text:
After All These Years,(Chao-Ming Teng)
Sound Meridians
After All These Years, Archival Giclée Print, Digital Offset Prints, Sound Installation, 2020 © You-Wei Chen
The diagram for the carpet installation, denoting the positions of all the names of the "collaborators" appeared in the chronology of Rainy Night Flowers
Rainy Night Flowers, a song that was born in 1934, was then one of the most popular songs in Taiwan. Since its release, there have been more than 30 singers who have released the song, with a dozen versions in different languages. Widely performed in political and memorial campaigns, this song elicits nostalgia and a sense of collective identity in Taiwanese people. "Rainy night flowers" has been used to describe Xie Xue-Hong (1901–1970), who cofounded the Taiwanese Communist Party; it has also been used as a metaphor for the tragic fatalism of Taiwan's democracy, as well as for Taiwan's past economic development. This song has been banned by the government on numerous occasions; it has also been featured in plays, films, and television shows. The phenomenon of Rainy Night Flowers has become the crux of extensive research in Taiwan. Former Taiwanese president Lee Teng-Hui (1923–2020) had publicly expressed his wish that Taiwanese people would stop singing this song. Rainy Night Flowers was the only Taiwanese song that world-renowned tenor Plácido Domingo chose to perform in his 2002 concert in Taiwan. Eighty-three years have passed before the song was designed as the key clue to the mystery in Taiwanese survival horror video game Detention in 2017. A Taiwanese cinematic adaptation of Detention became an instant blockbuster in 2019, where Rainy Night Flowers is featured as a crucial music element that sets the tone of the film.
The tenacious, unrelenting life of Rainy Night Flowers is transduced in Chao-Ming Teng's hands into an unorthodox interpretation. Suavely personified, the song in Teng's eye seeks survival after its inception, wangling its way to eternity through devious maneuvers of the times and those who sang it. The artist has assembled written archives of the song's over 80 years of history into a chronology, printed out as flyers at the exhibition for viewers to take. He has also penned a first-person narrative of Rainy Night Flowers: A voice-over of the song's soliloquy is broadcast at the exhibition, where the song looks back on the vicissitudes of its journey, as well as the lessons it has learned. A conspicuous indication that an object is not merely dictated by humanity, but it also defines humanity. This work proposes a question centered on the personification of the song: an existence of multiple identities, ready to be accessed at any given moment─does it count as a loss of identity or a form of true proactiveness?
Just like Rainy Night Flowers, the work After All These Years, reinvents itself in every rendition. The way it is shown adapts to the exhibition space. In addition to the chronology and the voice-over, recurring motifs include the black rectangle that serves as the background of the chronology, as well as the number of mirrors that corresponds to the number of names in the chronology. The specifically proportioned black color block and the mirrors appear to be a means of Rainy Night Flowers to beguile you, or perhaps, the beginning of its secret machinations.
Chao-Ming Teng
Born and currently lives in Taipei. Teng graduated from the Media Arts and Sciences program from MIT School of Architecture and Planning. He had been invited to be the resident artist at Villa Arson (France, 2009) and Para Site (Hong Kong, 2014). Recently shows (selected) include Taipei Biennial (Taiwan, 2012), Dojima River Biennial (Japan, 2013), ALTERing-NATIVism—Sound Cultures in Post-War Taiwan (Taiwan, 2014), Discordant Harmony (Art Sonje Center, Korea, 2015), RR ZZ (Gluck 50, Italy, 2015), Hiroshima Trilogy: Part III (Hiroshima MOCA, Japan, 2015), Public Spirits (Warsaw CCA, Poland, 2016), Metahistory (TKG+ Gallery, Taiwan, 2018), Mercurial Boundaries (Museum of NTUE, Taiwan, 2019) and Rotating Exploded View Diagram of Historiography (Galerie Nichido, Taiwan, 2019).
<< Liquid Love / Sound Meridians
Exhibition| 14th November 2020 – 24th January 2021
Venue | MoCA Taipei


text:
January 14, 2016 - 10:00am PST
Electrophysiology Data Discovery Index (EDDI) - Raimond L. Winslow, PhD
Dynamic data are produced by sampling continuous-time waveforms to generate time-series. They are ubiquitous throughout biomedical research. Examples include the electrocardiogram, other patient monitoring data, and single- and multi-electrode whole-cell and patch-clamp recording data. Unlike the case for static data, there is no culture of data sharing, standards and general purpose tools for publishing and discovering dynamic biomedical data. Currently, these data are either lost forever or stored on digital media in laboratories of individual investigators where they at best must be accessed manually, or at worst can't be accessed at all. In light of the huge investment made by NIH to collect these data, it is remarkable that such a broad class of biomedical data is "ephemeral".
To help address this problem, in a supplement to the CardioVascular Research Grid (CVRG), we have developed the Electrophysiology Data Discovery Index (EDDI), a web-based platform for annotating, publishing, and discovering biomedical time-series data. Built using DSpace, EDDI supports the annotation, publication and discovery of electrophysiology time-series data. Users publish data by entering the URLs where data sets are located in web-forms, annotate data sets, and associate data sets with peer-reviewed publications. EDDI uses Apache Solr to index these entries and provides a search interface to aid data discovery. EDDI has integrated Globus OAuth authentication which allows researchers with a Globus Online account to quickly register to use EDDI using their existing Globus credentials. This Globus integration enables EDDI users to quickly reference data repositories defined as Globus endpoints, and seamlessly integrate with Globus Connect to initiate high-speed data transfers. While tailored to the needs of the electrophysiology data communities, EDDI is designed to allow for the addition of new dataset communities and also to individual community dataset schemas as the needs of researchers evolve. We will do a live demonstration of the functionality provided by EDDI.
Raimond L. Winslow, Ph.D., The Johns Hopkins University School of Medicine & Whiting School of Engineering
Dr. Winslow is the Raj and Neera Singh Professor of Biomedical Engineering, and Director of the Institute for Computational Medicine at Johns Hopkins University. Work in his laboratory is focussed in two areas. The first is understanding the relationship between the perturbed function of molecular networks and generation of arrhythmia in heart disease through development and application of computational models. The second is developing informatics tools that enable sharing and analysis of cardiovascular clinical research data. He is PI of the CardioVascular Research Grid Project (cvrgrid.org), an NHLBI-funded R24 resource developing web-based tools in this area.

redpajama_set_name: RedPajamaCommonCrawl


text:
This story is from June 11, 2018
Rajasthan RBSE 10th Result 2018 released @ rajresults.nic.in
TOI-Online | Jun 11, 2018, 15:41 IST
TOI-Online
Representational Image
NEW DELHI: The
Board of Secondary Education (RBSE), Ajmer released the Class 10 board results on Monday i.e., June 11, 2018.
The RBSE declared the Class 10 results today around 3:15 pm on the official websites rajresults.nic.in and rajeduboard.rajasthan.gov.in
Check your Rajasthan Board Class 10 Result here
The exams for RBSE Class 10 were conducted from March 15 to 26, 2018. The first paper was of English and the exams were concluded with science paper.
Around 11 lakh students wrote their paper this year. This is also, the Rajasthan board is not going to release the merit list of the candidates.
This year, a total of 33564 students did not turn up for the examination.
Last year also the class 10 results were declared in June. In 2017, around 10,72,799 students had appeared for the examination, out of which 4,83,282 were boys and 3,63,781 were girls.
Against the national trends, in the Rajasthan Class 10 results boys had outperformed girls by scoring 79.01 per cent against girls' 78.89 per cent. The overall pass percentage of Class 10 was 78.96 per cent.
On June 6, the Rajasthan board declared the class 8 results and on June 1 the Class 12 Arts stream results were declared by the board.
In Class 12 Arts, around 88.92 per cent students cleared the examination successfully. Around 91.46 per cent girls have cleared the exams while 86.67 per cent boys emerged successful.
In 2018, about 5,37,259 students had registered for the Rajasthan Board Class 12 Arts examination, of which 2,14,411 got the first division, a total 2,25,291 students scored second division while the third division has been bagged by 24,864 students.
The results of Science, Commerce examinations were declared on May 23.
STEPS TO CHECK RAJASTHAN BOARD CLASS 10TH RESULT 2018
Logo on to the official website: rajeduboard.rajasthan.gov.in
In the side bar, click on the link that says "Rajasthan Class 10 or SSC Exam result 2018"
A new page will appear on your screen
Enter your roll number and date of birth in DD/MM/YYYY format
View and download your result for Class 10
Take a print out for future use
About Rajasthan Board
Rajasthan Board of Secondary Education was established in the year 1957 in Jaipur. The Board aims to provide quality education to the students of all the public and private schools affiliated with it in the state. The Board is presently headquartered in Ajmer. The RBSE conducts the exam of Rajasthan Board 8th, 10th, and 12th class and declares the results.
The Board has its offices in various offices districts, viz. Bharatpur,
, Churu, Dausa, Dholpur, Jaipur, Sikar,
etc. The candidates can visit the official web portal of the Rajasthan Board to get the necessary information relating to the Rajasthan 8th Board Result 2018.
Download the Times of India News App for Latest Home News
Raju Raika


Thermorossi moves the borders between space and function. The heater becomes a masterpiece to be exhibited, a "piece of furniture" to be placed at will, a picture to be hanged. Equipped with heat exchanger THERMOCORE® and simplified cleaning system EASY CLEANING.
Product SlimQuadro 11 Maiolica add to comparison.


