text:
\section{Introduction}

One of the most important methods used in characterizing young stars, and the only method that can establish membership in specific nearby young moving groups, is their kinematics. Kinematics exploits the property that young stars (at ages less than a billion years) are still tracing the space motion of the gas from which they formed. Several membership identification codes are available publically: BANYAN \cite[(Malo et al. 2013)]{Malo2013}, BANYAN II \cite[(Gagne et al. 2014)]{Gagne2014}; a convergence code \cite[(Rodriguez et al. 2013)]{Rodriguez2013}; and (soon), LACEwING \cite[(Riedel et al. in prep)]{Riedel2015}\footnote{See also https://github.com/ariedel/lacewing}.

These codes use the six basic kinematic elements (RA, DEC, $\pi$, $\mu_{RA}$, $\mu_{DEC}$, and RV) to predict probabilities of membership in nearby young moving groups, and can handle incomplete data. It is, however, important to know how to use and interpret the results of LACEwING (and codes like it) to obtain the most accurate results.

\section{Overview of LACEwING}
The LACEwING code computes membership probabilities in 10 moving groups ($\epsilon$ Chameleon, TW Hydra, $\beta$ Pic, Octans, Tucana-Horologium, Columba, Argus, AB Doradus, Hercules-Lyra, and Ursa Major) and 4 open clusters ($\eta$ Chameleon, The Pleiades, Coma Berenices, and The Hyades). The groups are represented as freely oriented triaxial ellipsoids, which are computed from new lists of bona-fide (high confidence) members taken from existing lists (e.g. \cite[Malo et al. 2013]{Malo2013}), and filtered using epicyclic tracebacks to limit membership to only the stars that were plausibly within the group's boundaries at the time of formation.

The LACEwING code matches stars to groups by computing up to four metrics of membership, matching the proper motion, distance, radial velocity, and spatial location of the star to those estimated for a group member at the same RA and DEC. Those metrics are combined into a single numerical goodness-of-fit value. The goodness-of-fit values are transformed into membership probabilities by creating a large simulation (using 32 million stars; \cite[Riedel et al. submitted]{Riedel2015}) of the Solar Neighborhood, which is then binned by goodness-of-fit value relative to a group. This is shown for $\beta$ Pic and Coma Ber in Fig.\,\ref{fig:beta_pic}, where all seven different possible combinations of data (RA and DEC are assumed to be known) are given as different colors. Curves are fit to those distributions to provide membership probabilities.

\begin{figure}[h]
\begin{center}
\includegraphics[width=2.5in]{beta_Pic.eps}
\includegraphics[width=2.5in]{Coma_Ber.eps}
 \caption{Histograms of matches to the simulated $\beta$ Pictoris Moving Group (left) and Coma Berenices open cluster (right), showing the percentage of stars in each bin that are genuine members of the group/cluster. Curves fit to these histograms (dotted lines) are used to translate the LACEwING code's goodness-of-fit parameters to actual membership probabilities.
   \label{fig:beta_pic}}
\end{center}
\end{figure}

\section{Lessons from LACEwING}

\subsection{\bf Groups Are Not Equally Easy to Identify}

Some groups (like the Coma Berenices open cluster) have very distinctive UVW space velocities and space positions (Fig.\,\ref{fig:ellipses}), which translate into uniquely identifying proper motion and radial velocity estimates for any given star.

\begin{figure}[h]
\begin{center}
\includegraphics[width=5in]{ellipses.eps}
 \caption{2D projections of the freely oriented triaxial ellipsoids that constitute LACEwING's kinematic models (in UVW motion and XYZ spatial position) of the 14 nearby young moving groups and open clusters. As shown in these figures, some groups have overlapping UVW space velocities or XYZ space positions. This can make it difficult to determine to which group individual stars belong.
   \label{fig:ellipses}}
\end{center}
\end{figure}

As an example, the large simulation of stars in Fig.\,\ref{fig:beta_pic} shows us that for the case in which all kinematic elements are available (RA, DEC, plus proper motion, distance, and rv), a goodness-of-fit value of 0.75 relative to $\beta$ Pic yields a roughly 50\% probability that the object is a member of $\beta$ Pic. The stars matched to Coma Ber show that, for the equivalent case using all available data, a goodness-of-fit of 0.75 yields a nearly 100\% probability that the object is a member of Coma Ber.

\subsection{\bf Additional Kinematic Data Vastly Improves Membership Probabilities}
Fig.\,\ref{fig:beta_pic} shows the seven different data availability scenarios LACEwING expects: RA and DEC plus all combinations of proper motion, radial velocity, and distance. For $\beta$ Pic, the proper motion method (red dashed line) demonstrates that even in the best possible case of a goodness-of-fit = 0, the probability of membership is only 35\%, i.e. 65\% of those perfect matches to $\beta$ Pic are actually (simulated) members of other moving groups and the generic field population. Adding a radial velocity (pink triangles) increases the best-case probability to 80\%/20\% contamination.

Another good reason to obtain more kinematic information is to reduce the false positive contamination (Fig.\,\ref{fig:beta_pic_falsepositive}) of a survey. With only proper motion and a fixed cutoff of 20\% probability, over 80\% of the objects in a survey will be contaminants (to be weeded out by some other technique, such as spectroscopy). Having complete data for the star of interest reduces the contamination rate to just above 40\%.

\begin{figure}[h]
\begin{center}
\includegraphics[width=2.5in]{beta_Pic_falsepositive.eps}
 \caption{In this figure we see the cost of lowering the membership probability threshold: the false positive contamination of the sample increases. The best way to reduce the false positive contamination is to obtain more of the 6 kinematic elements for the sample of stars, but even in this case, additional methods (spectroscopic confirmation, for example) will be needed to weed out the sample.}
   \label{fig:beta_pic_falsepositive}
\end{center}
\end{figure}

\subsection{\bf Not All Young Stars Belong to a Known Moving Group}
Virtually all lithium is primordial and easily fused by objects over roughly 60 Jupiter masses. Therefore, a low mass object with detectable lithium must either be a brown dwarf, or a young star. \cite[Riedel et al. (submitted)]{Riedel2015} assembled a sample of roughly 500 lithium-detected nearby stars and used those stars as a test sample for LACEwING, reasoning that these should be the best nearby young moving group members. However, nearly half the sample did not match any of the known nearby young moving groups LACEwING tests for. The same lithium-detected sample was run through other moving group codes - BANYAN I and II, and the Convergence code - with similar results. Some lithium-detected stars were not sorted into moving groups (Fig.\,\ref{fig:nongroup}).

\begin{figure}[h]
\begin{center}
\includegraphics[width=3.4in]{04473187-7412323.eps}
 \caption{2MASS 0447-7412 has a significant lithium equivalent width, 50$\pm$20 m\AA \cite[(Murphy \& Lawson 2015)]{Murphy2015}, a proper motion, a radial velocity, and no distance. If we generate a range of reasonable distances for the star, it could spatially be within the Octans (cyan) moving group, but it cannot possibly share the space velocity of Octans, and cannot be a member (as previously noted by Murphy)
   \label{fig:nongroup}}
\end{center}
\end{figure}

There are three potential reasons for this: 1.) There are additional nearby young stellar groups not currently known or accounted for; 2.) there is a young field population not part of any previously identified groups; 3.) our current understanding of the kinematics of moving groups is incomplete. The fact that multiple moving group codes (each with a different algorithm and kinematic model of the nearby young moving groups) could not identify large numbers of stars as members of nearby young moving groups suggests that the explanation probably involves some combination of the first two points. Ultimately, the message is clear: Young stars do not need to be members of a known nearby young moving group. ``None of the above'' can be a correct answer.

\section{Conclusions}

As we enter the third decade of nearby young star research, there is much more to be done from the standpoint of membership statistics. Moving group identification codes such as LACEwING will continue to provide essential tools in studies of stellar and planetary formation and evolution, but care must still be taken in interpreting their results.

redpajama_set_name: RedPajamaArXiv


Rocheston Customer Happiness Accreditation Standards is based on Nebula Metrics. It is a scientific methodology that measures customer satisfaction using proprietary patent-pending customer-oriented key measurement drivers.
Nebula Metrics is a scientific measurement of tracking Customer Happiness. It is reported on a scale of 1-100 and driven by three critical dimensions: Tangible Value, Intangible Value, and Organizational Impact.
An organization's business growth and survival is closely linked to and decided by Customer Satisfaction. However, in the new digital world, Customer Satisfaction is no longer enough. Organizations have to look beyond and target "Customer Happiness" as the means to long-term growth. As the saying goes, when the customer is happy, the business takes care of itself.
Presents a picture of the organization's standing on delivering Customer Happiness.
An organization's long-term business growth and survival are closely linked and decided by customer satisfaction. However, in the new digital world, customer satisfaction is no longer enough. Organizations have to look beyond and target "Customer Happiness" as the means to long-term growth. As the saying goes, when the customer is happy, the business takes care of itself.
Rocheston's Customer Happiness Index Accreditation Framework provides an opportunity for organizations across the globe to assess themselves on where they stand in the Customer Happiness scale on an independent and comparative basis with some of the best organizations in the world.
Organizations are invited very selectively, and they can choose to get accredited by responding to a self-assessment questionnaire that evaluates all the above mentioned critical dimensions that lead to Customer Happiness. The assessment generates a set of scores which are then objectively weighted across the various attributes and dimensions to arrive at the final CHI Score. The CHI score presents an objective picture of how well the organization currently delivers Customer Happiness from an introspective view.
Rocheston then closes the loop for you by providing a dedicated, live and online Customer Happiness Portal (Nebula Metrics Portal). This portal captures real concurrent customer feedback on various aspects of their engagement and experience with the products and services of the client organization.
This information is fed back to the organization continually to calibrate their performance on the Customer Happiness journey, and valuable inputs, advice, mentoring and coaching is provided by us to improve Customer Happiness scores significantly, going forward.
The Rocheston CHI accreditation framework has the following dimensions with multiple attributes embedded therein.
How well these components are linked with each other is explained by a well-validated Cause-Effect Model. The Cause-Effect model uses advanced algorithms to establish the magnitude of the relationship between the various attributes and dimensions that lead to Customer Happiness.
Globally it has been proven that the top 100 companies never stay there for more than 20 years, except for a few rare exceptions. Those companies that continue to remain in the top 100 do one thing very well - they keep their Customers happy and also stay profitable. If you wish to join them, Rocheston's Customer Happiness Accreditation is the way ahead! We are ready to take off, are you ready.
Let's energize, synergize and help you succeed!
Nebula Metrics Customer Happiness Index methodology is based on customer to business relationships covering service and satisfaction areas. The measurement contains 500+ questions spanning every aspect influencing Customer Happiness.


