prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the SI unit of power?

Read the following text and answer briefly.

Context 1: For other uses, see Electricity (disambiguation) 
"Electric" redirects here  For other uses, see Electric (disambiguation) 
Lightning is one of the most dramatic effects of electricity 
Electricity is the set of physical phenomena associated with the presence and motion of matter that has a property of electric charge  In early days, electricity was considered as being not related to magnetism  Later on, many experimental results and the development of Maxwell's equations indicated that both electricity and magnetism are from a single phenomenon: electromagnetism  Various common phenomena are related to electricity, including lightning, static electricity, electric heating, electric discharges and many others 
The presence of an electric charge, which can be either positive or negative, produces an electric field  The movement of electric charges is an electric current and produces a magnetic field 
When a charge is placed in a location with a non-zero electric field, a force will act on it  The magnitude of this force is given by Coulomb's law  Thus, if that charge were to move, the electric field would be doing work on the electric charge  Thus we can speak of electric potential at a certain point in space, which is equal to the work done by an external agent in carrying a unit of positive charge from an arbitrarily chosen reference point to that point without any acceleration and is typically measured in volts 
electronics which deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes and integrated circuits, and associated passive interconnection technologies 
Electrical phenomena have been studied since antiquity, though progress in theoretical understanding remained slow until the seventeenth and eighteenth centuries  Even then, practical applications for electricity were few, and it would not be until the late nineteenth century that electrical engineers were able to put it to industrial and residential use  The rapid expansion in electrical technology at this time transformed industry and society, becoming a driving force for the Second Industrial Revolution  Electricity's extraordinary versatility means it can be put to an almost limitless set of applications which include transport, heating, lighting, communications, and computation  Electrical power is now the backbone of modern industrial society 
Long before any knowledge of electricity existed, people were aware of shocks from electric fish  Ancient Egyptian texts dating from 2750 BCE referred to these fish as the "Thunderer of the Nile", and described them as the "protectors" of all other fish  Electric fish were again reported millennia later by ancient Greek, Roman and Arabic naturalists and physicians  Several ancient writers, such as Pliny the Elder and Scribonius Largus, attested to the numbing effect of electric shocks delivered by catfish and electric rays, and knew that such shocks could travel along conducting objects  Patients suffering from ailments such as gout or headache were directed to touch electric fish in the hope that the powerful jolt might cure them  Possibly the earliest and nearest approach to the discovery of the identity of lightning, and electricity from any other source, is to be attributed to the Arabs, who before the 15th century had the Arabic word for lightning ra‘ad (رعد) applied to the electric ray 
Ancient cultures around the Mediterranean knew that certain objects, such as rods of amber, could be rubbed with cat's fur to attract light objects like feathers  Thales of Miletus made a series of observations on static electricity around 600 BCE, from which he believed that friction rendered amber magnetic, in contrast to minerals such as magnetite, which needed no rubbing  Thales was incorrect in believing the attraction was due to a magnetic effect, but later science would prove a link between magnetism and electricity  According to a controversial theory, the Parthians may have had knowledge of electroplating, based on the 1936 discovery of the Baghdad Battery, which resembles a galvanic cell, though it is uncertain whether the artifact was electrical in nature 
Benjamin Franklin conducted extensive research on electricity in the 18th century, as documented by Joseph Priestley (1767) History and Present Status of Electricity, with whom Franklin carried on extended correspondence 
Electricity would remain little more than an intellectual curiosity for millennia until 1600, when the English scientist William Gilbert wrote De Magnete, in which he made a careful study of electricity and magnetism, distinguishing the lodestone effect from static electricity produced by rubbing amber  He coined the New Latin word electricus ("of amber" or "like amber", from ἤλεκτρον, elektron, the Greek word for "amber") to refer to the property of attracting small objects after being rubbed  This association gave rise to the English words "electric" and "electricity", which made their first appearance in print in Thomas Browne's Pseudodoxia Epidemica of 1646 
Further work was conducted in the 17th and early 18th centuries by Otto von Guericke, Robert Boyle, Stephen Gray and C  F  du Fay  Later in the 18th century, Benjamin Franklin conducted extensive research in electricity, selling his possessions to fund his work  In June 1752 he is reputed to have attached a metal key to the bottom of a dampened kite string and flown the kite in a storm-threatened sky  A succession of sparks jumping from the key to the back of his hand showed that lightning was indeed electrical in nature  He also explained the apparently paradoxical behavior of the Leyden jar as a device for storing large amounts of electrical charge in terms of electricity consisting of both positive and negative charges 
In 1791, Luigi Galvani published his discovery of bioelectromagnetics, demonstrating that electricity was the medium by which neurons passed signals to the muscles  Alessandro Volta's battery, or voltaic pile, of 1800, made from alternating layers of zinc and copper, provided scientists with a more reliable source of electrical energy than the electrostatic machines previously used  The recognition of electromagnetism, the unity of electric and magnetic phenomena, is due to Hans Christian Ørsted and André-Marie Ampère in 1819–1820  Michael Faraday invented the electric motor in 1821, and Georg Ohm mathematically analysed the electrical circuit in 1827  Electricity and magnetism (and light) were definitively linked by James Clerk Maxwell, in particular in his "On Physical Lines of Force" in 1861 and 1862 
While the early 19th century had seen rapid progress in electrical science, the late 19th century would see the greatest progress in electrical engineering  Through such people as Alexander Graham Bell, Ottó Bláthy, Thomas Edison, Galileo Ferraris, Oliver Heaviside, Ányos Jedlik, William Thomson, 1st Baron Kelvin, Charles Algernon Parsons, Werner von Siemens, Joseph Swan, Reginald Fessenden, Nikola Tesla and George Westinghouse, electricity turned from a scientific curiosity into an essential tool for modern life 
In 1887, Heinrich Hertz:843–44 discovered that electrodes illuminated with ultraviolet light create electric sparks more easily  In 1905, Albert Einstein published a paper that explained experimental data from the photoelectric effect as being the result of light energy being carried in discrete quantized packets, energising electrons  This discovery led to the quantum revolution  Einstein was awarded the Nobel Prize in Physics in 1921 for "his discovery of the law of the photoelectric effect"  The photoelectric effect is also employed in photocells such as can be found in solar panels and this is frequently used to make electricity commercially 
The first solid-state device was the "cat's-whisker detector" first used in the 1900s in radio receivers  A whisker-like wire is placed lightly in contact with a solid crystal (such as a germanium crystal) to detect a radio signal by the contact junction effect  In a solid-state component, the current is confined to solid elements and compounds engineered specifically to switch and amplify it  Current flow can be understood in two forms: as negatively charged electrons, and as positively charged electron deficiencies called holes  These charges and holes are understood in terms of quantum physics  The building material is most often a crystalline semiconductor 
The solid-state device came into its own with the invention of the transistor in 1947  Common solid-state devices include transistors, microprocessor chips, and RAM  A specialized type of RAM called flash RAM is used in USB flash drives and more recently, solid-state drives to replace mechanically rotating magnetic disc hard disk drives  Solid state devices became prevalent in the 1950s and the 1960s, during the transition from vacuum tubes to semiconductor diodes, transistors, integrated circuit (IC) and the light-emitting diode (LED) 
The presence of charge gives rise to an electrostatic force: charges exert a force on each other, an effect that was known, though not understood, in antiquity :457 A lightweight ball suspended from a string can be charged by touching it with a glass rod that has itself been charged by rubbing with a cloth  If a similar ball is charged by the same glass rod, it is found to repel the first: the charge acts to force the two balls apart  Two balls that are charged with a rubbed amber rod also repel each other  However, if one ball is charged by the glass rod, and the other by an amber rod, the two balls are found to attract each other  These phenomena were investigated in the late eighteenth century by Charles-Augustin de Coulomb, who deduced that charge manifests itself in two opposing forms  This discovery led to the well-known axiom: like-charged objects repel and opposite-charged objects attract 
The force acts on the charged particles themselves, hence charge has a tendency to spread itself as evenly as possible over a conducting surface  The magnitude of the electromagnetic force, whether attractive or repulsive, is given by Coulomb's law, which relates the force to the product of the charges and has an inverse-square relation to the distance between them :35 The electromagnetic force is very strong, second only in strength to the strong interaction, but unlike that force it operates over all distances  In comparison with the much weaker gravitational force, the electromagnetic force pushing two electrons apart is 1042 times that of the gravitational attraction pulling them together 
Study has shown that the origin of charge is from certain types of subatomic particles which have the property of electric charge  Electric charge gives rise to and interacts with the electromagnetic force, one of the four fundamental forces of nature  The most familiar carriers of electrical charge are the electron and proton  Experiment has shown charge to be a conserved quantity, that is, the net charge within an electrically isolated system will always remain constant regardless of any changes taking place within that system  Within the system, charge may be transferred between bodies, either by direct contact, or by passing along a conducting material, such as a wire :2–5 The informal term static electricity refers to the net presence (or 'imbalance') of charge on a body, usually caused when dissimilar materials are rubbed together, transferring charge from one to the other 
The charge on electrons and protons is opposite in sign, hence an amount of charge may be expressed as being either negative or positive  By convention, the charge carried by electrons is deemed negative, and that by protons positive, a custom that originated with the work of Benjamin Franklin  The amount of charge is usually given the symbol Q and expressed in coulombs; each electron carries the same charge of approximately −1 6022×10−19 coulomb  The proton has a charge that is equal and opposite, and thus +1 6022×10−19 coulomb  Charge is possessed not just by matter, but also by antimatter, each antiparticle bearing an equal and opposite charge to its corresponding particle 
The movement of electric charge is known as an electric current, the intensity of which is usually measured in amperes  Current can consist of any moving charged particles; most commonly these are electrons, but any charge in motion constitutes a current  Electric current can flow through some things, electrical conductors, but will not flow through an electrical insulator 
By historical convention, a positive current is defined as having the same direction of flow as any positive charge it contains, or to flow from the most positive part of a circuit to the most negative part  Current defined in this manner is called conventional current  The motion of negatively charged electrons around an electric circuit, one of the most familiar forms of current, is thus deemed positive in the opposite direction to that of the electrons  However, depending on the conditions, an electric current can consist of a flow of charged particles in either direction, or even in both directions at once  The positive-to-negative convention is widely used to simplify this situation 
