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

Question: How are the relationships between catch per set and fishing behavior variables different for different measures of catch per unit effort (CPUE)?

Read the following text and answer briefly.

Context 1: Overfishing is a major threat to the survival of shark species, primarily driven by international trade in high-value fins, as well as meat, liver oil, skin and cartilage  The Convention on the International Trade in Endangered Species of Wild Fauna and Flora (CITES) aims to ensure that commercial trade does not threaten wild species, and several shark species have recently been listed on CITES as part of international efforts to ensure that trade does not threaten their survival  However, as international trade regulations alone will be insufficient to reduce overexploitation of sharks, they must be accompanied by practical fisheries management measures to reduce fishing mortality  To examine which management measures might be practical in the context of a targeted shark fishery, we collected data from 52 vessels across 595 fishing trips from January 2014 to December 2015 at Tanjung Luar fishing port in East Lombok, Indonesia  We recorded 11,920 landed individuals across 42 species, a high proportion of which were threatened and regulated species  Catch per unit effort depended primarily on the number of hooks and type of fishing gear used, and to a lesser degree on month, boat engine power, number of sets and fishing ground  The most significant factors influencing the likelihood of catching threatened and regulated species were month, fishing ground, engine power and hook number  We observed significant negative relationships between standardised catch per unit effort and several indicators of fishing effort, suggesting diminishing returns above relatively low levels of fishing effort  Our results suggest that management measures focusing on fishing effort controls, gear restrictions and modifications and spatiotemporal closures could have significant benefits for the conservation of shark species, and may help to improve the overall sustainability of the Tanjung Luar shark fishery  These management measures may also be applicable to shark fisheries in other parts of Indonesia and beyond, as sharks increasingly become the focus of global conservation efforts 
Copyright: © 2018 Yulianto et al  This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited 
Funding: Data collection of this study was funded by the Darwin Initiative (grant number 2805)  The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript 
Overfishing is the greatest global threat to marine fish stocks [1–5]  Several shark species (Selachimorpha) are particularly vulnerable to overexploitation due to their conservative life history strategies, large body sizes and the high economic value of their preserved body parts [6–8]  With increasing fishing pressure in recent decades, primarily driven by international demand for a range of consumer goods (including fins, liver oil, skin, cartilage and meat), it is estimated that annual fishing mortality now exceeds the intrinsic rebound potential of most commercially exploited species [5, 9, 10]  This fishing pressure is taking its toll, with an estimated one in four Chondrichthyan species now threatened with extinction, making sharks amongst the most threatened species groups in the world  
It is also increasingly acknowledged that sharks play a critical role in maintaining functional and productive ocean ecosystems , as well as providing an important source of food and income for many coastal communities   Recognising both the plight and importance of shark populations, there is growing professional and public interest to improve shark conservation, and the management of shark fisheries and trade   This is reflected in several recent policy decisions to afford new international regulations for 12 species of sharks across seven genera under the Convention on the International Trade of Endangered Species of Wild Fauna and Flora (CITES)  This is a promising step for shark conservation; however, in order to create tangible outcomes for species conservation CITES must be implemented through domestic measures that are adapted to national and local contexts 
Indonesia is the world’s largest shark fishing nation [9, 14], and a global priority for shark conservation   Until recently Indonesia’s shark fishery has largely functioned as de facto open-access [12, 16]  However, in the past five years the Indonesian government has demonstrated a clear commitment to shark conservation and resource management, with domestic measures put in place to implement international obligations under CITES   Exploitation of all CITES-listed species is now regulated, either through full species protection or export controls (these species are hereafter referred to as ‘regulated’ species)  However, CITES only affords protection to a small number of Indonesia’s 112 known shark species , of which 83 are threatened with extinction according to the IUCN Red List of Threatened Species (i e  Vulnerable (VU), Endangered (EN) or Critically Endangered (CR) , these species are hereafter referred to as ‘threatened’ species), many of which continue to be landed throughout the country   Further, these policy measures predominantly regulate trade at the point of export, but do not necessarily influence fisher behaviour or local demand at the point of catch, such that the ‘trickle-down’ impacts on species mortality are unknown  In addition, effectively implementing species-specific shark mortality controls remains challenging due to the non-selectivity of fishing gears, and practical and cultural barriers to changing fisher preferences for certain gear-types and fishing methods  As such, existing regulations alone (e g  Indonesian Law on Fisheries 31/2004 and its derivative regulations) will likely be insufficient to curb mortality of threatened and regulated species, as fishers must be both willing and able to change their fishing behaviour   Moreover, most of Indonesia’s shark fisheries are small-scale, and in relatively poor coastal communities where there are often no legal, sustainable marine-based alternatives to shark fishing that offer similar financial returns [22, 23]  It is therefore imperative to consider the ethical and socioeconomic impacts of shark trade controls  Most shark species listed under CITES are listed on Appendix II, which is designed for sustainable use  International trade is permitted for CITES Appendix II species provided it is non-detrimental to wild populations of the species, as proven through a scientific non-detriment finding (NDF) report and implemented through a system of export permits  However, in Indonesia there is currently a lack of species-specific trade data for conducting NDFs and setting sustainable export quotas, such that the Indonesian government has to introduce trade bans for these species in order to meet CITES obligations  With new CITES-listings for thresher sharks (Alopias spp ) and silky shark (Carcharhinus falciformis) recently coming into force, this is likely to have huge implications for Indonesia’s economically important shark industry, and the coastal communities depending on it  In order to balance conservation and socioeconomic objectives, robust management systems must be put in place that ensure and allow sustainable fishing and trade  This necessitates the identification of practical management measures that can reduce mortality of threatened and regulated species at the point of catch, and provide realistic options for fishers to effectively and measurably improve the sustainability of their fishing practices 
This study analyses two years of qualitative and quantitative data from one of Indonesia’s targeted shark fisheries in Tanjung Luar, West Nusa Tenggara Province  We outline the key characteristics of the fishery, including fishing behaviour and overall catch volumes and composition  We analyse the impacts of different fishing techniques, and present factors influencing overall catch per unit effort (CPUE) of individual shark fishing trips, as well as factors influencing the likelihood of catching threatened and regulated species  Finally, we discuss the implications of our findings, and provide practical recommendations for fisheries management measures, which can support CITES implementation for sharks and reduce the catch of threatened and regulated species, in Indonesia and beyond 
This work was conducted under a Memorandum of Understanding (MoU) and Technical Cooperation Agreement (TCA) between the Wildlife Conservation Society (WCS) and the Ministry of Environment and Forestry (MoEF), Ministry Marine Affairs and Fisheries (MMAF) and the Marine and Fisheries Agency (MFA) of West Nusa Tenggara Province  These documents were approved and signed by Sonny Partono (Director General of Conservation of Natural Resources and Ecosystem MoEF), Sjarief Widjaja (Secretary General MMAF), and Djoko Suprianto (Acting Head of MFA of West Nusa Tenggara Province)  Due to this MoU and TCA no specific research permit was required  We collected data by measuring sharks that were already caught, dead, and landed by fishers in Tanjung Luar, with no incentives, compensation or specific requests for killing sharks for this study  WCS participates in the Conservation Initiative on Human Rights and the rules and guidelines of our Internal Review Board ensures that any research protects the rights of human subjects  We did not apply for an IRB permit for this study because our study design focused on collecting fish and fisheries data as opposed to personal socio-economic data  The FDGs and interviews were conducted to obtain early scoping information about fishing practices, and to establish protocols for more detailed fisheries data collection (as used in this study), and socio-economic data collection (as used in a later study (Lestari et al ), which underwent further ethical review due to the specific focus on human subjects) 
Tanjung Luar, located in East Lombok, West Nusa Tenggara Province, Indonesia (Fig 1), is a landing site for one of Indonesia’s most well-known targeted shark fisheries  Tanjung Luar serves at least 1,000 vessels, and the majority of these are less than 10 gross tonnes (GT) in size   A group of specialised fishers operating from Tanjung Luar village and a neighbouring island, Gili Maringkik, specifically target sharks  Shark catch is landed in a dedicated auction facility at the Tanjung Luar port  The shark industry is well established in Tanjung Luar, with product processing facilities and trade connections to local, national and international markets  Research by Lestari et al   indicates that the shark industry is significantly more profitable than non-shark fisheries in Tanjung Luar, particularly for boat owners  Strong patron-client relationships exist between boat owners and fishers, with shark fishers exhibiting high dependency on shark fishing, limited occupational diversity and low adaptive capacity for shifting into other fisheries  
Fig 1  Sharks landing monitoring site and fishing grounds of shark fishers that land at Tanjung Luar 
