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Oil Spill Response

# Responding to oil spills in the U.S.

October 7, 2010, 11:14 am
Source: EPA
 Topics:

National Service response to the Deepwater Horizon oil spill. Credit: Serve.gov

# Response to Hazardous Substances Releases

When a hazardous substance release is reported in the United States, the Environmental Protection Agency's (EPA) Emergency Response Program sets its response procedures into motion. Many steps and safety precautions must be followed to ensure a swift and effective response to the emergency.

 Regional Response Team (RRT) responding to hazardous substance release in Massachusetts. Credit: Mass.gov

The first step in any response action is to investigate the site. When a release is first reported, responders may not know all the necessary information such as how the release occured, the extent of the damage, or even what hazardous substances are involved. All this information must be learned before any effective response effort can be carried out. Site investigation also allows responders to determine the appropriate safety measures to take during the response effort.

Response actions fall into three main categories, depending on the urgency of the situation. Once information has been gatherered about the release, responders can determine what type of response action should be taken. A clean-up effort may be long-term or short-term. Depending on the circumstances, responders may employ some or all of these methods:

• Removing hazardous substances in soil or in containers
• Burning or otherwise treating hazardous substances
• Draining waste ponds or repairing leaky waste disposal pits so that hazardous substances do not seep into the ground
• Using chemicals to stop the spread of the hazardous substance release
• Encasing hazardous substances in place or otherwise ensuring that winds or rain do not move them around
• Providing a safe supply of drinking water to people affected by hazardous substance contamination
• Temporarily moving residents affected by hazardous substance contamination while cleanup activities take place
• Installing fences to prevent direct contact with hazardous substances

EPA has conducted several thousand response actions since the Emergency Response program began in 1980, and has directed and monitored many other actions carried out by those responsible for the contamination. The threats confronted by the EPA Emergency Response program vary greatly in size, nature, and location, and have involved EPA in incidents requiring unusual or complex emergency response actions.

## Response Alternatives

Experience has demonstrated that not all emergency response or removal actions classified under the National Contingency Plan (NCP) are equally urgent. For example, situations involving fire or explosions or imminent, catastrophic contamination of a reservoir may require prompt and expeditious attention, while certain situations involving abandoned hazardous waste drums or cleanup of abandoned industrial facilities may not. Based on the NCP, EPA defines the following types of removal actions:

• Classic Emergencies: Those actions where the release requires that on-site activities be initiated within minutes or hours of the determination that a removal action is appropriate.
• Long-term clean-up efforts may respond to less urgent recent releases, or may involve clean-up at long-term Superfund sites:
• Time-Critical Actions: Those actions where, based on an evaluation of the site, EPA determines that less than six months is available before site activities must be initiated.
• Non-Time-Critical Actions: Those actions where, based on an evaluation of the site, EPA determines that more than six months is available before on-site activities must begin.

# Responding to Oil Spills

National Response System Flowchart. Source: EPA

Despite the nation's best efforts to prevent spills, almost 14,000 oil spills are reported each year, mobilizing thousands of specially trained emergency response personnel and challenging the best-laid contingency plans. Although many spills are contained and cleaned up by the party responsible for the spill, some spills require assistance from local and state agencies, and occasionally, the federal government. Under the National Contingency Plan, EPA is the lead federal response agency for oil spills occurring in inland waters, and the U.S. Coast Guard is the lead response agency for spills in coastal waters and deepwater ports.

Whether or not it manages the response, EPA tracks all reports of oil spills. EPA usually learns about a spill from the responsible party, who is required by law to report the spill to the federal government, or from state and local responders. Once the federal government receives the report, either through the National Response Center, EPA, or another agency, it is recorded in the Emergency Response Notification System, or ERNS. ERNS contains historical spill information for the entire country dating from 1986, and is currently available for downloading.

## National Response Center (NRC)

 EPA responding to a hazardous substance release. Credit: EPA

The National Response Center (NRC) is the federal government's national communications center, which is staffed 24 hours a day by U.S. Coast Guard officers and marine science technicians. The NRC is the sole federal point of contact for reporting all hazardous substances and oil spills. The NRC receives all reports of releases involving hazardous substances and oil that trigger the federal notification requirements under several laws.

Reports to the NRC activate the National Contingency Plan and the federal government's response capabilities. It is the responsibility of the NRC staff to notify the pre-designated On-Scene Coordinator (OSC) assigned to the area of the incident and to collect available information on the size and nature of the release, the facility or vessel involved, and the party(ies) responsible for the release. The NRC maintains reports of all releases and spills in a national database.

## Investigating Sites

Once EPA learns of a potential hazardous substance release, an established set of procedures is followed to investigate the site, evaluate the threat, and determine the best course of action. First, EPA designates an On-Scene Coordinator (OSC) to evaluate the incident and determine the appropriate response agency. If the OSC determines that EPA will take the lead in responding to the incident, the OSC will evaluate the urgency of the situation to determine the appropriate response alternative.

Prior to initiating an emergency response action, the OSC will conduct an off-site preliminary assessment of the release and the site's characteristics. The preliminary assessment helps to identify specific hazards and determine the appropriate response measures and safety measures needed to ensure the health and safety of the responders. The OSC may rely on a variety of methods to collect the necessary information, including interviewing witnesses, first responders, and others present at the site; reviewing records and documents at the facility or on the vehicle; analyzing photographs taken at the site; or conducting a visual (off-site) inspection using binoculars. Thanks to the Community Right-to-Know law, important information about the released substances is often easily and quickly obtained.

Once EPA determines the type of emergency response action needed, response personnel take special precautions to ensure that they are protected from the threats posed at the site. When entering the site, response personnel wear personal protective gear to shield or isolate them from the chemical, physical, and biological hazards that may be encountered on the site. The selection of the type or level of personal protective equipment is based on the identification of the hazards or suspected hazards, potential exposure pathways, and the performance of the equipment in providing a barrier to these hazards. Because there is often little known information on specific hazards during the initial phase of an emergency response, the OSC typically directs that the most protective equipment be used at first; as more information about the hazards and conditions become available, the OSC can decide to downgrade the level of protection to match the site hazards.

As response personnel enter the site of the release, they gather additional information and further evaluate the site risks and hazards present. Response personnel use this information to further refine their response activities and the safety measures being taken. Generally, response workers entering the site conduct a visual survey for potential hazards and may perform air monitoring for potential dangers to life and health. For example, a visual survey might note the condition of waste containers (e.g., rusted or other unusual conditions), determine potential exposure pathways, and identify other possible dangers, such as confined space and oxygen-deficient environments, ground subsidence, visible vapor clouds, or areas that contain biological indicators, such as dead vegetation or wildlife. Response personnel use direct-reading instruments and testing equipment when performing air monitoring. One important goal of monitoring during initial site entry is to establish work or safety zones at the site. As the emergency response action continues, response personnel conduct periodic monitoring to ensure that any new hazards are identified promptly and that appropriate controls are implemented to protect the responders and nearby communities.

Site investigations are essential to protect the health and safety of response personnel and others during emergency response actions. The information gathered is absolutely critical to enable responders to proceed confidently and safely, and to ensure that local communities receive accurate information about the potential for adverse health effects.

## Oil Spill Response Management

 Fish & Wildlife Service personnel participate as members of an integrated response team, responding to chemical and oil spills. Credit: FWS

As the federal government's lead agency in responding to oil spills in inland waters, EPA is responsible for monitoring and, if necessary, directing spill response efforts. Under the Oil Pollution Act of 1990, EPA is required to direct the response in cases where the spill "is of such a size or character as to pose a substantial threat to the public health or welfare." EPA may also take the lead in managing the response if requested to do so by state or local response officials, or if EPA determines that the responsible party is incapable of responding adequately to the spill.

The On-Scene Coordinator (OSC) is the federal official responsible for monitoring or managing federal responses to oil spills. If the spill affects inland waters, then an EPA OSC will be designated for the incident; a USCG OSC will be designated for spills to coastal waters and the great lakes. Once notified of an oil spill, the OSC also will conduct an immediate assessment to evaluate several factors, including the size and nature of the spill, the type of oil spilled, its potential hazards, and the resources needed to contain and clean it up. The OSC also will monitor any existing response efforts to determine whether additional technical support or federal involvement is necessary.

If the OSC determines that federal involvement is needed, the OSC will assume control of all spill response operations at the site and will obtain and direct all needed resources, such as cleanup personnel and equipment. If the OSC determines that the personnel and equipment already deployed at the spill site are inadequate, the OSC will employ spill contractors using available federal response funds or, if necessary, funds from the Oil Spill Liability Trust Fund.

For oil spills that require a greater scope of federal support and resources, the OSC may activate the Regional Response Team to provide broader technical advice, equipment, or manpower to assist with a response. In addition, the OSC can request support from EPA's Environmental Response Team , which is available 24 hours-a-day to provide oil spill expertise and special response equipment to the spill responders. The OSC also can access various National Oceanic and Atmospheric Administration (NOAA) Scientific Support Coordinators with expertise in different aspects of oil spills, as well as support from non-governmental organizations that specialize in wildlife rescue and rehabilitation.

### On-Scene Coordinators (OSCs)

The On-Scene Coordinator (OSC) is the federal official responsible for monitoring or directing responses to all oil spills and hazardous substance releases reported to the federal government. The OSC coordinates all federal efforts with, and provides support and information to, local, state and regional response communities. The OSC is an agent of either EPA or the U.S. Coast Guard, depending on where the incident occurs. EPA OSCs have primary responsibility for spills and releases to inland areas and waters, while U.S. Coast Guard OSCs have responsibility for coastal waters and the Great Lakes. In general, the OSC has the following key responsibilities during and after a response to a hazardous substance release or an oil spill: (1) assessment; (2) monitoring; (3) response assistance; and (4) evaluation.

