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Home & Studio Recording - Sep 1984	Studio Earthing Techniques
Studio Earthing Techniques
Studio Earthing Techniques (Part 1)
by Ben Duncan
Studio 'earthing' techniques.
Ben Duncan offers a firm grounding on the abolition of the GLC (Ground Loop Connections).
Let's begin by identifying two distinct reasons for having 'earth' or ground connections in the first place. One is to do with defining a common point, or 'reference' between your equipment necessary to make it work. The other isn't essential to the equipment's operation, but is all about safety. Good grounding practice is little more than keeping the signals in each ground-path separated physically as well as conceptually. On this tack, you might find it helpful to use the word ground for common connections in general, keeping earth as an alternative term for the occasions where actual soil is involved, namely when dealing with the mains safety connection, the green/yellow wire.
Types Of Ground
Signal ground is the earth or screened side of your cables, when they're unbalanced. In transformer balanced systems, there may be no through signal ground connection (ie. between equipment), but some transformer-balanced set-ups, treat the cable screen as the signal ground, as do actively balanced inputs and outputs. And in all systems (balanced or not), signal ground is the common or '0V' (zero volts) connection to the circuitry inside the enclosure.
Mains ground is the stake outside the green/yellow wire on a 3 core mains lead, and the big pin on your mains plug (UK only). Professional equipment is generally enclosed in aluminium and steel, and this is normally connected to mains ground, viz 'earthed', for safety. Another method, beloved by 'consumer' equipment manufacturers is 'double insulated' construction. Originally, this was meant to relieve manufacturers of hairdriers and such like (ie. goods in plastic cases), from the legal requirement to provide a mains ground connection, enabling them to cut costs.
To spot this category of gear then, you need only look out for a two core mains lead. Oddly, the 'double insulated' category now appears to extend beyond bona fide plastic enclosures; many oriental tape machines with acres of exposed aluminium can be seen sporting 2 core mains leads.
Hum-Loop
When two currents flowing in a ground (ie. common) connector are vastly different in size, the bigger one modulates the smaller, and if we get our signal and mains grounding paths mixed up, this is exactly what happens. Mains related 'hash' impinges on our music. Hum caused by loops varies between a 'rasping hum' and an 'edgy buzz', depending on how 'filthy' your mains supply is.
Hands up anyone who's spent hours tracking down a phantom hum? After unplugging every lead, the hum turned out to be incurable, because it wasn't caused by a hum-loop in the first place! Valve gear usually hums of its own accord, and power supply faults in any equipment or naff connections can give rise to a variety of hums and buzzes. Beware also of acoustic hum from badly made transformers, especially if the offending equipment is next to the monitors. Putting your ear right against the speaker will confirm that the hum isn't acoustic in origin.
Returning to hum-loop hum, whenever you track down a loop, listen to the characteristic harmonic structure, and try to learn to recognise it by ear alone - ground loop buzzes have a distinctive sound, and knowing what to listen for may help if you encounter problems at a future date, especially in a situation where there's no time to be spared.
Now to prevent hum-loops, we'd ideally like to keep both types of ground electricity separated (Figure 1). But electronics technology abhors a vacuum, and in the next section, we'll see two reasons why they should be connected; one good, the other a nuisance. Why? Well it's handy to know that whilst one low impedance (ie. direct) link between the two grounds is hunky-dory, any additional connections will set up the ideal conditions for a hum-loop. Our problem then, is to take control of connections between the grounds and place one link in sole command.
Two Earths Collide
Ideally our equipment's circuitry shouldn't need to be tied to mains ground. It's isolated from the mains power and the absence of a mains earth connection won't prevent it from working (think of portable, battery-operated gear, Walkman to you). Trouble is, the mains transformer that's supposed to isolate electronics from the mains has imperfections, in particular interwinding capacitance. It's rather as if a very small value capacitor were connected between live mains and signal ground. With a low power transformer (ie. gear other than power amps) the current passed by this capacitance is small, circa 10μA, and just enough to transmit a mild tingling sensation if you've got sweaty paws. At least, it tickles if the electronics inside the box 'floats'; in other words, if it's not tied to the mains ground (Figure 3).
The level of leakage current available when a lone item of equipment floats isn't particularly dangerous, although it wouldn't be pleasant if you were 20 feet up scaffolding. But if we hook together the usual home studio's complement of gear, lo and behold, the capacitances multiply. Pretty soon, the sum of our interwinding leakage currents adds up to produce a fairly nasty bite. The remedy, of course, is to connect the signal ground to the safety electrical earth. This swamps out the leakage, so you can touch signal ground - or indeed, any part of the audio electronics - in complete safety.
Moving over to the enclosure per se, with the notable exception of oriental cassette machines (which masquerade as 'doubly-insulated') the majority of equipment with an extensive area of exposed metalwork has this tied to the mains earth for safety. The head-scratching begins when we encounter a processor (say) where the signal ground and the metalwork are tied together, or worse, are one and the same.
Examples are when metal-bodied jacks are mounted directly on the metalwork, or when the area of copper on the PCBs dedicated to signal ground is screwed down in contact with the chassis. These practices are especially naughty when the system employs unbalanced connections. You can test for a suspected connection beween signal and mains ground by looking for a low ohms reading with a meter going between the signal ground terminal on any other connectors (ie. where the cable's screen would be connected), and the earth pin on the mains plug. Do this with the gear unplugged of course!
If you get a low reading - below 40 ohms (some meters won't accurately read this low), you'll need to sort out the grounding inside. Above 40 ohms and up to 1k or a little higher, indicates that the processor (or whatever) has been groundlifted. Higher - or infinite - resistance readings mean that there's no immediate hum problem, but you might want to add groundlift. We'll come to this in a moment.
Grounding Point
The first stage to proper grounding relationships is to decide on the nerve centre of your system. Normally it's the mixing console, but the choice is yours. Anyhow, this is the ONLY point where we tie the mains earth to signal ground - at least directly. Let's call it the central grounding point (Figure 2).
If your test meter shows no such connection (ie. an infinite reading, see above), you'll have to make a simple internal modification, taking a wire from the chassis (or at least, from the mains earth) to a suitable signal ground point, typically the 'dirty' 0V busbar (Figure 3). Ask a boffin if you need help identifying the correct connections.
For the remainder of the equipment, we need to ensure that the opposite is true. Essentially, this means checking out with an ohmeter, seeking out direct 'mains ground' to 'signal ground' connections, and removing them wherever possible. However, gear which might be used on its own, and therefore without the benefit of the mixing console's central ground connection, shouldn't be wholly isolated in this way. Instead, we groundlift it. Rather than separate the two grounds 100%, we link them together via a resistor. Any value under 10kohms will swamp out the leakage interwinding current, but better safe than sorry. So we'll pick a much lower value to be double safe. "But surely, hum-loops..." NO! The hum arises out of large currents flowing in the loop. With a groundlift, we're essentially placing a resistor in series with the loop. And any resistor in excess of 40 ohms or so will reduce the current level to such an extent that it's effect is no longer audible. But careful: if ten equipments have 40 ohm groundlifts, the overall loop resistance falls to 40/10 = 4 ohms. This is because the groundlift resistors appear in parallel when all the gear is connected together. With this in mind, a 1k, ½ watt groundlift resistor has become something of an industry standard (Figure 2). This lets us connect large quantities of equipment without risk of a low impedance developing.
Groundlifting will only normally apply to units with a 3 core mains cable. In theory, anything double-insulated (ie. with a two core mains lead) shouldn't exhibit any significant leakage reactance. We hope so, because there's no mains ground to tie down to!
Back to the 'lifting' as it's known. The resistor is normally connected between the chassis or enclosure, and the signal ground appearing on an adjacent input connector - or any other convenient point. You can also wire a choke in series while you're at it. A 10μH inductor will preserve the 50/60Hz groundlift impedance, but decouple your audio ground from the whine of electric drills and other RF trash appearing on the mains ground. It's particularly worthwhile if your setup is already beset by RF (radio) interference nasties. The choke should be rated at 1Amp; a suitable part is available from RS Components' suppliers.
As time goes by, an increasing number of leading UK manufacturers are making groundlifting and ground isolation easy. One of the first was Chas Brooke: his BSS crossovers have featured a groundlift switch (plus dinky green LED!) since 1978. Other manufacturers offering similar contrivances are Klark-Teknik and HH. There's either a switch, or a link-bar at which two grounds are tied - and can be untied. With the link bar, you simply screw your resistor - with or without choke - across the terminals. These facilities are hardly an earth-shattering high-tech development... but let's hope that more manufacturers adopt these features in future, because if they did, correct grounding would be made a lot easier for everyone.
The next stage is to sort out any units (other than the mixing console of course) with defective standards. For clarity, we'll refer to it as bonded-ground gear. As I implied above, enemy number one in this class is the cassette machine or amp with a direct connection between the case and the signal ground. This sorry situation frequently relates to uninsulated panel connectors, particularly jacks and phono sockets.
Sometimes we can remedy this by putting insulated connectors in their place, but often this sort of modification isn't feasible, for a variety of reasons. Even if you're not borrowing or hiring the gear, the scale of the modifications required may be in the major surgery league. For instance, the signal ground may be bonded to the case at dozens of places on the PCB, not to mention the control pot brackets. I have had to hack away large chunks of PCB track on more than a few dbx 165s in my time! The best way to do this is with a very hot soldering iron, and a very sharp chisel. It's a job for the confident though; if my chisel had slipped, I would have had to explain to the band's manager why I'd destroyed all their compressor/limiters..!
So if you're not up to modifying equipment bought from a shop, we suggest you ask the supplier, dealer, manufacturer or importer if they can make the necessary grounding mods. If you request this at the time of purchase, the charge should be small, because you can truthfully insist that the work is necessary for you to use the equipment properly ie. without him. In fact, there's probably a good case for having the modification done on the house, because without it, the offending article isn't "fit for its intended purpose", namely high quality audio. But I'll leave you to argue about the Sale of Goods Act in the county court...
Bonded-Ground
We'll finish this section by highlighting the problems that bonded-grounds entail:
1) With the signal ground linked to chassis, any attempt to connect the case to mains earth (for safety) will set up a hum-loop (because we've already made the sole direct link at the console).
2) And as soon as we mount-up the unit in a rack, the metal fixing strip will bring the offending article into electrical contact with the other equipment enclosures, some with metalwork which is tied directly (and quite legitimately) to the mains earth (Figure 4). This has elements of Catch 22, of course: the very connection we're trying to avoid is inherent in the mechanics of the situation.
Alternative Solutions
Let's assume that the actual physical amendments to the grounding discussed in the preceding section aren't practical. Now if you don't use a rack, problem no. 2 (above) is easily remedied by keeping incompatible enclosures separated. Of course, you'll want to stack up your gear, and this is okay so long as sensitive equipment is insulated from adjacent units with rubber feet or any suitable insulating material, in the past, I have used scraps of cork tile for this purpose, as it also damps down tinny lids which are apt to rattle in response to loud bass notes.
To solve the no. 1 objection, you'll also have to disconnect the mains earth cable. This is strictly very naughty, but there are mitigating circumstances. First, most of the gear we're discussing at this point has a two-core mains lead. But even if not, and we end up unhooking the green/yellow wire at the plug, safety can be maintained, providing you fit a 30 milli Amp current-balance circuit-breaker in line with your master mains socket.
If you do decide to disconnect any mains earth connections, be sure to chop back the redundant wire to the cable sheath, so it can't float around and touch anything nasty. Also, mark the plug accordingly: U/G in big red letters will remind you that it's UNGROUNDED.
Returning to the trail, what happens to our rack-mounted gear? Well, here, we need to keep units with bonded-grounds isolated from the rack strips (these latter are tied to mains ground in the usual fashion). To isolate, removable metal rack-tabs can be replaced by plastic members. These are often available as accessories from Pro-audio and Hi-Fi dealers, and can be readily adapted to fit equipment other than whatever they're aimed at specifically. All you need do is drill out 4 fixing holes in the enclosure's side panels.
Next month, we'll look at an altogether different way around hum-loop elimination - that of balancing.
Read the next part in this series:
Patchbay Tips
Home & Studio Recording - Copyright: Music Maker Publications (UK), Future Publishing.
Home & Studio Recording - Sep 1984
Electronics / Build
Maintenance / Repair / Modification


