ATE}) is calculated \cite{Sturm_IROS_2012}.

Fig.~\ref{fig:slam}~(\textbf{Left})
shows the ground truth and estimated trajectories for a randomly chosen type-1 trajectory with a randomly chosen model, in three different scenarios:
a regular scene~(\textbf{Left Top image}),
the same scene with different lighting~(\textbf{Left Middle image}),
and the same scene but with objects displaced~(\textbf{Left Bottom image}).
Trajectory estimation errors for these cases are given in Table~\ref{tb:scene}, rows 1, 2, and 3 respectively. Note that the dense reconstruction is generated by the \textit{ElasticFusion}~\cite{Whelan2015RSS} algorithm.
In the same table, rows 4 and 5 show two other type-1 trajectories with very high position and angular velocities ($v$ and $\omega$ respectively).
\textit{ORBSLAM2.0} is not able to track all frames, indicating the challenge that the trajectories provide.
Rows 5, 6, and 7 show ATE for other types of trajectories with different difficulty levels.

\begin{figure*}[t]
  \centering
  \includegraphics[width=0.95\linewidth]{mapping_combo_small.pdf}
\caption{ (\textbf{Left}) SLAM under change in lighting and object rearrangement. Estimated trajectories along with the ground truth trajectory are shown overlaid on the dense reconstruction. (\textbf{Right}) Percentage of the frames tracked for 50 trajectories, running \textit{ORBSLAM2.0}.}
\label{fig:slam}
\vspace{-4mm}
\end{figure*}

\begin{table}[t]

\begin{minipage}{0.6\columnwidth}
\centering
\begin{tabular}{lll|l|l}
\scriptsize{No} & \scriptsize{Length($m$)} &   \scriptsize{($v$,$\omega$, type)} & \scriptsize{ATE($m$)} & \scriptsize{Description}\\
\hline
\hline
\scriptsize{1} & \scriptsize{21.93} &  \scriptsize{(1,1,1)} & \scriptsize{0.0428} & \scriptsize{a sample model}\\
\scriptsize{2} &  \scriptsize{22.19} &  \scriptsize{(1,1,1)} &  \scriptsize{0.0352}& \scriptsize{.. with different lighting}\\
\scriptsize{3} &  \scriptsize{21.84} &  \scriptsize{(1,1,1)} &  \scriptsize{0.0515}& \scriptsize{.. with objects displaced}\\
\hline
\scriptsize{4} &  \scriptsize{13.88} &  \scriptsize{(9,9,1)} & \scriptsize{0.1701} & \scriptsize{16$\%$ tracked}\\
\scriptsize{5} &  \scriptsize{20.83} &  \scriptsize{(5,6,1)} & \scriptsize{0.0454}&\scriptsize{39$\%$ tracked}\\
\hline
\scriptsize{6} &  \scriptsize{17.46} &  \scriptsize{(1,5,1)} & \scriptsize{0.0172}& \scriptsize{type-1}\\
\scriptsize{7} &  \scriptsize{22.67} &  \scriptsize{(1,1,2)} & \scriptsize{0.0193}& \scriptsize{type-2}\\
\scriptsize{8} &  \scriptsize{4.79} &  \scriptsize{(1,1,3)} & \scriptsize{0.3840}& \scriptsize{type-3, 11$\%$ tracked}
\end{tabular}
\end{minipage}
\hfill
\begin{minipage}{0.39\columnwidth}
\caption{Absolute trajectory error (ATE) for sample sequences when running ORBSLAM2.0. For trajectories, $v$ and $w$ are maximum position and angular velocities in metric units,
with the type explained in Sec.~\ref{sec:traj}:
({type-1}): two-body random; ({type-2}): hand-held; and ({type-3}): look-forward.}
\label{tb:scene}
\end{minipage}
\vspace{-6mm}
\end{table}

Additionally, another random trajectory (length of 24.91~m) was used with several random models. The generated sequences were used to evaluate ORBSLAM2.0. The average ATE was 0.0345~m, with standard deviation of 0.02~m {amongst different scenes}. This indicates that the variation in the models affects the results. To further demonstrate the challenge levels in trajectories,
Fig.~\ref{fig:slam}~{(\textbf{Right})}
shows the percentage of the tracked frames by \textit{ORBSLAM2.0} for 50 different trajectories with different difficulty levels based on maximum position and angular velocities. As the difficulty level increases, the percentage of the tracked frames drops.