#### Assessment

The OSC typically conducts assessment activities at the beginning of a response. Assessment involves evaluating the size and nature of a release or spill, its potential hazards, the resources needed to contain and clean it up, and the ability of the responsible party or local authorities to handle the incident. The results of the assessment are used to determine the need for personnel, equipment, and other resources to promptly and effectively combat the release.

#### Monitoring

Most releases or spills are small and are cleaned up by the responsible party or local response agencies. Monitoring comprises those activities taken to ensure that the actions taken to control and clean up a chemical release or oil spill are appropriate. Monitoring can be conducted from the site when necessary, or from an agency office if the situation appears to be under control. In the case of oil spills, the OSC is legally required to monitor the response if the spill poses a substantial threat to the health and welfare of the public due to its size or characteristics.

#### Response Assistance

Once a release or spill has been assessed, the OSC determines whether federal assistance will be http://www.eoearth.org/articles/edit/158425/necessary to help control and contain it. If the OSC decides that federal assistance is required, the OSC will obtain needed resources such as personnel and equipment. If sufficient resources are not available for an incident, the OSC decides who pays and can secure federal funding either from the Superfund trust fund for hazardous substance releases or the Oil Spill Liability Trust Fund for oil spills. This assistance ensures that cleanup will not be hindered by a lack in availability of personnel or equipment on behalf of the local or state or responsible party resources.

#### Evaluation

Evaluating response actions provides information that is useful for designing or improving spill response plans. The National Oil and Hazardous Substances Pollution Contingency Plan (NCP) requires that the OSC report all activities that take place during and after an incident. For example, following an oil spill, the OSC is required to file a summary report that outlines the actions taken to remedy the spill and the level of assistance provided by local, state, and federal agencies. These reports can be used to identify problem areas and can be shared with other agencies who may make recommendations for improvement.

# National Response System

The National Response System (NRS) routinely and effectively responds to a wide range of oil and hazardous substance releases. It is a multi-layered system of individuals and teams from local, state, and federal agencies, industry, and other organizations that share expertise and resources to ensure that oil spill control and cleanup activities are timely and efficient, and that they minimize threats to human health and the environment.

At the heart of the system is the National Contingency Plan (NCP), which ensures that the resources and expertise of the federal government are available immediately for oil or hazardous substance releases that are beyond the capabilities of local and state responders. The NCP provides the framework for the NRS and establishes how it works.

When releases are serious enough to be considered "Nationally Significant Incidents," the National Response Framework (NRF) is activated, and works in conjunction with the NRS and NCP. The NRF is the federal government's comprehensive, all-hazard approach to crisis management, and provides a mechanism for coordinating federal assistance to state governments and localities.

### Responding Agencies

Because a hazardous substance release or an oil spill could occur virtually anywhere and at any time, EPA and a network of federal, state, and local responders stand ready 24 hours a day to contain and clean-up the discharged oil and released chemicals. EPA's Emergency Response program has a leadership role in this National Response System that promotes coordinated emergency response actions and guarantees the availability of resources to cover all possible release scenarios. This coordination allows federal, state, and local agencies to work together to respond to all emergencies efficiently. EPA also provides other financial and technical support as needed to assist local communities in responding to the broad range of emergency response incidents that may occur.

### Contingency Planning

One of EPA's major tasks is to coordinate contingency planning efforts with other agencies to ensure that emergency responses are carried out quickly and with maximum effectiveness. To further ensure the readiness of its response teams, EPA provides training to emergency responders so that they have the necessary skills and use appropriate precautions when undertaking emergency response measures.

Contingency plans describe the information and processes for responding to hazardous substance emergencies, including the roles and responsibilities of the different responding agencies, the location and availability of response resources, the process for conducting the response, and other actions necessary to ensure a safe and effective cleanup. When used properly by trained personnel, a well-designed contingency plan enables response efforts to proceed smoothly and effectively, minimizes danger to cleanup personnel, reduces overall costs of cleanup by avoiding unnecessary effort, and ensures the protection of human health and the environment. Because the approaches and methods for responding to releases are constantly evolving, contingency plans also are constantly evolving and improving.

A network of contingency plans with different levels of geographical scope form the backbone of our country's efforts to prepare for and coordinate responses to emergency incidents:

• The National Contingency Plan Overview (National Oil and Hazardous Substances Pollution Contingency Plan - 40 CFR Part 300) is the federal government's primary plan for preparing for, and coordinating with, other emergency responders. The National Contingency Plan establishes the principles and structure of the unified command system and identifies the roles and responsibilities of the key players within the system.
• The federal government also prepares Regional and Area Contingency Plans that coordinate effective responses within each of the 10 standard federal regions and other designated Areas covering Alaska, the Caribbean, and several islands in the Pacific. These plans include preparedness information on a regional level and identify useful response facilities and resources available from government, commercial, academic, and other sources.
• At the local level, Local Contingency Plans are developed to prepare and organize local resources in the event of the accidental release of hazardous substances. Under the Emergency Planning and Community Right-to-Know Act of 1986 (EPCRA), state governors are required to establish State Emergency Response Commissions, which in turn establish Local Emergency Planning Committees (LEPCs) for districts within the state. These emergency planning organizations are responsible for developing local contingency plans using chemical inventory information collected as part of the law's community right-to-know provisions.
• Federal on-scene coordinators, who are the federal government's frontline staff during an incident, may develop an On-Scene Coordinator (OSC) Contingency Plan for responses in the OSC's area of responsibility. These plans identify probable locations of releases, the availability and location of emergency response resources, and the local structure for responding to release incidents.

Taken together, these activities and resources form the cornerstone of our country's ability to respond to hazardous substance emergencies regardless of their nature, size, or location.

## Federal, State, and Local Cooperation

Together, EPA and state and local governments form an effective partnership at all points in the emergency response process as part of our country's National Response System, a multi-layered network of individuals and teams from local, state, and federal agencies, industry, and other response organizations.

### Federal Response Actions

The Superfund law grants the Emergency Response program and the Superfund program the authority to respond to hazardous substance emergencies. On the national level, the Superfund law distinguishes between short-term and long-term responses to threats posed by hazardous substances. Short-term responses address immediate threats to public health and the environment. These short-term national responses are primarily coordinated by the Emergency Response program. EPA differentiates among three types of emergency response alternatives according to the urgency of the situation, and the Emergency Response program ensures that all elements of the response system are ready to respond immediately to any hazardous substance emergency, wherever it occurs. Since its inception in 1980, the program has compiled an impressive record of accomplishments.

The Superfund Response program is primarily responsible for long-term responses, which involve complex and highly contaminated sites where it often requires several years to fully study the problem, develop a permanent remedy, and clean up the hazardous waste. As of August 1996, there were over 1200 Superfund sites undergoing some form of long-term cleanup. Together, the Superfund and the Emergency Response programs coordinate safe and effective response techniques for any type of hazardous substance emergency.

### Response Teams and the National Response System

Sometimes, when an EPA On-Scene Coordinator (OSC) or other emergency responder comes to the scene of a hazardous substance release, he or she immediately knows that extra technical help will be needed. In these cases, additional support may come from several response teams established under the National Contingency Plan:

 Members of EPA's Radiological Emergency Response Team (RERT) inspecting a potentially hazardous site. Credit: EPA

Environmental Response Team: The Environmental Response Team (ERT) is a group of EPA technical experts who provide around-the-clock assistance at the scene of hazardous substance releases, offering expertise in such areas as treatment, biology, chemistry, hydrology, geology, and engineering. The ERT can provide support to the full range of emergency response actions, including unusual or complex emergency incidents such as underwater releases.

National Response Team: The National Response Team (NRT) is an inter-agency group that provides guidance prior to an incident and, when requested, technical and financial assistance during an incident.

Regional Response Teams: Regional Response Teams (RRTs) also are interagency groups that consist of representatives from federal, state, and local governments. They conduct pre-response planning and preparedness activities, as well as coordinate and provide advice during response actions. The two principal components of the RRT are thirteen regional standing teams that provide region-wide support on communications, planning, coordination, training, evaluation, and preparedness, as well as incident-specific teams for which participation depends on the technical nature and location of the incident.

### State and Local Responders

Individual states are members in their respective Regional Response Teams, which are established and maintained in the National Response System to provide On-Scene Coordinator (OSCs) with support and assistance in responses to release incidents at the Regional level. Prior to emergency incidents, EPA coordinates with states to ensure that state contingency plans are consistent with what was accomplished through national and regional contingency planning.

Local responders have, perhaps, the most vital role in the National Response System. Because firefighters and local police are usually the first responders at the scene of an incident, they are the first to assess the situation, identify the hazards, and take emergency measures, such as fighting a fire, securing the area, or re-routing traffic. Their assessment and initial activities help the EPA OSC determine what EPA actions are necessary.

## National Response Team

Response planning and coordination is accomplished at the federal level through the U.S. National Response Team (NRT), an interagency group co-chaired by the EPA and the U.S. Coast Guard. Although the NRT does not respond directly to incidents, it is responsible for three major activities related to managing responses: (1) distributing information; (2) planning for emergencies; and (3) training for emergencies. The NRT also supports the Regional Response Teams.