Today in Munchies That Society Approves Of, we have crackers. Crispy, homemade, baked crackers with not one terrible ingredient or unpronounceable preservative. It's all healthy and crunchy and fabulous, and there's some cheese because YOLO. They stay perfectly crispy in an airtight tin for days and you'll be popping them in your mouth every waking minute….


Felix and I have been busy beavers! We want to share with you what we have been up to since the "Puppy Pals". I really need your feed back on the train ornament so take a look and let me know what you think.


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Угледно е село в Североизточна България. То се намира в община Омуртаг, област Търговище.

Религии
Селото основно е заселено с помаци

Външни препратки

Села в област Търговище
Населени места в община Омуртаг

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Back to News Front
AMC leadership visits JBLM
Drives home message of readiness, innovation
By Airman 1st Class Mikayla A. Heineck, 62nd Airlift Wing Public Affairs on July 3, 2019
Gen. Maryanne Miller and Chief Master Sgt. Terrence Greene, AMC command chief, make boxed meals at the 627th Force Support Squadron flight kitchen June 26 at Joint Base Lewis-McChord. Photo credit: Airman 1st Class Sara Hoerichs
General Maryanne Miller, commander of Air Mobility Command, and Chief Master Sgt. Terrence Greene, AMC command chief, saw first-hand what makes Joint Base Lewis-McChord (JBLM) the global-reaching airlift power that it is during their visit to JBLM June 24-27.
Team McChord airmen showcased their units' humanitarian airlift and airdrop capabilities, offering Miller and Greene hands-on experience operating deicing vehicles and practicing combatives taught by the 627th Security Forces Squadron's Phoenix Raven members.
The AMC leaders spoke with several groups across base, including a session where they expressed to Team McChord's first sergeants the importance of taking care of airmen. On the last day of the visit, Miller and Greene addressed McChord airmen at an all call, covering topics including readiness, mobility operations and innovation.
They spoke on new practices that the Air Force is beginning to integrate into exercises to increase lethality and readiness under any condition.
"We're going to start practicing different ways of doing things," Miller said. "We've got to be ready to face any threat and remain connected to the fight even without the Internet. We must be able to do our job, delivering strength, in any environment."
While there is an increasing demand signal for a high state of readiness in contested and degraded environments, Greene also stressed the importance of installation resiliency at home.
"We've got to change our mindset, because we often think about installation resiliency only when we're deployed," Greene said. "When we talk base resiliency, especially for us in AMC, we've got to start looking at it through a different lens. We have to start thinking about installation resiliency right here at home, in the United States."
Miller and Greene also expressed the impact McChord's C-17 fleet had on Air Force operations in 2018. During their presentation, they displayed a world map with hundreds of lines branching out from JBLM, illustrating the vast reach of JBLM C-17s as they execute missions all over the world, including in Antarctica.
"It's not an understatement to say we're global," said Miller. "The tempo we operate at is incredible. So I thank you for everything that you do, because your support to the mobility mission achieves global impacts every day."
Innovation also ranks as an important topic across the Air Force. AMC leaders highlighted the work airmen are doing around the AMC community. Miller spoke of an airman who represented AMC as an Air Force Phoenix Spark Tank finalist in February who 3D-printed a device that would reduce cargo parachute failures during airdrops, thus allowing airlift missions to be more cost-efficient and reliable.
Gen. Miller and Chief Greene also emphasized their awareness of the challenges airmen face. They encouraged AMC leaders to empower their airmen to innovate and foot-stomped their commitment to placing the right leaders at every level to prioritize the ever-changing needs of the Air Force.
"You all are doing phenomenal work across the command," said Greene. "We felt the energy and camaraderie of AMC's airmen, we've seen a lot of things going right, going well. Keep that energy going."
Alec Clayton on July 3rd, 2019
New trauma procedures
By Suzanne Ovel on July 11th, 2019
AAFES -- 124 years strong
By JBLM Army & Air Force Exchange Service on July 11th, 2019
By Sgt. Sean Harding on July 11th, 2019
Humanitarian airmen
By 62nd Airlift Wing Historian Office on July 11th, 2019
Where it's cold we go
By Airman 1st Class Mikayla A. Heineck, 62nd Airlift Wing Public Affairs on June 8th, 2019

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HomeMARINEWhat Does Cabotage Law Mean, What Is Its Importance? When Was the Cabotage Law Enforced?
What Does Cabotage Law Mean, What Is Its Importance? When Was the Cabotage Law Enforced?
01/07/2021 MARINE, GENERAL, TURKEY
What does the cabotage law mean?
Cabotage is the privilege that a state grants to its ports regarding maritime trade. Since only their citizens can benefit from this privilege, it will make a significant contribution to the national economy, so the states have resorted to imposing a cabotage ban on foreign flagged ships. Some international conventions also contain provisions regarding the authority to impose a ban on cabotage.
When Was the Cabotage Law Enforced?
The cabotage privilege granted by the Ottoman Empire to foreign ships within the framework of the capitulations was abolished in 1923 with the Lausanne Peace Treaty. It was also accepted on 20 April 1926. The Cabotage Law came into effect on July 1, 1926. According to this law; Keeping vehicles driven by machinery, sails and oars in rivers, lakes, the Sea of Marmara and the straits, in all territorial waters and in gulfs, harbors, bays and similar places within them; Turkish citizens were given the right to transport goods and passengers with them. Also; It was stated that diving, piloting, captaining, engineering, crew and similar professions can be fulfilled by Turkish citizens. It was accepted that foreign ships could only carry people and cargo between Turkish ports and ports of foreign countries.
What Does Cabotage Law Mean, What Is Its Importance?
Cabotage, literally, is the name given to the privileges granted by states to their ports in maritime trade. Benefiting from privileges only by its own people makes a great contribution to the national economy. For this reason, states have the right to impose a ban on cabotage to foreign ships.
The Ottoman Empire applied some cabotage privileges to the ships of foreign countries with the capitulations. These privileges ended in 1923 with the Lausanne Peace Treaty. The Cabotage Law came into force on 1 July 1926.
According to the law; Only Turkish citizens have the right to have vehicles that move with sails, oars, machinery, and to transport goods or passengers with these vehicles in the Marmara Sea and the straits, lakes, all territorial waters, in all gulfs, ports, bays and similar places.
Today in History: Cabotage Law Regulating the Principles of Maritime Trade Entered into Force
What is Cabotage Day, Why is it Celebrated? The History, Importance and Meaning of Cabotage Festival
Mayor Kadir Topbas: Say We're the Metro
Environmental Law Including Mucilage and Zoning Regulations Entered into Force
GPS and Camera Period in Public Transport Starts So What Does It Mean?
What is TAI? What does TAI mean, what does it mean, what does it stand for?
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What is SADAT? What does SADAT mean, what does it stand for? What does SADAT do?
What is Drezin? What Does Drezin Mean?
What does oligarch mean? What is an oligarch, what does it mean? Who Are The Oligarchs?
What Does Tem Mean? What does the word Tem Highway stand for? How Many Kilometers on TEM Highway?
What does TCDD mean and what does it mean? What is TCDD Abbreviation?
What Does Esfeli Safilin Mean? What is the dictionary meaning? Who Is Esfeli Safilin Called?
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What Is Recession, What Does It Mean? What Does Recession Mean in the Economy?
What is Pedophilia, What is Pedophilia? Pedophilia in Law
What Does Abuse Mean? Abuklama What is TDK Dictionary Meaning?
Minister Turhan: Tas Liquidation Decree will be in force in the coming days "
Items Postponed In The Lease Of The Workplace Comes Into Force!
Today in History: Istanbul Convention Entered into Force
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«Jun Aug »