The process by which electric current passes through a material is termed electrical conduction, and its nature varies with that of the charged particles and the material through which they are travelling  Examples of electric currents include metallic conduction, where electrons flow through a conductor such as metal, and electrolysis, where ions (charged atoms) flow through liquids, or through plasmas such as electrical sparks  While the particles themselves can move quite slowly, sometimes with an average drift velocity only fractions of a millimetre per second,:17 the electric field that drives them itself propagates at close to the speed of light, enabling electrical signals to pass rapidly along wires 
Current causes several observable effects, which historically were the means of recognising its presence  That water could be decomposed by the current from a voltaic pile was discovered by Nicholson and Carlisle in 1800, a process now known as electrolysis  Their work was greatly expanded upon by Michael Faraday in 1833  Current through a resistance causes localised heating, an effect James Prescott Joule studied mathematically in 1840 :23–24 One of the most important discoveries relating to current was made accidentally by Hans Christian Ørsted in 1820, when, while preparing a lecture, he witnessed the current in a wire disturbing the needle of a magnetic compass  He had discovered electromagnetism, a fundamental interaction between electricity and magnetics  The level of electromagnetic emissions generated by electric arcing is high enough to produce electromagnetic interference, which can be detrimental to the workings of adjacent equipment 
In engineering or household applications, current is often described as being either direct current (DC) or alternating current (AC)  These terms refer to how the current varies in time  Direct current, as produced by example from a battery and required by most electronic devices, is a unidirectional flow from the positive part of a circuit to the negative :11 If, as is most common, this flow is carried by electrons, they will be travelling in the opposite direction  Alternating current is any current that reverses direction repeatedly; almost always this takes the form of a sine wave :206–07 Alternating current thus pulses back and forth within a conductor without the charge moving any net distance over time  The time-averaged value of an alternating current is zero, but it delivers energy in first one direction, and then the reverse  Alternating current is affected by electrical properties that are not observed under steady state direct current, such as inductance and capacitance :223–25 These properties however can become important when circuitry is subjected to transients, such as when first energised 
The concept of the electric field was introduced by Michael Faraday  An electric field is created by a charged body in the space that surrounds it, and results in a force exerted on any other charges placed within the field  The electric field acts between two charges in a similar manner to the way that the gravitational field acts between two masses, and like it, extends towards infinity and shows an inverse square relationship with distance  However, there is an important difference  Gravity always acts in attraction, drawing two masses together, while the electric field can result in either attraction or repulsion  Since large bodies such as planets generally carry no net charge, the electric field at a distance is usually zero  Thus gravity is the dominant force at distance in the universe, despite being much weaker 
A hollow conducting body carries all its charge on its outer surface  The field is therefore zero at all places inside the body :88 This is the operating principal of the Faraday cage, a conducting metal shell which isolates its interior from outside electrical effects 
The principles of electrostatics are important when designing items of high-voltage equipment  There is a finite limit to the electric field strength that may be withstood by any medium  Beyond this point, electrical breakdown occurs and an electric arc causes flashover between the charged parts  Air, for example, tends to arc across small gaps at electric field strengths which exceed 30 kV per centimetre  Over larger gaps, its breakdown strength is weaker, perhaps 1 kV per centimetre  The most visible natural occurrence of this is lightning, caused when charge becomes separated in the clouds by rising columns of air, and raises the electric field in the air to greater than it can withstand  The voltage of a large lightning cloud may be as high as 100 MV and have discharge energies as great as 250 kWh 
A pair of AA cells  The + sign indicates the polarity of the potential difference between the battery terminals 
The concept of electric potential is closely linked to that of the electric field  A small charge placed within an electric field experiences a force, and to have brought that charge to that point against the force requires work  The electric potential at any point is defined as the energy required to bring a unit test charge from an infinite distance slowly to that point  It is usually measured in volts, and one volt is the potential for which one joule of work must be expended to bring a charge of one coulomb from infinity :494–98 This definition of potential, while formal, has little practical application, and a more useful concept is that of electric potential difference, and is the energy required to move a unit charge between two specified points  An electric field has the special property that it is conservative, which means that the path taken by the test charge is irrelevant: all paths between two specified points expend the same energy, and thus a unique value for potential difference may be stated :494–98 The volt is so strongly identified as the unit of choice for measurement and description of electric potential difference that the term voltage sees greater everyday usage 
For practical purposes, it is useful to define a common reference point to which potentials may be expressed and compared  While this could be at infinity, a much more useful reference is the Earth itself, which is assumed to be at the same potential everywhere  This reference point naturally takes the name earth or ground  Earth is assumed to be an infinite source of equal amounts of positive and negative charge, and is therefore electrically uncharged—and unchargeable 
Electric potential is a scalar quantity, that is, it has only magnitude and not direction  It may be viewed as analogous to height: just as a released object will fall through a difference in heights caused by a gravitational field, so a charge will 'fall' across the voltage caused by an electric field  As relief maps show contour lines marking points of equal height, a set of lines marking points of equal potential (known as equipotentials) may be drawn around an electrostatically charged object  The equipotentials cross all lines of force at right angles  They must also lie parallel to a conductor's surface, otherwise this would produce a force that will move the charge carriers to even the potential of the surface 
Ørsted's discovery in 1821 that a magnetic field existed around all sides of a wire carrying an electric current indicated that there was a direct relationship between electricity and magnetism  Moreover, the interaction seemed different from gravitational and electrostatic forces, the two forces of nature then known  The force on the compass needle did not direct it to or away from the current-carrying wire, but acted at right angles to it  Ørsted's slightly obscure words were that "the electric conflict acts in a revolving manner " The force also depended on the direction of the current, for if the flow was reversed, then the force did too 
Ørsted did not fully understand his discovery, but he observed the effect was reciprocal: a current exerts a force on a magnet, and a magnetic field exerts a force on a current  The phenomenon was further investigated by Ampère, who discovered that two parallel current-carrying wires exerted a force upon each other: two wires conducting currents in the same direction are attracted to each other, while wires containing currents in opposite directions are forced apart  The interaction is mediated by the magnetic field each current produces and forms the basis for the international definition of the ampere 
This relationship between magnetic fields and currents is extremely important, for it led to Michael Faraday's invention of the electric motor in 1821  Faraday's homopolar motor consisted of a permanent magnet sitting in a pool of mercury  A current was allowed through a wire suspended from a pivot above the magnet and dipped into the mercury  The magnet exerted a tangential force on the wire, making it circle around the magnet for as long as the current was maintained 
Experimentation by Faraday in 1831 revealed that a wire moving perpendicular to a magnetic field developed a potential difference between its ends  Further analysis of this process, known as electromagnetic induction, enabled him to state the principle, now known as Faraday's law of induction, that the potential difference induced in a closed circuit is proportional to the rate of change of magnetic flux through the loop  Exploitation of this discovery enabled him to invent the first electrical generator in 1831, in which he converted the mechanical energy of a rotating copper disc to electrical energy  Faraday's disc was inefficient and of no use as a practical generator, but it showed the possibility of generating electric power using magnetism, a possibility that would be taken up by those that followed on from his work 
Italian physicist Alessandro Volta showing his "battery" to French emperor Napoleon Bonaparte in the early 19th century 
The ability of chemical reactions to produce electricity, and conversely the ability of electricity to drive chemical reactions has a wide array of uses 
Electrochemistry has always been an important part of electricity  From the initial invention of the Voltaic pile, electrochemical cells have evolved into the many different types of batteries, electroplating and electrolysis cells  Aluminium is produced in vast quantities this way, and many portable devices are electrically powered using rechargeable cells 
A basic electric circuit  The voltage source V on the left drives a current I around the circuit, delivering electrical energy into the resistor R  From the resistor, the current returns to the source, completing the circuit 
An electric circuit is an interconnection of electric components such that electric charge is made to flow along a closed path (a circuit), usually to perform some useful task 
Electric power is the rate at which electric energy is transferred by an electric circuit  The SI unit of power is the watt, one joule per second 
Electricity generation is often done with electric generators, but can also be supplied by chemical sources such as electric batteries or by other means from a wide variety of sources of energy  Electric power is generally supplied to businesses and homes by the electric power industry  Electricity is usually sold by the kilowatt hour (3 6 MJ) which is the product of power in kilowatts multiplied by running time in hours  Electric utilities measure power using electricity meters, which keep a running total of the electric energy delivered to a customer  Unlike fossil fuels, electricity is a low entropy form of energy and can be converted into motion or many other forms of energy with high efficiency 
Electronics deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes, optoelectronics, sensors and integrated circuits, and associated passive interconnection technologies  The nonlinear behaviour of active components and their ability to control electron flows makes amplification of weak signals possible and electronics is widely used in information processing, telecommunications, and signal processing  The ability of electronic devices to act as switches makes digital information processing possible  Interconnection technologies such as circuit boards, electronics packaging technology, and other varied forms of communication infrastructure complete circuit functionality and transform the mixed components into a regular working system 
Today, most electronic devices use semiconductor components to perform electron control  The study of semiconductor devices and related technology is considered a branch of solid state physics, whereas the design and construction of electronic circuits to solve practical problems come under electronics engineering 
Thus, the work of many researchers enabled the use of electronics to convert signals into high frequency oscillating currents, and via suitably shaped conductors, electricity permits the transmission and reception of these signals via radio waves over very long distances 
Early 20th-century alternator made in Budapest, Hungary, in the power generating hall of a hydroelectric station (photograph by Prokudin-Gorsky, 1905–1915) 