\vspace{-2 mm}
\section{Conclusions}
\label{sec:conclusions}
\vspace{-2 mm}

We have presented a very large synthetic dataset of indoor video sequences that accesses millions of interior design layouts, furniture and object models which were all professionally designed to a highest specification. We then provide variability in terms of lighting and object rearrangement to further devise our scenes and simulate the environment of daily life.
As a result, we obtain highly photo-realistic footage at a high frame-rate.
Furthermore, a large variety of different trajectory types was synthesized, as we believe the temporal aspect should be given closer attention. We demonstrate the usefulness of our dataset by evaluating SLAM algorithms.

In this work, we configured lighting and scene changes in a random fashion due to lack of real-world ground truth for lighting and scene changes. Also, the scene rearrangement was obtained via a physics engine accessing physical parameters e.g. mass, size, friction coefficient etc. Alternatively, a data-driven approach could be used -- which we leave as future work.

\vspace{-2 mm}
\section{Acknowledgements}
\label{sec:acknowledgement}
We would like to thank \url{Kujiale.com} for providing their database of production furniture models and layouts, as well as access to their GPU/CPU clusters. We also thank the Kujiale artists and other professionals for their great efforts into editing and labelling millions of models and scenes. We also highly appreciate the comments and technical support from \textit{Kujiale ExaRendering Group}, as well as helpful discussions and comments from Prof. Andrew Davison and other members of \textit{Robot Vision Group} of Imperial College London. This research is supported by the EPSRC grants PAMELA EP/K008730/1, Aerial ABM EP/N018494/1, and Imperial College London.
\vspace{-2 mm}

redpajama_set_name: RedPajamaArXiv


text:
Most Parents Say Games Have a Positive Impact on Their Kids
A new survey of parents finds a positive outlook toward games. Maybe that's because while many claim to understand ESRB ratings, most didn't have a clue what is age-appropriate.
https://www.pcmag.com/news/most-parents-say-games-have-a-positive-impact-on-their-kids
TV used to be considered a sufficient babysitter. But these days, the screen of choice is a little more interactive. Games for kids are now considered more influential in their lives than almost any other kind of content.
That's according to a survey run by Frontier.com, the internet service provider, which was conducted in May 2021. The surveyed were 1,002 parents with at least one child who plays video games.
These results underscore the power gaming has in the market today, especially when it comes to 5- to 17-year-old kids. Nearly 63% of parents said that video games have a positive impact, across all age groups, though that wanes a bit as the kids get older—only because social media takes over as the "most influential" content for kids ages 14 to 17.
Note again that the info above doesn't include responses from parents who said they don't let their kids play games at all.
Those that do allow games have to keep track of them somehow, right? Well, most do—but 5.4% across all age groups said they don't bother monitoring. That number goes up to 16.3% for kids ages 14 to 17. That's probably because the most popular method of monitoring is sitting in the same room with kids.
There are, of course, pros and cons to allowing all that gaming. The biggest con: Addiction, a worry of 63.7% of the adults. But parents like the fact that gamer kids have good logical thinking skills (62.9%) and, well, they're happier (60.5%).
Recommended by Our Editors
10 Things Every Parent With a Connected Kid Needs to Know
The Best Parental Control Software for 2022
Parents Strive to Reconfigure Kids' Screen Time During Quarantine
All parents dislike anything sexually suggestive in their kids' video games; it's their number-one gripe. This might be why more of them should learn how to determine whether an Entertainment Software Rating Board (ESRB) score on a game indicates it's right for their kid. The most telling thing in the survey turned out to be that while 78.2% of parents claimed they understood the ratings, most parents got ESRBs completely wrong. Not only can't they peg the minimum age for each rating (from Everyone up to Adults Only), but few could even get it right when it came to specific games that were pretty obvious. Most parents defaulted to saying games were either for Everyone (E) or Mature (M); the latter is meant for ages 13 and up, but most parents think it's meant for ages 17 and up.
For more details, read the full report at Frontier.com.
Surprise! Console Gamers Cheat More Than Those on PC
Take-Two Financial Projections Hint at GTA VI Launch in 2024
By Mark Knapp
The Best VR Games for 2022
By Ben Moore & Gabriel Zamora
The Best Sega Genesis Games
By Benj Edwards & Jordan Minor
About Eric Griffith
Eric Griffith has been writing about computers, the internet, and technology professionally for 30 years, more than half of that time with PCMag. He was previously on the founding staff of publications like Windows Sources, FamilyPC, and Access Internet Magazine, all of which are now defunct, and it's not his fault. He spent six years writing exclusively about Wi-Fi, but don't ask him to fix your router. At PCMag he runs several special projects including the Readers' Choice and Business Choice surveys, and yearly coverage of the Fastest ISPs and Best Gaming ISPs, plus regularly writes features on all tech topics. He's the author of two novels: BETA TEST ("an unusually lighthearted apocalyptic tale" according to Publishers' Weekly) and KALI: THE GHOSTING OF SEPULCHER BAY, which you can still get as ebooks. He works from his home in Ithaca, NY, and did it long before pandemics made it cool.
Read the latest from Eric Griffith
Haters Gonna Hate: The Most-Despised Tech Brands
Must-Watch: The Most-Streamed TV Shows and Movies This Week
What to Stream This Weekend
You Talking to Me? These iOS Apps Support the Most Languages
The Best Tech Brands for 2022
More from Eric Griffith