text:
Alphaville (Kino Lorber, NR)
By Sarah Boslaugh July 9, 2019 July 9, 2019
Leave a Comment on Alphaville (Kino Lorber, NR)
The brilliantly-named Lemmy Caution (American B-movie actor Eddie Constantine) is the quintessence of all the American tough guy private eyes you've ever seen in the movies—he's got the trench coat, the fedora, the craggy features, the vocal fry, the snappy repartee—and of course, a taste for brawling and a Colt semi-automatic in his pocket. Lemmy is the central character in Jean-Luc Godard's Alphaville, a 1965 science fiction film without futuristic sets or fantastic gadgets, in which the future looks like a cross between an American film noir and some of the more modern but otherwise completely ordinary sections of Paris.
Caution is a secret agent traveling under the pseudonym "Ivan Johnson;" his cover is that he's a journalist working for Figaro-Pravda, an occupation presumably chosen to give him a cover story in case any asked why he was constantly photographing everything (although why he uses a plastic consumer camera with a tiny flashbulb might be harder to explain). And if the name of that paper sounds a bit suspect to you, you're not the only one—I think Godard is having a bit of a laugh, as he seems to be doing throughout much of this film, as if to say that he understands that filmmaking is a game and if everything in this film is just too much of whatever it is, that's totally deliberate on his part.
The science fiction aspect of Alphaville comes in more in the concept than in the film's visuals: Alphaville is a town controlled by a computer, the Alpha 60 (and yes, Rod Serling did recycle that idea a few times in The Twilight Zone). The computer was created by a professor with the on-the-nose name of von Braun (Howard Vernon), and it's quite the dictator, having banned both free speech and the expression of emotion (much too individualist, don't you know). In a concept that seems lifted from George Orwell's 1984, dictionaries are even regularly revised to remove words that might invoke emotion, and neon lights flash scientific formulas at everyone as if they were advertising peep shows.
Caution's job is to find Henri Dickson (Akim Tamiroff), a fellow secret agent who has gotten way too comfortable drowning his sorrows (so it seems the pleasures of booze, at least, have not been banned in Alphaville; sex is also available, although presumably not love). He's also planning to hunt down von Braun, and for that mission he enjoys the assistance of von Braun's daughter Natacha (Anna Karina).
The French subtitle of Alphaville, "une étrange aventure de Lemmy Caution" ("a strange adventure of Lemmy Caution") offers a good clue that this is a film to be viewed with at least one arched eyebrow. So do various indications within the film, including the habit of some characters to directly address the camera, and some film processing tricks that are pure amateur hour. Godard's fellow directors either saw the joke not at all, or only too well, because Constantine, who had played the character Lemmy Caution in a number of French films before this one, never got another offer to play the role.
Above all, Alphaville looks amazing. That's largely due to the work of cinematographer Raoul Coutard, who had a real feel for night shots, production designer Pierre Guffroy, who comes up with all kinds of telling props to express the strangeness of Alphaville society, and whoever scouted the locations for this film (if you like 1960s architecture and interiors, this is definitely the film for you). The script, by Godard, draws on a variety of influences, perhaps most remarkably the writings of Jorge Luis Borges, while the soundtrack by Paul Misraki heightens both the noir atmosphere (with regular interruptions by electronic beeps, to remind us that it's also futuristic) and the absurdity of it all. | Sarah Boslaugh
Alphaville is distributed on Blu-ray by Kino Lorber. Extras on the disc include an audio commentary by film historian Tim Lucas, an introduction by film scholar Colin McCabe (5 min.), an interview with Anna Karina (4 min.), and the trailers for 5 films.
Tags: akim tamiroff alphaville anna karina Eddie Constantine featured film noir Home Viewing howard vernon jean luc godard kino-lorber science fiction
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The Shawshank Redemption (Columbia Pictures, R)
Notable Albums of the 2010s
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This was a rant a while back… for a beautiful friend… That made me realize that flirting did not have to be "dirty" and having a good time did not have to involve "sexting", and etc. etc. You get the dealio.
"I think perhaps it's only when we're at your worst that we come to realize what we truly miss. It never really did hit me as to what I lost until now. On a night when the stars don't shine as bright and the room feels just a little rocky and the world feels just a little blurry, I would look back and remember you. Remember my best friend who was there for me through so much. The one who was forever innocent. Who made me feel innocent. The one who was always there for me. Just lingering, never judging. The one who never made me feel like I was on my own, because there is nothing worse, then feeling alone… On your own.
Despite everything we went through, I don't think I ever truly forgave you, until now… When I finally come to realize that despite all the crap thrown our way, you were my constant. You were my shining star. You were the one thing that I always counted on to be there for me. You were the one person, who stood by me… When I pushed everyone else away.
And maybe it was a let down… Just to see that despite all your flaws, you weren't superman. You were not perfect, not even to me. But I'm not either. I never will be. You loved me. Unconditionally. And that is all I could've ever asked for.
I've swerved. Off our path. Off of what I want to be and what I want to be known for… I've swerved… So much. I'm not in control. I haven't been in control. And it takes a lot to ask for help… But you were the one, that never made me ask. You just did.
So on this night, when I just keep switching back and forth between my emotions and between my friends and just between my head…. I keep wishing that something would just help me. But I have come to realize, that they really can't. Because they are just not you. No one else is you. No one else, can be you.
Special thanks to Wil… For always being original. My special little innocent star.


text:
Чимадолмо је насеље у Италији у округу Тревизо, региону Венето.

Према процени из 2011. у насељу је живело 1504 становника. Насеље се налази на надморској висини од 29 м.

Становништво
Према резултатима пописа становништва 2011. у општини је живело 3.413 становника.

Види још
 Покрајине Италије
 Италијани

Референце

Литература

Спољашње везе

Насеља у округу Тревизо
Википројект географија/Насеља у Италији

redpajama_set_name: RedPajamaWikipedia


5th November 2009 - Scotland's favourite whisky, The Famous Grouse has unveiled its limited edition carton designed by well known Scottish designers Timorous Beasties, and announced an exclusive listing with Waitrose over the Christmas period.
The nostalgic illustration takes the style of the iconic Timorous Beastie toile designs, and depicts a visual history of the brand and its origins in Perth, Scotland at Bordeaux House, along with the famous bird itself.
The UK is one of four main markets* distributing the bespoke packaging, and will be releasing only 24,000 of the limited edition cartons across the country, providing a real collectors' item for whisky lovers over the Christmas period.
Timorous Beasties is famous for its uncompromisingly contemporary images on traditional textiles and wallpapers, and has become famous throughout the world for its work with clients ranging from Vivienne Westwood, Phillip Treacy, Nike, Liberty and the V&A museum.
The limited edition Timorous Beasties packaging contains a 70cl bottle of The Famous Grouse. It is available exclusively in Waitrose, or to order online visit www.thefamousgrouse.com (RRP £14.32. Supermarket prices may vary). For more information on the design please visit: www.famouslydesigned.com.


Introduction: Elderly orthopedic patients are frequently associated with systemic co morbidities like cardiac, pulmonary or endocrinal disorders. Regional anaesthesia has benefits over general anaesthesia in elderly. But the hypotension caused by regional anaesthesia is a limiting factor which can be overcome by using low dose of local anaesthetics. Low-dose local anaesthetics can limit the block level, but may not provide an adequate ansesthesia level and duration for surgery, requiring more analgesic consumption postoperatively. These shortcomes are overcome by adding intrathecal adjuvants with local anaesthetics. Among them α2 agonists are gaining popularity. Our aim was to compare the characteristics of spinal block, haemodynamic changes following administration of low dose of α2 agonist's intrathecally combined with low-dose levobupivacaine in elderly patients undergoing orthopaedic surgeries.
Material and Methods: In this prospective randomized double blind study, 90 patients of more than 65 years of age posted for lower limb surgeries were allotted into three groups. Group-LS received 1.5cc of 0.5% isobaric levobupivacaine with 0.5cc of normal saline, Group –LC received 1.5 cc of 0.5% isobaric levobupivacaine with 30g of clonidine and Group-LD received 1.5 cc of 0.5% isobaric levobupivacaine with 5g of dexmedetomidine. Onset of sensory and motor block, duration of sensory and motor block, haemodynamic parameters, sedation and side effects if any were evaluated.
Results: The onset of sensory and motor block were faster in dexmedetomidine group than clonidine or plain levobupivacaine group. Duration of the sensory and motor block were also prolonged in dexmedetomidine group when compared with clonidine or plain levobupivacaine group. Haemodynamic stability was maintained in all the three groups.
Conclusion: The addition of dexmedetomidine 5µg to7.5mg 0.5% isobaric levobupivacaine hastens the onset of sensory and motor block and also prolongs the duration of analgesia with good haemodynamic stability in elderly patients.


Here is the update to the gold color. I was able to color pick two pantone colors based off of the RAL colors you sent.
Here are the two colors side by side.
Here is a snapshot of the two Pantone colors next to each other.
Here are some color options for the logo. I'm showing two version of the gold here. Pantone 124C is a coated color that could replace the gold color when not printing a metallic ink. Pantone 871C is a metallic gold. It will look very different in it's digital form (as seen below). The color chip next to it shows how it looks when printed. Also some printed samples I found online.
The only difference here is that the burgundy pantone color is a bit richer and darker allowing you to see the metallic gold better.
Here is the update to the logo with the type drawn out and a little bit of worn texture. There's a version with and without the popcorn badge. You should have both for future marketing needs. The one without the badge has a drop shadow behind the word 'Heart'. I tried using it within the badge layout, but it made the word 'Heart' look too bulky (3rd logo shown bellow). It might look better once we start playing with colors.
*** Click on the images for a larger preview.
At first I thought about doing something cute with a heart holding or munching on some popcorn (left). Cute and silly is always fun, but might not be the right approach for your type of clients. Then a minimal and subtle idea hit me of placing a heart inside of a piece of popcorn for the icon. Still a fun idea, but a bit more grown up. Also a smart way of playing off the name.
Since you product is package base, I think it might look good within a logo badge/crest.
I'm sure if you thought of a tagline already, but I started to toy around with a few ideas that might help.
What do think about having "All Natural" at the top? Seems like everyone's drawn to naturally made items.
And lastly, what everything could look like all together. 1) Icon. 2) Logo (standalone). 3) Logo with a classy badge. 4) Logo with cloud shape badge. Please note that the typeface for "Heart" is very rough. If you like this font style, I'll could recreate it making it a bit smoother. Maybe keeping a little of the roughness to give it that homemade feel.
*** Please feel free to click on the images for a lightbox preview.


text:
\section{Introduction}
\label{sec:introduction}

Learning 3D shape representation is important for many downstream applications in 3D computer vision \cite{2DProjectionMatching,Han2020Reconstructing3S,Han2020DRWRAD,Han2021HierarchicalVP,Han20193D2SeqViewsAS,han2019SeqViews2SeqLabels,Han2019MultiAnglePC}. Explicit 3D representations such as meshes, voxels and point clouds have been widely used in various tasks \cite{Kato2018Neural3M,Qi2017PointNetDL,Qi2017PointNetDH,Maturana2015VoxNetA3,xiang2021snowflakenet,wen2021pmp,Wen2020PointCC,wen2021c4c,Wen2020Point2SpatialCapsuleAF,Wen2020CFSISSS}. Recently, deep implicit function (DIF) \cite{Mescheder2019OccupancyNL,Park2019DeepSDFLC,Chen2019LearningIF,Xu2019DISNDI,NeuralPull,Ma2022RS,Ma2022SR} has received more popularity as an efficient 3D shape representation, which learns latent codes of 3D shapes by predicting a signed distance or inside/outside for each query point. Different from explicit 3D representations, DIF can be stored compactly and learn shape priors by the network. Besides, it is simple and natural to use DIF in learning-based tasks because of its differentiable ability.

