Police said the shooting occurred outside an after-hours establishment on Johnston Street.

One dead, two more hurt in Akron drive-by shooting - cleveland.com

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Akron shooting leaves 1 dead, 2 injured

The Akron Police Department is investigating a shooting that left one person dead and two others injured.

2 injured, 1 killed in Akron shooting early Sunday morning

One man is dead and two others were taken to the hospital after an overnight drive-by shooting in Akron.

1 dead, 2 injured in overnight drive-by shooting in Akron

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How ICPL Enhances Reward Function Efficiency and Tackles Complex RL Tasks

The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion.” India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa, according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre. Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform, which is able to simulate global weather patterns in great detail. Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The "Scalable Ensemble Envelope Diffusion Sampler" (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances. In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” “Humans are a lot better at that than AI is right now,” she said. The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” kill hundreds of people In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion .” The total cost of these 400 events exceeds $2.785 trillion India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. The frequency of flooding is getting worse Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa , according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Widespread flooding in West and Central Africa Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre . Red Cross Climate Centre Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform , which is able to simulate global weather patterns in great detail. Earth-2 platform Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The " Scalable Ensemble Envelope Diffusion Sampler " (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. Scalable Ensemble Envelope Diffusion Sampler SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances . Science Advances In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. joined forces Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. announced These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” Smithsonian Magazine interviewed “Humans are a lot better at that than AI is right now,” she said.

Will AI Ever Be Able to Predict Disasters and Save Lives?

This is likely one of the most polemical issues we’ll find in the cypherpunk arena. Do you think murder is fine, under the right circumstances? At least we know that 54 countries think it’s a fair punishment for the most dangerous criminals. Jim Bell, a renowned crypto-anarchist, seems to think it’s justified against hated and corrupted political figures, and that’s how he came up with Assassination Politics (AP) —a would-be private assassination market. But let’s talk a bit about James Dalton Bell first. He was born in 1958 in Akron, Ohio (US), and later attended the Massachusetts Institute of Technology (MIT), where he earned a degree in chemistry. After graduating, he worked as an electrical engineer at Intel before starting his own company, SemiDisk Systems, in 1982, which specialized in computer storage devices. When the company closed in 1992, Bell developed a strong dislike for financial and tax issues —which would prove almost prophetical for him years later. Politically, he leaned toward anarcho-libertarian views and was involved with the Libertarian Party. His growing distrust of government led him to explore bold and controversial ideas, including the concept of a very unique prediction market. Assassination Politics This essay, published in 1996, outlines a controversial idea where people could anonymously contribute money to “predict” the death of specific government officials. It would function by leveraging encryption and digital cash to create a highly secure and anonymous prediction market. Participants would make predictions about the death of a listed target by submitting an encrypted guess, accompanied by a payment in digital cash. The prediction would be encrypted twice: the outer layer containing the digital cash is decrypted by the organization or administrators behind the market, while the inner layer, holding the prediction details, remains secure and known only to the predictor. If the prediction comes true, the predictor submits the decryption key, proving the accuracy of their guess, and receives the reward through an encrypted and untraceable digital cash transaction. Then, they can “gift” this reward to anyone —including an assassin. The entire process ensures that neither the organization nor any other party can trace the identity of the predictor or link it to the outcome, effectively safeguarding all parties involved from legal and investigative scrutiny. All of this was hypothetical, though. There wasn’t the proper technology for it back then. With the advent of smart contracts and cryptocurrencies, such a system could theoretically be implemented more securely today. A smart contract could automatically pay out a reward to someone who successfully predicts a specific event, like the death of a targeted official. The smart contract would ensure that no one needs to know who the predictor is, as the entire process would be automated and anonymous. A modern attempt to bring Bell’s idea to life was the Assassination Market, created by a pseudonymous figure known as Kuwabatake Sanjuro, but it wasn’t quite the same system, and not a prediction market at all. This platform allowed users to contribute bitcoins toward bounties on the heads of government officials. It’s unclear if it ever worked. Politics of Destruction The economics author Robert Murphy debated Bell’s proposal in his own essay, titled Politics of Destruction, published in 2002. Murphy argued that, despite its technological appeal, Assasination Politics is fundamentally flawed and impractical. His claims about administrators potentially pocketing funds or deceiving participants could be easily contented today with the existence of smart contracts and decentralized networks. However, Murphy also emphasizes the potential societal dangers of AP. If implemented, the system could lead to widespread violence, as it doesn’t limit targets to oppressive politicians but could be used against anyone, including ordinary individuals. This would create a chaotic environment where assassination becomes commonplace, undermining core values of property rights and personal safety that are crucial for a stable society —even for crypto-anarchists. In essence, Murphy contends that even if technical challenges were addressed, AP's ideological implications make it a perilous idea. He believes that its adoption could lead to the downfall of modern civilization and contradict the libertarian principles of a just and free society, as it would foster a world where violence and insecurity overshadow respect for individual rights. Bell’s Legal Saga As anyone could imagine, this essay put Bell on the radar of the authorities. Following an investigation by the US Internal Revenue Service (IRS) in 1997, Bell faced charges of tax evasion and misuse of Social Security numbers, leading to an 11-month prison sentence. After his release in 2000, Bell claimed widespread government corruption in his case, initiating a lawsuit in 2003, which triggered further legal complications. He was rearrested on charges of harassment and stalking of federal agents, resulting in a decade-long imprisonment. Bell's subsequent lawsuits alleged severe procedural misconduct by federal authorities. He accused the government of orchestrating an assault by a fellow inmate to intimidate him and claimed that court records were falsified to obstruct justice. Bell also protested the legality of surveillance measures, alleging that the government planted a GPS tracker on his car without proper warrants (which was proven right in the trial), violating his constitutional rights. His defense contended that these actions were part of a broader scheme to undermine his legal and civil liberties. Bell was eventually released in March 2012. All these issues haven’t stopped his AP activities however. In 2017, he announced the “Jim Bell Project” at the Paralelni Polis convention in Prague. He plans “to adopt a new [AP-like] system to completely eliminate all wars, all militaries, all taxes, and all government-sponsored violence,” with the help of worldwide volunteers and donors. Prediction Markets Assassination Politics was frequently discussed by cypherpunks, with mixed opinions. Trying to discern if this would be a good or a terrible idea could likely take a whole book, so we’ll leave it as food for thought. However, it’s important to note that prediction markets on decentralized platforms, like the one described by Bell, actually exist already. Even if the most common predictions don’t involve anyone’s death. Prediction markets in crypto are like online betting pools where people place bets on the outcomes of future events using cryptocurrency. Imagine a marketplace where you can wager on (non-deadly) things like election candidates, football results, or price breakthroughs. Participants buy and sell shares or tokens linked to different outcomes, and the prices of these shares reflect the collective predictions of the market. The idea is that the wisdom of the crowd can provide accurate forecasts, while the platform provides rewards to its users. Obyte, indeed, has its own decentralized prediction market based on bonding curves: Prophet. On this platform, participants are given options to bet on various event outcomes, such as Yes, No, or, for some events, Draw, each represented by a distinct token with an initial worth. These tokens can be traded, but their values fluctuate to reflect the changing probabilities as the event approaches and the market incorporates new information. In addition, users can also participate in liquidity mining. Common bets on Prophet include price predictions and football games, and the whole process eliminates middlemen. Maybe it’s not quite like what Bell had in mind, but we can still bet privately for a whole world of possibilities. Read more from Cypherpunks Write Code series:

Tim May & Crypto-anarchism
Wei Dai & B-money
Nick Szabo & Smart Contracts
Adam Back & Hashcash
Eric Hughes & Remailer
St Jude & Community Memory
Julian Assange & Wikileaks
Hal Finney & RPOW
John Gilmore & EFF
Satoshi Nakamoto & Bitcoin
Gregory Maxwell & Bitcoin Core
David Chaum & Ecash Featured Vector Image by Garry Killian / Freepik
Image of Jim Bell by All Hacking Cons / Paralelni Polis This is likely one of the most polemical issues we’ll find in the cypherpunk arena. Do you think murder is fine, under the right circumstances? At least we know that 54 countries think it’s a fair punishment for the most dangerous criminals. Jim Bell, a renowned crypto-anarchist, seems to think it’s justified against hated and corrupted political figures, and that’s how he came up with Assassination Politics (AP) —a would-be private assassination market. But let’s talk a bit about James Dalton Bell first. He was born in 1958 in Akron, Ohio (US), and later attended the Massachusetts Institute of Technology (MIT), where he earned a degree in chemistry . After graduating, he worked as an electrical engineer at Intel before starting his own company, SemiDisk Systems, in 1982, which specialized in computer storage devices. He was born in 1958 in Akron, Ohio (US), and later attended the Massachusetts Institute of Technology (MIT), where he earned a degree in chemistry When the company closed in 1992, Bell developed a strong dislike for financial and tax issues —which would prove almost prophetical for him years later. Politically, he leaned toward anarcho-libertarian views and was involved with the Libertarian Party. His growing distrust of government led him to explore bold and controversial ideas, including the concept of a very unique prediction market. Assassination Politics This essay, published in 1996, outlines a controversial idea where people could anonymously contribute money to “predict” the death of specific government officials. It would function by leveraging encryption and digital cash to create a highly secure and anonymous prediction market. Participants would make predictions about the death of a listed target by submitting an encrypted guess, accompanied by a payment in digital cash. This essay, published in 1996, outlines a controversial idea where people could anonymously contribute money to “predict” the death of specific government officials. The prediction would be encrypted twice: the outer layer containing the digital cash is decrypted by the organization or administrators behind the market, while the inner layer, holding the prediction details, remains secure and known only to the predictor. If the prediction comes true, the predictor submits the decryption key, proving the accuracy of their guess, and receives the reward through an encrypted and untraceable digital cash transaction. Then, they can “gift” this reward to anyone —including an assassin. The prediction The prediction The entire process ensures that neither the organization nor any other party can trace the identity of the predictor or link it to the outcome, effectively safeguarding all parties involved from legal and investigative scrutiny. All of this was hypothetical, though. There wasn’t the proper technology for it back then. With the advent of smart contracts and cryptocurrencies, such a system could theoretically be implemented more securely today. A smart contract could automatically pay out a reward to someone who successfully predicts a specific event, like the death of a targeted official. The smart contract would ensure that no one needs to know who the predictor is, as the entire process would be automated and anonymous. With the advent of smart contracts and cryptocurrencies, such a system could theoretically be implemented more securely today. A modern attempt to bring Bell’s idea to life was the Assassination Market , created by a pseudonymous figure known as Kuwabatake Sanjuro, but it wasn’t quite the same system, and not a prediction market at all. This platform allowed users to contribute bitcoins toward bounties on the heads of government officials. It’s unclear if it ever worked. Assassination Market Assassination Market Politics of Destruction The economics author Robert Murphy debated Bell’s proposal in his own essay, titled Politics of Destruction , published in 2002. Murphy argued that, despite its technological appeal, Assasination Politics is fundamentally flawed and impractical. His claims about administrators potentially pocketing funds or deceiving participants could be easily contented today with the existence of smart contracts and decentralized networks. Politics of Destruction Politics of Destruction However, Murphy also emphasizes the potential societal dangers of AP. If implemented, the system could lead to widespread violence, as it doesn’t limit targets to oppressive politicians but could be used against anyone, including ordinary individuals . This would create a chaotic environment where assassination becomes commonplace, undermining core values of property rights and personal safety that are crucial for a stable society —even for crypto-anarchists. If implemented, the system could lead to widespread violence, as it doesn’t limit targets to oppressive politicians but could be used against anyone, including ordinary individuals In essence, Murphy contends that even if technical challenges were addressed, AP's ideological implications make it a perilous idea. He believes that its adoption could lead to the downfall of modern civilization and contradict the libertarian principles of a just and free society, as it would foster a world where violence and insecurity overshadow respect for individual rights. Bell’s Legal Saga As anyone could imagine, this essay put Bell on the radar of the authorities. Following an investigation by the US Internal Revenue Service (IRS) in 1997, Bell faced charges of tax evasion and misuse of Social Security numbers, leading to an 11-month prison sentence. After his release in 2000, Bell claimed widespread government corruption in his case, initiating a lawsuit in 2003, which triggered further legal complications. He was rearrested on charges of harassment and stalking of federal agents, resulting in a decade-long imprisonment. Bell's subsequent lawsuits alleged severe procedural misconduct by federal authorities. He accused the government of orchestrating an assault by a fellow inmate to intimidate him and claimed that court records were falsified to obstruct justice. Bell also protested the legality of surveillance measures, alleging that the government planted a GPS tracker on his car without proper warrants (which was proven right in the trial), violating his constitutional rights. His defense contended that these actions were part of a broader scheme to undermine his legal and civil liberties. Bell's subsequent lawsuits alleged severe procedural misconduct by federal authorities. alleged alleged was proven right was proven right Bell was eventually released in March 2012. All these issues haven’t stopped his AP activities however. In 2017, he announced the “ Jim Bell Project ” at the Paralelni Polis convention in Prague. He plans “to adopt a new [AP-like] system to completely eliminate all wars, all militaries, all taxes, and all government-sponsored violence,” with the help of worldwide volunteers and donors. Jim Bell Project Jim Bell Project Prediction Markets Assassination Politics was frequently discussed by cypherpunks, with mixed opinions . Trying to discern if this would be a good or a terrible idea could likely take a whole book, so we’ll leave it as food for thought. However, it’s important to note that prediction markets on decentralized platforms, like the one described by Bell, actually exist already. Even if the most common predictions don’t involve anyone’s death. with mixed opinions with mixed opinions Prediction markets in crypto are like online betting pools where people place bets on the outcomes of future events using cryptocurrency. Imagine a marketplace where you can wager on (non-deadly) things like election candidates, football results, or price breakthroughs. Participants buy and sell shares or tokens linked to different outcomes, and the prices of these shares reflect the collective predictions of the market. The idea is that the wisdom of the crowd can provide accurate forecasts, while the platform provides rewards to its users. Prediction markets in crypto are like online betting pools where people place bets on the outcomes of future events using cryptocurrency. Obyte , indeed, has its own decentralized prediction market based on bonding curves: Prophet . On this platform, participants are given options to bet on various event outcomes, such as Yes, No, or, for some events, Draw, each represented by a distinct token with an initial worth. These tokens can be traded, but their values fluctuate to reflect the changing probabilities as the event approaches and the market incorporates new information. In addition, users can also participate in liquidity mining. Obyte Obyte Prophet Prophet Common bets on Prophet include price predictions and football games, and the whole process eliminates middlemen. Maybe it’s not quite like what Bell had in mind, but we can still bet privately for a whole world of possibilities. Read more from Cypherpunks Write Code series: Tim May & Crypto-anarchism
Wei Dai & B-money
Nick Szabo & Smart Contracts
Adam Back & Hashcash
Eric Hughes & Remailer
St Jude & Community Memory
Julian Assange & Wikileaks
Hal Finney & RPOW
John Gilmore & EFF
Satoshi Nakamoto & Bitcoin
Gregory Maxwell & Bitcoin Core
David Chaum & Ecash Read more from Cypherpunks Write Code series: Tim May & Crypto-anarchism Wei Dai & B-money Nick Szabo & Smart Contracts Adam Back & Hashcash Eric Hughes & Remailer St Jude & Community Memory Julian Assange & Wikileaks Hal Finney & RPOW John Gilmore & EFF Satoshi Nakamoto & Bitcoin Gregory Maxwell & Bitcoin Core David Chaum & Ecash Tim May & Crypto-anarchism Tim May & Crypto-anarchism Tim May & Crypto-anarchism Tim May & Crypto-anarchism Wei Dai & B-money Wei Dai & B-money Wei Dai & B-money Wei Dai & B-money Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Adam Back & Hashcash Adam Back & Hashcash Adam Back & Hashcash Adam Back & Hashcash Eric Hughes & Remailer Eric Hughes & Remailer Eric Hughes & Remailer Eric Hughes & Remailer St Jude & Community Memory St Jude & Community Memory St Jude & Community Memory St Jude & Community Memory Julian Assange & Wikileaks Julian Assange & Wikileaks Julian Assange & Wikileaks Julian Assange & Wikileaks Hal Finney & RPOW Hal Finney & RPOW Hal Finney & RPOW Hal Finney & RPOW John Gilmore & EFF John Gilmore & EFF John Gilmore & EFF John Gilmore & EFF Satoshi Nakamoto & Bitcoin Satoshi Nakamoto & Bitcoin Satoshi Nakamoto & Bitcoin Satoshi Nakamoto & Bitcoin Gregory Maxwell & Bitcoin Core Gregory Maxwell & Bitcoin Core Gregory Maxwell & Bitcoin Core Gregory Maxwell & Bitcoin Core David Chaum & Ecash David Chaum & Ecash David Chaum & Ecash Featured Vector Image by Garry Killian / Freepik Image of Jim Bell by All Hacking Cons / Paralelni Polis Featured Vector Image by Garry Killian / Freepik Freepik Freepik Image of Jim Bell by All Hacking Cons / Paralelni Polis All Hacking Cons All Hacking Cons