Over the years, our cooperation with Holscher design has developed into an important partnership that we have the greatest respect for here at Pressalit."
integrated release-buttons, and automatic closing systems.
direct contact wih the seat.
Small changes, that make a necessity a good experience.
on creating good toilet seat design that unites aesthetics, comfort and functionality.
and easy cleaning are important ingredients for a good product."
Pressalit Sign Art is Nordic in its design. The toilet seat is functional but far from cool. Sign Art is a small piece of everyday art that honors the ability to live life. It has quick-release and soft close. The design allows three shapes to meet in an obvious interaction. The bow of the bolt, the cylinder's shape and the horizontal surfaces of the grips. The toilet seat is designed specifically for Ifö Sign Art.
Pressalit Spira Art. Clean language and pure enjoyment. Spira Art is the natural connection between the bathroom's curved design and the bare curved human body. Thoughtful design has resulted in a seat that is inviting and intuitive to open with the tip of a finger tip. It has quick-release and soft close. Designed specifically for Ifö Spira Art and Ifö Spira. The designer behind the seat is Knud Holscher.


text:
Mazurka in A Minor, Op. 7, No. 2 performed by Rubinstein (1966)
Label: BMG 09026-63050-2
Track: 6 Track Duration: 3:29 minutes
Related Performances: Ashkenazy (1981), Bacha (1997), Barbosa (1983), Biret (1990), Blet (2003), Block (1995), Brailowsky (1960), Chiu (1999), Clidat (1994), Cohen (1997), Cortot (1951), Csalog (1996), Czerny-Stefanska (1989), Ekier (1987), Ezaki (2006), Falvay (1989), Ferenczy (1958), Fiorentino (1958), Flière (1977), Fou (1978), Fou (2005), François (1956), François (1966), Friedman (1930), Frugoni (1962), Groot (1988), Hatto (1993), Hatto (2006), Indjic (1988), Iturbi (1959), Kapell (1951), Kushner (1990), Lilamand (2001), Luisada (1990), Magaloff (1977), Magin (1975), Milkina (1970), Mohovich (1999), Niedzielski (1931), Ohlsson (1999), Olejniczac (1990), Osinska (1989), Pobłocka (1999), Rangell (2001), Rubinstein (1939), Rubinstein (1952), Shebanova (2002), Smith (1975), Sztompka (1959), Uninsky (1959), Vardi (1988), Wasowski (1980)
There are two types of MIDI file available on the right for each entry below:
Type 0 = Standard MIDI File type 0. There is only one track of data for all notes and tempo markings.
Type 1 = Standard MIDI File type 1. There are multiple tracks of data. The first track contains tempo change information. The second track typically contains the notes of the left hand. The third typically contains the notes of the right hand.
MIDI files labeled PerfViz are in a format which is compatible for loading into the PerfViz program by Martin Gasser which displays 3D performance worms. The "match-file" links the PerfViz performance MIDI file to the musical score and is a text-based file.
Type 0 Type 1
Constant Tempo Performance Simulations
Plain simulated performance -- These MIDI files contain a constant tempo throughout the performance, and are basically literal performances of the written score. However, the performance tempo is set from the average tempo of the model performance. Thus, the duration of the MIDI files will match the duration of the original performance.
plainsd0
MM 105 plainsd
Beat-Level Tempo Performance Simulations
Beat-level tempo -- These MIDI files contain a tempo which changes once per beat based on the duration between beats in the originating performance. Sub-beat level note times are estimated from a constant tempo throughout the beat. Score dynamics are used.
Average tapped performance -- These MIDI files contain a tempo which changes once per beat throughout the performance based on an average of several reverse conductions of the performance. Score dynamics are used.
avgtapsd0 avgtapsd
PerfViz
[match file]
Subbeat-Level Tempo Performance Simulations
Note-Level Tempo Performance Simulations


