text:
The Strokes – pochodzący z Nowego Jorku zespół grający rock alternatywny.

Historia
Do tej pory grupa wydała sześć płyt: Is This It (2001), Room on Fire (2003), First Impressions of Earth (2006), Angles (2011), Comedown Machine (2013) i The New Abnormal (2020). 3 lipca 2011 roku zespół wystąpił po raz pierwszy w Polsce na festiwalu Heineken Open'er jako jeden z głównych headlinerów.

Ich pierwsze utwory zaczęły powstawać w 1998 roku. Na samym początku zespół, jeszcze niezbyt dopracowany, składał się tylko z Juliana (wokal), Nicka (gitara prowadząca) i Fabrizia (perkusja). Dopiero po dojściu do tej grupki Alberta (gitara rytmiczna) i Nikolaia (bas) rozpoczęły się prawdziwe próby i plany założenia solidnego zespołu. Początkowo nosili nazwę "De Niros", ale nie spotkało się to ze zbyt dużym entuzjazmem, więc po rozmyślaniu nad czymś innym powstało "The Strokes".

Młody wówczas zespół odnalazł "łowca talentów" Ryan Gentles podczas ich występu w nowojorskim klubie Mercury Lounge w 2000 roku. Zachwycił się ich utworami demo, następnie polecił ich szefowi wytwórni Rough Trade, który od razu zaangażował zespół do wydania pierwszego EP.

W 2003 zespół wydał drugi album, Room on Fire, w 2006 First Impressions of Earth, po 5-letniej przerwie w  2011 wydano kolejny, czwarty album zespołu, zatytułowany Angles. Premiera płyty odbyła się 21 marca. W 2013 zespół wydał 5 i ostatni album, który musiał wydać z powodu kontraktu z RCA.  Po ponad 7 latach przerwy, 10 kwietnia 2020 roku zespół powrócił z kolejnym długogrającym albumem zatytułowanym The New Abnormal.

Skład
 Julian Casablancas – śpiew, autor tekstów
 Albert Hammond, Jr. – gitara
 Nick Valensi – gitara
 Fabrizio Moretti – perkusja
 Nikolai Fraiture – gitara basowa

Dyskografia

Albumy
 Is This It (2001)
 Room on Fire (2003)
 First Impressions of Earth (2006)
 Angles (2011)
 Comedown Machine (2013)
 The New Abnormal (2020)

Single
 The Modern Age EP (styczeń 2001)
 Hard To Explain (kwiecień 2002)
 Someday (wrzesień 2002)
 Last Nite (kwiecień 2003)
 12:51 (październik 2003)
 Reptilia (Luty 2004)
 The End Has No End (październik 2004)
 Juicebox (grudzień 2005)
 Heart In A Cage (marzec 2006)
 You Only Live Once (lipiec 2006)
 Under Cover Of Darkness (luty 2011)
 Taken For A Fool (lipiec 2011)

Przypisy

Linki zewnętrzne
 Oficjalna strona zespołu
 Strona fanów

Strokes, The

redpajama_set_name: RedPajamaWikipedia


text:
from pathlib import Path

import pandas as pd

def _to_dollars(s: str):
    s = s.replace(' USD per Hour', '')
    return float(s)

def _cols_to_delete():
    return [
        'Instance family',
        'Instance size',
        'Availability zones',
        'Free-Tier eligible',
        'Bare metal',
        'Hypervisor',
        'Dedicated Host support',
        'On-Demand Hibernation support',
        'Burstable Performance support',
        'Valid cores',
        'Local instance storage',
        'Storage type',
        'Storage disk count',
        'EBS encryption support',
        'EBS optimization support',
        'Network performance',
        'ENA support',
        'Maximum number of network interfaces',
        'IPv4 addresses per interface',
        'IPv6 addresses per interface',
        'IPv6 support',
        'Supported placement group strategies',
        'Auto Recovery support',
        'Supported root devices',
        'Current generation',
    ]

def report():
    in_fspec = Path(f'{__file__}/../instancetypes.csv').resolve()
    df = pd.read_csv(in_fspec)
    del df['On-Demand Windows pricing']
    df['On-Demand Linux pricing'] = (
        df['On-Demand Linux pricing'].apply(_to_dollars))
    df = df.rename(columns={'On-Demand Linux pricing': 'price',
                            'Threads per core': 'tpc',
                            'Valid threads per core': 'vtpc',
                            'Sustained clock speed (GHz)': 'GHz'})
    df = df[df['Current generation']]
    df = df[df.vCPUs > 8]
    for col_name in _cols_to_delete():
        if col_name:
            del df[col_name]
    df['price/core'] = df['price'] / df['vCPUs']
    df.sort_values(by=['price/core', 'vCPUs'], inplace=True)
    df.to_csv('/tmp/t.csv', index=False)

    print(df.head(40))

if __name__ == '__main__':
    report()

redpajama_set_name: RedPajamaGithub


We are The Evolved Traveler, an international travel brand that promotes an increased sense of global community and humanity. We custom-tailor personalized, luxury journeys which promote philanthropic endeavors that allow our discerning guests to experience their dreams while helping other members of our global family realize theirs. We provide exceptional journeys so our evolved travelers can help change the world as it expands their own.
In the early 20th century, author G.K. Chesterton reportedly responded to a query from The Times of London asking, "What is wrong with the world today?" As the story goes, Chesterton replied simply, "I am." This was not a personal commentary on his part, but an observation on the behavior of humanity and the flawed paradigms we live under.
After 30 years in the travel industry, having the good fortune to travel to a great many places and experience things I would have never imagined, a personal fork-in-the-road in 2015 led me to believe there was a more productive way to approach travel, even luxury travel. It seemed to me that we were only looking at one side of the experience—our own. This thought, and my personal journey at the time, was what lead to the concept and creation of The Evolved Traveler.
Our team at The Evolved Traveler has big dreams, and we know you do too! Let us know what they are today and we can get started planning one of the most unique journeys you have ever taken.


We are prepared and ready to respond to reports of trees that may have blocked roads or caused damage to structures.
Report damage to property from high winds contact the Building Control Team, who will be able to assist you.
Our officers and gritting vehicles are on standby and continually monitor the weather conditions.
Please follow us on Twitter for gritting updates and other service updates.
Due to the cold weather, there may be an increased risk of burst water pipes. If you spot a burst water pipe down your road, please contact your water provider.
When there is extreme cold weather our services can be disrupted. Please follow us on Twitter for updates on school closures and our services.
Those properties which did not receive a collection due to the adverse weather conditions should present all of their refuse and recycling as normal on their next scheduled day of collection and everything will be taken.
Please check our Twitter and Facebook for any updates. Thank you for your understanding.
During hot weather the heat affects everyone particularly the young, elderly and people who are fasting.
When temperatures are high, keep safe and healthy by finding shade to cool down and stay hydrated by drinking of plenty of cool fluids.


His date of birth is Dec. 6, 1982.
So Rauschenberger had Kander by more than a year and, apparently, now holds the title of the nation's youngest elected statewide official.
We saw "apparently" because neither the National Conference of State Legislatures nor the National Governors Association keeps track of such matters. It's up to Google searches and the like.
For the record, Rauschenberger's office didn't know if their boy-commissioner was the youngest. It took a reporter from The Buzz to fill them in.
Last month, Missouri Gov. Jay Nixon appointed Nicole Galloway, the Boone County treasurer, as state auditor. As an appointed official rather than an elected one, Galloway falls into a different category. Kander's office reports that several appointed statewide officials dot the national landscape who are in their 20s.
Galloway's DOB? June 13, 1982, making her 32.
That's way, way too old to be the youngest. Poor Nicole. She's already over the hill.


Brandessence Market Research has published a new report title "LiDAR Market: Global Size, Trends, Competitive, Historical & Forecast Analysis, 2018-2024. Increasing demand for the 3D imaging technology and technological developments are key drivers for Global LiDAR Market.
The regions covered in this LiDAR market report are North America, Europe, Asia-Pacific and Rest of the World. On the basis of country level, market of LiDAR market is sub divided into U.S., Mexico, Canada, U.K., France, Germany, Italy, China, Japan, India, South East Asia, GCC, Africa, etc.
Global LiDAR market is growing with increasing demand for drone with higher accuracy, fast acquisition and processing. Moreover, market is also growing with technological development and rising demand for 3D imaging application. However, high operating costs in some applications and require skilled data analysis techniques are expected to restrains of the Global LiDAR market. Nonetheless, untapped market and technological advancements may generate new opportunities in forecast period.
Global LiDAR market report covers in depth historical and forecast analysis.
Global LiDAR market research report provides detail information about Market Introduction, Market Summary, Global market Revenue (Revenue USD), Market Drivers, Market Restraints, Market opportunities, Competitive Analysis, Regional and Country Level.
Global LiDAR market report helps to identify opportunities in market place.
Global LiDAR market report covers extensive analysis of emerging trends and competitive landscape.
Our 172 pages report provides 55 tables, 63 charts and graphs. The report covers historical and forecast data of North America, Europe, Asia Pacific, Latin America and Middle East & Africa. Our study focuses on the most lucrative areas in the industry and the future market prospects. Market covers financial results, trends, opportunities and revenue predictions from 2018 to 2024, significant rise in research and development activities by leading vendors.
This report features news, insights, the latest developments and an in depth survey of the Global LiDAR with up-to-date analysis as well as tables, graphs and charts. This report is a vital addition to gaining an understanding of this market sector and will give your company 'the edge' on your competitors.
North America LiDAR Market Revenue (USD Million) and Growth Rate (%), 2014 – 2024.
North America LiDAR Market Revenue (USD Million) By Country, 2014 – 2024.
North America LiDAR Market Share (%) By Country, 2014 – 2024.
North America LiDAR Market Revenue (USD Million), Market Share (%) and Growth Rate, By Market Segmentation, 2014 – 2024.