text:
Maysville man faces homicide charge in 2019 crash that killed a Gainesville woman
Basketball scoreboard: East Hall boys take down Cedar Shoals
Gainesville ghosts: Alta Vista Cemetery
Local paranormal society president Melanie Baez alleges to have seen a ghostly figure near the mausoleum columns - photo by Scott Rogers
Alta Vista Cemetery in Gainesville may seem like a peaceful spot to remember the dead, but Melanie Baez finds it alight with activity.
Baez, who lives in Flowery Branch, said she started the Paranormal Society of Northeast Georgia nearly two years ago in hopes of gathering like-minded people to share in her passion for the paranormal. She regularly comes out to the cemetery to listen and look for ghosts.
The property has been in operation for over 140 years and includes graves and memorials for many notable people, including veterans of the Revolutionary and Civil wars such as Lt. Gen. James Longstreet, as well as former governors, mayors, sheriffs and police chiefs.
When walking over the grave-stone covered grounds, Baez keeps an eye out for the activity of two ghosts she claims to observe — the bride and groom.
Baez said she has seen the figures — one dressed in a black suit and the other in a white dress with a veil — with both her eyes and through photos she's taken in their preferred "hang out spot" next to the cemetery's mausoleum.
"They come up from somewhere up there," she said pointing to the hill overlooking the mausoleum. "They're always together. I think they just keep to themselves."
Since she first spotted the couple two years ago, Baez has conducted research to figure out their identities. She said her mother believes the groom figure is Longstreet. However, Baez said she can't be sure.
A week ago, she took her 3-month old German shepherd puppy, Laveau — who is named after the famous Voodoo priestess of New Orleans, Marie Laveau — out for a walk in the cemetery. She said he strayed away from her on his leash and begun performing basic commands while looking into the open air.
Baez suspects that he might have encountered a military veteran ghost familiar with dog training.
"He sat, he laid, he jumped on two feet, and nobody was there," Baez recounted. "He could see somebody that I couldn't see."

redpajama_set_name: RedPajamaCommonCrawl


text:
New year, emergent impact
Blog 31 Dec 2015 3 Comments
As 2016 dawns can the RSA pioneer a new way of thinking about progressive change?
2015 was an interesting year for the RSA. We had good outputs in the form of research reports covering a diverse set of issues ranging from social networks in deprived areas, public service volunteering and the case for a basic income to recycling furniture and makers' spaces to supplementary schools and creative teaching. The reports were accompanied by a steady stream of blogs and other on line content, helping to give the RSA a growing global social media profile. Our events programme and Journal were as strong as ever. We also achieved unprecedented levels of Fellowship activity and recruitment.
But this was also the year of implementing our strategic review. The goals we had set in 2014 were to be more mission driven, collegiate and impactful. This led to new ways of working, in particular cross cutting panels in our three key areas of focus: public service and communities; economy, enterprise and manufacturing; creative learning and development.
The aim of these panels was to bring together the RSA's unique range of methods and tools behind a rigorous model of change. There has been a lot of organisational learning, both through success and failure, but when colleagues get together the enthusiasm for this more aligned and impactful way of working is tangible.
There was until a few weeks ago one big fly in the ointment. The Society relies on external funding for our research programme and just as our model of change and operation seemed to be coming together we had a bad run of funding rejections. Fortunately, just as disappointment was turning to dismay, our luck changed and we have received support for some fascinating programmes of work in the New Year. Most gratifying of all, one bid explicitly involved support for engaging and mobilising RSA Fellows. Persuading a third party of what we in the RSA have long believed – that Fellows can be a powerful force for change - is a key moment.
Indeed it was the prompt for us to make explicit a new way of thinking about change; what we are calling 'emerging impact'. This is in part an alternative to the emphasis on social impact. On the one hand, social impact assessment has often failed to provide the consistent, reliable, useful metrics its advocates promised, while, on the other hand, a focus on measurable cause and effect tends to favour narrow, linear interventions over more emergent system level change.
The breadth of the RSA's interests and mission can make us hard to describe and risks a lack of focus. But being able to look at problems from many angles and to combine different areas of expertise can also be a major advantage. Whether we think that the problem to address is one of policy, research, practical innovation, public discourse or civic mobilisation, we have the tools ready to act.
For example a current project with the Heritage Lottery Fund began with the question: why is heritage only occasionally seen as a strategic asset when local political leaders think about the future of their place? An initial inquiry, which benefited from the input of Fellows who are local leaders and heritage enthusiasts, suggested that the problem lay in an ambivalence about the very idea of distinctive local identity.
Seeing the need to widen the debate, the team mashed together nearly a hundred data sets to develop a national heritage assets and participation index. This generated national coverage and local controversy. On the back of this interest we hosted well attended heritage question time events in major cities. Most encouragingly, we are now seeing local groups developing – often with Fellows at their heart – wanting to strengthen the role and voice of heritage in place shaping. As the project develops into its next phases, we hope to identify place leaders who will work with us, the sector and the community to use heritage as a strategic asset in their future planning and to run the index again, informed by what we now know about its value as a tool for engagement and activism.
What emerged almost accidentally in this project is now becoming the Society's modus operandi.
'Emergent impact' involves setting out with a clear mission and set of goals but then being able to shift focus and method as the project develops. If a research report generates interest, we can develop on line content to deepen that engagement. If an idea mobilises people in one place, we can push it out into other places through our network of Fellows. If we feel close to persuading a minister or official of the need for a shift in policy, we can swiftly scan our networks and pull together an expert round table to refine the idea and cement support. If an idea needs to be proven practically, we can develop collaborations to test on the ground innovations.
Emergent impact isn't easy. It requires continuous inquiry and dialogue and an ability to shift focus and method quickly. It keeps RSA staff on their toes, encouraging them to appreciate all the skills and tools at our disposal and to keep the focus always on what we are trying to achieve. Our existing and potential funding partners share our commitment to change and are attracted to our ideas, but emergent impact challenges them to work in a more flexible, engaged and adaptive way.
The world is changing faster and becoming ever more complex. In such a world impact is a moving target. Combining ambitious long term goals with diverse and flexible methods seems the right way to go but it's a demanding approach. Whether the RSA can prove the value of emergent impact remains to be seen, but right now it feels like we are onto something.
Isabella M H Wesoly
Heritage must be the 'Society's modus operandi' and at cultural levels is certain to expand. History still shows us little of how life in the past was, we may read much of territorial conquests, scientific breakthroughs and limelight attractions, but little of social life.
The every day life of the UK population is disparate enough to consider the diverse human architecture that made Britain Great. In fact, in any Heritage story there should also be links to the far away lands that connect in trade. For example, this way we come to see how the mills that produced cloth, employed locals but also created work in cotton producing lands far from 'home'. Around the town where I grew up are some orchard remnants and several human references to the many laundries that 'grew' around the natural waters that ran through (in the Thames Basin). Parts of that local community grew indirectly from cloth trade.
You mentioned 'dialogue' and I agree wholeheartedly. We not only need to be talking to each other buy also in a way when every society member can also interact or understand.
Emergence is a time in space, time is art, space is open. We are definitely 'onto something' Matthew.
Hoping to engage more now, after a first-year-limited curve of learning more about the RSA.
Alex Barker
Hi Alan,
A member of the team will be in touch shortly to sort out your life membership and ensure that you receive the journal and have access to other Fellows in China, once your Fellowship is up and running.
In the meantime, if you have any further questions you get in touch on fellowship@rsa.org.uk
Michael Reardon wrong
I find January to be not the best month .it's my birthday. I think the October campaign has also had some success....I have up almost completely for three months as a result. Today. ..new years eve...I am very frustrated by the coverage of NYE being centred around getting drunk and hangovers. ..even in the Guardian. Makes it so much harder for those who want to drink moderately or not at all.
Four levels of accountability
Building the foundations for intelligent accountability
Working towards an improved Fellowship experience
Lucy Griffiths FRSA
The Fellowship Roadmap project commenced in March 2020 with the aim aim of better understanding the RSA Fellowship's needs.
Creating a more inclusive post Covid-19 NHS
Bhavani EsapathI
We need to prepare and understand what long-term care looks like from both a governance perspective.