### Distributing Information

The NRT is responsible for distributing technical, financial, and operational information about hazardous substance releases and oil spills to all members of the team. This information is collected primarily by NRT committees whose purpose is to focus attention on specific issues, then collect and disseminate information on those issues to other members of the team. Standing committees of the NRT and the topics that are addressed include:

• Response Committee, chaired by EPA, addresses issues such as response operations, technology employment during response, operational safety, and interagency facilitation of response issues (e.g., customs on transboundary issues). Response specific national policy/program coordination and capacity building also reside in this committee.
• Preparedness Committee, chaired by the U.S. Coast Guard, addresses issues such as preparedness training, monitoring exercises/drills, planning guidance, planning interoperability, and planning consistency issues. Preparedness specific national policy/program coordination and capacity building also reside in this committee.
• Science and Technology Committee, chaired by EPA and the National Oceanic and Atmospheric Administration in alternating years, provides national coordination on issues that parallel those addressed by the Scientific Support Coordinator on an incident by incident basis. The focus of this committee is on identifying developed technology and mechanisms for applying those technologies to enhance operational response. The committee monitors research and development of response technologies and provides relevant information to the RRTs and other members of the National Response System to assist in the use of such technologies.

### Planning for Emergencies

 EPA inspectors look for hazardous spills in obvious places such as chemical plants and factories, but also visit sites like this high school chemistry lab to ensure no materials are creating a danger for students. Credit: EPA

The NRT ensures that the roles of federal agencies in the NRT for emergency response are clearly outlined in the National Contingency Plan (see the National Oil and Hazardous Substances Pollution Contingency Plan Overview). After a major incident, the effectiveness of the response is carefully assessed by the NRT. The NRT may use information gathered from the assessment to make recommendations for improving the National Contingency Plan and the National Response System. The NRT may be asked to help Regional Response Teams (RRTs) develop Regional Contingency Plans. The NRT also reviews these plans to determine whether they comply with federal policies on emergency response.

### Training for Emergencies

Training is the key to the federal strategy for preparing for oil spills or hazardous substance releases. Although most training is performed by state and local personnel, the NRT develops training courses and programs, coordinates federal training efforts, and provides information to regional, state, and local officials about training needs and courses.

### Supporting Regional Response Teams

The NRT supports Regional Response Teams (RRTs) by reviewing Regional or Area Contingency Plans to maintain consistency with national policies on emergency response. The NRT also supports RRTs by monitoring and assessing RRT effectiveness during an incident. The NRT may ask an RRT to focus on specific lessons learned from a particular incident and to share those lessons with other members of the National Response System. In this way, the RRTs can improve their own regional contingency plans while helping to solve problems that might be occurring elsewhere within the National Response System.

### National Response Team (NRT) Member Roles and Responsibilities

EPA personnel chair the National Response Team and cochair all Regional Response Teams (RRTs). The agency provides On-Scene Coordinators (OSCs), scientific support coordinators for inland spills, and Remedial Project Managers for hazardous waste remedial actions under Superfund. EPA funds the Environmental Response Team (ERT), which is dispatched at the OSC's request to any response episode exceeding available regional resources. The ERT can provide support for site assessments, health and safety issues, action plan development, and contamination monitoring. Legal expertise is also available from EPA to interpret environmental statutes.

• U.S. Coast Guard: An agency of the U.S. Department of Transportation, the U.S. Coast Guard serves as ViceChair for the National Response Team. U.S. Coast Guard provides OSCs for coastal zones and cochairs all RRTs. Twenty four hour-a-day staffed facilities in 46 "Captain of the Port Zones" are maintained by the U.S. Coast Guard, for command, control, and surveillance of releases in coastal waters. The U.S. Coast Guard manages the National Response Center and maintains a National Strike Force, specially trained and equipped to respond to major marine pollution incidents. The U.S. Coast Guard's Strike Teams are based on the Pacific and Gulf Coasts. The U.S. Commandant of the Coast Guard also serves as fund manager for the Oil Pollution Liability Trust Fund set up under the Clean Water Act.
• Federal Emergency Management Agency (FEMA): During a response effort, FEMA advises and aids lead agencies in coordinating relocation assistance. The agency provides guidance, policy, and technical assistance in emergency preparedness planning, training, and exercising activities for state and local governments.
• Department of Defense (DoD): DoD acts when oil or hazardous substances are released from a facility or vessel under its jurisdiction. Upon request, DoD will provide U.S. Navy oil spill containment and recovery equipment and manpower, as well as equipment for ship salvaging, shipboard damage control, and diving. DoD may also make U.S. Army Corp of Engineer equipment and expertise available for removing navigational obstructions and performing ship structural repairs.
• Department of Energy (DOE): This agency provides OSCs when hazardous substances are released from DOE facilities, or when materials being transported under DOE's control are spilled. DOE staff aids in the control of immediate radiological hazards.
• Department of Agriculture (USDA): USDA measures, evaluates, and monitors situations where natural resources, including soil, water, wildlife, and vegetation have been affected by hazardous substances. USDA contributes expertise from the following organizations:
• Forest Service
• Agriculture Research Service
• Natural Resources Conservation Service (formerly Soil Conservation Service)
• Food Safety and Inspection Service
• Animal and Plant Health Inspection Service
• Department of Commerce (DOC): This department, through the National Oceanic and Atmospheric Administration (NOAA), provides scientific support for resources and contingency planning in coastal and marine areas including hazard assessment and spill trajectory (direction) monitoring to predict movement and dispersion of oil and other hazardous substances. NOAA contributes information about sensitive coastal environments, and furnishes data about actual and predicted meteorological, hydrological, ice, and oceanographic conditions. NOAA also serves as the natural resource trustee for the living marine resources it manages and protects.
• Department of Health and Human Services (HHS): Health hazards at a response are assessed by HHS. Agencies within HHS that maintain and provide information on health effects include the Agency for Toxic Substances and Disease Registry (ATSDR) and the National Institutes for Environmental Health Sciences (NIEHS). NIEHS also offers training on the health effects of oil spills.
• Department of Interior (DOI): This department contributes expertise on natural resources, endangered species, and Federal lands and waters, and is responsible for native Americans and U.S. territories. Regional Environmental Officers (REO) of DOI are designated members of RRTs DOI serves as a natural resource trustee for the resources it manages or protects. Bureaus within the department with expertise include:
• Fish and Wildlife Service
• Geological Survey Bureau of Indian Affairs
• Bureau of Land Management
• Minerals Management Service
• Bureau of Mines
• National Park Service
• Bureau of Reclamation
• Office of Surface Mining and Reclamation Enforcement
• Office of Territorial Affairs
• Department of Justice (DOJ): Expert advice on legal questions arising from discharges or releases, and Federal agency responses, can be obtained from this agency. DOJ represents the Federal government in litigation relating to discharges or releases.
• Department of Labor (DOL): Through the Occupational Safety and Health Administration (OSHA), DOL conducts safety and health inspections of hazardous waste sites to ensure that onsite employees are protected from hazards and to determine if a site is in compliance with safety and health standards and regulations.
• Department of Transportation (DOT): In addition to the activities of the U.S. Coast Guard, response expertise is provided by DOT pertaining to transportation of oil or hazardous substances through the Research and Special Programs Administration (RSPA). RSPA offers specialized advice on requirements of packaging, handling, and transporting regulated hazardous materials. RSPA serves other functions, including promulgating and enforcing hazardous materials regulations (49 CFR Part 100199), producing emergency response guidebooks, and supporting protective action decision strategies and exercise scenarios.
• Nuclear Regulatory Commission (NRC): When radioactive materials by its licensees are released, this agency responds in accordance with the its incident response plan.
• Department of State: This agency takes the lead in developing international contingency plans. It helps coordinate international response efforts, when discharges or releases cross international boundaries or involve foreign flag vessels. The agency also coordinates requests for aid from foreign governments.
• Treasury Department

## Regional Response Teams

There are thirteen Regional Response Teams (RRTs) in the U.S., each representing a particular geographic region (including the Caribbean and the Pacific Basin). RRTs are composed of representatives from field offices of the federal agencies that make up the National Response Team, as well as state representatives. The four major responsibilities of RRTs are: (1) response; (2) planning; (3) training; and (4) coordination.

### Response

RRTs provide a forum for federal agency field offices and state agencies to exchange information about their abilities to respond to On-Scene Coordinator's (OSC's) requests for assistance. As with the National Response Team (NRT), RRT members do not respond directly to releases or spills, but may be called upon to provide technical advice, equipment, or manpower to assist with a response.

### Planning

Each RRT develops a Regional Contingency Plan to ensure that the roles of federal and state agencies during an actual incident are clear. Following an incident, the RRT reviews the OSC's reports to identify problems with the Region's response to the incident and improves the plan as necessary.

### Training

Federal agencies that are members of the RRTs provide simulation exercises of Regional plans to test the abilities of federal, state, and local agencies to coordinate their emergency response activities. Any major problems identified as a result of these exercises may be addressed and changed in the Regional Contingency Plan so the same problems do not arise during an actual incident.

### Coordination

The RRTs identify available resources from each federal agency and state within their regions. Such resources include equipment, guidance, training, and technical expertise for dealing with chemical releases or oil spills. When there are too few resources in a region, the RRT can request assistance from federal or state authorities to ensure that sufficient resources will be available during an incident. This coordination by the RRTs assures that resources are used as wisely as possible, and that no Region is lacking what it needs to protect human health and the environment from the effects of a hazardous substance release or oil spill.

