Tellent Recruitee is a cloud-based applicant tracking system (ATS) for talent acquisition owned by Tellent. It is used by internal HR teams for processes including job postings, candidate sourcing, reporting, and applicant tracking. == History == Perry Oostdam and Pawel Smoczyk founded Recruitee after working on a mobile gaming startup. The Recruitee was launched in August 2015. In September 2015, it received a seed funding round with participation from investors Robert Pijselman and Luc Brandts. Merger In February 2021, Recruitee and the Finnish HR software provider Sympa merged their operations, backed by the growth equity firm Providence Strategic Growth (PSG). Acquisition In 2022, the group acquired the French company Javelo and the German company kiwiHR. The parent company was subsequently renamed as Tellent while Recruitee renamed as Tellent Recruitee and continues to operate as a product unit within the Tellent group. == Platform == Tellent Recruitee is a customizable recruitment software. It functions as an ATS and talent acquisition platform and includes tools to create and publish job listings, source candidates, manage recruitment agencies, and track applicants through customizable pipelines. The interface allows drag-and-drop organization of candidates. The platform also includes features for team collaboration, such as shared notes, task assignments, and candidate evaluations. It also has integrated scheduling tools and automated email communication. Tellent Recruitee also provides analytics and reports on hiring and career site metrics. The software allows for customization of career site pages and application forms. It supports integrations with other HR and productivity software, such as WhatsApp, and has various AI functionalities to support with manual recruitment tasks.
Representation collapse
Representation collapse is a phenomenon in machine learning and representation learning where a model maps different inputs to the same or very similar embeddings, which means it loses important information about how the data is spread out. It is frequently encountered in self-supervised learning, especially within contrastive and non-contrastive frameworks, when training objectives or model architectures do not maintain variance across representations. Collapse results in degenerate solutions characterized by uninformative learned features, significantly impairing downstream task performance. Various techniques have been proposed to mitigate representation collapse, including the use of negative samples, architectural asymmetry, stop-gradient operations, variance regularization, and redundancy reduction objectives, as seen in methods such as SimCLR, BYOL, and VICReg. Comprehending and averting representation collapse is regarded as a fundamental challenge in the advancement of stable and efficient self-supervised learning systems.
Linagora
Linagora is a French open source software editor, founded in June 2000 by Alexandre Zapolsky and Michel-Marie Maudet. Located in France, as well as in Belgium, Canada, Vietnam, the United States and Tunisia, the company employs around 200 people. In 2023, Linagora created the OpenLLM France community, alongside other French Artificial Intelligence companies and organizations. In 2025, the company launched Lucie, an opensource Large Language Model. == History == Linagora was founded on June 28, 2000. Its name is a contraction of the words "Linux" and "Agora". The company was founded by Alexandre Zapolsky and Michel-Marie Maudet. Soon after, the two entrepreneurs were joined by Alexandre Zapolsky's wife and brother, who took on the roles of commercial director and administrative and financial director of the SME. In 2007, the company was selected by the French National Assembly to provide the software for Linux computers, replacing Microsoft Windows. Linagora then claimed the position of the leading French open source software company by revenue. In 2015, French Prime Minister Manuel Valls allocated €10.7 million from the "Investments for the Future" fund for a research program aimed at developing a new generation of open source software platforms based on Linagora's offerings. In September 2016, Linagora launched the social network "La Cerise" for the newspaper L'Humanité. This app offered a service and tool for readers and citizens mobilizing for causes. It aimed to share engagement through petitions, discussions, agendas, and contacts. In October 2016, the company won two public contracts for supporting open source software in forty-two French ministries and other administrative entities. In May 2019, Linagora organized a fundraising event in the presence of the French Secretary of State for Digital Affairs, Cédric O, to celebrate its 19th anniversary. The funds were intended for: Supporting parents of hospitalized Polynesian children in France. Equipping primary school students with digital devices (tablets or PCs). Establishing a digital academy "OpenHackademy" in French Polynesia to train unemployed youth in digital skills and help them find jobs. In December 2022, Linagora acquired a property known as "Maison Rocher" and later "Maison Chocolat," located on the Île Saint-Germain in Issy-les-Moulineaux. Renamed "Villa Good Tech" by Linagora, this award-winning