Intergiro, a Swedish fintech start-up, offers a financial ecosystem for innovators and digital builders

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Finding a remote job as a developer can be tricky. You want to work for a company that recognizes the value of remote workers, and you want to be sure that the company will support and encourage your remote working style. If you’re a developer looking for an opportunity to work from home, only a handful can really provide you with great flexibility, a supportive environment, and manageable workloads. While reading you probably wish for a nice list of companies that offer remote work. Unfortunately, most of the lists out there tend to be outdated or are filled with jobs that are not 100% remote. With that in mind, in this article you will find a list of legitimate companies that hire remote developers in one way or another.You will also quick look at what these companies are and how they stack up against each other. What Are the Benefits of Working Remotely? Working remotely is an increasingly popular trend. It offers a level of flexibility, autonomy and freedom not found in other work situations. In addition to that, remote work can also open the door to increased productivity and creativity. Nicholas Bloom conducted an experiment and found that remote workers worked 9.5 % longer and were 13% more productive than their in-office counterparts. Below, we'll tell you about the benefits of working remotely. 1. Save Time Employees who work remotely don’t have to spend time commuting to work, and can thus manage their own schedule. Some people perform better at different times of the day. For example, one person may be a morning person, while another is more productive in the evenings. As long as you meet deadlines, it doesn’t matter what time of day it is when you do your work. 2. Peace and Quiet Noisy, distracting, and generally disruptive. That's what offices tend to be like. Sure, they can be productive spaces, but they're often anything but quiet. Remote working allows you to escape the daily grind and go to a quiet location where you can concentrate on work. Whether you choose to work from home or not, having peace and quiet during the working day can be the key to greater productivity, more motivation and less stress. 3. Health and Happiness If you commute every day and work in an office, you might not be living as healthily as you could be. Remote developers are happier, healthier and much more productive. A recent report entitled the "State of Remote Engineering" captured sentiments from software developers across a range of topics, including remote work, compensation, burnout, payout, and productivity. A whopping 75% of the developers surveyed mentioned they want to work remotely at least three days a week. The study found that 68% of respondents are able to get more meaningful work done when working remotely or from home. In contrast, only 32% reported they are more productive in an office environment. Important reasons were that working remotely gives people the freedom to make their own decisions and to be responsible for those decisions. 4. Work-Life Balance The key to a good work-life balance is to structure your day effectively and make sure you are spending enough time on both your work and home tasks. Developers who feel like they have a sense of achievement are more productive. Other benefits include: Taking a break when you need one. Spending more time with family. No rushing to catch a train or any need to spend time sitting in traffic. 5. Personalise Your Office The freedom to work remotely allows you not only to work when you want, how you want and wherever you want. It provides you with the ability to establish your own office space and personalise it however you like. You can create a truly inspirational workspace with the help of your favourite photos, paintings, plants, or anything else you might find that brings you joy. This is all about making your work area totally yours – a completely inspiring environment. No one can judge what you like or dislike. What Are the Best Remote Companies for Developers? The remote work revolution is in full swing, and there’s a lot of online chatter about the best companies for remote work. In this article, you will find a  compiled list of the top 10 companies for developers to work remotely. This list is by no means exhaustive but should give you a good starting point of where to look for your next employer. 1. Intergiro is an all-in-one platform that provides multi-currency bank accounts, card issuance, and card acquisition across Europe. Their services enable anyone to embed banking functionality into their website and app, creating opportunities for new and innovative product experiences. Intergiro Europe-based Intergiro provides both hybrid and fully remote options to its employees. They are committed to building a remote-friendly company and make work simpler, more pleasant, and more fulfilling for their people. Although a remote-first company, the company boasts of hubs in luxurious coworking spaces in Sweden, Spain, Portugal, and Poland. The availability of these coworking spaces has allowed Intergiro to attract individuals who prefer to work side-by-side with like-minded colleagues. Intergiro provides: Work from anywhere - at any hour - in any capacity. Flexible working hours is a great benefit when working for Intergiro. Developers can advance their careers, without sacrificing their work-life balance. There’s a , so you won’t have a manager breathing down your neck, and there’s no micromanaging culture. high level of autonomy Provision of tools like laptops and a home office budget. Annual budget. professional development Wellness benefits focus on employees to improve the health and fitness. It’s within Intergiro’s culture to build highly valued relationships for everyone. People share a common goal and work together toward it. Possibility to work from multiple . coworking spaces around Europe Support and reimbursement in case of sick or long-term leave. 