text:
Kick-off for SFF "Porous media Laboratory" (PoreLab)
By PoreLab 11 September, 2017 meetings
The new Center of Excellence "Porous media Laboratory" (PoreLab) had its kick-off meeting, its first international workshop, at the historical Soria Moria hotel in Oslo from 6th to 8th September 2017.
The vision for PoreLab is ambitious and high risk. It addresses fundamental scientific questions in a field which, historically, have been dedicated to specific applications. The center focuses on the physics of porous media using experimental, theoretical and computational methods. It is led by scientists from physics, chemistry and reservoir engineering.
The PoreLab team is now seeking to bring the field to a new general level, creating possibilities to solve new applications, and to anchor the foundation of PoreLab nationally and internationally by assembling the best scientists in the field.
The Center Director, Professor Alex Hansens welcome address:
Here is how I see it: after ten years, I need to be able to look 250 average taxpayers in the eyes and tell them "What we did was worth every penny you have paid in taxes for the last ten years." My aim is to be able to look them in the eyes with pride. Center Director Alex Hansen.
The PoreLab team and co-workers. . Foto: Marie-Laure Olivier
Building the platform for years to come
The workshop presented the foremost scientific circles from academia and industry with an opportunity to meet, and develop research interests. Lectures in various disciplines of porous media were given by national and international invited speakers, followed by poster sessions.
The goal is to build the platform for years to come and lay the foundation for the assignment: to advance the understanding of flow in porous media.
The PoreLab kick off workshop was led by the Center vice Director, Professor Knut Måløy, and was opened by the Center Director, Professor Alex Hansen.
Center director Alex Hansen (in front), with Morten Dæhlen (to the left), Dean at the Faculty of Mathematics and Natural Sciences at the University of Oslo, and Øyvind Gregersen, Dean at the Faculty of Natural Sciences, NTNU. Foto: Marie-Laure Olivier
Welcome to the Grand opening of PoreLab – with historical significance
PoreLab will edit a volume of Frontiers in Physics


The Cove is a privately owned, certified tree farm. The Cove covers about three thousand acres ranging from one to three thousand feet in altitude in the Appalachian Mountains and a part of The Cove has been developed to provide outdoor recreation for the general public. It is the desire of the present owners to share the charm, solitude, and beauty of The Cove with those who love the outdoors.
The Cove also hosts outdoor music festivals and off-road four wheel drive trail riding events.


text:
US Polo Assn watch brand makes its UK debut at Jewellery & Watch show
The US Polo Assn. has licensed its name and branding to a watch collection that will be shown in the UK at next month's Jewellery & Watch show in Birmingham.
The watches bring a classic vintage American styling with modern day twist to its 2017 collection that will be seen for the first time at the show.
There are 300 styles across thirty collections for both men and women being launched for 2017. Retail prices start at £79.
US Polo Association was founded in 1890 and premiered its first watch collection at Baselworld in 2009.
Since then distribution has expanded to the US, Asia and throughout Europe, but the brand has not previously been available in the UK.
"US Polo Assn. has captured the world's imagination with its style and flair, with over 2.8 million Likes on Facebook, a dedicated TV channel and 34,800 Instagram followers. Its status as a truly global brand is unquestionable. The design team have combined the enduring icons of classic American style with modern day fashion flair and I believe it is these design elements that will capture the imagination of the British consumer. I am delighted to be launching the US Polo Assn. Watch Collection, at J&W 2017" – John Claffey, managing director, Gablestone Partners, which is representing the brand in the UK.
The US POLO ASSN Watch Collection is a range confident in its' style and unrelenting in its' demand for the highest quality standards.