"We have 900 remedies ran up to the 10M (10,000), 50M (50,000), CM (100,000). These potencies are made with the Dr. James Tyler Kent potentizer. This machine is the most accurate centesimal potentizing implement ever invented. The starting point of these potencies is commenced with the 1000 Ehrhart's hand made potency. The motive power being water pressure. The menstrum which is used for potentizing is filtered water.
"There are four dials on this machine. The dials indicate the actual number of times the receiver has been emptied. This machine differs from all others as the remedy being potentized receives quite a succussion, this no other machine will do. These are the only machine made high potencies on the market that are made with filtered water; all others that we know of are made with the ordinary tap water."
Little is known about the history of the Kent Potentizer. That Kent designed it is alluded to in the Ehrhart and Karl Catalog. Kent himself left no writings about the machine.
The machine was in use from before Kent's death in 1916 probably through the 1940s.
At one point in the late 1970s when Luyties bought much of the equipment from E&K, the potentizer was taken to St. Louis. It remains at the Luyties facility.
I have seen it in partial operation.
The machine held two potentizing tubes, mounted on a flat plate made of a thick piece of Bakelite.
These tubes were open at the bottom with a slight return. (Picture at the left).
The hole in the bottom of the tube was stopped with a leather plug, held in place by a cam. (See picture below) When the cam pulled the plug back, the contents of the tube would flow out, but the return held some of the previous content.
I make the assumption that the amount in the return was seen as "1" and the when the tube was re-filled, it then held "99" parts of filtered water.
The machine is powered by a motor that drives an axle. Connected to the axle is a square "succussing cam" and a worm gear, which drives an axle at right angles to the first. This axle holds the "raising cam" and the "opening cam."
The operation is fairly straight-forward.
Starting with a tube full of remedy ready to be succussed, the axle turns the square "succussing cam" which moves the Bakelite plate up. The plate is then driven down by two springs located behind the potentizing tubes.
After the plate has moved up and down ten times, the "raising cam" lifts the plate away from the square "succussing cam."
At this point, the "opening cam" pushes down on the arm holding the leather pads in contact with the potentizing tubes, thus opening the bottom of the tubes.
All the liquid flows out the tube, leaving one part behind in the return.
The "opening cam" returns the leather seal to the closed position.
The tubes are filled with water.
The "raising cam" lowers the plate, and the cycle repeats.
The one missing piece is the mechanism by which the potentizing tubes were filled.
Forrest Murphy, of Luyties, suggested that, perhaps, the water ran continuously through the tubes, being timed to just fill the tube during the ten succussions.
But, with the accuracy inherent in the the rest of the machine, It is hard to believe that Kent would have accepted such an inaccurate measure.
Although the report in the catalogue speaks of "four dials," none were evident on the machine.
There was obviously some way of feeding a measured amount of water through rubber tubes that fit tightly into the glass potentizing tubes, but the exact mechanism remains a mystery.
There is, unfortunately, no one still alive that has seen the machine in operation.


text:
"""
Implementation of a feature which maps integers to an array containing
their binary representation, modulo the length of the array.
"""
import numpy as np
from functools import partial
from flib.abstract import BinaryFeature

class Int2Bin(BinaryFeature):
    """
    Convert integer to its bit vector representation.

    On initialization, it precomputes an array which is used to extract the
    individual bits of each integer.

    .. note ::
        The function only extracts the first `length` bits of the integers
        supplied to it, so it's essentially converting modulo `length`.
        For example, `2**length` will have the same representation as `0`, and
        `-1` will be represented the same way as `2**length -1`.
    """
    def __init__(self, length: int):
        super().__init__(1, length)
        # Precompute the array for converting integers to bit vectors
        self._array = (1 << np.arange(length))

        # Create the function to apply to inputs
        self.apply = partial(self.func, self._array)

    @staticmethod
    def func(array, x):
        return ((x & array) > 0)

    def __call__(self, x):
        # TODO: Add proper vectorization here / as an abstract mixin
        x = np.array(x)
        if x.ndim > 0:
            ret = np.empty(shape=(x.size, self.n_output), dtype=np.uint8)
            for ix, i in enumerate(x.flat):
                ret[ix] = self.apply(i)
            return ret
        else:
            return self.apply(x)


> Giveaways > Not-So-Secret Santa Holiday Giveaway!
We want to be your not-so-secret Santa this year! That's why we're giving away weekly prizes between November 14, 2016 and December 18, 2016. Enter to win weekly prizes and be automatically entered into our grand prize drawing for a $75 SunRose Aromatics gift certificate!
This week, we're giving away a $15 SunRose Aromatics gift certificate! Enter between 11/14/2016 and 11/20/2016.


Fans of Nintendo's Wii U console can get their hands on Game & Wario this week, featuring a collection of minigames that utilise the GamePad controller.
Game & Wario is the latest entry in the Wario series and, like many of its predecessors, is composed of several minigames. The game uses the Wario games' trademark bizarre humour and integrates with the Miiverse.
The game drew criticism in GameSpot's review for its use of the "bulky Wii U GamePad as a precision aiming device" and for offering too few minigames, but saw praise for a few well-designed levels and its implementation of the Miiverse.
Fans of MotoGP racing games will be happy to hear that developer Milestone has made a return to the series with the release of MotoGP 13.
The game includes tracks, riders, and teams from the 2013 MotoGP, Moto 2, and Moto 3 World Championships. Players can choose from four single-player game modes: Grand Prix, World Championship, Career, and Time Attack.
MotoGP 13 includes commentary from international Moto GP commentator Gavin Emmett, and introduces a new helmet cam. The game will be released on July 4 for the Xbox 360 and PlayStation 3.
For the full details on games out this week, check out the list below.


Who Are the Thought Adjusters?
What is your focus? Is it war, lack, negativity, problems, etc.? What would happen if you were able to shift your thoughts toward abundance, harmony, and joy? The Thought Adjusters not only guide us, but are actively re-tuning our frequencies. Learn more in this week's webinar.
This entry was posted in Healer's Journey and tagged frequency vibration, Healer's Journey, solfeggio tuning forks, thought adjusters, tuning forks on August 29, 2017 by rosemary.
As healers on the Healer's Journey we sometimes take on the symptoms and suffering of others. It may look like you're ill, and in fact, you may have a serious diagnosis! Don't ignore the symtoms, but don't also ignore the possibility that your dis-ease may have more to do with your SOUL DNA than your physical DNA.
This entry was posted in Healer's Journey, Uncategorized on August 25, 2017 by rosemary.
As Healers on the Healer's Journey you recognize growth in daily practice, utilize skills and tools, and raise your awareness to become more "illuminated". What is the Illuminated Path of becoming a qualified, experienced healer? How can you build your skills to achieve effortless spiritual awareness?
This entry was posted in Healer's Journey, Spiritual Energy Healing, Uncategorized and tagged advanced Reiki training, effortless spiritual awareness, illumination Reiki, spiritual energy healing on August 1, 2017 by rosemary.


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text:
\section{Introduction}

Deep learning has demonstrated impressive performance on a variety of tasks such as image classification, object detection, semantic segmentation, among others. Typically, these models create internal abstract representations from raw data in a supervised manner. Nevertheless, supervised learning is a limited approach since it requires large amounts of labeled data. It is not always possible to obtain labeled data since it requires time, effort and resources. As a consequence, semi-supervised or unsupervised algorithms have been developed to reduce the required number of labels. Unsupervised learning is intended to learn useful representations of data easily transferable for further usage. As using smart data representations is important, another desirable property of unsupervised methods is to perform dimensionality reduction while keeping the most important characteristics of data. Classical methods are principal component analysis (PCA) or matrix factorization. For the same purpose, autoencoders learn to compress data into a low-dimensional representation and then, to uncompress that representation into the original data. An autoencoder variant is the variational autoencoder (VAE) introduced by Kingma and Welling \cite{Kingma2013} where the low-dimensional representation is constrained to follow a prior distribution. The VAE provides a way to extract a low-dimensional representation while learning the probability distribution of data. Other unsupervised methods of learning the probability data distribution have been recently proposed using generative models. A generative model of particular interest is generative adversarial networks (GANs) introduced by Goodfelow \etal \cite{Goodfellow2014, Goodfellow2016}.

In this work, we present a model that combines the VAE and GAN methods in order to create a useful representation of satellite image time series in an unsupervised manner. To create these representations we propose to learn the image-to-image translation task introduced by Isola \etal \cite{isola2017image} and Zhu \etal \cite{Zhu2017}. Given two images from a time series, we aim to translate one image into the other one. Since both images are acquired at different times, the model should learn the common information between these images as well as their differences to perform translation. We also aim to create a disentangled representation into a shared representation that captures the common information between the images of a time series and an exclusive representation that contains the specific information of each image. For instance, the knowledge about the specific information of each image could be useful to perform change detection.

Since we aim to generate any image of the time series from any of its images, we address the problem of multimodal generation, \ie multiple output images can be generated from a single input image. For instance, an image containing harvested fields could be translated into an image containing growing crop fields, harvested fields or a combination of both.

Our approach is inspired by the BicycleGAN model introduced by Zhu \etal \cite{zhu2017toward} to address multimodal generation and the model presented by Gonzalez-Garcia \etal \cite{gonzalez2018image} to address representation disentanglement.

In this work, the following contributions are made. First, we propose a model that combines the cross-domain autoencoder principle proposed by Gonzalez-Garcia \etal \cite{gonzalez2018image} under the GAN and VAE constraints to address representation disentanglement and multimodal generation. Our model is adapted to satellite image time series analysis using a simple architecture. Second, we show that our model is capable to process a huge volume of high-dimensional data such as Sentinel-2 image time series in order to create feature representations. Third, our model generates a disentangled representation that isolates the common information of the entire time series and the exclusive information of each image. Finally, our experiments suggest that these feature representations are useful to perform several tasks such as image classification, image retrieval, image segmentation and change detection.

\section{Related work}

\textbf{Variational autoencoder (VAE)}. In order to estimate the data distribution of a dataset, a common approach is to maximize the log-likelihood function given the samples of the dataset. A lower bound of the log-likelihood is introduced by Kingma and Welling \cite{Kingma2013}. To learn the data distribution, the authors propose to maximize the lower bound instead of the log-likelihood function which in some cases is intractable. The model is implemented using an autoencoder architecture and trained via a stochastic gradient descent method. It is an interesting method since it creates a low-dimensional representation where relevant attributes of data are captured.