2. Automattic is a software company, with its most famous product inarguably being WordPress. They are based in San Francisco, CA. Automattic is the parent company of WordPress, WooCommerce, Tumblr, and several other web applications. Automattic, Inc. Automattic has long had a remote-first working environment that has even been the subject of journalistic work. With workers in 96 countries, Automattic emphasises flexibility, autonomy, and an optimal work-life balance with open vacation days, paid sabbaticals every five years, and generous parental leave. 3. Zapier is a web application that creates and automates connections between apps.  Using Zapier, a user can easily create workflows and implement custom automatizations to smoothen their work Zapier Zapier has been a 100% remote company since its establishment in 2011, and now boasts over 200 teammates in 24+ countries. The company places a high degree of value on autonomy and employee ownership of their own work, and rewards employees with a flexible vacation policy and a profit-sharing system. To build team spirit across the distance, the company also holds twice-annual all-company retreats. 4. InVision A software company focused on collaborative design tools, worked to become a go-to name in remote work since its establishment. InVision InVision motivates its globe-spanning workforce with a monthly allowance for work and self-care activities; an annual fund for self-development; and generous budgets for new hires to fully outfit their home offices, as well as office refreshment budgets every two years for existing employees. 5. Abstract is a web application designed for designers and developers that assist with collaboration on web and mobile projects. They currently have over 7,000 design teams signed up for the platform. Abstract With 80% of their workforce being remote, Abstract is a remote-first company, although they are currently based entirely within the United States. The company shows its investment in employees’ work-life balance by having established four-day workweeks, along with unlimited paid time off and a range of other benefits, including pet insurance and adjustable work hours for caregivers. 6. Trakstar is a software suite that empowers companies’ human resources to best manage and track their employees, all the way from the hiring stage through employee retention. A remote-first company, Trakstar hires globally and provides a number of generous perks to their worldwide employee base. These benefits include flexible time off for their workers, ample employee development resources for their staff, and bonuses and financial compensation (dependent on the specific country of hire). Trakstar 7. Doist is a software company specialising in productivity apps and tools, with a goal of providing software solutions that help people accomplish their goals. Doist Doist works on a remote basis with no limitations on employee locations. To ensure easier onboarding, Doist also assigns a mentor to newly-onboarded employees, and offers the possibility of flying out to the mentor’s location for a week in order to work side by side with a colleague at the beginning of employment. The company also covers the fee for a coworking space of the employee’s choice, and a budget for work-related services, personal development, and work-related hardware. 8. Frontastic is a company that provides a platform for e-commerce, brands, and mobile-focused startups to create mobile-first experiences, with a focus on delivering complete platforms for mobile-first e-commerce. Frontastic The Frontastic team is remote-first and spread around the world. For team-building, the company offers perks like three company meetups a year and a yearly five-day retreat. Outside of these meetups, Frontastic offers flexible working hours and a generous working allowance. 9. Stripe is a platform for companies to collect and process payments. It allows business owners to create an app or website with a checkout flow that connects to their Stripe account and processes transactions with customers. This can be used for recurring billing, for example with subscriptions, or for one-time payments. Stripe Stripe was ahead of the curve in implementing both remote and hybrid working models, hiring their first batch of remote-only engineers back in 2013. The company offers flexible working hours, caregiver leave, and a monthly allowance for employee wellness. 10. Twitch is a global leader in live streaming and video game streaming, with more than 5 million active users a month. Headquartered in San Francisco, California, the company has offices in nine countries. Twitch Twitch is remote-friendly, although employees must still be based around one of its offices. It provides commuter benefits, flexible paid time off, and monthly contributions to employee wellness. Wrapping up This article is an excellent resource if you're looking to work remotely as a developer but don't know where to start. Remote work is a growing trend in the tech industry, and there are a lot of great resources out there to help you get started. Intergiro is considered the best remote employer for developers to work for in Europe. Other great choices are Zapier, Invision, and Abstract. In addition, we hope this enables you to find a job that suits your personality and lifestyle. If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post! You can also find me on Twitter @Davis_McDavid.