# Oil Spill Clean-Up Safety Measures

## Personal Protective Equipment

Vapors, gases, and particulates from hazardous substance response activities place response personnel at risk. For this reason, response personnel must wear appropriate personal protective clothing and equipment whenever they are near the site. The more that is known about the hazards at a release site, the easier it becomes to select personal protective equipment. There are basically four levels of personal protective equipment:

• Level A protection is required when the greatest potential for exposure to hazards exists, and when the greatest level of skin, respiratory, and eye protection is required. Examples of Level A clothing and equipment include positive-pressure, full face-piece self contained breathing apparatus (SCBA) or positive pressure supplied air respirator with escape SCBA, totally encapsulated chemical- and vapor-protective suit, inner and outer chemical-resistant gloves, and disposable protective suit, gloves, and boots.
• Level B protection is required under circumstances requiring the highest level of respiratory protection, with lesser level of skin protection. At most abandoned outdoor hazardous waste sites, ambient atmospheric vapors or gas levels have not approached sufficiently high concentrations to warrant level A protection -- Level B protection is often adequate. Examples of Level B protection include positive-pressure, full face-piece self contained breathing apparatus (SCBA) or positive pressure supplied air respirator with escape SCBA, inner and outer chemical-resistant gloves, face shield, hooded chemical resistant clothing, coveralls, and outer chemical-resistant boots.
• Level C protection is required when the concentration and type of airborne substances is known and the criteria for using air purifying respirators is met. Typical Level C equipment includes full-face air purifying respirators, inner and outer chemical-resistant gloves, hard hat, escape mask, and disposable chemical-resistant outer boots. The difference between Level C and Level B protection is the type of equipment used to protect the respiratory system, assuming the same type of chemical-resistant clothing is used. The main criterion for Level C is that atmospheric concentrations and other selection criteria permit wearing an air-purifying respirator.
• Level D protection is the minimum protection required. Level D protection may be sufficient when no contaminants are present or work operations preclude splashes, immersion, or the potential for unexpected inhalation or contact with hazardous levels of chemicals. Appropriate Level D protective equipment may include gloves, coveralls, safety glasses, face shield, and chemical-resistant, steel-toe boots or shoes.

While these are general guidelines for typical equipment to be used in certain circumstances, other combinations of protective equipment may be more appropriate, depending upon specific site characteristics.

## Safety Zones

An essential element of any hazardous substance release site is the establishment of safety or work zones. These zones are established primarily to reduce the accidental spread of hazardous substances by workers or equipment from contaminated areas to clean areas. Safety zones specify the type of operations that will occur in each zone, the degree of hazard at different locations within the release site, and the areas at the site that should be avoided by unauthorized or unprotected employees. The three most frequently identified zones are below:

• The exclusion zone (or hot zone) is the area with actual or potential contamination and the highest potential for exposure to hazardous substances.
• The support zone (or cold zone) is the area of the site that is free from contamination and that may be safely used as a planning and staging area.
• The contamination reduction zone (or warm zone) is the transition area between the exclusion and support zones. This area is where responders enter and exit the exclusion zone and where decontamination activities take place.

# Paying for the Clean-Up

By law, the parties responsible for the use, transportation, storage, and disposal of hazardous substances and oil are liable for the cost of containment, cleanup, and damages resulting from a release, or threat of release, related to their own activities. EPA's goal is to identify the responsible parties and ensure that they pay these costs.

Unfortunately, the responsible party sometimes cannot be identified or simply refuses to cooperate with the response effort. In such instances, EPA and other participants in the National Response System will step in without delay to ensure that the emergency is dealt with in a timely and appropriate manner. During and after the cleanup, EPA will continue to search for the responsible party and seek payment for the costs incurred. These efforts do not always result positively; on some occasions, the responsible party is never identified.

Congress established two funds to cover the costs of federal cleanup activities when the responsible party does not or cannot pay: the Superfund and the Oil Spill Liability Trust Fund. Both of these funds are financed through taxes on industry, reflecting Congressional recognition of the need for unhesitating response to serious health and environmental hazards and an unwillingness to burden the taxpayer with the sometimes very large costs involved. In a further step, Congress directed that a percentage of the Superfund be used by the Local Governments Reimbursement (LGR) Program to help offset costs involved with the clean up of hazardous substance releases.

## Oil Spill Liability Trust Fund

Under the Oil Pollution Act of 1990, the owner or operator of a facility from which oil is discharged (also known as the responsible Party) is liable for the costs associated with the containment or cleanup of the spill and any damages resulting from the spill. The EPA's first priority is to ensure that responsible parties pay to clean up their own oil releases. However, when the responsible party is unknown or refuses to pay, funds from the Oil Spill Liability Trust Fund can be used to cover removal costs or damages resulting from discharges of oil.

The primary source of revenue for the fund is a five-cents per barrel fee on imported and domestic oil. Collection of this fee ceased on December 31, 1994 due to a "sunset" provision in the law. Other revenue sources for the fund include interest on the fund, cost recovery from the parties responsible for the spills, and any fines or civil penalties collected. The Fund is administered by the U.S. Coast Guard's National Pollution Funds Center (NPFC).

The Fund can provide up to $1 billion for any one oil pollution incident, including up to$500 million for the initiation of natural resource damage assessments and claims in connection with any single incident. The main uses of Fund expenditures are:

• State access for removal actions;
• Payments to Federal, state, and Indian tribe trustees to carry out natural resource damage assessments and restorations;
• Payment of claims for uncompensated removal costs and damages; and
• Research and development and other specific appropriations.

# Factors that Impact Response Efforts

## Spill Location

Different factors affect the ability to contain and clean up an oil spill. Issues concerning geographic isolation, weather conditions, body of water, and type of shoreline are all considered when creating a spill site scenario.

The remote location of an oil spill can present many logistical problems. Lodging, communication resources, and the ability to access an accident scene are all factors for consideration. Small communities may not have adequate facilities to shelter emergency response teams, nor adequate telephone lines and radio transmitters to handle the sudden increase in long distance correspondence. In some cases, cleanup equipment will have to be moved over great distances to reach a spill site. Large planes carrying this equipment may not be able to land at the nearest airstrip.

In addition, most spill response equipment and materials are greatly affected by such factors as shifting tides, water currents, wind, and environmental factors, including water salinity and temperature. (For example, biodegradation and dispersing agents both tend to work best in warm water environments, and moderate wave activity can enhance the effectiveness of gelling agents.) Standing water such as marshes or swamps with little water movement are likely to incur more severe impacts than flowing water because spilled oil tends to "pool" in the water and can remain there for long periods of time. In calm water conditions, the affected habitat may take years to restore. Flowing water is less impacted by oil spills than standing water because the currents provide a natural cleaning mechanism.

Surrounding habitats and type of shoreline must also be considered when determining the best form of response. Biological communities differ in their sensitivity to the effects of oil spills and the physical intrusion that may be associated with various cleanup methods. Spilled oil and cleanup operations can threaten different types of marine habitats, with different results.

## Geology of Shorelines

Shorelines can vary dramatically in their forms and compositions. Some shorelines are narrow with beaches formed from rounded or flattened cobbles and pebbles; some are wide and covered in a layer of sand or broken shell fragments; and still others are steep cliffs with no beach at all. The composition and structure of the beach will determine the potential effects of oil on the shoreline.

Oil tends to stick to mud and to the surfaces of cobbles and pebbles. It also flows downward in the spaces between cobbles, pebbles, and sand grains, and accumulates in lower layers of beach sediments. Oil that sticks to mud particles suspended in the water column and to cobbles and pebbles on the beach is exposed to the action of sunlight and waves, which helps it to degrade and makes it less hazardous to organisms that come into contact with it. Oil that sticks to rocks and pebbles can be wiped or washed off. Oil that flows onto sandy beaches, however, can "escape" downward into sand, making it difficult to clean up and reducing its ability to degrade.

Natural processes such as evaporation, oxidation, and biodegradation help to clean the shoreline. Physical methods such as wiping with sorbent materials, pressure washing, raking, and bulldozing can be used to assist these natural processes. Choosing the most effective yet potentially least damaging cleaning methods helps to assure that the natural systems of shorelines will be preserved and protected for future generations.

Glossary


text:
## 66.52 Zariski's Main Theorem (representable case)

This is the version you can prove using that normalization commutes with étale localization. Before we can prove more powerful versions (for non-representable morphisms) we need to develop more tools. See More on Morphisms of Spaces, Section 75.34.

Lemma 66.52.1. Let $S$ be a scheme. Let $f : X \to Y$ be a morphism of algebraic spaces over $S$ which is representable, of finite type, and separated. Let $Y'$ be the normalization of $Y$ in $X$. Picture:

$\xymatrix{ X \ar[rd]_ f \ar[rr]_{f'} & & Y' \ar[ld]^\nu \\ & Y & }$

Then there exists an open subspace $U' \subset Y'$ such that

1. $(f')^{-1}(U') \to U'$ is an isomorphism, and

2. $(f')^{-1}(U') \subset X$ is the set of points at which $f$ is quasi-finite.

Proof. Let $W \to Y$ be a surjective étale morphism where $W$ is a scheme. Then $W \times _ Y X$ is a scheme as well. By Lemma 66.48.4 the algebraic space $W \times _ Y Y'$ is representable and is the normalization of the scheme $W$ in the scheme $W \times _ Y X$. Picture

$\xymatrix{ W \times _ Y X \ar[rd]_{(1, f)} \ar[rr]_{(1, f')} & & W \times _ Y Y' \ar[ld]^{(1, \nu )} \\ & W & }$

By More on Morphisms, Lemma 37.43.1 the result of the lemma holds over $W$. Let $V' \subset W \times _ Y Y'$ be the open subscheme such that

1. $(1, f')^{-1}(V') \to V'$ is an isomorphism, and

2. $(1, f')^{-1}(V') \subset W \times _ Y X$ is the set of points at which $(1, f)$ is quasi-finite.

