A new provision in the tax cut bill that is headed for a vote in the Senate could undercut the financing mechanism that has been a major driver in the rapid growth of renewable energy projects.
Four trade organizations representing a broad swath of the renewable energy industry sent an urgent letter to members of the Senate, asking them to reconsider the Base Erosion Anti-Abuse Tax (BEAT) provision, which they say could have a "devastating" effect on renewable energy projects.
The BEAT provision is designed to make it harder for corporations to dodge taxes, but it would also sweep up tax credits — such as the renewable energy Production Tax Credit (PTC) for wind power projects and the Investment Tax Credit (ITC) for solar projects — making the credits more difficult to monetize.
Microgrids have garnered renewed interest recently, but will these facilities become mainstream? Find out in the free ebook.
Until recently, the renewable energy industry had been hopeful that the Senate's tax cut bill would leave in place existing incentives for wind power.
The House's version of the tax cut bill makes changes in the calculation of the PTC that could "kill" over half of planned wind farms, Tom Kiernan, CEO of the American Wind Energy Association, has said.
Members of the renewable energy industry took some comfort in the prospect that the House provisions would never make it past the Senate, where there is strong support for wind power. Until last week.
It appears that the BEAT provision was inserted in a last minute mark-up of the Tax Cut and Jobs Act in the Senate finance committee on Thanksgiving eve.
The provision means that the Senate bill "keeps the credits alive, but eliminates their value," Greg Wetstone, president and CEO of the American Council on Renewable Energy, told Utility Dive.
Wetstone pointed out that the BEAT provision would affect $50 billion in annual investment in renewable energy projects. His organization is one of four that sent a letter to members of Senate. The others are the American Wind Energy Association, Citizens for Responsible Energy Solutions, and the Solar Energy Industries Association.
"We do not believe it fair or appropriate for Congress to reduce the value of tax incentives that have been relied upon in good faith by investors and developers," they wrote in the letter.
The BEAT provision targets "earnings stripping" — cross border payments multi-national companies make to overseas affiliates to reduce their tax bills. The provision aims to close that loophole by setting a minimum tax of 10% of taxable income.
The provision would require every company to quantify 10% of their taxable income, including cross-border payments and, in a second calculation, quantify their tax liability, excluding any tax credits. If the tax liability is less than the taxable income calculation, the government would collect the difference as a tax. The provision applies to all tax credits, except research and development credits.
The BEAT provision would make it harder for "banks and other large companies that are the principal source of tax equity for renewable energy to know, when closing on tax equity investments, whether they will receive the tax credits on offer for making the investments," Keith Martin, a partner with Norton Rose Fulbright, wrote in a blog post.
"Any large bank or insurance company in the tax equity market is potentially affected," Martin told Utility Dive. That could include JPMorgan, Bank of America, Citibank, Goldman Sachs, Morgan Stanley and foreign banks such as RBC, Credit Suisse and BNP.
Because the BEAT calculations would have to be performed every year, they would, in essence, be retroactive. Wind projects collect the PTC over a 10-year term. So, in any given year, a tax equity investor would not know the value of a tax credit until the end of the year.
Solar projects are often funded with the ITC, but the value of that credit would not be certain at the outset of the project — only at the end of the year in which construction is completed.
The BEAT tax would apply starting in 20118, but "credits would be clawed back on [PTC] deals closed as far back as 2008," Martin said.
Wetstone says the trade groups are advocating that the BEAT provision be revised so that the PTC and ITC are given the same treatment as R&D tax credits. That, the trade groups say, would restore the certainty that underpins the tax equity market and, by extension, the growth in renewable energy investment.
We bring homeowners together into a group, or co-op. We provide unbiased, installer neutral support to co-op participants through each state of the process of going solar. Our experienced team ensures you understand how solar works, how it can be financed, and how it can be installed on your home.
Co-ops take advantage of the group's bulk-purchasing power to get discounted pricing and a quality installation. Volunteer co-op members choose an installer on behalf of the entire group through an open and competitive bidding process. The selected installer provides co-op participants a personalized proposal for their consideration.
Watch the video below to learn about how solar co-ops work and what the benefits of going solar with our co-op, or view our FAQs.
The 51st State co-op will be open to all District residents, regardless of income levels. As a DOEE-funded Solar for All project, the co-op will focus on expanding access to solar for low-income households. Low to moderate income households may qualify for Solar for All grant funding from DOEE that Solar United Neighbors is administering to cover the cost of solar arrays. For qualifying households, the solar array paid for by Solar United Neighbor's Solar for All grant could reduce your electric bills by 50% over the course of a year.
The 51st State Co-op is open to all District residents.
You could qualify for a free solar installation under Solar United Neighbor of D.C.'s DOEE grant.
Can't make our info session?
With support from The Department of Energy and Environment (DOEE) Solar for All program, certain income qualified homeowners may be eligible for the installation of a solar system at no cost.
SunShot expects to make 7-10 awards under Solar Desalination, each ranging between $500,000 and $5 million for a total of $15 million. Prior to submitting a full application for this opportunity, a brief, mandatory concept paper is due on December 4, 2017. See all application deadlines in the table below.
NASA is currently accepting applications for Summer 2018 internships.
NASA's live-streamed Virtual Career Summit held Wednesday, September 27th is still available online for students interested in internships, fellowships and scholarships at the space agency. The NASA Internships, Fellowships and Scholarship program (NIFS) showcases some of its missions, programs, interns and mentors. Students can learn about the current available internship, fellowship and scholarship opportunities in the STEM (Sciences, Technology, Engineering and Mathematics) fields and others, and eligibility requirements, tips for the application process and hear from previous NIFS awardees. The virtual event engaged students and NASA professionals in a question and answer session recorded during the event and also available online where you can continue to ask questions.
The National Science Foundation launched the Historically Black Colleges and Universities – Excellence in Research Program (HBCU-EIR) as a track within the new HBCU-UP Solicitation NSF 18-522. In response to the Consolidated Appropriations Act of 2017, this is a high priority program for the Agency. It will provide strategic opportunities for HBCUs to stimulate further research and strengthen research capacity.
a) Collaborative research projects of up to $1 million that are expected to build and support the development of research capacity at HBCUs. Projects must have a research focus in one of the research areas supported by NSF, and a direct connection to the long-term plans of the host department(s) and the institutional mission. Proposers are encouraged to establish a research collaboration with one or more additional HBCU partner institutions to work with faculty and to build and expand research capacity.
b) Research projects of up to $500,000 to support research by individual PIs. The project should help to further the PI's research, to improve research capacity at the institution, and to involve students in research experiences. Collaborations are encouraged.
For further information, please go to the above linked document. Of course, HBCU-UP still supports STEM research and education at HBCUs through the existing funding opportunities, including Broadening Participation Research Centers, Implementation Projects, Targeted Infusion Projects, Research Initiation Awards and Broadening Participation Research Projects.
Announces funds to support community-based organizations in their efforts to collaborate and partner with local stakeholder groups (e.g., local businesses and industry, local government, medical providers, and academia) as they develop and implement solutions that address environmental and/or public health issues for underserved communities.
The company SolarReserve built the 110 megawatt Crescent Dunes Solar Energy Plant in southwestern Nevada. It creates enough electricity for 75,000 homes and stores thermal energy with molten salt. The plant uses mirrors instead of conventional solar panels and is one of several such facilities in the Southwest.
Peiwen Li: Molten salt keeps as a liquid for a wide range of temperatures, and it stores a lot of thermal energy, so you can store the energy for use during the night or during the bad weather time.
Ryan Heinsius: So, essentially it's just a big battery.
PL: Correct. To some sense, yes, it's a battery. It's a huge tank of salt. It stores energy in the form of thermal energy, not electrical energy. And when you use thermal energy you need to pump up this hot fluid and then go to the power plant to use the thermal power plant to generate electricity.
RH: Maybe you could explain to me why energy storage is such a big problem facing the renewables industry.
PL: Generally, after sunset people need another six hours to have electricity—people go home and they use a lot of electricity. So you need to supply the power if you rely on renewable energy to do that, and then energy storage is so critical, it's important. And compared to any other energy storage method, thermal storage can be a very good choice because you can store a large capacity of energy. People use batteries but the storage capacity is very limited. So you cannot expect a power plant to have battery to store that huge amount of energy.
Huge tanks of molten salt store thermal energy at SolarReserve's solar facility in Nevada.
RH: Are there any negative environmental aspects or effects from the use of molten salt energy storage?
PL: All the salt we use there is no toxicity and they are natural materials. For example, potassium nitrate is a kind of fertilizer salt. Sodium chloride and magnesium chloride salt, these are the materials people in the north use to melt snow during winter. Sodium nitrate is also a well-known material. So, they are friendly salts. There is no big concern regarding the environmental impact.
RH: Hundreds of people stand to be laid off if the Navajo Generating Station here in northern Arizona closes. Could this technology potentially save those jobs?
PL: So far, I've never heard of any country, or in the U.S., any plan to convert a coal-fired plant into a solar thermal-based power plant, yet. Once it starts running the jobs for maintenance probably will be much less than the coal fired plant, because in a coal-fired plant you have workers to deal with the waste from the power plant and so on. But once the solar thermal plant is constructed it's really clean and the maintenance jobs are reduced.
The 110 megawatt solar facility in Nevada.
RH: Is solar power using molten salt storage a viable alternative to coal-generated power or other fossil fuels?
PL: Yes, I think it will be widespread particularly in this region where the solar energy resources are rich. For example, California is pursuing this technology very actively; Nevada and also Arizona. So, we might completely give up gas and coal energy resources and we can completely rely on our solar energy.
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Royal Nickel Corp. Announced New Drilling Results for Qiqavik, Canada.
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Glencore plc Announced an Acquisition of Shares.
Cobalt mining is hot and getting hotter. Here are the 10 top cobalt-producing countries of 2016, based on US Geological Survey numbers.
Cobalt mining and exploration are flourishing, buoyed by excitement about Tesla's (NASDAQ:TSLA) lithium-ion battery gigafactories and increased worldwide demand for electric vehicles. Lithium-ion batteries require lithium, graphite and cobalt, among other components, and demand for these metals is expected to surge as electric vehicles become more mainstream. Anticipated demand and limited supply have led to strong gains for cobalt in particular this year, and investors are becoming more interested in this metal.
With that context, here's a look at the top 10 cobalt-producing countries of 2016, as identified by the US Geological Survey. Cobalt is usually produced as a by-product of copper and nickel, so countries that produce high quantities of those metals generally put out a lot of cobalt as well.
The DRC is the world's largest producer of cobalt, accounting for roughly 53 percent of global production. The country has been the top producer of the metal for some time. The DRC's output in 2016 increased by 3,000 MT compared to 2015.