\textbf{Generative adversarial networks (GAN)}. Due to its great success in many different domains, GANs \cite{ Goodfellow2014, Goodfellow2016} have become one of the most important research topics. The GAN model can be thought of as a game between two players: the generator and the discriminator. In this setting, the generator aims to produce samples that look like drawn from the same distribution as the training samples. On the other hand, the discriminator receives samples to determine whether they are real (dataset samples) or fake (generated samples). The generator is trained to fool the discriminator by learning a mapping function from a latent space which follows a prior distribution to the data space. However, traditional GANs (DCGAN \cite{radford2015unsupervised}, LSGAN \cite{Mao2017}, BEGAN \cite{Berthelot2017}, WGAN \cite{arjovsky2017wasserstein}, WGAN-GP \cite{Gulrajani2017}, LaplacianGAN \cite{Denton2015}, EBGAN \cite{zhao2016energy}, among others) does not provide a means to learn the inverse mapping from the data space to the latent space. To solve this problem, several models were proposed such us BiGAN \cite{donahue2016adversarial} or VAE-GAN \cite{pmlr-v48-larsen16} which include an encoder from the data space to the latent space in the model. The data representation obtained in the latent space via the encoder can be used for other tasks as shown by Donahue \etal \cite{donahue2016adversarial}.

\textbf{Image-to-image translation}. It is one of the most popular applications using conditional GANs \cite{Mirza2014}. The image-to-image translation task consists of learning a mapping function between an input image domain and an output image domain. Impressive results have been achieved by the pix2pix \cite{isola2017image} and cycleGAN \cite{Zhu2017} models. Nevertheless, most of these models are monomodal. That is, there is a unique output image for a given input image.

\textbf{Multimodal image-to-image translation}. One of the limitations of previous models is the lack of diversity of generated images. Certain models address this problem by combining the GAN and VAE methods. On the one hand, GANs are used to generate realistic images while VAE is used to provide diversity in the output domain. Recent work that deals with multimodal output is presented by Gonzalez-Garcia \etal \cite{gonzalez2018image}, Zhu \etal \cite{zhu2017toward}, Huang \etal \cite{huang2018multimodal}, Lee \etal \cite{lee2018stochastic} and Ma \etal \cite{ma2018exemplar}. In particular, to be able to generate an entire time series from a single image, we adopt the principle of the BicycleGAN model proposed by Zhu \etal \cite{zhu2017toward} where a low-dimensional latent vector represents the diversity of the output domain. However, while the BicycleGAN model is mainly focus on image generation, we only consider the image-to-image translation task as a way to learn suitable feature representations. For image generation purpose, the output diversity is conditioned at the encoder input level in the BicycleGAN model. Instead the output diversity is conditioned at the decoder input level in our model.

\textbf{Disentangled feature representation}. Recent work is focused on learning disentangled representations by isolating the factors of variation of high-dimensional data in an unsupervised manner. A disentangled representation can be very useful for several tasks that require knowledge of these factors of variation. Chen \etal \cite{Chen2016} propose an objective function based on the maximization of the mutual information. Gonzalez-Garcia \etal \cite{gonzalez2018image} propose a model based on VAE-GAN image translators and a novel network component called cross-domain autoencoders. This model separates the feature representation of two image domains into three parts: the shared part which contains common information from both domains and the exclusive parts which only contain factors of variation that are specific to each domain. We propose a model that combines the cross-domain autoencoder component under the VAE and GAN constraints in order to create representations containing the common information of the entire time series and the exclusive information of each image. The VAE is used to create a low-dimensional representation that encodes the image variations related to acquisition time and the GAN is used to evaluate the generated image at a given time. We introduce a model adapted for satellite image time series using a simpler architecture since only four functions (the shared representation encoder, the exclusive representation encoder, the decoder and the discriminator) must be learned.

\begin{figure*}[t]
\begin{center}
\includegraphics[width=0.6\linewidth]{ModelOverview.png}
\end{center}
\caption{Model overview. The model goal is to learn both image transitions: $\mathbf{x} \rightarrow \mathbf{y}$ and $\mathbf{y} \rightarrow \mathbf{x}$. Both images are passed through the network $\mathbf{E}_{sh}$ in order to extract their shared representations. On the other hand, the network $\mathbf{E}_{ex}$ extracts the exclusive representations corresponding to images $\mathbf{x}$ and $\mathbf{y}$. The exclusive representation encoder output is constrained to follow a standard normal distribution. In order to generate the image $\mathbf{y}$, the decoder network $\mathbf{G}$ takes the shared feature of image $\mathbf{x}$ and the exclusive feature of image $\mathbf{y}$. A similar procedure is performed to generate the image $\mathbf{x}$. Finally, the discriminator $\mathbf{D}$ is used to evaluate the generated images.}
\label{fig:ModelOverview}
\end{figure*}

\section{Method}

Let $\mathbf{x}, \mathbf{y}$ be two images randomly sampled from a given time series $\mathbf{t}$ in a region $\mathbf{c}$. Let $\mathcal{X}$ be the image domain where these images belong to and let $\mathcal{R}$ be the representation domain of these images. The representation domain $\mathcal{R}$ is divided into two subdomains $\mathcal{S}$ and $\mathcal{E}$, $\mathcal{R} = \left[\mathcal{S}, \mathcal{E}\right]$. The subdomain $\mathcal{S}$ contains the common information between images $\mathbf{x}$ and $\mathbf{y}$ and the subdomain $\mathcal{E}$ contains the particular information of each image. Since images $\mathbf{x}$ and $\mathbf{y}$ belong to the same time series, their shared representations must be identical, \ie $\mathcal{S}_{\mathbf{x}} = \mathcal{S}_{\mathbf{y}}$. On the other hand, as images are acquired at different times, their exclusive representations $\mathcal{E}_{\mathbf{x}}$ and $\mathcal{E}_{\mathbf{y}}$ correspond to the specific information of each image.

We propose a model that learns the transition from $\mathbf{x}$ to $\mathbf{y}$ as well as the inverse transition from $\mathbf{y}$ to $\mathbf{x}$. In order to accomplish this, an autoencoder-like architecture is used. In Figure \ref{fig:ModelOverview}, an overview of the model can be observed. Let $\mathbf{E}_{sh}:\mathcal{X} \rightarrow \mathcal{S}$ be the shared representation encoder and $\mathbf{E}_{ex}:\mathcal{X} \rightarrow \mathcal{E}$ be the exclusive representation encoder. To generate the image $\mathbf{y}$, the shared feature of $\mathbf{x}$, \ie $\mathbf{E}_{sh}(\mathbf{x})$, and the exclusive feature of $\mathbf{y}$, \ie $\mathbf{E}_{ex}(\mathbf{y})$ are computed. Then both representations are passed through the decoder function $\mathbf{G}:\mathcal{R} \rightarrow \mathcal{X}$ which generates a reconstructed image $\mathbf{G}(\mathbf{E}_{sh}(\mathbf{x}), \mathbf{E}_{ex}(\mathbf{y}))$. A similar process is followed to reconstruct the image $\mathbf{x}$. Then, these images are passed through a discriminator function $\mathbf{D}:\mathcal{X} \rightarrow \left[0,1\right]$ in order to evaluate the generated images.

The model functions $\mathbf{E}_{ex}$, $\mathbf{E}_{sh}$, $\mathbf{G}$ and $\mathbf{D}$ are represented by neural networks with parameters $\theta_{\mathbf{E}_{ex}}$, $\theta_{\mathbf{E}_{sh}}$ and $\theta_{\mathbf{G}}$ and $\theta_{\mathbf{D}}$, respectively. The training procedure to learn these parameters is explained below.

\subsection{Objective function}

As the work presented by Zhu \etal \cite{zhu2017toward} and Gonzalez-Garcia \etal \cite{gonzalez2018image}, our objective function is composed of several terms in order to obtain a disentangled representation.

Concerning the shared representation, images $\mathbf{x}$ and $\mathbf{y}$ must have identical shared feature representations, \ie $\mathbf{E}_{sh}(\mathbf{x}) = \mathbf{E}_{sh}(\mathbf{y})$. A simple solution is to minimize the $L_1$ distance between their shared feature representations as can be seen in Equation \ref{eq:sharedloss}.
\begin{equation}\label{eq:sharedloss}
\mathbf{L}_{1}^{sh} = \mathbb{E}_{\mathbf{x}, \mathbf{y} \sim \mathcal{X}} \left[ \lvert \mathbf{E}_{sh}(\mathbf{x}) - \mathbf{E}_{sh}(\mathbf{y}) \rvert \right]
\end{equation}

The exclusive representation must only contain the particular information that corresponds to each image. To enforce the disentanglement between shared and exclusive features, we include a reconstruction loss in the objective function where the shared representations of $\mathbf{x}$ and $\mathbf{y}$ are switched. The loss term corresponding to the reconstruction of image $\mathbf{x}$ is represented in Equation \ref{eq:vaereconstructionloss_x}. Moreover, this loss term can be thought of as the reconstruction loss in the VAE model \cite{Kingma2013} which maximizes a lower bound of the log-likelihood function. As we enforce representation disentanglement, we simultaneously maximize the log-likelihood function which is equivalent to Kullback-Leibler divergence minimization between the real distribution and the generated distribution.
\begin{equation}
\mathbf{L}_{1}^{\mathbf{x}, \mathbf{y}} = \mathbb{E}_{\mathbf{x}, \mathbf{y} \sim \mathcal{X}} \left[ \lvert \mathbf{x} - \mathbf{G}(\mathbf{E}_{sh}(\mathbf{y}), \mathbf{E}_{ex}(\mathbf{x})) \rvert \right] \label{eq:vaereconstructionloss_x}
\end{equation}

On the other hand, the lower bound proposed in the VAE model constraints the feature representation to follow a prior distribution. In our model, we only force the exclusive feature representation to be distributed as a standard normal distribution $\mathcal{N}(0, I)$ in order to generate multiple outputs by sampling from this space during inference. In contrast to the approach employed by Gonzalez-Garcia \etal \cite{gonzalez2018image} which uses a GAN approach to constraint the exclusive feature distribution, a simpler solution which proves to be effective is to include a Kullback-Leibler divergence term between the distribution of the exclusive feature representation and the prior $\mathcal{N}(0, I)$. Assuming that the exclusive feature encoder $\mathbf{E}_{ex}(\mathbf{x})$ is distributed as a normal distribution $\mathcal{N}(\mu_{\mathbf{E}_{ex}(\mathbf{x})}, \sigma_{\mathbf{E}_{ex}(\mathbf{x})})$, the Kullback-Leibler divergence can be written as follows
\begin{equation}\label{eq:kullbackleiblerdistance}
\resizebox{.9\hsize}{!}{$\mathbf{L}_{KL}^{\mathbf{x}} = -\frac{1}{2} \mathbb{E}_{\mathbf{x} \sim \mathcal{X}} \left[ 1 + \log(\sigma_{\mathbf{E}_{ex}(\mathbf{x})}^2) - \mu_{\mathbf{E}_{ex}(\mathbf{x})}^2 - \sigma_{\mathbf{E}_{ex}(\mathbf{x})}^2 \right]$}
\end{equation}