By Lemma 66.34.7 there is a maximal open set of points $U \subset X$ where $f$ is quasi-finite and $W \times _ Y U = (1, f')^{-1}(V')$. The morphism $f'|_ U : U \to Y'$ is an open immersion by Lemma 66.12.1 as its base change to $W$ is the isomorphism $(1, f')^{-1}(V') \to V'$ followed by the open immersion $V' \to W \times _ Y Y'$. Setting $U' = \mathop{\mathrm{Im}}(U \to Y')$ finishes the proof (omitted: the verification that $(f')^{-1}(U') = U$). $\square$

In the following lemma we can drop the assumption of being representable as we've shown that a locally quasi-finite separated morphism is representable.

Lemma 66.52.2. Let $S$ be a scheme. Let $f : X \to Y$ be a morphism of algebraic spaces over $S$. Assume $f$ is quasi-finite and separated. Let $Y'$ be the normalization of $Y$ in $X$. Picture:

$\xymatrix{ X \ar[rd]_ f \ar[rr]_{f'} & & Y' \ar[ld]^\nu \\ & Y & }$

Then $f'$ is a quasi-compact open immersion and $\nu$ is integral. In particular $f$ is quasi-affine.

Proof. By Lemma 66.51.1 the morphism $f$ is representable. Hence we may apply Lemma 66.52.1. Thus there exists an open subspace $U' \subset Y'$ such that $(f')^{-1}(U') = X$ (!) and $X \to U'$ is an isomorphism! In other words, $f'$ is an open immersion. Note that $f'$ is quasi-compact as $f$ is quasi-compact and $\nu : Y' \to Y$ is separated (Lemma 66.8.9). Hence for every affine scheme $Z$ and morphism $Z \to Y$ the fibre product $Z \times _ Y X$ is a quasi-compact open subscheme of the affine scheme $Z \times _ Y Y'$. Hence $f$ is quasi-affine by definition. $\square$

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# Both x and y are positive integers.

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Kudos [?]: 1199 [0], given: 5712

Both x and y are positive integers. [#permalink]  09 Apr 2019, 02:16
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Both x and y are positive integers. If $$x^2 + 2xy + y^2 = 49$$ and x$$^2 − y^2 = − 7$$, then $$y =$$

A. 2

B. 3

C. 4

D. 5

E. 7
[Reveal] Spoiler: OA

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Kudos [?]: 1575 [1] , given: 8

Re: Both x and y are positive integers. [#permalink]  09 Apr 2019, 06:26
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Expert's post
Carcass wrote:
Both x and y are positive integers. If $$x^2 + 2xy + y^2 = 49$$ and x$$^2 − y^2 = − 7$$, then $$y =$$

A. 2

B. 3

C. 4

D. 5

E. 7

GIVEN: x² + 2xy + y² = 49
Factor to get: (x + y)² = 49

So, EITHER x + y = 7 OR x + y = -7
Since we're told that x and y are POSITIVE, we can be certain that x + y = 7

GIVEN: x² - y² = −7
Factor to get: (x + y)(x - y) = -7
Replace x + y with 7 to get: (7)(x - y) = -7
So, we know that x - y = -1

We now have two linear equations:
x + y = 7
x - y = -1

Subtract the BOTTOM equation from the TOP equation to get: 2y = 8
Solve: y = 8/2 = 4

Cheers,
Brent
_________________

Brent Hanneson – Creator of greenlighttestprep.com

Re: Both x and y are positive integers.   [#permalink] 09 Apr 2019, 06:26
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text:
The pool() function combines the estimates from m repeated complete data analyses. The typical sequence of steps to do a multiple imputation analysis is:

1. Impute the missing data by the mice function, resulting in a multiple imputed data set (class mids);

2. Fit the model of interest (scientific model) on each imputed data set by the with() function, resulting an object of class mira;

3. Pool the estimates from each model into a single set of estimates and standard errors, resulting is an object of class mipo;

4. Optionally, compare pooled estimates from different scientific models by the D1() or D3() functions.

A common error is to reverse steps 2 and 3, i.e., to pool the multiply-imputed data instead of the estimates. Doing so may severely bias the estimates of scientific interest and yield incorrect statistical intervals and p-values. The pool() function will detect this case.

pool(object, dfcom = NULL)

## Arguments

object An object of class mira (produced by with.mids() or as.mira()), or a list with model fits. A positive number representing the degrees of freedom in the complete-data analysis. The default (dfcom = NULL) is to extract this information from the first fitted model. When that fails the warning "Large sample assumed" is printed, and the parameter is set dfcom = 999999. Use the dfcom parameter to specify the correct degrees of freedom.

## Value

An object of class mipo, which stands for 'multiple imputation pooled outcome'.

## Details

The pool() function averages the estimates of the complete data model, computes the total variance over the repeated analyses by Rubin's rules (Rubin, 1987, p. 76), and computes the following diagnostic statistics per estimate:

1. Relative increase in variance due to nonresponse r;

2. Residual degrees of freedom for hypothesis testing df;

3. Proportion of total variance due to missingness lambda;

4. Fraction of missing information fmi.

The function requires the following input from each fitted model:

1. the estimates of the model, usually obtainable by coef()

2. the standard error of each estimate;

3. the residual degrees of freedom of the model.

The pool() function relies on the broom::tidy and broom::glance function for extracting this information from a list of fitted models.

The degrees of freedom calculation uses the Barnard-Rubin adjustment for small samples (Barnard and Rubin, 1999).

## References

Barnard, J. and Rubin, D.B. (1999). Small sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955.

Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.

van Buuren S and Groothuis-Oudshoorn K (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. https://www.jstatsoft.org/v45/i03/

with.mids, as.mira, glance, tidy

## Examples

# pool using the classic MICE workflow
imp <- mice(nhanes, maxit = 2, m = 2)#>
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chlfit <- with(data = imp, exp = lm(bmi ~ hyp + chl))
summary(pool(fit))#>          term   estimate  std.error  statistic        df      p.value
#> 1 (Intercept) 21.3937741 4.22217002  5.0670091 17.192034 9.211353e-05
#> 2         hyp -1.4382464 2.56874915 -0.5599014  4.912371 6.001139e-01
#> 3         chl  0.0346279 0.02516489  1.3760400  6.688939 2.131088e-01


text:
Interacting with the file system is a common task when you are doing any kind of system programming with Python. A common sub-task in this is to iterate over files and folders in a directory in the file system.

## The os.walk function

Python provides the excellent os.walk directory and file iterator function for this purpose. This function returns a generator that allows you to walk over the files and folders starting from a given directory in an iterative fashion.

For example, this is how you can iterate over the list of mp3 music files say in your $HOME/Music folder and perform some action, in this case, just count them. import os def count_mp3(top='~/Music'): count = 0 for root,dirs,files in os.walk(os.path.expanduser(top)): for f in files: if f.lower().endswith('.mp3'): count += 1 print('Found {} mp3 files in {}'.format(count, top)) return count  And this is how you can generalize this using the fnmatch module say for any type of file. import os import fnmatch def count_files(top, pattern): count = 0 for root,dirs,files in os.walk(os.path.expanduser(top)): count += len(fnmatch.filter(files, pattern)) print('Found {} files in {} matching {}'.format(count, top, pattern)) return count  The fnmatch module provides Unix style file matching in Python, so this is how you can call the above function. >>> count_files('~/Videos', '*.mp4') Found 118 files in ~/Videos matching *.mp4 118  ## The find command If you are familiar with Linux or any other *nix system, you must be used to the very versatile find command. This command allows you to search for files matching a variety of conditions. For example, the command matching our mp4 video file counter using find is, $ find ~/Videos -name \*.mp4 | wc -l
118

However find can do much more than filter files by their extensions. Here are some examples.

Find filenames greater than a given size (Say 5MB from your ~/Pictures folder)

$find ~/Pictures -type f -size +5M ...  Find filenames smaller than a given size. $ find ~/Pictures -type f -size -5M
...

Find files modified in the last n days (Say 10 days from your ~/Pictures folder)

\$ find ~/Pictures -type f -mtime -10
...

Can we write code in Python similar to the find program which allows you to filter files from a folder which satisfies a set of conditions without having to write a new function every time ?

That is the focus of this post.

## Composing functions - Predicate based filtering

The problem with os.walk is that it is a general purpose iterator. It does not accept any predicate (a filter function) as argument which allows one to pass in a condition as a function to it, to customize the directories/files it generates.

How difficult it is to write one ?

As it turns out this is not a very difficult thing to do. Here is a function which returns a generator over files in a directory named top which matches a given predicate. Since it generates only a list of files (with full pathnames) I am calling it as fwalk.

def fwalk(top, predicate=None):
""" Generator that filters results of os.walk
using a predicate function """

for root,dirs,files in os.walk(os.path.expanduser(os.path.expandvars(top))):
for f in files:
cond = (predicate == None) or predicate(root, f)
if cond:
yield os.path.join(root, f)

The idea here is simple. We pass in a predicate function - think of it as filter functions that return either True or False - to fwalk and it generates only those file paths which match the predicate. The predicate function accepts a tuple of (directory, filename) pair.

The fwalk function allows one to create new functions by composing predicate functions and layering their logic on top to achieve the final logic we want.

Here are some examples, which repiclates what find did in the section above.