text:
'Regret' as US begins exit from UN climate accord
US-The US has begun the process of withdrawing from the Paris Agreement, notifying the UN of its intention to leave, as other countries express regret and disappointment at the move.
The notification begins a one-year process of exiting the global climate change accord, culminating the day after the 2020 US election. The Paris accord, agreed in 2015, committed the US and 187 other countries to keeping rising global temperatures below 2C above pre-industrial levels and attempting to limit them even more, to a 1.5C rise.
The decision to withdraw - taken by President Donald Trump after he came to office in 2017 - made the US the world's sole non-signatory and prompted high-level efforts by the European Union to keep the agreement on track.
However, hundreds of local governments, businesses and organisations in the US have joined the We Are Still In movement, pledging to cut emissions and move to renewable energy.
Trump had made withdrawing from the agreement one of his election campaign pledges but UN rules had meant it was not possible for the US to start the withdrawal process until 4 November 2019.
The withdrawal is still subject to the outcome of next year's US presidential election - if Mr Trump loses, the winner may decide to change course.
Animated chart showing that most of the coldest 10 years compared to the 20th century average were in the early 1900s; while the warmest years have all been since 2000
But scientists and environmentalists fear the effect the Trump administration will have on climate protections in the meantime.
A report issued in December 2018 by the Institute of International and European Affairs suggested President Trump's decision to leave had done "very real damage" to the Paris agreement, creating "moral and political cover for others to follow suit".
The decision by the US - one of the world's biggest emitters of greenhouse gases - drew condemnation from environmentalists and expressions of regret from world leaders.
An official for France's presidential office said "we regret this and this only makes the Franco-Chinese partnership on the climate and biodiversity more necessary."
French President Emmanuel Macron and his Chinese counterpart Xi Jinping are due to meet in Beijing on Wednesday, where they are expected to sign a statement declaring the "irreversibility of the Paris accord", the official added.
Chinese Foreign Ministry spokesman Geng Shuang said he hoped the US could take more responsibility in what was a multilateral process instead of adding "negative energy".
Pakistan, US to discuss vital issues today
Demand From The US
Afghan war will end after US withdrawal: Taliban
US-China trade 'deal' key to ease downward pressure on Global Economy: IMF
ALL OPPOSITION PARTIES ARE UNITED AGAINST GOVT! AZADI
Shahzad Ishaq & Qasim Ali
Navigating banks: Why Omni channel?
Dr Kamal Monnoo
October Revolution – Is there any relevance to today's events?
Shumayl Aziz
Revamping the NAB Ordinance
Exiting Paris Agreement
Student Unions In Sindh
Importance of sports
Curfew in Kashmir
Fractured OIC and Kashmir dispute


Welcome to our brand new website, we're glad you're here!
We want to give a big thank you to Luke at Simple Expertise for building and hosting TheBugGuys.ca. We hope the user-friendly design makes it easy for you to navigate through our latest blog articles, resource sheets, and social media feeds. From our new site you can learn more about our company values and history, and you can easily contact us to schedule a free inspection for your home or business. Be sure to check back often for new content, and follow us on social media to keep up on everything #bugnation!
As always, we look forward to working with you all!
Error: Error validating access token: Session has expired on Monday, 25-Mar-19 11:34:40 PDT. The current time is Monday, 22-Apr-19 19:14:08 PDT.


I don't give myself credit sometimes. Ok, most of the time.
There's a lot I want to change, mainly about the way that I view myself.
This is the perfect opportunity for me to do all the self-discovering that I think I need. And whether I like it or not, a lot of change is about to take place, at a rapid pace. At a much faster pace than I'm used to, at least.
I'm scared, I'm excited, I'm anxious. But all at the same time, it isn't hitting me.
I can't wait to get out of my comfort zone. I'm scared to leave behind what's familiar. Risk makes me nervous. But I can't wait to get lost a couple times, have a couple melt downs. Realize I'm stronger than I thought I was. I can't wait to smile at the epiphanies. I can't wait to dismiss the trivial things in life that, at this moment, I waste too much time stressing over. I can't wait to learn things I never knew about myself. I can't wait for all that I'll discover.
This will be good for me. Stay tuned.
What are you talking about? I'm stay tuned…Hope everything is well girl.


After Jammer starained his hamstring against Houston on October 28, he started at CB against Minnesota the following week and re-aggravated the injury. As a result, he has not practiced all week, and Chargers head coach Norv Turner said it would be surprising if Jammer played at all. In the likely event that Jammer doesn't play on Sunday, reserve CB Warren Cromartie would get his first start.


