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Defendants Acquitted in Panama Papers Money-Laundering Trial

The case focused on the law firm at the heart of the 2016 scandal, in which leaked documents revealed a vast network of offshore tax havens.

By Leila Miller

Reporting from Mexico City

In a striking verdict for a money-laundering case associated with the Panama Papers scandal, a Panamanian judge on Friday acquitted all 28 defendants, among them former employees of the law firm Mossack Fonseca, the source of the leaked documents that set off a worldwide furor in 2016.

The verdict came eight years after a partnership of media outlets published an explosive investigation into 11.5 million documents leaked from the Panama-based firm. The leak exposed the offshore banking industry, prompted international tax investigations and brought down heads of state.

Among the original 29 defendants were the shuttered firm’s co-founders, Jürgen Mossack, 76, and Ramón Fonseca, who died in May at age 71 while awaiting the verdict. In her 339-page ruling, the judge, Baloísa Marquínez, said that the case against Mr. Fonseca was dropped because of his death.

Prosecutors had alleged that Mossack Fonseca had created shell companies with the purpose of hiding money earned in illicit activities, and that the firm failed to act with due diligence and take the necessary care when reviewing its clients.

In a written statement published Friday evening, Panama’s judicial branch said that the judge had found that electronic evidence presented by prosecutors did not meet chain of custody protocols and had suffered from authentication issues. It also said that the judge had not found sufficient evidence to hold the accused responsible.

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Judge acquits 28 people accused in Panama Papers case, including law firm co-founder

FILE - Juergen Mossack, partner of the law firm Mossack-Fonseca, leaves the Supreme Court during the trial of the “Panama Papers” money laundering case in Panama City, April 8, 2024. On Friday, a Panamanian judge acquitted Mossack and 27 other people who were accused of money laundering in the notorious Panama Papers case. (AP Photo/Agustin Herrera, File)

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PANAMA CITY (AP) — A judge has acquitted 28 people accused of money laundering in an international case known as the Panama Papers, including the co-founder of a law firm that authorities say was at the center of a conspiracy to hide money linked to illegal activities.

Jürgen Mossack founded Mossack & Fonseca with then associate Ramón Fonseca, who died in May. Mossack was acquitted on Friday along with others after a Panamanian judge found that the evidence against Mossack didn’t comply with the chain of custody after authorities raided the office of the now defunct firm.

Prosecutors had accused Mossack, Fonseca and others of creating offshore companies and using complex transactions to hide money from illegal activities related to the so-called car wash corruption scandal involving Brazilian construction company Odebrecht, which pleaded guilty in U.S. federal court to a charge related to using shell companies to hide millions of dollars in bribes paid worldwide to win public contracts.

The judge noted that other evidence in the Panama Papers case “was not sufficient and conclusive to determine the criminal responsibility of the accused.”

In addition, the judge lifted personal and property precautionary measures against all the defendants, according to a judicial statement.

“We feel satisfied in the midst of mixed emotions, because many lives were affected along the way,” Guillermina Mc Donald, who was the defense attorney for Mossack and Fonseca, told The Associated Press. Her firm also represented 80% of the accused firm’s collaborators.

Judge Balaoisa Marquínez had decided to combine the Panama Papers case with another known as “Operation Car Wash,” a major anti-corruption investigation that began in Brazil.

On Friday, she ruled that in the car wash case, “it was not possible to determine the entry of money from illicit sources, coming from Brazil, into the Panamanian financial system with the purpose of hiding, concealing, disguising or helping to evade the legal consequences of the preceding crime.”

In June 2022, Mossack, Fonseca and 37 other people were acquitted in a separate money laundering case.

The investigation in Brazil began in 2014, with the Mossack & Fonseca firm later coming under scrutiny after 11 million financial documents tied to the company were leaked.

The repercussions of the leak were widespread: it led to the resignation of a prime minister in Iceland and brought scrutiny to now former leaders of Argentina and Ukraine, Chinese politicians and Russian President Vladimir Putin, among others.

Harnessing the Facebook for Online Marketing in Papua New Guinea: How does it Transpire?

22 Pages Posted: 27 Jun 2024

Geetha Rani Prakasam

National Institute of Educational Planning and Administration (NIEPA)

Amanda Towi

University of papua new guinea.