architectural work by Éric Daniel-Lacombe became the company's new headquarters, aiming to provide a space for associative actors and companies to develop technologies that contribute to a better world. In July 2023, Linagora launched OpenLLM France, a community initially comprising around twenty actors focused on generative AI. The goal was to develop a sovereign and open source large language model. This initiative, led by co-founder and CEO Michel-Marie Maudet, had more than four hundred French members by early 2024. and announced its expansion to the European sphere during Fosdem 2024. In February 2024, the CNRS and Linagora signed a framework agreement to strengthen their research collaboration. In January 2025, Linagora released Lucie, an open source and sovereign AI that faced ridicule due to tests on an unfinished, uncensored version designed for scientific and experimental use. The platform divided opinions between those who saw it as a technological achievement and those who criticized it as "French bashing" compared to American and Chinese AIs. == Acquisitions == The company acquired: In July 2007, the SME AliaSource, based in Ramonville-Saint-Agne and led by its founder, Pierre Baudracco. In 2008, the open source web hosting company Netaktiv, a member of the GIE Gitoyen, announced during the 2008 Solutions Linux trade show. In 2012, the Toulouse-based company EBM Websourcing, the publisher of the open-source software Petals Link, and took over its development. In 2016, the digital agency Neoma Interactive, specializing in UX design and digital communication strategy. == Locations == In 2017, the company's headquarters was located in Issy-les-Moulineaux, with branches in Lyon, Toulouse, Marseille, and internationally in Brussels, San Francisco, Montreal, Vietnam, and Tunisia. In 2005, the company attempted to establish a presence in Nantes. In 2024, the headquarters was moved to Issy-les-Moulineaux. == Activity == === Software === Twake Workplace One of Linagora's flagship products is Twake Workplace, which stands out as a 100% open-source solution compared with those of the GAFAMs. Twake Workplace is available as a complete platform or module by module. It includes : Twake Mail, a powerful modern messaging solution based on the JMAP protocol and the James email server from the Apache Foundation, for which Linagora provides technical management; Twake Chat, an instant communications solution for businesses developed using the Matrix protocol and compatible with the French government's chat solution, Tchap; Twake Drive, an easy-to-use collaborative platform for group work using OnlyOffice. ==== OpenPaaS ==== In 2018, the search engine Qwant announced that its email service Qwantmail would be based on the OpenPaaS product. In 2022, Qwant announced the abandonment of its Qwantmail project due to Linagora's collection of personal email addresses and serious security breaches. The site Next (formerly PC INpact) published an article in January 2020 criticizing the "failures and delays" of the Qwantmail project led by Linagora, which led to the CNIL's intervention regarding Qwant and Linagora. ==== LinTO ==== In 2017, Linagora launched its open source voice assistant project named LinTO. This enterprise voice assistant, described as "GAFAM Free," was presented at CES 2018 in Las Vegas. The LinTO voice framework was developed as part of the eponymous research project funded by Bpifrance (Grands Défis du Numérique instrument). === Services === ==== OSSA (Open Source Software Assurance) ==== One of the company's main activities is OSSA. Through OSSA, Linagora provided support for open source software for 42 ministries and other administrative entities in 2012. == Legal issues == === Dispute with BlueMind === In 2012, a legal dispute arose between BlueMind and Linagora. Linagora accused BlueMind of copyright infringement, unfair competition, and breach of a non-compete clause, leading to several legal actions. Linagora sued BlueMind for copyright infringement and unfair competition in the Bordeaux court, which ruled in Linagora's favor for unfair competition and parasitism but rejected the copyright claim. BlueMind was ordered to pay nearly €170,000 to Linagora. Linagora sued former associates Pierre Baudracco and Pierre Carlier in the Paris Commercial Court for breach of a non-compete clause and violation of a warranty of eviction. The court dismissed Linagora's claims and ordered it to pay €20,000 each to Baudracco and Carlier. Linagora appealed, and the Paris Court of Appeal partially overturned the decision, awarding Linagora €480,000. BlueMind sued Linagora for defamation and public insult in the Toulouse Criminal Court. The court ruled against Linagora, but the decision was overturned by the Court of Cassation in January 2024, and the case was remanded for retrial. === Conviction for wrongful termination and harassment === On June 14, 2017, France 3 reported on a decision by the Versailles Court of Appeal, which ruled that Linagora had wrongfully terminated an employee and subjected them to moral harassment. The court ordered Linagora to pay the employee €22,000 for wrongful termination, €11,000 for notice pay, €6,600 for legal severance pay, €3,200 for conservative suspension, and €3,000 for moral harassment.