In January 2014 we conducted preliminary scoping research to better understand the operational and socioeconomic characteristics of Tanjung Luar’s shark fishery  During a three-week scoping visit a team of four trained Indonesian enumerators conducted semi-structured interviews and focus group discussions (FDGs) with fishers, boat owners and traders, alongside naturalistic observation in the field  Respondents were selected through purposive sampling, since the research was exploratory in nature and a priori sampling decisions were not possible   We conducted a total of 34 semi-structured interviews (S1 File) and four FDGs, which were attended by a total of 30 individuals  All interviews and discussions took place in Indonesian, with the help of a local enumerator who was fluent in the Tanjung Luar local dialect  Interviews took approximately 30 minutes, with no remuneration for participating  All respondents gave their full prior and informed consent before contributing to the research  During the interviews and FDGs we gathered information on number of boats, fishing gears used, fishing grounds, fishery operational characteristics, and shark supply chain, including estimated volumes and value of shark catch relative to other fisheries  We improved the accuracy of information on shark fishery characteristics and fishing behaviour through informal daily interactions and discussions with 131 shark fishers during our daily landings data collection and community engagement activities  More detailed socioeconomic data were collected in a full household survey in 2016, as outlined in Lestari et al   
Shark landings data were collected by three experienced enumerators, who were trained in species identification and data collection methods during a two-day workshop and three weeks of field mentoring to ensure the accuracy of the data collected  Landings were recorded every morning at the Tanjung Luar shark auction facility where shark fishers usually landed dead sharks, from 5am to 10am from January 2014 to December 2015  The enumerators recorded data on catch composition and fishing behaviour (Table 1) from 52 different vessels across a total of 595 fishing trips  The enumerators also measured the weight of selected sharks to calculate biomass and length-weight relationship 
Table 1  Types of data collected on fishing behaviour and catch composition during daily landings data collection at Tanjung Luar 
From fishing behaviour and catch data we calculated the overall species composition of catch  We calculated catch per unit effort (CPUE) by number of individuals using both catch per set (hereafter CPUE per set) and catch per 100 hooks per set (hereafter standardised CPUE) [25,26]  This was deemed necessary since different vessels and gear-types systematically deploy different numbers of hooks, and standardised CPUE allows for a more meaningful comparison 
To understand factors influencing overall CPUE we log transformed CPUE per trip to fit a normal distribution, and fitted linear models (LMs) of CPUE per trip to fishing behaviour variables (Table 1)  We considered all variables and used minimum AIC values with stepwise analysis of variance to identify the best fit and most significant influencing variables 
To inform the development of practical fisheries management measures (e g  gear restrictions), we also specifically analysed differences in CPUE for surface and bottom longline gears employed in the fishery, using two-way ANOVAs 
Factors affecting catch of threatened and regulated species 
To identify variables influencing the catch of threatened and regulated species we conducted a two-step process  In the first step, we identified factors influencing the likelihood of catching any threatened/regulated species during a given fishing trip, by creating binary response variables for whether a threatened species had been caught during a trip (yes = 1, no = 0), and separately for whether a regulated species had been caught during a trip (yes = 1, no = 0)  We then fitted generalised linear models (GLMs) with binomial errors to the binary response variables, separately for catch of threatened species and catch of regulated species  In the second step we identified variables that significantly influenced the CPUE of threatened species and the CPUE of regulated species, given that any were caught  We removed all records in which no threatened or regulated species were caught, log transformed standardised CPUE of threatened and regulated species, and fitted linear models (LMs) of standardised CPUE of threatened species and standardised CPUE to regulated species to fishing behaviour variables  Again, we considered all meaningful models and used minimum AIC values with stepwise analysis of variance to identify the best fit  and most significant influencing variables  This approach was necessary since catch of threatened and regulated species is zero-inflated, and creating binary response variables with a binomial error structure allowed for a simpler and more powerful statistical analysis  Note that we conducted two separate analyses, one for threatened species only and one for regulated species only, but used the same methods and process, as outlined above, for each analysis  We did not group threatened and protected species together, since although some species are both threatened and protected, this is not the case for all shark species landed in Tanjung Luar 
A total of 52 shark fishing vessels operate from Tanjung Luar, all of which are classified as small-scale according to the Indonesian Ministry of Marine Affairs and Fisheries (MMAF) vessel categorisation system, with <7GT capacity  These vessels are operated by approximately 150 highly-specialised shark fishers, from Tanjung Luar village and Gili Maringkik, who make up roughly 5% of the local fisher population  The shark industry is more profitable than non-shark fisheries, and shark fishers report high household dependency on shark resources, low occupational diversity, and limited capacity and aspirations to move into other fisheries or industries 
Surface and bottom longlines are used as the primary fishing gears to target sharks, with pelagic fish (e g  Euthynnus spp , Rastrellinger spp ) used as bait  Surface and bottom longlines systematically vary in length, depth deployed, number of sets, number of hooks used, and soak times (Table 2)  Gear types are typically associated with certain vessel types, and fishers–captain and crew—tend to exhibit preferences for specific gear types  Shark fishers also use gillnets and troll lines as secondary gears, to catch bait and opportunistically target other species, such as grouper, snapper, skipjack and mackerel tuna 
Table 2  Characteristics of surface and bottom longlines 
The shark fishing vessels can be divided into two broad categories according to fishing behaviour: larger vessels (≥14 m) with higher horsepower (HP) engines spend more time at sea than smaller vessels (≤12m) (p<0 001), and reach fishing grounds outside of West Nusa Tenggara  These vessels primarily fish in southern Sumbawa and Sumba Islands, however, they also reach as far as eastern Flores, Timor Island, and the Java Sea (Fig 1)  Larger, higher HP vessels also tend to employ surface longlines (p<0 001), and since they spend more time at sea, have a higher number of sets per trip than smaller vessels (p<0 001)  Smaller vessels (≤12 m) with smaller engines tend to remain in waters around West Nusa Tenggara only, carrying out shorter fishing trips using bottom longlines (Table 3) 
Table 3  Characterisation of the different fishing vessels used to target sharks in Tanjung Luar 
During the study period we recorded shark catch from a total of 595 fishing trips  We recorded 11,678 individual sharks, with an average total catch of 963 individuals per month (SD ± 434) and 19 7 individuals per trip (SD ± 15 6)  Standardised CPUE (per 100 hooks per set) ranged from 0 05 to 22 13 individuals, with an average of 0 96 and a mode of 0 20  Catch consisted of 42 different species from 18 families (Table 4)  22% of all landings were classified as threatened species (i e  VU, EN, CR) according to the IUCN Red List of Threatened Species, and 73% were near threatened  Almost half (46 3%) of landings were regulated (i e  CITES-listed) species  The most commonly caught species were silky shark (Carcharhinus falciformis), black tip shark (Carcharhinus limbatus) and scalloped hammerhead (Sphyrna lewini) 
Table 4  Sharks species landed in Tanjung Luar from January 2014 –December 2015 (VU = Vulnerable, EN = Endangered, NT = Near Threatened, LC = Least Concern, NE = Not Evaluated (VU and EN classified as ‘threatened’ in this study); II = CITES Appendix II, N = Not CITES-listed (II species classified as ‘regulated’ in this study)) 
Measures of CPUE for the Tanjung Luar shark fishery vary spatially and temporally, and with several aspects of fishing effort including gear type, hook number, engine power and number of sets  An initial comparison of average catch per trip and catch per set of the two major gear types, surface longline and bottom longline, indicates that CPUE of surface longlines was significantly higher than that of bottom longlines (ANOVA, p<0 001)  CPUE (individuals per set) was also positively associated with number of hooks, engine power, and number of sets (Fig 2)  However, these relationships are for unstandardised CPUE i e  without controlling for number of hooks 
Plots of CPUE: Number of individuals per set (A) and number of individuals per 100 hooks per set (standardised CPUE) (B) by gear type (1), number of hooks (2), number of sets (3) and engine horsepower (4) 
When controlling for hook number using standardised CPUE (individuals per 100 hooks per set) the relationships were reversed, with standardised CPUE of bottom longlines significantly higher than that of surface longlines (ANOVA, p<0 001; Fig 2)  A similar pattern was observed when comparing relationships between CPUE (individuals per set) and standardised CPUE for other measures of fishing effort, including numbers of hooks, engine power and number of sets (Fig 2)  There was a positive relationship between unstandardised CPUE (individuals per set) and number of hooks, number of sets and engine power, but a negative relationship between CPUE and these fishing behaviour variables when CPUE was standardised by hook number (individuals per 100 hooks per set) 
The best fit LM of standardised CPUE indicated that the most significant factors influencing standardised CPUE were fishing gear and number of hooks (p<0 001)  Month, engine power, number of sets and fishing ground were also identified as significant variables (Table 5), although there was considerable covariance between these factors  Standardised CPUE was significantly lower in January, and decreased with higher numbers of hooks, despite a higher total catch per trip and set (Fig 2) 
Table 5  Analysis of variance for linear model of standardised CPUE (individuals per 100 hooks per set) data from Tanjung Luar; significant values (p<0 05) are given in bold 
Best fit GLMs indicated that the most significant factors influencing the likelihood of catching threatened species were month (January and November were significantly lower: p<0 001 and p<0 05, respectively) and fishing ground (Other (i e  fishing grounds outside of WNTP and ENTP) was significantly higher: p<0 01)  Significant factors associated with standardised CPUE of threatened species were number of hooks (p<0 001), fishing ground (other: p<0 001, ENTP p<0 05), engine power (p<0 001) and trip length (p<0 001) (Table 6 and Fig 3) 
Plots of most significant factors affecting standardised CPUE (number of individuals per 100 hooks per set) of threatened species: a) hook number, b) fishing ground, c) engine power and d) trip length 
Analysis of variance for the best fit models of factors affecting: a) the likelihood of catching and the standardised CPUE of threatened species b) the likelihood of catching and the standardised CPUE of regulated species 
The most significant factors influencing the likelihood of catching regulated species were month (January was significantly lower: p<0 001), number of hooks (p<0 001) and engine power (<0 01)  Significant factors associated with standardised CPUE of regulated species were number of hooks (p<0 001), fishing gear (<0 001), number of sets (p<0 001), engine power (p<0 01) and month (November and January: p<0 05) (Table 5 and Fig 4) 
Plots of most significant factors affecting standardised CPUE (number of individuals per 100 hooks per set) of regulated species: a) hook number, b) gear type, c) number of sets 