1. Finding a list of mp4 files in the ~/Videos folder.

>>> fwalk('~/Videos',predicate=lambda x,y: fnmatch.fnmatch(y, '*.mp4'))
<generator object fwalk at 0x7fcf43cdf891>
>>> len(list(fwalk('~/Videos',predicate=lambda x,y: fnmatch.fnmatch(y, '*.mp4'))))
118

2. Find pictures of size > 5MB in the ~/Pictures folder.

>>> predicate = lambda x,y: os.path.getsize(os.path.join(x,y)) > 5*1024**2
>>> fwalk('~/Pictures', predicate=predicate)
<generator object fwalk at 0x7fcf43ef7c78>
>>> len(list(_))
492

3. Find pictures of size < 5MB in the ~/Pictures folder.

>>> predicate = lambda x,y: os.path.getsize(os.path.join(x,y)) < 5*1024**2
>>> fwalk('~/Pictures', predicate=predicate)
<generator object fwalk at 0x7fcf43ef7b88>
>>> list(_)
['/home/anand/Pictures/skeptichacker_old.png', '/home/anand/Pictures/pp.jpeg',
...
...
...
]

## Adding flexibility - Combining Predicates

It does not take much effort to extend the above fwalk function to a more generic use-case which accepts a set of predicate functions than a single one. Here is the rewritten function.

def fwalk(top, predicates=[]):
""" Generator that filters results of os.walk using a set of predicate functions """

for root,dirs,files in os.walk(os.path.expanduser(os.path.expandvars(top))):
for f in files:
cond = all(pred(root, f) for pred in predicates)
if cond:
yield os.path.join(root, f)

How does this work ?

As you may have noted, the function accepts a list of predicates and only generates those filepaths which match all the predicates. It makes use of Python’s wonderful all built-in function to do this.

With this function as a tool, we can now start building a generic find like function in Python which can be extended with any set of predicate functions to give us a general purpose, customizable directory iterator in Python.

May I present to you the find function without much ado!

import re

def find(folder, **kwargs):
""" General purpose find emulator using Python """

# Built-in predicates we support
# any pattern using keyword 'pattern'
# max size using keyword 'max_size'
# min size using keyword 'min_size'

pdict = {'pattern':  lambda x, y: re.search(re.escape(kwargs['pattern']), os.path.join(x,y), re.IGNORECASE),
'min_size': lambda x, y: os.path.getsize(os.path.join(x,y)) > kwargs['min_size'],
'max_size': lambda x, y: os.path.getsize(os.path.join(x,y)) < kwargs['max_size']}

predicates = []

for k in kwargs:
if k in pdict:
predicates.append(pdict[k])

return fwalk(folder, predicates)

NOTE: Note how we are using re module for general purpose string matching instead of fnmatch.

How does this work ?

Our find function supports some built-in predicate functions using its pdict dictionary. The functions are defined using specific keywords as keys in the dictionary. We support the following keys namely pattern, min_size and max_size. The values are anonymous functions defined using lambda and accepting a pair of (x,y) arguments where (x,y) stands for the (directory, filename) pair.

When someone calls the function using one or more of the keys as optional argument, we use the value of the key to build out the predicate function. This is then appended to the list of predicates. Finally fwalk is called using the built predicate list and the result is returned.

Here is our find in action.

## Generator for files matching my vacation pics in Hawaii!
>>> find('~/Pictures', pattern='hawaii')
...
## Generator for Videos above 1G in size
>>> find('~/Videos', min_size=1024**3)
...
## Combine both
## Videos of vacation in hawaii which are over 1G in size
>>> find('~/Videos', min_size=1024**3, pattern='hawaii')
...
## Pictures which are in the size range 1 to 2 MB
>>> find('~/Pictures', min_size=1024**2, max_size = 2*1024**2)
...

The find function demonstrates the power of Python’s keyword based functions on top of the ability to compose functions giving the programmer a lot of flexibility by dynamically composing functions and layering logic. This is a very good example of functional programming in Python utilized to its fullest extent.

Our find function is extensible by easily adding new predicates in its pdict .

If you understood the code in this post, you can take it upon as an exercise to add predicates for filtering files based on modification and/or creation times. You will need to make use of the stat module in Python for this purpose.


text:
# How to Mute Animation Nodes Subprogram

Just wonder whether subprograms can be totally muted in ways other than just deleting it. I ask bacause:

For example, I tried to setup a loop in another nodetree, Then disable auto execution for that nodetree. However, it can still be invoked in other nodetrees, because subprograms seem to be kinda "global" data.

In some cases I want to mute the subprogram to improve overall performance by disabling the execution. Is that helpful? Can I do that? Or other better solutions? Thanks!

• I think there is no good solution to this yet.. I'm thinking about an additional boolean input in the Invoke Subprogram node that tells the node wether to execute the subprogram or not. Will see. – Jacques Lucke Nov 2 '16 at 14:33
• Oh I bet that can be helpful especially for complex setups, you know, I mean those with hundreds of nodes, though not sure whether it can make enough sense for a performance leap. – Leon Cheung Nov 2 '16 at 15:42
• I thought it might have been possible with the "loop break", but I just tried it out and it does not cause the loop to stop. Which makes me wonder: what is the "loop break" node for and how it is used? but i"ll open another question for that :) – Kristof Meirlaen Nov 2 '16 at 23:09
• @KristofMeirlaen There was a discussion on loop break between Jacques and me. FYI – Leon Cheung Nov 3 '16 at 0:53

EDIT: New Version

Same Principle, but how about ditching additional Subprogram and using Get List Element instead:

Old Version

I put actual Subprogram(red) inside another one (blue) as a Parameter to get ability to dynamically change number of iterations. Condition is controling execution of blue Subprogram

New:

Old:

• 0 iteration, interesting, quite dirty huh. X-D @JacquesLucke Your opinion here? – Leon Cheung Jan 29 at 16:04
• Tried to combine both Subprograms inside one – Serge L Jan 30 at 11:18


text:
# Information Theory III — Information for Prediction

Lecture 17 (23 October 2019)

$\newcommand{\Expect}[1]{\mathbb{E}\left[ #1 \right]}$

# Information for Prediction

• We’ve seen the basic ideas of information theory:
• How much information/uncertainty is in one random variable ($$H[X]$$)
• How much information/uncertainty is in two RVs ($$H[X,Y]$$)
• How much information/uncertainty is left after conditioning ($$H[Y|X]$$)
• How much is uncertainty reduced by conditioning ($$H[Y] - H[X|Y]$$)
• How much information does one variable give about another ($$I[X;Y]=H[Y]-H[X|Y]$$)
• And conditional versions of all these
• We’ve seen how to use this for feature selection
• Rank features $$X_1, \ldots X_p$$ by $$H[Y|X_i]$$ or $$I[X_i;Y]$$
• Picking collects of features by evaluating $$I[Y; X_1, \ldots X_q]$$
• Now: What would success look like?
• Good features
• Good synthetic features

# Predictive Information

• We observe $$X$$ (possibly multivariate) and want to predict $$Y$$
• Conditioning on $$X$$ gives us some amount of information about $$Y$$ $I[X;Y] = H[Y] - H[Y|X]$
• Our prediction is a function of $$X$$, say $$\hat{Y}(X)$$
• Conditioning on $$\hat{Y}$$ never gives us more information: $I[\hat{Y};Y] = H[Y] - H[Y|\hat{Y}] \leq I[X;Y]$
• Because: (i) Conditioning never increases entropy, $$H[Y|X, f(X)] \leq H[Y|f(X)]$$
• But (ii) Conditioning on $$X$$ and $$f(X)$$ is the same as conditioning on $$X$$, so $$H[Y|X, f(X)] = H[Y|X]$$
• So $$H[Y|f(X)] \geq H[Y|f(X), X] = H[Y|X]$$
• This is (one form of) the data-processing inequality
• $$I[X;Y]$$ is the predictive information $$X$$ has about $$Y$$ and limits how good any prediction using $$X$$ can be

# Predictive Information vs. Accuracy

• For discrete $$Y$$, say $$M$$ (for “mistake”) $$=1$$ if $$Y\neq \hat{Y}$$ and $$=0$$ if $$Y=\hat{Y}$$
• Then one can show (see backup) that $H[Y|X] \leq H[M] + \Pr(M=1)\log{(|\mathcal{Y}|-1)}$
• Perfect classification ($$\Pr(M=1) = 0$$) implies $$H[Y|X] = 0$$
• RHS is an increasing function of $$\Pr(M=1)$$ so higher $$H[Y|X] \Rightarrow$$ higher inaccuracy, no matter what method we use

# Predictive Information vs. Accuracy

• Upshot: $$H[Y|X]$$ lower bounds classification accuracy
• Even if we just care about accuracy, we should want to maximize information!

# Reducing the Features

• Suppose we look not at $$X$$ but $$T=\tau(X)$$
• Could be just picking out some dimensions of a multivariate $$X$$
• Could be applying transformations
• Could be creating new features out of old ones (as in PCA)
• Data-processing inequality says: $I[T;Y] \leq I[X;Y]$
• When will $$I[T;Y] = I[X;Y]$$?