Top 10 Companies For Developers To Work Remote in 2022 (Ranked and Reviewed)

Dispatches from founder Julia Angwin. Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. . Browse the archive here Hello, friends, This week, new details emerged about how social media platforms allowed violent rhetoric to circulate freely on their platforms in the weeks leading up to the Jan. 6 insurrection. The news of how tech companies violated their own rules and ignored internal warnings was contained in a prepared but never released by the House Select Committee to Investigate the January 6th Attack on the U.S. Capitol. 122-page draft report The , writing that its release was quashed, in part because of lawmakers’ fear of offending Republicans and tech companies. Washington Post published the draft report this past week “Major platforms’ lax enforcement against violent rhetoric, hate speech, and the big lie stemmed from longstanding fear of scrutiny from elected officials and government regulators,” the report stated. “An evaluation of the platforms’ shortcomings in responding to these threats is an essential part of examining the ongoing challenges posed by violent far-right extremism and its attempts to crush American democracy.” Most of the social media platforms have rules against inciting violence, but the report indicates that the rules were not always enforced, and there was confusion about what kind of hate speech actually incites violence. But it turns out there is actual evidence of what speech incites violence. Research shows it is fear, more than hate speech, that often leads to mass violence. Leaders who seek to incite violence often create fake threats so that people will feel they must defend themselves. Hate can of course be part of the equation, but fear is almost always a key ingredient. Susan Benesch has spent years , and she has found a consistent pattern where fear of a looming threat is used to prod groups into preemptive violence. cataloging the types of speech that have led to genocide In a recent article, she writes that and needs to be countered. dangerous speech is on the rise in the United States Benesch is the founder and executive director of the , which studies speech that can inspire violence and works to find ways to prevent that violence without infringing on freedom of expression. Dangerous Speech Project Benesch is also faculty associate of the Berkman Klein Center for Internet and Society at Harvard University. Our conversation, edited for brevity and clarity, is below. Let’s start at the beginning. What is dangerous speech? Angwin: It is any kind of human communication that makes people more likely to condone or even commit violence against another group of people. It can be speech, a photo, even the color of a T-shirt that convinces people to perceive other members of a group as a terrible threat. Benesch: That makes violence seem acceptable, necessary, or even virtuous. I coined this term after looking at the rhetoric that malevolent civilian political leaders have used in the months and years leading up to mass intergroup violence. I was stunned at how similar this rhetoric is from case to case. It’s as if there’s some horrible school that they all attend. It made me think, If this stuff is a consistent precursor to intergroup violence, it could, at a minimum, be a useful early-warning signal for that violence. There’s also a lot of discussion about hate speech. Can you draw out the distinction between dangerous speech and hate speech? Angwin: If you attack and denigrate another group of people because of their membership in that group, that’s hate speech. But the boundaries are contested, and the definitions vary a lot. Benesch: Dangerous speech is deliberately not subjective since the definition is consequentialist. It’s a prediction about the effect of speech as it is disseminated in the world. It is about the capacity of the speech to motivate somebody to commit or condone mass violence, which is a consequence that the vast majority of people don’t want. This means it’s easier to get people to agree that dangerous speech is bad, compared to getting people to agree that hate speech is bad, since it’s hard to agree that something is hate speech in the first place. You can’t make a list of words that count as dangerous speech. You can only classify it with reference to our analytical framework, which asks, Who made or disseminated this content? Who received the message? What’s the content of the message itself that might make it convincing? In what social and historical context was the message disseminated? And finally, What was the means of dissemination? Those five factors can help us assess whether a message is more or less dangerous. As far as I know, there isn’t a similar framework for hate speech. Can you talk about some of the common characteristics of dangerous speech? Angwin: I mentioned earlier that I originally became interested in this topic because of how similar it was from case to case. I noticed certain rhetorical moves in dangerous speech, and I began to list them. Benesch: These characteristics are striking and repeated, but they don’t define speech as dangerous by themselves. The most well-known one is dehumanization. It’s extremely common for humans to refer to other humans as some disfavored creature that is perceived as less than human. For example, it’s Hutus calling Tutsis “cockroaches” during the Rwandan genocide of 1994. Rats and cockroaches seem to be the most common creatures in dangerous speech, I suppose because they are universally despised. If you can get people to regard another human as a cockroach, then they think, It’s O.K. to do to that person what you would do to a cockroach. It’s all about threat and fear. Another common hallmark of dangerous speech is called “accusation in a mirror.” That is when a dangerous speaker tells his own people that the other group is planning to attack the in-group when, in fact, the speaker wants the in-group to condone an attack against the other group. This term was coined not by me or any other researcher; it was found after the Rwandan genocide in a manual for making anti-Tutsi propaganda. Accusation in a mirror is a major feature of White supremacist language. The whole idea of the “great replacement” is that there’s another group planning to wipe you out, and they will do that unless