Although Tanjung Luar’s targeted shark fishery is small in scale, considerable numbers of shark are landed, including a large proportion of threatened and regulated species  A key finding is that measures of CPUE, for all sharks and for threatened and regulated species, vary spatially and temporally, and with several aspects of fishing effort including gear type, hook number, engine power and number of sets  Moreover, the relationships between CPUE and fishing behaviour variables are different for different measures of CPUE (CPUE per trip, CPUE per set, CPUE per 100 hooks per set)  This highlights the importance of using appropriate standardisation for meaningful comparisons of CPUE across different gears and vessel types, and has important implications for fisheries management 
Unstandardised CPUE (individuals per set) was significantly lower in January  This is during the west monsoon season, which is characterised by high rainfall and adverse conditions at sea for fishing  Unstandardised CPUE was also significantly lower in West Nusa Tenggara Province (WNTP) than East Nusa Tenggara Province (ENTP) and other provinces, suggesting a lower abundance of sharks in this area  Engine power had a significant positive influence on unstandardised CPUE, and was also associated with longer trips and more sets, which was likely due to the ability of vessels with larger engines to travel longer distances, over longer time periods, and with higher numbers of sets, to favoured fishing grounds  Unstandardised CPUE was also significantly higher for surface longlines than bottom longlines  However, when standardising CPUE for the number of hooks (i e  individuals per 100 hooks per set) this relationship was reversed  Bottom longlines exhibit a higher standardised CPUE, with negative relationships between catch per 100 hooks per set and number of hooks and frequency of sets  Vessels with moderate engine horsepower (50-59hp) also had the highest standardised CPUE  Since surface longlines systematically employ significantly more hooks than bottom longlines (400–600 vs 25–200 hooks), and tend to be associated with larger boats, longer trips and more sets, these findings suggest that although increasing fishing effort increased total catch for these gears and trips, there were diminishing returns of this increased effort above low to moderate levels 
A large proportion of Tanjung Luar’s shark catch consisted of threatened (22%) and regulated species (46%)  Month is a significant factor in explaining standardised CPUE of both threatened and regulated species, which could indicate seasonal variation in the abundance of these species in the Tanjung Luar fishing grounds, or seasonal impacts on CPUE due to poor weather conditions  Fishing ground was a significant factor in explaining the catch of threatened species but not the catch in regulated species  This may be due to differences in range, distribution and relative abundance of species within these groups  Threatened species make up a relatively small proportion of Tanjung Luar’s catch in comparison to regulated species, which make up almost half of the catch (46%)  As such, regulated species may generally be more abundant and spatially diffuse than threatened species, and therefore caught more uniformly across fishing grounds  For example, regulated species catch is dominated by silky sharks (Carcharhinus falciformis), which are circum-tropical and coastal-pelagic, and exhibit limited site-fidelity or aggregation behaviour, while threatened species catch is dominated by scalloped hammerheads (Sphyrna lewini), which are known to aggregate in schools  These schools of scalloped hammerheads may be more restricted to specific aggregation sites outside of WNTP and ENTP waters, while silky sharks are found in uniform abundance throughout fishing grounds 
As with CPUE of all catch, there was a positive relationship between unstandardised CPUE (catch per set) of threatened and regulated species and number of hooks, but a significant negative relationship between standardised CPUE (catch per 100 hooks per set)  This was likely due to diminishing returns of adding additional hooks, and indicates that the effort for threatened and regulated species was exceeding maximum sustainable yield effort, such that increases in effort (e g  hook number) were leading to decreases in catch [28–30] 
Due to the profitability of the shark industry in Tanjung Luar, and limited adaptive capacity and willingness of shark fishers to move into other industries, it is necessary to identify practical and ethical management interventions that can improve the sustainability of the fishery whilst also mitigating the negative socio-economic consequences for coastal communities  Our findings indicate that spatiotemporal closures and restrictions on fishing effort could improve the overall catch per unit effort and sustainability of the Tanjung Luar shark fishery, and lead to positive conservation outcomes for priority species 
Since the location of shark fishing grounds plays a significant role in determining the likelihood of catching threatened species and their associated CPUE, improved marine spatial planning, with the identification of marine protected areas (MPAs) that protect critical shark habitat and shark populations, could reduce catch of species of conservation concern [31–33] and increase abundance of sharks [34, 35]  Provincial governments in West Papua and West Nusa Tenggara have already established ‘shark sanctuary’ MPAs, which protect critical shark habitat and ban shark fishing within their boundaries [16, 36], and monitoring data indicates positive impacts of shark-specific closures on shark abundance [37, 38]  Strengthening Indonesia’s existing MPA network for shark conservation, such as making all MPAs no-take zones for sharks and expanding spatial protection to critical shark habitat, including aggregation sites or pupping and nursery grounds for species of conservation concern, could have considerable conservation benefits  It should be noted, however, that MPAs may only be effective for certain species, such as those with small ranges or site-fidelity   More research is required to identify critical shark habitat and life history stages  For Tanjung Luar these efforts could focus on better understanding scalloped hammerhead (Sphyrna lewini) aggregation sites  Well-targeted spatial closures for this species could significantly reduce catch of threatened species in this fishery 
The relationships between gear type, several aspects of fishing effort (i e  hook number, engine power, number of sets, trip length), standardised CPUE of all shark species and standardised CPUE of threatened and regulated species suggest that there is an optimal effort that could increase overall CPUE of the fishery and significantly reduce fishing mortality of species of conservation concern  For example, our data suggest that CPUE peaks with low to intermediate trip lengths and gear sets, intermediate engine power and hook numbers of less than 75 per set longline  Although standardised CPUE of threatened and regulated species is also higher when fewer hooks are deployed, the catch per set and overall mortality is significantly lower  Regulations that control the number of hooks in combination with incentives for shark fishers to tightly manage the number of hooks they deploy could significantly reduce mortality of threatened and endangered species, maximise the overall CPUE of the fishery, and reduce operational costs for fishers, making shark fishing in Tanjung Luar more sustainable and more cost effective [39–41] 
Acknowledging that almost half of Tanjung Luar’s shark catch consists of CITES-listed species, developing measures that ensure both the sustainability of the fishery, and full traceability and control of onward trade, will be crucial for implementing CITES   The Indonesian government has demonstrated a strong commitment to regulating shark trade and implementing CITES [17–18], as demonstrated through several policy decisions to confer full and partial protection to CITES-listed shark and ray species (Marine Affairs and Fisheries Ministerial Decree No 4 /KEPMEN-KP/2014, Regulation No  48/PERMEN-KP/2016)  This includes zero quotas/export bans for hammerhead and oceanic whitetip sharks  However, these export bans should be considered intermediate policy measures as monitoring systems and data availability are improved, and sustainable quotas are established  This will be challenging, as shark products are often traded in large volumes of fresh and/or preserved body parts, with high morphological similarity between products from regulated species and non-regulated species  To guarantee that trade is not detrimental to the survival of species, sustainable fisheries management will need to be complemented with species-specific trade quotas  This will require catch documentation systems which trace shark products from point of catch to point of export and rapid, low-cost species identification methods 
As baseline data on shark population health are limited, and there is no standardised, fisheries-independent system for monitoring long-term changes in shark populations, indirect bio-indicators (e g  endo- and ectoparasites, [43–45]) could help to elucidate the impact of management measures on fisheries and populations of wild species  In the future, shark conservation and fisheries management could benefit from long-term monitoring of agreed indices of population abundance and health status 
These lessons may also apply to shark fisheries in other parts of the world  As sharks increasingly become the focus of global conservation efforts it should be acknowledged that species protection alone will not be enough to reduce mortality of priority species  More needs to be done to identify practical fisheries management measures that can reduce pressure on the most vulnerable species and populations, but also support sustainable use of species that are less susceptible to overfishing  Shark fishing forms an integral part of the livelihood strategies of many coastal communities [22, 23], and prohibiting catches will not necessarily lead to positive conservation outcomes [21, 46]  Management interventions must take into account local context and the motivations and well-being of fisher communities in order to be ethical, feasible and impactful 
S1 Dataset  Data of landed sharks at Tanjung Luar auction that had been used for this study 
S1 File  Questionnaires have been used to interview shark fishers, collector, traders, and processors 
We wish to acknowledge the support provided by fishers in Tanjung Luar for their great cooperation during fieldwork  We also thank I Made Dharma Aryawan, Muhsin, Abdul Kohar, and Abdurrafik for their assistance during field research, Benaya M Simeon, Peni Lestari, and Siska Agustina for helping with data processing, Ken Kassem for carefully reading the manuscript and providing useful inputs, and the anonymous reviewers for their constructive comments 
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Context 2: Brooksley Elizabeth Born (born August 27, 1940) is an American attorney and former public official who, from August 26, 1996, to June 1, 1999, was chair of the Commodity Futures Trading Commission (CFTC), the federal agency which oversees the U S  futures and commodity options markets  During her tenure on the CFTC, Born lobbied Congress and the President to give the CFTC oversight of off-exchange markets for derivatives, in addition to its role with respect to exchange-traded derivatives, but her warnings were ignored or dismissed, and her calls for reform resisted by other regulators <ref name="nytimes">Goodman, Peter S  The Reckoning - Taking Hard New Look at a Greenspan Legacy, The New York Times, October 9, 2008 </ref> Born resigned as chairperson on June 1, 1999, shortly after Congress passed legislation prohibiting her agency from regulating derivatives 