# Sufficiency

• Re-write mutual informations: $I[X;Y] = \sum_{x}{p(x) \sum_{y}{p(y|x) \log{\frac{p(y|x)}{p(y)}}}}$ and $I[T;Y] = \sum_{t}{p(t) \sum_{y}{p(y|t)\log{\frac{p(y|t)}{p(y)}}}}$
• Suppose that $$\tau(x) = \tau(x^{\prime}) \Rightarrow p(y|x) = p(y|x^{\prime})$$
• Then for any $$x$$ with $$\tau(x) = t$$, $p(y|t) = p(y|x)$
• In more symbols, if $$x\in\tau^{-1}(t)$$ then $$p(y|t) = p(y|x)$$
• Why is this true?
• Use this to group terms in the $$x$$ sum for $$I[X;Y]$$: $\begin{eqnarray} I[X;Y] & = & \sum_{x}{p(x) \sum_{y}{p(y|x) \log{\frac{p(y|x)}{p(y)}}}}\\ & = & \sum_{t}{\sum_{x \in \tau^{-1}(t)}{p(x)\sum_{y}{p(y|x)\log{\frac{p(y|x)}{p(y)}}}}}\\ & = & \sum_{t}{\sum_{x \in \tau^{-1}(t)}{p(x) \sum_{y}{p(y|t)\log{\frac{p(y|t)}{p(y)}}}}}\\ & = & \sum_{t}{p(t) \sum_{y}{p(y|t)\log{\frac{p(y|t)}{p(y)}}}}\\ & = & I[T;Y] \end{eqnarray}$
• We say that $$T=\tau(X)$$ is sufficient for predicting $$Y$$ (from $$X$$)

# Sufficiency cont’d.

• Suppose that $\rho(x) = \rho(x^{\prime}) ~ \Leftrightarrow ~ p(y|x) = p(y|x^{\prime})$
• Then $$R=\rho(X)$$ will be sufficient
• And for any other sufficient $$T$$, $$R=g(T)$$ for some function $$g$$
• $$H[R] \leq H[T]$$ for any other sufficient $$T$$
• $$R$$ is minimal sufficient
• The way $$\rho$$ divides up $$X$$ is a statistical relevance basis (for predicting $$Y$$) (Salmon 1971, 1984; Shalizi and Crutchfield 2002)
• “Distinctions that make a difference”

# From Sufficiency to the Information Bottleneck

• We can’t compress below $$R$$ without losing some predictive information
• What if we’re willing to give up some predictive information?
• Pick $$\beta > 0$$ and do $\max_{\tau}{I[Y;\tau(X)] - \beta H[\tau(X)]}$ or $\max_{\tau}{I[Y;\tau(X)] - \beta I[\tau(X);X]}$
• In words: Our benefit is the predictive information, our cost is the memory of the features
• $$\beta$$ is the price at which we trade predictive information against memory
• Lagrange multipliers: equivalent to maximizing $$I[Y;\tau(X)]$$ with a constraint on $$I[\tau(X);X]$$
• $$T=\tau(X)$$ is called the bottleneck variable
• Searching for this optimal $$\tau$$ (given $$\beta$$) is the information-bottleneck method (Tishby, Pereira, and Bialek 1999)

# Why This Matters

• Sometimes, we can explicitly work out the bottleneck or even the sufficient statistic
• Sometimes, it gives us a benchmark to evaluate against
• What’s $$I[Y;T]$$ for our favorite transformation of $$T$$ of the features?
• What’s $$H[T]$$ as compared to $$H[X]$$? How much information loss are we tolerating to get that compression?
• Sometimes it just inspires how we select features, or synthesize new features

# Dimension Reduction with a Target Variable

• Start with $$p$$-dimensional feature vector $$X$$
• Consider functions $$\tau$$ which map $$X$$ down to $$q$$-dimensional vectors $$T$$
• Maybe constrained, e.g., only functions linear in $$X$$
• Still want to maximize $$I[Y;T]$$, or maybe a bottlenecked version
• If calculating $$I[Y;T]$$ is too hard, or not quite right for the job, look at prediction error

# Dimension Reduction without a Target Variable

• Start with $$p$$-dimensional feature vector $$X$$
• Consider functions $$\tau$$ which map $$X$$ down to $$q$$-dimensional vectors $$T$$
• Think about how we’d reconstruct $$X$$ from $$T$$, $$\hat{X}(T)$$
• Maximize $$I[X;\hat{X}(T)]$$ with constraint on $$I[T;X]$$
• Again, can swap in minimizing prediction error if you want
• This is basically what we did in PCA!

# word2vec

• Start with words being binary features, $$p\approx$$ number of entries in the dictionary
• i.e., which word do we see at this position?
• Try to predict this word from neighboring words
• A huge problem!
• Map each word in to a vector of dimension $$q \ll p$$, maybe say $$q=700$$
• Adjust the mapping of words to vectors to maximize predictive information
• Experimentally, works better than PCA on the bag-of-word vectors
• But a lot more costly computationally (and in actual \$)

# Clustering

• Start with high-dimensional feature vectors $$X$$
• Now map them to a discrete set of categories, say $$k$$ of them
• $$I[\tau(X);X] \leq \log{k}$$ (why?)
• Try to maximize $$I[X;\hat{X}(T)]$$
• Or, if that’s too hard, some measure of the error of recovering $$X$$ from $$T$$
• This is clustering, and we’ll look at it for the next few lessons

# Summing up

• $$I[X;Y]$$ tells us about how well any method using $$X$$ can predict $$Y$$
• Sufficient statistics maximally compress $$X$$ without losing predictive information
• The information bottleneck method tells us about how to trade off compression against predictive information
• Dimension reduction $$\approx$$ looking for a continuous bottleneck
• Clustering $$\approx$$ looking for a discrete bottleneck

# Backup: Information for Continuous Variables

Replace sums with integrals as needed.

• $$X$$ and $$Y$$ both continuous: $\begin{eqnarray} I[X;Y] & = & \int{p(x,y) \left(\log{\left(\frac{p(x,y)}{p(x)p(y)}\right)}\right) dx dy}\\ & = & \int{p(x) \left(\int{p(y|x) \log{\left(\frac{p(y|x)}{p(y)}\right)} dy}\right) dx} \end{eqnarray}$ with $$p$$ being the pdf everywhere

• $$X$$ continuous, $$Y$$ discrete: $I[X;Y] = \int{p(x) \left(\sum_{y}{p(y|x) \log{\left(\frac{p(y|x)}{p(y)}\right)}}\right) dx}$ with $$p(x)$$ being the pdf but $$p(y|x)$$, $$p(y)$$ being the (conditional) pmf

• In any case:
• $$I[X;Y] = I[Y;X]$$
• $$I[X;Y] \geq 0$$
• $$I[X;Y] = 0$$ iff $$X$$ and $$Y$$ are independent
• $$I[f(X);Y] \leq I[X;Y]$$, with equality iff $$f(x) = f(x^{\prime}) \Rightarrow p(y|x) = p(y|x^{\prime})$$ (i.e., sufficiency)

# Backup: Conditional entropy and classification accuracy

$\begin{eqnarray} H[M|Y,\hat{Y}] & = & 0\\ H[Y|\hat{Y}] & = & H[Y|\hat{Y}] + H[M|Y,\hat{Y}]\\ & = & H[Y,M|\hat{Y}]\\ & = & H[M|\hat{Y}] + H[Y|M,\hat{Y}]\\ & = & H[M|\hat{Y}] + \Pr(M=0)H[Y|M=0,\hat{Y}] + \Pr(M=1)H[Y|M=1,\hat{Y}]\\ & \leq & H[M] + \Pr(M=0)H[Y|M=0,\hat{Y}] + \Pr(M=1)H[Y|M=1,\hat{Y}]\\ & = & H[M] + \Pr(M=0)\cdot 0 + \Pr(M=1) H[Y|M=1,\hat{Y}]\\ & = & H[M] + \Pr(M=1) H[Y|M=1,\hat{Y}]\\ & \leq & H[M] + \Pr(M=1)\log{(|\mathcal{Y}|-1)} \end{eqnarray}$

because if $$M=1$$, we know $$Y\neq \hat{Y}$$ and there are at most $$|\mathcal{Y}|-1$$ values $$Y$$ could have

Finally, remember that $$H[Y|\hat{Y}(X)] \geq H[Y|X]$$

This result is called Fano’s inequality

• Originally about recovering the true message ($$Y$$) from a noisy signal ($$X$$)
• For $$Y$$ uniformly distributed on $$\mathcal{Y}$$, Fano’s inequality implies $\Pr(M=1) \geq 1 - \frac{I[X;Y] + \log{2}}{\log{|\mathcal{Y}|}}$
• (Can you show this?)
• Fano’s inequality out to have many, many uses in prediction and estimation (Scarlett and Cevher, n.d.)
• Prediction: Obvious
• Estimation: Think of the parameter as the message ($$Y$$) we’re trying to recover from the noisy signal of the data set ($$X$$)

# Backup: Sufficiency and the Bottleneck, Take 2 (Shalizi and Crutchfield 2002)

• Initial bottleneck problem: $\max_{\tau}{I[Y;\tau(X)] - \beta H[\tau(X)]}$
• Willing to give up $$1$$ bit of predictive information if it saves at least $$\beta$$ bits of memory
• Lagrange multiplier form: $\max_{\tau}{I[Y;\tau(X)]} ~ \mathrm{subject\ to}~ H[\tau(X)] \leq c$
• Equivalently: compressing by 1 bit is only worthwhile if it reduces predictive information by $$\leq 1/\beta$$ bits $\min_{\tau}{H[\tau(X)] - (1/\beta) I[Y;\tau(X)]}$
• As $$\beta \rightarrow\infty$$, we become less and less willing to give up any predictive information
• Lagrange-multiplier form: $\min_{\tau}{H[\tau(X)]} ~ \mathrm{subject\ to} ~ I[Y;\tau(X)] \geq c^{\prime}$
• The limit $$\beta=\infty$$ is the minimal sufficient statistic / statistical relevance basis: $\min_{\tau}{H[\tau(X)]} ~\mathrm{subject\ to} ~ I[\tau(X);Y]=I[X;Y]$

# Backup: word2vec in a little more detail

• Each word $$w$$ corresponds to a vector $$v_w$$
• Each “context” = window of $$k$$ words around the focal word corresponds to a vector $$v_c$$
• Try to maximize $\max_{v_c, v_d}{\sum_{\mathrm{word}~ w ~\mathrm{appears\ in\ context} ~c}{\log{\frac{e^{v_c \cdot v_w}}{\sum_{c^{\prime}}{e^{v_{c^{\prime}} \cdot v_w}}}}}}$
• Words which appear in similar contexts should get similar vectors
• The actual maximization is too hard, so the word2vec software does something easier but related (Goldberg and Levy 2014)
• … which turns out to be (implicitly) factorizing a matrix of pointwise mutual informations between words and contexts (Levy and Goldberg 2014)

# References

Goldberg, Yoav, and Omer Levy. 2014. “word2vec Explained: Deriving Mikolov et Al.’s Negative-Sampling Word Embedding Method.” Electronic preprint, arxiv:1402.3722. https://arxiv.org/abs/1402.3722.