In order to minimize the distance between the real distribution and the generated distribution of images, a LSGAN loss \cite{Mao2017} is included in the objective function. The discriminator is trained to maximize the probability of assigning the correct label to real images and generated images while the generator is trained to fool the discriminator by classifying generated images as real, \ie $\mathbf{D}(\mathbf{G}(\mathbf{E}_{sh}(\mathbf{y}), \mathbf{E}_{ex}(\mathbf{x}))) \rightarrow 1$. The corresponding loss term for image $\mathbf{x}$ and its reconstructed version can be seen in Equation \ref{eq:gan_x} where the discriminator maximizes this term while the generator minimizes it.
\begin{align}
\begin{split}
\mathbf{L}_{GAN}^{\mathbf{x}} =& \ \mathbb{E}_{\mathbf{x} \sim \mathcal{X}} \left[ \left(\mathbf{D}(\mathbf{x})\right)^{2} \right] + \\[0.20em]
& \ \mathbb{E}_{\mathbf{x}, \mathbf{y} \sim \mathcal{X}} \left[ \left(1 - \mathbf{D}(\mathbf{G}(\mathbf{E}_{sh}(\mathbf{y}), \mathbf{E}_{ex}(\mathbf{x})))\right)^{2} \right]
\end{split}\label{eq:gan_x}
\end{align}

To summarize, the training procedure can be seen as a minimax game (Equation \ref{eq:totallosses}) where the objective function $\mathcal{L}$ is minimized by the generator functions of the model ($\mathbf{E}_{ex}$, $\mathbf{E}_{sh}$, $\mathbf{G}$) while it is maximized by the discriminator $\mathbf{D}$.
\begin{equation}\label{eq:totallosses}
\begin{split}
\min_{\mathbf{E}_{ex}, \mathbf{E}_{sh}, \mathbf{G}} \max_{\mathbf{D}} \ \mathcal{L} =& \ \mathbf{L}_{GAN}^{\mathbf{x}} + \mathbf{L}_{GAN}^{\mathbf{y}} + \lambda_{L_1} \left(\mathbf{L}_{1}^{\mathbf{x}, \mathbf{y}} + \mathbf{L}_{1}^{\mathbf{y}, \mathbf{x}}\right)\\[0.25em]
 & + \lambda_{L_{KL}} (\mathbf{L}_{KL}^{\mathbf{x}} + \mathbf{L}_{KL}^{\mathbf{y}}) + \lambda_{L_1}^{sh} \mathbf{L}_{1}^{sh}
\end{split}
\end{equation}

Where $\lambda_{L_1}$, $\lambda_{L_1}^{sh}$ and $\lambda_{L_{KL}}$ are constant coefficients to weight the loss terms.

\subsection{Implementation}

\textbf{Network architectures}: Our model is architectured around four network blocks: the shared feature encoder, the exclusive feature encoder, the decoder and the discriminator. The shared feature encoder is composed of 5 convolutional layers while the exclusive feature encoder is composed of a first convolutional layer and three consecutive ResNet blocks \cite{He2016a}. Since the exclusive feature encoder must provide a normally distributed vector, 2 fully-connected layers of size 64 are appended on top of the ResNet blocks to estimate its mean value $\mu$ and standard deviation $\sigma$. The decoder consists of 4 transposed convolutional layers. Finally, the discriminator is composed of 5 convolutional layers. During training and test experiments, we use batch normalization in all the networks. All the layers use a kernel of size $4 \times 4$, a stride of 2 and leaky ReLU as activation function (except the discriminator and the decoder outputs where the sigmoid and hyperbolic tangent functions are used, respectively).

\textbf{Optimization setting}: To train our model, we use batches of 64 randomly selected pairs of images of size $64 \times 64 \times 4$ from our time series dataset. Every network is trained from scratch by using randomly initialized weights as starting point. The learning rate is implemented as a staircase function which starts with an initial value of 0.0002 and decays every 50000 iterations. We use Adam optimizer to update the network weights using a $\beta = 0.5$ during 150000 iterations. Concerning the loss coefficients, we use the following values: $\lambda_{L_1} = 10$, $\lambda_{L_1}^{sh} = 0.5$ and $\lambda_{L_{KL}} = 0.01$ during training. The training procedure is summarized in Algorithm \ref{alg:trainingalgorithm}.

\begin{algorithm}
\caption{Training algorithm.}
\label{alg:trainingalgorithm}
  \begin{algorithmic}[1]
			\State Random initialization of model parameters \\ $(\theta_{\mathbf{D}}^{(0)}, \theta_{\mathbf{E}_{sh}}^{(0)}, \theta_{\mathbf{E}_{ex}}^{(0)}, \theta_{\mathbf{G}}^{(0)})$
      \For{$k=1$; $k= k + 1$; $k<\text{number of iterations}$}
				\State Sample a batch of  $m$ time series $\{\mathbf{t}_{s}^{(1)},...,\mathbf{t}_{s}^{(m)}\}$
				\State Sample a batch of  $m$ image pairs  \\ \hspace{0.5cm} $\{ (\mathbf{x}^{(1)}, \mathbf{y}^{(1)}),...,(\mathbf{x}^{(m)}, \mathbf{y}^{(m)})\}$ from $\{\mathbf{t}_{s}^{(i)}\}$
				\State Compute $\mathcal{L}^{(k)}(\mathbf{x}^{(i)}, \mathbf{y}^{(i)}, \theta_{\mathbf{D}}^{(k)}, \theta_{\mathbf{E}_{sh}}^{(k)}, \theta_{\mathbf{E}_{ex}}^{(k)}, \theta_{\mathbf{G}}^{(k)})$
			  \begin{equation}\label{eq:loss_estimated}
				\begin{split}
				\mathcal{L}^{(k)} =& \ \frac{1}{m} \sum_{i=1}^{m} \left[ \left(\mathbf{D}(\mathbf{x}^{(i)})\right)^{2} + \left(\mathbf{D}(\mathbf{y}^{(i)})\right)^{2} \right. \\
				& + \left(1 - \mathbf{D}(\mathbf{G}(\mathbf{E}_{sh}(\mathbf{y}^{(i)}), \mathbf{E}_{ex}(\mathbf{x}^{(i)})))\right)^{2}\\
				& + \left(1 - \mathbf{D}(\mathbf{G}(\mathbf{E}_{sh}(\mathbf{x}^{(i)}), \mathbf{E}_{ex}(\mathbf{y}^{(i)})))\right)^{2} \\
				& + \lambda_{L_1} \left( \lvert \mathbf{x}^{(i)} - \mathbf{G}(\mathbf{E}_{sh}(\mathbf{y}^{(i)}), \mathbf{E}_{ex}(\mathbf{x}^{(i)})) \rvert \right. \\
				& + \left. \lvert \mathbf{y}^{(i)} - \mathbf{G}(\mathbf{E}_{sh}(\mathbf{x}^{(i)}), \mathbf{E}_{ex}(\mathbf{y}^{(i)})) \rvert \right) \\
				& + \lambda_{L_1}^{sh} \left( \lvert \mathbf{E}_{sh}(\mathbf{x}^{(i)}) - \mathbf{E}_{sh}(\mathbf{y}^{(i)}) \rvert \right) \\
				& - \frac{1}{2} \lambda_{L_{KL}} \left(2 + \log(\sigma_{\mathbf{E}_{ex}(\mathbf{x}^{(i)})}^2) - \mu_{\mathbf{E}_{ex}(\mathbf{x}^{(i)})}^2  \right. \\
				& - \sigma_{\mathbf{E}_{ex}(\mathbf{x}^{(i)})}^2 + \log(\sigma_{\mathbf{E}_{ex}(\mathbf{y}^{(i)})}^2) - \mu_{\mathbf{E}_{ex}(\mathbf{y}^{(i)})}^2 \\
				& \left. \left. - \sigma_{\mathbf{E}_{ex}(\mathbf{y}^{(i)})}^2 \right) \right]
				\end{split}
				\end{equation}
				\State Update the model parameters:
				\begin{align}\label{eq:model_parameters}
				\theta_{\mathbf{D}}^{(k+1)} &\leftarrow \text{Adam} \left(-\nabla_{\theta_{\mathbf{D}}^{(k)}}\mathcal{L}^{(k)}, \theta_{\mathbf{D}}^{(k)}\right)\\
				\theta_{\mathbf{E}_{sh}}^{(k+1)} &\leftarrow \text{Adam} \left(\nabla_{\theta_{\mathbf{E}_{sh}}^{(k)}}\mathcal{L}^{(k)}, \theta_{\mathbf{E}_{sh}}^{(k)} \right)\\
				\theta_{\mathbf{E}_{ex}}^{(k+1)} &\leftarrow \text{Adam} \left(\nabla_{\theta_{\mathbf{E}_{ex}}^{(k)}}\mathcal{L}^{(k)}, \theta_{\mathbf{E}_{ex}}^{(k)}\right)\\
				\theta_{\mathbf{G}}^{(k+1)} &\leftarrow \text{Adam} \left(\nabla_{\theta_{\mathbf{G}}^{(k)}}\mathcal{L}^{(k)}, \theta_{\mathbf{G}}^{(k)}\right)
				\end{align}
      \EndFor
  \end{algorithmic}
\end{algorithm}

\section{Experiments}

\subsection{Sentinel-2}

The Sentinel-2 mission is composed of a constellation of 2 satellites that orbit around the Earth providing an entire Earth coverage every 5 days. Both satellites acquire images at 13 spectral bands using different spatial resolutions. In this paper, we use the RGBI bands which correspond to bands at 10m spatial resolution. In order to organize the data acquired by the mission, Earth surface is divided into square tiles of approximately 100 km on each side. One tile acquired at a particular time is referred to as a granule.

To create our dataset, we selected 42 tiles containing several regions of interest such as the Amazon rainforest, the Dead Sea, the city of Los Angeles, the Great Sandy Desert, circular fields in Saudi Arabia, among others. As explained by Kempeneers and Soille~\cite{kempeneers2017optimizing}, many of the acquired granules might carry useless information. In our case, the availability of granules for a given tile depends on two factors: the cloud coverage and the image completeness. Therefore, we defined a threshold in order to avoid these kind of problems that affect Earth observation by setting a cloud coverage tolerance of 2\% and completeness tolerance of 85\%. For each tile, we extracted 12 granules from March 2016 to April 2018. Then, we selected 25 patches of size $1024 \times 1024$ from the center of the tiles to reduce the effect of the satellite orbit view angle. Finally, our dataset is composed of 1050 times series each of which is composed of 12 images of size $1024 \times 1024 \times 4$.