you (which usually means the men of the group who want to see themselves as its protectors) defend yourself and your women and children. Accusation in a mirror makes violence—and people who commit it—seem noble and virtuous. How much dangerous speech is out there? A 2021 survey found that more than , “The traditional American way of life is disappearing so fast that we may have to use force to save it.” Angwin: one-third of Americans agreed with the statement I’ve been working on this topic for a decade, and I’ve been very cautious ever to say that it’s getting worse or there’s an increase, since it’s hard to measure that in a robust, definitive way. Benesch: But I have recently concluded that in the United States at the moment, there is, at minimum, a striking and alarming shift in the extent to which dangerous speech is used and condoned by political leaders and other influential people. Take the [Republican Senate candidate] Eric Greitens’ campaign ad, in which he said, “Let’s go RINO hunting.” He illustrated that with some other men all dressed up in paramilitary gear, kicking down the door of a house and blasting into it as if to massacre the people inside it. That ad was criticized and eventually taken down. But there was not a major outcry from other Republicans, who therefore are condoning dangerous speech. Obviously, Trump and Jan. 6 are one of the most striking and egregious examples. Large numbers of Republican leaders and far-right leaders condoned incitement to violence before, during, and after Jan. 6. They’re still doing it. It’s not true that all of the dangerous speech in this country is coming from the right. There are absolutely examples from the left. However, the amount of dangerous speech as far as I can see, and its intensity, is much, much greater on the far right. It’s this speech, and the habit of condoning it directly or implicitly by not saying anything, that has moved into the mainstream. How should we be responding to dangerous speech? Angwin: What I really want to do is find ways of making what we now call dangerous speech less convincing and less effective without impinging on freedom of expression. Benesch: In any society at any time, there is some small proportion of people who are extremist. But the majority of people are not extremist, and their views are moderate and change gradually. Those people are absolutely vital, so we all have a tremendous responsibility to keep people from going over the edge and becoming convinced by dangerous speech and especially dangerous disinformation. Influential people of various spheres need to refrain from dangerous speech themselves and denounce it when other influential people use it. Wherever possible, we need to all put pressure on people who are producing and disseminating dangerous speech. : Do any of the big tech platforms include dangerous speech metrics in their content moderation policies? And what do you think they could do to improve? Angwin : No company has incorporated the dangerous speech framework whole hog into content moderation, as far as I know, but several have used dangerous speech ideas to decide where to draw the line between permissible and impermissible content, including Facebook, Twitter, Google, and Spotify. Benesch They have also used dangerous speech ideas in downranking, which is a major, often overlooked method on which platforms increasingly rely. Here are some ideas for what they should do: Distinguish between hate speech and dangerous speech, in part so that they can better protect freedom of speech, for example by allowing content that is provocative but not dangerous. Try to build classifiers for dangerous speech, for review by humans. It’s very important to detect it quickly, after all. Identify ambiguous content as inflammatory by observing how people are reacting to it, as I suggested in and . Noema magazine in the Los Angeles Times Recruit more people with detailed knowledge of particular cultures, places, and languages, to let platforms know when dangerous content proliferates. As always, thanks for reading. Best, Julia Angwin The Markup (Additional Hello World research by Eve Zelickson.) Credits: Julia Angwin Also published here Photo by on Hugo Tasca Unsplash

Dangerous Speech Is on the Rise

This paper is available on arxiv under CC 4.0 license. Authors: (1) Eleonora Alei, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (2) Björn S. Konrad, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (3) Daniel Angerhausen, ETH Zurich, Institute for Particle Physics & Astrophysics, National Center of Competence in Research PlanetS & Blue Marble Space Institute of Science; (4) John Lee Grenfell, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR) (5) Paul Mollière, Max-Planck-Institut für Astronomie; (6) Sascha P. Quanz, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (7) Sarah Rugheimer, Department of Physics, University of Oxford; (8) Fabian Wunderlich, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR); (9) LIFE collaboration, www.life-space-mission.com. Table of Links Abstract & Introduction Methods Results Discussion Conclusions Next Steps & References Appendix A: Scattering of terrestrial exoplanets Appendix B: Corner Plots Appendix C: Bayes’ factor analysis: other epochs Appendix D: Cloudy scenarios: additional figures ABSTRACT Context. An important future goal in exoplanetology is to detect and characterize potentially habitable planets. Concepts for future space missions have already been proposed: from a large UV-Optical-Infrared space mission for studies in reflected light, to the Large Interferometer for Exoplanets (LIFE) for analyzing the thermal portion of the planetary spectrum. Using nulling interferometry, LIFE will allow us to constrain the radius and effective temperature of (terrestrial) exoplanets, as well as provide unique information about their atmospheric structure and composition. Aims. We explore the potential of LIFE in characterizing emission spectra of Earth at various stages of its evolution. This allows us (1) to test the robustness of Bayesian atmospheric retrieval frameworks when branching out from a Modern Earth scenario while still remaining in the realm of habitable (and inhabited) exoplanets, and (2) to refine the science requirements for LIFE for the detection and characterization of habitable, terrestrial exoplanets. Methods. We