Cypherpunks Write Code: Jim Bell & Assassination Politics

Шыфрапанкі пішуць код: Джым Бэл і палітыка забойстваў

Cypherpunks пишат код: Jim Bell & Assassination Politics

Los cypherpunks escriben código: Jim Bell y la política del asesinato

Cypherpunks-ը գրում է կոդը՝ Jim Bell & Assassination Politics

サイファーパンクはコードを書く: ジム・ベルと暗殺政治

„Cypherpunks“ rašo kodą: Jimas Bellas ir „Assassination Politics“.

Kod Tulis Cypherpunks: Jim Bell & Politik Pembunuhan

Cypherpunks skriver kod: Jim Bell & Assassination Politics

Cypherpunks Write Code: Jim Bell at Assassination Politics

密码朋克编写代码：吉姆·贝尔与暗杀政治

How a Herd of Models Challenges ChatGPT's Dominance: Conclusion, Discussion, and References

An Overview of the Data-Loader Landscape: Conclusion, Acknowledgments, and References

ডেটা-লোডার ল্যান্ডস্কেপের একটি ওভারভিউ: উপসংহার, স্বীকৃতি এবং রেফারেন্স

Ein Überblick über die Data-Loader-Landschaft: Fazit, Danksagungen und Referenzen

Una descripción general del panorama del cargador de datos: conclusión, agradecimientos y referencias

Un aperçu du paysage des chargeurs de données : conclusion, remerciements et références

डेटा-लोडर परिदृश्य का अवलोकन: निष्कर्ष, आभार और संदर्भ

データローダーの概要: 結論、謝辞、参考文献

Data-Loader 환경 개요: 결론, 승인 및 참고 자료

Uma visão geral do cenário do Data-Loader: conclusão, agradecimentos e referências

Обзор ландшафта загрузчиков данных: заключение, благодарности и ссылки

Veri Yükleyici Ortamına Genel Bakış: Sonuç, Teşekkür ve Referanslar

Tổng quan về bối cảnh trình tải dữ liệu: Kết luận, lời cảm ơn và tài liệu tham khảo

数据加载器概况：结论、致谢和参考文献

In the ever-evolving landscape of creative work, efficiency, collaboration, and precision are the keystones of success. Creative teams grapple with the challenge of maintaining a smooth workflow while ensuring the highest quality output. This is where online proofing software steps in as a game-changer, providing a huge number of benefits that streamline the creative process and enhance team collaboration. Here we explore 15 advantages that make indispensable for creative teams. online proofing software Real-Time Collaboration Online proofing software facilitates real-time collaboration among team members, irrespective of geographical location. Team members can review and provide feedback on projects instantly, eliminating delays caused by time zone differences or physical distances. Efficient Feedback Loops Gone are the days of lengthy email chains and confusing comments. Aproove’s help to centralize feedback, and as a result create a clear and organized space for team members to comment, suggest changes, and discuss their creative (and practical) ideas. This streamlined feedback loop accelerates project timelines and helps to ensure that projected deadlines are met. Online proofing tools Version Control Maintaining version control is a common challenge in creative projects. Online proofing tools help to keep track of necessary revisions, ensuring that team members are always working on the latest version. This eliminates the risk of errors caused by outdated files and working on incorrect versions, as well as boosting overall productivity. Reduced Errors and Revisions By providing a centralized platform for feedback, online proofing software significantly reduces the likelihood of errors and missed communication. Having the ability to maintain clear communication, as well as visual annotations, ensures that team members understand revisions and any expectations, minimizing the need for multiple iterations and speeding up project completion. Time Efficiency Efficiency is paramount when it comes to creative endeavors. Online proofing eliminates the time-consuming process of waiting for physical proofs or coordinating multiple rounds of revisions. Projects progress seamlessly, saving valuable time for both team members and clients. Enhanced Visual Collaboration Visual projects demand visual feedback. to annotate directly on images, designs, or documents, making the feedback process more intuitive and effective. This visual collaboration ensures that everyone is on the same page creatively. Online proofing software allows team members Accessibility One of the most significant advantages of online proofing is accessibility. Team members can access projects from any device with an internet connection, fostering flexibility and enabling people to work remotely from anywhere in the world. This accessibility promotes a healthy work-life balance and accommodates diverse work preferences. Centralized Asset Management Online proofing software serves as a centralized hub for all the necessary project assets. No more searching through endless email threads or shared drives—everything is neatly organized in one place. This centralized approach enhances organization and project tracking. Customizable Workflows Every creative project is unique, and online proofing tools ensure that this diversity is recognized. Online proofing tools often offer customizable workflows to align with the specific needs of different projects or teams. This flexibility ensures that the software adapts to the team, not the other way around. Client Collaboration For creative agencies, collaborating with clients and stakeholders is a critical aspect of the success of a project. Online proofing tools provide a client-friendly interface, allowing clients to review and comment on projects easily. This transparency strengthens client relationships and fosters a sense of involvement in the creative process. Audit Trails For Accountability Maintaining accountability is hugely vital for project success. Online proofing software often includes audit trails that track every action taken on a project. This not only ensures accountability but also provides valuable insights into the project’s evolution. Integration Capabilities To further streamline workflows, many online proofing tools integrate seamlessly with other essential software such as project management tools, design software, or cloud storage. This integration minimizes friction between different tools and enhances overall productivity and ease when it comes to working on the project. Cost Savings The efficiency and collaboration improvements offered by online proofing software translate into cost savings. Reduced project timelines, fewer errors, and optimized workflows contribute to a more cost-effective creative process. Automated Notifications Staying on top of project timelines is crucial. Online proofing tools often come with automated notification features that alert team members about upcoming deadlines, new feedback, or changes in the project status. This ensures that everyone is informed and can act promptly. Data-Driven Insights Online proofing software often provides analytics and reporting features that offer valuable data-driven insights. Teams can analyze feedback trends, track project completion times, and identify areas for improvement, fostering continuous growth and refinement in the creative process. Online proofing software is a transformative asset for creative teams, providing a holistic solution to collaboration, efficiency, and quality control. By embracing these tools, creative professionals can unlock their full potential, delivering outstanding results while adapting to the dynamic demands of the digital age. The 15 benefits discussed here illuminate the profound impact that online proofing software can have on the creative landscape, paving the way for a future where innovation knows no bounds!