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KS3 Units
BTEC Unit 1
Knowledge Org.
Music from the Caribbean
Music from the Caribbean
A diverse group of musical genres originate from the Caribbean. Popular types of Caribbean music include reggae, salsa and calypso.
The Caribbean islands lie in a chain from the Bahamas in the north to Trinidad and Tobago in the south. Central in the chain are the largest islands of Cuba, Haiti, the Dominican Republic, Jamaica and Puerto Rico. Most of the Caribbean population are descendants of West African slaves and settlers from Europe, India and China. The music of the Caribbean differs from island to island, but each kind has its origins in a mixture of African and European influences.
African musical influences include:
syncopations (offbeat accents) and cross-rhythms
use of percussion
European musical influences include:
tonal (based in a key) harmonies and melodies
instruments eg guitar
There are similarities in the types of instruments used across the Caribbean islands. The most popular instruments are percussion and drums, different types of guitar and homemade instruments.
There are many types of drums, including congas (a pair of tall, narrow, single-headed drums played with the hands) and their smaller version bongos.
Percussion instruments include:
claves - two short pieces of wood
maracas - hand-held shakers
cabasa - loops of steel balls wrapped round a cylinder, hand-held
reco-reco or guiro - scrapers
tragogo - double-headed bells
These types of instruments are often known as Latin percussion.
The guitar is found in a number of sizes including the:
guitarrone (a large four-stringed bass guitar)
tres (a small guitar with three sets of double strings)
cuatro (a small guitar with four strings)
Salsa is a lively style of dance music with African and Spanish influences that originated in Cuba.
It has:
clave rhythms (usually played by claves), which provide the rhythmic foundation
melodic and rhythmic syncopation
several one or two bar patterns each played by a different instrument
much use of Latin percussion eg bongos, congas, timbales (shallow single-headed drums) and cowbells
piano often features strongly
rhythm section with a front line of two to five horns
simple harmony based on repeated chord patterns, often in the minor key.
call and response vocals
simple melodies followed by coro (improvised sections)
lyrics of love and everyday life
Listen to the opening of the salsa song "Se quema la chumbamba" played by Familia Valera Miranda. Notice the cuatro, the Latin percussion (maracas, bongo and claves) and the clave rhythm.
Originally the national dance of Trinidad and Tobago, calypso is particularly associated with carnival. It has:
4/4 time with syncopation
acoustic and bass guitar or band with trumpets, saxophones, electric guitars, drum kit and Latin percussion
often uses three-beat rhythms with two long beats followed by a short beat
often uses call and response
simple harmony
verse and chorus songs
topical, witty lyrics - often satirical
Here is a sample Calypso rhythm:
Soca means 'soul-calypso' and is an energetic, disco-influenced offshoot of salsa.
Soca is characterised by:
use of horns and Latin percussion with loud fast percussion beats
very loud bass guitar
repeated chord sequences
rhythmic nonsense words
lyrics about love and partying
Mento is a form of Jamaican folk music similar to calypso. It was most popular in the 1950s.
Mento is characterised by:
guitar, banjo and drums
strophic (verse-repeating) form
lighthearted lyrics of everyday life
precursor to ska and reggae
Ska is a fast dance music that emerged in the late 1950s. Ska music:
fuses American rhythm 'n' blues (R&B) with mento rhythms
uses electric guitars and jazzy horn section (trumpets, saxophones and trombones)
uses characteristic offbeat jumpy rhythms
has lyrics about local issues
Here is an example of a ska rhythm:
Listen to the offbeat quavers in the ska song "El Pussy Cat" by Roland Alphonso.
Reggae is a much slower form of music than ska which emerged in the 1960s. It is characterised by:
electric guitars and drums line-up
amplified bass guitar riffs (short repeated patterns)
an association with Rastafarianism (a religious movement worshipping Haile Selassie)
characteristic rhythm in 4/4 with missing beat emphasis
use of repeated offbeat quavers
use of dub remixing techniques where effects such as delay are added
simple chord sequences
verse and chorus form
political themes in lyrics
Listen to the reggae song Freedom Fighters by Delroy Washington. Notice the repeated bass line, the rim shots and the bass drum playing on beats 2 and 4.
Rocksteady is a dance music that emerged in the mid-1960s. It is characterised by:
rhythms more relaxed than ska
stresses on beats 2 and 4
loud bass guitar playing steady 4/4 beat
Listen to the rocksteady song "You Can Get It If You Really Want" played by Desmond Dekker and the Aces. Notice the drum emphasis on beats 2 and 4 and the steady 4/4 beat of the bass guitar.
Merengue
Merengue was originally the national dance of the Dominican Republic and has African and Spanish influences. It is characterised by:
traditional instruments - tambora (a type of double-headed drum), guiro, cuatro, accordion
trumpets and saxophones playing an important part
a fast 2/4 beat
catchy melodies and rhythms
syncopated rhythms played by tambora
simple harmony - much use of tonic and dominant
mambo - instrumental interludes
slick choreographed stage presentation
Listen to the opening of the merengue song "No Me Faltes Nunca" played by Cana Brava. Notice the fast 2/4 beat and the use of the tambora.
Test your knowledge of MUSIC FROM THE CARIBBEAN
reggae listening questions...audio below
bob marley - 'three little birds'
Origins of Calypso
Percussionist Val Serrant sings a calypso, accompanying himself on a drum. He demonstrates alternating the two tones in rhythmic patterns by using the sticks in the centre of an oil drum and towards the edge of the drum for a contrasting sound. The words of the song give a brief synopsis of the origins of calypso. For use in teaching percussion techniques, studying the origins of calypso and the use of junk materials in music making.
Salsa Beat Machine
Go to the Salsa Beat Machine website via the button above. Have a play with the music.
Try taking the different instruments out and hearing each individual part.
Caribbean composition brief
Back to GCSE Music Home


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\section{Introduction}

    Planning is a core area in Artificial Intelligence (AI), which emerged in the early days of AI as part of robotics research \cite{blum1997fast,hoffmann2001ff}. A planning problem consists of a start state, one or more goal states, and a set of actions that specifies how one can move from one state to the next. The generality of the planning formalism captures a surprisingly wide range of tasks, including robotic path planning, program synthesis, general theorem proving (a proof can be seen as a plan), combinatorial puzzles, etc.

    In this paper, we focus on solving hard Sokoban instances that are challenging for general AI planners and even for domain-specialized solvers. In addition, we also conduct experiments on N-puzzle problems to further study the quality of discovered plans. See Figure~\ref{fig:example} for example instances for both domains. These two domains demonstrate core aspects of planning problems, e.g.,  irreversible actions vs. reversible actions, finding an arbitrary plan vs. finding a shorter plan. Both domains are representative of hard planning problems, despite their simple conceptual rules. Finding the shortest solution for N-puzzle is NP-hard and Sokoban was proved to be PSPACE-complete \cite{culberson1997sokoban,hearn2005pspace}. A regular-sized Sokoban board can have the shortest solution with hundreds or even thousands of pushes. The fact that pushes are irreversible makes solving harder. Modern N-puzzle and Sokoban solvers are based on combinatorial search augmented with carefully designed heuristic functions, pruning techniques, and various dead-end detection rules to avoid sub-search space with no solution. While we focus on Sokoban and N-puzzle problems, we stress that our approach only uses the minimum domain knowledge required to describe any planning problem, namely the state representation, the state-action transition function, and routines deciding the start state and goal states.
    \begin{figure*}[t]
        \centering
        \includegraphics[width=0.8\textwidth]{images/concat.png}
        \caption{\textbf{Left panel:} An example of a 15-puzzle instance. \textbf{Middle panel:} An example of a Sokoban instance \protect\cite{SokobanVis2021}. The blue circle represents the player (or "pusher"), the red circles represent boxes, and the dark squares are goal squares. Light-colored squares form walls. The player has to push all the boxes onto goal squares. \textbf{Right panel:} An example of applying the sub-instance creation strategy for Sokoban used in \protect\cite{DBLP:conf/ijcai/FengGS20}. The approach randomly selects an equal number of boxes and goal squares from the original ones and leaves the locations of walls and empty squares unchanged to form a new sub-instance. The created sub-instance is easier to solve compared with the original one. In section~\ref{sec:experiment}, we show various experimental results for deep RL for Sokoban with and without sub-instances.}
        \label{fig:example}
    \end{figure*}

    One major difficulty for RL in planning domains is the extreme sparsity of rewards \cite{weng2020curriculum}. Unlike other domains, e.g., video games, where a well-shaped reward function can often provide immediate learning signals for each state-action pair, planning domains require a complete valid plan to generate useful learning signals. A random exploration strategy cannot ``accidentally'' encounter a valid plan consisting of hundreds of steps. \cite{DBLP:conf/nips/FengGS20,DBLP:conf/ijcai/FengGS20} proposed a curriculum approach that creates a pool of sub-instances from the input instance, selects adequate instances to try, and gradually increases the power of the framework by solving harder and harder sub-instances. Sub-instances are easier to solve and make the learning smoother. As a result, the curriculum framework can solve challenging Sokoban instances that are out of reach for domain-specialized solvers.  See the right panel of Figure~\ref{fig:example} for an example. Nevertheless, while the sub-instance creation strategy was shown to be effective for the Sokoban domain, it may not be clear how to generate such sub-instances for an arbitrary domain. So it is desirable to develop an approach that generalizes to arbitrary domains.