perform Bayesian retrievals on simulated spectra of 8 different scenarios, which correspond to cloud-free and cloudy spectra of four different epochs of the evolution of the Earth. Assuming a distance of 10 pc and a Sun-like host star, we simulate observations obtained with LIFE using its simulator LIFEsim, considering all major astrophysical noise sources. Results. With the nominal spectral resolution (R = 50) and signal-to-noise ratio (assumed to be S/N = 10 at 11.2 µm), we can identify the main spectral features of all the analyzed scenarios (most notably CO2, H2O, O3, CH4). This allows us to distinguish between inhabited and lifeless scenarios. Results suggest that particularly O3 and CH4 yield an improved abundance estimate by doubling the S/N from 10 to 20. Neglecting clouds in the retrieval still allows for a correct characterization of the atmospheric composition. However, correct cloud modeling is necessary to avoid biases in the retrieval of the correct thermal structure. Conclusions. From this analysis, we conclude that the baseline requirements for R and S/N are sufficient for LIFE to detect O3 and CH4 in the atmosphere of an Earth-like planet with an abundance of O2 of around 2% in volume mixing ratio. Doubling the S/N would allow a clearer detection of these species at lower abundances. This information is relevant in terms of the LIFE mission planning. We also conclude that cloud-free retrievals of cloudy planets can be used to characterize the atmospheric composition of terrestrial habitable planets, but not the thermal structure of the atmosphere. From the inter-model comparison performed, we deduce that differences in the opacity tables (caused by e.g. a different line wing treatment) may be an important source of systematic errors. Methods: statistical – Planets and satellites: terrestrial planets – Planets and satellites: atmospheres Keywords. 1. Introduction Temperate terrestrial exoplanets are predicted to be very abundant in our galaxy (Bryson et al. 2021). These planets are ideal candidates when searching for life beyond our Solar System. A powerful way to characterize a terrestrial exoplanet in the context of its habitability is by detecting and studying its atmosphere with the goal to constrain its surface conditions. Atmospheric spectra are influenced by many parameter and processes, such as the chemical composition, the temperature structure of the atmosphere, the presence of clouds, as well as emission and scattering from the surface. The detection and characterization of potentially habitable, rocky exoplanets is challenging with current facilities. For this reason, there is a widespread interest in the community to build new instruments for the search of life in the universe, as reported in the White Paper series in the context of the ESA “Voyage 2050” process[1] , as well as the US Astro 2020 Decadal survey (National Academies of Sciences, Engineering, and Medicine 2021). Space missions that aim at characterizing terrestrial exoplanets have been proposed, such as HabEx (Gaudi et al. 2020) and LUVOIR (Peterson et al. 2017) focusing on the reflected (visible and near-infrared) portion of the planetary spectrum, as well as LIFE (Large Interferometer for Exoplanets, Quanz et al. 2021, hereafter Paper I), which will characterize terrestrial planets in the thermal (mid-infrared), emitted portion of the planetary spectrum. Using nulling interferometry, LIFE will allow us to constrain the radius and effective temperature of (terrestrial) exoplanets, as well as provide unique information about their atmospheric structure and composition (Dannert et al. 2022; Konrad et al. 2021, hereafter Paper II and III, respectively). Due to the current lack of high-quality observational data, we must rely momentarily on simulated observations of terrestrial planets to create and improve the analyses algorithms, but also to provide scientific and technical requirements when planning a mission. This effort is currently ongoing within the LIFE Initiative and in a previous study (Paper III), we built a Bayesian retrieval routine to estimate the planetary and atmospheric parameters of a simulated Modern Earth twin at 10 pc distance as it would be observed by LIFE. In this work, we extend this exercise to other stages in the evolution of Earth’s atmosphere. Our planet has been habitable for about 4.4 billion years (see e.g. Heller et al. 2021, and references therein). In this context, we define a planet as habitable if its physical and chemical conditions would allow water, if present, to be liquid on the surface. In the prebiotic stage of Earth’s evolution, the atmosphere lacked O2 (currently about 21% of the atmospheric composition by volume). It was instead a CO2-N2-H2O-rich atmosphere, with traces of CH4 from volcanism. The early forms of life developed under a reducing environment and survived under anaerobic conditions (Olson et al. 2018, and references therein). Methanogenesis was thought to be a dominant metabolism at this stage (around 3.5 Ga), which would explain the increase in CH4 in the atmosphere (see e.g. Wolfe & Fournier 2018). Around 3 Ga, life forms that could use carbon dioxide to produce oxygen (via oxygenic photosynthesis) appeared (Marais 2000). These eventually led to a significant increase of O2 maximally up to ∼ 1% PAL[2] (see Gregory et al. 2021; Lyons et al. 2014, 2021, and references therein) around 2.33 Ga (Luo et al. 2016), during the so-called "Great Oxygenation Event" (GOE). There is also evidence pointing to a second increase in the O2 abundance (up to ∼ 10% PAL) occurred around 0.8 Ga, in the "Neoproterozoic Oxygenation Event" (NOE) (Shields-Zhou & Och 2011; Campbell & Squire 2010). The high abundance of carbon dioxide in the early Earth would have enhanced the atmospheric greenhouse effect, allowing Earth to be habitable despite the fainter solar irradiation (see e.g. Feulner 2012, and references therein). The positive feedback between the carbon-silicate cycle and the increase in irradiation would have then allowed to maintain temperatures conducent to liquid water over the last 4 Ga. The increase in irradiation from the Sun over the eons has made the weathering of CO2 more efficient, decreasing the amount of carbon dioxide in the atmosphere and thus dampening the atmospheric greenhouse effect (see e.g. Graham 2021, and references therein). The