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When you buy a high-tech device like an iPhone, you realize much of the value is in the software, and you expect to agree to a handful of End-User License Agreements (EULAs) in order to use its apps.
After all, the creators of those apps have spent untold millions of dollars and person-hours creating them. They deserve to protect their intellectual property.
Smartphones, OK – but what about that tractor? John Deere is now in hot water with the 'Right to Repair' movement for making farmers sign EULAs in order to use their farm equipment – EULAs that prevent farmers from repairing their own gear.
There's more to this controversy than meets the eye, however. John Deere is but one of thousands of enterprises undergoing digital transformation as it becomes a software company that runs its technology on tractors, rather than the other way around.
The origins of the John Deere controversy arose in the debate over 'jailbreaking' iPhones and other high tech devices. At issue: the right of Apple to protect its intellectual property had come into conflict with consumers' right to run whatever software they wanted to on the smartphones they purchased.
The legal question behind this controversy centered on a part of the Digital Millennium Copyright Act (DMCA) that Congress passed in 1996. As written, DMCA criminalizes production and dissemination of technology, devices, or services intended to circumvent measures that control access to copyrighted works and also criminalizes the act of circumventing such an access control, according to Wikipedia.
However, subsequent to the passing of the DMCA, regulators took up the question of whether there should be exceptions to the law – situations where consumers might have the right to circumvent technical protection measures (TPMs) intended to protect intellectual property in certain limited situations that would also protect the rights of the intellectual property holders.
After an extensive discussion period, the US Copyright Office delineated several such exceptions in 2015, including guidelines that would allow consumers to jailbreak their smartphones. In fact, the new regulation specified 27 classes of intellectual property that the regulator would exempt from TPMs.
In other words, hacking your tractor may be allowed under the fair use doctrine that gives consumers the right to back up their software, as long as the purpose of such modifications is repair or modification-related, but not, for example, to create a derivative work for sale.
That final statement essentially vindicated the right-to-repair movement's position, giving vehicle owners free reign over their ability to repair or modify their own equipment.
Whereas the strict terms of the DMCA caused outcries of regulatory overreach from the right-to-repair community, the adoption of the Class 21 exemption ironically solicited a similar response from John Deere.
Subsequent to the US Copyright Office's Class 21 exemption, then, John Deere required its customers to sign an updated EULA that restricted the ability of equipment owners to repair or modify their equipment, in essence requiring them to use John Deere-certified diagnostic and repair software – software that some farmers claim the company has not made available to them.
Farmers in the market for mechanized farm equipment may not like the software-based innovations that John Deere is bringing to market, along with the constraints they are insisting on to protect the functionality, safety, and emissions compliance of their vehicles. If they don't like what John Deere has to offer, of course, they are welcome to take their business elsewhere.
In the two years since Wiens' Wired article, a number of state legislatures have taken up the controversy with various right-to-repair bills. For example, Legislative Bill (LB) 67, The Nebraska Fair Repair Act, which specifically focuses on embedded software.
Given the vitriol from activists like Gordon-Byrne, it would be logical to conclude that customers will simply seek farm equipment elsewhere – leaving the market to decide whether John Deere's strategy is rational, as the company suggests.
However, John Deere's reluctance to offer the diagnostic, repair, and modification software some of its more vocal customers are demanding is having an unintended effect – a burgeoning grey market of unlicensed alternative software and associated diagnostic equipment, much of it from Ukraine.
In fact, the Motherboard article from March 2017 focuses on the growing Ukrainian grey market. "What you've got is technicians running around here with cracked Ukrainian John Deere software that they bought off the black market," claims Kevin Kenney, a farmer and right-to-repair advocate in Nebraska.
Given the Class 21 exemption, such software and equipment is more likely to qualify as grey market – violating John Deere's EULA and thus the equipment warranty – more so than consisting of illegal black market products.
Nevertheless, when it comes to such gear, the watchword is buyer beware, as the chance of malware finding its way into such applications is quite high. The end result may be far worse than violating the terms of John Deere's warranty or even circumventing emission controls. It's possible infected farm equipment might participate in illegal botnets, or worse, the malware might impact the safety of the operators.
In spite of activists' positioning of John Deere as the bad guy, therefore, its lawyers may be choosing the only path that makes sense given the dangers of giving its customers too much control over the software in its products. After all, the lawyers' job is essentially to protect the company from the liability that might result from an ill-advised DIY modification or repair.
In the final analysis, there's no question that John Deere's business – as well as the business of any enterprise in a similar situation – has permanently changed. For better or worse, the company is now in the software business.
Regardless of which side of this controversy you are on, one thing is clear: as digitally transformed enterprises become software companies, they are wading into unknown waters. Just ask John Deere.
Intellyx publishes the Agile Digital Transformation Roadmap poster, advises companies on their digital transformation initiatives, and helps vendors communicate their agility stories. As of the time of writing, none of the organizations mentioned in this article are Intellyx customers. Image credit: Ted Van Pelt.