text:
What Lorca wrote in his short life made it impossible to imagine the poetry not only of Spain and the Spanish language everywhere but of the whole Western world, since then, without him.
Selected Poems of Federico García Lorca
In Search of Duende
In The Green Morning
Selected Letters
The Public And Play Without A Title
The Cricket Sings
Deep Song And Other Prose
Five Plays
Three Tragedies
Federico García Lorca (1898–1936) was a Spanish poet and dramatist. He attended the University of Granada, the University of Madrid (where he studied law), and Columbia University. In 1936, at the young age of thirty-eight, Lorca was killed by fascist rebels.
Poetry by Federico García Lorca
Federico García Lorca is admired the world over for the lyricism, immediacy, and beauty of his poetry. The superb translators of this collection — Stephen Spender, Langston Hughes, Ben Belitt, William Jay Smith, and W. S. Merwin — have produced English versions that catch the spirit and intensity of the originals. Presented bilingually, this is a selection of Lorca's very best.
More Information Buy
Your Independent Bookstore Barnes & Noble Amazon
The notion of "duende" became a cornerstone of Federico García Lorca's poetics over the course of his career. In his lecture "Play and Theory of the Duende," he says, "there are no maps nor disciplines to help us find the duende. We only know that he burns the blood like a poultice of broken glass, that he exhausts, that he rejects all the sweet geometry we have learned…" The duende is portrayed by Lorca as a demonic earth spirit embodying irrationality, earthiness, and a heightened awareness of death.…
Fiction by Federico García Lorca
In The Green Morning: Memories of Federico, Francisco García Lorca tells of the charmed childhood he, his sisters, and his older brother, the Spanish poet and playwright Federico García Lorca, spent in the "quiet, very fragrant" Andalusian village of Fuente Vaqueros. Digressions into family history enable us to see Federico, the son of a well-off landowning family with a tradition of literacy, poetry-writing, and musical accomplishment—as the culmination of a particular family type his brother describes as "happy, spontaneous, and instinctive.…
This first English-language edition of Federico Garcia Lorca's Selected Letters presents an intimate autobiographical record of the Spanish poet from the age of twenty to a month before his death at the hands of Franco's forces in 1936. "I was born for my friends," Lorca wrote to Melchor Fernández Almagro in 1926, and these letters reveal the personality his friends found so magical. ("A happiness, a brilliance…" Pablo Neruda called him.…
Federico Garcia Lorca called The Public "the best thing I've written for the theater." Yet, he acknowledged, "this is for the theater years from now." Now, half a century later, The Public and another of Lorca's most daring works, Play without a Title, are available in English translation for the first time. Surrealism, folk theater, poetry, vivid costumes, black humor––in the The Public, dramatic traditions are ransacked to develop themes as timely in the 1980s as they were taboo when Lorca was writing: if Romeo were a man of thirty and Juliet a boy of fifteen, would their passion be any less authentic?…
A special aspect of Federico Garcia Lorca's personality was the ease with which he made friends with children and the delight he took in entertaining them. In addition to the little puppet shows he created for his sisters and their friends and for the children of other writers, he wrote many little poems as well as words set to folk tunes that are still read and sung by Spanish children today and which, in their rarity and charm, are just the right size for anyone of any age.…
A collection of prose writing and lectures from the great Spanish poet.
Federico García Lorca's position as one of the few geniuses of the modern theatre was firmly established in the English-speaking world with his Three Tragedies. Here, with an introduction by the dramatist's brother, Francisco García Lorca, are five of his "comedies," in the authorized translations, extensively revised to reflect recent Lorca scholarship and to convey the sparkle, freshness, and magic of the original Spanish. The Shoemaker's Prodigious Wife tells of a young beauty married to an old man, a theme that often concerned Lorca.…
Here in the authorized translation by James Graham-Lujan and Richard L. O'Connell, with an illuminating biographical introduction by the poet's brother, Francisco García Lorca, are three tragic dramas by the great modern Spanish poet and playwright which have caught the imagination and won the critical acclaim of the literate world: Blood Wedding: Concerned with love that cannot become marriage among the primitive hill people of Castile, this is a play of the workings of tremendous passions and tribal ritual toward an inescapable tragic end.…
He has been a man of extraordinary influence on both my political and personal work.
Spain's great poet and playwright is being rediscovered.
What a poet! I have never seen grace and genius, a winged heart and a crystalline waterfall, come together in anyone else as they did in him. Federico García Lorca was the extravagant 'duende,' his was a magnetic joyfulness that generated a test for life in his heart and radiated it like a planet.
angle-line
< Fleur Jaeggy Ezra Pound >
Keep up to date with New Directions
New Directions Publishing
New Directions was founded in 1936, when James Laughlin (1914–1997), then a twenty-two-year-old Harvard sophomore, issued the first of the New Directions anthologies. "I asked Ezra Pound for 'career advice,'" Laughlin recalled. "He had been seeing my poems for months and had ruled them hopeless. He urged me to finish Harvard and then do 'something' useful."
© 2020 New Directions Publishing Cookie Policy Privacy Policy Terms of Service
site by the New Dynamic


Jun 23, 2018· mining environmental issues impact in india . Mines In India And Its Environmental Issu Mining Basalt Issues In India, one of the major problems caused by coal Mining environmental issues impact in india . Get Price Mining and its Effects on the Environment Palawan.
Odisha's story about pollution, mining and the environment ... india Updated: Jun 05, ... but it did manage to cause a constructive debate on environmental issues in the region.
For proposed mining projects, it is necessary to address the potential environmental impact issues that may arise due to proposed mining activities i.e. an assessment of the potential impacts of a project on the pre-mining environment.
Environmental Impacts and Health Aspects in the Mining Industry – A Comparative Study of the Mining and Extraction of Uranium, Copper and Gold ... discuss environmental issues in the mining industry respectively. Bengt Lilljha (Ranstad Mineral AB) for showing us the former Swedish uranium mine Ranstadsverket.
25-5-2016 Due to its unique properties and consequent environmental issues, coals in northeast India ... environmental implications of coal mining in ... Environmental issues in coal mining 5-7-2000 ``AMONG the fossil fuels, coal has acquired the dubious distinction of being the dirtiest.
Tourism and the environment : case studies on Goa, India, and the Maldives (English) Abstract. The two papers included in this set review the impacts of tourism on the environment in the State of Goa, India and in the Republic of the Maldives.
Impact of Mining Industry on Environmental Fabric -A Case Study of Rajasthan State in India S.K. Maanju, K. Saha, Vikram University, Ujjain (M.P.) ... Industrialization and urban development has posed problems of groundwater pollution and environmental hazards. Ground water quality vulnerable areas can be grouped broadly.
environmental impacts of line mining in northeast india Glass Recycling Machine Wholesale, Recycling Machine Suppliers . ... environmental activities iron ore processing in india; environmental problems with ore mining; good quality environmental grinding mill production line; environmental protection; About Us.


text:
\section{Ablation Study}
\label{sec:ablation}

In this section, we conduct extensive studies to better understand {SimCVD}. We justify the inner working of {SimCVD} from two perspectives: (1) boundary-aware contrastive distillation (Section \ref{subsec:component}), and (2) the projection head (Section \ref{subsec:projection}). In these studies, we evaluate our proposed method on the LA dataset with 10\% labeled ratio (8 labeled and 72 unlabeled).

\subsection{Analysis on Boundary-aware Contrastive Distillation}
\label{subsec:component}

\myparagraph{Ablation on Model Component}
In the model formulation, our motivation is to advance state-of-the-art voxel-wise representations by capturing the geometric and semantic information in 3D space. Rather than transferring knowledge across confidence score maps directly, our {SimCVD} distills ``boundary-aware'' knowledge from the teacher network. To validate the idea of boundary-aware contrastive distillation, we compare {SimCVD} to an ablative baseline (\textit{i.e.}, {SimCVD}  w/o SDM). Table \ref{tab:component_ablation} {\bf(a)} compares each component of {SimCVD} in the 10\% labeled setting. First, we observe that removing the SDMs in training hurts the segmentation performance by $\!-0.79\%$, $\!-1.27\%$, $\!-1.6$, and $\!-3.09$ absolute differences in terms of Dice, Jaccard, ASD, and 95HD. This confirms our intuition that the learned boundary-aware representations provide a good prior for improving segmentation accuracy. We also find that using adaptive max pooling strategy (\textit{i.e.}, {SimCVD}  w/ adaptive max pooling) largely degrades the segmentation performance. Our segmentation results demonstrate that {{SimCVD}} is an effective approach, outperforming the best previous method with $\!+0.71\%$, $\!+1.08\%$, $\!+1.20$, and $\!+6.35$ absolute differences in terms of Dice, Jaccard, ASD, and 95HD. We hypothesize that it is because ``w/ adaptive max pooling'' leads to information loss during training.