appearance of photosynthetic life forms and the onset of plate tectonics also contributed to the depletion of atmospheric CO2. Numerous processes including biology and geology have driven the wide-ranging evolution of Earth’s atmosphere during the various epochs of its development. Our modern atmosphere represents however only a small fraction of Earth’s evolutionary states. It is therefore important to simulate a suitable range of different atmospheric epochs from Earth’s history when in vestigating Earth-like atmospheres. For this study, we simulated observations obtained by LIFE starting from theoretical spectra of 4 distinct epochs of Earth’s atmospheric evolution, produced from a self-consistent 1D climate and photochemistry model coupled with a line-by-line radiative transfer model (Rugheimer & Kaltenegger 2018). The observed spectra were simulated using the LIFE noise simulator LIFEsim (for details on the simulator see Paper II). We then used the Bayesian retrieval routine presented in Paper III to characterize the different atmospheres. We aim to address the following research questions: – science-driven questions: How well could LIFE characterize atmospheres of habitable planets? Could LIFE differentiate between different atmospheres, and with what confidence? What is the impact of clouds on this assessment? What are the most promising (combinations of) detectable biosignatures? – technology- and computationally-driven questions: Is the combination of spectral resolution (R = λ/∆λ), signal to noise ratio (S/N) and wavelength range defined in Paper III still adequate for this case study? What are the caveats and limitations of the Bayesian retrieval routine? What systematics may arise when comparing two different models (e.g. in terms of differences in line lists, scattering treatment, identification of biomarkers)? We discuss how we adapted the input spectra to simulate LIFE observations and describe the grid of scenarios in Section 2. We show and describe the results in Section 3. A thorough discussion of our findings and of the potential systematic uncertainties of the retrieval routine is provided in Section 4. In Section 5 we report the main takeaway points from this study, and in Section 6 we trace an outlook of the ongoing and future studies. [1] https://www.cosmos.esa.int/web/voyage-2050 [2] Present Atmospheric Level This paper is available on under CC 4.0 license. arxiv

Large Interferometer For Exoplanets (LIFE): Looking Beyond the Stars

Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, 1Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References 5 Materials and Methods In this section, we endeavor to elucidate key mathematical and computational foundations that are instrumental for the work presented in this study. Specifically, we delve into spectral graph theory, simplicial complex, and persistent Laplacian methods, highlighting their significance in capturing topological and spectral properties essential for the characterization of proteins. Additionally, we discuss machine learning and deep learning paradigms, focusing on their role in processing, analyzing, and interpreting these complex features, especially within the confines of test datasets and validation settings. 5.1 Persistent Laplacian characterization of proteins A simplicial complex is made up of a set of simplices and generalises beyond graph networks at higher dimensions [44, 45, 46, 47]. Every simplex is a finite set of vertices which can be interpreted as the atoms in a protein structure. Essentially, simplices can be a point (0-simplex), an edge (1-simplex), a triangle (2-simplex), a tetrahedron (3-simplex), or in higher dimensions, a p-simplex. In other words, a k-simplex σ k = {v0, v1, · · · , vk} is the convex hull formed by k + 1 affinely independent points v0, v1, · · · , vk as follows, Simplicial complex A geometric simplicial complex K is a finite set of geometric simplexes that satisfy two essential conditions. First, any face of a simplex from K is also in K. Second, the intersection of any two simplexes in K is either empty or shares faces. Commonly used methods to construct simplicial complexes are Cech complex, Vietoris-Rips complex, Alpha complex, Clique ˇ complex, Cubic complex, and Morse complex [44, 45, 46, 47]. In the case k = 0, then L0 = B1B⊤ 1 since ∂0 is a zero map Persistent Laplacian (PL) were first introduced by integrating graph Laplacian and multiscale filtration [25]. Analyzing the spectra of k-combinatorial Laplacian matrix allows both topological and geometric information (i.e. connectivity and robustness of simple graphs) to be obtained. However, this method is genuinely free of metrics or coordinates, which induced too little topological and geometric information that can be used to describe a single configuration. Persistent Laplacian Therefore, PL was extended to simplicial complexes. This allows a sequence of simplicial complexes from a filtration process to generate persistent Laplacian which is largely inspired by persistent homology and in earlier works in multiscale graphs. For the rest of this section, we introduce mainly on the construction of PL. First, a k-combinatorial Laplacian matrix is symmetric and positive semi-definite. Therefore, its eigenvalues are all real and non-negative. The multiplicity of zero spectra (also called harmonic spectra) reveals the topological information, and the geometric information will be preserved in the non-harmonic spectra. This nested sequence of simplicial complexes induces a family of chain complexes In order to illustrate the difference between PL and PH, Figure 5 describes a point cloud, basic simplices, a filtration process and the comparison between persistent Laplacian and persistent homology barcodes of 13 points. The filtration process in Figure 5(c) shows the different stages of a Rips filtration process for the 13 points. Figure 5(d) shows the persistent homology barcodes (in blue) and persistent non-harmonic spectra (in red). It can be seen that the nonharmonic spectra provides the additional homotopic shape evolution that is missing in persistent homology in the later part of the filtration process. 