Early life and education
Born graduated from Abraham Lincoln High School (San Francisco, California) at the age of 16  She then attended Stanford University, where she majored in English and was graduated with the class of 1961   She initially wanted to become a doctor, but a guidance counsellor at Stanford advised her against medicine, so she majored in English literature instead 

She then attended Stanford Law School, one of only seven women in her class   She was the first female student ever to be named president of the Stanford Law Review  She received the "Outstanding Senior" award and graduated as valedictorian of the class of 1964 

Legal career
Immediately after law school Born was selected as a law clerk to judge Henry Edgerton of the U S  Court of Appeals for the District of Columbia Circuit  It was during this time that she met her first husband, Jacob C  Landau, who was a journalist covering the Federal courts at the time  Following her clerkship, she became an associate at the Washington, D C -based international law firm of Arnold & Porter  Born was attracted to Arnold & Porter because it was one of the few major law firms to have a woman partner at that time, Carolyn Agger, who was the head of the tax practice  Born took a two-year leave of absence from Arnold & Porter to accompany her first husband to Boston, where he had received a fellowship  During that time she worked as a research assistant to law professor Alan Dershowitz 

Born's early career at Arnold & Porter focused on international trade law, in which she represented a number of Swiss industries and the government of Switzerland  She developed a practice representing clients in numerous complex litigation and arbitration cases involving financial market transactions  Among her high-profile cases was the matter of the Hunt Brothers attempt to corner the market in silver in the 1970s  She made partner at Arnold & Porter, after moving to a three-day schedule to help raise her second child, and eventually rose to be the head of the firm's derivatives practice 

Born was among the first female attorneys to systematically address inequities regarding how the laws treated women  Born and another female lawyer, Marna Tucker, taught what is considered to have been the first "Women and the Law" course at Catholic University’s Columbus School of Law  The class exclusively concerned prejudicial treatment of women under the laws of the United States, past and present   Born and Tucker were surprised to discover that there was no textbook on the issue at the time   Born is also one of the co-founders of the National Women's Law Center  Born also helped rewrite the American Bar Association rules to make it possible for more women and minorities to sit on federal bench 

During her long legal career, and into her retirement, Born did much pro bono and other types of volunteer work  She was active in the American Bar Association, the largest professional organization of lawyers in the United States   Initially Born was named a member of the governing council of the ABA's Individual Rights Section, eventually becoming chairperson   Born and Tucker founded the ABA Women's Caucus, the first organization of female lawyers in the ABA   She held several other senior positions in the ABA, including being named the first woman member of the ABA's Standing Committee on the Federal Judiciary   As a member of the Judiciary Committee, Born provided testimony and opinion on persons nominated for federal judgeships  In 1980 she was named chair of the committee   As chair of the committee, Born was invited to address the U S  Congress regarding the nomination of Judge Sandra Day O'Connor to the U S  Supreme Court 

In 1993, Born's name was floated as a possible candidate for Attorney General of the United States, but Janet Reno was nominated 

In July 2009, Nancy Pelosi appointed Brooksley Born as a commissioner to the Financial Crisis Inquiry Commission (FCIC) 

Born and the OTC derivatives market
Born was appointed to the CFTC on April 15, 1994, by President Bill Clinton  Due to litigation against Bankers Trust Company by Procter and Gamble and other corporate clients, Born and her team at the CFTC sought comments on the regulation of over-the-counter derivatives, a first step in the process of writing CFTC regulations to supplement the existing regulations of the Federal Reserve System,  the Options Clearing Corporation, and the National Association of Insurance Commissioners  Born was particularly concerned about swaps, financial instruments that are traded over the counter between banks, insurance companies or other funds or companies, and thus have no transparency except to the two counterparties and the counterparties' regulators, if any   CFTC regulation was strenuously opposed by Federal Reserve chairman Alan Greenspan, and by Treasury Secretaries Robert Rubin and Lawrence Summers  On May 7, 1998, former SEC Chairman Arthur Levitt joined Rubin and Greenspan in objecting to the issuance of the CFTC's concept release  Their response dismissed Born's analysis and focused on the hypothetical possibility that CFTC regulation of swaps and other OTC derivative instruments could create a "legal uncertainty" regarding such financial instruments,  hypothetically reducing the value of the instruments  They argued that the imposition of regulatory costs would "stifle financial innovation" and encourage financial capital to transfer its  transactions offshore  The disagreement between Born and the Executive Office's top economic policy advisors has been described not only as a classic Washington turf war, but also a war of ideologies,  insofar as it is possible to argue that Born's actions were consistent with Keynesian and neoclassical economics while Greenspan, Rubin, Levitt, and Summers consistently espoused neoliberal, and neoconservative policies 