In the 6th century BC, the Greek philosopher Thales of Miletus experimented with amber rods and these experiments were the first studies into the production of electrical energy  While this method, now known as the triboelectric effect, can lift light objects and generate sparks, it is extremely inefficient  It was not until the invention of the voltaic pile in the eighteenth century that a viable source of electricity became available  The voltaic pile, and its modern descendant, the electrical battery, store energy chemically and make it available on demand in the form of electrical energy  The battery is a versatile and very common power source which is ideally suited to many applications, but its energy storage is finite, and once discharged it must be disposed of or recharged  For large electrical demands electrical energy must be generated and transmitted continuously over conductive transmission lines 
Electrical power is usually generated by electro-mechanical generators driven by steam produced from fossil fuel combustion, or the heat released from nuclear reactions; or from other sources such as kinetic energy extracted from wind or flowing water  The modern steam turbine invented by Sir Charles Parsons in 1884 today generates about 80 percent of the electric power in the world using a variety of heat sources  Such generators bear no resemblance to Faraday's homopolar disc generator of 1831, but they still rely on his electromagnetic principle that a conductor linking a changing magnetic field induces a potential difference across its ends  The invention in the late nineteenth century of the transformer meant that electrical power could be transmitted more efficiently at a higher voltage but lower current  Efficient electrical transmission meant in turn that electricity could be generated at centralised power stations, where it benefited from economies of scale, and then be despatched relatively long distances to where it was needed 
Since electrical energy cannot easily be stored in quantities large enough to meet demands on a national scale, at all times exactly as much must be produced as is required  This requires electricity utilities to make careful predictions of their electrical loads, and maintain constant co-ordination with their power stations  A certain amount of generation must always be held in reserve to cushion an electrical grid against inevitable disturbances and losses 
Electricity is a very convenient way to transfer energy, and it has been adapted to a huge, and growing, number of uses  The invention of a practical incandescent light bulb in the 1870s led to lighting becoming one of the first publicly available applications of electrical power  Although electrification brought with it its own dangers, replacing the naked flames of gas lighting greatly reduced fire hazards within homes and factories  Public utilities were set up in many cities targeting the burgeoning market for electrical lighting  In the late 20th century and in modern times, the trend has started to flow in the direction of deregulation in the electrical power sector 
The resistive Joule heating effect employed in filament light bulbs also sees more direct use in electric heating  While this is versatile and controllable, it can be seen as wasteful, since most electrical generation has already required the production of heat at a power station  A number of countries, such as Denmark, have issued legislation restricting or banning the use of resistive electric heating in new buildings  Electricity is however still a highly practical energy source for heating and refrigeration, with air conditioning/heat pumps representing a growing sector for electricity demand for heating and cooling, the effects of which electricity utilities are increasingly obliged to accommodate 
Electricity is used within telecommunications, and indeed the electrical telegraph, demonstrated commercially in 1837 by Cooke and Wheatstone, was one of its earliest applications  With the construction of first intercontinental, and then transatlantic, telegraph systems in the 1860s, electricity had enabled communications in minutes across the globe  Optical fibre and satellite communication have taken a share of the market for communications systems, but electricity can be expected to remain an essential part of the process 
The effects of electromagnetism are most visibly employed in the electric motor, which provides a clean and efficient means of motive power  A stationary motor such as a winch is easily provided with a supply of power, but a motor that moves with its application, such as an electric vehicle, is obliged to either carry along a power source such as a battery, or to collect current from a sliding contact such as a pantograph  Electrically powered vehicles are used in public transportation, such as electric buses and trains, and an increasing number of battery-powered electric cars in private ownership 
Electronic devices make use of the transistor, perhaps one of the most important inventions of the twentieth century, and a fundamental building block of all modern circuitry  A modern integrated circuit may contain several billion miniaturised transistors in a region only a few centimetres square 
A voltage applied to a human body causes an electric current through the tissues, and although the relationship is non-linear, the greater the voltage, the greater the current  The threshold for perception varies with the supply frequency and with the path of the current, but is about 0 1 mA to 1 mA for mains-frequency electricity, though a current as low as a microamp can be detected as an electrovibration effect under certain conditions  If the current is sufficiently high, it will cause muscle contraction, fibrillation of the heart, and tissue burns  The lack of any visible sign that a conductor is electrified makes electricity a particular hazard  The pain caused by an electric shock can be intense, leading electricity at times to be employed as a method of torture  Death caused by an electric shock is referred to as electrocution  Electrocution is still the means of judicial execution in some jurisdictions, though its use has become rarer in recent times 
Electricity is not a human invention, and may be observed in several forms in nature, a prominent manifestation of which is lightning  Many interactions familiar at the macroscopic level, such as touch, friction or chemical bonding, are due to interactions between electric fields on the atomic scale  The Earth's magnetic field is thought to arise from a natural dynamo of circulating currents in the planet's core  Certain crystals, such as quartz, or even sugar, generate a potential difference across their faces when subjected to external pressure  This phenomenon is known as piezoelectricity, from the Greek piezein (πιέζειν), meaning to press, and was discovered in 1880 by Pierre and Jacques Curie  The effect is reciprocal, and when a piezoelectric material is subjected to an electric field, a small change in physical dimensions takes place 
§Bioelectrogenesis in microbial life is a prominent phenomenon in soils and sediment ecology resulting from anaerobic respiration  The microbial fuel cell mimics this ubiquitous natural phenomenon 
Some organisms, such as sharks, are able to detect and respond to changes in electric fields, an ability known as electroreception, while others, termed electrogenic, are able to generate voltages themselves to serve as a predatory or defensive weapon  The order Gymnotiformes, of which the best known example is the electric eel, detect or stun their prey via high voltages generated from modified muscle cells called electrocytes  All animals transmit information along their cell membranes with voltage pulses called action potentials, whose functions include communication by the nervous system between neurons and muscles  An electric shock stimulates this system, and causes muscles to contract  Action potentials are also responsible for coordinating activities in certain plants 
In the 19th and early 20th century, electricity was not part of the everyday life of many people, even in the industrialised Western world  The popular culture of the time accordingly often depicted it as a mysterious, quasi-magical force that can slay the living, revive the dead or otherwise bend the laws of nature  This attitude began with the 1771 experiments of Luigi Galvani in which the legs of dead frogs were shown to twitch on application of animal electricity  "Revitalization" or resuscitation of apparently dead or drowned persons was reported in the medical literature shortly after Galvani's work  These results were known to Mary Shelley when she authored Frankenstein (1819), although she does not name the method of revitalization of the monster  The revitalization of monsters with electricity later became a stock theme in horror films 
As the public familiarity with electricity as the lifeblood of the Second Industrial Revolution grew, its wielders were more often cast in a positive light, such as the workers who "finger death at their gloves' end as they piece and repiece the living wires" in Rudyard Kipling's 1907 poem Sons of Martha  Electrically powered vehicles of every sort featured large in adventure stories such as those of Jules Verne and the Tom Swift books  The masters of electricity, whether fictional or real—including scientists such as Thomas Edison, Charles Steinmetz or Nikola Tesla—were popularly conceived of as having wizard-like powers 
With electricity ceasing to be a novelty and becoming a necessity of everyday life in the later half of the 20th century, it required particular attention by popular culture only when it stops flowing, an event that usually signals disaster  The people who keep it flowing, such as the nameless hero of Jimmy Webb’s song "Wichita Lineman" (1968), are still often cast as heroic, wizard-like figures 
Ampère's circuital law, connects the direction of an electric current and its associated magnetic currents 
^ Diogenes Laertius  R D  Hicks (ed )  "Lives of Eminent Philosophers, Book 1 Chapter 1 "  Perseus Digital Library  Tufts University  Retrieved 5 February 2017  Aristotle and Hippias affirm that, arguing from the magnet and from amber, he attributed a soul or life even to inanimate objects 
^ Aristotle  Daniel C  Stevenson (ed )  "De Animus (On the Soul) Book 1 Part 2 (B4 verso)"  The Internet Classics Archive  Translated by J A  Smith  Retrieved 5 February 2017  Thales, too, to judge from what is recorded about him, seems to have held soul to be a motive force, since he said that the magnet has a soul in it because it moves the iron 
^ a b c Guarnieri, M  (2014)  "Electricity in the age of Enlightenment"  IEEE Industrial Electronics Magazine  8 (3): 60–63  doi:10 1109/MIE 2014 2335431 
^ Srodes, James (2002), Franklin: The Essential Founding Father, Regnery Publishing, pp  92–94, ISBN 0-89526-163-4 It is uncertain if Franklin personally carried out this experiment, but it is popularly attributed to him 
^ a b Guarnieri, M  (2014)  "The Big Jump from the Legs of a Frog"  IEEE Industrial Electronics Magazine  8 (4): 59–61, 69  doi:10 1109/MIE 2014 2361237 
^ Hertz, Heinrich (1887)  "Ueber den Einfluss des ultravioletten Lichtes auf die electrische Entladung"  Annalen der Physik  267 (8): S  983–1000  Bibcode:1887AnP   267  983H  doi:10 1002/andp 18872670827 
^ "The Nobel Prize in Physics 1921"  Nobel Foundation  Retrieved 2013-03-16 
^ John Sydney Blakemore, Solid state physics, pp  1–3, Cambridge University Press, 1985 ISBN 0-521-31391-0 
^ Richard C  Jaeger, Travis N  Blalock, Microelectronic circuit design, pp  46–47, McGraw-Hill Professional, 2003 ISBN 0-07-250503-6 
^ "The repulsive force between two small spheres charged with the same type of electricity is inversely proportional to the square of the distance between the centres of the two spheres " Charles-Augustin de Coulomb, Histoire de l'Academie Royal des Sciences, Paris 1785 
^ Sewell, Tyson (1902), The Elements of Electrical Engineering, Lockwood, p  18   The Q originally stood for 'quantity of electricity', the term 'electricity' now more commonly expressed as 'charge' 
^ a b Berkson, William (1974), Fields of Force: The Development of a World View from Faraday to Einstein, Routledge, p  370, ISBN 0-7100-7626-6 Accounts differ as to whether this was before, during, or after a lecture 
^ "Lab Note #105 EMI Reduction – Unsuppressed vs  Suppressed"  Arc Suppression Technologies  April 2011  Retrieved March 7, 2012 
^ Almost all electric fields vary in space  An exception is the electric field surrounding a planar conductor of infinite extent, the field of which is uniform 
^ Paul J  Nahin (9 October 2002)  Oliver Heaviside: The Life, Work, and Times of an Electrical Genius of the Victorian Age  JHU Press  ISBN 978-0-8018-6909-9 
^ "The Bumpy Road to Energy Deregulation"  EnPowered  2016-03-28 
^ a b c d e f g h Van Riper, op cit , p  71 
Look up electricity in Wiktionary, the free dictionary 
Basic Concepts of Electricity chapter from Lessons In Electric Circuits Vol 1 DC book and series 
Context 2: Paper Info