    We thus propose a graph value iteration component to enhance the learning. Traditional value iteration uses Bellman equation to update state value estimates on {\bf sampled trajectories}. For planning domains, graph search, e.g., Best-First Search (BFS) or Monte Carlo Tree Search (MCTS), provides search space with more informative combinatorial structure than trajectories, and enables value iteration to collect and exploit the information from local search graph of failed attempts. The update rule of graph value iteration is rather simple --- we update the state value estimate of each node using a variant of Bellman equation that deals with multiple outgoing edges in the reversed topological order. Compared with previous works, the graph value iteration combined with graph search and curriculum learning framework can solve more hard planning instances with less input instances, as shown in the Experiments section.

    \textbf{Our contributions:} We introduce a novel Graph Value Iteration (GVI) component to automatically explore and learn from planning instances with little domain knowledge. GVI caches information about the search space from each (potentially failed) search attempt and generates training data to learn the structure of local search space. Thereby the plan search network can still learn from failed search attempts, i.e., before finding a solution plan to the goal. So, even for a set of input instances where no easily solvable instance exists, we will show that GVI can still learn useful information to improve the plan search network. GVI naturally generates learning signals containing intrinsic rewards that encourage graph search to explore previously unseen states and eventually find one solution given enough curriculum iterations. We show that our approach allows us to solve substantially harder instances than previous works with less input instances.

\section{Formal Framework}
    \begin{figure*}[t]
        \centering
        \includegraphics[width=0.8\textwidth]{images/workflow.pdf}
        \caption{\textbf{The main workflow.} The input of the framework is a task pool consisting of multiple planning instances to solve, and the output after learning is the set of solved instances with the shortest found solutions attached. In each iteration, the sampler picks a batch of planning instances from the task pool, and the graph search component, which is BFS or MCTS, attempts to solve these instances. The success/failure status of each instance is then sent back to the sampler to adjust its weights. A graph value iteration is performed on each search graph generated by graph search to provide further training data for the training phase. The updated parameters are then sent back to the search component and a new iteration starts.}
        \label{fig:workflow}
    \end{figure*}

    An overview of the workflow of one full iteration of the curriculum framework is depicted in Figure~\ref{fig:workflow}. The framework aims to solve hard planning instances (namely Sokoban or 15-puzzle instances) with no labeled solutions given. Subsection~\ref{sec:data_gen} describes the details about how to generate learning signals from local search graph, which is the \emph{core novel component} of our approach.

    \subsection{Curriculum Learning Framework and Training Process}
    \label{sec:curriculum}
    We adapted the curriculum strategy of \cite{DBLP:conf/nips/FengGS20} for our approach. The input to the learning framework is a set of planning instances to solve, and after learning the output contains solved instances with the shortest found solutions attached. In each iteration, we randomly sample a batch of input instances and run the search on each instance with a fixed search budget. Training data is then collected from the search graph to improve the parameters of the network. Initially, the model can only solve the easiest instances of the input set. As the learning goes, the prediction of the network gradually becomes more accurate and the model can solver harder instances. And training data collected from solved hard instances can further improve the network and this procedure iterates until the model cannot solve any more new instances and terminates. This offers a natural curriculum view of the input planning instances.

    For training, each batch samples state-action pairs from discovered plans and those generated by GVI. Specifically, labels for state $s_i$ on a discovered plan $(s_0,a_0,...,a_{n-1},s_n)$ is set to $n - i$ and labels for GVI-generated learning signals are described in Algorithm~\ref{alg:data_gen}. The percentage of GVI-generated samples in each batch is control by a constant $p$. In Section~\ref{sec:ab} we do an ablation test to see how $p$ affects the overall performance.

    \subsection{Graph Search with Restarts}
    \label{sec:astar}

    Our search component is an augmentation of vanilla graph search. Specifically, instead of using a manually crafted heuristic function, a deep neural network $v = h_\theta(s)$ with parameters $\theta$ takes a state $s$ as input and predicts a scalar value $v$ indicating the estimated remaining steps to the closest goal state. We compare two search methods, namely BFS and MCTS, in this paper.

    Given any start state $s$, BFS tries to find solution paths from $s$ to any goal state. The search maintains an open set which contains boundary nodes that are not expanded. At each search step of the main loop, BFS determines the next node to expand from the open set by picking the node with minimum evaluation function $f(n)$. We adopt the evaluation function of weighted A\textsc{*} and set
    \[f(n) = g(n) + 2 \cdot h_\theta(\textrm{state of } n),\]
    where $g(n)$ is the cost from the start node to $n$ \cite{pohl1970first}. Notice that unlike A\textsc{*} which guarantees optimality and requires the heuristic function to be admissible and consistent, our learning objective is to shape the heuristic predictor as close as possible to the ground truth. Thus discovered solution plans by BFS are not guaranteed to be optimal.

    MCTS tries to find the next best action given any state $s$. Since the output of the network $h_\theta(s)$ is the remaining distance estimate, we use $r(s) = -\log(h_\theta(s))$ as the reward of leaf nodes. At each selection stage, MCTS selects the next node with maximum
    \[
        U(s, a) = Q(s, a) + \sqrt{\frac{2 \cdot \ln \sum_b N(s, b)}{N(s, a)}},
    \]
    where $N(s, a)$ is the visit count of the action $a$ on the state $s$ and $Q(s, a)$ is the average rewards from previous simulations.

    We use graph search with \textit{restarts}~\cite{Biere2018,restart}. For each planning instance, instead of performing a long search, we divide the search budget into small pieces and each BFS run can only expand at most $S$ nodes. Multiples short runs can avoid exponential mean runtime when the runtime distribution has heavy-tailed behavior~\cite{restart}.

    \subsection{Dataset and Graph Value Iteration}
    \label{sec:data_gen}

    \noindent The study of the performance of search methods is greatly hampered by the difficulty in collecting real data and using procedurally generated instances may result in too regular search space. To make our result more diverse and general, we collected the Large Test Suite Dataset from the Sokobano website\footnote{http://sokobano.de/en/levels.php}, resulting in 3272 instances in total. All these instances were designed by different human authors in the past few decades, have great variation in the underlying structure, serve as the benchmark for specialized solvers, and also exhibit practical interest for humans to solve.

    \begin{figure*}[t]
        \centering
        \includegraphics[width=0.6\textwidth]{images/dijkstra.pdf}
        \caption{\textbf{The use of graph value iteration to calculate new training labels.} \textbf{Left panel:} The graph generated by graph search. \textbf{Right panel:} The graph with adjusted training labels. Solid circles represent closed nodes, dashed circles represent open nodes, and red circles are found goal states. Blue labels are remaining distance predicted by the network and yellow labels are calculated using graph value iteration. Each time the search hits the maximum node expansions, we fix the predicted labels of open nodes (found goal states are considered as open nodes too with labels modified to zeros) and calculate the labels of the remaining graph according to a variant of Bellman equation in the reversed topological order (see Algorithm~\ref{alg:data_gen} for more details). The calculated yellow labels reflect information about the local search structure near the start state and are then used as training signals.}
        \label{fig:dijkstra}
    \end{figure*}

    \begin{algorithm}[t]
        \caption{The Graph Value Iteration Component}
        \label{alg:data_gen}
        \textbf{Input}: Search graph $\mathcal{G}$ generated by graph search, open set $\mathcal{O}$
        \begin{algorithmic}[1]
            \STATE Let $\mathcal{U} = \textrm{the whole node set of }\mathcal{G}$.
            \FOR{$u\in \mathcal{U}$}
                \IF{$u$ is a goal state}
                    \STATE $d(u) = 0$ and remove $u$ from $\mathcal{O}$.
                \ELSIF{$u\in\mathcal{O}$}
                    \STATE $d(u) = h_\theta(s(u))$ (raw network prediction of the state $s(u)$.
                \ELSE
                    \STATE $d(u) = \infty$\ (set infinite to all closed nodes).
                \ENDIF
            \ENDFOR
            \WHILE{$\mathcal{U} \neq \emptyset$}
                \STATE Let $v = \textrm{arg\,min}_u d(u)$.
                \FOR{each node $w$ in incoming neighbors of $v$}
                    \STATE Let $d(w) = \min(d(w), d(v) + 1)$.
                \ENDFOR
                \STATE Let $\mathcal{U} = \mathcal{U} \setminus \{v\}$.
            \ENDWHILE
            \STATE \textbf{return} each node $u$ that are not in $\mathcal{O}$ with label $d(u)$
        \end{algorithmic}
    \end{algorithm}

    Unlike previous works that only collect training data from found plans, we generate training data from both successful and failed search attempts. When a failed search has been done, we fix the deep net's predictions of nodes from the open set (boundary/leaf nodes) and then perform a graph value iteration to calculate the adjusted heuristics of the remaining search graph. For each goal state, we force its heuristic to be zero instead of using the predicted value. See Figure~\ref{fig:dijkstra} and Algorithm~\ref{alg:data_gen} for more details. We will show that data generated from failed search attempts can behave as curiosity-driven intrinsic rewards that encourage the search to explore previously nonvisited states.

    \subsection{Network Architecture}
    \label{sec:network}
    The planning domains considered in this paper are all board games and we used ResNet \cite{he2016deep} as the feature extractor. Commonly, different planning instances have varying board sizes. We adopt a masking strategy used in \cite{wu2019accelerating} that allows mixing multiple board sizes into the same batch during both training and evaluating. Specifically, we size each input tensor to the size of the largest height and width in the batch and zero-pad each entry independently in the batch to fill out the excess space. The network also takes 0-1 mask as input indicating which parts of the tensor are real entries instead of excess space. For each network operation that will make excess space non-zero, we multiple intermediate tensors with the mask to make excess space zero again. Special attention is needed for operations involving global statistics to ensure only real entries are taken into account.

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

    The setup of all experiment runs takes a set of planning instances as input and terminates until no new instance of the input set can be solved for 10 iterations. For BFS, node expansion budget $S$ was set to 20000 for Sokoban and 500 for N-puzzles. For MCTS, we use 100 simulations for both domains. Each experiment run takes at most 3 days with 5 Nvidia Tesla V100 16GB GPUs and 2x18 core Xeon Skylake 6154 CPUs with base clock 3GHz.