Date Written: May 29, 2024

Digital Marketplaces in the context of Papua New Guinea is at its nascent stage. Mobile penetration rate including one bang (basic phone) subscribers is 30 per cent of the population, with a highly skewed distribution tilted toward the capital city. Further, the enabling technology for online marketing have just begun or in the process of emerging. In this backdrop the questions raised here are (i) ‘Is E-commerce happening in Papua New Guinea (PNG) if at all other than Port Moresby? (ii) As an Alternative, is S-commerce the possibility? (iii) If so, is it the same S-commerce operating elsewhere works out for PNG? (iv) What is it that is working in PNG? To understand this, we explore the sample of online sellers via Facebook using the Snowballing Method of data collection. Having explored these questions, the research further examines how the Facebook is used as somewhat an onsite marketing place in PNG. And lastly, we try to answer why E-commerce or S-commerce could not work in PNG? With this discussion, insight is drawn on the challenges of digital marketing in PNG. Purpose: The purpose of the paper is to explore series of sequential questions, viz., (i) Is E-commerce happening in Papua New Guinea (PNG) if at all other than Port Moresby? (ii) As an Alternative, is S-commerce the possibility? (iii) If so, is it the same S-commerce operating elsewhere works out for PNG? (iv) What is it that is working in PNG? Design/Methodology/Approach: With one of the poorly developed digital technologies and hence the online marketing in Papua New Guinea reporting one of the least internet and subscribers’ rates, Facebook is used as somewhat an onsite marketing place in PNG. The paper uses the research design of capturing these quasi-online sellers using their Facebook profiles. These online sellers via Facebook have been collected using somewhat a snowballing sampling methodology. We approached the online sellers through a Snowballing Method. Facebook online sellers are identified during the period from April-May, 2022. In this manner, we identified 242 Facebook online sellers during survey period.

Keywords: Digital Markets, internet, E-commerce, S-commerce, Facebook

JEL Classification: M00, M21

Suggested Citation: Suggested Citation

Geetha Rani Prakasam (Contact Author)

National institute of educational planning and administration (niepa) ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, philosophy of science ejournal.

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NeurIPS 2024 Datasets and Benchmarks Track

If you'd like to become a reviewer for the track, or recommend someone, please use this form .

The Datasets and Benchmarks track serves as a venue for high-quality publications, talks, and posters on highly valuable machine learning datasets and benchmarks, as well as a forum for discussions on how to improve dataset development. Datasets and benchmarks are crucial for the development of machine learning methods, but also require their own publishing and reviewing guidelines. For instance, datasets can often not be reviewed in a double-blind fashion, and hence full anonymization will not be required. On the other hand, they do require additional specific checks, such as a proper description of how the data was collected, whether they show intrinsic bias, and whether they will remain accessible. The Datasets and Benchmarks track is proud to support the open source movement by encouraging submissions of open-source libraries and tools that enable or accelerate ML research.

The previous editions of the Datasets and Benchmarks track were highly successful; you can view the accepted papers from 2021 , 2002 , and 2023 , and the winners of the best paper awards 2021 , 2022 and 2023

CRITERIA. W e are aiming for an equally stringent review as the main conference, yet better suited to datasets and benchmarks. Submissions to this track will be reviewed according to a set of criteria and best practices specifically designed for datasets and benchmarks , as described below. A key criterion is accessibility: datasets should be available and accessible , i.e. the data can be found and obtained without a personal request to the PI, and any required code should be open source. We encourage the authors to use Croissant format ( https://mlcommons.org/working-groups/data/croissant/ ) to document their datasets in machine readable way.   Next to a scientific paper, authors should also submit supplementary materials such as detail on how the data was collected and organised, what kind of information it contains, how it should be used ethically and responsibly, as well as how it will be made available and maintained.

RELATIONSHIP TO NeurIPS.  Submissions to the track will be part of the main NeurIPS conference , presented alongside the main conference papers. Accepted papers will be officially published in the NeurIPS proceedings .

SUBMISSIONS.  There will be one deadline this year. It is also still possible to submit datasets and benchmarks to the main conference (under the usual review process), but dual submission to both is not allowed (unless you retracted your paper from the main conference). We also cannot transfer papers from the main track to the D&B track. Authors can choose to submit either single-blind or double-blind . If it is possible to properly review the submission double-blind, i.e., reviewers do not need access to non-anonymous repositories to review the work, then authors can also choose to submit the work anonymously. Papers will not be publicly visible during the review process. Only accepted papers will become visible afterward. The reviews themselves are not visible during the review phase but will be published after decisions have been made. The datasets themselves should be accessible to reviewers but can be publicly released at a later date (see below). New authors cannot be added after the abstract deadline and they should have an OpenReview profile by the paper deadline. NeurIPS does not tolerate any collusion whereby authors secretly cooperate with reviewers, ACs or SACs to obtain favourable reviews.