As far back as the 19th century, North Carolina anchored Southern Appalachia's status as a supplier for a wide range of plant-based ingredients like goldenseal, black cohosh, wild cherry, slippery elm, white willow, and many analogs, to traditional Chinese herbs like astragalus and ginseng.
That tradition continues with the equipment and services available with the Natural Products Manufacturing Facility at Blue Ridge Food Ventures and our partners in the natural products service industry.
The Natural Products Manufacturing Facility at Blue Ridge Food Ventures is the nation's only GMP-compliant shared-use facility for companies developing dietary supplements, body care products, functional ingredients, all-natural cleaning solutions, and pet care products.
We have made the equipment investments so you don't have to! Your company can rent time in our facilities to manufacture and package your proprietary formulas, all within an FDA-inspected and registered manufacturing facility or we can make them for you. We produce small runs, develop prototypes for test marketing, and fill short-term production needs.
Our Natural Products Manufacturing Facility is part of a group of North Carolina nonprofit natural products service organizations that have banded together as the Natural Products Alliance of North Carolina. The purpose of the collaboration is to help natural products companies grow, formulate, test, make, and brand their botanically-based products in one vertical pipeline.
The Alliance's member organizations can facilitate everything from grower contracts and sourcing of wild crafted botanicals, to testing, authentication, formulation, and pilot-scale manufacturing, to workforce GMP training, access to capital and branding of finished products.
The project is supported by the Golden LEAF Foundation.


text:
ФК "Салют" е футболен клуб от гр. Белгород, Русия. Дълги години носи името "Салют-Енергия".

История
Основан през 1960 г. под името "Циментик". През 1970 получава името "Салют". През 1983 отборът завършва на 5 място в 1 съветска лига. След разпадането на СССР играе в 1 лига на Русия, но още първият сезон изпада. През 1993 печели в звоята зона, но отказва да играе в 1 лига. През 1995 Салют изпада в Трета лига, но на следващия сезон благодарение на спонсорите от ЮКОС отборът се завръща в професионалния футбол. През 1999 след като нефтената компания спира да финансира отбора, Салют изпада в аматьорските дивизии. През 2000, под името "Салют-Енергия", белгородският тим се завръща. През 2001 президентът Николай Головин успява да намери спонсори и привлича ветеранът Валерий Масалитин. Въпреки 35те си години, Масалитин става голмайстор на Салют. На отборът не достигат 2 точки, за да влезе в 1 дивизия. През 2002 отборът остава на 3 позиция в шампионата. На следващия сезон са привлчени много футболисти, играли във Висшата дивизия, но белгородци отново са трети. В началото на 2004 е създаден президентски съвет на Салют-Енергия, а Головин се оттегля от поста. През 2004 Салют тръгва много лошо, заемайки 14 място на полусезона. Все пак отборът се засилва и финишира пети. В сезон 2005 Салют печели втора дивизия и се класира в 1 дивизия. В първите си 2 сезона отборът едва не изпада, но завършва съответно на девето и дванадесето място. В сезон 2009 Салют записва най-силното постижение в историята си-7 място. През 2010 отборът заема 18 позиция и изпада. Следващият сезон е много силен за отборът, който си осигурява титлата 4 кръга преди края Във ФНЛ завършват на 13 място през 2012/13.