    \subsection{Experiment Results on Sokoban}
    \label{sec:data_eff}

     \begin{figure}[t]
        \centering
        \includegraphics[width=\linewidth]{images/compare.v2.pdf}
        \caption{\textbf{Scaling comparison between MCTS and BFS with or without GVI on a set of hard Sokoban instances.} Figure~\ref{fig:example} illustrates the sub-instance creation strategy. \protect\cite{DBLP:conf/nips/FengGS20} focused on a set of hard instances that are not solvable with any other method. They collected 180 of such instances. Without sub-instance creation, both MCTS and BFS can only solve at most one hard instance. While enhanced by GVI, the number of solved instances raises to $>10$. With the sub-instance creation strategy, \protect\cite{DBLP:conf/nips/FengGS20} could solve 146 using MCTS. The remaining 34 unsolved instances could only be solved by adding almost 3000 other easier Sokoban instances into the input set. While enhanced by GVI, even without any extra instance, both MCTS and BFS can solve $\geq 170$ hard instances, which shows GVI is highly data-efficient.}
        \label{fig:compare}
    \end{figure}

    In \cite{DBLP:conf/nips/FengGS20}, the framework based on MCTS can solve 180 extremely hard Sokoban instances from the Large Test Suite \cite{SokobanRep2020} with the sub-instance creation strategy and extra input instances. This is a remarkable result since modern fine-tuned specialized Sokoban solvers cannot solve any of them. Introducing extra instances or creating sub-instances can greatly increase the number of solvable hard instances because more instances can prevent overfitting, smooth the whole learning procedure, and fill the ``difficulty gap'' between hard instances. In the first experiment, we disable the usage of sub-instance creation since it utilizes extensive domain knowledge. We gradually added more remaining instances from the Large Test Suite to the input set to test how many hard instances the algorithm can solve as the number of input instances increases. In the second experiment, we enabled the usage of sub-instances to match the result listed in \cite{DBLP:conf/nips/FengGS20}. See Figure~\ref{fig:compare} for more details.

    MCTS (\cite{DBLP:conf/nips/FengGS20}) cannot solve any hard instance without the sub-instance creation strategy --- thousands of extra instances are not enough to fill the ``difficulty gap''. In contrast, MCTS + GVI can solve 13 hard instances given the whole Large Test Suite as input. With the help of sub-instance creation, the overall impact of GVI is marginal given enough input instances. However, GVI still solves more instances when only a few input instances are available. Moreover, since in many practical applications one does not have collections of thousands of (non-random) problem instances, the data efficiency of GVI offers a significant advantage.

     \begin{table}[t]
        \centering
        \begin{tabular}{|l|l|l|l|l|l|l|}
            \hline
            Number of input instances & 500 & 1000 & 1500 & 2000 & 2500 & 3000 \\
            \hline
            MCTS (previous S.O.T.A) & 417 & 810 & 1190 & 1513 & 1937 & 2371 \\
            \hline
            MCTS + GVI & 471 & 923 & 1310 & 1792 & 2104 & 2763 \\
            \hline
            BFS & 217 & 629 & 1037 & 1501 & 1901 & 2316 \\
            \hline
            BFS + GVI & 297 & 817 & 1207 & 1561 & 1998 & 2413 \\
            \hline
        \end{tabular}
        \caption{Number of solved instances based on MCTS and BFS with or without GVI.}
        \label{tab:gvi}
    \end{table}
    Besides the hard instances, we test the performance of GVI on all 3272 instances with varying difficulties. As shown in Table~\ref{tab:gvi}, GVI can consistently improve the number of solved instances for both MCTS and BFS, regardless the number of input instances.

    \subsection{The Ablation Test of GVI}
    \label{sec:ab}
     \begin{table}[t]
        \centering
        \begin{tabular}{|l|l|l|l|l|l|l|l|}
            \hline
            $p$ & $0\%$ & $20\%$ & $40\%$ & $60\%$ & $80\%$ & $90\%$ & $100\%$ \\
            \hline
            MCTS (previous S.O.T.A) & 146 & 156 & 171 & {\bf 175} & 163 & 97 & 0 \\
            \hline
            BFS & 143 & 147 & {\bf 170} & 164 & 117 & 83 & 0 \\
            \hline
        \end{tabular}
        \caption{Number of solved hard instances according to the percentage $p$ of training samples from GVI. The table shows the best performance is reached when mixing learning signals from discovered plans and GVI.}
        \label{tab:ablation}
    \end{table}
    In this section, we do an ablation test of GVI to test the overall performance based on the weight factor of GVI. Specifically, each batch of the training procedure samples state-value pairs from discovered plans and those generated by GVI from failed search attempts. We can control the percentage $p$ of GVI samples in one batch to test how it affects the learning. Notice that $p=0$ means no GVI while $p=1$ means that learning signals purely come from failed attempts. We enables sub-instance creation and disables extra instance to show the impact of GVI. See Table~\ref{tab:ablation} for more details.

    Without GVI, both BFS and MCTS can solve around 145 hard instances. The number of solved instances increases when we start to mix in the learning signals from GVI, and drops to 0 when using pure GVI.

    \subsection{How Pure GVI Shapes the Local Search Space}
    \label{sec:no_sol}

       \begin{figure*}[t]
        \centering
        \includegraphics[width=0.9\textwidth]{images/explore.pdf}
        \caption{\textbf{Exploration behavior of best-first search + GVI with all failed attempts.} The three panels represent data collected from learning iteration 1, 50, and 100, respectively. In a 50x50 board, the robot starts at the bottom-left corner and tries to reach the goal cell located at the top-right corner. For each iteration, the maximum node expansion of the search is 300. The estimated remaining distance to the goal is represented by the color of each cell and is initialized by a uniform distribution $\mathcal{U}(0, 50)$. Cells with a centered black dot represent expanded nodes by the search at the corresponding iteration. Initially, best-first search behaves similarly to breadth-first search, and expanded states are all near the start state. As the learning goes, with learning signals from GVI, the search picks a path quickly leading to the boundary of previously visited cells and starts exploring from there. The middle panel shows the search is exploring two areas of sub-space simultaneously (blue areas).}
        \label{fig:explore}
    \end{figure*}

    Herein, we show that pure GVI can still learn usable local search structure even without any solution found. To demonstrate how learning shapes local search structure, we construct a toy setting with a 50x50 grid board and the goal for the robot is to reach the upper-right corner from the lower-bottom corner. For each step, the robot can move to one of its four adjacent cells and the maximum search steps for best-first search is set to 300, which is one magnitude smaller than the total number of cells. To simplify and demonstrate how learning goes, we use a table instead of a deep network to store remaining distance predictions. All heuristic values are initialized by a uniform distribution $\mathcal{U}(0, 50)$. Figure~\ref{fig:explore} shows the learning procedure. In the first iteration, the search degrades to breadth-first search and explores local cells near the start cell. After learning the local structure near the start cell, the search picks a path that leads to the boundary of previously visited cells and starts exploring from there. The middle panel of Figure~\ref{fig:explore} shows best-first search is exploring two areas of sub-search-spaces (colored with a blue cluster of cells) simultaneously. After the first time the search reaches the goal cell, the path leading to the goal cell is quickly solidified and repeatable in future iterations as shown in the right panel.

    \subsection{High Quality of Discovered Solutions}
    \label{sec:n_puzzle}

    \begin{figure*}[t]
        \centering
        \includegraphics[width=0.9\textwidth]{images/npuzzle.pdf}
        \caption{\textbf{Percentage of solved instances as the size of input instances increases}. Horizontal axes represent different $m$, ranging from 200 to 1000. For each $m$ we generated 500 boards with different difficulty, which were obtained by randomly scrambling $k$ times a goal board ($k=1,...,500$). The lower the $k$, the ``easier'' the board. So, our setting involves training datasets ranging from $200 \times 500=10,000$ to $1,000 \times 500=500,000$ input instances. Vertical axes show the percentage of all input instances solved by our algorithm. Different curves show the percentage of solutions found with different length relaxation compared with the shortest solutions (ground truth) searched by the optimal N-puzzle solver. For example, the curve ``shortest + 5'' means the percentage of solved instances whose found plan has length less than or equal to the ground truth length plus five. The figure shows that even with a network-generated heuristic function that is neither admissible nor consistent, solutions found by GVI still have higher quality than non-GVI
        BFS. Notice that the overall solvability ratio increases as the number of input instances increases.}
        \label{fig:n_puzzle}
    \end{figure*}

    A\textsc{*} is guaranteed to find the shortest solution if the heuristic function is admissible and consistent. Herein we show that even with a network-predicted heuristic function, solutions found by GVI are still close to the shortest ones, and adding GVI can greatly improve the quality of found plans compared with the framework without GVI. We test on 15-puzzle, 24-puzzle, and 35-puzzle since optimal solvers for these board sizes exist \cite{felner2004additive}. For an input set of N-puzzle instances, we first calculated the length of the shortest solution for each instance as the ground truth. We did not prune any node whose depth exceeds the shortest length to avoid leaking ground truth information and making the search easier. We calculated the percentage of the input instances whose found shortest plans are exactly the ground truth, or do not exceed the ground truth plus a certain constant value. To generate the set of input instances, for each $k=1,...,500$, we generate $m$ different boards by randomly scrambling the goal state $k$ times. We only test on BFS for this experiment setting. See Figure~\ref{fig:n_puzzle} for more details.

    As shown in Figure~\ref{fig:n_puzzle}, even without any hint about the length of shortest solutions, solutions found by GVI still have higher quality than non-GVI BFS. For each instance of 15-puzzle, GVI can find solutions of length no longer than the ground truth plus ten. The number of input instances greatly affects the overall percentage of solved instances. This result shows the potential of GVI since generating more planning instances is cheap (we do not need to provide any other information, e.g., ground truth solutions) and in many planning domains, a large set of unlabeled instances is available.