15 Benefits of Online Proofing Software for Creative Teams

সৃজনশীল দলের জন্য অনলাইন প্রুফিং সফটওয়্যারের 15 সুবিধা

15 Vorteile von Online-Proofing-Software für Kreativteams

15 beneficios del software de revisión en línea para equipos creativos

15 avantages du logiciel de vérification en ligne pour les équipes créatives

क्रिएटिव टीमों के लिए ऑनलाइन प्रूफ़िंग सॉफ़्टवेयर के 15 लाभ

クリエイティブ チームにとってのオンライン校正ソフトウェアの 15 の利点

크리에이티브 팀을 위한 온라인 교정 소프트웨어의 15가지 이점

15 benefícios do software de revisão online para equipes criativas

15 преимуществ программного обеспечения для онлайн-проверки для творческих команд

Çevrimiçi Prova Yazılımının Yaratıcı Ekipler İçin 15 Faydası

15 lợi ích của phần mềm soát lỗi trực tuyến dành cho nhóm sáng tạo

在线校对软件为创意团队带来的 15 个好处

When it comes to the increasingly fast-paced world of marketing, effective project management is the key to long-term success. However, with a plethora of options available, selecting the right software for your business can be a daunting task. In this blog, we'll explore the essential factors to consider when choosing the best marketing project management software for your unique needs. Assess Your Marketing Workflow and Team Dynamics The first step in choosing the right is understanding your business's unique needs and workflow. Start by assessing the intricacies of your marketing campaigns, team dynamics, and the specific challenges you face. marketing project management software Consider factors such as the size of your team, the nature of your projects, and the collaboration requirements. Understanding these aspects will guide you in identifying the features and functionalities that are critical for your business. Features to Look For: Task Management and Planning Efficient task management is the foundation of successful marketing project execution. Look for software that allows you to create, assign, and track tasks seamlessly. Gantt charts and project timelines can provide a visual representation of project progress and help in identifying bottlenecks. Collaboration Tools Robust collaboration features are essential for teams working on marketing projects. Look for project management software for marketing that facilitates real-time communication, file sharing, and feedback loops. Integrated chat, discussion boards, and comment threads within the project management tool can also significantly enhance collaboration with both team members and external stakeholders. Resource Allocation and Tracking Ensure the PM software allows you to allocate resources efficiently, including tracking workloads, in order to prevent any burnout among team members. By making the most of resource management features, you can help when it comes to optimizing team capacity and ensuring that everyone is working on tasks that align with their skills and expertise – also a key factor in enabling job satisfaction. Integration Capabilities Seamless integration with other tools your marketing team uses is crucial for a cohesive workflow. Look for project management software that integrates with popular marketing tools, CRM systems, and communication platforms. Integration with email marketing software, design tools, online proofing, and analytics platforms can streamline processes and improve overall efficiency. Customization Options A one-size-fits-all approach might not suit the unique requirements of your marketing projects. Choose software that allows for the customization of workflows, task lists, and project structures. The ability to tailor the software to match your team's specific needs can significantly improve adoption and effectiveness. Consider Scalability and Future Growth As your business expands, so will your marketing initiatives. It's crucial to choose a marketing project management software that can scale with your business. Consider the software's capacity to accommodate growing teams, increasing project complexity, and evolving workflows. Scalability ensures that the software remains an asset rather than a hindrance as your business matures. User-Friendly Interface and Adoption No matter how feature-rich a marketing project management software is, it's only valuable if your team adopts it seamlessly. Opt for a platform with an intuitive and user-friendly interface to reduce the learning curve. Conducting user onboarding and training sessions, and providing adequate support during the transition can facilitate smoother adoption. Additionally, consider platforms that offer mobile accessibility, enabling your team to stay connected and productive on the go- essential in an increasingly remote-working world. Budgetary Constraints and Return on Investment (ROI) While it's tempting to opt for the software with the most extensive feature set, it's crucial to align your choice with your budget constraints. Evaluate the pricing models of different software solutions and consider the long-term ROI. Some platforms offer tiered pricing based on the number of users or projects, while others may have a flat-rate model. Be mindful of any hidden costs and ensure that the chosen software provides tangible value for your investment. Security and Compliance Given the sensitive nature of marketing data and campaigns, security should be a top priority when selecting project management software. Look for platforms that prioritize data encryption, secure user authentication, and compliance with industry regulations. Ensure that the software adheres to data protection standards and offers features like role-based access control to safeguard sensitive information. Evaluate Customer Support and Training Effective customer support can be a lifesaver when encountering issues or during the initial onboarding phase. providers that offer responsive customer support, comprehensive documentation, and training resources. Aproove’s work management software Assess the availability of tutorials, webinars, and user forums to facilitate ongoing learning and troubleshooting. Choosing the best marketing project management software for your business requires a careful analysis of your unique needs, team dynamics, and project requirements. By focusing on features such as task management, collaboration tools, resource allocation, integration capabilities, customization options, scalability, user-friendliness, budget considerations, security, and customer support, you can make an informed decision that aligns with your business goals. Remember that the right software is not just a tool; it's a strategic asset that empowers your team to navigate the complexities of the digital marketing world successfully.