\input{tables/table4_projection}

\myparagraph{Ablation on Loss Formulation}
In the loss formulation, our main idea is to pull closer similar (\ti{positive}) pairs upon the same threshold, while pushing apart dissimilar (\ti{negative}) pairs. Our learning objective is designed to jointly exploit effective correlations in the \ti{prediction} and \ti{feature} space in an informative way. To evaluate the effectiveness of each objective term, we conduct ablation studies by removing each term separately. As shown in Table \ref{tab:component_ablation} {\bf(b)}, we observe that {SimCVD} outperforms the ablative baseline ``{SimCVD} w/o $\mathcal{L}_\mathrm{contrast}$'' by a large margin and achieves a $\!+3.90\%\!$ increase in Dice. It clearly demonstrates it can effectively capture global context structure and local cues of 3D shapes. Next, we study whether enforcing similarity constraints in the \ti{feature} space is effective enough to exploit structured knowledge in practice. We find that, under the 10\% labeled setting, removing $\mathcal{L}_\mathrm{pd}$ leads to a performance drop, and the accuracy measures decreases by $\!-0.92\%\!$ and $\!-1.45\%\!$ in Dice and Jaccard, respectively. Our qualitative results confirm that \textit{pair-wise distillation} is effective for {SimCVD} in improving the network performance. When we remove $\mathcal{L}_\mathrm{sdm}$, the network performance does not drop significantly. We speculate that our boundary-aware contrastive distillation framework is capable of eliciting ``boundary-aware'' knowledge from the teacher model with high accuracy. This further highlights the effectiveness of our proposed {SimCVD}. To further verify the robustness of {SimCVD}, we perform pair-wise t-test between {SimCVD} and the other methods, using the per-case test Dice score as samples. The null hypothesis states that the test scores come from the same distribution, and thus the methods do not differ; whereas the alternative hypothesis is that {SimCVD} yields higher Dice score. For all the alternative methods in the Table below, the p-values are close to or less than $0.05$. Small p-values indicates that we are confident in rejecting the null hypothesis and conclude that {SimCVD} indeed outperforms the baseline models.

\subsection{Analysis on Projection Head}
\label{subsec:projection}
To further understand how different aspects of our projection head contribute to the superior model performance, we conduct extensive experiments and discuss our findings below.

\myparagraph{How to Interpret Dropout?}
Our experimental results have shown that {{SimCVD}} is an effective approach. In the following, we aim to answer two questions. First, how can we interpret {SimCVD}'s \ti{dropout} training strategy? Can we view \ti{dropout} as a form of data augmentation? Second, is it capable of exploiting additional informative cues in practice?

First, we examine whether removing \ti{dropout} during training can achieve comparable performance. Table \ref{tab:projection_ablation} shows the ablation result of our \ti{dropout} on LA. As shown in Table \ref{tab:projection_ablation}, we observe that using \ti{dropout} achieves a much better result on LA dataset. Compared to the setting $p\!=\!0.1$, we find that ``no dropout'' ($p\!=\!0$) leads to a dramatic performance degradation by $\!-1.34\%$, $\!-2.11\%$, $\!-1.71$, $\!-2.69$ absolute differences in terms of Dice, Jaccard, ASD, and 95HD, respectively. While in the case of $p\!=\!0.5$, it also significantly hurts the network performance. On the other hand, we observe slight improvements on the other $p$ settings, compared to ``no dropout'', but eventually underperform {SimCVD}. This clearly demonstrates the superiority of our \ti{dropout} strategy to learn better representations with respect to different pairs of augmented images. We speculate that adding \ti{dropout} can be interpreted as a minimal form of data augmentation, in which the positive pair takes two views of the same images, and their representations make a clear difference in dropout masks.

\myparagraph{Effect of Augmentation Techniques}
To further examine our hypothesis, we compare common data augmentation techniques (\textit{i.e.}, local shuffle pixel, non-linear transformation, in-painting, out-painting) in Table \ref{tab:data_aug}. As is shown, the quantitative results reveal interesting behavior of different data augmentation: adding more data augmentation does not further contribute to the good model performance. We note that, somewhat surprisingly, it hurts the final prediction performance, and none of them outperforms the basic \ti{dropout} mask. This suggests that by including these data augmentation techniques, it is possible to introduce additional noise during training, which leads to the representation collapse.

\input{tables/table5_data_aug}

\myparagraph{Effect of Pooling Size}
In Table \ref{tab:component_ablation}, we demonstrate the network improvements from using adaptive mean pooling instead of adaptive max pooling. We investigate the effects of different pooling sizes in Table \ref{tab:projection_ablation}. Empirically, we observe that using a larger pooling size clearly improves performance consistently. However, we find that the results can not be improved further by increasing the pooling size to $256$. In our implementation, we set the pooling size as $128$.
\section{Conclusion}
\label{section:conclusion}

In this work, we propose {SimCVD}, a simple contrastive distillation learning framework, which largely advances state-of-the-art voxel-wise representation learning on medical segmentation tasks. Specifically, we present an unsupervised training strategy, which takes two views of an input volume and predicts their signed distance maps of their object boundaries in a contrastive objective, with only two different dropout masks. We further conduct extensive analyses to understand the state-of-the-art performance of our approach, and demonstrate the importance of learning distinct boundary-aware representations and using dropout as the minimal data augmentation technique. We also propose to perform structural distillation by distilling pair-wise similarities, which achieves good performance improvements. Our experimental results show that {SimCVD} obtained new state-of-the-art results on two benchmarks in an extreme few-annotation setting.

We believe that our unsupervised training framework provides a new perspective on data augmentation along with unlabeled 3D medical data. We also plan to extend our method to solve multi-class medical image segmentation tasks.

\section{Introduction}
\label{sec:intro}

\input{figures/main_figure}

Medical image segmentation is a popular task in both machine learning and medical imaging communities \cite{staib1996model,yang2004neighbor,yang20043d,chakraborty1996deformable,staib1992boundary}. Compared to traditional segmentation approaches, deep neural network based segmentation methods have achieved much stronger performance in recent years with huge advances in representation learning \cite{ronneberger2015u,milletari2016v,bai2017semi,ganaye2018semi,you2020unsupervised,wang2020deep,xue2020shape,li2020transformation}. However, previous state-of-the-art approaches are mostly trained with a large amount of labeled data, which pose significant practical challenges in many medical segmentation tasks where there is a scarcity of labeled data due to  the heavy burden of annotating images.

In recent years, a wide variety of semi-supervised methods \cite{laine2016temporal,zhang2017deep,li2018semi,nie2018asdnet,qiao2018deep,bortsova2019semi,li2021assessing,yu2019uncertainty,li2020shape,zhu2020rubik,luo2020semi,chaitanya2020contrastive,you2021momentum} have been designed to tackle these issues, which learn from limited labeled data along with a large amount of unlabeled data, achieving significant improvements in accuracy and greatly reducing the labeling cost. The common paradigms include adversarial learning, knowledge distillation, and self-supervised learning. Contrastive learning, a sub-area of self-supervised learning, has recently been noted as a promising direction since it has shown great promise in learning useful representations with limited human supervision~\cite{chen2020simple,hjelm2018learning,bai2019self,chaitanya2020contrastive,peng2021self}. This is often best understood as pulling together semantically similar (\ti{positive}) samples and pushing apart non-similar (\ti{negative}) samples in a shared latent space. The representations uncovered by these contrastive objectives are capable of boosting the performance of any vision system especially in scenarios where the amount of annotated data available for the downstream tasks is extremely low, which is well suited for medical image analysis.

Despite advances in semi-supervised learning benchmarks, previous methods still face several major challenges: (1) \ti{Suboptimal performance}: although prior works have achieved promising segmentation accuracy in the setting of limited annotations, semi-supervised models are usually not robust due to some information loss, compared with fully-supervised counterparts; (2) \ti{Geometric information loss}: previous segmentation networks are poor at characterizing geometry, \textit{i.e.}, leveraging the intrinsic geometric structure of the images, such as the object boundary. As a consequence, it is often hard to accurately recognize object contours; and (3) \ti{Generalization ability}: considering the limited amount of training data, training deep models is usually deficient due to over-fitting and co-adapting \cite{yang2019snapshot,zhuang2020deep}.

In this work, we address the question: can we advance state-of-the-art voxel-wise representation learning in a more extreme few-annotation-setting for medical image segmentation? To this end, we present {SimCVD}, a \underline{sim}ple \underline{c}ontrastive \underline{v}oxel-wise representation \underline{d}istillation framework, which can be utilized to produce superior voxel-wise representations from unlabeled data for improving network performance. Our proposed {SimCVD}, built upon the mean-teacher framework \cite{tarvainen2017mean}, can address the above-mentioned challenges as follows. First, {SimCVD} predicts the output geometric representations with only two different \ti{dropout}~\cite{srivastava2014dropout} masks (Figure~\ref{fig:structure}). In other words, we pass two views of the geometric representations to the mean-teacher model, obtain two representations as ``positive pairs'', by applying two independent dropout masks, and learn effective representations by efficiently associating positives and disassociating negatives in the shared latent space. Though this unsupervised learning strategy is simple, we find this approach is strikingly effective compared to other common data augmentation techniques (\textit{e.g.}, inpainting and local shuffle pixel). More importantly, as we will show, it achieves comparable performance to previous fully-supervised approaches. Through a series of thorough analyses, we find that dropout can be viewed as minimal data augmentation for performance improvement, and it can effectively regularize the training of deep neural networks, avoid representation collapse and enhance model generalization.

Second, we attribute the cause of the \ti{geometric information loss} to the need for geometric shape constraints. We address this challenge by performing multi-task learning that jointly predicts a segmentation map along with a signed distance map (SDM) \cite{perera2015motion,dangi2019distance, park2019deepsdf,xue2020shape,li2020shape}. The SDM calculates the signed distance function of the object, \textit{i.e.}, the distance of a voxel from the boundary of the object, with the sign determined by whether the voxel is within the object. Thus, it can be viewed as a global shape constraint on the labeled data. Considering that the SDM can provide a more flexible geometric measure of the object boundary, we move beyond the supervised learning scheme and exploit the regularity in geometric shapes among different object classes through distilling ``boundary-aware'' knowledge via a contrastive objective among the unlabeled data. This enables the model to learn boundary-aware features more effectively by encouraging the networks to produce segmentation maps with similar distance map distributions on the entire dataset.