In 1998, a trillion-dollar hedge fund called Long Term Capital Management (LTCM) was near collapse   Using mathematical models to calculate debt risk, LTCM used derivatives to leverage $5 billion into more than $1 trillion, doing business with fifteen of Wall Street's largest financial institutions   The derivative transactions were not regulated, nor were investors able to evaluate LTCM's exposures   Born stated, "I thought that LTCM was exactly what I had been worried about"   In the last weekend of September 1998, the President's working group was told that the entire American economy hung in the balance   After intervention by the Federal Reserve, the crisis was averted   In congressional hearings into the crisis, Greenspan acknowledged that language had been introduced into an agriculture bill that would prevent CFTC from regulating the derivatives which were at the center of the crisis that threatened the US economy   U S  Representative Maurice Hinchey (D-NY) asked "How many more failures do you think we'd have to have before some regulation in this area might be appropriate?"  In response, Greenspan brushed aside the substance of Born's warnings with the simple assertion that "the degree of supervision of regulation of the over-the-counter derivatives market is quite adequate to maintain a degree of stability in the system"  Born's warning was that there wasn't any regulation of them   Born's chief of staff, Michael Greenberger summed up Greenspan's position this way: "Greenspan didn't believe that fraud was something that needed to be enforced, and he assumed she probably did  And of course, she did "  Under heavy pressure from the financial lobby, legislation prohibiting regulation of derivatives by Born's agency was passed by the Congress   Born resigned on June 1, 1999 

The derivatives market continued to grow yearly throughout both terms of George W  Bush's administration  On September 15, 2008, the bankruptcy of Lehman Brothers forced a broad recognition of a financial crisis in both the US and world capital markets   As Lehman Brothers' failure temporarily reduced financial capital's confidence, a number of newspaper articles and television programs suggested that the failure's possible causes included the conflict between the CFTC and the other regulators Faiola, Anthony, Nakashima, Ellen and Drew, Jill  The Crash: Risk and Regulation - What Went Wrong, The Washington Post, October 15, 2008 

Born declined to publicly comment on the unfolding 2008 crisis until March 2009, when she said: "The market grew so enormously, with so little oversight and regulation, that it made the financial crisis much deeper and more pervasive than it otherwise would have been " She also lamented the influence of Wall Street lobbyists on the process and the refusal of regulators to discuss even modest reforms 

An October 2009 Frontline documentary titled "The Warning"  described Born's thwarted efforts to regulate and bring transparency to the derivatives market, and the continuing opposition thereto  The program concluded with an excerpted interview with Born sounding another warning: "I think we will have continuing danger from these markets and that we will have repeats of the financial crisis -- may differ in details but there will be significant financial downturns and disasters attributed to this regulatory gap, over and over, until we learn from experience "

In 2009 Born, along with Sheila Bair of the FDIC, was awarded the John F  Kennedy Profiles in Courage Award in recognition of the "political courage she demonstrated in sounding early warnings about conditions that contributed" to the 2007-08 financial crisis   According to Caroline Kennedy, "Brooksley Born recognized that the financial security of all Americans was being put at risk by the greed, negligence and opposition of  powerful and well connected interests     The catastrophic financial events of recent months have  proved them [Born and Sheila Bair] right "  One member of the President's working group had a change of heart about Brooksley Born   SEC Chairman Arthur Levitt stated "I've come to know her as one of the most capable, dedicated, intelligent and committed public servants that I have ever come to know", adding that "I could have done much better  I could have made a difference" in response to her warnings 

In 2010, a documentary film Inside Job further alleged that derivatives regulation was ineffective from the Clinton administration on  Along with fellow whistleblower, former IMF Chief Economist Raghuram Rajan, who was also scorned by the economic establishment, Brooksley Born was cited as one of the authorities arguing that financial derivatives increase economic risk 

 Personal life 
Born is married to Alexander E  Bennett (also retired from Arnold & Porter)   She has five adult children - two from a previous marriage to Jacob Landau and three stepchildren  Notably, Born was named a partner at Arnold & Porter while working part-time so she could raise her two young children   When both of her children were school-age, Born returned to practice full-time 

References

External links
Attorney profile at Arnold & Porter
Brooksley Born (2009 Winner) of the Profiles in Courage Award, with acceptance speech transcript and NECN video

Profile at MarketsWiki
Speeches and statements
"Testimony Of Brooksley Born Chairperson of the CFTC Concerning The Over-The-Counter Derivatives Market", before the House Committee On Banking And Financial Services, July 24, 1998 
"The Lessons of Long Term Capital Management L P ", Remarks of Brooksley Born, Chairperson of the CFTC, Chicago-Kent-IIT Commodities Law Institute, Chicago, Illinois, October 15, 1998 
 Interview: Brooksley Born for "PBS Frontline: The Warning", PBS, (streaming VIDEO 1 hour), October 20, 2009 
Articles
Manuel Roig-Franzia  "Credit Crisis Cassandra:Brooksley Born's Unheeded Warning Is a Rueful Echo 10 Years On", The Washington Post, May 26, 2009
 Taibbi, Matt  "The Great American Bubble Machine", Rolling Stone'', July 9–23, 2009

1940 births
American women lawyers
Arnold & Porter people
Clinton administration personnel
Columbus School of Law faculty
Commodity Futures Trading Commission personnel
Heads of United States federal agencies
Lawyers from San Francisco
Living people
Stanford Law School alumni
21st-century American women
Stanford University alumni



ground_truth: The relationships between catch per set and fishing behavior variables differ when comparing unstandardized CPUE and standardized CPUE.

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

Question: What award did Brooksley Born receive in 2009?

Read the following text and answer briefly.

Context 3: Paper Info

Title: Conflict Optimization for Binary CSP Applied to Minimum Partition into Plane Subgraphs and Graph Coloring
Publish Date: 25 Mar 2023
Author List: Loïc Crombez (from LIMOS, Université Clermont Auvergne), Guilherme Da Fonseca (from LIS, Aix-Marseille Université), Florian Fontan (from Independent Researcher), Yan Gerard (from LIMOS, Université Clermont Auvergne), Aldo Gonzalez-Lorenzo (from LIS, Aix-Marseille Université), Pascal Lafourcade (from LIMOS, Université Clermont Auvergne), Luc Libralesso (from LIMOS, Université Clermont Auvergne), Benjamin Momège (from Independent Researcher), Jack Spalding-Jamieson (from David R  Cheriton School of Computer Science, University of Waterloo), Brandon Zhang (from Independent Researcher), Da Zheng (from Department of Computer Science, University of Illinois at Urbana-Champaign)

Figure

Figure 1: A partition of the input graph of the CG:SHOP2022 instance vispecn2518 into 57 plane graphs It is the smallest instance of the challenge with 2518 segments On top left, you see all 57 colors together On top right, you see a clique of size 57, hence the solution is optimal Each of the 57 colors is then presented in small figures 
Figure 2: Number of colors over time for the instance vispecn13806 using different values p The algorithm uses σ = 0 15, easy vertices, q max = 59022, but does not use the BDFS nor any clique 
Figure 3: Number of colors over time with different values of q max obtained on the instance vispecn13806 Parameters are σ = 0 15, p = 1 2, no clique knowledge, and no BDFS 
Figure 4: Number of colors over time with and without clique knowledge and BDFS obtained on the instance vispecn13806 Parameters are σ = 0 15, p = 1 2, and q max = 1500000 
Figure 5: Number of colors over time for the instance vispecn13806 for different values of σ In both figures the algorithm uses p = 1 2, easy vertices, q max = 59022, but does not use the BDFS nor any clique For σ ≥ 0 25, no solution better than 248 colors is found 
Figure 6: Number of colors over time (in hours) for the instance vispecn13806 
Several CG:SHOP 2022 results We compare the size of the largest known clique to the smallest coloring found by each team on a selection of 14 CG:SHOP 2022 instances 
[20][21][22][23][24][25] with state-of-the-art graph coloring algorithms The conflict optimizer underperforms except on the geometric graphs r* and dsjr* CE39-0007), SEVERITAS (ANR-20-CE39-0005) and by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP[20][21][22][23][24][25] The work of Luc Libralesso is supported by the French ANR PRC grant DECRYPT (ANR-18-CE39-0007) 

abstract

CG:SHOP is an annual geometric optimization challenge and the 2022 edition proposed the problem of coloring a certain geometric graph defined by line segments  Surprisingly, the top three teams used the same technique, called conflict optimization  This technique has been introduced in the 2021 edition of the challenge, to solve a coordinated motion planning problem 
In this paper, we present the technique in the more general framework of binary constraint satisfaction problems (binary CSP)  Then, the top three teams describe their different implementations of the same underlying strategy  We evaluate the performance of those implementations to vertex color not only geometric graphs, but also other types of graphs 