How to Choose the Best Marketing Project Management Software for Your Business

আপনার ব্যবসার জন্য সেরা মার্কেটিং প্রজেক্ট ম্যানেজমেন্ট সফ্টওয়্যার কীভাবে চয়ন করবেন

So wählen Sie die beste Marketing-Projektmanagement-Software für Ihr Unternehmen aus

Cómo elegir el mejor software de gestión de proyectos de marketing para su empresa

Comment choisir le meilleur logiciel de gestion de projet marketing pour votre entreprise

अपने व्यवसाय के लिए सर्वश्रेष्ठ मार्केटिंग प्रोजेक्ट प्रबंधन सॉफ़्टवेयर कैसे चुनें

あなたのビジネスに最適なマーケティング プロジェクト管理ソフトウェアを選択する方法

귀하의 비즈니스에 가장 적합한 마케팅 프로젝트 관리 소프트웨어를 선택하는 방법

Como escolher o melhor software de gerenciamento de projetos de marketing para o seu negócio

Как выбрать лучшее программное обеспечение для управления маркетинговыми проектами для вашего бизнеса

İşletmeniz için En İyi Pazarlama Proje Yönetimi Yazılımını Nasıl Seçersiniz?

Cách chọn phần mềm quản lý dự án tiếp thị tốt nhất cho doanh nghiệp của bạn

如何为您的企业选择最佳的营销项目管理软件

Authors: (1) Nathan Lambert, Allen Institute for AI; (2) Roberto Calandra, TU Dresden. Table of Links Abstract & Introduction Related Work Background Understanding Objective Mismatch Discussions Conclusion Acknowledgments, and References 2 Related Work 2.1 Reinforcement learning from human feedback Early work in RLHF focused on continuous control domains with various methods for altering the behavior across trajectories (Christiano et al., 2017; Wirth, Akrour, Neumann, Fürnkranz, et al., 2017). The impacts of RLHF today primarily has been centered around its use with LLMs. Initial work on RLHF for LLMs utilized user preferences from a batch of 4 options (Ziegler et al., 2019) to train a reward model across general LLM benchmarks. Group preferences were changed to pairwise preferences, and rather than general benchmarks the reward model was focused on the task of summarization (Stiennon et al., 2020; J. Wu et al., 2021). Next emerged general question-answering models (Ouyang et al., 2022) and web crawling agents (Nakano et al., 2021), primarily from scaling the initial model and human datasets. Now, RLHF is used to train general chat models across a variety of tasks (Bai et al., 2022; Schulman et al., 2022; Touvron et al., 2023) and in specific domains such as harm reduction (Glaese et al., 2022) or information accuracy (Menick et al., 2022). The development of these methods has accelerated markedly, with many variations on the methodology for integrating feedback into language models (Fernandes et al., 2023). The most popular reinforcement learning optimizer is still Proximal Policy Optimization (PPO) (Schulman, Wolski, Dhariwal, Radford, & Klimov, 2017), with its many stable and scalable implementations. Recent works have been developing methods for the RL optimizer, such as the offline RL algorithm Implicit Language Q Learning (ILQL) (Snell, Kostrikov, Su, Yang, & Levine, 2022), direct preference optimization (DPO) (Rafailov et al., 2023) for utilizing preference data without a reward model, or Advantage-Leftover Lunch RL (A-LOL) (Baheti et al., 2023) which is designed to act on the entire response as a single action (which RLHF generally does). 2.2 Problem mis-specification in RLHF There is a substantial emerging literature on varieties of numerical issues, unexpected behaviors such as verbosity and evasiveness (Schulman, 2023), and potential solutions in RLHF, which can be mitigated by progress on solving objective mismatch. A prominent recent example is the behavior of the flagship Llama 2 chat model refusing to answer a request asking “How do I kill a Linux process,” conflating the computer process with the morals of killing a living creature. It has been shown that there are predictable behaviors of reward model overoptimization with PPO and best-of-N optimization techniques (Gao, Schulman, & Hilton, 2022), which can be partially mitigated by training ensemble reward models (Coste, Anwar, Kirk, & Krueger, 2023). Other issues have emerged in RLHF models that demonstrates the need for improved reward models, such as a bias towards long responses (Singhal, Goyal, Xu, & Durrett, 2023), a lack of language consistency Shen et al. (2023) (invariance over changes that maintain meaning), or a reduction of output diversity Kirk et al. (2023). A similar argument is made in A. Wei, Haghtalab, and Steinhardt (2023), where the authors argue that “competing objectives and mismatched generalization” mislead the models – we present how objective mismatch covers both of these limitations and more possible failure cases. There are also papers studying how the reward model training formulation does not align with the data collection process and downstream RL optimization, suggesting the models should model advantage estimates rather than direct value functions (Knox & Stone, 2008; Peng et al., 2023). 2.3 Evaluating LLMs trained with RLHF Core to the ideas of objective mismatch with LLMs is the methods of evaluation used to correlate performance. Historically, LLMs have been evaluated across a wide variety of tasks trying to capture specific characteristics of models, making evaluation a extremely broad process (Liang et al., 2022) where progress is saturating (Kiela, Thrush, Ethayarajh, & Singh, 2023). Now, many models are focused on hard to specify tasks such as chat, where existing benchmarks were not well correlated with performance (Zheng et al., 2023), so new chat based evaluations such as MT-Bench (Zheng et al., 2023) and AlpacaEval (Li et al., 2023) have been introduced, but substantial further work is needed. This paper is under CC 4.0 license. available on arxiv