\begin{figure}[t]
    \centering
    \includegraphics[scale=0.45]{images/intro.pdf}
    \caption{Illustration comparison between our method and other methods. In (a), we select the grid-based DIF (ConvONet \cite{Peng2020ECCV}). In (b), we show the octree-based DIF (NGLOD \cite{Takikawa2021NeuralGL}). (c) is our DCC-DIF. (d) is Reference.
    The first row shows the code positions of different methods, where the warmer color indicates the local codes are closer to the surface. Compared with other methods, in which code positions are discrete and regular, our code positions are continuous and more flexible. The second row shows the reconstruction results, where our method can reconstruct highly detailed geometry of complex shapes, like teeth.
}
    \label{fig:intro}
\end{figure}

Previous DIF approaches \cite{Mescheder2019OccupancyNL,Park2019DeepSDFLC,Chen2019LearningIF} encode the entire 3D shape into a single global latent code through the auto-encoder or auto-decoder \cite{Tan1995ReducingDD} framework, which leads to information loss of local regions. As a result, those approaches can not capture local geometry details well and struggle to represent complex shapes. To address this problem, some methods \cite{Chabra2020DeepLS,Jiang2020LocalIG} divide the 3D space into small local volumes and assign each volume with a latent code. Then each local volume is reconstructed separately and all volumes are combined together to get the final reconstruction. Since small local volumes contain simple shapes and common patterns are shared among volumes, these methods can represent 3D shapes with high accuracy and generalize to different shapes. Similarly, some approaches \cite{Genova2020LocalDI, Genova2019LearningST} decompose 3D shapes into local parts, each of which is associated with a latent code for learning local details.
On the other hand, more recent methods discretize the space into a regular 3D grid \cite{Peng2020ECCV,Chibane2020ImplicitFI,Chen_2021_ICCV} (or octree) \cite{Martel2021ACORNAC,Takikawa2021NeuralGL} and store the local codes in the grid points (or octree nodes). Given a query point in 3D space, the local feature is computed by interpolating its neighboring local codes with their positional weights. Next, the local feature is fed into a decoder to predict a signed distance or inside/outside. As the resolution of grids or depth of octree increases, these methods achieve the state-of-the-art results in several shape reconstruction
tasks. However, the increase of resolution or depth will result in a significant growth of the number of local codes. Moreover, the local codes in these methods are constrained at discrete and regular positions like grid points or octree nodes, which makes the code positions difficult to be optimized \cite{Peng2020ECCV,Chen_2021_ICCV,Chibane2020ImplicitFI,Takikawa2021NeuralGL,Martel2021ACORNAC} and limits the representation ability.

\begin{figure}[t]
    \centering
    \includegraphics[scale=0.4]{images/optimization_process.pdf}
    \caption{\textbf{Illustration of moving code positions during optimization.} Our code positions are dynamically updated during optimization, which makes 3D shape representation more efficiently. We typically show four states during optimization, where the first and second rows display code positions and reconstructions, respectively. Initial and final states are shown in (a) and (d), respectively, while (b) and (c) show two intermediate states.}
    \label{fig:optimization_process}
\end{figure}

To address the above-mentioned problems, we propose a novel method to learn DIF for 3D shape with Dynamic Code Cloud, named DCC-DIF. Specifically, we represent a 3D shape with a set of local latent codes, each of which is explicitly associated with a learnable position vector. Using these position vectors, the local feature for a query point is computed by interpolating local codes with their positional weights, which are computed using the distances relative to this query point.
In contrast with previous local DIF methods \cite{Chibane2020ImplicitFI,Chen_2021_ICCV,Peng2020ECCV,Takikawa2021NeuralGL}, which store the local codes in discrete and regular grids, the positions of local codes used in our method are continuous and flexible.
Specially, our code positions can be dynamically optimized, where the position vectors is learnable and can be updated by back-propagation and gradient descent. Therefore, we name our method \emph{Dynamic Code Cloud} (DCC), as shown in \cref{fig:intro} and \cref{fig:optimization_process}.
In addition, we design a novel \emph{Code Position} (CP) loss to optimize the positions of local codes, where more local codes are heuristically guided to distribute around complex geometric details. With the help of CP loss, our method can represent 3D shapes more efficiently with a small amount of local codes. As a result, when using the same number of local codes as previous methods, our method achieves better results and reconstructs highly detailed geometry of 3D shapes.
Our main contributions are summarized as follows.
\begin{itemize}
    \item We propose a novel DCC-DIF to learn deep implicit function of 3D shapes. Compared with previous methods which limit the local codes at discrete and regular grid points, the code positions in DCC-DIF are continuous and can be dynamically optimized, which improves the representation ability.
    \item We further propose a novel code position (CP) loss to optimize the positions of local codes, so that more local codes are distributed around complex geometric details. With the help of CP loss, our DCC-DIF can represent 3D shapes with higher quality and efficiency.
    \item Compared to previous methods, our method can achieve better accuracy with fewer number of local codes when reconstructing highly detailed geometry of 3D shapes. Experiments demonstrate our DCC-DIF can achieve the state-of-the-art results.
\end{itemize}

\section{Related Work}
\label{sec:related_work}

The recent emerged implicit representation research has drawn a growing attention in 3D computer vision. Compared with the previous explicit representation based methods (e.g. voxel \cite{Maturana2015VoxNetA3}, mesh \cite{Kato2018Neural3M} and point cloud \cite{Qi2017PointNetDL,Qi2017PointNetDH}), the implicit methods can represent 3D shapes at arbitrary resolution. In this paper, we take the advantage of implicit 3D representation and focus on the task of reconstructing high-quality 3D signed distance functions (SDF). Relevant work of this area can be roughly divided into two categories, which are global DIF methods and the local DIF methods.

\paragraph{Global DIF methods.}
For the previous global DIF methods, a common practice is to take the benefit from the traditional implicit representation methods, and integrate them into the deep learning based framework.
Typical method like DeepSDF \cite{Park2019DeepSDFLC} implicitly represents a 3D shape by its zero-level set. It optimizes a global latent code for each 3D shape, and predicts signed distances to the shape surface for sampled points using a decoder. On the other hand, OccNet \cite{Mescheder2019OccupancyNL} represents the surface of a shape by decision boundary of a deep neural network. It leverages an auto-encoder framework to predict inside/outside values for sampled points in 3D space. Following pioneers, some recent emerged methods have further improved the frontier of DIF research. For example, Duan et al. \cite{duan2020curriculum} learn DIF by a curriculum strategy, and Zheng et al. \cite{Zheng2021DeepIT} develop the deformation based method to predict DIF from shape templates. However, the problem is that these methods are still hard to  preserve the details of local surfaces, due to the fixed dimensionality of single global code.

\paragraph{Local DIF methods.} To overcome the limitation of global DIF methods, the local DIF methods have been developed to learn 3D shapes at more detailed geometric level.
For example, LIG and DeepLS \cite{Jiang2020LocalIG,Chabra2020DeepLS} divide shapes/scenes into volumes, where each volume is independently reconstructed with an assigned latent code. After that, all volumes are combined together to get the final reconstruction. SIF, LDIF and PatchNets \cite{Genova2019LearningST,Genova2020LocalDI,Tretschk2020PatchNetsPG} decompose shapes into local patches and represent each patch using a latent code. More recently, IMLSNets \cite{Liu2021DeepIM} adapts implicit moving least squares surface formulation for learning based method. ConvONet \cite{Peng2020ECCV} builds 3D grids or 2D grids on each axes plane and stores a latent code in each grid point. Then given a query point in 3D space, the positions of this point and its neighboring grid points are leveraged to interpolate the stored latent codes into a vector. IF-Nets \cite{Chibane2020ImplicitFI} constructs hierarchical latent grids with different resolutions to capture local geometric information of different scales. Similarly, MDIF \cite{Chen_2021_ICCV} also constructs hierarchical latent grids. Moreover, it sets top level latent grids to be a global latent code and connects latent codes between different levels by transposed convolutions \cite{Kim2018MovingTC} and concatenations, which enables it to do global operations like completion. However, a high grid resolution is required for latent grids based methods to achieve good results, which leads to cubic growth of number of latent codes. NGLOD \cite{Takikawa2021NeuralGL} leverages the sparse octree instead of uniform grids to reduce the number of latent codes, and achieves state-of-the-art reconstruction accuracy. ACORN \cite{Martel2021ACORNAC} also adopts the octree, and the structure of octree can be adjusted during optimization. However, this step is non-differentiable and needs to solve an integer linear program problem. These grids or octree-based methods constrain that latent codes are located at discrete and regular positions like grid points or octree nodes. And code positions are static \cite{Takikawa2021NeuralGL,Chen_2021_ICCV,Chibane2020ImplicitFI,Peng2020ECCV} or non-trivial to be optimized \cite{Martel2021ACORNAC}. In contrast, positions of latent codes in our method are flexible and continuous. And we dynamically optimize code positions by back-propagation and gradient descent, which is more efficient than solving an integer linear program problem.

\begin{figure}[t]
    \centering
    \includegraphics[scale=0.62]{images/network.pdf}
    \caption{Illustration comparison between architecture of our method and other methods. We show the overall architecture of global DIF methods in (a), local DIF methods in (b), and our method in (c).}
    \label{fig:network}
\end{figure}

\section{Method}
\label{sec:method}

Our goal is to design a flexible 3D shape representation which can efficiently fit a single shape or reconstruct 3D datasets with high quality. \cref{fig:network} illustrates the overall architecture of our method and differences among global, local and our method. To represent a 3D shape, as shown in \cref{fig:network}(a), global methods leverage a single global latent code, and optimize the latent code by minimizing errors between outputs and the ground truth. Local methods replace the global latent code with a set of local latent codes, as shown in \cref{fig:network}(b). In our method, as shown in \cref{fig:network}(c), we explicitly assign a position vector to each local code, which indicates the $(x,y,z)$ coordinates of corresponding local code in 3D space, and a novel CP loss is further proposed to optimize position vectors.
In this section, we firstly introduce background knowledge about neural signed distance functions (SDF) in \cref{sec:deep_implicit_function}. Then the design of our method is explained in detail in \cref{sec:dynamic_code_cloud}. Next, we present our novel CP loss in \cref{sec:code_position_loss}. And lastly we describe the training process in \cref{sec:training}.