Introduction

The CG:SHOP challenge (Computational Geometry: Solving Hard Optimization Problems) is an annual geometric optimization competition, whose first edition took place in 2019  The 2022 edition proposed a problem called minimum partition into plane subgraphs  The input is a graph G embedded in the plane with edges drawn as straight line segments, and the goal is to partition the set of edges into a small number of plane graphs (Fig  )  
This goal can be formulated as a vertex coloring problem on a graph G defined as follows  The vertices of G are the segments defining the edges of G, and the edges of G correspond to pairs of crossing segments (segments that intersect only at a common endpoint are not considered crossing)  The three top-ranking teams (Lasa, Gitastrophe, and Shadoks) on the CG:SHOP 2022 challenge all used a common approach called conflict optimization while the fourth team used a SAT-Boosted Tabu Search  
Conflict optimization is a technique used by Shadoks to obtain the first place in the CG:SHOP 2021 challenge for low-makespan coordinated motion planning , and the main ideas of the technique lent themselves well to the 2022 challenge  Next, we describe the conflict optimizer as a metaheuristic to solve constraint satisfaction problems (CSP)  
We start by describing a CSP  A CSP is a triple of • variables X = (x 1 ,       , x n ), Each of the 57 colors is then presented in small figures  • domains D = (D 1 ,       , D n ), and • constraints R  Each variable x i must be assigned a value in the corresponding domain D i such that all constraints are satisfied 
In general, the constraints may forbid arbitrary subsets of values  We restrict our attention to a particular type of constraints (binary CSP ), which only involve pairs of assignments  A partial evaluation is an assignment of a subset of the variables, called evaluated, with the remaining variables called non-evaluated 
All constraints involving a non-evaluated variable are satisfied by default  We only consider assignments and partial assignments that satisfy all constraints  The conflict optimizer iteratively modifies a partial evaluation with the goal of emptying the set S of non-evaluated variables, at which point it stops 
At each step, a variable x i is removed from S  If there exists a value x ∈ D i that satisfies all constraints, then we assign the value x to the variable x i   Otherwise, we proceed as follows  For each possible value x ∈ D i , we consider the set K(i, x) of variables (other than x i ) that are part of constraints violated by the assignment x i = x 
We assign to x i the value x that minimizes where w(j) is a weight function to be described later  The variables x j ∈ K(i, x) become non-evaluated and added to S  The weight function should be such that w(j) increases each time x j is added to S, in order to avoid loops that keep moving the same variables back and forth from S  Let q(j) be the number of times x j became non-evaluated 
A possible weight function is w(j) = q(j)  More generally, we can have w(j) = q(j) p for some exponent p (typically between 1 and 2)  Of course, several details of the conflict optimizer are left open  For example, which element to choose from S, whether some random noise should be added to w, and the decision to restart the procedure from scratch after a certain time 
The CSP as is, does not apply to optimization problems  However, we can, impose a maximum value k of the objective function in order to obtain a CSP  The conflict optimizer was introduced in a low makespan coordinated motion planning setting  In that setting, the variables are the robots, the domains are their paths (of length at most k) and the constraints forbid collisions between two paths 
In the graph coloring setting, the domains are the k colors of the vertices and the constraints forbid adjacent vertices from having the same color  The conflict optimizer can be adapted to non-binary CSP, but in that case multiple variables may be unassigned for a single violated constraint  The strategy has some resemblance to the similarly named min-conflicts algorithm , but notable differences are that a partial evaluation is kept instead of an invalid evaluation and the weight function that changes over time 
While the conflict optimization strategy is simple, there are different ways to apply it to the graph coloring problem  The goal of the paper is to present how the top three teams applied it or complemented it with additional strategies  We compare the relative benefits of each variant on the instances given in the CG:SHOP 2022 challenge 
We also compare them to baselines on some instances issued from graph coloring benchmarks  The paper is organized as follows  Section 2 presents the details of the conflict optimization strategy applied to graph coloring  In the three sections that follow, the three teams Lasa, Gitastrophe, and Shadoks present the different parameters and modified strategies that they used to make the algorithm more efficient for the CG:SHOP 2022 challenge 
The last section is devoted to the experimental results 

Literature Review

The study of graph coloring goes back to the 4-color problem (1852) and it has been intensively studied since the 1970s (see for surveys)  Many heuristics have been proposed , as well as exact algorithms   We briefly present two classes of algorithms: greedy algorithms and exact algorithms  Greedy algorithms 
These algorithms are used to find good quality initial solutions in a short amount of time  The classic greedy heuristic considers the vertices in arbitrary order and colors each vertex with the smallest non-conflicting color  The two most famous modern greedy heuristics are DSATUR and Recursive Largest First (RLF )  
At each step (until all vertices are colored), DSATUR selects the vertex v that has the largest number of different colors in its neighbourhood  Ties are broken by selecting a vertex with maximum degree  The vertex v is colored with the smallest non-conflicting color  RLF searches for a large independent set I, assigns the vertices I the same color, removes I from G , and repeats until all vertices are colored 
Exact algorithms  Some exact methods use a branch-and-bound strategy, for example extending the DSATUR heuristic by allowing it to backtrack   Another type of exact method (branch-and-cut-and-price) decomposes the vertex coloring problem into an iterative resolution of two sub-problems   The "master problem" maintains a small set of valid colors using a set-covering formulation 
The "pricing problem" finds a new valid coloring that is promising by solving a maximum weight independent set problem  Exact algorithms are usually able to find the optimal coloring for graphs with a few hundred vertices  However, even the smallest CG:SHOP 2022 competition instances involve at least a few thousands vertices 

Conflict Optimization for Graph Coloring

Henceforth, we will only refer to the intersection conflict graph G induced by the instance  Vertices will refer to the vertices V (G ), and edges will refer to the edges E(G )  Our goal is to partition the vertices using a minimum set of k color classes C = {C 1 ,       , C k }, where no two vertices in the same color class C i are incident to a common edge 

Conflict Optimization

TABUCOL inspired neighbourhood One classical approach for the vertex coloring involves allowing solutions with conflicting vertices (two adjacent vertices with the same color)  It was introduced in 1987 and called TABUCOL  It starts with an initial solution, removes a color (usually the one with the least number of vertices), and assigns uncolored vertices with a new color among the remaining ones 
This is likely to lead to some conflicts (i e  two adjacent vertices sharing a same color)  The local search scheme selects a conflicting vertex, and tries to swap its color, choosing the new coloring that minimises the number of conflicts  If it reaches a state with no conflict, it provides a solution with one color less than the initial solution 
The process is repeated until the stopping criterion is met  While the original TABUCOL algorithm includes a "tabu-list" mechanism to avoid cycling, it is not always sufficient, and requires some hyper-parameter tuning in order to obtain a good performance on a large variety of instances  To overcome this issue, we use a neighbourhood, but replace the "tabu-list" by the conflict optimizer scheme presented above 
PARTIALCOL inspired neighbourhood PARTIALCOL another local search algorithm solving the vertex coloring problem was introduced in 2008  This algorithm proposes a new local search scheme that allows partial coloring (thus allowing uncolored vertices)  The goal is to minimize the number of uncolored vertices 
Similarly to TABUCOL, PARTIALCOL starts with an initial solution, removes one color (unassigning its vertices), and performs local search iterations until no vertex is left uncolored  When coloring a vertex, the adjacent conflicting vertices are uncolored  Then, the algorithm repeats the process until all vertices are colored, or the stopping criterion is met 
This neighbourhood was also introduced alongside a tabu-search procedure  The tabu-search scheme is also replaced by a conflict-optimization scheme  Note that this neighbourhood was predominantly used by the other teams 

Finding Initial Solutions

Lasa team used two approaches to find initial solutions: 1  DSATUR is the classical graph coloring algorithm presented in Section 1  2  Orientation greedy is almost the only algorithm where the geometry of the segments is used  If segments are almost parallel, it is likely that they do not intersect (thus forming an independent set) 
This greedy algorithm first sorts the segments by orientation, ranging from − π 2 to π 2   For each segment in this order, the algorithm tries to color it using the first available color  If no color has been found, a new color is created for coloring the considered segment  This algorithm is efficient, produces interesting initial solutions and takes into account the specificities of the competition 

Solution Initialization

The gitastrophe team uses the traditional greedy algorithm of Welsh and Powell to obtain initial solutions: order the vertices in decreasing order of degree, and assign each vertex the minimum-label color not used by its neighbors  During the challenge Gitastrophe attempted to use different orderings for the greedy algorithm, such as sorting by the slope of the line segment associated with each vertex (as the orientation greedy initialization presented in Section 3), and also tried numerous other strategies 
Ultimately, after running the solution optimizer for approximately the same amount of time, all initializations resulted in an equal number of colors 