    \subsection{Exploration Efficiency Comparison}
    \label{sec:exp_eff}

    \begin{table}[t]
        \centering
        \begin{tabular}{|l|l|l|l|l|l|}
            \hline
            & Time & Iteration & Node & Unique node \\
            \hline
            MCTS & 61 mins & 197 & 7674642 & {\bf 1307017}  \\
            \hline
            BFS & {\bf 35 mins} & {\bf 153} & {\bf 1457029} & 1339486  \\
            \hline
        \end{tabular}
        \caption{Resource efficiency between BFS and MCTS. We use a hard Sokoban instance, XSokoban\_29, as the test instance. Both experiments use the sub-instance creation strategy and terminate once the solution for XSokoban\_29 is found. The number of uniquely visited nodes of both algorithms is similar, but MCTS expands significantly more repreated nodes than BFS. This is mainly caused by the simulation mechanism of MCTS that visits branches under the root  node multiple times.}
        \label{table1}
    \end{table}
    \noindent In this section, we compare the efficiency of using resources between BFS and MCTS when both are augmented with GVI. For AlphaZero-style frameworks, self-plays take the majority of the time because MCTS needs to spend multiple simulations to find the best move and each node will be visited multiple times during the construction of Monte Carlo trees. In contrast, best-first search visits each node at most once and the search only needs to query a min-heap to find the best node to expand. We pick XSokoban\_29 as our test instance. See Table~\ref{table1} for more details. We found the number of unique nodes visited is roughly the same to solve the original instance. However, MCTS visits more repeated nodes because of its simulation mechanism. The total running time of MCTS is still close to BFS mainly because MCTS uses a hash table to save previously estimated boards. Whenever a repeated board is encountered, the saved estimate is fetched instead of evaluating the network. However, for larger and more complex planning instances such hashing method will have huge memory usage. For BFS, the numbers of visited nodes and uniquely visited nodes are very close, indicating the high efficiency of node expansion.

\section{Related Work}
    The recent breakthrough of deep RL on two-player games, e.g, Go and Chess, which are also discrete combinatorial tasks, shed a light on using deep RL for AI planning \cite{silver2017mastering}. Multiple methods have been proposed to tackle hard planning problems with deep neural networks. \cite{trunda2020deep,yonetani2020path} used a supervised method that learns the heuristic function from a set of solved planning instances. \cite{DBLP:conf/ijcai/FengGS20,DBLP:conf/nips/FengGS20} used a curriculum framework with sub-instances and solved challenging Sokoban instances that specialized solvers cannot solve. \cite{crippa2022analysis} compared MCTS and IDA\textsc{*} on Sokoban and has shown that IDA\textsc{*} provides the best performance. \cite{shoham2021solving} augmented traditional RL with backward search and solved 88 of the 90 XSokoban levels, which is the default benchmark for Sokoban. \cite{czechowski2021subgoal} used an approach of generating $k$-th step ahead subgoals on Sokoban. However, the method requires an expert dataset which consists of suboptimal solutions. \cite{agostinelli2021search} used Deep Approximate Value Iteration (DAVI) to learn a heuristic function for Rubik's cube in an unsupervised manner. States for training are generated by randomly backward scrambling the goal state $k$ times. However, for hard planning domains that require hundreds of steps to solve, pure random backward scrambling cannot generate challenging enough states.
    \cite{graves2017automated} proposed the idea of \emph{automatic curriculum learning} to learn an adaptive policy on a N-task curriculum. \cite{pathak2017curiosity,burda2018large} used prediction error as an intrinsic reward to help the agent explore the search space more efficiently. The method achieves high scores on multiple Atari games purely guided by curiosity-driven rewards. GoExplore further achieved significant score improvement on two hard-exploration domains of Atari: Montezuma's Revenge and Pitfall \cite{ecoffet2019go}. GoExplore remembers states that have previously been visited, return to a promising state (without exploration), and then explore from it.

    Herein we listed three main features GVI offers: 1) it doesn't require any expert dataset; 2) it can solve challenging Sokoban instances that are out of reach for specialized solvers and other approaches; 3) it is data-efficient. Without sub-instance creation, GVI can scale up way better than previous MCTS-based methods.

\section{Conclusions}
\label{sec:conclusions}
    Recently, there has been significant progress on using deep RL to solve highly challenging AI planning problems in the Sokoban domain with over thousand-step solution plans \cite{DBLP:conf/ijcai/FengGS20}. The curriculum framework learns from a collection of easier sub-instances. Training is effective because the simpler instances can be solved with MCTS, which provides useful reward signals to train a deep net incrementally to guide the search for solving harder instances.

    Here we show how we can also learn without sampling sub-instances but from other problem instances available in problem benchmark sets. Most importantly, we introduce a mechanism for learning search guidance even when the search does not yield any valid plans (i.e., no goal state is reached). Our graph value iteration algorithm (\ref{fig:dijkstra}) provides learning signals about the heuristic function (estimating the distance to the goal) that is derived from the structure of the local search space.

    Our experiments show that there is useful information in the updated heuristic function, even before a problem instance is solved. This opens up a promising new direction in general for deep RL on hard combinatorial planning domains where true rewards (reaching a goal state) can be thousands of steps away. In such settings, the probability for MCTS with random roll-outs to accidentally find a solution path is too small. However, GVI can learn an approximate search policy long before reaching the goal state, which is in turn used to boost the remaining search.

\clearpage

redpajama_set_name: RedPajamaArXiv


text:
Selected Regions: Baden-Württemberg, Bayern, Berlin, Brandenburg, Bremen, Hamburg, Hessen, Mecklenburg-Vorpommern, Niedersachsen, Nordrhein-Westfalen, Rheinland-Pfalz, Saarland, Sachsen, Schleswig-Holstein, Schleswig-Holstein, Thüringen, International
4GENE
4GENE offers its FLAVOR-ON-DEMAND technology platform of activatable flavor and fragrance glucosides to its Customers in the Cosmetics, Food and Industrial Application Market Segments. 4GENE is a biotech start-up company founded at the Professorship for Biotechnology of Natural Products led by Prof. Dr. Wilfried Schwab at the Technical University Munich. During the EXIST Funding Program 4GENE cooperated with Prof. Dr.-Ing. Ralf Takors, Head of the Institute for Biotechnical Engineering at the University of Stuttgart to provide the Proof-of-Concept for the production of flavor glucosides. Today the company has 6 Employees and operates from Offices in Freising and Stuttgart in Germany with its customers worldwide.
Sector: Life Sciences
Website: www.4gene.de
Added to portfolio 21. Sep 2018
4GENE GmbH
Lise-Meitner-Str. 30
adivo GmbH
adivo is a biotech company generating therapeutic antibodies for companion animals, addressing the medical needs in dogs, with an upside potential for a future in care for other species.
Antibody selection is based on a novel fully synthetic, dog-specific phage display library that allows de-novo identification of antibodies to select a drug candidate with optimal properties.
adivo is a spin-off from MorphoSys AG, a leading European biopharmaceutical company. The team transfers an in-depth expertise in discovery and development of human therapeutic antibodies to the veterinary market.
Website: www.adivo.vet
Added to portfolio 19. Jul 2018
Am Klopferspitz 19
82152 Martinsried/ Planegg
denovoMATRIX
denovoMATRIX develops and manufactures biomimetic coatings for the cultivation of human stem cells. Our focus is on enabling culture conditions for stem and primary cells closer to the natural conditions found in the body. Our products offer a biologically relevant solution, which is both chemically defined, and modular for adapting to specific applications. Our vision is to enable human biology in vitro, beginning with Life Science research and onwards to cell therapies of the future.
Website: www.denovomatrix.com
Added to portfolio 15. Oct 2018
Tatzberg 47/49
GWA Hygiene GmbH
GWA Hygiene is committed to reducing the number of hospital infections. For this purpose, the company has developed NosoEx, a modern monitoring system for measuring hand hygiene. The IoT system can be seamlessly integrated into the infrastructure of the health facilities. For this purpose, the existing disinfectant dispensers are equipped with sensors. The software can then be used to see how the disinfection figures for each station, donor and occupational group are developing. Measures to improve the hygiene situation can then be initiated.
The Stralsund start-up was founded in 2015 and now has 21 employees. In addition to Maik Gronau and Tobias Gebhardt, Dirk Amtsberg, Marcel Walz and Daniel Neuendorf are among the founding partners.
The NosoEx system is currently being used by a double-digit number of customers in the DACH region.
Website: gwa-hygiene.de/de
Added to portfolio 07. Aug 2018
Heinrich-Mann-Str. 11
The founders of Kumovis GmbH have set themselves the goal of enabling the additive production of plastic implants and are developing innovative 3D printers specifically for medical products. Kumovis focuses on the processing of high-performance polymers, which are already established in medical technology. With the Kumovis system these plastics can be processed individually, decentrally and with a high degree of design freedom. Through the integration of a unique air circuit including filter units, the technology enables a clean room environment within the build chamber. The contamination of the component with foreign particles can be avoided. As a result, the Kumovis printer can produce products such as implants cost-effectively and at the same time patient-individualized.
Website: www.kumovis.com
Trimburgstr. 2
mediaire
mediaire GmbH was founded in 2018 in Berlin by experienced MRT and AI experts in order to support radiologists with innovative software solutions for the automatic diagnosis of MRT images. mediaire's solutions can be fully integrated into the internal IT infrastructure of radiology centers to ensure a smooth workflow and the highest possible protection of sensitive patient data. The first product md.Brain, which can be used for early detection and follow-up of neurodegenerative diseases, is already certified as a medical device and is therefore available for immediate use. Among the co-founders are two medical physicists with expertise in MRI and CI (Dr. Andreas Lemke and Dr. Jörg Döpfert), as well as two experienced scientists and specialists in radiology (Prof. Dr. med. Henrik Michaely and Prof. Dr. med. Michael Fenchel).
Website: mediaire.de
Added to portfolio 15. Mar 2019
mediaire GmbH
Bismarckstrasse 10
neotiv
neotiv is a spin-off of the Otto von Guericke University in the German city of Magdeburg in close collaboration with the Magdeburg site of the German Centre for Neurodegenerative Diseases (DZNE). neotiv incorporates the latest scientific findings from the fields of neuroscience and dementia research into mobile software that can be used on smartphones and tablets. Our work focuses on the development of a digital assistant that can monitor and aid users' memories and provide support for patients with dementia. neotiv is also designed to improve the early detection of Alzheimer's dementia and to facilitate efficacy monitoring for new therapies as they become available in the future. Biologically and anatomically valid and user-oriented measurements of specific memory functions, which can be carried out on a regular basis, form the core of the development of neotiv. The measurements focus on regions of the brain that are affected in the early stages of Alzheimer's disease. The vision behind neotiv is to (i) help reducing risk factors for cognitive decline by making lifestyle change simple and cognitive health measurable and (ii) to enable the innovative early detection of cognitive difficulties and dementia risk.
Website: www.neotiv.com/de
neotiv GmbH
Noscendo
In order to successfully treat the infected patient, you have to recognize and diagnose most reliably the infecting microbe.
Noscendo changes the way infection causative microbes are identified. Through a paradigm shift we introduce by using proprietary algorithms and software harnessing next generation sequencing, we identify pathogens from cell free nucleic acids in patient samples and thereby aid intensive care clinicians in their daily task to find the right decisions, enabling a targeted and successful treatment in an actionable timeframe.
Website: noscendo.com
Noscendo GmbH
Purenum
Purenum is a spin-off of the Fraunhofer-IFAM and develops biocompatible adhesives for medical use. As part of a BMBF project (GO-Bio 6 "mediNiK"), an adhesive was developed that can be used to glue together and remove previously ungrippable kidney stone fragments after lithotripsy. In addition to work on the certification of the kidney stone adhesive, Purenum is also developing adhesives for other medical indications.
Fahrenheitstr. 1
RIMASYS
RIMASYS offers prefractures specimen for educational purposes in the field of orthopedic surgery. The innovative MedTech company is setting new standards in surgical education and implant development through innovative education and validation with life-like fractured human specimen.
Website: www.rimasys.com/de
Rimasys GmbH
Nattermannallee 1