According to Mining Weekly, the DRC will receive increasing amounts of foreign investment in the coming years, mostly from China, as a result of its low production costs and high-quality minerals. But cobalt mining in the DRC has also been related to human right abuses — earlier this year, Apple (NASDAQ:AAPL) announced that it has temporarily stopped buying cobalt mined by hand in the DRC.
China saw no change in its cobalt production from 2015 to 2016. Much of the country's output comes from ore and partially refined cobalt imported from the DRC, though scrap and stocks of cobalt material also contribute to its output, the US Geological Survey says. The Asian nation is the world's leading refined cobalt producer, and is the top supplier of cobalt to the US.
Canada's cobalt production increased in 2016, rising from 6,900 MT the year before to 7,300 MT. Canadian cobalt comes mostly from large nickel and copper mines that produce cobalt as a by-product of their normal operations. Some of these major nickel and copper deposits are Kidds Creek, Sudbury and Raglan. Several companies are now rushing to Canada stake land in the hunt for cobalt. In the coming years, the country's production is likely to expand as new sites are identified.
Russia bumped Australia out of the number-four spot to become the world's fourth-largest cobalt producer in 2016. Russia's production remained the same from 2015 to 2016, at 6,200 MT. That said, the country plans to significantly increase its cobalt production over the next several years, as per a statement from an official spokesperson for Denis Manturov, Russia's minister of industry and trade.
Australia saw a drop in cobalt production from 2015 to 2016, with output sinking from 6,000 MT to 5,100 MT. Like many other countries on this list, Australia's cobalt is a by-product of copper and nickel mining. The country's nickel mines are located in the western part of the country, mostly around the Kalgoorlie-Leonara regions.
In 2016, Zambia climbed the ranks to become the world's sixth-largest cobalt producer. The country's cobalt production for 2016 remained the same as it was in 2015 — it came in at 4,600 MT for both years.
Cuban cobalt production remained fairly steady in 2016 at 4,200 MT; that's up slightly from 4,300 MT in 2015. The country's Moa region is home to a joint venture nickel-cobalt operation between Canadian firm Sherritt International (TSX:S) and General Nickel Company of Cuba. The venture struggled last year due to a bridge collapse brought on by Hurricane Matthew; it resulted in the deaths of four employees.
Last year, the Philippines produced 3,500 MT of cobalt, down 800 MT from the amount produced in 2015. The fall was largely due to the country's decision to shut down 28 mines due to environmental concerns. The Chamber of Mines of the Philippines has since warned that the value of the country's metallic mineral production could go down by as much as 50 percent this year. The fate of mining in the Philippines is up in the air as President Rodrigo Duterte and recently appointed Environment Secretary Roy Cimatu deliberate over a country-wide ban on open-pit mining.
Madagascar's cobalt production dropped in 2016 from 3,700 MT to 3,300 MT. According to MBendi Information Services, the Ambatovy nickel-cobalt mine is the largest and most advanced mineral project in the country. It is located about 80 kilometers east of Antananarivo.
Sherritt International owns 40 percent of Ambatovy, which cost more than $5 billion to develop. Last year, the company announced a C$1.6-billion writedown of the value of its stake in the mine.
Rounding out the list is New Caledonia, whose cobalt production dropped slightly from 3,650 MT in 2015 to 3,300 MT in 2016. A subsidiary of France's Eramet (EPA:ERA) mines substantial deposits of oxidized ore (garnierites) at four mining centers located in the north and south parts of the country. The ore is processed at the company's Doniambo smelter, which is the world's largest ferronickel-producing plant.
The sun shines everywhere in Maryland. But too few Marylanders could take advantage of solar energy before the advent of a statewide community solar 3-year pilot program of approximately 196 MW. Community, or shared, solar makes it possible for anyone with an electric bill to access solar energy, even if they can't put it where they live. Shared solar means photovoltaic (PV) systems can be somewhere else in the community (in a field, on a building, over a parking lot, and elsewhere) but provide the benefits of solar electricity to participating subscribers. Community solar also represents a significant opportunity to expand direct access to Maryland's renewable energy economy to everyone in the state. A successful pilot program that delivers energy savings to a wide variety of participants and builds a diverse portfolio of shared renewable projects will broaden and deepen the state's constituency for renewable, clean, distributed energy. It will also make the case for a permanent and expanded program.
Now that the program is officially launched across much of the state, Solar United Neighbors decided to take a closer look at community solar in Maryland to date. In our recently released report that is the first of its kind about the Maryland program, we cover its history and structure, and highlight some projects happening across the state before investigating some of the programs successes and challenges so far. The report is meant to establish a baseline for future analysis of Maryland's community solar program. We will be revisiting the program to track its progress in future reports, so that a future, permanent program can build on the pilot's successes and learn from any mistakes made.
This post captures some of the report's highlights and key takeaways from the status of Maryland's community solar program.
With a specific provision in the legislation to "encourage developers to promote participation by renters and low-income and moderate-income retail electric customers," the legislature made its intent clear. The regulations attempt to comply with this intent by allocating 30% of the program's system size allocations (~196 MW over three years) to projects that provide at least 30% of the kilowatt hour output to low-and-moderate income (LMI) subscribers. In effect, the program has created a scarce and desirable resource (space in the program) and then sections off part of that resource for LMI projects. So far, the LMI category is unfilled in Pepco territory, but, in other territories covered by BG&E, Delmarva, and Potomac Edison, LMI project capacity is completely allocated for the first year. This is very encouraging. What remains to be seen is whether these LMI-focused projects will be able to find financing, find customers, and deliver meaningful savings to low-to-moderate income subscribers.
Community solar is often assumed to mean large-scale, multi-megawatt installations with shares divided among a large number of people. The concept however, also has the potential for smaller-scale and community-focused projects to flourish if the program allows them the space to do so. These smaller projects, while not as cost effective on a per-watt basis as a larger installation, hold the potential for community members to take a more active role in project development and management. They can also allow for communities to retain more of the economic value it produces. The Maryland program regulations include several provisions to support these types of projects.
The U.S. International Trade Commission last week made public a report sent to President Donald Trump detailing three proposed trade duties aimed at protecting domestic solar panel manufacturers.
The ITC report, filed on Friday, details how the agency arrived at its unanimous decision in September that the importation of cheap solar panels is unfairly harming U.S. solar panel producers. None of the proposed remedies changed between their announcement at the end of October and the official filing.
President Donald Trump has 90 days to review the recommendations and make a final decision, which could come as late as January. Meanwhile, a final hearing will take place at the Office of the U.S. Trade Representative on Dec. 6.
There isn't a lot of new information in the detailed report sent to Trump by the ITC, but it does compile hours of testimony and research, providing insight into how the agency arrived at its unanimous injury ruling in September.
At the end of October, the four sitting commissioners recommended a series of remedies composed of quotas, tariffs and a licensing fee to impose on imported chrystalline silicon photovoltaic cells and modules — the most popular type of material for solar panels.
The decision is a result of a rare Section 201 investigation, requested in April by Georgia-based solar manufacturer Suniva, which filed for bankruptcy protection earlier in the same month. SolarWorld, another domestic solar manufacturer, later joined.
The two companies are pushing for a combination of tariffs, quotas and a floor price to remain in effect for four years — the longest period duties can remain in effect before review and potential renewal.
The Commission did not accept the companies' proposed remedies, instead proposing a more modest set of duties that included a suggested licensing fee from Solar Energy Industries Association — a solar trade group vehemently opposed to the tariffs. The proposed remedies would be impose on imports from South Korea and Mexico. China and Taiwan already had duties imposed on their CSPV modules and cells in 2012 and 2015.
The proposed remedies sparked mixed reactions from both sides of the trade case. SEIA and its supporters were relieved that remedies were not as stringent as feared, but continue to aggressively lobby the Trump administration. Suniva and SolarWorld, on the other hand, expressed disappointment and said the proposed remedies were not strong enough to help the companies restart domestic operations.
The ITC report is an official recommendation to the president, but Trump ultimately holds the final decision. The White House has the option to do nothing, devise stiffer tariffs or other remedies or impose the ITC's recommendations. Trump has reportedly expressed a general desire for tariffs, and the solar case may fit the bill.
However, proposing tariffs could provoke retaliatory actions from other countries. Representatives from China said at the ITC remedy hearing in October question the legality of the injury ruling, likely setting up a fight at the World Trade Organization. According to GTM Research's Shayle Kann, few remedies from past Section 201 cases lasted the full four years because of challenges at WTO.
A Bloomberg report noted China could target Kentucky bourbon and Wisconsin cheese in a similar manner, indirectly affecting House Majority Leader Mitch McConnell (R-KY) and Speaker of the House, Paul Ryan (R-WI). That would create natural opponents, R Street Institute trade analyst Clark Packard told Bloomberg.
The solar trade case already created unusual alliances. Conservative think tank Heritage Foundation and the American Legislative Exchange Council came out against the proposed tariffs. Should Trump impose the ITC's remedies, it would mark a departure from the economic blueprint laid out by his conservative allies. The case even made headlines at Fox News as the two sides seek to sway Trump through his preferred media outlet.
The House of Representatives last Thursday passed a tax overhaul bill that slashes incentives for wind power projects, turning the focus to the Senate, which is considering its own tax reform legislation that would leave those incentives in place.
"There is $50 billion of investment at risk," Evan Vaughan, spokesman for the American Wind Energy Association (AWEA), told Utility Dive.
While the House bill poses a considerable threat to the wind industry, analysts contacted by Utility Dive believe the friendlier Senate version will prevail, at least when it comes to wind-specific items. But broader changes that exist in both bills could have a mixed impact on the wind power sector.
The flow of U.S. wind power development has been lumpy over the past couple of decades as developers have rushed projects into development to beat the expiration of the production tax credit (PTC), which has had a two-year shelf life and has gone through a biennial ritual of expiration and renewal since 1992. That cycle was broken in 2015 when Congress extended the PTC (and the investment tax credit for solar power) by five years, on a schedule that calls for the PTC to end in 2020.
The House tax reform bill (HR 1) keeps that schedule and the PTC itself intact, but changes two crucial details. It retroactively ends the inflation adjustment and it changes the definition for construction start.
Removing the inflation adjustment rolls back the PTC to 1993 levels, lowering the value to 1.5 cents/kWh from the current 2.4 cents/kWh. "It is a retroactive tax hike," Vaughan said.
The construction start date sets an eligibility deadline for the PTC. That is particularly important in the current environment because developers have been rushing to get their wind projects into construction ahead of each step-down. In 2018, for example, the PTC credit is set go down to 60% of the value of the credit.
The Internal Revenue Service administers the PTC and sets the eligibility requirements. Under existing IRS rules, there are two ways to qualify for the PTC: physical work of a significant nature or the 5% safe harbor rule. Most developers have taken the safe harbor option, under which a project qualifies for the PTC if the developer spends 5% of capital costs by the eligibility deadline.