Third, it is challenging to train the segmentation model on small training sets since deep neural networks trained on a limited amount of data are prone to over-fitting. To this end, we propose to use knowledge distillation (KD), which has been shown to be effective in segmentation and classification tasks \cite{hinton2015distilling,romero2014fitnets,liu2019structured}. The key idea of KD is that a teacher model is first trained, and then used to guide the training of the student model for improving generalization ability. In the medical domain, most existing KD methods~\cite{yu2019annotation,li2020dual} simply consider the segmentation problem as a pixel/voxel-level classification problem. In contrast, considering that medical image semantic segmentation is a structured prediction problem, we present a novel structured knowledge \ti{pair-wise distillation}, which further use the structural knowledge from the mean-teacher model, while avoiding co-adapting and over-fitting.

Our contributions are summarized as follows. First, we propose a novel contrastive distillation model termed {SimCVD} featured by (i) boundary-aware representations that incorporate rich information of the object shape, (ii) a distillation objective which contrasts different distance map distributions jointly in the shared latent space, and (iii) a pair-wise distillation objective to further distill pair-wise structural knowledge. Second, we demonstrate that, in the setting of very limited annotation, simply using dropout can deliver more robust end-to-end segmentation performance compared to heavily relying on a large amount of labeled data. Third, we conduct experiments on two popular benchmark datasets to evaluate {SimCVD}. The results demonstrate that {SimCVD} significantly outperforms other state-of-the-art semi-supervised approaches, while achieving competitive performance compared to fully-supervised counterparts.

\section{Experimental Setup}
\label{sec:experiments}

\subsection{Dataset and Pre-processing}
We evaluated our approach on two popular benchmark datasets: the Left Atrium (LA) MR dataset from the Atrial Segmentation Challenge\footnote{http://atriaseg2018.cardiacatlas.org/}, and the NIH pancreas CT dataset \cite{roth2016data}. For the Left Atrium dataset, it comprises 100 3D gadolinium-enhanced MR imaging scans (GE-MRIs) with expert annotations, with an isotropic resolution of $0.625\times 0.625 \times 0.625 \text{mm}^3$. Following the experimental setting in \cite{yu2019uncertainty}, we use 80 scans for training, and 20 scans for evaluation. We employ the same pre-processing methods by cropping all the scans at the heart region and normalizing the intensities to zero mean and unit variance. All the training sub-volumes are augmented by random cropping to~$112\times 112\times 80 \text{mm}^3$. For the pancreas dataset, it contains 82 contrast-enhanced abdominal CT scans. Following the experimental settings in \cite{luo2020semi}, we randomly select 62 scans for training, and 20 scans for evaluation. In the pre-processing, we first truncate the intensities of the CT images into the window [$-125$, $275$] HU~\cite{zhou2019prior}, and then resample all the data into a fixed isotropic resolution of $1.0\times1.0\times1.0 \text{mm}^3$. Finally, we crop all the scans centering at the pancreas region, and normalize the intensities to zero mean and unit variance. All the training sub-volumes are augmented by random cropping to~$96\times 96\times 96 \text{mm}^3$.  In this study, we compare all the
methods on LA and the pancreas dataset with respect to 20\% labeled ratio. To emphasize the effectiveness of {SimCVD}, we further validate all the methods with respect to 10\% labeled ratio on LA dataset.

\subsection{Implementation Details}
In this study, all evaluated methods are implemented in PyTorch, and trained for $6000$ iterations on an NVIDIA 1080Ti GPU with a batch size of $4$. For data augmentation, we use standard data augmentation techniques (\textit{i.e.}, random rotation, flipping, and cropping). We set the hyper-parameters $\alpha$,\,$\lambda$,\,$\beta$,\,$\gamma$,\,$\tau$ as $0.1$,\,$0.5$,\,$0.1$,\,$0.1$,\,$0.5$, respectively. For the projection head, we set $p = 0.1$ in the \ti{AlphaDropout} layer, and output size $128\times128$ for \ti{AdaptiveAvgPool2d}. We use SGD optimizer with a momentum of \!$0.9$ and a weight decay of \!$0.0005$ to optimize network parameters. The initial learning rate is set as $0.01$ and divided by $10$ every $3000$ iterations. For EMA updates, we follow the experimental setting in \cite{yu2019uncertainty}, where the EMA decay rate $\alpha$ is set to $0.999$. We use the time-dependent Gaussian warming-up function \,$\Psi_{\text{con}}(t)\!=\!\exp{\left(-5\left(1-t / t_{\max}\right)^{2}\right)}$\, to ramp up parameters, where $t$ and $t_{\max}$ denote the current and the maximum training step, respectively. For fairness, we do not adopt any post-processing step.

In the testing stage, we adopt four metrics to evaluate the segmentation performance: Dice coefficient (Dice), Jaccard Index (Jaccard), 95\% Hausdorff Distance (95HD), and Average Symmetric Surface Distance (ASD). Following~\cite{yu2019uncertainty,luo2020semi,ssl4mis2020}, we adopt a sliding window strategy, which uses a stride with $18\times18\times4$  for the LA and $16\times16\times16$ for the pancreas.
\section{Related Work}

\myparagraph{Semi-Supervised Medical Image Segmentation}
In recent years, substantial efforts~\cite{zhang2017deep,li2018semi,nie2018asdnet,he2019dpa,zhou2019models,yang2020nuset,laine2016temporal,yu2019uncertainty,chen2021deep,shanlin2022CVPR,tang2019clinically,tang2021spatial,zhang2018fully,zhang2021fully,tang2019nodulenet,zhang2018fully,zhang2020fully,sun2020attentionanatomy,yan2022after,tang2021recurrent,xue2020shape,bortsova2019semi,ma2020distance,castillo2020auxiliary,murugesan2019psi,xia2020uncertainty,zheng2019semi,zhuang2019self,taleb20203d,you2022class} have been devoted to incorporating unlabeled data to improve network performance due to limited annotations. Yu~{\textit{et~al.}}\cite{yu2019uncertainty} investigated an uncertainty map based on the mean-teacher framework~\cite{tarvainen2017mean} to guide the student network to capture better features. Li~{\textit{et~al.}}\cite{li2020shape} proposed to use signed distance fields for boundary prediction to improve the performance. Also, Luo~{\textit{et~al.}}\cite{luo2020semi} proposed a dual-task-consistency (DTC) model for semi-supervised medical image segmentation by jointly predicting the pixel-wise segmentation maps and the global-level level set representations on the unlabeled data. Our method aims at a more practical and challenging scenario: we train our model in a more extreme few-annotation setting that relies only on a small number of annotations, while achieving superior segmentation accuracy.

\myparagraph{Contrastive Learning}
Self-supervised learning (SSL)~\cite{hadsell2006dimensionality,doersch2015unsupervised,noroozi2016unsupervised,zhuang2019self} has provided robust benefits to vision tasks by learning effective visual representations from unlabeled data in an unsupervised setting. It is based on a commonly-held belief that superior performance gains can be achieved through improved representation learning. Recently, contrastive learning, a type of self-supervised learning, has received a lot of interests~\cite{hadsell2006dimensionality,wu2018unsupervised,tian2019contrastive,misra2020self,chen2020simple,Federici2020Learning,chaitanya2020contrastive,peng2021self,chen2021self,you2021self}. The key idea of contrastive learning is to learn powerful representations that optimize similarity constraints to discriminate similar pairs (\ti{positive}) and dissimilar pairs (\ti{negative}) within a dataset. The primary stream of subsequent work focuses on the choice of dissimilar pairs, which is critical to the quality of learned representations. The loss function used to quantify the contrast is chosen from several options, such as InfoNCE~\cite{oord2018representation}, Triplet~\cite{wang2015unsupervised}, and so on. Recent studies~\cite{wu2018unsupervised,misra2020self} introduced memory bank or momentum contrast to use more negative samples for contrast computation. In the context of medical imaging, Chaitanya~{\textit{et~al.}}\cite{chaitanya2020contrastive} extended a contrastive learning framework to extract global and local cues in a stage-wise way, which requires human intervention and extensive training time. In contrast to Chaitanya~{\textit{et~al.}}\cite{chaitanya2020contrastive}, our unified work focuses on explicit modeling of the intrinsic geometric structure of the semantic objects in an end-to-end manner, and hence is able to recognize object boundaries more effectively and efficiently.

\myparagraph{Knowledge Distillation}
The idea of knowledge distillation is to minimize the KL-divergence between the output distributions of the teacher model and the student model, and thus avoid over-fitting. KD has been applied to a variety of tasks \cite{you2020data,you2020contextualized,you2021mrd,you2021knowledge,ma2020undistillable,ma2021good}, including image classification \cite{hinton2015distilling,furlanello2018born,yang2018knowledge,li2017learning} and semantic segmentation \cite{xie2018improving,liu2019structured}. Recent works \cite{yang2018knowledge,furlanello2018born} found that the student model can outperform the teacher model when they share the same network architecture. Zhang~{\textit{et~al.}}\cite{zhang2018deep} proposed to collaboratively train multiple student models with co-distillation, which improves performance of those individual models. At the same time, in the context of medical imaging, among those existing state-of-the-art KD methods, the self-ensemble mean-teacher framework \cite{tarvainen2017mean} is widely explored for image segmentation. Different from the existing methods that separately exploit class probabilities for each voxel, we consider knowledge distillation as a structured prediction problem by matching the relational similarity among all pairs of voxels from the encoded feature maps of the mean-teacher model. We have found that our approach significantly improves learning better voxel-wise representations.
\section{Method}
\label{sec:method}

In this section, we introduce {SimCVD}, a semi-supervised segmentation network, which is built from scratch by effectively leveraging scarce labeled data and ample unlabeled data for improving end-to-end voxel-wise representation learning (See Figure~\ref{fig:structure}). We first overview our proposed {SimCVD} and then describe the task formulation of {SimCVD}. Finally, we detail each component of {SimCVD} in the following subsections.