Rnn (software)
rnn is an open-source machine learning framework that implements recurrent neural network architectures, such as LSTM and GRU, natively in the R programming language, that has been downloaded over 100,000 times (from the RStudio servers alone). The rnn package is distributed through the Comprehensive R Archive Network under the open-source GPL v3 license. == Workflow == The below example from the rnn documentation show how to train a recurrent neural network to solve the problem of bit-by-bit binary addition. == sigmoid == The sigmoid functions and derivatives used in the package were originally included in the package, from version 0.8.0 onwards, these were released in a separate R package sigmoid, with the intention to enable more general use. The sigmoid package is a dependency of the rnn package and therefore automatically installed with it. == Reception == With the release of version 0.3.0 in April 2016 the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", which further increased usage. The book Neural Networks in R by Balaji Venkateswaran and Giuseppe Ciaburro uses rnn to demonstrate recurrent neural networks to R users. It is also used in the r-exercises.com course "Neural network exercises". The RStudio CRAN mirror download logs show that the package is downloaded on average about 2,000 per month from those servers , with a total of over 100,000 downloads since the first release, according to RDocumentation.org, this puts the package in the 15th percentile of most popular R packages .
Chainer
Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia. Chainer is notable for its early adoption of "define-by-run" scheme, as well as its performance on large scale systems. The first version was released in June 2015 and has gained large popularity in Japan since then. Furthermore, in 2017, it was listed by KDnuggets in top 10 open source machine learning Python projects. In December 2019, Preferred Networks announced the transition of its development effort from Chainer to PyTorch and it will only provide maintenance patches after releasing v7. == Define-by-run == Chainer was the first deep learning framework to introduce the define-by-run approach. The traditional procedure to train a network was in two phases: define the fixed connections between mathematical operations (such as matrix multiplication and nonlinear activations) in the network, and then run the actual training calculation. This is called the define-and-run or static-graph approach. Theano and TensorFlow are among the notable frameworks that took this approach. In contrast, in the define-by-run or dynamic-graph approach, the connection in a network is not determined when the training is started. The network is determined during the training as the actual calculation is performed. One of the advantages of this approach is that it is intuitive and flexible. If the network has complicated control flows such as conditionals and loops, in the define-and-run approach, specially designed operations for such constructs are needed. On the other hand, in the define-by-run approach, programming language's native constructs such as if statements and for loops can be used to describe such flow. This flexibility is especially useful to implement recurrent neural networks. Another advantage is ease of debugging. In the define-and-run approach, if an error (such as numeric error) has occurred in the training calculation, it is often difficult to inspect the fault, because the code written to define the network and the actual place of the error are separated. In the define-by-run approach, you can just suspend the calculation with the language's built-in debugger and inspect the data that flows on your code of the network. Define-by-run has gained popularity since the introduction by Chainer and is now implemented in many other frameworks, including PyTorch and TensorFlow. == Extension libraries == Chainer has four extension libraries, ChainerMN, ChainerRL, ChainerCV and ChainerUI. ChainerMN enables Chainer to be used on multiple GPUs with performance significantly faster than other deep learning frameworks. A supercomputer running Chainer on 1024 GPUs processed 90 epochs of ImageNet dataset on ResNet-50 network in 15 minutes, which is four times faster than the previous record held by Facebook. ChainerRL adds state of art deep reinforcement learning algorithms, and ChainerUI is a management and visualization tool. == Applications == Chainer is used as the framework for PaintsChainer, a service which does automatic colorization of black and white, line only, draft drawings with minimal user input.