Title: CONTOUR COMPLETION USING DEEP STRUCTURAL PRIORS
Publish Date: 9 Feb 2023
Author List: Ali Shiraee, Morteza Rezanejad, Mohammad Khodadad, Dirk Walther, Hamidreza Mahyar

Figure

Figure 1: Just by looking at subfigure (a), we, as humans, can easily perceive a shape like the one in subfigure (b) This is an extraordinary capability of our human brain and in this paper, we tried to see whether convolutional neural networks can show such capabilities 
Figure 2: The trajectory from random noise X N to the incomplete image X I in image space The network will pass a completed version of the image, X C , throughout this trajectory 
Figure 4: This figure shows our iterative process to complete the fragmented contours of an image given as input to our pipeline 
Figure 5: This example shows how different scores change throughout a single run All three scores change in the range of [0, 100] Our goal is to maximize reconstruction_score and minimize the overfit_score, but we should consider that the minimization lower bound is data dependent and is not zero 
Figure 6: Evolutionary process of the deep structure prior The right column shows the incomplete shapes given to the model and the rest of the columns show how the model is overfitting gradually to produce the incomplete shapes In each column, we are showing an intermediate iteration of this process The loss-term setup enables our pipeline to let the completed image appears during this iterative process 
Average MSE and IoU values between the incomplete (Raw) images, the output of DIP and DSP methods, and ground truth for each image are provided in this table 
For this experiment, we ran the model over a subset of complex dataset with 500 incomplete images at various levels of alpha for 250 iterations After the image completion is done, we compared the evaluation metrics between the completed image and the ground truth to examine the performance of the model for different values of alpha 
In this table, we show the effect of the receptive filter size on our algorithm's capability to fill in bigger gap sizes The numbers in this table are showing the percentage of the time that DIP was successful to complete shapes with each gap size and corresponding receptive field size As predicted, the bigger the filter size, the more successful the algorithm is in filling in the gaps 

abstract

Humans can easily perceive illusory contours and complete missing forms in fragmented shapes  This work investigates whether such capability can arise in convolutional neural networks (CNNs) using deep structural priors computed directly from images  In this work, we present a framework that completes disconnected contours and connects fragmented lines and curves 
In our framework, we propose a model that does not even need to know which regions of the contour are eliminated  We introduce an iterative process that completes an incomplete image and we propose novel measures that guide this to find regions it needs to complete  Our model trains on a single image and fills in the contours with no additional training data 
Our work builds a robust framework to achieve contour completion using deep structural priors and extensively investigate how such a model could be implemented 

Introduction

The human visual system is used to seeing incomplete outlines  Our brains can effortlessly group visual elements and fragmented contours that seem to be connected to each other  This power enables us to make shapes, organize disconnected visual features, and even properties of 3D surfaces when projected on 2D planes 
demonstrated how early vision may quickly complete partially-occluded objects using monocular signals  This capability of perceptual grouping has been studied in vision science for decades   Although there has been some work on perceptual grouping in the past couple of years, it has been less studied in the past decade due to the enormous progress of deep neural networks and their success in dealing with the pixel-by-pixel inference of images 
Different types of lines and curves have been studied to maximize the connectivity of two broken ends in the planer contour completion problem   Different types of lines and curves have been studied to maximize the connectivity of two broken ends in the planer contour completion problem  Geometry-based constraints can be utilized to address some challenges of contour completion problems, such as smoothness and curvature consistency  
However, such approaches only work for simple, smooth contours and usually fail in more complex settings  On the other hand, we currently have deep models that could easily take an incomplete image and complete the missing regions using enough training data   The amazing capability of such models especially those that are trained on different modalities with millions or billions of training data raises the question of whether we need such a large amount of training to perceive all the visual cues that are present in an image, which underlies visual perception by humans 
In human vision, Gestalt psychology suggests that our brain is designed to perceive structures and patterns that are grouped by some known rules  In this work, we show that some perceptual structures can also be learned from the image itself directly using architectures that enable such learning  Earlier work has shown  This is an extraordinary capability of our human brain and in this paper, we tried to see whether convolutional neural networks can show such capabilities 
that some forms of perceptual grouping can be achieved using computational models, such as stochastic completion fields   This type of learning resonates with some of the Gestalt perceptual grouping principles including "proximity", "good continuation" and "similarity"  In scenarios where color and/or texture are present, the cue of "similarity" helps us group regions with consistent patterns  
When color and texture are present, they provide a collection of rich information for such cues  In the present article, we probe convolutional neural networks in a scenario where both are absent, and the neural network is dealing with just forms and shapes  Specifically, we explore whether the convolutional neural network architecture itself can give rise to some of these grouping cues when they are fed just contours and shapes alone 
For years, neural networks have been treated as black boxes that can generalize images very well to multiple classes when there are enough training exemplars  One of the reasons that neural networks are trained on many exemplars is to avoid the problem of overfitting  On the other hand, we know that CNNs that generalize well to large classes of exemplars can easily overfit when those class labels are randomly permuted  
Inspired by this observation, suggest that image priors can be learned to a large extent through a generator network architecture that is solely trained on a single image  This encouraged us to take a deeper look at what structural information can be learned from a single-shape image and whether we can reconstruct some of those perceptual grouping capabilities using a generator network 
Inspired by , in this work, we adopt a novel training regime to complete shapes and contours where we use a UNet architecture with random initial weights and try to complete the contours within a single image without any training data  In our model, we imagine that the input image (i e , the only image used to update the model's weights) is an image of fragmented contours 
In this work, instead of training the model on multiple images fetched from a big image dataset, we imagine a random fixed tensor noise image as input to this model  At each iteration, the random noise tensor is inferred through our generative network and the network produces an outcome image  We introduce a novel loss function that enables this network to complete contours 
This process repeats, and the weights of our network are updated gradually based on this loss function, which is an energy term defined based on the input image and the output of the network  The model will reconstruct the missing structures i e , group fragmented contours that perceptually seem to be connected, before it fully overfits to the incomplete input image 
Contributions of our work are summarized as follows: 1  In our pipeline, we propose a novel algorithm that enables us to complete contours that appear to be connected to each other in an illusory form  2  Our model is trained on just one single query image and does not need any training data  3  Our model does not need to know which regions of the image are masked or occluded, i e , we remove the dependency of the algorithm on the guiding mask (a guiding mask is a mask that informs the model on where the missing regions are located at) 
We also introduce two metrics to produce a stopping criterion to know when to stop training before the model fully overfits to the incomplete image, i e , we guide the model to stop when the completed image is produced 

Methods

Our eyes are trained to predict a missing region of an occluded object within a scene  We can easily perceive or make guesses about parts of objects or shapes that we do not necessarily see  Even when we are looking at an image, we might guess about the shape, property, or other attributes of an unknown object within a scene 
Such capability extends beyond just known objects or shapes  We can look at a disconnected set of contours and guess what the connected form may look like  This capability is rooted in our prior knowledge about the world  (see Figure )  In this work, we aim to achieve a similar capability using deep generative networks 
Most neural networks that we work with these days are trained with a massive amount of data and one might think that this is the only way that a neural network can obtain prior information  Authors of Deep Image Prior (DIP) suggest that the convolutional architecture can capture a fair amount of information about image distribution 
They show that the hourglass architectures like UNet can show some good performances in some inverse problems such as image denoising, super-resolution, and inpainting  In this work, we focus on completing fragmented contours end-to-end just by using a single image  To be able to address this problem, we first look at a similar problem in image editing, known as image inpainting 
Image inpainting is the task of completing an image where some regions of that image are covered or filtered by a mask  In image inpainting, the generative model receives a masked image with the mask that guides the algorithm to fill in those missing regions  Although in the problem of contour completion, we have a very similar goal, the additional challenge that we suffer from is that we do not necessarily have a mask that covers the regions of interest for us 
For example, when we look at Figure (left), we are not provided that which regions of the image are incomplete by a guiding mask  Our brain figures this out just by looking at the form and predicting those missing regions  Inspired by the image inpainting work of DIP , we propose a novel algorithm for the contour completion problem (see Figure ), where, unlike DIP, we do not have a guiding mask to know where to fill in the missing regions of our disconnected contours 
Let us assume that we are given a degraded image x I containing a fragmented contour  We propose an iterative process (see Figure ) that can connect those discontinuities and glue those fragmented pieces together as follows  We propose an hour-glass model structure (f ) that is initially set up with completely random parameters (θ 0 ) at first 
Through an iterative process, we start feeding our network with a fixed random tensor noise z signal and obtain the inferred output (f (z)) from that network  We then back-propagate the difference between the inferred output and the incomplete image to the network  We then repeat this process until the difference between the generated outcome of the network (f θ (z)) and the incomplete image (x I ) gets smaller and smaller and finally overfits the incomplete image (x I ) 
In this work, we propose a novel error metric to backpropagate in the model and update its weights  we set the metric in a way that enables us to complete the incomplete image before it overfits the incomplete image  This is where the magic of our algorithm happens  We also propose a stopping criterion, so that when the image is complete, we no longer overfit the outcome of the model and instead produce a plausible connected set of fragmented contour pieces 
As illustrated in Figure , this trajectory will pass through a complete version of the image in image space, which is close to the actual connected ground truth x gt , which we do not have access to directly 