Related Work on Reinforcement Learning from Human Feedback 

Scientific American, Volume XXIV., No. 12, March 18, 1871 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Official lists of patents here Official lists of patents ISSUED BY THE U. S. PATENT OFFICE. FOR THE WEEK ENDING MARCH 7, 1871. Reported Officially for the Scientific American. SCHEDULE OF PATENT FEES On each Caveat $10 On each Trade-Mark $25 On filing each application for a Patent, (seventeen years) $15 On issuing each original Patent $20 On appeal to Examiners-in-Chief $10 On appeal to Commissioner of Patents $20 On application for Reissue $30 On application for Extension of Patent $50 On granting the Extension $50 On filing a Disclaimer $10 On an application for Design (three and a half years) $10 On an application for Design (seven years) $15 On an application for Design (fourteen years) $30 For Copy of Claim of any Patent issued within 30 years $1 A sketch from the model or drawing, relating to such portion of a machine as the Claim covers, from upward, but usually at the price above-named. $1 The full Specification of any patent issued since Nov. 20, 1866 at which time the Patent Office commenced printing them $1.25 Official Copies of Drawings of any patent issued since 1836, we can supply at a reasonable cost, the price depending upon the amount of labor involved and the number of views. Full information, as to price of drawings, in each case, may be had by addressing MUNN & CO., Patent Solicitors, 37 Park Row, New York. 112,309.—Hose Sprinkler.—William Anderson, San Francisco, Cal. 112,310.—Locomotive Spark Arrester.—J. G. Armstrong, New Brunswick, N. J. 112,311.—Tool for Carriage Makers' Use.—George Atkinson, San Francisco, Cal. 112,312.—Potato Probe.—John A. Beal, Waterford, N. Y. 112,313.—Hinge for Carriage Doors.—George W. Beers, Bridgeport, Conn. 112,314.—Stove Leg.—James Birckhead, Jr., Baltimore, Md. 112,315.—Clothes Pin.—Orris A. Bishop, Chicago, Ill. 112,316.—Manufacture of Rochelle Salts and Borax.—V. G. Bloede, Brooklyn, N. Y. 112,317.—Beehive.—Felix Brewer, Waynesville, Mo. 112,318.—Thill Coupling.—Theodore Burr (assignor to Allen Muir and Henry Muir), Battle Creek, Mich. 112,319.—Evaporating Pan for Saccharine Liquids.—F. C. Butler, Bellows Falls, Vt., assignor to himself and James B. Williams, Glastonbury, Conn. 112,320.—Door Securer.—William H. Caldwell, Wheeling, W. Va. 112,321.—Toe-calk Bar.—R. B. Caswell, Springfield, Mass. Antedated March 2, 1871. 112,322.—Glass Flattening Furnace and Leer.—James Clabby, Lenox, Mass. 112,323.—Spring Bed Bottom.—Alex. Cole, Manamuskin, N. J. 112,324.—Water Wheel.—E. E. Coleman, West Cummington, Mass. 112,325.—Toy Horse and Carriage.—John B. Cuzner, Bridgeport, Conn. 112,326.—Mackerel-line Holder.—E. L. Decker, Southport, Me. 112,327.—Sewing Machine.—J. William Dufour, Stratford, Conn. 112,328.—Steam Boiler.—Edwards Evans, North Tonawanda, N. Y. 112,329.—Medical Compound for Cure of Catarrh and Asthma.—Erastus Field, Ostrander, Ohio. 112,330.—Machine for Grinding the Cutters of Mowers, etc.—H. C. Fisk, Wellsville, N. Y. 112,331.—Machine for Making Hooks and Eyes.—Jeremy T. Ford, San Francisco, Cal. 112,332.—Churn.—Thompson Freeman, Westfield, Ill. 112,333.—Attachment for Revolving Mold Boards for Plows.—J. S. Godfrey, Leslie, Mich., assignor to himself and S. M. Loveridge, Pittsburgh, Pa. 112,334.—Grain Cleaner and Fertilizer Sifter.—J. A. Green, Mill Dale, Va. 112,335.—Screw Propulsion.—E. C. Gregg (assignor to A. H. Gregg and C. P. Gregg), Trumansburg, N. Y. 112,336.—Seeding Machine.—P. M. Gundlach, Belleville, Ill. 112,337.—Compound for Kindling Fires.—J. L. Hannum and S. H. Stebbins, Berea, Ohio. 112,338.—Lawn Mower.—Benjamin Harnish, Lancaster, and D. H. Harnish, Pequea, Pa. 112,339.—Composition for Pavements.—C. B. Harris, New York city. Antedated February 25, 1870. 112,340.—Spring for Vehicles.—John R. Hiller, Woodland, Cal. 112,341.—Harvester Rake.—S. T. Holly, (assignor to John P. Manny), Rockford, Ill. 112,342.—Door Clamp.—Henry O. Hooper, Diamond Springs, Cal. 112,343.—Taper Holder.—Thomas W. Houchin, Morrisania, N. Y. 112,344.—Metallic Garter.—Henry A. House, Bridgeport, Conn. 112,345.—Bobbin Winder.—Henry A. House, Bridgeport, Conn. 112,346.—Method of Knitting Stockings, etc.—Henry A. House, Bridgeport, Conn. 112,347.—Apparatus for Evaporating and concentrating Liquids.—John Howarth, Salem, Mass. Antedated March 1, 1871. 112,348.—Apparatus for Evaporating and Concentrating Liquids.—John Howarth, Salem, Mass. Antedated March 1, 1871. 112,349.—Apparatus for Removing Oil from Vegetable and other Matters.—Elias S. Hutchinson, Baltimore, Md. 112,350.—Apparatus and Process for Removing Oil from Grain, Seeds, etc.—Elias S. Hutchinson, Baltimore, Md. 112,351.—Chandelier.—Charles F. Jacobsen, New York city. 112,352.—Culinary Vessel.—Carrie Jessup, New Haven, Conn. 112,353.—Machine for Cutting Leather.—Aberdeen Keith, North Bridgewater, Mass. 112,354.—Attaching Knobs to their Spindles.—John F. Keller and Nathaniel Sehner, Hagerstown, Md. 112,355.—Miter Machine.—T. E. King, Boston, Mass. 112,356.—Take-up for Corset Looms.—Julius Kuttner, New York city. 112,357.—Elevator and Carrier.—T. W. Lackore, Worth, Ill. 112,358.—Apparatus for Burning Hydrocarbon Oils.—James R. Lee, Grass Valley, Cal. 112,359.—Burglar Alarm.—Robert Lee, Cincinnati, Ohio. 112,360.—Telegraph Apparatus.—L. T. Lindsey, Jackson, Tenn. 112,361.—Harvester.—J. P. Manny, Rockford, Ill. 112,362.—Harvester.—J. P. Manny, Rockford, Ill. 112,363.—Harvester Rake.—J. P. Manny, Rockford, Ill. 112,364.—Cheese Curd Sink.—H. C. Markham, Collinsville, N. Y. 112,365.—Mowing Machine.—H. C. Markham and Dewitt C. Markham, Collinsville, N. Y. 112,366.—Propeller.—Alex. J. Marshall, Warrenton, Va. Antedated March 3, 1871. 112,367.—Oiler.—Edward McDuff and E. D. Forrow, Warwick, R. I. 112,368.—Wash Boiler.—John McInnes, Oxford, Pa. 112,369.—Propelling Canal Boats.—H. B. Meech, Fort Edward, N. Y. Antedated February 25, 1871. 112,370.—Water-proof Compound for Coating Cloth Wood, Metals, etc.—Peter E. Minor, Schenectady, N. Y. 112,371.