DABUS
DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) is an artificial intelligence (AI) system created by Stephen Thaler. It reportedly conceived of two novel products — a food container constructed using fractal geometry, which enables rapid reheating, and a flashing beacon for attracting attention in an emergency. The filing of patent applications designating DABUS as inventor has led to decisions by patent offices and courts on whether a patent can be granted for an invention reportedly made by an AI system. == History in different jurisdictions == === Australia === On 17 September 2019, Thaler filed an application to patent a "Food container and devices and methods for attracting enhanced attention," naming DABUS as the inventor. On 21 September 2020, IP Australia found that section 15(1) of the Patents Act 1990 (Cth) is inconsistent with an artificial intelligence machine being treated as an inventor, and Thaler's application had lapsed. Thaler sought judicial review, and on 30 July 2021, the Federal Court set aside IP Australia's decision and ordered IP Australia to reconsider the application. On 13 April 2022, the Full Court of the Federal Court set aside that decision, holding that only a natural person can be an inventor for the purposes of the Patents Act 1990 (Cth) and the Patents Regulations 1991 (Cth), and that such an inventor must be identified for any person to be entitled to a grant of a patent. On 11 November 2022, Thaler was refused special leave to appeal to the High Court. === European Patent Office === On 17 October 2018 and 7 November 2018, Thaler filed two European patent applications with the European Patent Office. The first claimed invention was a "Food Container" and the second was "Devices and Methods for Attracting Enhanced Attention." On 27 January 2020, the EPO rejected the applications on the grounds that the application listed an AI system named DABUS, and not a human, as the inventor, based on Article 81 and Rule 19(1) of the European Patent Convention (EPC). On 21 December 2021, the Board of Appeal of the EPO dismissed Thaler's appeal from the EPO's primary decision. The Board of Appeal confirmed that "under the EPC the designated inventor has to be a person with legal capacity. This is not merely an assumption on which the EPC was drafted. It is the ordinary meaning of the term inventor." === United Kingdom === Similar applications were filed by Thaler to the United Kingdom Intellectual Property Office on 17 October and 7 November 2018. The Office asked Thaler to file statements of inventorship and of right of grant to a patent (Patent Form 7) in respect of each invention within 16 months of the filing date. Thaler filed those forms naming DABUS as the inventor and explaining in some detail why he believed that machines should be regarded as inventors in the circumstances. His application was rejected on the grounds that: (1) naming a machine as inventor did not meet the requirements of the Patents Act 1977; and (2) the IPO was not satisfied as to the manner in which Thaler had acquired rights that would otherwise vest in the inventor. Thaler was not satisfied with the decision and asked for a hearing before an official known as the "hearing officer". By a decision dated 4 December 2019 the hearing officer rejected Thaler's appeal. Thaler appealed against the hearing officer's decision to the Patents Court (a specialist court within the Chancery Division of the High Court of England and Wales that determines patent disputes). On 21 September 2020, Mr Justice Marcus Smith upheld the decision of the hearing officer. On 21 September 2021, Thaler's further appeal to the Court of Appeal was dismissed by Arnold LJ and Laing LJ (Birss LJ dissenting). On 20 December 2023, the UK Supreme Court dismissed a further appeal by Thaler. In its judgment, the court held that an "inventor" under the Patents Act 1977 must be a natural person. === United States === The patent applications on the inventions were refused by the USPTO, which held that only natural persons can be named as inventors in a patent application. Thaler first fought this result by filing a complaint under the Administrative Procedure Act alleging that the decision was "arbitrary, capricious, an abuse of discretion and not in accordance with the law; unsupported by substantial evidence, and in excess of Defendants’ statutory authority." A month later on August 19, 2019, Thaler filed a petition with the USPTO as allowed in 37 C.F.R. § 1.181 stating that DABUS should be the inventor. The judge and Thaler agreed in this case that Thaler himself is unable to receive the patent on behalf of DABUS. In their August 5, 2022, Thaler decision, the US Court of Appeals for the Federal Circuit affirmed that only a natural person could be an inventor, which means that the AI that invents any other type of invention is not addressed by the "who" mentioned in the legislation. === New Zealand === On January 31, 2022, the Intellectual Property Office of New Zealand (IPONZ) decided that a patent application (776029) filed by Stephen Thaler was void, on the basis that no inventor was identified on the patent application. IPONZ determined that DABUS could not be "an actual devisor of the invention" as required by the Patents Act 2013, and that this must be a natural person as held by the previous patent offices above. The High Court of New Zealand confirmed the decision in 2023. === South Africa === On 24 June 2021, the South African Companies and Intellectual Property Commission (CIPC) accepted Dr Thaler's Patent Cooperation Treaty, for a patent in respect of inventions generated by DABUS. In July 2021, the CIPC released a notice of issuance for the patent. It is the first patent granted for an AI invention. === Switzerland === On June 26, 2025, the Swiss Federal Administrative Court ruled that artificial intelligence systems such as DABUS cannot be listed as inventors in patent applications. The court upheld the existing practice of the Swiss Federal Institute of Intellectual Property (IPI), which requires that only natural persons can be recognized as inventors under Swiss patent law. The case concerned a patent application, which sought to designate DABUS as the sole inventor of a food container designed with a fractal geometry to enhance heat distribution. The IPI had rejected the application, arguing that both the absence of a human inventor and the attribution of inventorship to an AI system were inadmissible. While the court dismissed Thaler's main request, it accepted a subsidiary request: if a human applicant recognizes and files a patent based on an AI-generated invention, that person may be considered the inventor. As a result, the application may proceed with Thaler listed as the inventor. The decision (B-2532/2024) can still be appealed to the Swiss Federal Supreme Court.