\subsection{Deep Implicit Function}
\label{sec:deep_implicit_function}

There are different approaches for deep implicit functions to represent surfaces. Mainstream approaches include occupancy functions \cite{Mescheder2019OccupancyNL} and signed distance functions (SDF) \cite{Park2019DeepSDFLC}.
In this paper, we follow the paradigm of SDF. SDF can be formulated as $f:\mathbb{R}^3 \rightarrow \mathbb{R}$, and $d = f(\mathbf{x})$ is the shortest signed distance from a query point $\mathbf{x}$ to the surface of underlying 3D shape. And the sign determines whether it is inside or outside the 3D shape. Thus, the surface of a 3D shape is the zero level-set of SDF,  denoted as
\begin{equation}
    \mathcal{S} = \{ \mathbf{x} \in \mathbb{R}^3 \mid f(\mathbf{x}) = 0 \}.
    \label{eq:SDF}
\end{equation}

Learning-based SDF usually encodes a 3D shape into a single global latant code or local latent codes. And a multi-layer perceptron is leveraged as the decoder, which takes latent codes and query points as input and predicts signed distances. Using sampled query points as training data and the corresponding ground truth signed distances as supervision, the latent codes and network parameters are optimized by minimizing the errors between predicted and ground truth signed distances. After SDF is learned, the Marching Cubes algorithm \cite{Lorensen1987MarchingCA} is usually applied to extract an isosurface and outputs a mesh for rendering or visualization.

\subsection{Dynamic Code Cloud}
\label{sec:dynamic_code_cloud}

In our method, we leverage an auto-decoder framework \cite{Tan1995ReducingDD}. And we learn DIF using a novel Dynamic Code Cloud (DCC-DIF). In \cref{fig:network}, we show the overall architecture of our method and the differences between our method and the compared methods. We represent a 3D shape using a set of latent codes and the corresponding code positions, which are denoted by a matrix $\mathbf{C} \in \mathbb{R}^{n \times m}$ and a matrix $\mathbf{P} \in \mathbb{R}^{n \times 3}$, respectively. Here, $n$ indicates the number of local codes we used, and $m$ indicates the dimension of local codes. Each row of $\mathbf{C}$ is a latent code $\mathbf{c}_i \in \mathbb{R}^m$, and each row of $\mathbf{P}$ is a position vector $\mathbf{p}_i \in \mathbb{R}^3$, where $1 \leq i \leq n$. Each $\mathbf{c}_i$ and $\mathbf{p}_i$ form a pair and $\mathbf{p}_i$ indicates the $(x,y,z)$ coordinate of corresponding latent code in 3D space.

Given a batch of query points $\mathbf{Q} \in \mathbb{R}^{B \times 3}$ in 3D space with the batch size $B$, in which each row is a query point $\mathbf{q}_{j} \in \mathbb{R}^3 \ (1 \leq j \leq B)$, we firstly obtain a distance matrix $\mathcal{D} \in \mathbb{R}^{B \times n}$ between query points $\mathbf{Q}$ and code positions $\mathbf{P}$, where each element of $\mathcal{D}_{ji}$ is computed as
\begin{equation}
    \mathcal{D}_{ji} = || \mathbf{q}_j - \mathbf{p}_i ||_2.
    \label{eq:distance}
\end{equation}
Then a weight matrix $\mathcal{W}$ is obtained based on $\mathcal{D}$, each element of which is computed as
\begin{equation}
    \mathcal{W}_{ji} = \frac{\mathcal{W}_{ji}^\prime}{\sum_{k=1}^{n}\mathcal{W}^\prime_{jk}},
    \label{eq:weight}
\end{equation}
where
\begin{equation}
    \mathcal{W}_{ji}^\prime = \frac{1}{\mathcal{D}_{ji}^3}.
    \label{eq:weight_}
\end{equation}

As we expect that the local codes far from the query point have small weights, we take the reciprocal of the cubic distance as the weight in \cref{eq:weight_}. Then we normalize the weights in \cref{eq:weight}.
After that, the matrix multiplication is applied to the weight matrix $\mathcal{W}$ and latent codes $\mathbf{C}$, which produces a matrix $\mathbf{Z}\in \mathbb{R}^{B \times m}$. Intuitively, each row of $\mathbf{Z}$ is a vector $\mathbf{z}_j \in \mathbb{R}^m$ for query point $\mathbf{q}_j$, which is interpolated from latent codes $\mathbf{C}$ based on distances. \cref{fig:interpolation} shows the interpolation process of our method, and the difference with traditional grid-based methods (e.g \cite{Peng2020ECCV}). At last, like most methods do, we leverage a multi-layer perceptron as the decoder. We concatenate $\mathbf{Q}$ and $\mathbf{Z}$ together as the input of decoder and get an output vector $\mathbf{d} \in \mathbb{R}^B$, in which each element $d_{j}$ is a predicted signed distance for $\mathbf{q}_j$.

As $\mathbf{p}_i$ can be any $(x,y,z)$ coordinates in the bounding box, our method is a more flexible representation whose code positions are continuous, while latent codes in other methods are usually constrained at regular and discrete positions. Moreover, we can dynamically update code positions during optimization directly by back-propagation and gradient descent, as there is no essential difference between $\mathbf{p}_i$ and other trainable parameters in the network.

\begin{figure}[t]
    \centering
    \includegraphics[scale=0.4]{images/interpolation.pdf}
    \caption{Illustration of interpolation. (a) Given a query point $\mathbf{q}_j$, previous grid-based methods \cite{Chibane2020ImplicitFI,Chen_2021_ICCV,Peng2020ECCV,Takikawa2021NeuralGL} leverage the position of $\mathbf{q}_j$ and its neighboring grid points to interpolate stored latent codes into a vector $\mathbf{z}_j$, where a trilinear interpolation is usually applied in these methods. (b) In our method, each latent code $\mathbf{c}_i$ is explicitly associated with a position vector $\mathbf{p}_i$ to indicate its position in 3D space, where the positions of both $\mathbf{q}_j$ and $\mathbf{p}_i$ are used to compute the weight $\mathcal{W}_{ji}$ which is used for interpolation.}
    \label{fig:interpolation}
\end{figure}

\subsection{Code Position Loss}
\label{sec:code_position_loss}

To fully utilize the latent codes, we further propose a novel Code Position (CP) loss. Our motivation is to guide more latent codes to be distributed near the regions with complex geometric details.

As shown in \cref{fig:code_position_loss}, we first define the prediction error of each query point $\mathbf{q}_j$ as $e_j$, i.e.
\begin{equation}
    e_j = \mid d_j - \bar{d}_j \mid,
    \label{eq:prediction_error}
\end{equation}
where $d_j$ is the predicted signed distance for query point $\mathbf{q}_j$ by our method and $\bar{d}_j$ is the ground truth. Intuitively, larger $e_j$ means it is more difficult to reconstruct the local region near the corresponding query point, which further indicates that complex geometries details may exist on this region. As we expect the latent codes to get closer to the query points with higher prediction errors, we assume that there is a certain attraction force between query points and latent codes. Moreover, such attraction force should be directly proportional to $e_j$ and decay with the growth of distances between latent codes and query points. As elements of the weight matrix $\mathcal{W}$ in \cref{sec:dynamic_code_cloud} have the property of decreasing with increasing distances, we use $\mathcal{W}$ as a decay of attraction force. Therefore, we define the \emph{attraction matrix} $\mathcal{A} \in \mathbb{R}^{B \times n}$ between query points $\mathbf{Q}$ and latent codes $\mathbf{C}$ as
\begin{equation}
    \mathcal{A}_{ji} = e_j * \mathcal{W}_{ji}.
    \label{eq:attraction}
\end{equation}
Then, we apply element-wise multiplication between the attraction matrix $\mathcal{A}$ and the distance matrix $\mathcal{D}$, and take the average of all elements as the final Code Position Loss $L_{CP}$, denoted by
\begin{equation}
    L_{CP} = \frac{1}{B*n}\sum_{j=1}^B \sum_{i=1}^n \mathcal{A}_{ji} * \mathcal{D}_{ji}.
    \label{eq:code_position_loss}
\end{equation}
Note that we cut off gradient back propagation to $\mathcal{A}$. Thus, the distances between latent codes and query points are optimized based on the attraction, leading to further update of code positions $\mathbf{P}$.

With the guidance of CP loss, more latent codes will be distributed near the regions with complex geometric details, which enables our method to capture fine local geometric details. On the other hand, since there are fewer latent codes around simple geometric regions, this allows our method to represent a 3D shape with a small amount of latent codes, compared with previous grid-based methods \cite{Chibane2020ImplicitFI,Chen_2021_ICCV,Peng2020ECCV,Takikawa2021NeuralGL}. Although other methods can also assign more latent codes to complex regions, such as by refining the depth of octree \cite{Martel2021ACORNAC}, our method is more flexible and effective since our code positions are continuous. Furthermore, we optimize the code positions directly by back-propagation and gradient descent, which is more efficient.

\begin{figure}[t]
    \centering
    \includegraphics[scale=0.8]{images/code_position_loss.pdf}
    \caption{\textbf{Code Position Loss.} We assume query points are attractive to latent codes. And we define attractions based on prediction errors of query points. As elements of the weight matrix $\mathcal{W}$ in \cref{sec:dynamic_code_cloud} have the property of decreasing with increasing distances, we use $\mathcal{W}$ as a decay of attractions. Lastly, we apply the element-wise multiplication to the distance matrix and the attraction matrix, and take the average of all values as the final CP loss.}
    \label{fig:code_position_loss}
\end{figure}

\subsection{Training}
\label{sec:training}

We train our network in an auto-decoder \cite{Tan1995ReducingDD} framework. To optimize latent codes and code positions, sampled query points and their ground truth signed distances should be provided as training data. For fair comparisons, we adopt different sampling schemes in different experiments to keep the same setting with compared methods.