\subsection{Overview}

We aim to construct an end-to-end voxel-wise contrastive distillation algorithm to learn boundary-aware representations in the setting of extremely few annotations for volumetric medical imaging segmentation. Although the accuracy of supervised models is usually higher than that of semi-supervised models, the former requires much more labeled data than the latter. In many clinical situations, we only have few annotated data but a large amount of unlabeled data. This situation necessitates a semi-supervised segmentation algorithm that can utilize the unlabeled data to improve the segmentation performance.

To this end, we propose a novel contrastive distillation framework to advance state-of-the-art voxel-wise representation learning. In particular, our base multi-task segmentation network tackles two tasks simultaneously: classification and regression. Specifically, the segmentation network takes the input volume batch and jointly predicts the probability maps (classification) and the SDMs of the object (regression). To obtain better representations, we propose to perform structured distillation in the latent \ti{feature} space, followed by contrasting the boundary-aware features in the \ti{prediction} space, to learn more effective boundary-aware representations from 3D unlabeled data by regularizing the embedding space and exploring the geometric and spatial context of training voxels. At test time, we remove the mean teacher and two projection heads, and only the student network is deployed for the medical segmentation tasks.

\subsection{Task Formulation}
In this work, we consider a set of training data (3D images) including $N$ labeled data and $M$ unlabeled data, where $N\ll M$. For simplicity, we denote the small set of labeled data as $\mathcal{D}_{l}=\left\{\left(\mathbf{X}_{i}, \mathbf{Y}_{i},\mathbf{Y}^{\mathrm{sdm}}_{i}\right)\right\}_{i=1}^{N}$, and abundant unlabeled data as $\mathcal{D}_{u}=\{\mathbf{X}_{i}\}_{i=N+1}^{N+M}$, where~$\mathbf{X}_{i} \in \mathbb{R}^{H \times W \times D}$ is the volume input, $\mathbf{Y}_{i} \in \{0, 1\}^{H \times W \times D}$ is the ground-truth label, and $\mathbf{Y}^{\mathrm{sdm}}_i \in \mathbb{R} ^{H \times W \times D}$ is the computed ground truth SDMs from $\mathbf{Y}_i$, which measures the distance from each voxel to the object boundary. Every 3D image $\mathbf{X}_{i}$ consists of a set of 2D image slices $\mathbf{X}_{i} = [\mathbf{x}_{i,1}, ..., \mathbf{x}_{i,D}]$ where $\mathbf{x}_{i,j}\in\mathbb{R}^{H \times W}$.

Our proposed {SimCVD} framework consists of a mean-teacher network, $\mathcal{F}_t(\mathbf{X};\theta_t)$, and a student network, $\mathcal{F}_s(\mathbf{X};\theta_s)$. Inspired by recent work~\cite{laine2016temporal,tarvainen2017mean}, the optimization of these two networks can be achieved with an exponential moving average (EMA) which uses a weighted combination of the parameters of the student network and the parameters of the teacher network to update the latter. This strategy has been widely shown to improve training stability and the model's final performance. Motivated by this idea, our training strategy is divided into two steps. At each iteration, we first optimize the student network $\mathcal{F}_s$ by stochastic gradient descent. Then we update the teacher weights $\theta_{t}$ using an exponential moving average of the student weights $\theta_{s}$.

The inputs to the two networks are perturbed versions of the same image. That is, given a volume input $\mathbf{X}_i$, we first add different perturbations (\textit{i.e.}, affine transformation and random crop) to generate two different images $\mathbf{X}_{i}^t$ and $\mathbf{X}_{i}^s$. We then feed $\mathcal{F}_t$ and $\mathcal{F}_s$ with these two corresponding augmented images to obtain two confidence score (probability) maps $\mathbf{Q}^t_i$ and $\mathbf{Q}^s_i$.

Before we present our proposed {SimCVD} in detail, we first describe our base architecture below.

\subsection{Base Architecture}
\label{sec:model}

Our base architecture adopts V-Net~\cite{yu2019uncertainty} as the network backbone, which consists of an encoder network $e_t:\mathbb{R}^{H \times W \times D}\rightarrow \mathbb{R}^{H^{\prime} \times W^{\prime} \times D^{\prime} \times D_e}$ and a decoder network $d_t:\mathbb{R}^{H^{\prime} \times W^{\prime} \times D^{\prime} \times D_e}\rightarrow [0,1]^{H \times W \times D} \times [-1,1]^{H \times W \times D}$ for the teacher network, and similarly $e_s$, $d_s$ for the student network, \textit{i.e.}, $\mathcal{F}_t=d_t \circ e_t$ and $\mathcal{F}_s = d_s \circ e_s$. $H^{\prime}, W^{\prime}, D^{\prime}$ are the size of the hidden pattern and $D_e$ is the encoded feature dimension. Inspired by previous work on medical imaging segmentation \cite{xue2020shape,li2020shape}, we incorporate multi-task learning into $\mathcal{F}$ to jointly perform both classification and regression tasks.

Given input $\mathbf{X}_i$, the classification branch is designed to generate the probability map $\mathbf{Q}_i^s\in[0,1]^{H\times W\times D}$, and the regression branch is designed to predict the SDM $\mathbf{Q}_i^{s, \mathrm{sdm}}\in[-1,1]^{H\times W\times D}$. The design of the regression branch is simple yet effective, \ti{only} including the hyperbolic tangent function. This design brings two clear benefits: (1) we can eventually encode rich geometric structure information to improve segmentation accuracy, and (2) we can implicitly enforce continuity and smoothness terms for better segmentation maps. Similarly, we have outputs $\mathbf{Q}_i^{t}$ and $\mathbf{Q}_i^{t, \mathrm{sdm}}$ from the teacher network.

\myparagraph{Supervised Loss $\mathcal{L}_{\mathrm{sup}}$}
For training on labeled data, we define the supervised loss as:
\begin{align}
    \mathcal{L}_{\mathrm{sup}} &= \frac{1}{N}\sum_{i=1}^{N}\mathcal{L}_{\mathrm{seg}}(\mathbf{Q}_i^{s},\mathbf{Y}_i) +  \frac{\alpha }{N}\sum_{i=1}^{N} \mathcal{L}_{\mathrm{mse}}(\mathbf{Q}^{s, \mathrm{sdm}}_{i}, \mathbf{Y}_i^{\mathrm{sdm}})
    \label{eq:supervised_loss},
\end{align}
where $\mathcal{L}_{\mathrm{seg}}$ denotes the segmentation loss (Dice and Cross-entropy)~\cite{yu2019uncertainty}, and $\mathcal{L}_{\mathrm{mse}}$ is the mean squared error loss. $\alpha$ is a hyperparameter. Note that the SDM loss~\cite{xue2020shape} is imposed as geometric constraints in training.

\input{tables/table1_LA}

\begin{figure*}[t]
	\centering
	\includegraphics[width=0.9\textwidth]{figures/visual_results_LA.pdf}
    \vspace{-5pt}
	\caption{Visual comparisons with other methods on LA dataset. As observed, {SimCVD} achieves superior performance with more accurate borders and shapes. We train all the evaluated methods in the setting of 8 annotated images. \textcolor{red}{Red} and \textcolor{blue}{blue} denote the predictions and ground truths, respectively.}
    \vspace{-15pt}
	\label{fig:vis_la}
\end{figure*}

\subsection{Boundary-aware Contrastive Distillation}\label{sec:contrat}

Many prior methods distill knowledge merely in the shared prediction space by delivering the student network that matches the accuracy of the teacher network. However, this strategy is not robust for the following reasons: (1) the learned voxel-wise representations from the mean-teacher model are usually not robust due to the lack of geometric information; (2) the segmentation model still suffers from \textit{generalization} issues; and (3) the network performance needs to be further improved. Therefore, we propose to perform boundary-aware contrastive distillation to train our model for better segmentation accuracy.

Our method differs from previous state-of-the-art methods in three aspects: (1) {SimCVD} imposes the global consistency in object boundary contours to capture more effective \ti{geometric information}; (2) previous methods follow the standard setting in considering the relations of local patches, while {SimCVD} aims to exploit correlations among all pairs of voxels to improve robustness; and (3) due to the computational cost, {SimCVD} does not use a large memory bank. {SimCVD} trains the contrastive objective as an auxiliary loss during the volume batch updates. To specify our voxel-wise contrastive distillation algorithm on unlabeled sets, we define two discrimination terms: boundary-aware contrastive loss and pair-wise distillation loss.