The House tax bill retroactively removes the safe harbor option, meaning that developers that have already qualified for safe harbor would have to requalify under the revised rules of the new tax bill, and that would put them into the next step-down to the PTC, cutting the tax credits they would receive by 20%.
The head of Puerto Rico's embattled electric utility, Ricardo Ramos, has resigned following intense scrutiny over a contract signed with Montana-based Whitefish Energy to repair and rebuild the island's electric grid.
The news comes just days after Ramos, executive director of the Puerto Rico Electric Power Agency, answered questions about the contract's negotiation before the U.S. Senate Energy and Natural Resources Committee.
Days after Hurricane Maria devastated the island, PREPA contracted with Whitefish to repair the grid rather than utilizing mutual aid agreements with other utilities. As terms of the $300 million contract came to light, it set off a firestorm of criticism that resulted in the deal being canceled and has now led to Ramos' ouster.
It's been two months since Hurricane Maria made landfall, but in some ways the storm has not subsided. The island still only has less than half of its generation online and today the head of its electric utility stepped down amid fierce criticism over the slow recovery and its deal with Whitefish.
Details are still scarce, but NBC News reports a statement should be forthcoming form PREPA later on, and it is not yet known if Ramos will remain in his position until a successor is found.
Ramos appeared before a Senate committee on Tuesday, and told lawmakers the utility contracted with Whitefish because he believed PREPA was "unable to meet requirements" for mutual assistance with the American Public Power Association. Those requirements include points such as providing fuel, housing and communications; however, emails released by the House Natural Resources Committee show that PREPA purchasing managers did not follow advice from their lawyers in signing the Whitefish contract.
The contract remains the subject of a federal law enforcement probe.
In addition to eschewing mutual aid, the contract was not competitively bid, contained provisions limiting oversight and recourse, and specified hourly wages and expenses for linemen far in excess of what is typical on the island. To boot, much of those wages were not going to the workers.
Both Puerto Rico and its utility are mired in debt, and the storm recovery story has merged into one of mismanagement.Puerto Rico Gov. Rosselló requested $94 billion in disaster aid from Congress, but pushed lawmakers not to exercise more federal control over the territory's decision making. PREPA and Puerto Rico's federal oversight board will work together to reform the utility, which could include partial or entire privatization, he said. On Monday, a judge rejected a move from the federal oversight board that manages Puerto Rico's finances to appoint a "chief transformation officer" to direct the grid rebuild.
Ramos' departure is not the only one. The head of the Puerto Rico Emergency Management Agency resigned last week amid the recovery efforts. PREMA Commissioner Abner Gómez in a statement acknowledged the slow recovery efforts; government officials had indicated in the last two weeks that he was "exhausted" and had been on vacation.
Stephen Byrne will retire as senior vice president ofSCANA, and COO and president of generation and transmission of SCE&G.
The V.C. Summer debacle appears to be having an impact on SCANA's management, with Marsh's resignation coming just days after a key lawmaker in the state said he should step down.
Waste Management's innovative Solar-Powered Trash Compactor helps keep public spaces clean and can reduce trash collections by up to 80%.
Bigbelly was founded in 2003 with the goal of transforming one of the least efficient and resource-intensive industries: waste collection. Cities were either collecting too often and wasting fuel and labor while emitting excess CO2, or alternatively, they were not able to keep up with the demands and overflowing trash cans that created litter, health, and safety issues.
In the following years, we evolved to offer a unique and complete solution by leveraging renewable solar energyand information technology. Bigbelly is a Smart Waste Management, Smart City, and Internet of (IoT) Things industry leader and is recognized as a C40 Cities Climate Leadership Group best practice. Industry knowledge & longevity in the market provides us with unparalleled insights backed by extensive data, analytics, and customer successes.
Powered by the sun, our eco-friendly compactors work in every kind of weather — snow, rain or shine. These remarkable machines are available with or without companion recycling kiosks and can be covered with the exterior design of your choice. By compressing its contents, the unit gains capacity, so it doesn't have to be emptied as often. Fewer collections mean cost-savings for labor, fuel and maintenance and reduced greenhouse gas emissions.
Powered by the sun, our eco-friendly compactors work in every kind of weather — snow, rain or shine. These remarkable machines are available with or without companion recycling kiosks and can be covered with the exterior design of your choice. Solar-Powered Trash Compactors and recycling kiosks are perfect for cities, community centers, parks, beaches or other high-traffic areas.
The Washington Center has partnered with the U.S. Naval Research Laboratory (NRL) to encourage students from underrepresented backgrounds to pursue careers in scientific research. Participants receive an introduction to professional laboratory research and perform hands-on work at the Naval Research Laboratory in Washington, D.C.
The interdisciplinary research performed at NRL gives interns exposure to a variety of fields including materials science and engineering, chemical and biochemical engineering, aerospace engineering, physics, synthetic chemistry, biochemistry, molecular biology, computer science, mathematics, mechanical engineering and electrical engineering.
Research interns are active participants in major research programs at the NRL, working a minimum of 40 hours per week under the guidance of senior NRL staff.
Each intern will be assigned a mentor who will provide a 10-week planned research outline of assignments and expectations. In addition, interns will attend scientific and skill-set seminars and participate in select weekend activities arranged by Program Directors. Learn more here.
Travel is provided to and from Washington, D.C., up to $700 total. Once a student is accepted into the program, Washington Center staff will work with the student to book their flight, their train or to discuss gas reimbursement if the student chooses to drive a personal vehicle.
Students who elect to stay in Washington Center-provided housing will receive a reduced stipend for the program. Interns can also elect to make independent housing accommodations.
Students choosing to live in Washington Center-provided housing will have access to a complimentary shuttle that will transport them to and from the Naval Research Laboratory each work day.
Internships are awarded competitively to students who have completed at least one year of undergraduate training in science, mathematics or engineering.
U.S. citizens or have permanent residency.
Enrolled at a Historically Black College or University, Minority Serving Institution or Tribal College or University.
Currently maintaining a 3.0 cumulative GPA or higher.
Preference is given to students planning careers in science, technology, engineering and mathematics (STEM) disciplines. This program is not intended for pre-meds.
Prior laboratory experience is desirable.
Applicants should be U.S. citizens or U.S. permanent residents, be majoring in a STEM subject, and be interested in complex laboratory work.
California utilities are well-ahead of the state's renewables portfolio standard — already the most aggressive in the nation — and by some accounts may be supplying 50% carbon-free energy a decade ahead of schedule, according to a new report from California Public Utilities Commission (CPUC). As of this year, 43.2% of San Diego Gas & Electric's energy supply comes from renewable resources; 32.9% for Pacific Gas & Electric; and 28.2% for Southern California Edison. CPUC's report also concludes the state's aggressive RPS program has helped drive down the costs of renewable energy. Between 2008 and 2016, the price of utility solar contracts declined 77%. Between 2007 and 2015, prices of wind contracts reported to the CPUC have declined 47%.
Its been 15 years since California regulators directed utilities to include renewables into their energy mix, and in that time, much progress has been made. The state is now targeting 50% renewable power by 2030 and could reach that much sooner. But the report yesterday finds utilities well ahead of even interim targets, and possibly on their way to supply 50% green power within the next three years. CPUC Commissioner Clifford Rechtschaffen, who oversees the agency's renewables standard proceeding, touted California's position as a national leader on climate.
There have been signs the state could push even farther. Over the summer, California lawmakers considered setting interim goals that could lead to 100% renewable energy by 2045.
California Gov. Jerry Brown (D) was in Germany on Monday for international climate talks. "We don't want to do nothing and just sit there and let the climate get worse," he told the San Francisco Chronicle. With one of the largest economies in the world, the state has taken up the mantle as a leader on fighting climate change, even as the federal government has pulled out of the Paris climate accord, aimed at keeping global warming under control.
The CEO of the Puerto Rico Electric Power Authority told Senate lawmakers on Tuesday that he signed a $300 million contract with Whitefish Energy for power restoration due to difficulties with mutual assistance programs offered by other utilities. PREPA CEO Ricardo Ramos said that after Hurricane Maria he believed the utility was "unable to meet requirements" for mutual assistance with the American Public Power Association, such as providing fuel, housing and communications. Emails released by the House Natural Resources Committee, however, show that PREPA purchasing managers did not follow advice from their lawyers in signing the Whitefish contract, now the subject of a federal law enforcement probe.
Puerto Rico Gov. Rosselló requested $94 billion in disaster aid from Congress, but pushed lawmakers not to exercise more federal control over the territory's decisionmaking. PREPA and Puerto Rico's federal oversight board will work together to reform the utility, which could include partial or entire privatization, he said.
3 Strategies to Accommodate DER Growth Case study explores 3 North American regions and how utilities there are addressing challenges as they face DER integration and growth.
The Senate Energy and Natural Resources Committee and the House Natural Resources Committee both held hearings on Tuesday to investigate power restoration in Puerto Rico — and in both, the Whitefish contract was a central focus.
PREPA contracted with Whitefish on Sept. 26, days after Hurricane Maria struck Puerto Rico, signing an agreement without a competitive bidding process that did not allow the utility or government officials to review rates or profit margins. Ramos told lawmakers he and his team opted for the previously unknown company because he was unsure PREPA could provide logistical assistance for mutual aid workers from other utilities.
Ramos cancelled the Whitefish contract in late October at the urging of Gov. Rosselló, turning back to mutual aid agreements from APPA and other utility groups. APPA declined to address Ramos's comments about logistics after the storm, but said it is now working with the utility on power restoration.
Rosselló said he and PREPA are open to working with federal officials, including the oversight board, set up last year by Congress to oversee Puerto Rico's progress through a bankruptcy process. Last week, the head of that board asked Bishop's House committee to clarify that it has the authority to appoint new leaders to public agencies like PREPA — authority curtailed by Monday's court ruling.
"To avoid uncertainty and lengthy delays in litigation, congressional reaffirmation of our exercise of our authority is welcome," said Natalie Jaresko, head of the oversight board.
While an appeal could still be filed, the Monday ruling likely means Jaresko's board will need to work with existing PREPA management and Puerto Rico utility regulators to guide the grid rebuild. At the Senate hearing, Jaresko said the ruling was a "setback," but the board still has "the authority to review fiscal plans" for PREPA.
Jaresko laid out a three-part plan for PREPA, including the finalization of a new fiscal plan by Feb. 2 and contract reviews for every deal over $10 million. Rosselló said PREPA would be ready to work with Jaresko's board, but said it has only the power to review and suggest, not make final decisions for the utility.
Just how much power the fiscal board has over PREPA could change in the coming weeks. Congressional lawmakers could write in new oversight stipulations to any disaster aid package, or move to affirm the board's authority to intervene in financial decisions. After the hearing, Bishop said it was "premature" to speak about specifics, but that PREPA's record demands more oversight of their practices.