To optimize latent codes, we minimize the mean squared error (MSE) between predicted signed distances $d_j$ and ground truth $\bar{d}_j$, denoted as
\begin{equation}
    L_{MSE} = \frac{1}{B}\sum_{j=1}^B||d_j - \bar{d}_j||^2.
    \label{eq:l2_loss}
\end{equation}
We also minimize the CP loss to optimize code positions. As a result, our final loss $L$ is defined as
\begin{equation}
    L = L_{MSE} + \lambda L_{CP},
    \label{eq:loss}
\end{equation}
where $\lambda$ is a parameter to balance $L_{MSE}$ and $L_{CP}$.

\section{Experiments}
\label{sec:experiments}

In this section, we conduct experiments to evaluate the performance of DCC-DIF. Specifically, in \cref{sec:single_shape_fitting}, we demonstrate the ability of DCC-DIF for describing geometric details through the single shape fitting task. In \cref{sec:reconstructing_3d_datasets}, the ability of DCC-DIF for learning shape priors and generalizing to new objects is further evaluated, by applying DCC-DIF to reconstruct unseen shapes. In \cref{sec:ablation_study}, we validate the effects of each part of DCC-DIF. Limited by space, more discussions can be found in appendix.

\subsection{Single Shape Fitting}
\label{sec:single_shape_fitting}

We apply DCC-DIF to single shape fitting task to evaluate the ability of describing geometric details. In this experiment, the latest NGLOD \cite{Takikawa2021NeuralGL} is typically selected for our comparison, which is an octree based method and achieves state-of-the-art results in single shape fitting.

\begin{table*}[t]
  \centering
  \resizebox{14cm}{!}{
  \begin{tabular}{@{}l||cccccccc@{}}
    \toprule
    \multirow{2}*{Metrics} & \multicolumn{8}{c}{Methods} \\
    & DeepSDF \cite{Park2019DeepSDFLC} & FFN \cite{tancik2020fourfeat} & SIREN \cite{sitzmann2019siren} & NI \cite{Davies2020OverfitNN} & NGLOD3 \cite{Takikawa2021NeuralGL} & NGLOD4 \cite{Takikawa2021NeuralGL} & NGLOD5 \cite{Takikawa2021NeuralGL} & Ours \\
    \midrule
    IoU $\uparrow$ & 96.8 & 97.7 & 95.1 & 96.0 & 99.0 & 99.3 & 99.4 & \textbf{99.5} \\
    CD $\downarrow$ & $-$ & $-$ & $-$ & $-$ & 3.69 & 3.59 & 3.57 & \textbf{3.55} \\

    \textit{\#Codes} & $-$ & $-$ & $-$ & $-$ & 5.7K/0.9K & 41.7K/3.7K & 316K/15K & 5.6K \\

    \textit{\#Param.} & 1.8M & 527K & 264K & 7.6K & 4.7K & 4.7K & 4.7K &  4.7K \\
    \bottomrule
  \end{tabular}
  }
  \caption{Results on Thingi32 \cite{Zhou2016Thingi10KAD}. We compare the reconstruction quality and efficiency between our method and others (NGLOD \cite{Takikawa2021NeuralGL} with LODs equals to 3, 4 and 5 is denoted as NGLOD3, NGLOD4 and NGLOD5, respectively). For quality, we use the IoU and CD as metrics. For efficiency, we use \textit{\#Codes} and \textit{\#Param.} as metrics. The \textit{\#Codes} indicates the number of latent codes used in each method. And the \textit{\#Param.} means the number of network parameters used for a single distance query. In the \textit{\#Codes} row, for each LODs of NGLOD \cite{Takikawa2021NeuralGL}, we present the average number of latent codes before/after removing the empty nodes of octree. Our method simultaneously achieves the best quality and a high efficiency. }
  \label{tab:single_shape_fitting_results}
\end{table*}

\paragraph{Network settings.} For fair comparisons, we use the same settings with NGLOD \cite{Takikawa2021NeuralGL}. Specifically, we set the decoder to be a multi-layer perceptron with only one hidden layer, which is 128-dimensional and leverages a ReLU \cite{Glorot2011DeepSR} activation function. And we set $m$ to 32, which is the same with NGLOD \cite{Takikawa2021NeuralGL}. NGLOD \cite{Takikawa2021NeuralGL} has different numbers of latent codes for different shapes after removing the empty nodes of octree that contain no surface. We simply use $n = 5600$ latent codes for each shape, which is approximately equal to the number of latent codes used in NGLOD \cite{Takikawa2021NeuralGL} with 3 LODs (before removing the empty nodes of octree). As prediction errors $e_j$ and distances $\mathcal{D}_{ji}$ tend to be small, we choose $ \lambda $ as a relatively large number to balance $L_{MSE}$ and $L_{CP}$, where $\lambda$ is typically set to $7000$ in this experiment.

\paragraph{Data and metrics.} Following NGLOD \cite{Takikawa2021NeuralGL}, we also select the same 32 shapes from Thingi10K \cite{Zhou2016Thingi10KAD} and follow the same preprocessing practice as NGLOD. Specifically, following DualSDF \cite{Hao2020DualSDFSS}, we normalize the meshes and remove internal triangles. And we sign the distances with ray stabbing \cite{Nooruddin2003SimplificationAR}. We adopt the same schemes with NGLOD \cite{Takikawa2021NeuralGL} to obtain a point set for training. Specifically, we sample 500K points at each epoch, in which 100K points are sampled uniformly in the bounding box, 200K points from the object surface and the other points are sampled near the object surface. For metrics, we evaluate results using Chamfer Distance (CD) and Intersection over Union (IoU). Following NGLOD \cite{Takikawa2021NeuralGL}, we also pay attention to the efficiency of storage and computation. Here, the number of latent codes used in each method is denoted as \textit{\#Codes}, which roughly shows the storage cost. The number of network parameters used for a single distance query is denoted as \textit{\#Param.}, which roughly indicates the computation cost.

\begin{figure}
    \centering
    \includegraphics[scale=0.43]{images/thingi32.pdf}
    \caption{Visualization results on Thingi32 \cite{Zhou2016Thingi10KAD}. We visually compare with NGLOD \cite{Takikawa2021NeuralGL} with LODs that equals to 3 and 5, as denoted by NGLOD3 and NGLOD5, respectively. We achieve better results than NGLOD3, especially for local geometric details. Although NGLOD5 can achieve the similar results with our method, our method has a small number of latent codes than NGLOD5.}
    \label{fig:thingi32}
\end{figure}

\begin{table*}[t]
  \centering
  \resizebox{16cm}{!}{
  \begin{tabular}{@{}l||cccccc||cccccc@{}}
    \toprule
    \multirow{2}*{Category} & \multicolumn{6}{c}{Chamfer($\downarrow$)} & \multicolumn{6}{c}{F-Score($\uparrow$,\%)} \\
    & Occ. \cite{Mescheder2019OccupancyNL} & SIF \cite{Genova2019LearningST} & LDIF \cite{Genova2020LocalDI} & IF. \cite{Chibane2020ImplicitFI} & MDIF \cite{Chen_2021_ICCV} & Ours & Occ. \cite{Mescheder2019OccupancyNL} & SIF \cite{Genova2019LearningST} & LDIF \cite{Genova2020LocalDI} & IF. \cite{Chibane2020ImplicitFI} & MDIF \cite{Chen_2021_ICCV} & Ours \\
    \midrule
    airplane & 0.16 & 0.44 & 0.10 & 0.52 & 0.028 & \textbf{0.011} & 87.8 & 71.4 & 96.9 & 94.4 & 98.6 & \textbf{99.7} \\
    bench & 0.24 & 0.82 & 0.17 & 0.31 & 0.052 & \textbf{0.017} & 87.5 & 58.4 & 94.8 & 92.6 & 96.0 & \textbf{99.5} \\
    cabinet & 0.41 & 1.10 & 0.33 & 0.11 & \textbf{0.051} & 0.131 & 86.0 & 59.3 & 92.0 & 93.0 & \textbf{96.6} & 96.4 \\
    car & 0.61 & 1.08 & 0.28 & 0.30 & \textbf{0.088} & 0.218 & 77.5 & 56.6 & 87.2 & 87.4 & \textbf{93.0} & 92.7 \\
    chair & 0.44 & 1.54 & 0.34 & 0.10 & \textbf{0.035} & 0.037 & 77.2 & 42.4 & 90.9 & 94.5 & 97.6 & \textbf{99.1} \\
    display & 0.34 & 0.97 & 0.28 & 0.07 & \textbf{0.019} & 0.028 & 82.1 & 56.3 & 94.8 & 96.1 & 98.7 & \textbf{99.4} \\
    lamp & 1.67 & 3.42 & 1.80 & 1.17 & 0.795 & \textbf{0.327} & 62.7 & 35.0 & 84.0 & 89.1 & 93.5 & \textbf{97.3} \\
    rifle & 0.19 & 0.42 & 0.09 & 1.07 & 0.057 & \textbf{0.007} & 86.2 & 70.0 & 97.3 & 93.5 & 96.9 & \textbf{99.9} \\
    sofa & 0.30 & 0.80 & 0.35 & 0.13 & 0.037 & \textbf{0.036} & 85.9 & 55.2 & 92.8 & 92.5 & 98.4 & \textbf{99.1} \\
    speaker & 1.01 & 1.99 & 0.68 & 0.14 & \textbf{0.044} & 0.146 & 74.7 & 47.4 & 84.3 & 90.2 & \textbf{97.3} & 96.1 \\
    table & 0.44 & 1.57 & 0.56 & 0.17 & 0.046 & \textbf{0.029} & 84.9 & 55.7 & 92.4 & 93.4 & 97.6 & \textbf{99.3} \\
    telephone & 0.13 & 0.39 & 0.08 & 0.08 & \textbf{0.010} & 0.027 & 94.8 & 81.8 & 98.1 & 98.8 & \textbf{99.6} & 99.3 \\
    watercraft & 0.41 & 0.78 & 0.20 & 0.90 & 0.067 & \textbf{0.042} & 77.3 & 54.2 & 93.2 & 92.7 & 97.2 & \textbf{98.3} \\
    \midrule
    mean & 0.49 & 1.18 & 0.40 & 0.39 & 0.102 & \textbf{0.081} & 81.9 & 59.0 & 92.2 & 92.9 & 97.0 & \textbf{98.2} \\
    \bottomrule
  \end{tabular}
  }
  \caption{Results on ShapeNet \cite{Chang2015ShapeNetAI}. We use the Chamfer distance (CD) and F-Score to evaluate the reconstruction results of our and compared methods. Our method achieves the lowest mean CD and the highest mean F-Score, outperforming all other methods.}
  \label{tab:shapenet_results}
\end{table*}