\myparagraph{Boundary-aware Contrastive Loss $\mathcal{L}_{\mathrm{contrast}}$}
We describe our unsupervised boundary-aware contrastive objective as follows. Our key idea is to make use of ``boundary-aware'' knowledge by a contrastive learning objective that enforces the consistency of the predicted SDM outputs on the unlabeled set during training. The key ingredient to working with two views of input images is to apply \ti{dropout} as mask. Specifically, given the collection of an input volume $\mathbf{X}_i$, the student SDM $\mathbf{Q}_i^{s,\mathrm{sdm}}$, the teacher SDM $\mathbf{Q}_i^{t,\mathrm{sdm}}$, we first directly add them up to build two boundary-aware features: $\mathbf{Q}_i^{s, \mathrm{ba}} = \mathbf{X}_i + \mathbf{Q}_i^{s,\mathrm{sdm}}$ and $\mathbf{Q}_i^{t, \mathrm{ba}} = \mathbf{X}_i + \mathbf{Q}_i^{t,\mathrm{sdm}}$. Then, we feed them into the projection heads with two independent dropout masks $z_i^{s}, z_i^{t}$, and contrast~\textit{positives} and~\textit{negatives} by using the InfoNCE loss. We denote the same slice from the two boundary-aware features as \textit{positive}, and slices at different locations or from different inputs as \textit{negative}.

The boundary-aware features are created by adding the original 3D volume to the SDM because we want to fuse both the distance and the intensity information. Another way to achieve this is concatenation -- adding another dimension to the feature tensor -- requires a more complex projection head which is more prone to over-fitting. Thus, the projection head ${\mathcal{H}}\!:\!\mathbb{R}^{H \times W \times D} \!\!\rightarrow\! \mathbb{R}^{D_h \times D}$ encodes each 2D slice to a $D_h$-dimensional feature vector. The implementation is simple, which includes an alpha dropout \cite{klambauer2017self}, an adaptive average pooling, and a 3-layer multilayer perceptron (MLP). Here the MLP is designed to convert each 2D slice to a vector.

Denoting the output of the projection head as $\mf{H}_i^s =\mathcal{H}(\mathbf{Q}_i^{s, \mathrm{ba}}; z_i^s)$, $\mf{H}_i^t =\mathcal{H}(\mathbf{Q}_i^{t, \mathrm{ba}}; z_i^t)$, and the $jth$ row of $\mf{H}_i$ as $\mf{h}_{i,j}$, the InfoNCE loss~\cite{oord2018representation} is defined by:
\begin{equation}\label{eq:NCE}
\mathcal{L}(\mf{h}_{i,j}^{t}, \mf{h}_{i,j}^{s})=-\log\frac{\exp(\mf{h}_{i,j}^{t}\!\cdot\!\mf{h}_{i,j}^{s} / \tau)}{\sum_{k,l}\exp({\mf{h}_{i,j}^t\!\cdot\!\mf{h}_{k,l}^s /\tau})},\!\!
\vspace{-2pt}
\end{equation}
where $\tau$ is a temperature hyperparameter. The indices $k$ and $l$ in the denominator are randomly sampled from a mini-batch of images such that $B$ 2D slices are sampled in total. $i$ and $j$ denote the 3D image index and slice index, respectively. The $\mf{h}_{k,l}^s$'s in the denominator that are not $\mf{h}_{i,j}^s$ are called negative samples. Inspired by the recent success~\cite{chaitanya2020contrastive}, our boundary-aware contrastive loss is defined as:
\begin{equation}\label{eq:contrast_loss}
    \mathcal{L}_{\mathrm{contrast}}= \frac{1}{|\mathcal{N}^{+}|} \sum_{\forall(i,j) \in \mathcal{N}^{+}} [\mathcal{L}(\mf{h}_{i,j}^t,\mf{h}_{i,j}^s) + \mathcal{L}(\mf{h}_{i,j}^s,\mf{h}_{i,j}^t)],
    \vspace{-1pt}
\end{equation}
where $\mathcal{N}^{+}=\{ (i,j): i = N+1,\dots,N+M, j = 1,\dots, D \}$ denotes a collection of the positive 2D slice pairs. Note that the index $i$ in $\mathcal{N}^{+}$ is over all the unlabeled data, hence these data affect the training of $\mathcal{F}_s$ and $\mathcal{F}_t$.

\input{tables/table2_PA}
\begin{figure*}[t]
	\centering
	\includegraphics[width=0.9\textwidth]{figures/visual_results_PA.pdf}
    \vspace{-5pt}
	\caption{Visual comparisons with other methods on the pancreas dataset. We train all the evaluated methods in the setting of 12 annotated images. \textcolor{red}{Red} and \textcolor{blue}{blue} denotes the predictions and ground truths, respectively.}
    \vspace{-10pt}
	\label{fig:vis_pa}
\end{figure*}

\myparagraph{Pair-wise Distillation Loss $\mathcal{L}_{\mathrm{pd}}$}
On one hand, boundary-aware contrastive objectives uncover distinctive global boundary-aware representations that benefit the training of downstream tasks, \textit{e.g.}~object classification, when limited labeled data is available. On the other hand, dense predictive tasks,~\textit{e.g.}~semantic segmentation, may require more discriminative spatial representations. As complementary to boundary-aware contrastive objectives, a promising local pair-wise strategy is vital for the medical image segmentation tasks. With this insight, we propose to perform voxel-to-voxel pair-wise distillation to explicitly explore structural relationships between voxel samples to improve spatial labeling consistency.

In our implementation, we enforce such a constraint on the hidden patterns from the encoders $e_t$ and $e_s$. Specifically, let $\mf{V}_i^t\in \mathbb{R}^{H^{\prime} W^{\prime} D^{\prime} \times D_e}$ and $\mf{V}_i^s\in \mathbb{R}^{H^{\prime} W^{\prime} D^{\prime} \times D_e}$ be the first-3-dimension-flattened hidden patterns of $e_t(\mathbf{X}_i)$ and $e_s(\mathbf{X}_i)$ respectively, and $\mf{v}_{i,j}$ be the $jth$ row of $\mf{V}_{i}$. The pair-wise distillation loss is defined as:
\begin{equation}\label{eq:distillation_loss}
    \mathcal{L}_{\mathrm{pd}} = -\frac{1}{M}\sum_{i=N+1}^{N+M} \sum_{j=1}^{H^{\prime} W^{\prime} D^{\prime}}
    \log \frac{\exp (s(\mathbf{v}_{i,j}^s, \mathbf{v}_{i,j}^t))}{\sum_k \exp (s(\mathbf{v}_{i,j}^s, \mathbf{v}_{i,k}^t))},

    \vspace{-3pt}
\end{equation}
where $s(\mathbf{v}_1, \mathbf{v}_2) = \frac{\mathbf{v}_1 \cdot \mathbf{v}_2}{\| \mathbf{v}_1 \| \| \mathbf{v}_2 \|}$ measures the cosine of the angle between two $\mathbf{v}$'s as their similarity. Note again that this loss also involves all the unlabeled data.

\myparagraph{Consistency Loss $\mathcal{L}_{\mathrm{con}}$}
Inspired by recent work \cite{laine2016temporal,tarvainen2017mean}, consistency is designed to further encourage training stability and performance improvements on the unlabeled set. In our implementations, we first perform different perturbation operations on the unlabeled input volume $\mf{X}_{i}$, \textit{i.e.}, adding noise~$\eta_i$, and then define the consistency loss as:
\begin{equation}\label{eq:mean-tea}
    \mathcal{L}_{\mathrm{con}}=\frac{1}{M} \sum_{i=N+1}^{N+M}\mathcal{L}_{\mathrm{mse}}\left(\mathcal{F}_s\left(\mf{X}_{i}^s + \eta_i^s\right), \mathcal{F}_t\left(\mf{X}_{i}^{t} + \eta_i^t\right)\right).
    \vspace{-3pt}
\end{equation}

\myparagraph{Overall Training Objective}
{SimCVD} is a general semi-supervised framework for combining contrastive distillation with geometric constraints. In our experiments, we train {SimCVD} with two objective functions --- a supervised objective and an unsupervised objective. For the labeled data, we define the supervised loss in Section~\ref{sec:model}. For the unlabeled data, the unsupervised training objective consists of the boundary-aware contrastive loss, pair-wise distillation loss, and consistency loss in Section \ref{sec:contrat}. The overall loss function is:
\begin{equation}\label{eq:overall_obj}
    \mathcal{L} = \mathcal{L}_{\mathrm{sup}} + \lambda \mathcal{L}_{\mathrm{contrast}} + \beta \mathcal{L}_{\mathrm{pd}} + \gamma \mathcal{L}_{\mathrm{con}},
    \vspace{-3pt}
\end{equation}
where $\lambda,\beta,\gamma$ are hyperparameters that balance each term.

\section{Results}

\subsection{Experiments: Left Atrium}

We compare {SimCVD} with published results from previous state-of-the-art semi-supervised segmentation methods, including V-Net \cite{milletari2016v}, MT \cite{tarvainen2017mean}, DAN \cite{zhang2017deep}, CPS \cite{chen2021semi}, Entropy Mini \cite{vu2019advent}, UA-MT \cite{yu2019uncertainty}, ICT \cite{verma2019interpolation}, SASSNet \cite{li2020shape}, DCT \cite{luo2020semi}, and Chaitanya {\textit{et~al.}} \cite{chaitanya2020contrastive} on the LA dataset in two labeled ratio settings (\textit{i.e.}, 10\% and 20\%).