Energy Function

We can model this iterative process mathematically by maximizing a posterior distribution  Let us assume that the optimal image x * that we want to achieve is on a path that connects a random tensor noise z to the incomplete image x I   With this assumption, we can eventually overfit any random tensor noise to the incomplete image x I , and we can formulate the posterior distribution of our wanted optimal image x * as follows:
No Prior To better recapitulate what we want to achieve using our generative model, we solve an energy minimization problem on the parameter space of the model, rather than explicitly working with probability distributions and optimizing on x (image space)  Thus, we solve an energy minimization problem that incorporates the incomplete image (x I ) and model parameters (f θ (z)):
As shown in Figure , the pipeline starts from a random initialized set of parameters θ and updates those weights until it reaches a local minimum θ *   The only information provided for the network is the incomplete image x I   When we reach the optimal θ * , the completed image is obtained as x * = f θ * (z) where z is random tensor noise 
In this work, we use a U-Net architecture with skip connections as the generator model  " As we mentioned previously, in this work we were inspired by an earlier work known as Deep Image Prior (DIP)   In this work, the authors suggested a mean-squared-error loss term that enables the network to compare the output of the generator to the incomplete input image:
where x I is the incomplete image with missing pixels in correspondence of a binary mask m ∈ {0, 1} H×W and operator is for point-wise multiplication of two image matrices  In the inpainting tasks, the existence of a mask is essential as the algorithm needs to know where to fill in the missing area, whereas, in our work, we wanted to know whether the network can perform completion on its own without the need for the mask 
In other words, is it possible for the network to predict where to fill in at the same time that it is trying to reconstruct the incomplete image through the iterative process? To answer this question, we tried to solve a much harder problem in which the mask is not provided to the model and the model is agnostic to it 
To better understand how a solution could be hypothesized for this problem, we first imagine that we want to consider all the available regions in our image that could be potential places to fill in, i e , we set the mask in the previous formula 1 to be equal to the incomplete image x I   This is problematic as the model quickly tries to fill in all white space and quickly reconstructs the incomplete image by doing so 
On the other hand, we can take the inverse problem of the current problem, where the model tries to just fill in the regions that fragmented contour lives in  Taking these two at the same time, we came up with a novel loss term for energy minimization term that helps us remove the need for the mask in the case of the contour completion problem:
In this term, we introduce a linear combination of the two loss terms, where one focuses on reconstructing the missing regions in the foreground, and one focuses on avoiding inpainting regions in the background  The logic behind this is that, if we assume the original image to be representative of the mask, then the model tries to reconstruct in all white regions (the foreground), and in the inverse problem we just want to reconstruct the regions that are already part of the ground truth 

Stopping Criteria

As shown in Figure , knowing when to stop iterating to the over-fitted model is a key to obtaining a completed shape  Therefore, we equipped our model with a criterion that uses two individual novel terms to know when to stop and output the result of the network  These two metrics expand the capability of the generator network beyond what it does currently and achieve a full end-to-end contour completion model that trains and infers on a single image of divided contour fragments 
These new terms are: reconstruction_score (ρ) and overfit_score (ω) 

Reconstruction Score

The first score that this paper suggests is the reconstruction score, i  e , we have to make sure that the model is trained enough that it can reconstruct at least the entire set of fragmented contours within the image  This is a trivial score and to compute the reconstruction_score (ρ), we apply a k-dimensional tree (KDTree) nearest-neighbor lookup to find the ratio of points in the original incomplete image (x 0 ) 
This score ranges from [0 − 100] 

Overfit Score

It is evident that the model overfits the fragmented contours  This is due to the fact that the error in our loss term is minimized as the x overfits to x I , i  e , replacing x with x I in the loss term would give us zero  As we hypothesize iterative process also produces the complete image before it overfits to the incomplete image, we can imagine that at some point the image is complete (x C ) and does not need to be fine-tuned any more to overfit to x I  
We suggest a new score called overfit_score  overfit_score determines how much of the reconstructed outcome is over the number of pixels that are already in the incomplete image (x I )  To compute the overfit_score (ω), we apply a k-dimensional tree (KDTree) nearest-neighbor lookup of points in the outcome of the input image and see what portions of those points are novel and not already in the incomplete image (x I ) 
Similar to reconstruction_score, the overfit_score also ranges from [0 − 100]  Our goal is to maximize reconstruction_score and minimize the overfit_score, but we should consider that the minimization lower bound is data dependent and is not zero 

Combined Score

To be able to find the best possible set of completed contours, we combine the two and have a loop that tries to achieve close to full reconstruction and avoids over-fitting at the same time  This is what we call an "ideal" stopping point in the contour completion problem  In each run throughout all iterations, we pick an output of the network that minimizes a dissimilarity term:
where δ represents our dissimilarity score  The reconstruction_score and overfit_score are obtainable given network output and the incomplete image  Ideally, we want to achieve an output image that has a reconstruction_score equal to 100 and an overfit_score of γ which is a hyperparameter that is dependent on the image and complexity of the shape 
Empirically, we observed that this value is highly correlated with the distance between gaps that are observed in fragmented contours, i  e , the larger the gap results in a larger γ value  We will discuss this in more detail in the next section (see Section 3)  For one sample image, we computed the two metrics reconstruction_score and overfit_score and the combined value of dissimilarity (δ) and showed how these values change (see Figure ) 
Our initial observations show that the reconstruction_score will increase to 100 quickly for the incomplete image indicating that the already existing fragments of the contours have been reconstructed in the output  However, as mentioned previously, we cannot solely rely on this score since we also want to minimize the overfitting 
Remember that our goal is to produce an output that: a) preserves the original contours in the incomplete image and b) fills in the gaps between the fragmented contours  It is evident that overfit_score decreases throughout an iterative run of our process until it reaches zero  The dissimilarity will also decrease along with the overfit to a point, then it will increase, as the model tries to reproduce the incomplete image 
This is where an ideal γ value can be picked, i e , where to stop when the reconstruction is good but we have not done a full overfit to the incomplete image  Thus, one should pick the value of γ empirically in the scenario that the ground truth is not available, whereas, assuming that the ground truth is available, we can easily compute the best γ value 
In our experiments, we tried two datasets of images with different gap sizes  We observed that the best the γ for one set of samples is ∼ 5 (the set with shorter gaps) while it is ∼ 23 for samples from the other set, i  e, the set with longer gaps (see Figure for some completed examples) 

Experiments and Results

Performing unsupervised contour completion is a difficult task to benchmark as one can never know what fragments exactly are connected to each other in a real-world scenario  This makes the problem of contour completion a hard problem to solve  In this paper, we tried to create artificial shapes that are occluded by some masks and then tried to see if our model can regenerate the missing pieces and glue those divided contours together 
To demonstrate our model's behavior, we will conduct experiments on datasets created for this task and will report on them in this section  To compare network results in different settings, we will use pixel-wise Mean Squared Error (MSE) and Intersection over Union (IoU) between the produced result of the network and unmasked ground truth data and the reconstructed image on black pixels (where contours live) 

Data

We prepared two datasets, one labeled "Simple" and one "Complex", in accordance with the number of gaps in each shape  Both datasets contain nine different categories of shapes  In order to generate the Complex dataset, we used FlatShapeNet which is a dataset for the educational game Ariga  The dataset includes the following categories: Circle, Kite, Parallelogram, Rectangle, Rhombus, Square, Trapezoid, Triangle and Overlap 
The "overlap" category contains images that are made as a mixture of two shapes that are overlapping from the previous categories  These are some standard shapes with a few gaps in simple dataset, while the complex dataset has some hand-drawn shapes with fragmented lines and more gaps that produce more variety in general 
For each instance, a ground truth image is available for comparison  Most of our experiments have been conducted using the complex dataset in order to evaluate the generalization of our approach  For the analysis of how γ values should be set for each shape, we used the simple dataset as a reference 

Evaluation

In this section, we compare our model to the original Deep Image Prior (DIP) inpainting model  DIP's inpainting module accepts a degraded image and a binary mask corresponding to that image  In order to make a fair comparison, instead of providing a binary mask, we used the incomplete images both as input and as a mask in order to see whether it can produce a result similar to ours 
For DIP, we run the iterative process for a maximum number of 2500 iterations with the U-net backbone  We used the exact same architecture and setting in our model for a fair comparison  Using our ground truth dataset images, we calculate the MSE loss between the network output and ground truth during each iteration instead of relying on our stopping mechanism described in the previous section 
We then store the output with minimal loss throughout all the iterations  Finally, we select the best output among all iterations, report the MSE and IoU with the ground truth, and save the iteration number which resulted in the lowest MSE  Table compares the results that are obtained using the DIP method, the DSP method (ours), and the difference between raw images and the ground truth 
We have presented the average MSE-loss, average IoU, and the average number of iterations for the best output for different methods  As can be seen from the table, our model improves both MSE and IoU between the incomplete image and ground truth in fewer iterations  The DIP method can neither generate a better result than the raw image nor provide stopping criteria to prevent overfitting 
We provide a more detailed analysis of this result in Figure   As results show, our algorithm not only provides a much faster convergence but also consistently provides a better-completed image (consistently less MSE loss and better IoU), whereas it is challenging for the DIP method to accomplish better results without a guiding mask 
We compare MSE loss between the degraded raw images that these algorithms started with (shown in blue) (a) Mean Squared Error Loss: we clearly see that for almost all images, DSP (green) achieves a lower MSE than the incomplete images (blue) whereas, the DIP completed images either do not improve the MSE or even worsen that for the incomplete images 
Note that, the MSE is computed to an available ground truth image hidden from our methods (the lower is better)  (b) Intersection Over Union: here, we are looking at the IoU metric that specifies the amount of intersection over the union between the obtained images and the ground truth data  Again, we see that DSP produces images that are much closer to the ground truth (in most cases) whereas the DIP can not achieve a similar result 
While we see few DIP completed images produce a better IoU than the degraded images (in terms of IoU), most of them are worse than the starting image (the higher is better)  (c) The number of iterations that are needed for each algorithm to obtain its best results  Here, we see that DSP can quickly produce the best outcome with the least MSE loss whereas the DIP algorithm's best results are when we run the iterative process for more iterations (the lower is better) 