text:
\section{Introduction}

Word segmentation and part-of-speech (POS) tagging are core steps for higher-level natural language processing (NLP) tasks. Given the raw text, segmentation is applied at the very first step and POS tagging is performed on top afterwards. As by convention the words in Chinese are not delimited by spaces, segmentation is non-trivial, but its accuracy has a significant impact on POS tagging. Moreover, POS tags provide useful information for word segmentation. Thus, modelling word segmentation and POS tagging jointly can outperform the pipeline models \cite{ng2004chinese, zhang2008joint}.

POS tagging is a typical sequence tagging problem over segmented words, while segmentation also can be modelled as a character-level tagging problem via predicting the labels that identify the word boundaries. \newcite{ng2004chinese} propose a joint model which predicts the combinatory labels of segmentation boundaries and POS tags at the character level. Joint segmentation and POS tagging becomes a standard character-based sequence tagging problem and therefore the general machine learning algorithms for structured prediction can be applied.

The bidirectional recurrent neural network (RNN) using conditional random fields (CRF) \cite{lafferty2001conditional} as the output interface for sentence-level optimisation (BiRNN-CRF) achieves state-of-the-art accuracies on various sequence tagging tasks \cite{huang2015bidirectional, maend} and outperforms the traditional linear statistical models. RNNs with gated recurrent cells, such as long-short term memory (LSTM) \cite{hochreiter1997long} and gated recurrent units (GRU) \cite{cho2014properties} are capable of capturing long dependencies and retrieving rich global information. The sequential CRF on top of the recurrent layers ensures that the optimal sequence of tags over the entire sentence is obtained.

In this paper, we model joint segmentation and POS tagging as a fully character-based sequence tagging problem via predicting the combinatory labels. The BiRNN-CRF architecture is adapted and applied. The Chinese characters are fed into the neural networks as vector representations. In addition to utilising the pre-trained character embeddings, we propose a concatenated n-gram-representation of the characters. Furthermore, sub-character level information, namely radicals and orthographical features extracted by convolutional neural networks (CNNs), are also incorporated and tested. Three datasets of different sizes, genres and with different annotation schemes are employed for evaluation. Our model is thoroughly evaluated and compared with the joint segmentation and POS tagging model in ZPar \cite{zhang2010fast}, which is a state-of-the-art joint tagger using structured perceptron and beam decoding. According to the experimental results, our proposed model outperforms ZPar on all the datasets in terms of accuracy.

The main contributions of this work include: \begin{enumerate*}
\item We apply the BiRNN-CRF model for general sequence tagging to joint segmentation and POS tagging for Chinese and achieve state-of-the-art accuracy. The experimental results show that our tagger is robust and accurate across datasets of different sizes, genres and annotation schemes.
\item We propose a novel approach for vector representations of characters that leads to substantial improvements over the baseline model.
\item Additional improvements are obtained via exploring the feasibility of utilising sub-character level information.
\item We provide an open-source implementation of our method along with pre-trained character embeddings.\footnote{
https://github.com/yanshao9798/tagger
}
\end{enumerate*}
\section{Model}

\subsection{Neural Network Architecture}

Our baseline model is an adaptation of BiRNN-CRF. As illustrated in Figure \ref{fig:1}, the Chinese characters are represented as vectors and fed into the bidirectional recurrent layers. The character representations will be described in detail in the following sections. For the recurrent layer, we employ GRU as the basic recurrent unit as it has similar functionalities but fewer parameters compared to LSTM \cite{chung2014empirical}. Dropout \cite{srivastava2014dropout} is applied to the outputs of the bidirectional recurrent layers. The outputs are concatenated and passed to the first-order chain CRF layer. The optimal sequence of the combinatory labels is predicted at the end. There is a post processing step to retrieve both segmentation and POS tags from the combinatory tags.

\begin{CJK}{UTF8}{gbsn}
\begin{figure}
\scalebox{0.78}{
\begin{tikzpicture}[x=1.5cm, y=1.5cm]

\node (char1) {夏};
\node (char2) [right=1cm of char1]{天};
\node (char3) [right=1cm of char2]{太};
\node (char4) [right=1cm of char3]{热};

\node (eng2) [above=0.2cm of char3]{(too)};
\node (eng3) [above=0.2cm of char4]{(hot)};
\node (eng1) [left=1cm of eng2]{(summer)};

\foreach \x in {1,...,4}
	\node [circle, draw, minimum size=0.8cm] (emb1-\x) [below=0.2cm of char\x]{};
\foreach \x in {1,...,4}
	\node [circle, draw, minimum size=0.8cm] (emb2-\x) [below=0.05cm of emb1-\x]{};
\foreach \x in {1,...,4}
	\node [circle, draw, minimum size=0.8cm] (emb3-\x) [below=0.05cm of emb2-\x]{};
\foreach \x in {1,...,4}
	\node[fit=(emb1-\x)(emb3-\x),inner sep=0, draw](emb-\x) {};

\node (note1) [left=0.3cm of emb2-1, text width=2cm, align=center]{character \\ representations};

\foreach \x in {1,...,4}
	\node [circle, draw, minimum size=0.8cm] (fgru-\x) [below=1cm of emb-\x]{GRU};

\coordinate  [draw=none, left=0.6cm of fgru-1](fgru-0) ;
\coordinate  [draw=none, right=0.6cm of fgru-4](fgru-5);

\foreach \x [count=\xi from 1] in {0,...,4}
	\draw [-{Latex[length=2mm]}] (fgru-\x) -- (fgru-\xi);

\foreach \x in {1,...,4}
	\draw [-{Latex[length=2mm]}] (emb3-\x) -- (fgru-\x);

\node (note2) [left=0.3cm of fgru-1, text width=2cm, align=center]{forward\\ RNN};

\foreach \x in {1,...,4}
	\node [circle, draw, minimum size=0.8cm] (bgru-\x) [below=1cm of fgru-\x]{GRU};

\coordinate  [draw=none, left=0.6cm of bgru-1](bgru-0) ;
\coordinate  [draw=none, right=0.6cm of bgru-4](bgru-5);

\foreach \x [count=\xi from 1] in {0,...,4}
	\draw [{Latex[length=2mm]}-] (bgru-\x) -- (bgru-\xi);

\foreach \x in {1,...,4}
	\draw[-{Latex[length=2mm]}] (emb3-\x.south) to [out=210,in=135] (bgru-\x.north) ;

\node (note3) [left=0.3cm of bgru-1, text width=2cm, align=center]{backward\\ RNN};

\node [circle, draw, minimum size=0.8cm] (crf-1) [below=1cm of bgru-1]{B-NT};
\node [circle, draw, minimum size=0.8cm] (crf-2) [below=1cm of bgru-2]{E-NT};
\node [circle, draw, minimum size=0.8cm] (crf-3) [below=1cm of bgru-3]{S-AD};
\node [circle, draw, minimum size=0.8cm] (crf-4) [below=1cm of bgru-4]{S-VA};

\node (note4) [left=0.3cm of crf-1, text width=2cm, align=center]{CRF\\ Layer};

\foreach \x in {1,...,4}
	\draw [-{Latex[length=2mm]}] (bgru-\x) -- (crf-\x)[dashed];

\foreach \x in {1,...,4}
	\draw[-{Latex[length=2mm]}] (fgru-\x.south) to [out=335,in=40] (crf-\x.north)[dashed] ;

\foreach \x [count=\xi from 2] in {1,...,3}
	\draw [-] (crf-\x) -- (crf-\xi);

\node (post-3) [below=0.6cm of crf-3]{太\_AD};
\node (post-4) [below=0.6cm of crf-4]{热\_VA};
\node (post-12) [left=1cm of post-3]{夏天\_NT};

\node (note4) [left=0.95cm of post-12, text width=2cm, align=center]{Output};

\end{tikzpicture}
}
\caption{The BiRNN-CRF model for joint Chinese segmentation and POS tagging. The dashed arrows indicate that dropout layers are applied to the outputs of the recurrent layers.}\label{fig:1}
\end{figure}

\subsection{Tagging Scheme}

Following the work of \newcite{kruengkrai2009error}, the employed tags indicating the word boundaries are {\tt B}, {\tt I}, {\tt E}, {\tt S} representing a character at the beginning, inside, end of a word or as a single-character word. The CRF layer models conditional scores over all possible combinatory labels given the input characters. Incorporating the transition scores between the successive labels, the optimal sequence can be obtained efficiently via the Viterbi algorithm both for training and decoding.

The time complexity for the Viterbi algorithm is linear with respect to the sentence length $n$ as $\mathcal{O}(k^2n)$, where $k$ is constant and equals to the total number of combinatory labels. The efficiency can be improved if we reduce $k$. For some POS tags, combining them with the full boundary tags is redundant. For instance, only the functional word  的 can be tagged as {\tt DEG} in Chinese Treebank \cite{xue2005penn}. Since it is a single-character word, combinatory tags of {\tt B-DEG}, {\tt I-DEG}, and {\tt E-DEG} never occur in the experimental data and should therefore be pruned to reduce the search space. Similarly, if the maximum length of words under a given POS tag is two in the training data, we prune the corresponding label.

\subsection{Character Representations}

We propose three different approaches to effectively represent Chinese characters as vectors for the neural network.

\subsubsection{Concatenated N-gram}\label{sec1}

The prevalent character-based neural models assume that larger spans of text, such as words and n-grams, can be represented by the sequence of characters that they consist of. For example, the vector representation $V_{m, n}$ of a span $c_{m, n}$ is obtained by passing the vector representations $v_i$ of the characters $c_i$ to a functions $f$ as:
\begin{equation}\label{eq:1}
V_{m, n} = f(v_m, v_{m+1}, ..., v_n)
\end{equation}
where $f$ is usually an RNN \cite{wangfind} or a CNN \cite{dos2014learning}.

In this paper, instead of completely relying on the BiRNN to extract contextual features from context-free character representations, we encode rich local information in the character vectors via employing the incrementally concatenated n-gram representation as demonstrated in Figure \ref{fig:2}. In the example, the vector representation of the pivot character {\tt 太} in the given context is the concatenation of the context-free vector representation $V_{i, i}$ of {\tt 太} itself along with $V_{i-1, i}$ of the bigram {\tt 天太} as well as $V_{i-1, i+1}$ of the trigram {\tt天太热}.

\begin{figure}
\scalebox{0.78}{
\begin{tikzpicture}[x=1.5cm, y=1.5cm]

\node (char1) {夏};
\node (char2) [right=1.2cm of char1]{天};
\node (char3) [right=1.2cm of char2]{太};
\node (char4) [right=1.2cm of char3]{热};

\node (eng2) [above=0.4cm of char3]{(too)};
\node (eng3) [above=0.4cm of char4]{(hot)};
\node (eng1) [left=1.15cm of eng2]{(summer)};

\node[fit=(char3)(char3),inner sep=0, draw](char33)[dashed] {};
\node[fit=(char2)(char3),inner sep=0.1cm, draw](char23){};
\node[fit=(char2)(char4),inner sep=0.2cm, draw](char24)[dashed] {};

\node [circle, draw, minimum size=0.8cm] (gram1) [below=2cm of char2]{};
\node[fit=(gram1)(gram1),inner sep=0, draw](gram11){};

\node [circle, draw, minimum size=0.8cm] (gram2) [right=0.2cm of gram11]{};
\node [circle, draw, minimum size=0.8cm] (gram3) [right=0.05cm of gram2]{};
\node[fit=(gram2)(gram3),inner sep=0, draw](gram22){};

\node [circle, draw, minimum size=0.8cm] (gram4) [right=0.2cm of gram22]{};
\node [circle, draw, minimum size=0.8cm] (gram5) [right=0.05cm of gram4]{};
\node [circle, draw, minimum size=0.8cm] (gram6) [right=0.05cm of gram5]{};
\node[fit=(gram4)(gram6),inner sep=0, draw](gram33){};

\draw[-{Latex[length=2mm]}] (char33.south) to [out=210,in=95] (gram11.north);
\draw[-{Latex[length=2mm]}] (char23.south) to [out=190,in=95] (gram22.north);
\draw[-{Latex[length=2mm]}] (char24.south) to [out=270,in=95] (gram33.north);

\node (vec1) [below=0.4cm of gram11]{$V_{i, i}$};
\node (vec2) [below=0.4cm of gram22]{$V_{i-1, i}$};
\node (vec3) [below=0.4cm of gram33]{$V_{i-1, i+1}$};

\node (note1) [left=0.8cm of gram11, text width=2cm, align=center]{n-gram character\\ Representation $V_3$};

\end{tikzpicture}
}
\caption{Vector representations of the Chinese characters as incrementally concatenated n-gram vectors in a given context.}\label{fig:2}
\end{figure}

Instead of constructing the vector representation $V_{m, n}$ of an n-gram $c_{m, n}$ from the character representations as in Equation \ref{eq:1}, $V_{m, n}$ in different orders,  such as $V_{i, i}$, $V_{i-1, i}$, and $V_{i-1, i+1}$,  are randomly initialised separately. We use a single special vector to represent all the unknown n-grams per order. The n-grams in different orders are then concatenated incrementally to form up the vector representations of a Chinese character in the given context, which is passed further to the recurrent layers. As shown in Figure \ref{fig:2}, the neighbouring characters on both sides of the pivot character are taken into account.