In order to analyze the entire time series using smaller patches the following strategy is applied: a batch of time series composed of images of size $64 \times 64 \times 4$ is randomly sampled from the time series of size $1024 \times 1024 \times 4$. Since our model takes 2 images as input, at each iteration two images are randomly selected from the time series to be used as input for our model. Thus, the whole time series is learned as the training procedure progresses. Data sampling procedure is depicted in Figure \ref{fig:TimeSeriesDataSelection}.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.9\linewidth]{TimeSeriesDataSelection2.png}
\end{center}
\caption{Training data selection. A batch of smaller time series is randomly sampled from the dataset. At each iteration two images are randomly selected from each time series to be used as input for our model.}
\label{fig:TimeSeriesDataSelection}
\end{figure}

To evaluate the model performance and the learned representations, we perform several supervised and unsupervised experiments on Sentinel-2 data as suggested by Theis in \cite{theis2015note}. We evaluate our model on: a) image-to-image translation to validate the representation disentanglement; b) image retrieval, image classification and image segmentation to validate the shared representation and c) change detection to analyze the exclusive representation.

\subsection{Image-to-image translation}

\begin{figure*}[t]
\begin{center}
\begin{tabular}{c c c}
\raisebox{-0.5\totalheight}{\includegraphics[width=55mm, height=55mm]{cross_shared_sample.png}} & \raisebox{-0.5\totalheight}{\includegraphics[width=55mm, height=55mm]{cross_exclusive_sample.png}} & \raisebox{-0.5\totalheight}{\includegraphics[width=55mm, height=55mm]{cross_generated_sample.png}} \\[3.00cm]
(a) & (b) & (c)
\end{tabular}
\end{center}
\caption{Image translation performed on images of Brawley, California. (a) Images used to extract the shared features; (b) Images used to extract the exclusive features; (c) Generated images from the shared representation of (a) and the exclusive representation of (b).}
\label{fig:ImageTranslationExperiment0}
\end{figure*}

\begin{figure*}[h]
\begin{center}
\includegraphics[width=0.8\linewidth]{multimodal.png}
\end{center}
\caption{Multimodal generation. The first row corresponds to a time series sampled from the test dataset. The second row corresponds to a time series where each image is generated by using the same shared feature and only modifying the exclusive feature.}
\label{fig:multimodal}
\end{figure*}

It seems natural to first test the model performance at image translation. We use a test dataset which is composed of time series acquired from different tiles to guarantee that training and test datasets are independent. For each dataset, 150 batches of 64 time series are randomly selected. It represents around 20k processed images of size $64 \times 64 \times 4$.

An example of image-to-image translation can be observed in Figure \ref{fig:ImageTranslationExperiment0}. For instance, let us consider the image in the third row, fifth column. The shared feature is extracted from an image $\mathbf{x}$ which corresponds to growing crop fields while the exclusive feature is extracted from another image $\mathbf{y}$ where fields have been harvested. Consequently, the generated image contains harvested fields which is defined by the exclusive feature of image $\mathbf{y}$. In general, generated images look realistic in both training and test datasets except for small details which are most likely due to the absence of skip connections in the generator part of the model.

We quantify the $L_1$ distance between generated images $\mathbf{G}(\mathbf{E}_{sh}(\mathbf{x}), \mathbf{E}_{ex}(\mathbf{y}))$ and images $\mathbf{y}$ used to extract the exclusive feature. Results can be observed in Table \ref{tab:ImageTranslationExperiment} (first row). Pixel values in generated images and real images are in the range of $[-1, 1]$, thus a mean difference of $0.0152$ in the training dataset indicates that the model performs well at image-to-image translation. A slightly difference is obtained in the test dataset where the $L_1$ distance is $0.0207$.

A special image-to-image translation case is image autoencoding where the shared and exclusive features are extracted from the same image. Additionally, we compute the $L_1$ distance between images $\mathbf{x}$ and autoencoded images $\mathbf{G}(\mathbf{E}_{sh}(\mathbf{x}), \mathbf{E}_{ex}(\mathbf{x}))$ for comparison purpose in Table \ref{tab:ImageTranslationExperiment} (second row). Lower values in terms of $L_1$ distance are obtained with respect to those of image-to-image translation. It is important to note that input images are considerably well reconstructed even if this case is not considered during training. Finally, we perform times series reconstruction in order to show that the exclusive feature encodes the specific information of each image. An image is randomly selected from a time series to extract its shared feature. While keeping the shared feature constant and only modifying the exclusive feature, we reconstruct the original time series. Results in terms of $L_1$ distance between the original time series and the reconstructed one can be observed in Table \ref{tab:ImageTranslationExperiment} (third row). Similar values to those of image-to-image translation are obtained. An example of time series reconstruction can be seen in Figure \ref{fig:multimodal}.

\begin{table}[h]
\begin{center}
  \begin{tabular}{| c | c | c |}
    \hline
    Task & Training set & Test set \\ \hline
		\hline
    Image translation &  $0.0152 \pm 0.0573$ & $0.0207 \pm 0.0774$ \\ \hline
    Autoencoding      &  $0.0084 \pm 0.0309$ & $0.0087 \pm 0.0312$ \\ \hline
		Time series       &  $0.0177 \pm 0.0622$ & $0.0223 \pm 0.0828$ \\
    \hline
  \end{tabular}
\end{center}
\caption{Mean and standard deviation values of the $L_1$ distance for image-to-image translation (first row), image autoencoding (second row) and time series reconstruction (third row).}
\label{tab:ImageTranslationExperiment}
\end{table}

\subsection{Image retrieval}

In this experiment, we want to evaluate whether the shared feature provides information about the geographical location of time series via image retrieval. Given an image patch from a granule acquired at time $t_o$, we would like to locate it in a granule acquired at time $t_f$.
The procedure is the following: a time series of size $12 \times 1024 \times 1024 \times 4$ is randomly sampled from the dataset. Then, a batch of 64 image patches of size $64 \times 64 \times 4$ is randomly selected as shown in Figure \ref{fig:ImageRetrievalSharedFeatureBBPatches}(a). The corresponding shared features are extracted for each image of the batch. The main idea is to use the information provided by the shared feature to locate the image patches in every image of the time series. For each image of the time series, a sliding window of size $64 \times 64 \times 4$ is applied in order to explore the entire image. As the window slides, the shared features are extracted and compared to those of the images to be retrieved. The nearest image in terms of $L_1$ distance is selected as the retrieved image at each image of the time series. In our experiment, 150 time series of size $12 \times 1024 \times 1024 \times 4$ are analyzed. It represents around 115k images of size $64 \times 64 \times 4$ to be retrieved and 110M images of size $64 \times 64 \times 4$ to be analyzed.

\begin{figure}[!t]
\begin{center}
\begin{tabular}{c c}
\raisebox{-0.5\totalheight}{\includegraphics[width=40mm, height=40mm]{image_retrieval_shared_test_query_img_patches_0.png}} & \raisebox{-0.5\totalheight}{\includegraphics[width=40mm, height=40mm]{image_retrieval_shared_test_image_retrieval_0_2.png}} \\[2.00cm]
(a) & (b)
\end{tabular}
\end{center}
\caption{Image retrieval using shared feature comparison. (a) Selected image from a time series where a batch of 64 patches (colored boxes) are extracted from; (b) Another image from the same time series is used to locate the selected patches. The algorithm plots colored boxes corresponding to the nearest patches in terms of shared feature distance.}
\label{fig:ImageRetrievalSharedFeatureBBPatches}
\end{figure}

To illustrate the retrieval algorithm, let us consider a test image of agricultural fields. We plot the patches to be retrieved in Figure \ref{fig:ImageRetrievalSharedFeatureBBPatches}(a) and the retrieved patches by the algorithm in Figure \ref{fig:ImageRetrievalSharedFeatureBBPatches}(b). As can be seen, even if some changes have occurred, the algorithm is able to spatially locate most of the patches. In spite of the seasonal changes in the agricultural fields, the algorithm performs correctly since the image retrieval leverages the shared representation which contains common information of the time series. Results in terms of Recall@1 are displayed in Table \ref{tab:ImageRetrievalSharedFeatures} (first row). We obtain a high value in terms of Recall@1 even if it is not so close to 1. This result can be explained since the dataset contains several time series from the desert, forest and ocean tiles which could be notoriously difficult to retrieve even for humans. For instance, image retrieval performs better in urban scenarios since the city provides details that can be easily identified in contrast to agricultural fields where distinguishing textures can be confusing.

\begin{table}[h]
\begin{center}
\begin{tabular}{| c | c |}
    \hline
    Method &  Recall@1 \\ \hline
		\hline
    Shared features & 0.7372 \\ \hline
    Raw pixels & 0.5083 \\
    \hline
  \end{tabular}
\end{center}
\caption{Image retrieval results in terms of Recall@1  using the shared feature representation and the raw pixels of the image as feature.}
\label{tab:ImageRetrievalSharedFeatures}
\end{table}

As a baseline to compare to the retrieval image based on the shared features, we use the raw pixels of the image to find the image location. Our experiments show that using raw pixels as feature yields a poor performance to locate the patches (see Table \ref{tab:ImageRetrievalSharedFeatures}, second row). We note that even if the retrieved images look similar to the query images, they do not come from the same location. The recommended images using raw pixels are mainly based on the image color. Whenever a harvest fields is used as query image the retrieved images correspond to harvested fields as well. This is not the case when using shared features since seasonal changes are ignored in the shared representation.

\begin{figure}[b]
\begin{center}
\begin{tabular}{c c}
\raisebox{-0.5\totalheight}{\includegraphics[width=40mm, scale=1.0]{ImageSegmentation_original_more_brightness.png}} & \raisebox{-0.5\totalheight}{\includegraphics[width=40mm, scale=1.0]{ImageSegmentation_segmap.png}} \\[2.00cm]
(a) & (b)
\end{tabular}
\end{center}
\caption{Image segmentation in Shanghai, China. A sliding window is used to extract the shared features of the image which in turn are used to perform clustering with 7 classes. (a) Image to be segmented; (b) Segmentation map. }
\label{fig:SegmentationExperiment}
\end{figure}

\subsection{Image classification}

\begin{figure*}[!t]
\begin{center}
\begin{tabular}{c c c}
\raisebox{-0.5\totalheight}{\includegraphics[width=55mm, scale=1.0]{ChangeDetection_img0_Brawley_64.png}} & \raisebox{-0.5\totalheight}{\includegraphics[width=55mm, scale=1.0]{ChangeDetection_img1_Brawley_64.png}} & \raisebox{-0.5\totalheight}{\includegraphics[width=55mm, scale=1.0]{ChangeDetection_map_Brawley_64.png}} \\[3.00cm]
(a) & (b) & (c)
\end{tabular}
\end{center}
\caption{Change detection in Brawley, USA. A window slides on the images to be compared and extracts their exclusive features. (a) Image $\mathbf{x}$; (b) Image $\mathbf{y}$; (c) A red colored box is plotted in the regions where the $L_1$ distance of the exclusive features extracted by the window is higher than a user-selected threshold.}
\label{fig:ChangeDetectionExperiment}
\end{figure*}

A common method to evaluate the performance of unsupervised features is to apply them to perform image classification. We test the shared features extracted by our model using a novel dataset called EuroSAT \cite{DBLP:journals/corr/abs-1709-00029}. It contains 27000 labeled images in 10 classes (residential area, sea, river, highway, etc.). We divide the dataset into a training and test dataset using a 80:20 split keeping a proportional number of examples per class.