Levy, Omer, and Yoav Goldberg. 2014. “Neural Word Embedding as Implicit Matrix Factorization.” In Advances in Neural Information Processing Systems 27 [Nips 2014], edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 2177–85. Curran Associates. http://papers.nips.cc/paper/5477-neural-word-embedding-as.

Salmon, Wesley C. 1971. Statistical Explanation and Statistical Relevance. Pittsburgh: University of Pittsburgh Press.

———. 1984. Scientific Explanation and the Causal Structure of the World. Princeton: Princeton University Press.

Scarlett, Jonathan, and Volkan Cevher. n.d. “An Introductory Guide to Fano’s Inequality with Applications in Statistical Estimation.” In Information-Theoretic Methods in Data Science, edited by Yonina Eldar and Miguel Rodrigues. Cambridge, England: Cambridge University Press. https://arxiv.org/abs/1901.00555.

Shalizi, Cosma Rohilla, and James P. Crutchfield. 2002. “Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction.” Advances in Complex Systems 5:91–95. http://arxiv.org/abs/nlin.AO/0006025.

Tishby, Naftali, Fernando C. Pereira, and William Bialek. 1999. “The Information Bottleneck Method.” In Proceedings of the 37th Annual Allerton Conference on Communication, Control and Computing, edited by B. Hajek and R. S. Sreenivas, 368–77. Urbana, Illinois: University of Illinois Press. http://arxiv.org/abs/physics/0004057.


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# Electrical control of surface acoustic waves

## Abstract

Acoustic waves at microwave frequencies are widely used in wireless communication and are potential information carriers in quantum applications. However, most acoustic devices are passive components, and the development of phononic integrated circuits is limited by the inability to control acoustic waves in a low-loss, scalable manner. Here we report the electrical control of gigahertz travelling acoustic waves at room temperature and millikelvin temperatures. We achieve phase modulation by tuning the elasticity of a lithium niobate acoustic waveguide via the electro-acoustic effect. This phase modulator is then used to build an acoustic frequency shifter based on serrodyne phase modulation, and phase modulators in a Mach–Zehnder interferometer configuration are used to create an electro-acoustic amplitude modulator. By tailoring the phase matching between acoustic and quasi-travelling electric fields, we achieve reconfigurable non-reciprocal modulation with a non-reciprocity of over 40 dB. To illustrate the potential of the approach in quantum applications, we show that our electro-acoustic modulator can provide coherent modulation of single-phonon-level acoustic waves at 50 mK.

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## Data availability

Source data are provided with the paper. Other data that support the findings of this study are available from the corresponding authors upon reasonable request.

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## Acknowledgements

We thank C. Wang and C. Chia for fruitful discussion. This work is supported by the US Navy Office of Naval Research (ONR) QOMAND grant no. N00014-15-1-2761, DOE HEADS-QON grant no. DE-SC0020376, National Science Foundation (NSF) grant no. DMR-2004536, the Welch Foundation Grant F-1814 and NSF RAISE/TAQS grant no. NSF ECCS-1839197. N.S. is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the AQT Intelligent Quantum Networks and Technologies (INQNET) research programme. D.Z. is supported by the Harvard Quantum Initiative (HQI) postdoctoral fellowship and A*STAR Science and Engineering Research Council (SERC) Central Research Fund (CRF). L.S. is supported by the Virginia Tech Foundation.

## Author information

Authors

### Contributions

L.S.: conceptualization, methodology, investigation, formal analysis, visualization, writing (original draft). D.Z.: methodology, investigation, writing (original draft). M.C.: investigation, writing (review and editing). D.L.: investigation, writing (review and editing). N.S.: methodology, investigation, writing (original draft). Y.H.: writing (review and editing). P.T.R.: writing (review and editing). K.L.: resources, methodology, writing (review and editing, and supervision). K.K.B.: resources, writing (review and editing, and supervision). M.L.: resources, writing (review and editing, and supervision).

### Corresponding authors

Correspondence to Linbo Shao or Marko Lončar.

## Ethics declarations

### Competing interests

M.L. is involved in developing LN technologies at HyperLight Corporation. President and Fellows of Harvard College has a patent pending (Application number: PCT/US21/60426) on the electro-acoustic modulators, in which M.L. and L.S. are listed as inventors. The other authors declare no competing interests.

## Peer review

### Peer review information

Nature Electronics thanks Anton Kockum and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Extended data

### Extended Data Fig. 1 Simulated acoustic phase velocities for varying directions on X-cut LN.

The direction is defined by the angle respective to the crystal Z axis. The electromechanical coupling coefficient $$k^2 = 2\;(v_o - v_m)/v_o$$, where vo and vm are the phase velocities when the top surface is free and electrically shorted, respectively. The direction of the waveguide used in our device is 30°, as indicated by the dash line.

### Extended Data Fig. 2 Measured transmission spectra of the acoustic modulator at temperatures of 300 and 1.3 K.

The results indicate a 25 dB improvement in peak transmission at low temperature. The frequency shift of the spectrum is due to the temperature dependent elasticity of LN.

### Extended Data Fig. 3 Measured Peak-to-peak acoustic phase changes due to sinusoidal modulating signals of varying peak-to-peak voltage (Vpp) at room and cryogenic temperatures.

The sinusoidal modulating signals are of the frequency fmod =10 kHz. Linear fits show Vπ of 53 V at room temperature (300 K) and 135 V at 1.3 K, respectively. The same device is measured as that in Fig. 2.

### Extended Data Fig. 4 Modulation bandwidth of the 1-cm-long electro-acoustic phase modulator.

The modulation efficiency is indicated by the first sideband power due to the phase modulation. The measured 3-dB bandwidth is 110 kHz. The modulation approaches zero at fmod = 336 kHz when the acoustic traveling time through the modulator equals 1/fmod. The same device is measured as that in Fig. 2.

### Extended Data Fig. 5 Fifty millikelvin measurement setup.

The electro-acoustic modulator is mounted on a mixing plate of a dilution fridge with a base temperature below 50 mK. The input microwave signal is provided by a signal generator and, to ensure negligible contribution of thermal noise, passed through attenuators at various temperature stages of the fridge before going into our device. The output microwave signal from the modulator passes through a circulator, a high-electron-mobility transistor (HEMT) amplifier at 4 K, two low-noise amplifiers at room temperature, and is finally detected by a real-time spectrum analyzer (RSA). The modulation signal is provided by a function generator. A controlled and thermally isolated thermal source, which consists of a heater, temperature sensor, and a 30 dB attenuator, is installed in the microwave line before our electro-acoustic modulator to calibrate the gain and added noise in the output/readout line.

### Extended Data Fig. 6 Noise measurement of the electro-acoustic modulator at 50 mK.

Total noise power spectrum density near fc when (1) no signal is applied to the electro-acoustic modulator (black), (2) only the carrier microwave signal is applied (blue), and (3) both the carrier microwave and modulation signals are applied (red). The total noise power $$N_{tot} = N_{dev} + N_{add}$$, where Ndev is the noise of the electro-acoustic modulator and Nadd is the added noise from the readout chain. Nadd is mainly determined by the high-electron-mobility transistor (HEMT) at the 4 K stage. The electro-acoustic modulation adds negligible noise and is thus suitable for quantum phononics. The same device is measured as that in Fig. 2. The error bars indicate the standard deviation of measured noise power spectrum density.

### Extended Data Fig. 7 Phase matching between a traveling acoustic wave and a quasi-traveling electric field.

The modulation sideband power is measured with varied modulation frequency and phase delay between the three electrodes. The maximum modulations that satisfy the phase matching condition are indicated by the red line. The condition for the maximum nonreciprocity in phase modulation is indicated by the black dots, as the counter propagating acoustic waves experience opposite phase delays compared to the propagating waves. The sideband power is normalized to the unmodulated carrier acoustic wave power. The measured date is from the same device as in Figs. 4C and 4D, which consists of three electrodes with overall modulation length of 1 cm.

## Supplementary information

### Supplementary Video 1

Principle of non-reciprocal acoustic phase modulation. The first (last) 30 s depict a wavefront of the acoustic wave as a blue (red) dot travelling in the forward (backward) direction. The top window illustrates three electrodes of the modulator (labelled as V1, V2 and V3) that have sinusoidal signals applied that are 120° out of phase with each other. The bottom window depicts the signals in the time domain, depicting the electrode regions for each signal. The middle window indicates the phase accumulation experienced by the wavefront as it propagates through the modulator.

## Source data

### Source Data Fig. 1

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### Source Data Fig. 2

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### Source Data Fig. 3

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### Source Data Fig. 4

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### Source Data Extended Data Fig. 1

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### Source Data Extended Data Fig. 2

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### Source Data Extended Data Fig. 3

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### Source Data Extended Data Fig. 4

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### Source Data Extended Data Fig. 6

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### Source Data Extended Data Fig. 7

Source data for plots.

## Rights and permissions

Reprints and Permissions

Shao, L., Zhu, D., Colangelo, M. et al. Electrical control of surface acoustic waves. Nat Electron 5, 348–355 (2022). https://doi.org/10.1038/s41928-022-00773-3

• Accepted:

• Published:

• Issue Date:

• DOI: https://doi.org/10.1038/s41928-022-00773-3

• ### Electrical control of quantum acoustics

• Anton Frisk Kockum

Nature Electronics (2022)