SCOPE. This track welcomes all work on data-centric machine learning research (DMLR) and open-source libraries and tools that enable or accelerate ML research, covering ML datasets and benchmarks as well as algorithms, tools, methods, and analyses for working with ML data. This includes but is not limited to:

• New datasets, or carefully and thoughtfully designed (collections of) datasets based on previously available data.
• Data generators and reinforcement learning environments.
• Data-centric AI methods and tools, e.g. to measure and improve data quality or utility, or studies in data-centric AI that bring important new insight.
• Advanced practices in data collection and curation that are of general interest even if the data itself cannot be shared.
• Frameworks for responsible dataset development, audits of existing datasets, identifying significant problems with existing datasets and their use
• Benchmarks on new or existing datasets, as well as benchmarking tools.
• In-depth analyses of machine learning challenges and competitions (by organisers and/or participants) that yield important new insight.
• Systematic analyses of existing systems on novel datasets yielding important new insight.

Read our original blog post for more about why we started this track.

Important dates

• Abstract submission deadline: May 29, 2024
• Full paper submission and co-author registration deadline: Jun 5, 2024
• Supplementary materials submission deadline: Jun 12, 2024
• Review deadline - Jul 24, 2024
• Release of reviews and start of Author discussions on OpenReview: Aug 07, 2024
• End of author/reviewer discussions on OpenReview: Aug 31, 2024
• Author notification: Sep 26, 2024
• Camera-ready deadline: Oct 30, 2024 AOE

Note: The site will start accepting submissions on April 1 5 , 2024.

FREQUENTLY ASKED QUESTIONS

Q: My work is in scope for this track but possibly also for the main conference. Where should I submit it?

A: This is ultimately your choice. Consider the main contribution of the submission and how it should be reviewed. If the main contribution is a new dataset, benchmark, or other work that falls into the scope of the track (see above), then it is ideally reviewed accordingly. As discussed in our blog post, the reviewing procedures of the main conference are focused on algorithmic advances, analysis, and applications, while the reviewing in this track is equally stringent but designed to properly assess datasets and benchmarks. Other, more practical considerations are that this track allows single-blind reviewing (since anonymization is often impossible for hosted datasets) and intended audience, i.e., make your work more visible for people looking for datasets and benchmarks.

Q: How will paper accepted to this track be cited?

A: Accepted papers will appear as part of the official NeurIPS proceedings.

Q: Do I need to submit an abstract beforehand?

A: Yes, please check the important dates section for more information.

Q: My dataset requires open credentialized access. Can I submit to this track?

A: This will be possible on the condition that a credentialization is necessary for the public good (e.g. because of ethically sensitive medical data), and that an established credentialization procedure is in place that is 1) open to a large section of the public, 2) provides rapid response and access to the data, and 3) is guaranteed to be maintained for many years. A good example here is PhysioNet Credentialing, where users must first understand how to handle data with human subjects, yet is open to anyone who has learned and agrees with the rules. This should be seen as an exceptional measure, and NOT as a way to limit access to data for other reasons (e.g. to shield data behind a Data Transfer Agreement). Misuse would be grounds for desk rejection. During submission, you can indicate that your dataset involves open credentialized access, in which case the necessity, openness, and efficiency of the credentialization process itself will also be checked.

SUBMISSION INSTRUCTIONS

A submission consists of:

• Please carefully follow the Latex template for this track when preparing proposals. We follow the NeurIPS format, but with the appropriate headings, and without hiding the names of the authors. Download the template as a bundle here .
• Papers should be submitted via OpenReview
• Reviewing is in principle single-blind, hence the paper should not be anonymized. In cases where the work can be reviewed equally well anonymously, anonymous submission is also allowed.
• During submission, you can add a public link to the dataset or benchmark data. If the dataset can only be released later, you must include instructions for reviewers on how to access the dataset. This can only be done after the first submission by sending an official note to the reviewers in OpenReview. We highly recommend making the dataset publicly available immediately or before the start of the NeurIPS conference. In select cases, requiring solid motivation, the release date can be stretched up to a year after the submission deadline.
• Dataset documentation and intended uses. Recommended documentation frameworks include datasheets for datasets , dataset nutrition labels , data statements for NLP , data cards , and accountability frameworks .
• URL to website/platform where the dataset/benchmark can be viewed and downloaded by the reviewers. 
• URL to Croissant metadata record documenting the dataset/benchmark available for viewing and downloading by the reviewers. You can create your Croissant metadata using e.g. the Python library available here: https://github.com/mlcommons/croissant
• Author statement that they bear all responsibility in case of violation of rights, etc., and confirmation of the data license.
• Hosting, licensing, and maintenance plan. The choice of hosting platform is yours, as long as you ensure access to the data (possibly through a curated interface) and will provide the necessary maintenance.
• Links to access the dataset and its metadata. This can be hidden upon submission if the dataset is not yet publicly available but must be added in the camera-ready version. In select cases, e.g when the data can only be released at a later date, this can be added afterward (up to a year after the submission deadline). Simulation environments should link to open source code repositories
• The dataset itself should ideally use an open and widely used data format. Provide a detailed explanation on how the dataset can be read. For simulation environments, use existing frameworks or explain how they can be used.
• Long-term preservation: It must be clear that the dataset will be available for a long time, either by uploading to a data repository or by explaining how the authors themselves will ensure this
• Explicit license: Authors must choose a license, ideally a CC license for datasets, or an open source license for code (e.g. RL environments). An overview of licenses can be found here: https://paperswithcode.com/datasets/license
• Add structured metadata to a dataset's meta-data page using Web standards (like schema.org and DCAT ): This allows it to be discovered and organized by anyone. A guide can be found here: https://developers.google.com/search/docs/data-types/dataset . If you use an existing data repository, this is often done automatically.
• Highly recommended: a persistent dereferenceable identifier (e.g. a DOI  minted by a data repository or a prefix on identifiers.org ) for datasets, or a code repository (e.g. GitHub, GitLab,...) for code. If this is not possible or useful, please explain why.
• For benchmarks, the supplementary materials must ensure that all results are easily reproducible. Where possible, use a reproducibility framework such as the ML reproducibility checklist , or otherwise guarantee that all results can be easily reproduced, i.e. all necessary datasets, code, and evaluation procedures must be accessible and documented.
• For papers introducing best practices in creating or curating datasets and benchmarks, the above supplementary materials are not required.
• For papers resubmitted after being retracted from another venue: a brief discussion on the main concerns raised by previous reviewers and how you addressed them. You do not need to share the original reviews.
• For the dual submission and archiving, the policy follows the NeurIPS main track paper guideline .

Use of Large Language Models (LLMs): We welcome authors to use any tool that is suitable for preparing high-quality papers and research. However, we ask authors to keep in mind two important criteria. First, we expect papers to fully describe their methodology, and any tool that is important to that methodology, including the use of LLMs, should be described also. For example, authors should mention tools (including LLMs) that were used for data processing or filtering, visualization, facilitating or running experiments, and proving theorems. It may also be advisable to describe the use of LLMs in implementing the method (if this corresponds to an important, original, or non-standard component of the approach). Second, authors are responsible for the entire content of the paper, including all text and figures, so while authors are welcome to use any tool they wish for writing the paper, they must ensure that all text is correct and original.

REVIEWING AND SELECTION PROCESS

Reviewing will be single-blind, although authors can also submit anonymously if the submission allows that. A datasets and benchmarks program committee will be formed, consisting of experts on machine learning, dataset curation, and ethics. We will ensure diversity in the program committee, both in terms of background as well as technical expertise (e.g., data, ML, data ethics, social science expertise). Each paper will be reviewed by the members of the committee. In select cases where ethical concerns are flagged by reviewers, an ethics review may be performed as well.

Papers will not be publicly visible during the review process. Only accepted papers will become visible afterward. The reviews themselves are also not visible during the review phase but will be published after decisions have been made. Authors can choose to keep the datasets themselves hidden until a later release date, as long as reviewers have access.

The factors that will be considered when evaluating papers include:

• Utility and quality of the submission: Impact, originality, novelty, relevance to the NeurIPS community will all be considered. 
• Reproducibility: All submissions should be accompanied by sufficient information to reproduce the results described i.e. all necessary datasets, code, and evaluation procedures must be accessible and documented. We encourage the use of a reproducibility framework such as the ML reproducibility checklist to guarantee that all results can be easily reproduced. Benchmark submissions in particular should take care to ensure sufficient details are provided to ensure reproducibility. If submissions include code, please refer to the NeurIPS code submission guidelines .  
• Was code provided (e.g. in the supplementary material)? If provided, did you look at the code? Did you consider it useful in guiding your review? If not provided, did you wish code had been available?
• Ethics: Any ethical implications of the work should be addressed. Authors should rely on NeurIPS ethics guidelines as guidance for understanding ethical concerns.  
• Completeness of the relevant documentation: Per NeurIPS ethics guidelines , datasets must be accompanied by documentation communicating the details of the dataset as part of their submissions via structured templates (e.g. TODO). Sufficient detail must be provided on how the data was collected and organized, what kind of information it contains,  ethically and responsibly, and how it will be made available and maintained. 
• Licensing and access: Per NeurIPS ethics guidelines , authors should provide licenses for any datasets released. These should consider the intended use and limitations of the dataset, and develop licenses and terms of use to prevent misuse or inappropriate use.  
• Consent and privacy: Per  NeurIPS ethics guidelines , datasets should minimize the exposure of any personally identifiable information, unless informed consent from those individuals is provided to do so. Any paper that chooses to create a dataset with real data of real people should ask for the explicit consent of participants, or explain why they were unable to do so.
• Ethics and responsible use: Any ethical implications of new datasets should be addressed and guidelines for responsible use should be provided where appropriate. Note that, if your submission includes publicly available datasets (e.g. as part of a larger benchmark), you should also check these datasets for ethical issues. You remain responsible for the ethical implications of including existing datasets or other data sources in your work.
• Legal compliance: For datasets, authors should ensure awareness and compliance with regional legal requirements.

ADVISORY COMMITTEE

The following committee will provide advice on the organization of the track over the coming years: Sergio Escalera, Isabelle Guyon, Neil Lawrence, Dina Machuve, Olga Russakovsky, Joaquin Vanschoren, Serena Yeung.

DATASETS AND BENCHMARKS CHAIRS

Lora Aroyo, Google Francesco Locatello, Institute of Science and Technology Austria Lingjuan Lyu, Sony AI

Contact: [email protected]

Panama court acquits 28 people tied to Panama Papers, Operation Car Wash

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Reporting by Elida Moreno; Writing by Sarah Morland; Editing by Brendan O'Boyle and William Mallard

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Colombian armed group segunda marquetalia agrees unilateral ceasefire.

Colombian armed group the Segunda Marquetalia on Sunday agreed to a unilateral ceasefire, after several days of peace talks in Caracas, which are part of President Gustavo Petro's efforts to end 60 years of unrest in his country.

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Judge acquits 28 people accused in ‘Panama Papers’ case, including law firm co-founder

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A judge has acquitted 28 people accused of money laundering in an international case known as the Panama Papers, including the co-founder of a law firm that authorities say was at the center of a conspiracy to hide money linked to illegal activities.

Jürgen Mossack founded Mossack & Fonseca with then associate Ramón Fonseca, who died in May. Mossack was acquitted on Friday along with others after a Panamanian judge found that the evidence against Mossack didn’t comply with the chain of custody after authorities raided the office of the now defunct firm.

Prosecutors had accused Mossack, Fonseca and others of creating offshore companies and using complex transactions to hide money from illegal activities related to the so-called car wash corruption scandal involving Brazilian construction company Odebrecht, which pleaded guilty in U.S. federal court to a charge related to using shell companies to hide millions of dollars in bribes paid worldwide to win public contracts.

The judge noted that other evidence in the Panama Papers case “was not sufficient and conclusive to determine the criminal responsibility of the accused.”

In addition, the judge lifted personal and property precautionary measures against all the defendants, according to a judicial statement.

“We feel satisfied in the midst of mixed emotions, because many lives were affected along the way,” Guillermina Mc Donald, who was the defense attorney for Mossack and Fonseca, told the Associated Press. Her firm also represented 80% of the accused firm’s collaborators.

Judge Balaoisa Marquínez had decided to combine the Panama Papers case with another known as “Operation Car Wash,” a major anti-corruption investigation that began in Brazil.

On Friday, she ruled that in the car wash case, “it was not possible to determine the entry of money from illicit sources, coming from Brazil, into the Panamanian financial system with the purpose of hiding, concealing, disguising or helping to evade the legal consequences of the preceding crime.”

In June 2022, Mossack, Fonseca and 37 other people were acquitted in a separate money laundering case.

The investigation in Brazil began in 2014, with the Mossack & Fonseca firm later coming under scrutiny after 11 million financial documents tied to the company were leaked.

The repercussions of the leak were widespread: It led to the resignation of a prime minister in Iceland and brought scrutiny to now former leaders of Argentina and Ukraine, Chinese politicians and Russian President Vladimir Putin, among others.

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