We recover the shared feature encoder $\mathbf{E}_{sh}(\cdot)$ as feature extractor from the pretrained model. We append two fully-connected layers of 64 and 10 units, respectively on top of the feature extractor. We only train these fully-connected layers while keeping frozen the weights of the feature extractor in a supervised manner using the training split of EuroSAT. Results can be observed in Table \ref{tab:ClassificationResults}. We obtain an accuracy of $92.38\%$ while we achieve an accuracy of $94.54\%$ by not freezing the weights of the feature extractor during training. It is important to note that using pretrained weights reduces the training time and allows to achieve better performance with respect to randomly initialized weights ($62.13\%$ of accuracy after 50 epochs).

\begin{table}[h]
\begin{center}
  \begin{tabular}{| c | c | c |}
    \hline
		Model & Accuracy  & Epochs\\ \hline
		\hline
    Pretrained + Fine-tuning    & $92.38\%$ & $10$\\ \hline
		Pretrained + Full-training  & $94.54\%$ & $10$\\ \hline
		From scratch                & $62.13\%$ & $50$\\
    \hline
  \end{tabular}
\end{center}
\caption{Accuracy results in the test dataset from classification experiments.}
\label{tab:ClassificationResults}
\end{table}

Higher accuracy ($98.57\%$) in the EuroSAT dataset is claimed by Helber \etal \cite{DBLP:journals/corr/abs-1709-00029} using supervised pretrained GoogLeNet or ResNet-50 models. However, we show that using a very simple model trained in an unsupervised manner allows us to obtain excellent results.

\subsection{Image segmentation}

Since the shared feature representation are related to the location and texture of the image, we perform a qualitative experiment to illustrate that it can be used to perform image segmentation. An image of size $1024 \times 1024 \times 4$ is randomly selected from a time series. Then, a sliding window of size $64 \times 64 \times 4$ and stride of size $32 \times 32$ is used to extract the patches. The shared features extracted from these patches are used to perform clustering via k-means. A new sliding window with a stride of $8 \times 8$ is used to extract the shared features from the image. Each of the extracted shared features is assigned to a cluster. Since several clusters are assigned for each pixel, the cluster is decided by the majority of voted clusters. In Figure \ref{fig:SegmentationExperiment}, a segmentation map example in Shanghai is displayed. As can be seen, this unsupervised image segmentation method achieves interesting results. It is able to segment the river, the port area and the residential area, among others. We think that segmentation results can be improved by using a smaller windows to achieve better resolution. On the other hand, experiments using the raw pixels of the image as features produce segmentation maps of lower visual quality.

\subsection{Change detection}

Another qualitative experiment is performed in order to illustrate some properties of the exclusive feature representation. Since the particular information of each image of a time series is contained in the exclusive feature, we leverage this information to propose a naive change detection method. Two images of size $1024 \times 1024 \times 4$ are selected from a given time series. A sliding window of size $64 \times 64 \times 4$ is used to explore both images using a stride of size $32 \times 32$. As the window slides, the exclusive features are extracted and compared using the $L_1$ distance. Then, a threshold is defined to determine whether a change has occurred or not. If the $L_1$ distance is higher than the threshold, a red colored box is plotted indicating that change has occurred. An example can be seen in Figure \ref{fig:ChangeDetectionExperiment}. Despite the method simplicity, our experiments suggest that the low-dimensional exclusive feature captures the factors of variation in time series generating visually coherent change detection maps.

\section{Conclusion}

In this work, we investigate how to obtain a suitable data representation of satellite image time series. We first present a model based on VAE and GAN methods combined with the cross-domain autoencoder principle. This model is able to learn a disentangled representation that consists of a common representation for the images of the same time series and an exclusive representation for each image. We train our model using Sentinel-2 time series which indicates that the model is able to deal with huge amounts of high-dimensional data. Finally, we show experimentally that the disentangled representation can be used to achieved interesting results at multiple tasks such as image classification, image retrieval, image segmentation and change detection.

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


This and four other pieces surfaced after Beethoven's death, bringing with them several very puzzling mysteries. Most challenging of them all was the instrument for which they were written. Piano, strings, harp and most other common instruments were instantly ruled out, building on the perplexing mystery and adding to the frustration. Eventually, Albert Kopfermann set forth a convincing argument that their strange scoring seemed a perfect fit for the Flötenuhr or Spielühr, a mechanical clock.
While the musical clock of the eighteenth century might seem like a toy to the twenty-first century ear and eye, its owners (usually members of the aristocracy) regarded it as a quite sophisticated device, not least because it was the only way to hear music away from the concert hall and parlors. Its chime-like tones may have limited its expressive range, but it seems that famous composers like Beethoven took compositions for the musical clock quite seriously.


text:
Aug 14, 2020 25th Anniversary
Paving His Own Road to Success Through eBay
Donovan Mitchell's self-determination and work ethic outlasted the motocross accident that left him paralyzed, and drove him to create a thriving new career.
Editor's note: In celebration of eBay's 25th anniversary, we're spotlighting a seller every week from our global community in our Mini Stories column through 2020.
After receiving his first dirt bike at the tender age of four, eBay seller Donovan Mitchell realized he wanted to become a professional motocross racer. Throughout his childhood and teenage years in northern California near Sacramento, he opted out of school dances and weekend hangouts with friends to relentlessly pursue his goal. Chasing after his dream meant long days training and practicing. By age 17, Donovan's hard work paid off and he turned pro, competing on the national racing circuit. "It seemed unbelievable that my dream had become a reality," he said.
The late 90s for Donovan were spent traveling around the country racing; he used his race winnings to help foot the bill. Donovan said he was thrilled when the Red Bull Factory KTM team signed him to race the 2000 Supercross/Motocross season.
But at 19 years old, his emerging career came to a halt when in a racing crash in 1999, he broke his neck and suffered a spinal cord injury that left him paralyzed from the neck down. He was airlifted to the hospital and spent a month in the ICU on ventilator support, unable to breathe independently. "My mindset was still that if I worked hard enough, I would overcome this injury and return to my former glory," he said. "I learned very quickly that spinal cord injuries don't work that way."
"After being independent for so long, the hardest thing to grasp was asking for help," he said. "It wasn't until a year and a half later that I was finally able to gain a little movement in my arm; it took several years to get in the groove and find a solid routine."
Donovan found himself unsure about what was next for him. He spent the following 14 years "floating through life, trying to find a career that was compatible" with his physical disability, he said. He gave college a go, along with a handful of jobs in the racing industry, including a job as a coordinator for a race team, but none of those pathways truly sparked his passion in the same way motocross racing did. "In my heart and my brain, none of it brought any excitement," he said.
Then, one day, Donovan's curiosity around eBay was piqued after a conversation with an old friend, who was a former, motocross racing mechanic. His friend had quit his job to run his eBay store full-time; "He said, 'Hey, why don't you look at doing what I do on eBay and having an eBay store?'" Donovan said. His friend showed Donovan how to sell on eBay, including how to list items and add descriptions. "I was very fortunate that I had a pretty successful mentor in the early days to listen to all my questions and get me in the right direction to take off running."
In 2014, Donovan started selling parts from his garage that were left over from his years around the sport of motocross. As long as he could find someone else to help with photos and shipping, he could handle all of the listings and administrative eBay store management independently.
"[Selling on eBay] became my new obsession," Donovan said. "It was the first thing that reminded me, 'Okay, I do have this passion, and I want to channel some of my competitiveness, my determination, into it.'"
Donovan's parents encouraged him from an early age to pursue his goal to train and compete on the professional motocross circuit.
Donovan traveled the country to race in 1998 and 1999, paying his way with his race winnings.
Around the same time that Donovan discovered his love for eBay and entrepreneurship, he met Jenny. They had friends in common, and he learned they shared a mutual passion for motorcycles. Jenny worked for a motorcycle dealership nearby, and off-handedly, she mentioned her employer had a surplus inventory of parts available — would Donovan be interested? Saying yes would mean rapidly expanding his business operations, but he took the plunge. In 2015, with Jenny's help, he sold $100,000 of inventory on eBay and tripled his investment.
His connection with Jenny soon evolved to something long-lasting — the two married last spring and are now running their BikeNow Motorsports store full-time together. "Without her, there's a lot that's impossible," he said.
Donovan said their partnership thrives because their personalities are such a natural fit; while he is driven and strong-willed, Jenny is "easygoing and mellow, calm and carefree." Through all of the store's milestones, Jenny has celebrated alongside Donovan. He recalls moments when they'd achieve a goal for monthly sales: "We'd look at each other like 'Wow, babe, that's amazing. We did that.' It doesn't seem real."
Jenny and Donovan share a lot — including their love for motorcycles.
In 2017, BikeNow Motorsports became an authorized dealer with a motorcycle parts distributor and grew their inventory from "a few hundred items in our garage and spare bedrooms" to over 35,000 listings and a small office. The business has continuously grown year-over-year, since it started. This year, with people looking to fix up old bikes while in quarantine due to the COVID-19 pandemic, BikeNow Motorsports has seen a sharp increase in sales over this time last year, a rapid uptick that Donovan said can sometimes make him feel like he's on a "runaway train."
On a normal day, Donovan wakes up around 6 a.m. "Immediately, I'm jumping onto my iPad to place any orders that are drop shipping through our distributor warehouses; I'm always answering questions and providing customer service." Throughout the day Jenny handles inventory control, packaging and shipping of the items on hand, while Donovan processes orders, creates listings and continues to provide constant customer service: "I kind of always have my iPad in my lap."
BikeNow Motorssports is a family business — even Hollie is a valued associate.
For Donovan, eBay has helped foster old connections with friends from his racing days. Sometimes, a customer's name rings a bell or he notices that the shipping location is in his hometown. When that happens, he said, "I'll wonder if that's a person I used to race against when I was a kid, and I'd lost track of them years and years ago."
He'll send an eBay message, thanking them for their purchase and leaving his name. At least half a dozen times, he's reconnected with an old friend or acquaintance that way. "There was one gentleman reaching out about some parts, and it turned out that we'd raced each other back in the mid-80s when we were eight years old," he said.
Donovan remembers the way it felt to spend a weekend riding in his childhood, and is motivated to make that feeling happen for his customers.
Selling on eBay also stirs memories for Donovan, who recalls how fervently his dad would scour local bike shops if he needed an accessory or part before a big race. Before the days of the internet, if they couldn't find the right part, racing was off for the weekend.
Today, if a customer needs rush delivery for a part before a weekend race, Donovan strives to make it happen. "They can't be any more thankful, saying it saved their weekend. That's close to home, because I knew that feeling growing up," he said.
Donovan's now an ambassador with the spinal cord research foundation Wings for Life, which also keeps him busy. "As an ambassador, I speak at events and mentor other newly injured individuals from all walks of life, advocating for continued research so that hopefully we see a day where others like me can get back on our feet," he said. He added he feels a sense of purpose in advocating for a foundation that has given him a platform to spread awareness and which contributes 100 percent of donations directly to spinal cord research.
Through eBay, Donovan said he's found a way to chase new dreams — and revved his talents through eBay entrepreneurship. He considers himself a model of the platform's accessibility to all. "I truly embody what's possible on eBay," he said. "I'm able to run a business with just the use of one of my arms and no use of my fingers. I am a prime example that people with disabilities can have independence and be successful using eBay as a platform to better their lives."
Empowering entrepreneurship and creating economic opportunity for all has been our philosophy at eBay since we were founded 25 years ago. Our marketplace has been enriched by the hundreds of thousands of independent small businesses and entrepreneurs worldwide. We win when they do. Learn more about how we partner with our sellers.
Topic: Customers
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Originally built in 1865, Grandfather's Barn is the only remaining building from the farmstead existing when the land was purchased by the Fair in 1886.
Grandfather's Barn was closed in 1989 due to dangerous amounts of deterioration, but thanks to a generous $100,000 donation from Vermeer Manufacturing of Pella, the building was reopened on the first day of the 1994 Iowa State Fair.
Renovations were completed by Barn Renovators, Inc. of Winterset and included rebuilding the foundation and batten boards, leveling, re-roofing, painting, and repairing stairs, doors, and windows.
During the Fair, Grandfather's Barn hosts "The Wine Experience," an educational exhibit about Iowa's growing wine industry.