Resilience (mathematics)
In mathematical modeling, resilience refers to the ability of a dynamical system to recover from perturbations and return to its original stable steady state. It is a measure of the stability and robustness of a system in the face of changes or disturbances. If a system is not resilient enough, it is more susceptible to perturbations and can more easily undergo a critical transition. A common analogy used to explain the concept of resilience of an equilibrium is one of a ball in a valley. A resilient steady state corresponds to a ball in a deep valley, so any push or perturbation will very quickly lead the ball to return to the resting point where it started. On the other hand, a less resilient steady state corresponds to a ball in a shallow valley, so the ball will take a much longer time to return to the equilibrium after a perturbation. The concept of resilience is particularly useful in systems that exhibit tipping points, whose study has a long history that can be traced back to catastrophe theory. While this theory was initially overhyped and fell out of favor, its mathematical foundation remains strong and is now recognized as relevant to many different systems. == History == In 1973, Canadian ecologist C. S. Holling proposed a definition of resilience in the context of ecological systems. According to Holling, resilience is "a measure of the persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables". Holling distinguished two types of resilience: engineering resilience and ecological resilience. Engineering resilience refers to the ability of a system to return to its original state after a disturbance, such as a bridge that can be repaired after an earthquake. Ecological resilience, on the other hand, refers to the ability of a system to maintain its identity and function despite a disturbance, such as a forest that can regenerate after a wildfire while maintaining its biodiversity and ecosystem services. With time, the once well-defined and unambiguous concept of resilience has experienced a gradual erosion of its clarity, becoming more vague and closer to an umbrella term than a specific concrete measure. == Definition == Mathematically, resilience can be approximated by the inverse of the return time to an equilibrium given by resilience ≡ − Re ( λ 1 ( A ) ) {\displaystyle {\text{resilience}}\equiv -{\text{Re}}(\lambda _{1}({\textbf {A}}))} where λ 1 {\textstyle \lambda _{1}} is the maximum eigenvalue of matrix A {\textstyle {\textbf {A}}} . The largest this value is, the faster a system returns to the original stable steady state, or in other words, the faster the perturbations decay. == Applications and examples == In ecology, resilience might refer to the ability of the ecosystem to recover from disturbances such as fires, droughts, or the introduction of invasive species. A resilient ecosystem would be one that is able to adapt to these changes and continue functioning, while a less resilient ecosystem might experience irreversible damage or collapse. The exact definition of resilience has remained vague for practical matters, which has led to a slow and proper application of its insights for management of ecosystems. In epidemiology, resilience may refer to the ability of a healthy community to recover from the introduction of infected individuals. That is, a resilient system is more likely to remain at the disease-free equilibrium after the invasion of a new infection. Some stable systems exhibit critical slowing down where, as they approach a basic reproduction number of 1, their resilience decreases, hence taking a longer time to return to the disease-free steady state. Resilience is an important concept in the study of complex systems, where there are many interacting components that can affect each other in unpredictable ways. Mathematical models can be used to explore the resilience of such systems and to identify strategies for improving their resilience in the face of environmental or other changes. For example, when modelling networks it is often important to be able to quantify network resilience, or network robustness, to the loss of nodes. Scale-free networks are particularly resilient since most of their nodes have few links. This means that if some nodes are randomly removed, it is more likely that the nodes with fewer connections are taken out, thus preserving the key properties of the network.