—Cooking Stove.—W. N. Moore, Neenah, Wis. 112,372.—Boring Machine.—J. H. Pardieck (assignor to himself and S. M. Brown), Acton, Ind. 112,373.—Vapor Burner.—R. W. Park, Philadelphia, Pa. 112,374.—Machine for Pointing Blanks for Cultivator Teeth.—John Pedder and George Abel, West Pittsburgh, Pa. 112,375.—Bale Tie.—J. E. Perkins, San Francisco, Cal. 112,376.—Lining Walls with Felt, etc.—James Phillips, Chicago, Ill. 112,377.—Cooking Stove.—Samuel Pierce, Boston, Mass. 112,378.—Tack.—A. A. Porter, New Haven, Conn. Antedated Feb. 25, 1871. 112,379.—Machine for Shaping and Cutting Gear Cutters.—F. A. Pratt (assignor to the Pratt & Whitney Company), Hartford, Conn. 112,380.—Combination Camera and Developing Box.—E. C. Ratzell, Philadelphia, Pa. 112,381.—Punching Machine.—J. C. Rhodes, South Abington, Mass. 112,382.—Washing Machine.—J. W. Ricker, Chelsea, Mass. 112,383.—Curtain Fixture.—Charles Robin. Chester, Conn. 112,384.—Machine for Making Printers' Leads.—Isaac Schoenberg, New York city. 112,385.—Slide Valve for Steam Riveting Machines.—Coleman Sellers (assignor to William Sellers & Co.), Philadelphia, Pa. 112,386.—Machine for Polishing Thread.—Samuel Semple, Sr., John Semple, Samuel Semple, Jr., and R. A. Semple, Mount Holly, N. J. 112,387.—Paint Brush.—F. S. Shearer, Washington, Ill. 112,388.—Bee Hive.—S. A. Short, F. J. Short, J. B. Short, and Jasper Kile, Decatur, Ala. 112,389.—Apparatus for Removing Oil from Vegetable and other Matter.—Thomas Sim, Baltimore, Md. 112,390.—Retort for Producing Bisulphide of Carbon.—Thomas Sim, Baltimore, Md. 112,391.—Utilizing the Silky Down of the Wild Cotton. —M. H. Simpson, Boston, Mass. 112,392.—Pruning Shears.—Frank Smiley, Batavia, N. Y. 112,393.—Water-closet Valve.—A. J. Smith, San Francisco, Cal. 112,394.—Gang Plow.—J. W. Sursa, San Leandro, Cal. 112,395.—Grinding Pan and Amalgamator.—W. H. Thoss, West Point, Cal. 112,396.—Street Lantern.—Augustus Tufts, Malden, Mass. 112,397.—Cooking Stove.—Alvin Warren, Swanton, Ohio. 112,398.—Safety Bridle.—James Weatherhead, San José, Cal. 112,399.—Fire Grate.—George Wellhouse, Akron, Ohio. 112,400.—Hay Knife.—G. F. Weymouth, Dresden, Me. 112,401.—Claw Bar.—Charles Winter, Chillicothe, Ohio. 112,402.—Steam Generator.—J. C. Woodhead, Pittsburgh, Pa. 112,403.—Animal Trap.—W. D. Wrightson, Queenstown England. 112,404.—Brush.—John Ames, Lansingburg, N. Y. 112,405.—Clod Fender.—F. L. Bailey, Freeport, Ind. 112,406.—Ruler.—H. S. Ball, Spartanburg, S. C. 112,407.—Fanning Mill.—Benjamin Barney, Time, Ill. 112,408.—Ice-cutting Machine.—Lafayett Barnum (assignor to himself and A. R. Hale), Bridgeport, Conn. 112,409.—Manufacture of Ice.—T. J. Bigger, Kansas City, Mo. 112,410.—Machine for Heading Bolts and Spikes.—Reinhold Boeklen, Brooklyn, N. Y., assignor to himself and Henry Torstrick New York city. Antedated Feb. 28, 1871. 112,411.—Washing Machine.—Joseph Boswell, L. M. Boswell, Jonathan Palmer, and J. H. James (assignors to themselves and Thomas Starbuck), Wilmington, Ohio. 112,412.—Water Wheel.—E. C. Boyles, New York city. 112,413.—Cotton Press.—R. M. Brooks, Pike county, Ga. 112,414.—Paper-cutting Machine.—Samuel Brown (assignor to himself and C. R. Carver), Philadelphia, Pa. 112,415.—Governor for Direct-acting Engines.—A. S. Cameron, New York city. 112,416.—Governor for Direct-acting Engines.—A. S. Cameron, New York city. 112,417.—Butt Hinge.—J. W. Carleton (assignor to the Union Manufacturing Co.), New Britain, Conn. 112,418.—Machine for Cutting Sheet Metal.—C. R. Choate, East Saginaw, Mich. 112,419.—Bit Brace.—William Cleveland, Lawrence, Mass., assignor to himself and James Swan, Seymour, Conn. 112,420.—Steam Engine Governor.—C. A. Condé, Indianapolis, Ind. 112,421.—Carpet-cleaning Machine.—J. C. Craft (assignor to himself and Antonio Rosello), Baltimore, Md. 112,422.—Steam Regulator for Paper Dryers.—Daniel Crosby, Hampden, Me. 112 423.—Metallic Piston and Valve Rod Packing.—G. M. Cruickshank, Providence, R. I. 112,424.—Grain-thrashing and Separating Machine.—John Culham, Grand Rapids, Mich. Antedated Feb. 25, 1871. 112,425.—Cooking Stove.—David Curtis, Mishawaka, assignor to himself and C. B. Graham, South Bend, Ind. 112,426.—Lightning Rod.—S. D. Cushman, New Lisbon, Ohio. 112,427.—Hose Bridge.—Patrick Daily (assignor to himself and J. J. Kehoe), New York city. 112,428.—Cover for Openings in Sidewalks.—William Dale, New York city. 112,429.—Rotary Pump.—F. O. Deschamps, Philadelphia, Pa. 112,430.—Machine for Cutting Files.—James Dodge, Manchester, England, assignor to David Blake, Spencertown, N. Y. 112,431.—Coupling for Railway Cars.—Henry Dubs and S. G. Goodall-Copestake, Glasgow, Great Britain. 112,432.—Tobacco Pipe.—P. J. Dwyer, Elizabethport, N. J. 112,433.—Basket for House Plants.—Albert P. Eastman, Washington, D. C. 112,434.—Sulky Plow.—Milo A. Elliott, Stratford Hollow, N. H. 112,435.—Stretcher for Paintings.—James Fairman, New York city. 112,436.—Body Lantern Holder.—Samuel C. Fessenden, Stamford, Conn. 112,437.—Stove Leg.—Amon L. Finch, Sing Sing, N. Y. 112,438.—Pump Piston.—John S. Follansbee and George Doolittle (assignors to the Forrester Manufacturing Company), Bridgeport, Conn. 112,439.—Shoe.—Samuel W. Francis (assignor to himself and W. H. Newton), Newport, R. I. 112,440.—Guard-Finger for Harvesters.—George Fyfe and Chester Hard, Ottawa, Ill. 112,441.—Dining Table.—S. R. Gardner (assignor to himself and S. M. Marquette), Independence, Iowa. 112,442.—Step Ladder.—M. Boland Geary, New York City. 112,443.—Oilcloth Printing Machinery.—Ebenezer A. Goodes (assignor to Philadelphia Patent and Novelty Company), Philadelphia, Pa. 112,444.—Tenoning Machine.—Lyman Gould, Norwich, Conn. 112,445.—Printer's Case.—Wm. H. A. Gresham, Atlanta, Ga. 112,446.—Lamp Chimney.—Geo. W. Griswold, Factoryville, Pa. 112,447.—Grain Separator.—Philander Griswold, Hudson, Mich. 112,448.—Clamp for Thill Couplings.—John W. Guider (assignor to himself and John Kiefer), St. Joseph, Mo. 112,449.—Bird Cage.—Gottlob Gunther, New York city. 112,450.—Stop Cock and Valve.—William Haas, New York city. 112,451.—Valve for Steam Engines.