Modifications to the Conflict Optimizer

Taking inspiration from memetic algorithms, which alternate between an intensification and a diversification stage, the algorithm continually switched between a phase using the above conflict score, and one minimizing only the number of conflicts  Thus during the conflict-minimization phase, the random variables f (C j ) and w(u) are both fixed equal to 1 leading to a conflict score
Each phase lasted for 10 5 iterations  Adding the conflict-minimization phase gave minor improvements to some of the challenge instances 

Shadoks

In this section, we describe the choices used by the Shadoks team for the options described in Section 2 1  The Shadoks generally chose to eliminate the color with the smallest number of elements  However, if the multistart option is toggled on, then a random color is used each time  The conflict set S is stored in a queue 
The Shadoks tried other strategies, but found that the queue gives the best results  The weight function used is w(u) = 1 + q(u) p , mostly with p = 1 2  The effect of the parameter p is shown in Fig    Notice that in all figures, the number of colors shown is the average of ten executions of the code using different random seeds 
The algorithm uses σ = 0 15, easy vertices, q max = 59022, but does not use the BDFS nor any clique  If q(u) is larger than a threshold q max , the Shadoks set w(u) = ∞ so that the vertex u never reenters S  If at some point an uncolored vertex v is adjacent to some vertex u of infinite weight in every color class, then the conflict optimizer is restarted 
When restarting, the initial coloring is shuffled by moving some vertices from their initial color class to a new one  Looking at Fig  , the value of q max does not seem to have much influence as long as it is not too small  Throughout the challenge the Shadoks almost exclusively used q max = 2000 • (75000/m) 2 , where m is the number of vertices 
This value roughly ensures a restart every few hours  q max =0 5k q max =5k q max =50k q max =100k q max =250k The Shadoks use the function f as a Gaussian random variable of mean 1 and variance σ  A good default value is σ = 0 15  The effect of the variance is shown in Fig    Notice that setting σ = 0 gives much worse results 
Option (e) The goal of BDFS is to further optimize very good solutions that the conflict optimizer is not able to improve otherwise  Fig  shows the influence of BDFS  While on this figure, the advantages of BDFS cannot be noticed, its use near the end of the challenge improved about 30 solutions  The bounded depth-first search (BDFS) algorithm tries to improve the dequeuing process 
The goal is to prevent a vertex in conflict with some adjacent colored vertices from entering in the conflict set  At the first level, the algorithm searches for a recoloring of some adjacent vertices which allows us to directly recolor the conflict vertex  If no solution is found, the algorithm  In both figures the algorithm uses p = 1 2, easy vertices, q max = 59022, but does not use the BDFS nor any clique 
For σ ≥ 0 25, no solution better than 248 colors is found  could recolor some vertices at larger distances from the conflict vertex  To do so, a local search is performed by trying to recolor vertices at a bounded distance from the conflict vertex in the current partial solution  The BDFS algorithm has two parameters: adjacency bound a max and depth d 
In order to recolor a vertex v, BDFS gets the set C of color classes with at most a max neighbors of v  If a class in C has no neighbor of v, v is assigned to C  Otherwise, for each class C ∈ C, BDFS tries to recolor the vertices in C which are adjacent to v by recursively calling itself with depth d − 1 
At depth d = 0 the algorithm stops trying to color the vertices  During the challenge the Shadoks used BDFS with parameters a max = 3 and d = 3  The depth was increased to 5 (resp  7) when the number of vertices in the queue was 2 (resp  1)  Degeneracy order Given a target number of colors k, we call easy vertices a set of vertices Y such that, if the remainder of the vertices of G are colored using k colors, then we are guaranteed to be able to color all vertices of G with k colors 
This is obtained using the degeneracy order Y   To obtain Y we iteratively remove from the graph a vertex v that has at most k − 1 neighbors, appending v to the end of Y   We repeat until no other vertex can be added to Y   Notice that, once we color the remainder of the graph with at least k colors, we can use a greedy coloring for Y in order from last to first without increasing the number of colors used 
Removing the easy vertices reduces the total number of vertices, making the conflict optimizer more effective  The Shadoks always toggle this option on (the challenge instances contain from 0 to 23% easy vertices) 

Results

We provide the results of the experiments performed with the code from the three teams on two classes of instances  First, we present the results on some selected CG:SHOP 2022 instances  These instances are intersection graphs of line segments  Second, we execute the code on graphs that are not intersection graphs, namely the classic DIMACS graphs , comparing the results of our conflict optimizer implementations to previous solutions 
The source code for the three teams is available at: • Lasa: https://github com/librallu/dogs-color • Gitastrophe: https://github com/jacketsj/cgshop2022-gitastrophe • Shadoks: https://github com/gfonsecabr/shadoks-CGSHOP2022

CG:SHOP 2022 Instances

We selected 14 instances (out of 225) covering the different types of instances given in the CG:SHOP 2022 challenge  The results are presented in Table   For comparison, we executed the HEAD code on some instances using the default parameters  The table shows the smallest number of colors for which HEAD found a solution 
We ran HEAD for 1 hour of repetitions for each target number of colors on a single CPU core (the HEAD solver takes the target number of colors as a parameter and we increased this parameter one by one)  At the end of the challenge, 8 colorings computed by Lasa, 11 colorings computed by Gitastrophe, and 23 colorings computed by Shadoks over 225 instances have been proved optimal (their number of colors is equal to the size of a clique) 
In order to compare the efficiency of the algorithms, we executed the different implementations on the CG:SHOP instance vispecn13806  The edge density of this graph is 19%, the largest clique that we found has 177 vertices and the best coloring found during the challenge uses 218 colors  Notice that vispecn13806 is the same instance used in other Shadoks experiments in Section 5  Notice also that HEAD algorithm provides 283 colors after one hour compared to less than 240 colors for the conflict optimizers 
We ran the three implementations on three different servers and compared the results shown in Figure   For each implementation, the x coordinate is the running time in hours, while the y coordinate is the smallest number of colors found at that time 

Results on DIMACS Graphs

We tested the implementation of each team on the DIMACS instances to gauge the performance of the conflict optimizer on other classes of graphs  We compared our results to the best known bounds and to the state of the art coloring algorithms HEAD and QACOL   The time limit for Lasa's algorithms is 1 hour 
CWLS is Lasa's conflict optimizer with the neighbourhood presented in TABUCOL , while PWLS is the optimizer with the neighbourhood presented in PARTIALCOL   Gitastrophe algorithm ran 10 minutes after which the number of colors no longer decreases  Shadoks algorithm ran for 1 hour without the BDFS option (results with BDFS are worse) 
Results are presented in Table   We only kept the difficult DIMACS instances  For the other instances, all the results match the best known bounds  The DIMACS instances had comparatively few edges (on the order of thousands or millions); the largest intersection graphs considered in the CG:SHOP challenge had over 1 5 billion edges 
We notice that the conflict optimizer works extremely poorly on random graphs, but it is fast and appears to perform well on geometric graphs (r250 5, r1000 1c, r1000 5, dsjr500 1c and dsjr500 5), matching the best-known results   Interestingly, these geometric graphs are not intersection graphs as in the CG:SHOP challenge, but are generated based on a distance threshold 
On the DIMACS graphs, Lasa implementation shows better performance than the other implementations 



ground_truth: In 2009, Brooksley Born received the John F. Kennedy Profiles in Courage Award.

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

Question: What are the three teams that used conflict optimization in the challenge?

Read the following text and answer briefly.