\paragraph{Results and analyses.}

\cref{tab:single_shape_fitting_results} shows the result comparison of our method and other methods, including DeepSDF \cite{Park2019DeepSDFLC}, FFN \cite{tancik2020fourfeat}, SIREN \cite{sitzmann2019siren}, NI \cite{Davies2020OverfitNN} and NGLOD \cite{Takikawa2021NeuralGL}. Specially, the LODs of NGLOD are selected as 3, 4 and 5, denoted as NGLOD3, NGLOD4 and NGLOD5, respectively. As we have the exactly same experiment settings with NGLOD \cite{Takikawa2021NeuralGL}, some results in the table directly come from it. Since the CD can be influenced by some factors such as the number of surface points, we recompute the CD by ourselves. The results in \cref{tab:single_shape_fitting_results} show that our method achieves the highest IoU and lowest CD, which are beyond other methods. We show the average number of latent codes used by NGLOD \cite{Takikawa2021NeuralGL} before/after removing the empty nodes of octree. It is worth noting that, compared with NGLOD5, our method leverages a small number of latent codes, but still achieves slightly better results. This demonstrates that our method can represent 3D shapes more efficiently. Our method also has advantages in storage and computation efficiency. As visualized in \cref{fig:thingi32}, our method achieves the similar reconstruction quality compared with NGLOD5, while using a small number of latent codes. Compared with NGLOD3, our method achieves better reconstruction quality, especially for local geometric details.

\subsection{Reconstructing 3D Datasets}
\label{sec:reconstructing_3d_datasets}

We conduct an experiment to reconstruct 3D datasets using our method. Specifically, we optimize the latent codes, code positions and decoder parameters in the training phase. During inference, we fix decoder parameters and only optimize the latent codes and code positions on unseen shapes. This experiment shows the ability of our method to learn shape priors and generalize to new objects.

\paragraph{Network settings.} We leverage a decoder with the same structure as IM-Net \cite{Chen2019LearningIF}, which is a fully-connected network with connections between layers. We set $m=32$ and $n=1376$, thus we have the same number of parameters in latent codes with MDIF \cite{Chen_2021_ICCV}. To balance $L_{MSE}$ and $L_{CP}$, we set $\lambda = 3000$ in this experiment.

\paragraph{Data and metrics.} We use a subset of 13 categories in ShapeNet \cite{Chang2015ShapeNetAI} and divide the dataset with train/test splits from 3D-R$^2$N$^2$ \cite{Choy20163DR2N2AU}. And we generate watertight meshes with tools from OccNet \cite{Mescheder2019OccupancyNL}. In this experiment, we sample a point set from each shape, and use the same point set for all epochs during training. Each point set contains 200K samples where half of the points comes from uniform sampling and the others are sampled near the object surface. We use Chamfer L2 distance and F-Score as the metrics, which have the identical settings with LDIF and MDIF \cite{Genova2020LocalDI,Chen_2021_ICCV}.

\paragraph{Results and analyses.}

The results are shown in \cref{tab:shapenet_results}. As we keep exactly the same experiment settings with MDIF \cite{Chen_2021_ICCV}, some results in the table directly come from it. Our method surpasses all other methods both on the mean CD and the mean F-Score, which demonstrates the ability of our method to learn shape priors and generalize to new objects. Among different categories of ShapeNet \cite{Chang2015ShapeNetAI}, there are great differences in difficulty of reconstruction. Shapes in some categories tend to be complex and various, such as lamp and rifle. In contrast, shapes in some other categories are relatively simple and similar to each other, such as speaker and display. From \cref{tab:shapenet_results}, we find that our method has great advantages in reconstructing complex and various shapes. \cref{fig:shapenet} visually shows the quality of our reconstruction on ShapeNet \cite{Chang2015ShapeNetAI}, compared with IF-Net \cite{Chibane2020ImplicitFI}. Our method achieves better reconstruction results, especially in local regions with thin strips and holes. This demonstrates that our method has a ability to represent complex shapes and capture fine local geometry details.

\begin{figure}
    \centering
    \includegraphics[scale=0.42]{images/shapenet.pdf}
    \caption{Visualization on ShapeNet \cite{Chang2015ShapeNetAI}. Compared to IF-Net \cite{Chibane2020ImplicitFI}, our method achieves better reconstruction quality in local regions with complex geometric details, such as thin strips and holes.}
    \label{fig:shapenet}
\end{figure}

\subsection{Ablation Study}
\label{sec:ablation_study}

Among existing DIF methods, grid and octree based methods achieve good performance in both single shape fitting and 3D datasets reconstruction. Compared with these methods, there are three differences in DCC-DIF, including interpolation process, the novel position vectors and CP loss. To evaluate the effects of each difference, we conduct ablation studies on display and watercraft categories from ShapeNet \cite{Chang2015ShapeNetAI}, where display category contains relatively simple shapes and watercraft category tend to be complex. We keep other experiment settings the same as \cref{sec:reconstructing_3d_datasets}.

To evaluate the influence of interpolation process, we design two variations of DCC-DIF. The first variation fixes all latent codes to be located at grid points and applies trilinear interpolation. The second variation also fixes latent codes at grid points but leverages distance-based interpolation, as described in \cref{sec:dynamic_code_cloud}. As positions of latent codes are fixed, we remove the CP loss from both variations. As shown in \cref{tab:ablation_interpolation}, the variation using trilinear interpolation achieves better results. It demonstrates that the performance of our DCC-DIF does not benefit from the new interpolation algorithm, but from our proposed position vectors and CP loss.

\begin{table}
  \centering
  \resizebox{5cm}{!}{
  \begin{tabular}{@{}l||cc||cc@{}}
    \toprule
    \multirow{2}*{Category} & \multicolumn{2}{c}{Chamfer($\downarrow$)} & \multicolumn{2}{c}{F-Score($\uparrow$,\%)} \\
    & Trilinear & Ours & Trilinear & Ours \\
    \midrule
    display & 0.038 & 0.045 & 99.2 & 98.8 \\
    watercraft & 0.090 & 0.108 & 97.2 & 96.3 \\
    \bottomrule
  \end{tabular}
  }
  \caption{Comparison of interpolation process.}
  \label{tab:ablation_interpolation}
\end{table}

Next, we validate the effects of our proposed position vectors and CP loss. As a baseline, we remove the CP loss from our full version pipeline and fix all latent codes at grid points. Then we evaluate the benefit of position vectors and CP loss respectively by two variations of our DCC-DIF. The first variation only adds the position vectors to the baseline and the second variation adds both position vectors and CP loss to baseline. Results are shown in \cref{tab:ablation_pv_cp}. We can find that both position vectors and the CP loss play a positive role in 3D shape representation, which supports our proposal. Additionally, the CP loss shows more significant effects with complex shapes, which is consistent with our design to guide more latent codes to be distributed around complex geometric details.

\begin{table}
  \centering
  \resizebox{8cm}{!}{
  \begin{tabular}{@{}l||ccc||ccc@{}}
    \toprule
    \multirow{2}*{Category} & \multicolumn{3}{c}{Chamfer($\downarrow$)} & \multicolumn{3}{c}{F-Score($\uparrow$,\%)} \\
    & baseline & p.v. & p.v. + CP & baseline & p.v. & p.v. + CP \\
    \midrule
    display & 0.045 & 0.033 & 0.028 & 98.8 & 99.3 & 99.4 \\
    watercraft & 0.108 & 0.081 & 0.042 & 96.3 & 97.8 & 98.3 \\
    \bottomrule
  \end{tabular}
  }
  \caption{Ablation of position vectors and CP loss. The 'p.v.' denotes position vectors.}
  \label{tab:ablation_pv_cp}
\end{table}

\section{Conclusion and Limitation}
\label{sec:conclusion}

In this paper, we introduce a novel DCC-DIF to learn deep implicit functions for 3D shapes. Among existing DIF methods, the best results are achieved by grids or octree based methods. However, the latent codes in these methods are constrained to be located at discrete and regular positions, and the code positions are difficult to be optimized. In contrast, the code positions in our DCC-DIF are continuous and flexible by explicitly assigning a position vector to each latent code. We further propose a novel CP loss to optimize the positions of latent codes, so that more latent codes are distributed around complex geometric details. In experiments, our method outperforms other methods and achieves state-of-the-art results, which demonstrates its performance and efficiency. The ablation studies show effects of each part of our design, which supports our proposal.

Some limitations exist in our DCC-DIF, which are also the directions of improvement in our future work. First, the current DCC-DIF is unable to represent different levels of details like NGLOD\cite{Takikawa2021NeuralGL}. To address this problem, we plan to design a hierarchical network, in which each level leverages a DCC-DIF and the number of latent codes growth along with the increase of level. Another limitation is that current DCC-DIF is unsuited to global operations such as completion \cite{Dai2017ShapeCU}.
Inspired by MDIF \cite{Chen_2021_ICCV}, we can further set the first level of above-mentioned hierarchical DCC-DIF to be a single global latent code and design a novel module for information exchange between global and local codes.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


Hey Journey Group members! This Saturday was sadly our last committee meeting I'm honestly going to miss the awesome check-ins with the food. The one good thing about committee meetings ending is that we finally get to start the Arts Experience-yay! But that is not all that happened this Saturday we also had the Winter Ball-yay! It was so much fun and there was a lot of food there too. It was cool because two girls from my school, in my grade, actually showed up and I wasn't even the one to invite them! I guess they just sensed the awesomeness coming from that building!