text:
Articles Recently Tagged: Hearst Corp
Approved by Planning back in 2015 but waylaid by legal challenges, the ground for the massive 5M Project – which spans a 4-acre South of Market site stretching from Mission to Howard and Fifth to Mary – has officially broken ground. Now championed by Brookfield Properties, which acquired Forest City... Read More »
Massive SoMa Development Approved By Planning
San Francisco's Planning Commission has approved the plans for Forest City's massive 5M Project, a development which spans a 4-acre South of Market site bounded by Mission, Fifth, Howard and Mary Streets and includes the redevelopment of the iconic Chronicle Building. In addition to 688 residences, a mix of 400... Read More »
SoMa Mega-Project Preview, Key Votes In Two Weeks Time
With the key Planning Commission votes and public hearing for the massive 5M Project scheduled for September 17, the project sponsors are slated to prep the Commission tomorrow with a preview of what's to come, both in terms of the formal presentation and a possible clash between proponents and... Read More »
Testing The Waters To Develop Four Infill Acres At Fifth And Mission
It was back in 2007 that the Hearst Corp. first started to seek a developer for their four-acre mid-market site on which the Chronicle Building stands. And now, four years later, Forest City has filed a preliminary project assessment application with San Francisco's Planning Department to build "1.3 million... Read More »
Hearst Close To Calling It Quits With The San Francisco Chronicle?
From the San Francisco Chronicle itself: The Hearst Corp. today announced an effort to reverse the deepening operating losses of its San Francisco Chronicle by seeking near-term cost savings that would include "significant" cuts to both union and non-union staff. In a posted statement, Hearst said if the savings... Read More »
Recent Reader Activity
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Timing for Approved Tower About to Be Pushed Back
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Proposed Navigation Center on Post Closer to Reality
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Bigger Plans for Shuttered North Beach Site
"Bảo trì, bảo dưỡng & nạp gas điều hòa định kỳ. http://arkay.se/Anv%C3%A4ndare..." [more]
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New Home for Noriega Produce and Condos Closer to Reality
"This project has effectively stolen money from homeowners in the neighborhood as it obstructs views ..." [more]
Reduced to Incite a Bidding War in Bayview
"UPDATE: Having been bumped back up to $1.399 million in November, a sale at which would have still b..." [more]


text:
Did The Titanic Really Sink?
When the Titanic sank in 1912, it shocked the world. The limits of human innovation were cruelly displayed with the destruction of such a technically remarkable ship.
SINK IN.
A conspiracy theory that has crept up online in recent years asks the question, most recently on Reddit: Was it actually the Titanic that sank?
We know. It sounds bonkers. But bear with us.
Everyone seems to agree on one fact: A ship really did sink in the icy waters of the North Atlantic on April 15, 1912, and approximately 1,500 passengers aboard that ship died. The conspiracy simply suggests the Titanic wasn't actually the technical marvel the ship's parent company, the White Star Line, had promised.
Do you think the Titanic actually sank?
Of course. James Cameron wouldn't lie to us like that or are you not convinced?
Rather, the White Star Line swapped ships for the voyage from Southampton to New York and the ship billed as the top-of-the-line Titanic was actually an older ship: the Olympic.
What's more, the conspiracy theory suggests, the entire crash was an insurance scam gone wrong.
J.P. Morgan and the Heyday of Big Ships
The British White Star Line had stiff competition in England and across the globe. Locally, it had a fierce rivalry with the Cunard Steamship Company, Ltd., which in 1906-07 had sent on its maiden voyage the world's then-largest passenger ships, the Lusitania and the Mauretania.
To compete with the Lusitania and its mate, White Star Line entered into a giant ship war. The company was no stranger to such battles, but Cunard's Lusitania and Mauretania had out-gunned White Star's so-called "Big Four" ships in terms of top speed. This time around, the company had some extra backing.
The Olympic and Titanic being built side-by-side.
In 1902, White Star had become a property owned by the International Mercantile Marine Co. (IMM), a holding company bankrolled by famed financier J.P Morgan. With Morgan's permission, White Star chairman J. Bruce Ismay began work on what came to be known as Olympic-class ships. If they couldn't match the Lusitania's speed, Olympic-class ships would outclass Cunard ships: they'd be even bigger, and more luxurious. Three ships were commissioned: the Olympic, the Titanic, and the Britannic.
blog by Julez for Styles Rebel Radio
October 11, 2021 by JulieAnna Lightcap Categories: Uncategorized	Leave a comment
What Causes The Mandela Effect
How To Get NFTs for FREE


text:
Fifth Report Contents
Instruments not reported
At its meeting on 6 December 2017 the Committee considered the Instruments set out in the Annex to this Report, none of which were required to be reported to both Houses.
Draft instruments requiring affirmative approval
Draft S.I.
Transparency of Donations and Loans etc. (Northern Ireland Political Parties) Order 2018
Environmental Protection (Microbeads) (England) Regulations 2017
Instruments subject to annulment
S.I. 2017/3781
Housing and Planning Act 2016 (Consequential Provisions) (England) Regulations 2017
S.I. 2017/930
Immigration Act 2014 (Current Accounts) (Freezing Orders: Code of Practice) Regulations 2017
Immigration Act 2016 (Consequential Amendments) Regulations 2017
Proscribed Organisations (Name Change) (No. 2) Order 2017
Registration of Births, Deaths, Marriages and Civil Partnerships (Fees) (Amendment) Regulations 2017
NHS Business Services Authority (Awdurdod Gwasanaethau Busnes y GIG) (Establishment and Constitution) (Amendment) Order 2017
NHS Counter Fraud Authority (Investigatory Powers and Other Miscellaneous Amendments) Order 2017
Adoption and Children Act Register (Search and Inspection) Regulations 2017
Transfer of Functions (Secretary of State for Digital, Culture, Media and Sport) Order 2017
Democratic People's Republic of Korea (European Union Financial Sanctions) (Amendment) (No. 4) Regulations 2017
Social Security (Qualifying Young Persons Participating in Relevant Training Schemes) (Amendment) Regulations 2017
Social Security and Child Support (Care Payments and Tenant Incentive Scheme) (Amendment) Regulations 2017
Electronic Communications Code (Transitional Provisions) Regulations 2017
Communications Act 2003 and the Digital Economy Act 2017 (Consequential Amendments to Secondary Legislation) Regulations 2017
Conservation of Habitats and Species Regulations 2017
Conservation of Offshore Marine Habitats and Species Regulations 2017
Jobseeker's Allowance (Schemes for Assisting Persons to Obtain Employment) (Amendment) Regulations 2017
Healthy Start Scheme and Welfare Food (Miscellaneous Amendments) Regulations 2017
Family Procedure (Amendment No. 3) Rules 2017
Non-Contentious Probate (Amendment) Rules 2017
Court of Protection Rules 2017
General Medical Council (Miscellaneous Amendments) Order of Council 2017
Central Securities Depositories Regulations 2017
United Nations and European Union Financial Sanctions (Linking) (Amendment) (No. 2) Regulations 2017
Infrastructure Planning (Compulsory Acquisition) (Amendment) (No. 2) Regulations 2017
Coroners and Justice Act 2009 (Alteration of Coroner Areas) (No. 2) Order 2017
Teachers' Pensions Schemes (Miscellaneous Amendments) Regulations 2017
Traffic Signs (Amendment) (England and Wales) Regulations and General Directions 2017
General Anti-Abuse Rule Procedure (Amendment) Regulations 2017
Venezuela (European Union Financial Sanctions) Regulations 2017
Draft Instruments subject to negative procedure
Kingston upon Hull (Electoral Changes) Order 2017
Ribble Valley (Electoral Changes) Order 2017
Surrey Heath (Electoral Changes) Order 2017
Instruments not subject to Parliamentary proceedings not laid before Parliament
Immigration Act 2016 (Commencement No. 5) Regulations 2017
Neighbourhood Planning Act 2017 (Commencement No. 2) Regulations 2017
Energy Act 2016 (Commencement No. 4 and Transitory Provision) Regulations 2017
Welfare Reform Act 2012 (Commencement No. 17, 19, 22, 23 and 24 and Transitional and Transitory Provisions (Modification)) Order 2017
Criminal Finances Act 2017 (Commencement No. 2 and Transitional Provisions) Regulations 2017
Criminal Finances Act 2017 (Commencement No. 3) Regulations 2017
Technical and Further Education Act 2017 (Commencement No. 2 and Transitional Provision) Regulations 2017
Wales Act 2017 (Commencement No. 3) Regulations 2017
Cultural Property (Armed Conflicts) Act 2017 (Commencement) Regulations 2017
1 The Committee asked the Department for Communities and Local Government why these Regulations state that they apply to England only but include reference to Wales. The Department rightly explained that the reference to Wales is included only to ensure that a provision that formerly applied to England and Wales now operates only in relation to Wales: so the substantive effect of the amendment is confined to England.