—Joseph L. Harley, Baltimore, Md., and Xaver Fendrich, Georgetown, D. C. 112,452.—Metallic Hub.—John H. Harper, Pittsburgh, Pa. 112,453.—Composition for Lubricating Machinery.—E. Q. Henderson (assignor to John C. Burroughs and Richard A. Springs) Charlotte, N. C. 112,454.—Post-hole Digger.—Bryant B. Herrick, Decatur, Mich. 112,455.—Door Check.—Levi S. Hicks (assignor to himself, J. Perrin Johnson, and John Buell), Peoria, Ill. 112,456.—Railway-car Brake.—Luther Hill, Stoneham, and Seth D. Tripp, Lynn, Mass. 112,457.—Truss.—Adam Hinoult, Montgomery, N. Y. 112,458.—Feed Bag for Horses.—Walter A. Hough, South Butler, N. Y. 112,459.—Shade Holder for Lamps—Mark W. House, Cleveland, Ohio. 112,460.—Lamp Chimney.—Mark Wiggins House (assignor to the Cleveland Non-Explosive Lamp Company), Cleveland, Ohio. Antedated March 1, 1871. 112,461.—Horse Hay Rake.—James Howard and E. T. Bousfield, Bedford, England. 112,462.—Tongs for Rolling Barrels.—Mark W. Ingle, Indianapolis, Ind. 112,463.—Pitman.—George W. Jayson, Lodi, Ohio. 112,464.—Paste for Paper Hangings.—John Jones (assignor to himself and Henry A. Smith), New York city. 112,465.—Twine Holder.—Edward M. Judd, New Haven, Ct. 112,466.—Clothes Pin or Clasp.—Amos L. Keeports and William Yount, Littletown, Pa. 112,467.—Putting up Hams.—Samuel Edward Kelly, Philadelphia, Pa. 112,468.—Limn Kiln.—Thomas A. Kirk, Kansas City, Mo. 112,469.—Fastening for Seats for Wagons or Sleighs.—John G. Knapp and John F. Robertson (assignors of one third their right to James H. Holly), Warwick, N. Y. 112,470.—Potato Planter.—George Knowlton (assignor for one-half his right to N. Haynes), Johnstown, Pa. 112,471.—Revolving Firearm.—Edwin S. Leaycroft, Brooklyn, N. Y., assignor by mesne assignment, to "Colt's Patent Firearms Manufacturing Company," Hartford, Conn. 112,472.—Revolving Firearm.—Edwin S. Leaycroft, Brooklyn, N. Y., assignor, by mesne assignment, to "Colt's Patent Firearms Manufacturing Company," Hartford, Conn. 112,473.—Railroad Cattle-Guard Gate.—J. H. Mallory, La Porte, Ind. 112,474.—Back-reflecting Mirror.—Richard Mason (assignor to himself and Matthew Ely), Newark, N. J. 112,475.—Ventilator and Chimney Top.—James McGowan (assignor to himself and Daniel H. Waring), New York city. 112,476.—Apparatus for Rectifying and Refining Spirits.—Frederick Measey (assignor to himself and Henry D. Fling), Philadelphia, Pa. 112,477.—Tin Can.—John F. Merrill (assignor to himself and Alexander Stewart), Cincinnati, Ohio. 112,478.—Take-up Mechanism for Looms.—John Michna and Joseph Fischer, New York city. 112,479.—Combined Baker and Broiler.—Wm. H. Miller, Brandenburg, Ky. 112,480.—Shuttle for Sewing Machines.—James D. Moore, Grinnell, Iowa. 112,481.—Cotton Chopper and Grain Cultivator.—Daniel Mosely, Osark, Arkansas. 112,482.—Sad and Fluting Iron.—Frederick Myers, New York city. 112,483.—Reed for Organs and Melodeons.—Augustus Newell, Chicago, Ill. 112,484.—Straw Cutter.—Amon Park, Germanville, Iowa. 112,485.—Apparatus for Aging Whisky and Other Spirits.—Josiah Peiffer and Samuel Richards, Valonia, Pa. 112,486.—Combined Cotton and Corn Planter.—Louis A. Perrault (assignor to himself and Joseph Huber), Natchez, Miss. 112,487.—Faucet.—Solomon Pfleger, Reading, assignor to himself and J. S. Pfleger, Tamaqua, Pa. 112,488.—Treadle.—George K. Proctor, Salem, Mass. 112,489.—Lubricating Compound.—Victory Purdy, Poughkeepsie, N. Y. 112,490.—Fertilizer and Seeding Machine.—Archibald Putnam (assignor to Elizabeth Putnam), Owego, N. Y. 112,491.—Rotary Pump.—George W. Putnam, South Glens Falls, N. Y. 112,492.—Hat Brush.—Robert Dunbar Radcliffe, Palmyra, N. Y. 112,493.—Refrigerating Show Case.—Thomas L. Rankin, Lyndon, Kansas, assignor to himself and D. W. Rockwell, Elyria, Ohio. 112,494.—Device for Starting and Stopping Cars.—Philip Rhoads, Carlisle, Pa. 112,495.—Pipe-molding Machine.—George Richardson, Milwaukee, Wis. 112,496.—Sulky Cultivator.—Richard B. Robbins, Adrian, Mich. 112,497.—Hand Plow.—Nelson Rue, Harrodsburg, Ky. 112,498.—Mechanical Movement.—Edward G. Russell, Ravenna, Ohio. 112,499.—Railway Car Brake.—Lyman Alphonzo Russell, Shrewsbury, Vt. 112,500.—Stovepipe Cleaner.—David Sanford, Ashton, Ill. 112,501.—Twine Holder.—Joseph B. Sargent and Purmont Bradford (assignors to Sargent & Co.), New Haven, Conn. 112,502.—Dovetailing Machine.—James M. Seymour, Newark, N. J. 112,503.—Wooden Pavement.—Eaton Shaw, Portland, Me. 112,504.—Guard for Circular Saws.—George W. Shipman, Ischua, N. Y. 112,505.—Breech-loading Firearm.—Dexter Smith and Martin J. Chamberlin, Springfield, Mass. 112,506.—Spark Arrester.—James Smith, Altoona, Pa. 112,507.—Horse Hay Rake.—Solomon P. Smith, Waterford, N. Y. 112,508.—Plow.—S. M. Stewart, New Harrisburg, Ohio. 112,509.—Medical Compound for Treating Fever and Ague.—George E. Swan, Mount Vernon, Ohio. 112,510.—Device for Cooling Journals of Car Axles.—Henry G. Thompson, Milford, Conn. 112,511.—Cooling Journal of Car Axles.—Henry G. Thompson, Milford, Conn. 112,512.—Cooling Journal of Car-wheel Axles.—Henry G. Thompson, Milford, Conn. 112,513.—Device for Cooling Journals of Railway Cars.—Henry G. Thompson, Milford, Conn. 112,514.—Non-heating Handle for Sad Irons, etc.—William H. Towers, Boston, Mass. 112,515.—Lubricator.—John Erst Uhl, Renovo, Pa. 112,516.—Combined Corn Planter and Cultivator.—Franklin Underwood, South Rutland, N. Y. 112,517.—King Bolt.—Wendel Vondersaar, Indianapolis, Ind. 112,518.—Wheat Roaster.—George W. Waitt (assignor to himself and Robert B. Fitts), Philadelphia, Pa. 112,519.—Plaster Sower.—Thomas J. West, Alfred Center, N. Y. 112,520.—Ticket Holder.—Henry Wexel, Providence, R. I. 112,521.—Tobacco Press.—Abraham N. Zell, Lancaster, Pa. 112,522.—Combined Bag Holder and Scales.—William Zimmerman, Lebanon, Pa. Antedated February 25, 1871. 112,523.—Breech-loading Firearm.—James M. Mason, Washington, D. C. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American, Volume XXIV., No. 12, March 18, 1871. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19180/pg19180-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

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