\subsubsection{Radicals and Orthographical Features}

Chinese characters are logograms. As opposed to alphabetical languages, there is rich information encrypted in the graphical components. For instance, the Chinese characters that share the same part {\tt 钅} (gold) are all somewhat related to metals, such as {\tt 银} (silver), {\tt 铁} (iron), {\tt 针} (needle) and so on. The shared part {\tt 钅} is known as the radical, which functions as a semantic indicator. Hence, we investigate the effectiveness of using the information below the character level for our task.

Radicals are first represented as randomly initialised vectors and concatenated as parts of the character representations. Radicals are traditionally used as indices in Chinese dictionaries. In our approach, they are retrieved via the unicode representation of Chinese characters as the characters that share the same radical are grouped together. They are organised in consistent with the categorisation in Kangxi Dictionary (康熙字典), in which all the Chinese characters are grouped under 214 different radicals. We only employ the radicals of the common characters in the unicode range of (U+4E00, U+9FFF). For the characters out of the range and the non-Chinese characters, we use a single special vector as their radical representations.

\begin{figure}
\scalebox{0.78}{
\begin{tikzpicture}[x=1.5cm, y=1.5cm]

\node [draw, minimum size=1.0cm](char1) {\Huge 太};

\node [draw, minimum size=0.3cm, below right=-0.6cm of char1](cell1)[dashed] {};

\node [draw, minimum size=1.0cm,  right=1.2cm of char1, fill=white](cnn1) {};

\foreach \x  [count=\xi from 2] in {1,...,5}
	\node [draw, minimum size=1.0cm, below left=-1.2cm of cnn\x, fill=white](cnn\xi) {};

\node[fit=(cnn1)(cnn6),inner sep=0](cnn){};

\node (note1) [below=0.8cm of cnn, text width=1.5cm, align=center]{Conv. layers};

\node [draw, minimum size=0.3cm, below left=-0.6cm of cnn6](cell2)[dashed] {};

\node [draw, minimum size=0.3cm, above right=-0.6cm of cnn6](cell3)[dashed] {};

\draw[-{Latex[length=2mm]}] (cell1) -- (cell2);

\foreach \x  in {1,...,6}
	\node [draw, minimum size=0.8cm, right=1 cm of cnn\x, fill=white](cnnp\x) {};

\node [draw, minimum size=0.15cm, below left=-0.6cm of cnnp6](cell4)[dashed] {};

\draw[-{Latex[length=2mm]}] (cell3) -- (cell4);

\node[fit=(cnnp1)(cnnp6),inner sep=0](out1){};

\node (note2) [right=0.4cm of note1, text width=1.5cm, align=center]{Max pooling};

\node [right= 0.2cm of out1](do1) {\ldots};

\node [circle, draw, minimum size=0.5cm] (o2) [right=1 cm of out1]{};

\node [circle, draw, minimum size=0.5 cm, above = 0.02cm of o2, fill=white](o1) {};

\foreach \x  [count=\xi from 3] in {2, 3}
	\node [circle, draw, minimum size=0.5 cm, below = 0.02cm of o\x, fill=white](o\xi) {};

\node[fit=(o1)(o4),inner sep=0, draw](out){};

\node (note3) [right=0.2cm of note2, text width=1.5cm, align=center]{fully-connected layer};

\node [circle, draw, minimum size=0.5cm] (fo1) [right=1 cm of o2]{};
\node [circle, draw, minimum size=0.5cm] (fo2) [right=1 cm of o3]{};

\node[fit=(fo1)(fo2),inner sep=0, draw](fout){};

\draw[-{Latex[length=2mm]}] (out) -- (fout)[dashed];

\end{tikzpicture}
}
\caption{Convolutional Neural Networks for orthographical feature extraction. Only the first convolutional layer and its following max-pooling layer are presented.}\label{fig:3}
\end{figure}

Additionally, instead of presuming that only radicals encode sub-character level information, we use convolutional neural networks (CNNs) to extract graphical features from scratch by regarding the Chinese characters as pictures and feed their pixels as the input. As illustrated in Figure \ref{fig:3},  there are two convolutional layers, both followed by a max-pooling layer. The output of the second max-pooling layer is reshaped and passed to a regular fully-connected layer. Dropout is applied to the output of the fully-connected layer. The output is then concatenated as parts of the character representation. The CNNs are trained jointly with the main network.

\subsubsection{Pre-trained Character Embeddings}

The context-free vector representations of single characters introduced in section \ref{sec1} can be replaced by pre-trained character embeddings retrieved from large corpora. We employ GloVe \cite{pennington2014glove} to train our character embeddings on Wikipedia\footnote{https://dumps.wikimedia.org/} and the freely available Sogou News Corpora (SogouCS).\footnote{http://www.sogou.com/labs/resource/cs.php} We use randomly initialised vectors as the representations of the characters that are not in the embedding vocabulary. Pre-trained embeddings for higher-order n-grams are not employed in this paper.

\subsection{Ensemble Decoding}

At the final decoding phase, we use ensemble decoding, a simple averaging technique, to mitigate the deviations led by random weight initialisation of the neural network. For the chain CRF decoder, the final sequence of the combinatory tags $y$ is obtained via the conditional scores $S(y_i|x_i)$ and the transition scores $T(y_i, y_j)$ given the input sequence $x$. Instead of computing the optimal sequence with respect to the scores returned by a single model, both the conditional scores and transition scores are averaged over four models with identical parameter settings that are trained independently:
\begin{equation}
y^* = \argmax_{y \in L(x)} p(y|x; \bar{\{S\}}, \bar{\{T\}})
\end{equation}

Ensemble decoding is only applied to the best performing model according to the feature experiments at the final testing phase in this paper.

\section{Implementation}

Our neural networks are implemented using the TensorFlow 1.2.0 library \citep{abadi2016tensorflow}. We group the sentences with similar lengths into the same buckets and the sentences in the same bucket are padded to the same length accordingly. We construct sub-computational graphs respectively for each bucket. The training and tagging speed of our neural network on GPU devices can be drastically improved thanks to the bucket model. The training time is proportional to both the size of the training set and the number of POS tags.

\begin{table}
\centering
\scalebox{0.85}{
\begin{tabular}{l|c}
\hline
Char. embedding size & 64 \\
n-gram embedding size & 64 \\
Radical embedding size & 30 \\
\hline
Character font & simsun (宋体)\\
Character size & 30 $\times$ 30 \\
\hline
GRU state size & 200 \\
Conv. filter size & 5 $\times$ 5 \\
Conv. filter number & 32 \\
Max pooling size & 2 $\times$ 2 \\
Fully-connected size & 100 \\
\hline
Optimiser & Adagrad \\
Initial learning rate & 0.1 \\
Decay rate & 0.05 \\
Gradient Clipping & 5.0 \\
\hline
Dropout rate & 0.5 \\
Batch size & 10 \\
\hline
\end{tabular}
}
\caption{Hyper-parameters.}\label{tab:1}
\end{table}

Table \ref{tab:1} shows the adopted hyper-parameters. We use one set of parameters for all the experiments on different datasets. The weights of the neural networks, including the randomly intialised embeddings, are initialised using the scheme introduced in \newcite{glorot2010understanding}. The network is trained with the error back-propagation algorithm. All the embeddings are fine-tuned during training by back-propagating gradients. Adagrad  \cite{duchi2011adaptive} with mini-batches is employed for optimisation with the initial learning rate $ \eta_0 = 0.1$, which is updated with a decay rate $\rho = 0.05$ as $\eta_t = \frac{\eta_0}{\rho (t - 1) + 1} $,  where $t$ is the index of the current epoch.

The model is optimised with respect to the performance on the development sets. F1-scores of both segmentation ($F1_{Seg}$) and joint POS tagging ($F1_{Seg\&Tag}$) are employed as $F1_{Seg}*F1_{Seg\&Tag}$ to measure the performance of the model after each epoch during training. In our experiments, the models are trained for 30 epochs. To ensure that the weights are well optimised, we only adopt the best epoch after the model is trained at least for 5 epochs.

\section{Experiments}

\subsection{Datasets}

We employ three different datasets for our experiments, namely Chinese Treebank \cite{xue2005penn} 5.0 (CTB5) and 9.0 (CTB9) along with the Chinese section in Universal Dependencies (UD Chinese) \cite{nivre2016universal} of version 1.4.

CTB5 is the most employed dataset for joint segmentation and POS tagging in previous research. It is composed of newswire data. We follow the conventional split of the dataset as in \newcite{jiang2008cascaded, kruengkrai2009error, zhang2010fast}. CTB9 consists of source texts in various genres, CTB5 is a subset of it. We split CTB9 by referring to the partition of CTB7 in \newcite{wang2011improving}.
We extend the training, development and test sets from CTB5 by adding 80\% of the new data in CTB9 to training and 10\% each to development and test. The double-checked files are all placed in the test set. The detailed splitting information can be found in Table \ref{tab:a1} in Appendix. UD Chinese has both universal and language-specific POS tags. They are not predicted jointly in this paper. For the sake of convenience, we refer the universal tags as UD1 and the language-specific ones as UD2 in the following sessions. To make the model benefit from the pre-trained character embeddings, we convert the texts in UD Chinese from traditional Chinese into simplified Chinese.