Young aviator: Wartime pilot Graham Harrison in uniform.
When I was a lad, every older man – and many women – seemed to have been service veterans of the Second World War.
As a boy, I used to love prising out their stories and, dare I say, it was partly this initial experience that sparked my interest in history and helped land me this job as your Mr Leicester.
I found the best veteran stories were not necessarily the gung-ho, Boy's Own tales of derring-do, but, often, the unique, quirky, individual stories of what everyday life was like.
Now, as this oft-dubbed "greatest generation'' passes on, fewer and fewer of these fascinating tales are being told.
So, I was pleased to receive a copy of a uniquely-titled new book by 96-year-old Graham Harrison, called Ops In Wimpy.
The author was prompted to write his memoir after attending the memorial service of his friend and fellow veteran, Aircraftman Denis Sharpe, as reported in the Leicester Mercury on April 7, 2008.
Sharpe, also a Leicester lad, died when his RAF aircraft crashed near Whitley Bay, returning home after a mission to Germany.
Graham Harrison was born and bred in Leicester, attending Hazel Street School and the City Boys' grammar school.
Life in an archetypal 1930s semi-detached in Edward Avenue, Braunstone, with his mother, Nellie – his father, Alf, a troubled First World War veteran walked out on her and Graham – is the starting point of the book.
Graham's recollections of 1930s Leicester are interesting, as are the friends and characters he knew: some of the eyebrow-raising activities he got up to also make surprising reading!
He left school at 16 and went to work at the County Council headquarters at Greyfriars, providing a good insight of the local bigwigs and bureaucracy of the time.
After joining the city's Home Guard, Graham received his call-up papers and found himself heading to Canada, learning to fly.
Amusingly, as with many other young RAF men, he seems to have spent more time chasing girls than Germans!
Postings followed in England, Wales and Northern Ireland, serving as the pilot of a Wellington bomber in several Coastal Command squadrons, hunting German submarines.
However, as Graham admits, there is a certain futile pathos about his "ops''.
Operating at night, the Wimpy was equipped with a radar that could detect German U-boats on the surface.
Once located, the bomber used a powerful Leigh light to illuminate its target, whereupon it would attempt to sink the sub with depth charges.
The only thing is, the Germans invented a device that could detect the Wimpy's radar emissions, so the U-boat was fathoms-deep by the time hunter reached its prey. Thus, Graham and his crew never spotted the enemy.
At war: Ops In A Wimpy by Graham Harrison.
That said, make no mistake, it wasn't 'Oh, What A Lovely War'. The war-weary Wellingtons seemed to crash on take-off and landing – not helped by a large mountain next to the airfield. Engine failure over the Bay of Biscay could result in a sudden lesson in treading water – or meeting Davy Jones.
Plus, there were always roaming German Junkers looking to give Wellingtons the boot.
At the war's end, Graham flew in Asia, ferrying ENSA performers, including none other than (pre-Sir) John Gielgud.
With unpublished photos from his own album of home and active service, Graham Harrison's honest, humorous and carefree account of his war years makes an endearing and satisfying read. Recommended.


Preaching sounds like only 'preachers' can do it.
Sharing sounds like the exchange of good ideas or new recipes or secrets on where the fish are biting.
But telling. Ah telling. That sounds like talking about something that I can't wait to tell you about. Like an amazing thing that happened in my life. Let me tell you about it. Or about something I witnessed that I'm sure no one has ever noticed before. Let me tell you about it.
Tonight at 6:30, I'll start a three-week series on the Essentials in Telling the Gospel. If Jesus left us on the planet to tell others, then how come they don't know yet? Why do we spend so much time, energy, resources on Christians talking to other Christians each week in really nice buildings, while people drive or walk right by us unnoticed, untouched and unchanged. This isn't about guilt. It's about a decision we need to make.
I'll do my part to help you with the information you'll need. Truth is, God does most of the work. But He does need us. It's His plan, to allow us to be a part of the process.
Three Monday nights. That's the only commitment you need to get started.


A lot of love goes into the design of each sass & bide piece, so we want you to love them as much as we do. We are excited to now offer our Australian customers free & easy returns if you change your mind, or an item isn't right. You can simply request a return for a refund, exchange or eBoutique credit. We happily accept returns and exchanges on all full-priced items and sale items can only be returned for exchange or credit note. Please be aware that we are unable to place items on hold as exchanges are subject to availability once your return arrives at our head office.
All returns must be in the original condition as sent; the item must not be worn, altered or washed, and must have its original tags attached. All items must be returned within 14 days of receiving the order. For hygiene reasons, we do not offer returns on lingerie or earrings and we are unfortunately unable to offer returns on our Charity Collections.
Please note, items purchased during short term promotional sales such as Vogue Online Shopping Night are not classed as "sale items" and are subject to our normal returns policy.
If you receive an item that is faulty or wrongly described, please contact us to request a refund, an exchange or store credit. Faulty items can then be sent to us to be assessed by our Quality Management team. Please note this process can take several weeks. This policy does not exclude or replace your rights under any applicable law.
Refunds for faulty or wrongly described merchandise will be processed once sass & bide has inspected and accepted the return, using the original tender type. Debit and credit card returns must be processed against the exact card used to complete the original purchase.
1. Please contact us by email at eboutique@sassandbide.com to obtain a Return Authorisation number (RA#).
2. Complete the eBoutique Return Slip included with your order and package up your items.
sass & bide is not liable for the loss of an item which is being returned but rest assured our new free returns process is tracked.
If you are returning a faulty, damaged or incorrect item, we will reimburse any reasonable postage or shipping costs where you can provide us with proof of those costs.
Once we have accepted and processed your return, we will notify you via email. Please allow up to 5 business days for your return to be processed. Once the refund has been processed, depending on your card issuer, it can take up to 10 business days for the funds to show in your account.
If you change your mind about a full priced item and wish to return it, you may request an exchange for another item or a store credit, if: you can provide its receipt, it is unworn and in its original condition (not damaged or washed) and you make the return within 7 days of purchase or delivery. Please consider purchases carefully as we do not offer refunds for in-store purchases unless the item is deemed faulty.
Please also consider sale purchases carefully as we do not offer refunds or exchanges for sale items in-store.
For health reasons, change of mind returns will not be accepted on lingerie, shoes, jewellery and accessories.
We are unable to offer returns on gift cards.
Returns will be accepted on any faulty merchandise within a reasonable timeframe. If an item is faulty or wrongly described you may request a refund, an exchange for another item, or a store credit. This Policy does not exclude or replace your rights under any applicable law.
Purchases made in-store can be returned to any sass & bide store. Stores cannot accept returns or exchanges of sass & bide items which have been purchased online or from Myer stores.
Simply present your receipt or proof of purchase in-store and our store staff will be happy to assist you. Photo identification may be requested and recorded in some circumstances. All returns must be in the original condition. The item must not be worn, altered or washed, and must have its original tags attached.
Refunds for faulty merchandise will be processed using the same method of payment used to make your purchase. Where the purchase was a gift, a credit voucher will be issued.


Salem Square was inspired to highlight a focus fabric! It works scrappy or with just a few fabrics. Lee Chappell named it for her hometown of Winston-Salem where there is a lovely town square in Old Salem. Wall Hanging 24" x 24" Baby 48" x 48" Throw 72" x 72" Twin 60"
Georgia's Dresden was inspired by a traditional dresden made by Lee Chappell Monroe's great grandmother, Nancy Minerva Georgia Elizabeth Richardson Culbertson. She wanted to make it scrappy with a twist. Wall Hanging 30" x 30" Throw 60" x 60" Twin 60" x 90" Double/Queen 90" x 90" King 110" x 110"
Sunday afternoons are the best day to sew! This quilt started because Lee Chappell wanted something quick and easy (with a lot of impact!) that she could make in an afternoon. Happy Sunday Sewing! Throw 45.5" x 67" Twin 57" x 91" Double 79.5" x 91" Queen 91" x 102.5"
All Squared Up was designed using Lee Chappell Monroe's Curated Collection for Sew Lux Fabrics! She wanted to create a dynamic block that would showcase the fabrics she selected. Wall Hanging 24" x 24" Throw 60" x 60" Twin 60" x 84" Double/Queen 88" x 88" King 108" x 108"
It's a fun twist on the Irish Chain! This quilt is great for scraps or your favorite line of fabric. Originally designed for a Craftsy kit with their Boundless 1930 Delights. Finished size: 84" x 96"
Lee Chappell has always wanted to make an elongated dresden! It's was as fun for her to piece as it was to design. This quilt was named in honor of her great-grandmother May Chappell's sister, Hattie. Wall Hanging 18" x 18" Table Runner 16" x 32" Baby 40" x 40"