Cheese, charcuterie and fruits boards make the ultimate party treat. Last Sunday my dad celebrated his 60th birthday; and since he's a real fan of cheese, I decided to make him my best creation to date. To me it's really about that, letting my creativity flow with an array of melting cheeses and sugary fruits. Add in the best charcuterie in town and you've got yourself a colorful taste bud explosion. Or at least I think so, by the time I arrived in front of the buffet the whole thing vanished. Cheeses that I spent so much time on choosing gone in a second. Two whole hours of my life grocery shopping disappearing in two minutes. Comforting yet sad.
Cheese platters (or Halloween themed ones) are what dreams are made of. If you're part of Recettes sans prise de tête you already know the drill. I feel like it's kind of a light dinner; I mean light compared to what we're culturally used to eat : tons of meat. And there was that at the buffet, Moroccan tanjia and roasted chicken, but I was in charge of the salads, cheese boards and birthday cakes. The things your girl's about.
So we had a planning for our morning grocery shopping, first stop : Amsellem. The renowned local shop that sells kosher charcuterie and meat. My parents and their parents used to shop there, it's still the same store it's always been, and the prices never went up. I did my little usual selection, and picked up some spiced olives and tasty chorizo.
Olive bread, salami, smoked chicken and cumin gouda.
After that we went to the Maarif market where my husband reminded me that I wasn't here to take pictures. I don't think he's ever met me. Shopped for romaine, colored cherry tomatoes, baby spinach, avocados and blackberries for my salad, and went on to my favorite shop of all.
The cheese shop ! Creamy, melty cheese that was just waiting for me to be picked up. I usually go for soft cheeses, I like a firm crust and a gooey inside, and I also went for some blue cheese because for whatever reason my dad is fond of it. My selection pleased the owner so much he offered us a whole fresh camembert he'd just received from France. He says buyers go more for hard cheeses, but whatever it is I was so glad I received something rewarding my taste. That's like the one thing you never get rewarded for. Who cares if I like soft cheeses ? Apparently this nice old man does.
When I came home, I threw in some green & red grapes, salty crackers, fresh berries and some cheese spread (I used Tartare ail & fines herbes), and voilà ! You've got yourself the perfect cheese board. Easy to make and so refreshing to eat.
Till next time guys <3 !


Your focal point is the majestic Mt. Kinabalu. The highest mountain between the Himalayas and the Snow Mountains of the New Guinea Island, this magnificent granite massif stands at 4,095.2M. Although the climb requires no mountaineering skills, it is by no means an easy trek and demands a certain level of physical fitness. The ascent takes you through an Upper Montane Forest which graduates to sparsely vegetated, corresponding to a higher altitude and harsher environment. Stop for a packed lunch along the trail and end your day at Laban Rata, the mid-way resthouse (3345m). Overnight accommodation is in dormitories.


Hallam, Nebraska, received some good news this week. Volkswind USA Inc., an international wind developer, applied for permits to build 54 wind turbines in Lancaster and Gage counties. According to a recent Lincoln Journal Star article, Volkswind says the wind farm would provide more than 200 jobs during construction, additional full-time operating jobs and about $700,000 in total property tax benefits annually to both counties. Volkswind is submitting proposals to Lincoln Electric System (LES), which is looking to add up to 100 megawatts of wind energy to their portfolio.
The Hallam area is well positioned for wind development. The town is home to Sheldon Station, an aging, coal-fired, Nebraska Public Power District (NPPD) power plant. Hallam's proximity to Lincoln and Omaha and access to transmission lines make it an excellent choice for renewable energy development.
While wind provides jobs, property tax benefits and payments to landowners, Sheldon Station contributes to air and water pollution in the Hallam area, preventing area families from enjoying clean air, water, hunting and fishing. Sheldon Station has 147 reported Clean Water Act violations, the fourth most in the nation, posing a real threat to the health and happiness of area families.
A recent NPPD study found that phasing out the Sheldon Station Coal Plant and increasing wind energy in the area will save customers money. We encourage NPPD and LES to take advantage of the economic, environmental and health benefits of wind. Volkswind's proposed wind farm is an important step from coal-to-clean energy.


Use PayUMoney as a payment option while placing your order to get Rs.50 flat discount on your order. Coupon code needs to be applied at the checkout.
Dominos pizza is a favourite food joint among kids and elders. Started in 1960 Dominos has served a millions of pizzas and are now just a click away. With all the restaurants going online Dominos pizza also could be ordered online. Browse online site at Dominos.co.in and select the pizza you need and order it. You will get the pizza in half an hour. You could choose a vegetarian pizza or non-vegetarian pizza, you can also customise your pizza with your favourite toppings and what else you could visualise your pizza on the screen called the pizza builder. Isn't that amazing, once you place your order you could view your pizza on the screen. You could also order pasta, veggies or garlic bread. The order could either be a pickup or home delivery which you need to specify while placing the order. To encourage the online customers they have Dominos coupons, weekly offers and exciting prizes for them.
The online restaurants or food joints are the future of hotel industry as there is no time for reaching the joints and placing an order in the queue and to wait for the order. Online orders from them will reach your place while you continue with your busy schedule or you could pick up your order as soon as you reach the place without waiting for your order. The orders are taken care efficiently so there is no mistake in the orders until they reach the customers correctly. Dominos ensure the order reaches the customer on time and do cross verification if you place the order for a first time. Another feature of ordering online is the customer can also know the position of their pizza from the time of order until the time of delivery. Dominos online pizza delivery is getting better day by day to serve their customers more efficiently. Scroll down to get all the latest on-going Dominos offers & Dominos deals or you can simply use the Dominos coupons, Dominos discount coupons & Dominos coupon codes to enjoy the additional savings for your purchases.


A joint conference with UNCITRAL addressing the upcoming Guide to the New York Convention and other contemporary topics regarding the Convention. This conference is given in celebration of the Institute's Centenary.
The Conference was introduced by Peter L. Michaelson, Chair of the New York Branch of the Chartered Institute with comments immediately thereafter by Charles Brown, President of the Chartered Institute. Following Mr. Brown's comments, the substantive session began with each panel being introduced in turn by Thomas Halket, Vice Chair of the Branch.
The Conference occurred across two video-linked venues: in New York City at The Association of the Bar of the City of New York, and in Vienna at the Vienna International Arbitration Center (VIAC).
Enforcement of Awards: Have contracting states been true to the letter and spirit of Article V of the Convention?
Have contracting states been true to the letter and spirit of Article V of the Convention?


text:
#ifndef SKUI_CSS_PROPERTY_LINEAR_GRADIENT_H
#define SKUI_CSS_PROPERTY_LINEAR_GRADIENT_H

#include "css/angle.h++"
#include "css/position.h++"

#include "css/property/background_gradient.h++"

#include <variant>

#include <cinttypes>

namespace skui::css
{
  struct linear_gradient : background_gradient<length>
  {
    std::variant<position, angle> direction; // direction or explicit angle [0,360] degrees
  };

  constexpr bool operator==(const linear_gradient& lhs, const linear_gradient& rhs)
  {
    return static_cast<const background_gradient<length>&>(lhs) == static_cast<const background_gradient<length>&>(rhs)
        && lhs.direction == rhs.direction;
  }
}

#endif

redpajama_set_name: RedPajamaGithub


text:
RIGHT MOOD
rightmood.com.br
facebook.com/rightmoodmusic
instagram.com/rightmoodmusic
soundcloud.com/rightmood
Fernando Cabral is the music producer behind Right Mood.
"I believe that electronic music for the dancefloors is much more than huge fills and drops, which are amazing music production techniques, by the way, but we can go further. Instead of defining patterns, and then obey those standards, just because is easier that way, we have to innovate, search for something new. New timbres, new arrangements, new sounds, whatever it is. But the final goal always has to remain the same: Make people dance. That's what Right Mood is all about."
Fernando started producing music in the late 90's and since then, he's been learning and improving his music production skills. The first tracks came out in 2006 and they received a positive feedback from the audience. In 2007, Fernando released a track called "The Balance" at the german label "FOEM". In 2008 he started to use the artist name "Right Mood" and in 2009, Fernando started running his own label called "Loud Shout Music", to release tracks from Right Mood and others electronic musicians. 2015 was the year he released the EP "Messed Up House" which is his debut in vinyl records. In 2017, Fernando was included by the legendary DJ Meme, on a list which has revealed the most talented house music brazilian producers.
LATEST TRACKSPOPULAR TRACKS
1234...5
Original Mix (6:16)
Go High
Right Mood, Voxo
TRANSA RECORDS
Voxo
Room of Mirrors
TrueLife Recordings
Thoughts Are Clear
Right Mood, Samantha Breschak
Reflective Music
Samantha Breschak
Million Dollars
Right Mood, Alex Holmes
Stordisco
Alex Holmes
Vinjay Remix (6:34)
Vinjay
Hiroki Esashika Remix (5:03)
Hiroki Esashika
Labdab


Geneva — Today (14/12), the International Organization for Migration (IOM) is releasing A Framework for Assisted Voluntary Return and Reintegration (AVRR) to guide policymakers and practitioners' work in this important field.
AVRR programmes help migrants who are unwilling or unable to stay in their host countries return to their countries of origin. IOM started implementing AVRR programmes in 1979 and has since then provided return and reintegration support to over 1.6 million people.
Throughout the years, AVRR concepts and practices have undergone major changes. AVRR programmes have progressively expanded beyond Europe (where they have long been in place) to become embedded in national policies across Africa, Asia and the Pacific, and the Western Balkans. The number of stakeholders involved in the organization of voluntary returns has grown, as has the interest among development players in supporting sustainable reintegration. Dangerous migration routes continue to place migrants in vulnerable situations, therefore strengthened protection standards are necessary in the context of return and reintegration.
In recognizing this evolution, and based on IOM's longstanding experience, the framework was designed to reaffirm the key tenets of assisted voluntary return and reintegration in an increasingly interconnected and interdependent world; to guide decision-makers and practitioners in the design and implementation of AVRR-related policies and programmes; and to propose a roadmap to address voluntary return and reintegration holistically, based on complementary efforts between migration management and development actors at the international, national and local levels.
A Framework for Assisted Voluntary Return and Reintegration marks an important milestone for the Organization and its partners with regards to AVRR engagement.
The framework is available in full here.
A Framework for Assisted Voluntary Return and Reintegration.