Table \ref{tab:2} shows brief statistics of the employed datasets in numbers of words. The out-of-vocabulary (OOV) words are counted regardless of the POS tags. We can see that the size of UD Chinese is much smaller and it has a notably higher OOV rate than the two CTB datasets.

\begin{table}[h!]
\centering
\scalebox{0.88}{
\begin{tabular}{l|ccc}
\hline
 & CTB5 & CTB9 & UD Chinese \\
 \hline
 Train & 493,935 & 1,696,322 & 98,608 \\
 Dev & 6,821 & 136,468 & 12,663 \\
 Test & 8,008 & 242,317 & 12,012 \\
 \hline
OOV rate (dev) & 8.11 & 2.93 & 12.13 \\
OOV rate (test) & 3.47 & 3.13 & 12.46 \\
\hline
\end{tabular}
}
\caption{Statistics of the employed datasets in numbers of words.}\label{tab:2}
\end{table}

\begin{table*}[!htbp]
\centering
\scalebox{0.86}{
\begin{tabular}{c|cc|cc|cc|cc}
\hline
 & \multicolumn{2}{c|}{CTB5} & \multicolumn{2}{c|}{CTB9} & \multicolumn{2}{c|}{UD1} & \multicolumn{2}{c}{UD2} \\
\hline
& Seg & Seg\&Tag & Seg & Seg\&Tag & Seg& Seg\&Tag & Seg& Seg\&Tag  \\
\hline
size = 300 & 95.22 & 91.71 & 95.53 & 90.89 & 91.84 & 85.43 & 92.40 & 85.63  \\
\hline
1-gram & 95.14 & 91.52 & 95.25 & 90.43 & 91.74 & 85.07 & 91.83 & 84.93  \\
2-gram & 97.08 & 93.72 & 96.30 & 91.66 & \textbf{94.50} & 88.36 & 94.42 & 88.14 \\
3-gram & \textbf{97.14} & 94.01 & 96.47 & 91.75 & 94.36 & 88.27 & \textbf{94.43} & \textbf{88.32} \\
4-gram & 97.13 & \textbf{94.02} & 96.48 & \textbf{91.89} & 94.25 & 88.37 & 94.16 & 88.24 \\
5-gram & 96.94 & 93.84 & \textbf{96.50} & 91.88 & 94.40 & \textbf{88.47} & 94.25 & 88.03 \\
\hline
\end{tabular}
}
\caption{Evaluation of concatenated n-gram representations on the development sets in F1-scores}\label{tab:3}
\end{table*}

\begin{table*}[!htbp]
\centering
\scalebox{0.86}{
\begin{tabular}{c|cc|cc|cc|cc}
\hline
 & \multicolumn{2}{c|}{CTB5} & \multicolumn{2}{c|}{CTB9} & \multicolumn{2}{c|}{UD1} & \multicolumn{2}{c}{UD2} \\
\hline
& Seg& Seg\&Tag & Seg& Seg\&Tag & Seg& Seg\&Tag & Seg& Seg\&Tag  \\
\hline
3-gram & 97.14 & 94.01 & 96.47 & 91.75 & 94.36 & 88.27 & \textbf{94.43} & 88.32 \\
\hline
+radicals & \textbf{97.26} & \textbf{94.42} & 96.42  & 91.74  & 94.37 & 88.21 & 94.39 & \textbf{88.36}  \\
+graphical & 97.25 & 94.08 & \textbf{96.50}  & \textbf{91.78}  & \textbf{94.50} & \textbf{88.59} & 94.23  & 87.95  \\
\hline
\end{tabular}
}
\caption{Evaluation of sub-character level features on the development sets in F1-scores. }\label{tab:4}
\end{table*}

\begin{table*}[!htbp]
\centering
\scalebox{0.86}{
\begin{tabular}{c|cc|cc|cc|cc}
\hline
 & \multicolumn{2}{c|}{CTB5} & \multicolumn{2}{c|}{CTB9} & \multicolumn{2}{c|}{UD1} & \multicolumn{2}{c}{UD2} \\
\hline
& Seg& Seg\&Tag & Seg& Seg\&Tag & Seg& Seg\&Tag & Seg& Seg\&Tag  \\
\hline
1-gram & 95.14 & 91.52 & 95.25 & 90.43 & 91.74 & 85.07 & 91.83 & 84.93  \\
\hline
+GloVe & 95.82 & 92.45 & 95.44 & 90.57 & 92.77 & 86.48 & 93.01 & 86.48 \\
\hline
3-gram, radicals& 97.26 & 94.42 &   96.42  & 91.74  & 94.37 & 88.21 & 94.39 & 88.36 \\
\hline
+GloVe & \textbf{97.42}  & \textbf{94.58} & \textbf{96.56} & \textbf{91.96} & \textbf{95.12}  & \textbf{89.69} & \textbf{95.02} & \textbf{89.20}  \\
\hline
\end{tabular}
}
\caption{Evaluation of the pre-trained character embeddings on the development sets in F1-scores. }\label{tab:5}
\end{table*}

\subsection{Experimental Results}

Both segmentation (Seg) and joint segmentation and POS tagging (Seg\&Tag) are evaluated in our experiments.\footnote{The evaluation script is downloaded from: \\http://people.sutd.edu.sg/~yue\_zhang/doc/doc/joint\_files\\/evaluate.py}
We employ word-level recall (R), precision (P) and F1-score (F) as the evaluation metrics. A series of feature experiments are carried out on the development sets to evaluate the effectiveness of the proposed approaches for vector representations of the characters. Finally, the best performing model according to the feature experiment is applied to the test sets in the forms of single as well as ensemble and compared with ZPar.

\subsubsection{Feature Experiments}

Table \ref{tab:3} shows the evaluation results of using concatenated n-grams up to different orders as the character representations. By introducing 2-grams, we can obtain vast improvements over solely using the conventional character embeddings, which indicates that not all the local information can be effectively captured by the BiRNN using context-free character representations. Utilising the concatenated n-grams ensures that the same character has different but yet closely related representations in different contexts, which is an effective way to encode contextual features.

From the table, we see that notable improvements can be achieved further via employing 3-grams. 4-grams still help but only to CTB9 while adding 5-grams achieves almost no improvement on any of the datasets. The results imply that concatenating higher-order n-grams can be detrimental, especially on datasets in smaller sizes due to the fact that higher-order n-grams are more sparse in the training data and their vector representations cannot be trained well enough. Besides, adopting higher-order n-grams also substantially increases the numbers of weights and therefore both training and decoding become less efficient. Under the circumstances, we consider that 3-gram model is optimal for our task and it is employed in the following experiments for all the datasets.

The concatenated n-grams have a bigger size compared to the basic character representation. We conduct one additional experiment using a basic 1-gram character model with a larger character vector size of 300. The evaluation scores are similar to the basic character model with the size of 64, which shows that the improvements obtained by the n-gram model are not matched by enlarging the size of the vector representation.

The evaluation scores of the sub-character level features are reported in Table \ref{tab:4}. The relevant features are added on top of the 3-gram model. Employing radicals and graphical features achieves similar improvements for segmentation while utilising radicals obtains better results for joint POS tagging on CTB5. However, radicals are not a very effective feature on CTB9, UD1 and UD2 whereas a notable enhancement is observed when employing graphical features on UD1. Using CNNs to extract graphical features is computationally much more expensive than simply adopting radicals via a lookup table, especially when GPU is not available.

From Table \ref{tab:5}, we can learn that employing pre-trained embeddings as initial vector representations for the characters achieves improvements in general, whereas the improvements are comparatively smaller if the the concatenated n-gram representations and the radicals are added. Additionally, the improvements obtained on UD Chinese are more significant than on CTBs, which indicates that the pre-trained character embeddings are more beneficial to the datasets in smaller sizes.

In general, the feature experiments indicate that the proposed Chinese character representations are all sensitive to dataset size. Using higher-order n-grams requires more data for training. On the other hand, the pre-trained embeddings are more vital if the dataset is small. In addition, the different representations are sensitive to tagging schemes as the evaluation results on UD1 and UD2 are quite diverse. Taking both robustness and efficiency into consideration, we select 3-grams along with radicals and pre-trained character embeddings as the best setting for final evaluation.

\subsubsection{Final Results}

Table \ref{tab:6} shows the final scores on the test sets. The complete evaluation results in precision, recall and F1-scores are contained in Table \ref{tab:a2} and Table \ref{tab:a3} in Appendix. Our system is compared with ZPar. We retrained a ZPar model on CTB5 that reproduces the evaluation scores reported in \newcite{zhang2010fast}. We also modified the source code so that it is applicable to CTB9 and UD Chinese. In addition, we perform the mid-$p$ McNemar's test \cite{fagerland2013mcnemar} to examine the statistical significances.

\begin{table*}[!htbp]
\centering
\scalebox{0.84}{
\begin{tabular}{c|c|ll|ll|ll|ll}
\hline
 \multicolumn{2}{c|}{} & \multicolumn{2}{c|}{CTB5} & \multicolumn{2}{c|}{CTB9} & \multicolumn{2}{c|}{UD1} & \multicolumn{2}{c}{UD2} \\
\hline
\multicolumn{2}{c|}{} & Seg& Seg\&Tag & Seg& Seg\&Tag & Seg& Seg\&Tag & Seg& Seg\&Tag  \\
\hline
\multicolumn{2}{c|}{ZPar} & 97.77 & 93.82 & 96.28 & 91.62 & 93.75 & 88.11 & 93.98 & 88.16  \\
\hline
\multicolumn{2}{c|}{Single (3-gram, rad., GloVe)} & 97.89  & 94.07**  & 96.47**  & 91.89**  & 94.85** & 89.41** & 94.93**  & 89.00**  \\
\multicolumn{2}{c|}{Ensemble (4 models)} & \textbf{98.02}* & \textbf{94.38}** & \textbf{96.67}**  & \textbf{92.34}**  & \textbf{95.16}**  & \textbf{89.75}* & \textbf{95.09}*  & \textbf{89.42}**  \\
\hline
\hline
\multirow{3}{*}{OOV recall} & ZPar & 76.98 & 68.34 & \textbf{75.83} & 63.71 & 78.69 & 64.40 & 79.56 & 64.86 \\
\cline{2-10}
& Single & \textbf{78.78}  & 69.78  & 74.16  & 62.58  & 81.36  & 67.40  & 81.16 &  66.73 \\
& Ensemble & 77.34 & \textbf{70.50}  & 75.52  & \textbf{64.14}  & \textbf{82.16}  & \textbf{68.14} & \textbf{81.56} & \textbf{68.00}  \\
\hline
\end{tabular}

}
\caption{Evaluations of the best model on the final test sets in F1-scores as well as the recalls of out-of-vocabulary words.

Significance tests for Single are in comparison to ZPar, while tests for Ensemble are in comparison to Single (\textsuperscript{**}$p<0.01$,
 \textsuperscript{*}$p<0.05$)
 }\label{tab:6}
\end{table*}

As shown in Table \ref{tab:6}, the single model is worse than the ensemble model but still outperforms ZPar on all the tested datasets. ZPar incorporates discrete local features at both character and word levels and employs structured perceptron for global optimisation, whereas we encode rich local information in the character representations and employ BiRNN to effectively extract global features and capture long term dependencies. The chain CRF layer is used for sentence-level optimisation, which functions similarly to structured perceptron. As opposed to the taggers built with traditional machine learning algorithms, our model avoids heavy feature engineering and benefits from large plain texts via utilising pre-trained character embeddings. It is also very flexible to add sub-character level features as parts of the character representations. The model performs very well despite being fully character based. Moreover, it has clear advantages when applied to smaller datasets like UD Chinese, while the prevalence is much smaller on CTB5.

Both our model and ZPar segment OOV words in UD Chinese with higher accuracies than the ones in CTBs despite that UD Chinese is notably smaller and the overall OOV rate is higher. Compared to CTB, the words in UD Chinese are more fine-grained and the average word length is shorter, which makes it easier for the tagger to correctly segment the OOV words as \newcite{zhang2016trans} show that the longer words are more difficult to be segmented correctly. For joint POS tagging for OOV words, the two systems both perform significantly better on CTB5 as it is only composed of news text.