The quantitative results on the LA dataset are shown in Table \ref{tab:la}. {SimCVD} substantially improved the segmentation accuracy in both 10\% and 20\% labeled cases. The results are visualized in Fig \ref{fig:vis_la}. Specifically, in the setting of 20\% labeled ratio, our proposed {SimCVD} raises the previous best average results from 89.94\% to 90.85\% and from 81.82\% to 83.80\% in terms of Dice and Jaccard, even achieving comparable performance to the fully supervised baseline. Using the 10\% labeled ratio, {SimCVD} further advances the state-of-the-art results from 87.49\% to 89.03\% in Dice. The gains in Jaccard, ASD, and 95HD are also substantial, achieving 80.34\%, 2.59, and 8.34, respectively. This suggests that: (1) taking voxel samples with a contrastive objective yields better voxel embeddings; (2) incorporating pair-wise spatial labeling consistency can boost the performance by accessing more structural knowledge; and (3) utilizing a geometric constraint (\textit{i.e.}, SDM) is capable of helping identify more accurate boundaries. Leveraging all these aspects, we can observe consistent performance gains.

\subsection{Experiments: Pancreas}

To further evaluate the effectiveness of {SimCVD}, we compare our model on the pancreas CT dataset. Experimental results on the pancreas CT dataset are summarized in Table \ref{tab:pa}. We observe that our model consistently outperforms all previous methods, achieving up to 6.72\% absolute improvements in Dice. As shown in Figs. \ref{fig:vis_la} and \ref{fig:vis_pa}, our method is capable of predicting high-quality object segmentation, considering the fact that the improvement in such a setting is difficult. This demonstrates: (1) the necessity of comprehensively considering both boundary-aware contrast and pair-wise distillation; and (2) the efficacy of global shape information. Compared to the previous strong models, our approach achieves large improvements on all the datasets, demonstrating its effectiveness.


text:
Red Sandstone may refer to:

Sandstone appearing red due to the inclusion of iron oxides (hematite)
Old Red Sandstone, a British lithostratigraphic unit (a sequence of rock strata) to which stratigraphers accord supergroup status
New Red Sandstone, a chiefly British geological term for the beds of red sandstone and associated rocks laid down throughout the Permian to the end of the Triassic
Red Sandstone Varied Productions, Irish performance art company

See also
Redstone (disambiguation)
Ultisols

redpajama_set_name: RedPajamaWikipedia


Tuggy always scoring free stuff.
Grammy invited us to join her at the Los Alamitos Race Course today, where they had their Annual Wiener Dog Races to benefit the Seal Beach Animal Care Center. The dogs were so cute! I had heard about the wiener dog races at Old World, and it seemed like a cute thing to see with the family…. but the races at the Race Course were a huge deal! The place was packed and , from our viewpoint, the itty bitty pups were hard to see. Luckily, these guys were all over the place. My favorite was the little guy above with flames painted across his body. Diesel needs to get himself some flames too! Lol.
In between the trials, they had horse races too. Grammy showed Kristina and I how to place a bet on some horses. Not really sure what we were doing, we picked some numbers and went back to our seats to check out the race. After the race, we went to scan our bet card, and it turns out… we won some money back… we just about broke even! Pretty neat for not knowing what the heck we were doing.
We went to Atlantis Play Center to check out the new splash pad they opened up last month. Tuggy loved it. Glad to see that they are bringing in new "attractions" to this park.
Larry used to come here as a kid, and its fun to see that some of the things he loved about it as a kid, like this giant sea dragon slide, haven't changed.


text:
Das Naturschutzgebiet Lindenfeld liegt auf dem Gebiet der Stadt Schwäbisch Gmünd im Ostalbkreis in Baden-Württemberg. Das Gebiet erstreckt sich südlich von Bettringen, einem Stadtteil von Schwäbisch Gmünd. Westlich des Gebietes verläuft die Kreisstraße K 3275 und nördlich und östlich die Landesstraße L 1160. Im Gebiet fließt der Schupfenbach, südlich erstreckt sich das 10,9 ha große Naturschutzgebiet Bergrutschung Tannenwald.

Bedeutung
Das 75 ha große Gebiet steht seit dem 29. Juli 1994 unter der Kenn-Nummer 1.202 unter Naturschutz.
Es handelt sich um einen ehemaligen Truppenübungsplatz und um eine "am Hangfuß des Nordtraufes der Schwäbischen Alb gelegene, großflächige, extensiv als Schafweiden genutzte Wiesenhänge mit basenarmen Magerrasen in enger Verzahnung mit feuchten und wechselfeuchten Standorten auf der gesamten Fläche."

Siehe auch
 Liste der Naturschutzgebiete im Ostalbkreis

Weblinks
  1.202 Lindenfeld  Verordnung des Regierungspräsidiums Stuttgart über das Naturschutzgebiet »Lindenfeld« vom 29.Juli 1994

Einzelnachweise

Naturschutzgebiet im Ostalbkreis
Schutzgebiet in Schwäbisch Gmünd
Schutzgebiet (Umwelt- und Naturschutz) in Europa


text:
A Biography of Madame Butterfly
Brian Burke-Gaffney
Eastbridge Books | July 2004 | 228 pages
$7.99 (e-book) | ISBN 978-1-78869-115-4
$19.99 (paperback) | ISBN 978-1-78869-043-0
$34.99 (hardback) | ISBN 978-1-78869-044-7
Choose an optionE-bookPaperbackHardback Clear
Starcrossed quantity
Camphor Press is a British-Taiwanese publisher, focused on Taiwan and the wider East Asia region. As a specialist in English books about the country and its surroundings, we offer a window into this fascinating, vibrant, conflicted place.
Giacomo Puccini's opera Madame Butterfly has enjoyed tremendous popularity in Europe and America since its debut in 1904. It has also inspired a global-level debate about whether the tragic heroine of the opera, Cho-Cho-san, was based on a real-life model.
Starcrossed looks at this controversy and presents compelling evidence that, in fact, there was no real life Cho-Cho-san. Nonetheless, the author demonstrates that, despite the cultural disparities evoked by the opera and the clash of values embodied in it, there lie buried in the history of Nagasaki many untold tales of true international romance and cooperation.
Nagasaki, the setting for the opera, holds many of the answers to the international identity debate, however, the city has remained virtually silent in the matter, in large part, argues Burke-Gaffney, because the story of Madame Butterfly is locally viewed as a Western romance with values alien to many Japanese.
Penetrating beyond consideration of Madame Butterfly as a work of art, Starcrossed explores the literature from which the opera springs, including a wide variety of primary sources in both Japanese and Western languages. Drawing on these insights, Burke-Gaffney presents the opera as a window on Japan's changing relationship with Europe and America from the seventeenth century through the post World War II occupation.
Finally, Starcrossed looks at the sites in Nagasaki which were related to the development of Madame Butterfly and presents an engaging display of illustrations and photographs from the past hundred years.
Brian Burke-Gaffney went to Japan in 1972 and trained for nine years as a Zen monk. A resident of Nagasaki since 1982, he has published numerous books and articles on Nagasaki history and Japanese culture, including Nagasaki: The British Experience, 1854–1945, The Light of Morning: Memoirs of the Nagasaki Atomic Bomb Survivors, and Holme, Ringer & Company: The Rise and Fall of a British Enterprise in Japan, 1868-1940. He is currently professor of cross-cultural studies at Nagasaki Institute of Applied Science.
Southerners, Northerners
Lee Ho-chul
Paperback Hardback
Letters of Gilbert Little Stark
Gilbert Little Stark
$2.99 $14.99 $24.99
E-book Paperback Hardback
Oil for the Lamps of China
Alice Tisdale Hobart
The Son of Taiwan
Chen Shui-bian


Great Home in Move in Condition in Howard City. This home boasts a Huge entry way 3 Bedrooms 1 Bath with main floor laundry all custom kitchen cabinets and newer tiled counter tops and all appliances included. Outside there is an enormous 4 Stall attached garage that is accessible from both ends there is also a great large yard. This home is within walking distance to schools and stores. Stay warm and cozy all winter long with Natural Gas!


New Savings On GERBER BRASS TRIP LEVER DRAIN For ROMAN BATHTUBS CHROME Within Gerber Bathtub Drain Decor 59 is one of pictures thet are related with the picture before in the collection gallery. If you would like to see the New Savings On GERBER BRASS TRIP LEVER DRAIN For ROMAN BATHTUBS CHROME Within Gerber Bathtub Drain Decor 59 in High Resolution [HD Resolution] version, please press the right click on picures/image then choose "Save as Image" option, and done. You will get New Savings On GERBER BRASS TRIP LEVER DRAIN For ROMAN BATHTUBS CHROME Within Gerber Bathtub Drain Decor 59 pictures that you want. The exactly dimension of New Savings On GERBER BRASS TRIP LEVER DRAIN For ROMAN BATHTUBS CHROME Within Gerber Bathtub Drain Decor 59 was 320x400 pixels. You can also look for some pictures by collection on below this picture. Find the other picture or article about Gerber Bathtub Drain here. We hope it can help you to get information of the picture.


text:
Die Solre ist ein Fluss in Frankreich, der im Département Nord in der Region Hauts-de-France verläuft. Sie entsteht aus dem Zusammenfluss zweier Quellbäche bei Solre-le-Château, im Regionalen Naturpark Avesnois. Der Fluss entwässert generell in nordwestlicher Richtung und mündet nach 22 Kilometern östlich von Maubeuge, im Gemeindegebiet von Rousies, als rechter Nebenfluss in die kanalisierte Sambre.

Orte am Fluss
 Solre-le-Château
 Obrechies
 Ferrière-la-Petite
 Ferrière-la-Grande
 Rousies

Weblinks

Anmerkungen

Fluss in Europa
2Solre