We do the same thing for the graphs showing new likes (paid and organic) and unlikes. You get a daily graph and a monthly graph so you can keep an eye both on what's happened recently as well as the overall trend.
The Monthly New Likes and Unlikes graph especially helped us when we tried out Facebook ads for a brief time. We got more likes (which were highlighted in green, for paid), but we also got more unlikes (probably since it got us likes from people who weren't really interested in us). So we decided that advertising isn't the way for us.
Keep an eye on these graphs, and they should help you with your Facebook marketing decisions as well!
If you've connected your Facebook page to Markitty, click here to get to your graphs.
Don't have Markitty yet? Get it now.


Dr. Alexander is committed to providing state-of-the-art care to his patients with a personal touch.
Dr. Scott Alexander was born, raised, and graduated from college in Idaho. Scott received his dental training at the University of Louisville. He was then elected to OKU dental honor society at the University of Louisville before completing his specialty certification in orthodontics at Nova Southeastern University.
Dr. Alexander is a member of the American Dental Association, American Association of Orthodontists, and is a diplomat of the American Board of Orthodontics.
Dr. Alexander also enjoys music, white-water rafting, kayaking, fishing, golfing, running, snowboarding, and many other outdoor activities that are accessible in our gorgeous state.
Dr. Mary Hoffman grew up on the coast in Newport, Oregon. She received her dental training at Oregon Health and Sciences University, after which she was accepted into Texas A&M Baylor Orthodontics residency.
Dr. Hoffman is a member of the American Dental Association, American Association of Orthodontists, and is a diplomat of the American Board of Orthodontics.
Outside of the office, Dr. Hoffman can be found spending time with her husband, Ian, and their two labradoodles. She enjoys hiking, running, bicycling, snowboarding, and travelling.
All orthodontists are dentists, but not all dentists are orthodontists. In fact, only about six out of a hundred dentists have the proper training to be "orthodontists".
As a specialist in orthodontics, unlike general dentists who provide orthodontic care, Dr. Alexander is focused only on your orthodontic needs and has completed the residency necessary to be a licensed Orthodontist. Find out more about orthodontics.