Context 4: 

English acceded to the role of Finance Minister in the continuing wake of the financial crisis  In response to New Zealand's rising debt, English made budget deficit-reduction his main priority  His first budget outlined three focuses in New Zealand's financial recovery: "improving the business environment and removing roadblocks to growth; investment in productive infrastructure; and improving the way government works"  One of his first acts was creating the National Infrastructure Unit, charged with formulating a plan for infrastructure projects and investments  He commissioned a government-wide spending review, with an aim to reducing government expenditure—with the exceptions of a two-year stimulus package and long-term increases on infrastructure spending 

In April 2011, the Opposition criticised English for suggesting that New Zealand businesses could use New Zealand's low wages to help it compete with Australia  The National Government campaigned for re-election in 2011 on its economic record  The Government boasted growth for five consecutive quarters up to mid-2010, totalling 1 6% of real GDP 

Strong growth resulted in a surplus of $473 million for the 2015/16 financial year, projected to rise to $8 5 billion by 2020/21  In his 2016 Economic and Fiscal Update address, English stated that reducing debt and tackling the costs of the 2016 Kaikōura earthquake were higher priorities than reducing rates of tax 

Allowances issue
In 2009, the media, including TVNZ and TV3 revealed that English was receiving about NZ$900 a week as part of a living allowance for ministers, to live in his own NZ$1 2 million Wellington home  At the time, English also received $276,200 in his annual salary as Deputy Prime Minister  It was also revealed other ministers with homes in the capital city were also claiming accommodation allowances  On 3 August 2009, Prime Minister John Key started a review of the housing allowances claimed by cabinet ministers  English subsequently paid back $12,000 and only claimed about $24,000 a year in living allowances  The Auditor-General's office said in September 2009 that they were making "preliminary enquiries" into parliamentary housing expenses in response to a letter of complaint from Progressive party leader Jim Anderton  Two days later English stated that he would no longer take up any housing allowance and had paid back all the allowance he had received since the November 2008 election 

Prime Minister (2016–2017)

John Key resigned on 12 December, and endorsed English as his successor in the resulting leadership election  Following the drop-out of both Judith Collins and Jonathan Coleman from the leadership election, English was sworn in as the 39th Prime Minister of New Zealand on 12 December 2016 

English appointed his first cabinet on 18 December  In a reshuffle, he appointed Steven Joyce to succeed him as Finance Minister, while most ministerial portfolios remained the same 

In February 2017, English did not attend Waitangi Day commemorations at the historic treaty grounds, reportedly in response to the Ngāpuhi iwi's decision to stop the Prime Minister from speaking at the marae  Ngāpuhi have protested the Government's negotiation of the Trans Pacific Partnership Agreement (TPPA), which the iwi believe infringes upon Māori sovereignty, and thus does not adhere to the Treaty of Waitangi  English had been invited to attend in an official capacity; his non-attendance was criticised by a Ngāpuhi elder and Opposition leader Andrew Little 

In his first overseas trip as Prime Minister, English travelled to Europe to discuss trade ties, including a prospective New Zealand–European Union free trade agreement  He first travelled to London on 13 January 2017 to meet British Prime Minister Theresa May  Discussing trade relations, English said the two nations were "natural partners" and would "continue to forge ties" after the UK's withdrawal from the EU  He also arranged to meet with London Mayor Sadiq Khan, Belgian Prime Minister Charles Michel and German Chancellor Angela Merkel   In a meeting with Merkel, English received crucial backing from Germany for a trade deal with the EU  On 16 January, English stated that his government would continue to promote TPPA, despite the United States' decision to withdraw from the agreement  He explained that Southeast Asian countries would now be treated as a priority in negotiations—he also asserted that the United States was ceding influence to China by its rejection of the trade pact 

At a press conference at the Beehive on 1 February 2017, English announced that the 2017 general election would be held on 23 September  The Prime Minister later confirmed that his party would approach ACT, United Future and the Māori Party if confidence and supply agreements were required to form a government following the election  In his second cabinet reshuffle on 24 April, English appointed Gerry Brownlee as his new Foreign Affairs Minister; he also promoted Nikki Kaye to the portfolio of Education Minister, and moved Mark Mitchell into the cabinet to become Defence Minister  The reshuffle was perceived as an election preparation 

On 13 February 2017, English welcomed Australian Prime Minister Malcolm Turnbull to Wellington  The two leaders reaffirmed their shared trade agenda, and discussed changes to the Australian citizenship pathway which will affect permanent residents originating from New Zealand 

On 19 June, it was reported that Todd Barclay, who succeeded English as MP for Clutha-Southland, had clandestinely recorded one of his employee's conversations the previous year, and that John Key's leaders' budget was used to pay a confidential settlement after the employee resigned  English admitted that he had been aware of the illegal recording and the settlement, and thus implicated in the scandal 

During the 2017 National campaign launch, English introduced a $379 million social investment package including digital learning academies for high school students, more resources for mathematics, and boosting support for teaching second languages in schools, and maintaining National Standards in the school curriculum  Prime Minister English also sought to defend National's financial management and economic track record and claimed that the opposition Labour Party would raise taxes  Early opinion polling had forecast a poor showing in the election for the Labour Party, but in early August 37-year-old Jacinda Ardern took over as Labour leader and seemingly energised younger voters 

At the 2017 general election, National won the largest share of the party vote (44 4%) and the largest number of seats (56) in the House Representatives  However, National lacked enough seats to govern alone due to two of the party's support partners, the Māori Party and United Future, losing their parliamentary seats  In response, English stated that the party would be entering into talks to form a coalition with New Zealand First  Following talks with the two largest parties, New Zealand First entered a coalition arrangement with the Labour Party  English was succeeded as prime minister by Jacinda Ardern on 26 October 

Opposition (2017–2018)

Leader of the Opposition
English was re-elected as National Party leader on 24 October 2017  At the time of his re-election, English announced his intention to stay on as leader until the next general election  On 13 February 2018, however, he stood down as National Party leader due to personal reasons, and instructed the party to put into motion the processes to elect a new leader  He also retired from Parliament  English's resignation followed weeks of speculation that he would step aside for a new leader  On 27 February, he was succeeded as party leader by Simon Bridges as the result of the leadership election held that day 

Post-premiership 
In 2018, English joined the board of Australian conglomerate, Wesfarmers  English serves in Chairmanships of Mount Cook Alpine Salmon, Impact Lab Ltd and Manawanui Support Ltd  He is also a director of The Instillery, Centre for Independent Studies and The Todd Corporation Limited, and is a member of the Impact Advisory Group of Macquarie Infrastructure and Real Assets 

Political and social views

English is regarded as more socially conservative than his predecessor, John Key  He has stated his opposition to voluntary euthanasia and physician-assisted suicide, same-sex civil unions, and the decriminalisation of prostitution  As Prime Minister he opposed any "liberalisation" of abortion law 

In 2004, English voted against a bill to establish civil unions for both same-sex and opposite-sex couples  In 2005, he voted for the Marriage (Gender Clarification) Amendment Bill, which would have amended the Marriage Act to define marriage as only between a man and a woman  English voted against the Marriage (Definition of Marriage) Amendment Bill, a bill that legalised same-sex marriage in New Zealand  However, in December 2016 he stated, "I'd probably vote differently now on the gay marriage issue  I don't think that gay marriage is a threat to anyone else's marriage" 

In 2009, English voted against the Misuse of Drugs (Medicinal Cannabis) Amendment Bill, a bill aimed at amending the Misuse of Drugs Act so that cannabis could be used for medical purposes 

Personal life 
English met his future wife, Mary Scanlon, at university  She was studying medicine at the time, and became a general practitioner  Both her parents were immigrants, her father being Samoan and her mother Italian, born on the island of Stromboli  They have six children: a daughter and five sons 

English is a practising Roman Catholic, but has stated that he considers his religious beliefs personal and thus separate from politics 

In June 2002, English took part in TV3's Fight For Life, a celebrity boxing fundraiser to raise money for the Yellow Ribbon anti-youth-suicide campaign, influenced by the death of a teenage nephew in 1997  He lost a split decision to former university colleague Ted Clarke 

Honours
In the 2018 Queen's Birthday Honours, English was appointed a Knight Companion of the New Zealand Order of Merit, for services of over 27 years to the State 

See also

List of New Zealand governments
Politics of New Zealand

References

External links

Profile at National Party 
Profile on Parliament nz
Releases and speeches at Beehive govt nz

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1961 births
21st-century New Zealand politicians
Candidates in the 2017 New Zealand general election
Deputy Prime Ministers of New Zealand
Leaders of the Opposition (New Zealand)
Living people
Members of the Cabinet of New Zealand
Members of the New Zealand House of Representatives
New Zealand farmers
New Zealand finance ministers
New Zealand list MPs
New Zealand MPs for South Island electorates
New Zealand National Party MPs
New Zealand National Party leaders
New Zealand Roman Catholics
New Zealand people of Irish descent
People educated at St  Patrick's College, Silverstream
People from Dipton, New Zealand
People from Lumsden, New Zealand
Prime Ministers of New Zealand
University of Otago alumni
Victoria University of Wellington alumni
Knights Companion of the New Zealand Order of Merit
New Zealand politicians awarded knighthoods



ground_truth: Lasa, Gitastrophe, and Shadoks.