1/30/2019 · Learning how to do a handstand is one of the most important steps to becoming a good gymnast.Sooner or later, you'll be doing a handstand on just about every event, and learning a solid one will help you improve quickly in the sport.
8/24/2018 · A proper handstand actually starts to feel easy. That's because once you're balanced and aligned, it becomes uber efficient. Just as you don't exert yourself much if you stand straight with good posture, a good efficient handstand is the same way… it will soon start to feel effortless.
This is the 6 Weeks To Handstand Workout Guide. It tells you exactly which videos to do each day, and it even includes your optimal rest days for the fastest progress possible. To hit your Handstand with unwavering confidence, you need to have a plan! This Handstand course takes out the guesswork, and does the planning for you.
8/18/2010 · Thanks for the write up mate. I can currently hold a static handstand against the wall for about a minute but have been struggling building strength to actually do handstand pushups. Looks like negatives are the way to go! Cheers.
Fastest Way to Learn a Handstand (3 top tips for beginners) (self.bodyweightfitness) submitted 1 hour ago by eatsleephustle You might remember from this post where I shared my 12 months handstand transformation and the 3 things I learnt in the journey.
6 Easy Steps to Learn How to Handstand Walk See more. ironcompany.com. ... Variations to build your way up to handstands and handstand pushups. [Video] How to Do The Kettlebell Lunge Press Exercise Headstand Pushup (HSPU) progression routine from Crossfit London. Good step by step instructions on how to eventually get HSPU's.
You need to train the handstand pushups very often – this is the only way to get good at them. I recommend doing them 5-6 times per week. Rest. At least once per week take a day off completely from training. Do at least 3 sets. You need volume so the stress is enough for your muscles to develop. Alternate between repetition ranges.
4/24/2014 · Inversions are challenging and, yes, a little scary, but this sequence can help you learn freestanding Handstand (Adho Mukha Vrksasana)—as in, no wall, in the middle of a room as your practice progresses. Work with a partner who can spot whether your ankles, hips, and hands aren't aligned—common form mistakes that topple Handstand.
3/19/2016 · Prior to doing any type of handstand practice, you will need to strengthen your wrists. Out of all the joints the wrists are probably the least prepared for bigger loads in inversions. Especially because we use them so little throughout the day. Make sure you are able to do these wrist exercises prior to starting your handstand training. 2.
6/28/2011 · Because I am working on my Press to Handstand I have found myself checking out a lot of articles and videos about it.. The following is a list of things that helped me to understand the movement and identify common errors. I hope that will be of some help for you people who are also struggling with the press to handstand.
11/17/2014 · Today, he is sharing with us his process for achieving the perfect handstand!) The freestanding handstand is one of the coolest exercises out there. It displays strength, balance and coordination that only a few athletes have. However… most people don't understand how to properly learn how to do their first perfect freestanding handstand.
tips hand stand How to do a hand stand Learn how to do a handstand by Barbara Blanco Easy way on how to do a handstand by Barbara Blanco I will teach you how to do a handstand it will require lots of practice but you will accomplish your handstand.
7/8/2016 · If you aren't the lucky one, it will take a long time to finally master the handstand. You will learn the handstand by focusing on these two things: 1. General Mobility Work. Handstand isn't just a shoulder movement, it's a full body movement. This means that in order to be able to do a good handstand, you need to be strong and mobile everywhere.
I guess you mean to ask "What is the easiest way to learn a cartwheel?".. Some people, especially kids, are able to perform an approximate cartwheel by instinct. Older people often lose this ability and have problem approaching this basic pre-acrobatic movement.
It is very easy to follow. It is also very detailed about what to do, and what not to do. It convinced me that it is possible for me to learn to handstand.-Anders I. Chris Salvato did a really good job with The 15-Second Handstand. You can start your way into the progressions within 10 minutes, and learn the why and how later through the book.
Join me and learn how to do a headstand. A step-by-step tutorial to get upside down, reverse your blood flow, increase your balance, and boost your energy! ... Yoga Tutorial: How To Do a Handstand - The Journey Junkie. Includes prep poses, wrist stretches etc.
5/12/2016 · Handstands may seem like a move best left to gymnasts and super-yogis. But really, you don't have to have Olympian genes or spend all your free time in a yoga studio to learn how to do them.
I used to sit on my chins, do a little jump and press at the same time in a tuck. Do a tucked handstand, go up to a handstand, try to go down into a tucked handstand again, and if i was about to fall i just used all of my willpower to try and press up again. So in a way it almost became a tuck planche press up.


Drove the car for a few weeks but only owned it for 5 days. Was involved in a 5 car pileup (rearended and slammed into three cars) and wrote the car off. Loved the car, handled great, bought one like it after the insurance money came in as well!! Paid roughly $5000 for it from the dealership Trevor's Hyundai in Miramichi.
Prom. Calificación del concesionario: (12 evaluaciones) "Mo was so easy to work with. I'd do business there again."
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Prom. Calificación del concesionario: (2 evaluaciones) "Car was sold. Told to come look at other cars but the lot was sketchy looking and maybe had 5 cars total."
Prom. Calificación del concesionario: (10 evaluaciones) "Replied quickly, but knew nothing about the truck they had listed. Asked for pictures, then they said they wouldn't actually receive the truck until Thursday. Promised pictures then. That was 5 days ago. Not a peep...."
Prom. Calificación del concesionario: (87 evaluaciones) "Ok "
Prom. Calificación del concesionario: (19 evaluaciones) "Great service. I met Anthomy there and went for a test drive, immediately after I told him I wanted to buy. Anthony took me back to see my options and look at all the paperwork. I wanted to buy outright and didnt push financing on me like most other dealers. He even helped me get a really great rate and insurance policy as a young first time car buyer. Great deal and fair price as well. Will definitely be back and would reccomend this place to everyone. Definitely see Anthony. Great guy, very helpful and straightforward. Will work with what your price is and what direction you want to go. No scams. "
Prom. Calificación del concesionario: (34 evaluaciones) "Ok I have no regrets, it was a pleasant experience. Many thanks to Ross."
Prom. Calificación del concesionario: (3 evaluaciones) "Good experience "


Due to the fact that we are a small business and have only 6 chalets, unfortunately we turn down many bookings. Last minute cancellations therefore have a great impact on our business as well as denying others the opportunity. We require 5 days notice to cancel all reservations with the exception of Christmas and New Year's (see below). If you cancel within 5 days of your arrival date, you will be required to pay the full balance of your reservation. We reserve the right to charge the remaining amount owing for your booking, to the credit card you provided to us upon booking. Cancellations are confirmed by email so that both parties have a record of the cancellation. For your convenience, we have included a hyperlink below, directly to our email. If we have not received an email from you regarding your cancellation, we will consider your booking confirmed and you will be charged accordingly. If you book within 5 days of your arrival date, we reserve the right to ring your card through upon booking. Our cancellation date for Christmas and New Year's is December 15. All Christmas and New Year's Bookings will be rung through in full, on December 15, with no exceptions.


The couple had faced opposition from the Thakur community as well as local authorities who said they will not 'change the tradition'.
A Dalit groom in Uttar Pradesh's Kasganj district on Sunday took his wedding procession through the streets where mainly upper-caste Thakurs live after facing stiff opposition from local authorities.
Kumar said they had to fight against all odds "to just earn respect, dignity and equality for our community," reported The Times of India. "Neither my community nor I was against local Thakurs, but we were against the discrimination on the basis of caste," he added.
Kumar, a local worker with the Bahujan Samaj Party, rode on a horse-drawn buggy with a convoy of 30 cars, a police pilot vehicle and an armed security guard. The district administration deployed 10 police inspectors, 22 sub-inspectors, 35 head constables, 100 constables and a platoon of the state provincial armed constabulary around the bride's village on the outskirts of Nizampur village.
The bride, Sheetal's, family home is on the outskirts of Nizampur village, as are the homes of four other Jatav families – the caste to which the bride and groom belong. Usually, Jatav grooms stop their wedding processions at a ground outside the village and then walk to the bride's house without crossing any Thakur areas.
The bride and groom's families have awaited permission for their wedding procession for several months. They said they had approached authorities for permission for the procession in October, soon after the wedding was fixed. The bride, groom, and their families faced opposition, not just from the Thakur community but also from local authorities who said they will not "change tradition".
Kumar even approached the Allahabad High Court, which told him that only the local police could help him. In April, the district administration gave Kumar permission to take his 'baraat' procession through areas with Thakur houses, provided no "political persons" participated in it, no weapons were on display and no alcohol was allowed.


Welcome to the free community dedicated to mixed-media artists - where you can learn new techniques, share inspiration, and discover must-have resources. 2019!
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text:
Падь — деревня в Омском районе Омской области России. В составе Иртышского сельского поселения.

История
Основана в 1920 году. В 1928 г. хутор Падь состоял из 23 хозяйств, основное население — русские. В составе Черемуховского сельсовета Ачаирского района Омского округа Сибирского края.

Население

Примечания

Населённые пункты Омского района


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Sometimes I wish I lived in the medieval era, if only so that I wouldn't have to bother so much with personal grooming concerns. I mean come on, there are more important things to worry about than whether my eyelashes are curled or various body parts are plucked and waxed. What am I, a chicken? Back then few people bathed or bothered, or even considered their own bodies as much as we do. Everyone was pretty much in the same boat priority-wise: trying to stay alive. No worries if your hair looks greasy or unkempt! You survived past childhood! Of course, I'd also have to contend with disease, plague, lack of clean food or water, misogyny, extremely curtailed liberty...so maybe this century does have its perks.
It's a hard knock life, guys.
What it's like for the 1%, maybe.
I would still love to visit a medieval castle to see how everything operated, or wander around Elizabethan London for a day. But then I'd probably be burned at the stake for being an independent female.* If I opened my mouth: heresy! They tend to romanticize it in the movies, but it would be great to see a time-travel film or book that dealt with: culture shock, having to learn a different language or dialect, alienation, becoming a starving, outcast beggar and dying miserably of hypothermia (complicated by gangrenous frostbite and malnutrition)...you know, reality. None of this warmly-welcomed, false-backstory-easily-accepted, falling-in-love stuff.
I just thought I would point this out because our problems may be different then and now, but travel (time or otherwise) doesn't erase them, it just changes them. We all live better than kings did in the medieval era - the array of foods and goods and health and cleanliness we can afford is astounding. Thankfully, we also live in a world where you won't be burned at the stake for expressing your opinion. (Most of the time.) But there are still problems that demand our attention. Most of us in the world today don't have to worry about where our next meal is coming from, whether the lord of the manor is going to take all our crops, or whether two-thirds of our seventeen children will die before the age of 12. So where should we channel that energy instead? Making sure our eyebrows are perfectly plucked? Or seeing that people are fed and sheltered and heard and validated? I lean towards the latter. Of course we need to have our basic needs and desires met before we can tend to others, or study, create, invent, and so on. And necessities may look different for different people. As long as we're living, breathing, and being ourselves, we can't go too far wrong.
*Not an actual/stated reason for burning a woman at the stake, but you know, approximately.