Correlation of γ with the Gap Size

To better understand the γ parameter of our combined score, we conducted the following experiment  A total of 12000 samples were obtained by merging all of our images from the two datasets, simple and complex  As we have access to the ground truth for each degraded image in the combined dataset, we can easily calculate reconstruction_score and overfit_score for each degraded-ground truth pair 
As expected, we obtain a reconstruction_score of 100 for all samples, but the overfit_score varies among them  Intuitively, we hypothesized that an optimal value of overfit score should be intertwined with the total area of gaps  To test this hypothesis, we did the following experiment  We first define a function φ(x) which takes a binary, black and white image x and returns the number of black pixels in it 
Then we define a gap term as follows: where x I is the incomplete image and x gt is the ground truth  In this case, gap indicates the total area of the gap with respect to the whole shape  We found out that this term and the best result have a correlation of 97 43%  This indicates that the value of γ is highly correlated with the gap size, that is something expected in a way 

Effect of α

We conducted additional experiments concerning how α affects the quality of reconstruction  In the previous section, we defined Equation 2 as the loss term that guides our iterative process  The term α specifies the amount of emphasis the model should place on reconstructing missing regions, rather than filling in fragmented contours 
A lower α indicates a better grouping quality, as shown in Equation   However, we will not achieve completion if we remove the first term completely from the loss by setting α = 0  Therefore, the first term should be kept, but its weight should be very low in order to achieve a good completion  On the other hand, if we set α = 1 and omit the second term, we lose the contour completion regularization term and obtain the same output as a vanilla deep image prior, which does not complete shapes 

Effect of Receptive Field Size

To better understand the effect of receptive field size on our algorithm, we test the following hypothesis: can models with bigger receptive field size complete shapes with bigger gaps? In Table , we report showing the results of this experiment  As we can see, the bigger the receptive field size, the more complete shapes we can reconstruct using DSP 

Implementation Details

As shown in Figure , we use a model with 5 layers and 128 channels for downsampling and upsampling convolutions, and 64 channels for skip convolutions  The upsampling and downsampling modules use 3 × 3 filters, while the skip module uses 1 × 1 filters  In the upsample part of the network, the nearest neighbor algorithm is used 
We used 256 × 256 images with three channels in all of our experiments  In training, we use the MSE loss between the degraded image and the output of the network, and we optimize the loss using the ADAM optimizer and a learning rate equal to 0 01   In our experiments, we also used α = 0 15 as an optimal proportion coefficient for reconstruction loss 

Conclusion

In this work, we introduced a novel framework for contour completion using deep structure priors (DSP)  This work offers a novel notion of a maskless grouping of fragmented contours  In our proposed framework, we introduced a novel loss metric that does not require a strict definition of the mask  Instead, it lets the model learn the perceivable illusory contours and connects those fragmented pieces using a generator network that is solely trained on just the single incomplete input image 
Our model does not require any pre-training which demonstrates that the convolutional architecture of the hour-glass model is able to connect disconnected contours  We present an extended set of experiments that show the capability of our algorithm  We investigate the effect of each parameter introduced in our algorithm separately and show how one could possibly achieve the best result for their problem using this model 
In future work, we plan to extend this model and try to see how it performs with real images  In particular, we want to determine whether we can inpaint real-world photographs while retaining perceptually aware scene structures  The importance of shape in perception by deep neural networks has been highlighted in many adversarial examples to appearance-based networks  
The outcome of this work has strong potential to impact the designing and implementation of models that are robust to such perturbations 
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The smallest thing to buy is a refresh  We give you some of these as you level up so you can try them out  Refreshes will refill (or partially fill depending on the size of your tank) your bot bar or your energy bar  But after that, it depends on your goals  There is much to choose from  Do you want to be able to deploy more nanobots on every launch? Do you want to boost your offensive or defensive bots? Do you want to be able to launch missiles into towns far away? All of these things are possible  Look through the depot and see what you like  Most of the time you will need to buy an upgrade before you can buy the ordinance  For example, missiles are fairly inexpensive, but you need to buy the MX Rack Missile Launcher before you can launch them  Buy the upgrade first  What is the difference between qredits and cubes in the depot? A qredit (aka: credit, which looks like the child of a Q and € ) is the type of currency you earn in the game by harvesting your bases  Cubes (aka: power cubes) are purchased with real money in the Bank section of the Depot  We want everyone to be able to do everything in the game for free, by earning qredits, but for those who want to move a bit faster, you can purchase cubes to speed things along  Purchasing cubes is how QONQR makes money  We very much appreciate your support  Every purchase you make helps us to keep making improvements in the game  Future enhancements will enable you to earn cubes in game  Why can’t I create another base? Additional bases become available as you level up  At the start, you will get a new base every 5 levels  If you don’t have any more bases available to build, you will need to level up  If you have a base available, but aren’t allowed to use it, it is because you already have a base in that zone  You can only have one base in a zone  Get yourself to another zone, then create your base there  My bases are collecting credits at different rates  Your bases collect resources faster if your faction controls the zone  Do your best to either put your bases in zones you can control by yourself, or find zones with strong players in the same faction and put your bases there to maximize your credit collection  The game says I’m in a town that doesn’t exist  QONQR tracks almost 3 million battle zones of varying strategic value in 250 countries  Sometimes those zones include locations that haven’t existed for over 100 years  That’s pretty cool if you ask us  If you find a zone that looks like a duplicate or is just plain wrong, however, let us know on the forums under “Zone Corrections”  How do I move my current position on the map? You might need to take a car, bus, plane or train depending on which zone you are trying to get to, but if you want to move on the map, you need to move in the real world  QONQR is a location-based game, which means you play where you are  However we don't want to make you move to play, we want you to plan when you move  QONQR goes with you as you move through the daily activities in your life  Where do I find the Strategy Guide? Here: http://community qonqr com/index php?/topic/1191-the-official-qonqr-strategy-guide/ How do I win? That is for you to decide  There is still much to discover  We don’t even know if QONQR is good or evil  Why is it here? What is its purpose? Help your faction further its goals and unlock all the secrets of QONQR!
Congratulations to the Swarm on their overwhelming victory in Atlantis in May 2015 -- taking and retaining all Atlantis zones from beginning to end is hard to argue with -- most convincing -- well done Swarm!
Rumor has it that the Duggers have 20 scopes  Some families really do have a wife and ten kids    Ha Anyways     Multi scoping is generally not encouraged, usually if you mention that you do it on the forums or in groupme your not going to be a very liked person, even within your own faction  What tends to happen is if you start multiscoping then your enemies start doing it, then you get into a war where ever person has 6 phones and nobody is happy  I've seen or heard about situations like this in a lot of different locations 
Great job, faceless! It was actually an exciting Atlantis and I prefer it that way  Way to bring your A game 
You know what we need in this game? Nano-Nukes!!
Attention (insert faction here), We, the (insert faction here) are tired of the way you constantly (circle one)   A  Cube rage us B  Bully us with numbers C  Talk mean to us It hurts our feelings because (circle one)   A  We don't cube B  We don't have as many allies C  We have no sense of humor and/or no backbone Please refrain from participating in the above selected actions for above selected reasons so as the game is enjoyable for (insert faction here)  Regards, (insert name and faction here) There we go   this should streamline the entire complaint process of the forums  Copy and reuse as needed  You're welcome 
Silver, you mentioned in a blog that there are levels beyond 150, is it safe to say there is no level limit anymore or is that for us to discover? Another question, I'm not sure if I'm the only one noticing this but it seems like bot decay doesn't work against Zone Assault bots  I've hit two players I know have been inactive for well over 6 months but my attack had the same effect it did yesterday before the update  However I did notice against Deflection bots I am getting 2x the kill power  Also in response to decontaminatoR  Paid gaming isn't just something for adults  When I was 13 the best options for handheld gaming was GameBoy or GameGear and the games at the time cost anywhere between $29-$39 dollars  I had a paper route to pay for my games  So, no offense, but you can afford $0 99 for a game  You don't even need a paper route, just check under your couch cushions and I'm sure you'll find a few quarters 
I want to start by thanking Faceless  This round of Atlantis, Legion and Faceless were doubled in sized and probably spending by Swarm  I contacted some great players from the other side and put together a nonaggression pact, this pact was one of the most impressive agreements I've seen in the more than two years of playing  Hundreds of people worldwide stuck to this agreement and put past feelings behind us  It was awesome to see both sides stick so closely to each other in fighting against Swarm  I want to really thank everyone who showed honor by standing behind me and the faceless command when we suggested that, the people who really gave it a chance and then most importantly, to all the players who honored it  Faceless, thank you very much! We stood no chance of winning without you! The battle came down to literally one launch in one of three zones  I'd say that with that being said everyone fought incredibly hard, so I want to give Swarm the respect they deserve  You guys really show out and play to win  Good game, 2 against 1 is not easy, no matter how large your crew is  And Legion, we had many late, late nights, many very long days  You guys killed it this month! We didn't take home a trophy, but I would say we all have something to be proud of! The other leaders who helped me coordinate everything were awesome! So many great people kept everything moving forward 24 hours a day for the whole week  Thank you to everyone who gave it your all for the whole week even when we saw that Swarm had 4x our bots at the end of just one day  Many people would have given up, but we held in and **** near won! I've won Atlantis battles with Faceless and with Legion but I will say, nothing was as fun or incredible as this round! You guys are fantastic and I can't wait to do it all again next month! Hopefully we have more Legion and Faceless show up for the next round, but I know that even if we don't, we will figure out a way, just as warriors do  PEW! PEW! PEW!
I must start with an announcement: Camels are not the only animal in the middle east  You have overused it already  It's saddens me that anyone would still find it funny  Get a bit of originality  For anyone not wanting to read all this drivel, skip to the end (hint: Bold stuff)  At what point have I bullied anyone? When was the last aggressive or threatening message you or any of your members received from me? Never  Legion and Swarm (in the UK) have teamed up because Faceless are dominating in London  That makes sense  It's a three sided fight and if one force becomes too powerful, the other two can join forces to try and take them down  But: We are dominating in the London area while your alliance is attacking players outside of that area  Then you expect me to sit back and do nothing  You are specifically using me as an excuse for why you have teamed up but then you're attacking players outside of my reach, sometimes with Europe involved  If that is not reason enough to attack you then I'm not sure what is  You lot cube, multiscope and have multi faction accounts and still complain  I don't complain about anything you do  Play the way you want to, ill do the same  I am never rude to anyone, always polite no matter what the message is, never brag about what I do, can do or have done, never threaten anyone, try and keep in touch with the few swarm or legion who are civilised to me, listen to any message form any side and if I can help in any way, I try my best to  Formed agreements with enemy (that I didn't need to) that ended up slapping me in the face  I have even in the past taken the time to find out who some of the younger players are so I know to avoid them  When I'm in a different country I try and find who the bullies are  Those players who threaten and brag and laugh at others  Those are my targets (if they exist in those areas)  I don't see how anyone can think I'm a bully  Is it your money? Its none of your business how I spend it  Either way your numbers are way off  700 dollars a day? Did you just decide to blindly strike the number pad to come up with that figure? Best part of one of your posts is saying that the devs should worry about my welfare  What concerns you about me spending my money? Maybe I'll self harm due to overspending? I wont have enough to buy food because I bought too many cubes? I don't understand what they are supposed to worry about  I have issues? At what point did you deduce this oh mighty psychologist? Wait a minute are you my bank account manager? What do you know about how much I can or cant afford? Random guy spends money on something he enjoys  The end  Told you last time to give up on all the drama but I guess thank you for the concern  You play to make me spend more? And? What do you think that accomplishes? In fact how do you even make me spend? You don't even attack me  You sulk when I'm in the UK and when I fly out and you find out, you call in Europe to help you take an zone or two  As they say, whatever floats your boat  As for Atlantis: Please try again  I quit Atlantis when we were winning  I have on occasion involved myself after I was asked to help out but on the whole I give it a miss  The last time (months ago) was the last 10 minutes of Atlantis and Legion fought hard  We lost  I was not the only Faceless player to quit Atlantis  Quite a few of us thought it lasted too long and had too many zones to fight over