Известни играчи
 Валерий Масалитин
 Юрис Лайзанс
 Сергей Рижиков
 Алексей Поляков
 Максим Ромащенко
 Андрей Перов
 Андрей Кобенко
 Евгений Калешин
 Александър Гришин
 Максим Ромащенко
 Денис Ткачук

Източници
 Официален сайт

Руски футболни отбори
Белгород

redpajama_set_name: RedPajamaWikipedia


Join Vivian Von Brokenhymen has she hosts a Pop Up Runway Show at All Paws Retreat in Franklinton. Featuring adotable dogs by Friends of the Shelter, Grateful Acres Pet Sanctuary, I Have a Dream Rescue Organization, Peace for Paws-Ohio, and Speak for the Unspoken. Come out and mingle with the furbabies and have a great time. Phillips Coney Island and Chef Moe's on the Go! with be set up providing their delicious cuisine.
Entrance fee is a suggested donation of $15.
All procees will go to Rescue Runway Columbus 2019.


Welcome to our Legislative Page! Here you can learn about our organization's involvement with promoting children's healthcare, and protecting the professional practice of advanced practice nurses caring for children through advocacy, policy reform, and legislation.
This continuing education activity is crafted by the Legislative Chairs and policy enthusiasts from each of the NAPNAP Chapters in California (San Joaquin Valley, Los Angeles, Orange County, San Diego, and San Francisco). CLICK TO GET STARTED!
View our health policy agenda and learn about our philosophy and goals for legislative involvement.
Visit our National Office's advocacy website to learn more about NAPNAP's involvement with children's health advocacy across the country.
Learn more about our legislative advocacy initiatives! Access our legislative training document, links to other pediatric legislation trackers, as well as journals, books, and information about community partnerships and advocacy alliances to help you understand more.
Contact our Legislative Chair with your comments, questions, or concerns.


In the market full of rivals, it is impossible to make a presence if you are running far behind in the race. While all your competitors are coming up the with the most alluring websites filled with the engaging videos, arresting images and involving text, moving ahead without a well-defined content plan can turn out to be fatal for organization, especially if they deal in some serious content marketing. The content you post around the vast network of World Wide Web is always for a purpose and having a meaningful, goal-oriented and relevant content plan can boost your sales and help you excel among your opponents.
Before beginning with the details let's have a quick intro about what you will read further.
To put in the simplest word, a content plan is nothing but a blueprint of what you want to see on your website with other details about when, how, where and why you want to post it. You may find variegated definitions and meaning of the phrase "Content Plan" but save yourself from getting carried away, all the explanations might add volume into its scope but will lead you to the same spot.
How to Know You Have a Content Plan?
Figuring out whether your organization has a content plan is no big task. If you follow a documented strategy to post your content on the web then congrats! You have a content plan with you. But if you are still figuring out how to sum what you do or have done on the net, then you might be in an urgent need of one.
Are you happy with the cost per visitor your website extracts?
Do you remember when did anyone post the last blog on your website?
Are your social networking sites attracting large traffic?
If you are still quiet or the answer is no, then some serious planning and discussion is required to give a kick to you content marketing strategies. And to achieve this goal, a documented content plan can prove to be the best for your quest.
To get a rocking content plan for your business you may seek help of various web content development experts providing Content Writing Services or try to do it your way by using some simple tips we will be more than glad to tell you.
In case, you are a new baby in the Web market, start your mission with a thorough user research while the individuals with an established website can divide their research into the two broad categories.
The research will involve exploring the various platforms where similar brands are discussed to get an insight into what kind of content users seek for.
User Research will help to extract information using informal techniques like conducting small surveys or directly asking you known ones what they look for.
Content analyzing would help to explore what similar brands are doing to invite traffic to their websites. This will help you understand the trends and develop an engaging content structure that will keep the users coming back.
A proper timetable for content is crucial for any firm dealing in serious content marketing practices. The schedule involves the sequence of the content releases going to take pace further.
Content generation is the most challenging part as it demands a combined effort from all the stakeholders involved in content development. For success, you will be required to create relevant matter that hits the right target or get the relevant people who could get the right kind of content your marketing requires.
No matter how good the quality of your content is, it will lose its user value if not distributed at the right place and to the right people. Distribution includes optimizing your social media presence and your search engine ratings to make the most out of the content you published.
Last but not the least, analyzing the results give you an idea of what is bringing most of the users to your web site and what is still in the need of modification. The end results give you a direction to grow further.
With about 88% of content marketers indulged in content marketing, one thing is certain that in order to move ahead you've to embrace it. While there aren't any hard and fast rules written for how to market content right, the above -cited tips will definitely guide you in forming an effective content management strategy that will help your business grow.