In general, our model is more robust to OOV words than ZPar, except that ZPar yields better result for segmentation by a small margin on CTB9. ZPar also obtains higher accuracy for joint POS tagging than the single model on CTB9. The differences between ZPar and our model for both segmentation and POS tagging are more substantial on UD Chinese, which indicates that our model is relatively more advantageous for handling OOV words when the training sets are small, whereas ZPar is able to perform equally well when substantial amount of training data is available as they achieve similar results on the CTB sets.

The single model is further improved by ensemble-averaging four independently trained models. The improvements are not drastic but they are observed systematically across all the datasets. In general, ensemble decoding is beneficial to handling OOV words as well except that a small drop for segmentation on CTB5 is observed.

\begin{table}[h!]
\centering
\scalebox{0.87}{
\begin{tabular}{c|ll|cc}
\hline
 & \multicolumn{2}{c|}{Ensemble} &  \multicolumn{2}{c}{ZPar}  \\
\hline
 & Seg& Seg\&Tag & Seg& Seg\&Tag \\
 \hline
  BN & \textbf{97.89}* & \textbf{94.48}** & 97.68 & 94.22 \\
  CS & \textbf{96.67}** & \textbf{91.78}** & 95.61 & 90.15 \\
  FM & \textbf{96.54}** & \textbf{91.92}** & 96.30 & 91.51 \\
  MG & \textbf{94.54}** & \textbf{89.23}** & 94.22 & 88.60 \\
  NS & \textbf{97.56} & \textbf{93.92}** & 97.49 & 93.70 \\
  SM & \textbf{96.43}** & \textbf{91.78}** & 96.13 & 90.32 \\
  SP & \textbf{97.29}** & \textbf{93.93}** & 96.69 & 93.35 \\
  WB & \textbf{94.27}** & \textbf{88.44}** & 93.38 & 86.88 \\
\hline
\end{tabular}
}
\caption{Evaluation on Broadcast News (BN), Conversations (CS), Forum (FM), Magazine (MG), News (NS), Short Messages (SM), Speech (SP) and Weblogs (WB) in CTB9. (\textsuperscript{**}$p<0.01$,  \textsuperscript{*}$p<0.05$)
}\label{tab:7}
\end{table}

Table \ref{tab:7} displays the evaluation of the ensemble model and ZPar on the decomposed test sets of CTB9 in different genres. Our model surpasses ZPar on all the genres in both segmentation and joint POS tagging. The differences are subtle on the genres in which the texts are normalised, such as News and Broadcast News. This, to a very large extent, explains why our model is only marginally better than ZPar on CTB5, whereas the experimental results reveal that our model is substantially better at processing non-standard text as it yields significantly higher scores on Conversations, Short Messages and Weblogs. The evaluation results of both our model and ZPar vary substantially across different genres as some genres are fundamentally more challenging to process.

Our models are compared with the previous best-performing systems on CTB5 in Table \ref{tab:8}. Our models are not optimised particularly with respect to CTB5 but still yield competitive results, especially for joint POS tagging. We are the first to report evaluation scores on CTB9 and UD Chinese.

\begin{table}[!htbp]
\centering
\scalebox{0.98}{
\begin{tabular}{c|cc}
\hline
& Seg& Seg\&Tag  \\
\hline
\newcite{kruengkrai2009joint} & 97.98  & 94.00   \\
\newcite{zhang2010fast} & 97.78 & 93.67 \\
\newcite{sun2011stacked} & \textbf{98.17} & 94.02 \\
\newcite{wang2011improving} & 98.11 & 94.18 \\
\newcite{shen2014} & 98.02 & 93.80 \\
\hline
Single & 97.89 & 94.07 \\
Ensemble & 98.02 & \textbf{94.38} \\
\hline
\end{tabular}
}
\caption{Result comparisions on CTB5 in F1-scores. }\label{tab:8}
\end{table}

\subsection{Tagging Speed}

Our joint segmentation and POS tagger is very efficient with GPU devices and can be practically used for processing very large files.  The memory demand of decoding is drastically milder compared to training, a large batch size therefore can be employed. The tagger takes constant time to build the sub-computational graphs and load the weights.

With bucket size of 10 and batch size of 500, Table \ref{tab:81} shows the tagging speed of the tagger using a single Tesla K80 GPU card and the pre-trained model on CTB5. The tagging speed of ZPar is also presented for comparison. GPU devices are not supported by ZPar and therefore the tagging speed is calculated using an Intel Core i7 CPU.

\begin{table}[!htbp]
\centering
\scalebox{0.88}{
\begin{tabular}{c|c|rr}
\hline
& Init. Time (s)& Sentence/s & Chars/s  \\
\hline
Single & 20 & 299.40 & 40,188.17  \\
Ensemble & 23 & 230.41 & 30,928.22 \\
\hline
ZPar & 4 & 134.59 & 18,090.09 \\
\hline
\end{tabular}
}
\caption{Tagging speed in numbers of sentences and characters per second}\label{tab:81}
\end{table}

\section{Related Work}

The fundamental BiRNN-CRF architecture is task-independent and has been applied to many sequence tagging problems on Chinese. \newcite{peng2016multi} adopt the model for Chinese segmentation and named entity recognition in the context of multi-task and multi-domain learning. \newcite{dong2016character} employ a character level BiLSTM-CRF model that utilises radical-level information for Chinese named entity recognition. \newcite{ma2016new} use a similar architecture but feed the Chinese characters pair-wise as edge embeddings instead. Their model is applied respectively to chunking, segmentation and POS tagging.

\newcite{zhengdeep} model joint Chinese segmentation and POS tagging via predicting the combinatory segmentation and POS tags. They employ the adaptation of  the feed forward neural network introduced in \newcite{collobert2011natural} that only extracts local features in a context window. A perceptron-style training algorithm is employed for sentence level optimisation, which is the same as the training algorithm of the BiRNN-CRF model. Their proposed model is not evaluated on CTB5 and therefore difficult to be compared with our system. \newcite{kong2015segmental} apply segmental recurrent neural networks to joint segmentation and POS tagging but the evaluation results are substantially below the state-of-the-art on CTB5.

\newcite{bojanowski2016enriching} retrieve word embeddings via representing words as a bag of character n-grams for morphologically rich languages. A similar character n-gram model is proposed by \newcite{wieting2016charagram}. \newcite{sun2014radical} attempt to encode radical information into the conventional character embeddings. The radical-enhanced embeddings are employed and evaluated for Chinese segmentation. The results show that radical-enhanced embeddings outperform both skip-ngram and continues bag-of-word \cite{mikolov2013efficient} in word2vec.

\section{Conclusion}

We adapt and apply the BiRNN-CRF model for sequence tagging in NLP to joint Chinese segmentation and POS tagging via predicting the combinatory tags of word boundaries and POS tags. Concatenated n-grams as well as sub-character features are employed along with the conventional pre-trained character embeddings as the vector representations for Chinese characters. The feature experiments indicate that concatenated n-grams contribute substantially. However, both radicals and graphical features as sub-character level information are less effective. How to incorporate the sub-character level information more effectively will be further explored in the future.

The proposed model is extensively evaluated on CTB5, CTB9 and UD Chinese. Despite the fact that different character representation approaches are sensitive to data size and tagging schemes, we use one set of hyper-parameters and universal feature settings so that the model is robust across datasets. The experimental results on the test sets show that our model outperforms ZPar which is built on structured perceptron on all the datasets. We obtain state-of-the-art performances on CTB5. The results on UD Chinese and CTB9 also reveal that our model has great advantages in processing non-standard text, such as weblogs, forum text and short messages. Moreover, the implemented tagger is very efficient with GPU devices and therefore can be applied to tagging very large files.

\section*{Acknowledgments}

 We acknowledge the computational resources provided by CSC in Helsinki and Sigma2 in Oslo through NeIC-NLPL (www.nlpl.eu). This work is supported by the Chinese Scholarship Council (CSC) (No. 201407930015).

redpajama_set_name: RedPajamaArXiv


text:
Q: EXC_BAD_ACCESS (code=1, address=0x0) when runModalForWindow I have a window and a window controller which opens when the user clicks a button.
Sometimes I get EXC_BAD_ACCESS(code=1, address=0x0).
0x7fff6f2a59e0:  movq   (%rax), %rdi

Here is the code:
    ChooseProceduresWindowController *chooseProceduresWindowController = [[ChooseProceduresWindowController alloc] initWithWindowNibName:@"ChooseProceduresWindow"];
    [NSApp runModalForWindow:[chooseProceduresWindowController window]];

The error appears then runModalForWindow: is called.
I don't get this error every time, but I couldn't find a pattern.
Thanks

A: the best way to debug EXC_BAD_ACCESS errors is to use NSZombies.
Check out this video for an explanation :
http://youtu.be/LQtPr8bkB3g
NSZombie keeps all your objects in memory when you are trying to release an object that has already been released, so you get closer to finding your bug. As @Mark H said, it is a memory management issue.
Also you could put an NSLog in your dealloc method to have a better idea of what is getting deallocated when at runtime.

A: That error will be thrown when the object doesn't exist in memory. I'd start looking for memory management issues. The first would be to make sure you are releasing the ChooseProceduresWindowController after using it.

redpajama_set_name: RedPajamaStackExchange


text:
Q: Pandas - assign histogram bucket to each row Here is my dataframe:
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4, 6, 4, 3, 2, 7]})
buckets = [(0,3),(3,5),(5,9)]

I also have histogram buckets stated above. Now I would like to assign each row of dataframe to buckets index. So I would like to get new column with the following info:
df['buckets_index']  = [0,0,0,1,2,1,0,0,2]

Of course, I can do it with loops, but I have fairly big dataframe (2.5 mil rows), so I need to get it done quickly.
Any thoughts?

A: You can use pd.cut, with labels=False if you only want the index:
buckets = [0,3,5,9]
df['bucket'] = pd.cut(df['A'], bins=buckets)
df['bucket_idx'] = pd.cut(df['A'], bins=buckets, labels=False)

The resulting output:
   A  bucket  bucket_idx
0  1  (0, 3]           0
1  2  (0, 3]           0
2  3  (0, 3]           0
3  4  (3, 5]           1
4  6  (5, 9]           2
5  4  (3, 5]           1
6  3  (0, 3]           0
7  2  (0, 3]           0
8  7  (5, 9]           2

A: You could use np.searchsorted -
df['buckets_index'] = np.asarray(buckets)[:,1].searchsorted(df.A.values)

Runtime test -
In [522]: df = pd.DataFrame({'A': np.random.randint(1,8,(10000))})

In [523]: buckets = [0,3,5,9]

In [524]: %timeit pd.cut(df['A'], bins=buckets, labels=False)
1000 loops, best of 3: 460 µs per loop # @root's soln

In [525]: buckets = [(0,3),(3,5),(5,9)]

In [526]: %timeit np.asarray(buckets)[:,1].searchsorted(df.A.values)
10000 loops, best of 3: 166 µs per loop

Outside limits cases : For such cases, we need to use clipping, like so -
np.asarray(buckets)[:,1].searchsorted(df.A.values).clip(max=len(buckets)-1)


Cardiff Rivers Group (CRG) is a community voluntary action group that aims to visibly improve the cleanliness of the rivers, streams and lakes in Cardiff and surrounding areas, to help protect and enhance their biodiversity, and so that both residents and visitors to the area can enjoy them more.
Improving the habitat in and around the waterways of Cardiff also increases the diversity of wildlife and plants, adding to their attractiveness and importance.
In June 2015, Cardiff Rivers Group was honoured to have its work recognised by receiving the highest accolade possible — The Queen's Award for Voluntary Service, which is regarded as "the MBE for volunteer groups".
Everyone's welcome, so please come along to an event and help us Make A Difference!
Just want to say thank you for clearing the brook on Heol y Waun. I knew there were kingfishers there but have been unable to see them. I live in Heol Cattwg so the kingfishers come past us as well. Thank you for making an old man happy.
Why not watch our introductory video to find out more?