5.2 Persistent Laplacian descriptors where DE(·, ·) is the Euclidean distance between the two atoms and Loc(·) refers to the atom’s location which is either in the mutation site or in the rest of the protein. Here, we construct two types of simplicial complexes in our PL computation, such as Vietoris-Rips complex (VC) and Alpha complex (AC). Both complexes are used to characterize the first order interactions and higher order patterns respectively. To capture and characterize different types of atomatom interactions, we generate the PL based on different atom subsets by selecting one type of atom in the mutation site and one other atom type in the rest of the protein. Different types of atom-atom interactions characterize the different interactions in proteins. For example, interactions generated from carbon atoms are associated with hydrophobic interactions. Similarly, interactions between nitrogen and oxygen atoms correlate to hydrophilic interactions and/or hydrogen bonds. Both types of interactions are illustrated in Figure 6. Interactive PLs have the capability to unveil additional details about bonding interactions and offer a fresh and distinct representation of molecular interactions in proteins. For zero dimensions, we consider both the harmonic spectra and non-harmonic spectra information for each persistent Laplacian. Filtration using Rips complex with DI distance is used. The 0-dimensional PL features are generated from 0A to 6 ˚ A with 0.5 ˚ A gridsize. For the ˚ non-harmonic spectra information, we count the number of non-harmonic spectra and calculate seven statistical values of non-harmonic spectra such as sum, minimum, maximum, mean, standard deviation, variance and the sum of eigenvalues squared. This generates eight statistical values for each of the nine atomic pairs. Therefore, the dimension of 0-dimensional PL features for a protein is 72. In total, the 0-dimensional PL-based feature size after concatenating features at different dimensions for wild type and mutant is 1872. For one or two dimensions, we perform the filtration using Alpha complex with the DE distance. The limited number of atoms in the local protein structure can create only a few high-dimensional simplexes, resulting in minimal alterations in shape. As a result, it suffice to consider features from only harmonic spectra of persistent Laplacians by coding the topological invariants for the high-dimensional interactions. Using GUDHI[51], the persistence of the harmonic spectra can be represented by persistent barcodes. The topological feature vectors are generated by computing the statistics of bar lengths, births and deaths. Bars shorter than 0.1A are excluded as they do not exhibit any clear physical meaning. The remaining bars are ˚ then used for computing the statistics: (1) sum, maximum and mean for lengths of bars; (2) minimum and maximum for the birth values of bars; (3) minimum and maximum for the death values of bars. Each set of point clouds leads to a seven-dimensional vector. These features are calculated on nine single atomic pairs and one heavy atom pair. The dimension of oneand two-dimensional PL feature vectors for a protein is 140. In total, the higher-dimensional PL-based feature size after concatenating features at different dimensions for wild type, mutant and their difference is 420. 5.3 Persistent Homology Persistent homology is part of the harmonic spectra of PL. The homology groups in PH illustrate the persistence of topological invariants, hence providing the harmonic spectral information in PL. The site- and element-specific PH features are generated in a similar way as compared to PL. Similar to PL, filtration construction is also employed to PH. For the zero dimension, the filtration parameter can be discretized into several equally spaced bins, namely [0, 0.5], (0.5, 1], · · · , (5.5, 6]A. The death value of the bars are summed in each bin resulting in 12 ˚ ×18 features. For each bin, we count the numbers of persistent bars, resulting in a nine-dimensional vector for each point cloud. Similarly, this is performed for each of the nine single atomic pairs. Hence, the dimension of PH features for a protein is 216. For one or two dimensions, the identical featurization from the statistics of persistent bars in PH is used. The PH embedding combines features at different dimensions as described above and concatenated for wild type, mutant and their difference, resulting in a 648-dimensional vector 5.4 Transformer Features Recently, we have seen significant advancements in modelling protein properties using largescale protein transformer models trained on hundreds of millions of sequences. These models, like ESM [33] (evolutionary scale modeling) and ProtTrans[34, 35], have demonstrated impressive performance. Moreover, hybrid fine-tuning approaches that leverage both local and global evolutionary data have proven to enhance these models even further. For instance, eUniRep is an improved LSTM-based UniRep model achieved through fine-tuning with knowledge extracted from local multiple sequence alignments (MSAs). Additionally, the ESM model can be fine-tuned using either downstream task data or local MSAs. In our research, we employed the ESM-1b transformer, a model that falls under the transformer architecture. This particular variant was trained on a dataset of 250 million sequences using a masked filling procedure and boasts an architecture comprising 34 layers with a whopping 650 million parameters. The ESM transformer’s primary role in our work was to generate sequence embeddings. At each layer of the ESM model, it encoded a sequence of length L into a matrix sized at 1,280×L, excluding the start and terminal tokens. For our study, we utilized the sequence representation derived from the final (34th) layer and computed the average along the sequence length axis, resulting in a 1,280-component vector. 5.5 Performance Metrics PPV and NPV assesses the true positive and true negative proportion of the predicted results for each solubility class. PPV and NPV are computed based on TP, TN, FP and FN which represents the true positive, true negative, false positive and false negative values for each solubility class. For each solubility class, PPV and NPV can be computed by: This paper is under CC 4.0 license. available on arxiv

Interrogating TopLabGBT's Computational Framework

Retreat into the Interior of the Country