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Gay Bars and Their Importance to LGBTQ History
The LGBTQ community has grown in strength and numbers today and support has blossomed across the globe like never before but it wasn't always that way. It wasn't that long ago that people who identified with a sexuality that fell outside the cultural norm were outsiders, often unwelcome or shunned in public establishments including entertainment establishments. Lesbian love, gay love– it didn't matter. Gay and lesbian bars were either shut down or their patrons routinely arrested. However, LGBTQ bars through the years have been one of the few safe havens available for the LGBTQ community to gather, make friends, be ourselves, laugh, have fun, create community and even find some company.
With such a hostile attitude towards a segment of the human population, it was up to our community to create a place where we could feel free once inside. The bars are where we found those spaces. While LGBTQ bars have not been known by the dominant culture (except by allies) the bar scene has been and remains today one of the key touchstones of the LGBTQ community.
With June being LGBTQ Pride Month and the recent horrific shootings in Orlando, it seems that LGBT bars and hate crimes against individuals who identify as queer are receiving well-needed attention. Forty-nine people were shot dead in the wee hours of June 12, 2016, by a man who allegedly was set off months earlier by the sight of two men kissing in the city, at least according to his father. The incident left many devastated, particularly the friends, lovers, and families of the deceased. The incident went on for hours and was akin to the school shootings that had swept the country. But this was different; this was a deliberate act intended to hurt and ultimately kill LGBTQ people exclusively. It was a targeted, well thought out plan. To this man, gay love was not something to be celebrated or respected—but something is seen as so disturbing that it had to be violently cut out.
This was done by Omar Mateen, a 29-year-old male from Fort Pierce, Florida. The young man had been previously interviewed by the FBI twice — in 2013 and 2014, but not found to be a threat. On top of it, the shooter called 9-1-1 just hours before the shooting to apparently pledge his allegiance to ISIS. Mateen was well armed, prepared and well organized– he had an assault rifle and a pistol on him as he entered Pulse at two in the morning and started shooting. By the end 49 people were dead and the wounded were estimated to be 53. There was a standoff that lasted three hours, during which the people who were trapped inside with the shooter texted and called their friends and family to let them know they loved them. Many of the people on the receiving ends of those messages would never hear from their loved one again.
The police eventually were able to break into the building using an armored car and stun grenades where they were able to shoot and kill Matee finally stopping his reign of terror. The shooting is the deadliest mass murder in the history of the United States. The act was not random; it was an act of premeditated violence against an all too unprepared community out for a night of fun. And it turns out this man may have been gay himself which begs the conversation about internalized homophobia.
If he was gay, and the investigation is ongoing, then his hatred of himself as gay set off his night of terror. And where does that originate from – our heterocentric society that has been condemning same-sex love for thousands of years –it is a toxic message and gets inside people's psyches. It is dangerous and can lead to acts of violence if not dealt with. I will be discussing this more in my next article. Stay tuned.
It's a desperately sad incident and ultimately preventable. The average person may have thought that the gay community would have used this as a reason to bury their head and stay inside. Instead, they are coming out loud and proud, and gaining more support than ever before– from everyday people who want to show that they support love, not hate.
Gay and lesbian bars have also served an important place in the history of LGBTQ culture. They have traditionally been places where people who were lesbian, gay, bisexual or trans and attracted to the same sex, or identified somewhere outside the so-called normal sexual spectrum could not only go and feel safe, but find a place they belonged—with their people who understood precisely what it was like to be marginalized, to be on the fringe, to be hated. There was a feeling of community in these bars that were very rarely accessible anywhere else, and one of the only places that they could be together in public. Perhaps that is why LGBTQ watering holes are still so beloved and such a gathering place in the community to this day.
The alleged first gay bar in Europe and the world was in Cannes on the French Riviera– the Zanzibar. Opened in 1885 and only closed in 2010, the bar drew thousands over the years. In fact, Paris itself was a center for gay culture in the 1800s, becoming something of a "queer capital". Among other early pioneers of gay culture was the Slide at 157 Bleecker Street in New York, a bar for gay men that opened in 1890 and called the wickedest place in New York by the press at the time. Paris entered a period of toleration for gays in the fifties and sixties, but raids on gay bars were frequent.
Gay club Eldorado opened in Berlin in 1932 as well. Berlin became an attractive destination for gay and lesbian nightlife as early as 1900, and by 1920, the scene was swinging. The Schöneberg district near Nollendorfplatz hosted many cafes as well as bars and clubs, an attractive destination for gay people who had fled their countries out of fear of being persecuted. The club was known around the world for its transvestite shows, however, when the Nazis took over in 1933, many of the popular gay establishments were closed. The gay scene revived again after homosexuality was decriminalized in 1969, and West Berlin became a gay and lesbian-friendly district once more.
Another famous bar was The Cave of the Golden Calf, a London nightclub at 9 Heddon Street that opened up underground in 1912. The establishment quickly became popular among the artistic as well as the wealthy, thanks to Frida Strindberg's avant-garde vision for the club. It would become an influence on later nightclub models as well. Gay bar culture became more openly visible once homosexuality was decriminalized in 1967 in the UK, and Soho became the new nexus of the LGBTQ community with endless bars, restaurants, clubs, and cafes popping up. The area was considered established by the nineties, although other UK cities became known for their queer bars, such as Manchester's Canal Street, Liverpool's Stanley Street Quarter, and the gay village in Birmingham.
There have been others, from The Empire (1911 in Amsterdam to the 1930s) to the Café t Mandje, a gay bar opened by Bet van Beeren, a lesbian (1927-1982 and then opened again in 2008). Still more have been found in Copenhagen's bar Centralhhjørnet (opened 1917), the Atlantic House in Provincetown (1798), San Francisco's Black Cat Bar (1906), The Double Header in Seattle (1933), Julius Bar in NYC, Webster Hall on 125 East 11th street in New York and Rockland Palace on 280 W. 155th Street were famous for their drag balls. Eve's Hangout was a speakeasy run out of West Village by Eva Kotchever at 129 MacDougal Street. She was arrested and later deported after an undercover officer found out she was writing a short story collection called Lesbian Love. And let's not forget Stonewall of NYC – one of the most important bars of all – that catapulted the LGBTQ rights into our social justice movement in June of 1969.
There can be no doubt that the LGBTQ community has been through horrible things. Instead of this event forcing everyone to live in fear, we are determined to not let one deranged person keep us down. We are coming out stronger and louder and prouder than ever before, with cities all over the world banding together to support us. Our pride parades are going on uninterrupted and vigils are happening everywhere. By now it is certain: we will NOT be kept silent, and we will not cower in fear.
Pulse was not the first gay bar that was targeted in history but hopefully it will be the last. Here's hoping that the rest of the world catches on to what the gay community is really about; the beauty in diversity and sensitivity, self-actualization and love. One act of hate will NOT bring down an entire community. The past few days of pride, courage, kindness, outrage, love, and support have shown that all too clearly. Here's to the LGBTQ bars; long may she reign.
Author Lauren Costine discusses "Lesbian Love Addiction
(Facebook Live Video Feed, Friday, June 3, at 4:00 PM Pacific Daylight Time. To view live and comment, visit and "FOLLOW" this Facebook page). Nicholas Snow is excited to welcome BLVD Treatment Centers' Chief Clinical Officer Dr. Lauren Costine to speak about her latest book, Lesbian Love Addiction: Understanding the Urge to Merge and How To Heal When Things Go Wrong.
Everyone makes mistakes in relationships at one time or another. Somethings they learn from those mistakes – other times, they return to those behaviors and cycle through failed relationship after failed relationship. Sometimes those behaviors become an addiction to love that leave a person feeling unhappy, unfulfilled, lonely, or worse.
Dr. Lauren Costine explores these themes and more in her latest work, Lesbian Love Addiction, and closely examines the elements of
BLVD Treatment Centers' Chief Clinical Officer Dr. Lauren Costine, author, Lesbian Love Addiction: Understanding the Urge to Merge and How To Heal When Things Go Wrong
love addiction that many lesbians suffer from. A former addict turned educator, public speaker, and clinical psychologist with over 20 years of experience in private practice, she brings her expertise to bear as Chief Clinical Officer at BLVD Treatment Centers.
Love Addiction and Lesbians – Counselor Magazine
Love Addiction and Lesbians
2016/May-Jun
By: Dr. Lauren Costine
Have you heard the joke about the lesbian and the U-Haul? It goes like this: What does a lesbian bring on a second date? The punch line, an in-joke about the potent emotional entwinement that happens quickly in lesbian relationships, is a U-Haul. Two dates and it's a move-in. This harmless enigma makes nearly everyone laugh. Even straight people seem to appreciate the psychology and intensity of lesbian relationships.
Lesbians have long considered the U-Haul joke to be a complex, cute, funny, and sometimes embarrassing component of our unique lesbian lifestyles. It's sweet, yet intense. You see, we're close; we know how to bond and we're not afraid of intimacy. This is one of the things lesbians have to offer the world, the seemingly innate ability to attach to one another and to commit almost effortlessly.
It can be mesmerizing. We also know from a psychological perspective that the U-Haul metaphor does have a dark side. Far from being something ideal, this instant merging often points to a deeper problem: lesbian love addiction, a very common phenomenon with real consequences that millions of lesbians are adversely affected by. Like countless other addictions that have claimed our psyches, love addiction has, for lesbians, become the insidious superglue that offers a quick-fix solution that hides and prevents the real underlying, unmet needs inherent in our addiction to love, women, relationships, and fantasy.
There is a happier, less conflicted way of coupling—a relationship style that is less harmful to the individual self—but it takes awareness of the issues that complicate lesbian relationships for this healthier state to become the new norm.
Let's get a sense of what makes this lesbian love addiction issue so prevalent that it has turned an in-joke into an allegory. To understand love addiction, let's look at the three types that commonly manifest in lesbians: the love addict, the love avoidant, and a combination of both, the love ambivalent.
Love Addicts
Love addicts fall in love easily and quickly without really knowing the other woman. They are addicted to the way falling in love makes them feel, more specifically to the feel-good chemicals such as dopamine and oxytocin, which are emitted in the beginning stages of romance. Many love addicts have a sudden need to spend all their time with their new love, and many women move in together within a couple of dates or months (hence the U-haul joke).
Love addicts have difficulty setting boundaries, losing their sense of self once they are in a relationship. Some women even stop taking care of themselves to better fit into their new partner's life. They lose touch with their own friends, family, self-care, and personal interests. Additionally, love addicts can have a pattern of falling for women who are perpetually unavailable physically and/or emotionally, and have their heart broken over and over again. They can jump into one relationship after another to avoid being alone.
Love Avoidants
Love avoidants are addicted to the seducing and chasing—they get high from pursuing other women. Romeos and Casanovas aren't gender-specific roles! Like love addicts, love avoidants are also addicted to the high of falling in love. However, they are afraid of authentic intimacy and consequently distance themselves emotionally once the honeymoon period ends. They can feel emotionally smothered by their partners once the glitter wears off. They find fault, criticize, and blame in order to create the distance that makes them feel safe.
Love Ambivalent
Love ambivalents have love addict characteristics in one relationship and then love avoidant in the next. They can also vacillate between love addict and love avoidant behaviors within a single relationship. Love ambivalents are either lightly or deeply ambivalent about their partner and doubt or fear their ability to commit. This is a pattern found in every relationship.
The Female Brain
As we now know, men and women don't have the same exact brains as male researchers once assumed. Recent discoveries indicate there are significant differences in communication modes and the connections made between the sexes. For example, women are more wired to connect to others. It's a biological strategy that helps human beings form groups and thus create community, which in turn improves our chances of surviving in hostile environments. In other words, we crave relationships because our brains are wired to favor them. This explains, in part, why two women might be more inclined to connect more quickly than men traditionally do. This insight helps us understand how, following directions from the brain, lesbians suffering from love addiction slip into habitual patterns and roles that adversely shape their lives. We also omit oxytocin and dopamine when falling in love—men do not in the same way—therefore when two women get together the "oxyfest" is beyond intoxicating. It can also lead to addictive tendencies for those who cannot get enough of how these chemicals make them feel.
Typically, addictive relationships don't just fizzle out or finish with mutual understanding and a kiss goodbye. Instead, they end as abruptly as they started. They come crashing down with the force that sudden withdrawal produces in someone coming off of alcohol or drugs. The body and central nervous system go into a state of duress from physical withdrawal. Science has shown that breaking up, at least when love is involved, creates a type of physiological withdrawal in the body. Humans are wired to dislike this kind of rejection or sudden change in connection. When we throw love addiction into the mix—when a person is not only attempting to heal from a broken heart, but the added wound of a toxic relationship—the withdrawal can feel like a death. Recovering from lesbian love addiction can produce this kind of all-consuming grief, but the experience is essential to the healing process.
It is during this incredibly painful period that many women seek help through therapy or Sex and Love Addicts Anonymous (SLAA) or similar support groups. Without the support of others, many lesbians find it impossible to detach from their partner, to resist reaching out—calling and texting or visiting places they know they will see their ex. If they don't get help and support at this time, the pull to end the pain by returning to the drug is too powerful. Symptoms often include:
Cravings to act out irrationally with love addicted behaviors
Inexplicable aches and pains
Physical illness or exhaustion
Switching to new addictions
Changes in eating or sleeping patterns
Overwhelming self-doubt
Desperation and fear
Feeling crazy
Suicidal thoughts or impulses
Desire to isolate
Obsessive thinking or fantasizing about the partner
Sadness, despair or depression
Emotional highs and lows
Irritability, anger or rage
The healing process from love addiction can prove to be one of the most difficult things a person will ever have to endure, but there is a light at the end of the tunnel. I tell my clients that one day withdrawing will be over and they'll feel like a new person. In order to recover from love addiction, clients have to commit to the process of healing. This means experiencing the withdrawals and avoiding the urge to return to their partner. Once the psychological separation from toxic behaviors and ways of thinking is made, and the person who truly cares for herself and her needs emerges, a new person with a strong internal sense of liberation will step in. Allowing clients to go through (not around) the pain is the essential part of healing. Avoidance leads to repetitive behaviors; true insight comes from the ability to stop, notice, and experience what is taking place, no matter how painful.
Help Clients Develop a Relationship with Themselves
For clients, learning how to reestablish a relationship with themselves after going through withdrawals isn't easy. It takes time and determination to get to know themselves outside continual love relationships with other women. I advise my clients against jumping into dating again until they have a well-developed sense of who they are and why they became addicted to love, romance, sex, and fantasy. The reasons women suffer from this addiction become apparent once they can identify the root causes of their cravings, which are often deep-seated unmet needs. In my experience, women who do the work continue to experience greater and greater freedom in their lives. They are better prepared for healthy intimate relationships after this process, and are more likely to attract partners who are ready for this, too.
In many ways, it's simple. If clients spend all their time and attention focused on finding and maintaining girlfriends, partners or wives, they have less time for self-care. Ideally, they all make time in their lives for inner work such as therapy, SLAA, support groups, somatic healing work, experiential activities such as working with animals or at the very least, exercise, self-time, and mindfulness practice. The more clients see what stands in their way to a healthier self and what prevents them from creating healthier partnerships, the better they all become at healing the wounds.
– See more at: http://www.counselormagazine.com/detailpage.aspx?pageid=1443&id=6442456537 # # #
Learn more about Love Addiction by signing up for a Sneak Peak of Lesbian Love Addiction The Urge to Merge and How to Heal When Things Go Wrong