I signed up for tumblr.
Mainly in the hope of finding shiny fannish things. If you have a tumblr and want me to follow you, let me know.
Mark has also added Fullmetal Alchemist: Brotherhood to his slate, and I am SO DELIGHTED. Starting with Wednesday's review, I'm really trying to put in the extra effort to respond to every episode. He'll be at episode 11 as of Wednesday, and through the month of August he's only posting reviews of it on alternate days (due to traveling), so if you are not on board with this series yet, there *is* time to catch up. I would be so incredibly happy to have some friends from LJ come join the wonderful, beautiful, PAINFUL party that is Mark Watches FMA:B. Obviously for me this is a re-watch, so I'm mostly laughing at Mark's unpreparedness. If you've seen FMA:B before, come laugh with me. If you haven't, WHAT ARE YOU WAITING FOR? It's an awesome show, and YOU ARE NOT PREPARED.
The following is a beautiful fan-made trailer for the series that someone posted in the comments to one of Mark's reviews. It hints at some spoilers--and at least one rather big spoiler---so spoilerphobes beware. But the big spoiler goes by quick enough that you might blink and miss it, so...if watching it might encourage you to check the series out, I say go ahead and watch! Edit: I should also add that if you've seen the 2003 version of FMA, then the big spoiler I'm referring to is not a spoiler for you.
The Newsroom - The HBO series is back on for the summer and four episodes in to the second season (if my count is right). I'm really enjoying it, especially since my little pattern-finding brain loves to find ways to relate it two other stories I'm enjoying/have recently enjoyed: The West Wing (because Sorkin) and Newslflesh (because the News is Serious Business).
Veronica Mars - I've been re-watching, but have stalled out mid-season three, which is--say it with me, class--just not as good as the others. I'm debating whether to try finishing it this summer, or to just wait to watch the rest when Mark Watches gets to it (some time after FMA:B is finished).
ATLA: The Search (comics) - I'm just "meh" on this so far. Ask me if you want a more detailed opinion.
Newsflesh novella: How Green This Land, How Blue This Sea (in progress) - Australia and zombie kangaroos. It's pretty good, but I can't seem to get myself to finish it. My reading time is just too limited, and there are too many other things distracting me. But thanks to reading this around the same time The Newsroom started back up again, I now have Dev Patel = Mahir firmly planted in my mind.
Toby Daye series re-read - Since I listened to this first on audiobooks, I'd really like to re-read (as in, actually read) the series before the new book comes out in September, but I doubt I'll have time to get all the way through before the book's release date. Alas. That said, I'm going to try anyway.
InCryptid, another Seanan McGuire series (audiobooks) - I have Discount Armageddon, the first book in the series, loaded on my cell phone and just waiting for me. I just finished listening to The Neverending Story today, so I'm planning to start this tomorrow. Yay!
My review for the latest chapter of Naruto is up at narutoforadults.
It's like detective story/noir meets Hero's Journey against a backdrop of Celtic folklore (plus a few others) transposed onto modern-day San Fransisco. It reminds me a bit of Veronica Mars, with its young heroine who is smart, tough, and a bit jaded. The first two books are good, but from book three on, it's FANTASTIC. So worth checking out.
As six grey goslings walk by.
This entry was originally posted at http://hymnia.dreamwidth.org/174930.html.
I've posted my review for the latest chapter of Naruto at narutoforadults.
If you are following current manga chapters, please take a look, and don't forget to vote in the poll!


As plants grow, some trimming can be helpful, while others can be propagated by the simple technique of layering. Those young shoots might need protection from aphids, however. The warming soils allow tender courgettes to be sown and it is peak season for planting. Any fertile soil will produce enormous crops – three courgette plants is enough to feed most families.
Judicious shortening now by about half leads to compact plants less prone to flop, and usefully delays flowering. Aster, helenium and sedum are suitable. Alternatively, remove one in three stems – severed stems re-grow and flower later, prolonging the display. Even after flower buds appear in late spring, there is scope for trimming all the stems by degrees, generally by a third, resulting in a bushier plant and longer flowering season. Echinacea (right), monarda and rudbeckia respond well to this.
These can now be sown outdoors (below) or indoors in cell trays, and grow quickly. If outdoors, cover the seedbed with fleece or cloches to deter the bean seed fly that can damage them. Any fertile soil in reasonable sun will produce enormous crops – three plants is enough for most families. Allow 90cm between plants. For something unusual, consider climbing courgettes trained up bean poles. They crop abundantly, suit small gardens, are tasty and look pretty. Try "Black Forest" with its dark fruits and yellow "Shooting Star".
Aphids multiply exceedingly in late spring. By midsummer, natural enemies and diseases usually suppress them, but they can be damaging in the meantime. If spotted early, they can be wiped off. However, once their colonies are numerous and plant growth is distorted, an insecticide is usually required. Ones based on fatty acids – that is, soaps or oils – kill aphids, but if applied at dusk do little harm to bees, ladybirds and other useful insects.
Home-grown and bought plants go out now. Good planting avoids losses and gets good results. Water is key – soak dry soil the previous day and water the plants thoroughly before planting. Fertiliser is less important, but using liquid fertiliser during the pre-planting watering is good practice. With your trowel, make a suitable-sized hole, place the plant in it and fill with water. When it has drained, firm the plant, ensuring the rootball is only barely covered with soil.
Bending a suitable low-growing shoot to soil level, pinning it in place with a "U"-shaped wire "staple" and covering with a low mound of soil induces roots to form on a wide range of plants by autumn: acer, camellia, cornus, cotinus, lilac, rhododendron and of course climbers including clematis, grape vines, jasminium and wisteria. A useful extra is to slightly wound, with a knife or a light "twist", the lowest part of the bent stem to enhance rooting.


text:
Anthony Duclair (* 26. srpna 1995 Montréal, Québec) je kanadský hokejista, hrající na pozici levého křídla. V současné době nastupuje za tým Florida Panthers v severoamerické lize NHL.

Kariéra

Juniorská kariéra
Duclair strávil tři sezóny v klubu Quebec Remparts kanadské juniorské ligy QMJHL. V ročníku 2013–14 dosáhl na hranici 50 gólů v 59 odehraných zápasech. Za své výkony byl zařazen do prvního All-Star týmu soutěže.

Profesionální kariéra
V roce 2013 byl Duclair vybrán týmem New York Rangers ve třetím kole, jako celkově 80. volba vstupního draftu NHL a 2. ledna 2014 podepsal s Rangers vstupní smlouvu na tři roky.

Anthony Duclair pozitivně zapůsobil na vedení Rangers v přípravných zápasech pro sezónu 2014–15, kdy dokázal zaznamenat celkem pět bodů v pěti zápasech, nejvíce z celého týmu. 6. října byli obránce Steven Kampfer spolu s centrem Andrew Yoganem vyměněni do klubu Florida Panthers za útočníka Joey Crabba, čímž se potvrdilo Duclairovo místo v základní sestavě pro úvodní zápas nadcházejícího ročníku. 27. října 2014, v utkání 9. kola proti Minnesotě Wild, Duclair vstřelil svou první branku. 6. ledna 2015 byl poslán na zbytek sezóny do Quebecu k Remparts.

1. března 2015 byl Duclair vyměněn do Arizony Coyotes. Součástí obchodu byl na straně Rangers navíc obránce John Moore a dvě volby v draftu, za Coyotes pak obránci Keith Yandle a Chris Summers. Generální manažer Coyotes Don Maloney potvrdil spekulace fanoušků prohlášením, že získal Duclaira se záměrem vytvořit sehranou formaci s dalším jejich nováčkem Maxem Domim. Oba totiž hráli ve stejné řadě na Mistrovství světa juniorů 2015 a právě jejich souhra byla jedním z klíčových faktorů kanadské cesty za zlatem. Duo si v dresu Coyotes získalo přezdívku "The Killer D's".

14. října 2015 si Duclair připsal svůj první hattrick v NHL ve vítězném duelu 3. kola proti Anaheim Ducks. Utkání skončilo stavem 4–0, jediným dalším střelcem utkání byl Max Domi, který navíc přihrál na jednu z Duclairových branek. V lednu 2018 byl Duclair vyměněn spolu s obráncem Adamem Clendeningem do Chicaga Blackhawks za útočníky Richarda Pánika a Laurenta Dauphina. Posledních 10 zápasů před transakcí se nevešel do základní sestavy Coyotes. Za Blackhawks odehrál 23 utkání, připsal si 2 branky a 6 přihrávek a po skončení sezóny mu nebyla nabídnuta nová smlouva.

V červenci podepsal jednoletý kontrakt na 650 tisíc dolarů (nejnižší možný) s klubem Columbus Blue Jackets. V únoru 2019 byl vyměněn spolu se dvěma volbami v druhém kole draftu za Ryana Dzingela a volbu v sedmém kole s Ottawou Senators. S Ottawou po skončení sezóny podepsal smlouvu v hodnotě 1,65 milionu dolarů pro další ročník. Následující sezóna 2019–20 pro něj byla střelecky nejlepší v kariéře, když v 66 zápasech zaznamenal 23 gólů a na 17 dalších přihrál. V lednu 2020 byl Ottawou vybrán pro NHL All-Star Game v St. Louis.

Jako volný hráč v prosinci 2020 podepsal jednoletý kontrakt v hodnotě 1,7 milionu dolarů s Florida Panthers. Po 43 odehraných zápasech základní části, ve kterých vstřelil 10 branek a na 22 přihrál s ním Panthers po sezóně prodloužili smlouvu o tři roky, během kterých si přijde na 9 milionů dolarů. Sezóna 2021–22 byla jeho do té doby bodově nejúspěšnější a s Panthers postoupil do druhého kola play-off. Po sezóně si během tréninku přetrhl achilovku a musel podstoupit operaci, kvůli čemuž přišel o většinu základní části ročníku 2022–23.

Statistiky

Klubové statistiky

Reprezentační statistiky

Odkazy

Reference

Externí odkazy

Kanadští hokejoví útočníci
Mistři světa juniorů v ledním hokeji
Vítězové Memoriálu Ivana Hlinky
Hokejisté Quebec Major Junior Hockey League
Hráči Arizona Coyotes
Hráči Columbus Blue Jackets
Hráči Chicago Blackhawks
Hráči Florida Panthers
Hráči New York Rangers
Hráči Ottawa Senators
Narození v roce 1995
Narození 26. srpna
Narození v Montréalu
Aktivní lední hokejisté
Žijící lidé
Muži
Hráči draftovaní New Yorkem Rangers

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