ground_truth: Watt, one joule per second.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How does the receptive field size affect the completion of shapes?

Read the following text and answer briefly.

Context 3:  I hear the duration has been reduced  You can't honestly believe I changed my sleeping pattern for the game  I was in California for a month  I was jet lagged  My sleeping pattern was a bit off when I got back  You're not even accurate about when I deploy  Pay attention  You should know this by now: I play as and when I want, sometimes every 20 minutes and sometimes I do long stretches and sometimes I'm busy and don't deploy for hours  You wont always win, and nor will I  Try and get satisfaction form at least trying to win or putting up a good fight  About the limit on cubing  Please  I suggested that last year  Twice  Unfortunately multiscoping is allowed and so prevalent that it kind of ruins the idea  These are the facts: YOU and YOUR side threatened Faceless members who support London  One of the members threatened is actually London based  What do you expect him to do while his city is under attacked? A few of your members don't know when to keep quiet  They sent messages to us threatening specific players and telling them their zones will be dropped just because they have helped London  Basically if they help London then they get their zones wiped  And you have the cheek to call me a bully? We stood up to your members specifically because they were trying to bully  That is the reason we went strong months ago and took those big zones  How is this me bullying you? This is me answering your threats  I didn't attack those zones "just because i can", it's just because I should  Because of your threats  You can blame your members for the loss of those 2 or 3 big zones  These are the basics: You attack one of our zones  We look at the list of attackers and pick one of the players who deployed the most, find a zone of his or hers and attack it  We don't need to justify our attacks with "because there are Faceless players within 30 miles"  What, every time we attack a zone we need to send some letter explaining why? You attack us or we attack you, for any reason  That's the game  When some of you were cheating and you could not win you complained, when you cube and lose you complain, when you invite all of Europe to attack and win you still complain  I just think you like to make a fuss  Last words (for now): You think I attack zones as a means of getting attention? I get enough of it form your threads  The only attention seeker here is you with your victim attitude and pity us posts 
I've had some very angry emails today from a couple users who are upset their rival achieved the ability to switch factions freely having played for one of the factions for only 1 hour  I've been accused of doing favors, changing the rules, and various other backhanded deals  It appears it comes down reading the rules  You do not have to play for every faction for 60 days in order to earn free switching status  Here is a common scenario many players have used to achieve free switching status and avoiding playing for one faction they despise  1  Start with Swarm 2  Switch to Legion (earn Spy) play for 60 days 3  Switch back to Swarm (earn Double Agent) play for 60 days 4  Switch to Faceless (earn Mercenary) immediately switch back to Swarm or Legion Below is the complete text on the switch nanobots screen  It is the same text that has been there from Day 1 with the exception of the level 100 rules that went into place earlier this year, where you could switch as much as you want before level 100 , but those switches don't count towards the medals  This text has been part of this description since Jan 15, 2013  "Players that earn all three spy awards, may once again switch factions at any time as they could during the training levels 1 through 99 " Prior to Jan 15, the text said this  "Players that earn all three awards may be given the opportunity to switch factions more quickly in future updates (contact support for more information)" I pulled that right out of source control, which includes the entire change history  Here is the complete text from this page  http://portal qonqr    r/SwitchFaction WARNING: Defection has consequences! Self-destruct will be initiated on all your nanobots  Without the self-destruct, you would be required to battle against your former self to regain control of your zones  You will lose the capture and leadership of any zones you currently hold  Lifetime captures will be unaffected  If you are still completing the training levels and have not reached Level 100, you may switch as often as you like to find the faction that suits you best  Once you have reached Level 100 switching factions has rewards, but also has additional consequences beyond the self-destruct of all your nanobots  Defection will usually result in a demotion in rank  This is accomplished through awards with negative rank points  Those awards are: Spy - First switch to an opposing faction (-20 points) Double Agent - Return to a faction from which you had previously defected (-20 points) Mercenary - Become a member of all three factions (-20 points) Other Faction Change Details: You may not switch factions again until at least 60 days have passed since your last faction switch  Defection point penalties are applied only once per award Players that earn all three spy awards, may once again switch factions at any time as they could during the training levels 1 through 99  The decision to switch factions is one that must be made with strong determination  Nanobots cannot be reanimated once destroyed  You will retain your earned experience, level, formations, qredits, cubes, and upgrades  However, as far as your zones go, you will be starting over 
   has got to be one of the funniest moments I've seen in qonqr yet lol 
The **** change operation was a success!
As a relatively new player for faceless in a region dominated by swarm i can understand the OP  However, judging from the numbers i see here on a regular basis i think you are asking a bit much  My idea would be the opposite approach  Why not add a weapon with extremely short range, let's say like 5km that acts like a bomb and make it much stronk? That would add some serious home advantage  Or alternativley make attack formations lose power over range (exclude nanos and plasma)  Maybe something like that would allow newcomers to at least get a foothold in their homezones  It's just an idea, maybe i overlooked something?
This is by design  Some day it is possible (I said someday) we could offer skins for your scope  So we will need a uniform color scheme  You can tell the formation families based on the shape of the box  Trapezoid is attack, diamond is defense, and octagon is support  It will take some time to get comfortable with the change 
Geophysical based game  Anyways, probably not a bad idea but, considering the issues with the three platforms and the development of blue for those platforms, I doubt the resources are available for development on a new platform  Seen the blog? Its Qonqr meets wheel of fortune!
I am happy to announce that today I both completed the training and captured my first zone 
^THIS so now that qonqr has been thoroughly funded, can we have blue now? Or is that not happening still lol 
Atleast I am not legion and there for we can have this intelligent discussion rather than just compete over who has the best words XD ohhhhh someone bring the bill to legion cuz someone just served them extra double order of stir fried SNAPPPPPP If the devs wont make zone dueling for us I hope out there somewhere are those who would empty a zone and challenge one on one to a local battle  I'd like to see the transcript of deployments made / moves made as well that would be neat I think such events would be cool  I suppose if people give up on atlantis as it works now they can schedule their own tournements in empty atlantis zones   have a team clear the zone   put 1 vs 1 or teams vs teams   like fisticuffs challenges   find out what these warriors are really made of!
@Qonqrd everyone you know must face palm every time you make a post  Its embarrassing  Mega cubers or whatever you want to call them are not great for the opposing team surrounding them but are great for the game itself (money) and for the team they are part of  Multiscopers are not not great for the opposing team surrounding them and bring nothing to the game but are great for the team they are part of  Both have a negative impact on enemy teams/players but only one benefits the game itself  Both can make people want to quit out of frustration  And that's not great for the game  @OP unlimited refresh is over powered  Its frustrating to fight against a ridiculous amount of refreshes  Unfortunately i dont see anything changing unless this game gets a lot more people playing  More people might mean more money for the company from various sources  More money from various players might mean they can limit the players who spend a ton and still generate a healthy income  The main issue i see with limiting refreshes is someone multiscoping and spending money  He now has two, three, four accounts to refresh with and gets the advantage  Its tricky 
ey dun new ho to yet it uff 
Yet you complain almost everyday here, on your website, Twitter, and YouTube channel that the game needs to change because cubing has such an impact 
It was fun  Swarm had me scared at first, but it turned into kind of a bullying match between us and legion  Last hour became p obvious which way it was gonna go  Legion rly stepped up their game in the end there, respect 
We are investigating this  Here is what we know: Several of the accounts used the same password  Most of the accounts belonged to people who knew each other personally  The accounts were all switched from the same IP Addresses  The person who logged in, got into each account on the first attempt, so they knew the password for each account  What you should know: QONQR never stores passwords, not even in the logs  Passwords are hashed (one way encrypted) and can never be decrypted When you authenticate to our servers, we hash the password you gave us and compare it to the encrypted password in the database to see if they match  Access to our database in the could is restricted tightly and we are confident no one breached the system  What you should do: Don't use the same password as other people you play with  Don't share your password with anyone 
I heard all the French players fled to the UK after one German player accidentally shot a single missile into France 
Most factions now use GroupMe or Line as their means of communication, the forums are too slow as a means of communication and insecure for specific faction conversations  Think of the forums are more of a gaming information resource rather than a means of communication  Contact the top players of your faction in the leader boards of your state and they will likely point you in the right direction to chatting with your local faction  The developers are also building some sort of new chat system into the game, we don't know much about it but apparently beta testing for the chat will be happening very soon (next couple of weeks) according to their timelines 
A way to honor the dead? Nah, how bout a way to dishonor the dead 
Could just build it up and retain their capture  Remember Bizzy, staq to the heavens 
I just read this entire thread  I am now tuckered 
Does anyone know if Bot Booster has an effect on Seekers and how much dmg they do to attacking players? Also on the topic of seekers, does the amount of skeers in a battlefield have any effect on how much damage they do?
Interactive map of real-time zone captures 
You know that is something I didn't factor in there  Time  The player who can consistently and constantly launch wins against the guy who casually picks up the phone on occasion or has to work away from a cell phone for 8 hours  Good point  And yes I can't argue skill doesn't factor in, it just seems like less of a factor than other games is all 
I cant see why a closed forum, open only to registered Qonqr accounts, cant be used  **** spammers!
Gotta love synclock! I suspect linjin has a problem  Maybe the dvs should look into it 
No, Naamah   I clearly understood what you were trying to convey  I'll even go as far as to agree that what your facing now is, while fully allowed and deemed completely acceptable by the developers, unbalanced and wrong  However  the imbalance isn't in the game and isn't something that, from a business standpoint, is likely to be regulated  The game is fair  the advantages are provided to all players  It's the players themselves that throw the balance into chaos because, as you've said, not everyone can afford spending several thousand bucks a year in only one game  Truthfully, in my opinion, the moment you admitted to buying cubes yourself your complaint became silly  because I'm sure there's a player out there, who has bought NO cubes, who can make this same complaint about you that you are making about others here  I know these things because I've read them so   many   times in this forum  There was likely a time, ages ago when I was a young Massune, that I even posted a few myself  That was the purpose of my post  I was poking fun at addition of yet another cuber/bully/trash talk complaint on the forum  It wasn't directed at your personal plight so much as the idea that someone, yet again, finds it necessary to lobby for a spending cap on the only real way for this game to make money  As to your specific problem  you, like all those that have raised this topic before you, have few options to rectify the issue  Here are a few that seem to have worked for others  fight harder, recruit better, spend less time complaining and more time organizing, budget for more cubes or quit  I'd rather not see you opt for the latter  but to each their own 
I agree, we should find a way to honor the dead, but I don't think keeping their towers infinitely is necessarily the solution  The game must go on  I'm pretty sure the point of bot decay was to clear the game of inactive player bots so that new players can have a chance to rise up, not to dishonor the bots of dead players 
The following are frequently asked questions about the new server update (so far) It still says Training Complete on my iPhone  -	Download the update from iTunes It still says Training Complete on my Android  -	Sorry, Android will not be updated again until the QONQR Blue beta is released My XP per launch keeps going down -	This is XP throttling and is intended to limit the ability for people to leve1 from 1 to 100 in a single day through heavy cubing  The XP throttle was introduced with the original version of QONQR in 2012,and the throttle formula is the same for levels above 100  The throttle resets at midnight UTC every day  How do I buy the Bot Regeneration Accelerator? -	Currently the Bot Regen Accelerator can only be purchased through http://portal qonqr com  Go to the Depot and review your scope upgrades  The new QONQR Blue clients will allow for this purchase to be made in the app using your mobile billing  I don’t have a PayPal account -	For users interested in purchasing the Regen upgrade, but who do not have a PayPal account, PayPal does give you the option to checkout using your billing information without creating an account  PayPal is not allowed in my country, or I don’t have a credit or debit card -	Please contact support@QONQR com for alternate options Is Bot Regen Accelerator counted as part of the 100% scope upgrades? -	Yes, but there is a bug that does not increase scope upgrade percent when you purchase this upgrade, that will be fixed in the coming days  For all other questions, please read the 7 blog posts prior to 7/29/2015 for information on what was included in the update today 
Bye Fack, its been a pleasure being allied and against you 
The two big issues that are both killing the game slowly and keeping it from growing exponentially are cube injustice and new player ramp  The game obviously also needs to provide a consistent and growing revenue stream as well  I think Silver needs to rethink how revenue is generated if he is going to address cube injustice and new player ramp  For revenue generation I would suggest a model that doesn't give a significant combat advantages  Download and play for free from level 0-99 Pay small monthly fee to get full functionality or play for free at 50% of offensive/defensive funtionality Still buy cubes, but cubes are used for following: - Credit Boost: harvest more credits for a period of time - Range Extension: ability to use standard attack/defense formations at extended ranges - Base Share: get 100% credit attainment even in bases owned by another faction - Purchase additional ordinance - Zone Name Change - ability to customize zone names  "Breggland" - Faction Change with Bots - pay for the ability to keep up to 50% of your bots with faction change - Experience Boost: %increase in experienced gained while leveling - Other: anything that helps grow a player or provides enjoyment, but doesn't tip the battle capability of a scope  New Player Ramp/Integration into Game - Offer paid immediate ramp package: one price to become 100 with full upgrades - Like the changes in Blue - Create new zones in Metro areas that only 0-99 level can launch into, with statewide ranges Understand that catering to those who have money and like to use it for an advantage is a good business model and for those people it might be ok to offer very expensive options: - Shield generators: temporary energy shield that adds X% increase to defense or stops X% of damage - EMP's: turns Absorbs off for X minutes  Does not destroy, just turns off - Chain Lighting: Does damage across multiple players in a zone From a development standpoint I have no idea what is possible, easy or hard, but the general idea is to make the playing field more fair for the standard player while maintaining and growing a business revenue stream 
You need to come to the Northeast US  We handle our business like no other 