for automating any business processes you need. It combines the simplicity of no-code tools with quick drag-and-drop automations, and the ability to create complex architecture. Latenode is a low-code platform . All these possibilities allow everyone to tap into the full-code potential, even without technical hard skills. Choose integrations from our no-code library; use HTTP requests; connect NPM packages; add JavaScript modules; or ask the in-built AI assistant for custom code And no, it’s not just about business process automation. , unlocking the programming world for those who never saw themselves as coders. Latenode is also the perfect cloud environment for building services and applications Latenode’s Team Our founding team launched two successful multi million businesses behind them. One from 2014, another from 2018. And that’s exactly where building and automating businesses in a low-code environment started for us. During these years, our veteran team of developers, with its leader, CTO of Latenode – Oleg Zankov, has tried a whole variety of different tools. Starting from self-hosted open-source Huginn and ending with Zapier, Make, and n8n, . our team didn’t find the perfect solution for their businesses, so they started thinking about their own tool While Latenode is still a startup, our team counts 16 professionals. The dominant part is tech specialists focusing on our core product – the low-code platform itself: are constantly being added to our library, so the no-code journey becomes smoother for new people in the field. New applications keeps expanding, making the process of automating as flexible as possible. The variety of functions and operators are relentlessly extending, as long as building on Latenode is connected to all the AI advantages our modern world has. Coding capabilities The Latenode team has just introduced its . Just ask for it! Code AI Copilot that understands the context of your scenarios on the platform and suggests the exact code you need Vision & Mission As AI and other technologies evolve, . Low-code tools are one of the ways we can speed up this transition. And that’s precisely what modern businesses want! people shift their focus from boring routine tasks to more creative and fulfilling endeavors As we know from Forrester research, the low-code solutions market has a , with a total valuation around $25 billion. They expect this growth trend to continue and forecast the low-code market to reach $68 billion by 2026. 28.6% annual growth rate As true enthusiasts of automation, we aspire to make our significant contribution to this process and present the tool everyone (including ourselves :p) needs. Our founding story :-) Perfect time for Latenode! That's a very unique situation we've been put in... As we've said before, we have had two actively operating businesses since 2014, and our final decision about which low-code tools to use was Integromat (Make's parent). It was nice, and we built 150+ scenarios that supported one of our core products: some scenarios leveraged its development, while others established well-designed communication between the product and users. – a new product with a different architecture, 3x the price, and promises about a smooth transition to Make that didn't materialize even after a year. We were happy after our transition to Integromat, but in Feb 2022, the Integromat team announced Make Slightly later, they informed us that . Because the new terms didn't satisfy us, we began to build our own tool. That's precisely where our 9+ years of automation experience converged, allowing us to extract the maximum benefit from it. And… , and during this month we’re going to finally change our low-code tool from Integromat to our own solution – Latenode. After that, we’ll be launched publicly, but for now… Integromat would be fully deactivated in September 2023 we’re in September 2023 we’re staying in Beta and suggesting who found themselves in the same situation as we are. free access to our early adopters Latenode vs Make (post-Integromat) The differences lie in the details. . When we launched Latenode, our primary goal was to bring some vital features to life. It's not just us who view these features as essential; the Make community feels the same. They've expressed their agreement by leaving thousands of upvotes on feature requests on their community platform. Take a look: We spent considerable time learning about the limitations of Integromat & Make firsthand . Read more about it here: We've addressed the major blind spots that kept our automation process from being as efficient and convenient as possible latenode.com/blog/4-key-differences-make-vs-latenode Free access to everyone! to check how search engine robots perceive your website and categorize your content using Google NLP… This means you won't need several months to determine if your written articles align with your niche and boost your rank. Consider this simple scenario instead: Imagine building an SEO micro-service What about a to engage with your lead? Additionally, ensure you record all the information from the form and the generated email in a Google Sheet for later analysis: scenario where you receive information from a website form filled out by a potential client, send it to GPT-4, and immediately craft a customized email All of this and until the end of the year ( ). much more is available to you absolutely free then, prices will be much lower than Make's, with fair charging for consumed CPU, not for activated modules in your scenario So, even if you're a newbie in automation, ask ChatGPT about what can be automated in your business and dive in! If you'd rather not automate these processes yourself, , where you're sure to find assistance. you can place an order for automation on Latenode in our Discord community Our lovely low-code Community Currently, our community is taking its first steps. Which we'll then incorporate into our product, addressing any challenges you might encounter. Proactive low-code enthusiasts are accepting invitations to our Discord server – we're keen to receive your feedback! . Furthermore, our technical support promises swift responses directly from our platform developers. This provides a unique opportunity for you to build and create on Latenode in collaboration with us. Let us emphasize: while we're in Beta, we're offering free access to all early adopters of the Latenode platform See you on the platform & ( ) Latenode our Discord join now!

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