ground_truth: Bigger receptive field size leads to more successful shape completion.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How can you level up in the early levels?

Read the following text and answer briefly.

Context 4:  The departures of most of the experienced players have left the club's future to the youth  Along with two Ukrainian players, Ugandan international, Noah Kasule, has been signed 

The club headquarters are located on Jivani Street 2 of the Malatia-Sebastia District, Yerevan 

Domestic

European

Stadium

The construction of the Banants Stadium was launched in 2006 in the Malatia-Sebastia District of Yerevan, with the assistance of the FIFA goal programme  It was officially opened in 2008 with a capacity of 3,600 seats  Further developments were implemented later in 2011, when the playing pitch was modernized and the capacity of the stadium was increased up to 4,860 seats (2,760 at the northern stand, 1,500 at the southern stand and 600 at the western stand) 

Training centre/academy
Banants Training Centre is the club's academy base located in the Malatia-Sebastia District of Yerevan  In addition to the main stadium, the centre houses 3 full-size training pitches, mini football pitches as well as an indoor facility  The current technical director of the academy is the former Russian footballer Ilshat Faizulin 

Fans
The most active group of fans is the South West Ultras fan club, mainly composed of residents from several neighbourhoods within the Malatia-Sebastia District of Yerevan, since the club is a de facto representer of the district  Members of the fan club benefit from events organized by the club and many facilities of the Banants training centre, such as the mini football pitch, the club store and other entertainments 

Achievements
 Armenian Premier League
 Winner (1): 2013–14 
 Runner-up (5): 2003, 2006, 2007, 2010, 2018 

 Armenian Cup
 Winner (3): 1992, 2007, 2016 
 Runner-up (6): 2003, 2004, 2008, 2009, 2010, 2021–22

 Armenian Supercup
 Winner (1): 2014 
 Runner-up (5): 2004, 2007, 2009, 2010, 2016 

Current squad

Out on loan

Personnel

Technical staff

Management

Urartu-2

FC Banants' reserve squad play as FC Banants-2 in the Armenian First League  They play their home games at the training field with artificial turf of the Urartu Training Centre 

Managerial history
 Varuzhan Sukiasyan (1992–94)
 Poghos Galstyan (July 1, 1996 – June 30, 1998)
 Oganes Zanazanyan (2001–05)
 Ashot Barseghyan (2005–06)
 Nikolay Kiselyov (2006–07)
 Jan Poštulka (2007)
 Nikolay Kostov (July 1, 2007 – April 8, 2008)
 Nedelcho Matushev (April 8, 2008 – June 30, 2008)
 Kim Splidsboel (2008)
 Armen Gyulbudaghyants (Jan 1, 2009 – Dec 1, 2009)
 Ashot Barseghyan (interim) (2009)
 Stevica Kuzmanovski (Jan 1, 2010 – Dec 31, 2010)
 Rafael Nazaryan (Jan 1, 2011 – Jan 15, 2012)
 Volodymyr Pyatenko (Jan 17, 2013 – June 30, 2013)
 Zsolt Hornyák (July 1, 2013 – May 30, 2015)
 Aram Voskanyan (July 1, 2015 – Oct 11, 2015)
 Tito Ramallo (Oct 12, 2015 – Oct 3, 2016)
 Artur Voskanyan (Oct 3, 2016 – Aug 11, 2018)
 Ilshat Faizulin (Aug 12, 2018 –Nov 24, 2019)
 Aleksandr Grigoryan (Nov 25, 2019 –Mar 10, 2021)
 Robert Arzumanyan (10 March 2021–24 June 2022)
 Dmitri Gunko (27 June 2022–)

References

External links
 Official website 
 Banants at Weltfussball de  

 
Urartu
Urartu
Urartu
Urartu



ground_truth: Keep deploying and harvesting your bases to earn experience points and level up quickly.