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Campus

By , Founder and CEO at Washlava. on . Todd Belveal Originally published Quora Everything on a college campus today is an experience. Students don’t just go to a dingy basement gym to work out. They go to a multi-million dollar rec center complete with state-of-the-art equipment and a lazy river. Universities are competing for students, shelling out huge amounts of money to prove they have the best amenities. Lavish dining facilities, roomy dorms, and pricey athleisure at the bookstore are all there to attract students. But for all the structural and cosmetic improvements being made on campus, laundry has been overlooked entirely. The laundry facilities on every major college campus still look pretty much the same as they did 20 years ago. Small, white washing machines and stacks of cheap tumble dryers lined up against the walls. Some of the machines are almost the same age as the students. Universities are underestimating the importance of laundry by outsourcing it to old school equipment providers that don’t care. They figure it’s not a big deal to students, but they’re making a huge mistake. Anyone can see universities need to rethink laundry. It’s bizarre how forgotten an amenity laundry is on most campuses. At most campuses, this is how it works: A student walks out of her dorm room with her hamper, heads down the stairs to the basement of her brand new residence hall, and what greets her? A depressing sight. Rows of archaic laundry machines with coin slots — and maybe a card reader on the wall for her student ID card. The machines are old, the payment systems are faulty, and the process quickly becomes a bottleneck. Especially once students figure out which machines leave their clothes soaking wet. Most students are coming from homes with an LG, a Whirlpool, or some other machine with a digital display and six speeds for everything from wool sweaters to towels. Then they get to the university washing machines, and they have two options — gentle cycle or normal cycle. They just dropped $500 on overpriced school apparel at the bookstore, and now they’re supposed to wash it all in these antiquated machines. University laundry is undoubtedly ready for a change. Most universities haven’t raised the price of laundry in a decade or more. It’s still a dollar to do a load of wash. And why would they raise prices? The experience doesn’t justify it one bit. In reality, students aren’t going to care about a dollar increase if it comes with new machines and better technology. Technology they expect. No one notices the difference between one and two dollars when they’re using their iPhone to reserve a brand new washer and dryer. But universities aren’t making that upgrade, in part because they keep agreeing to ridiculous, decade-long contracts that serve no one but the laundry equipment provider. Once the contract has been signed, there’s no incentive for the provider to be helpful or provide good service. If a student has a problem with a machine, it’ll sit for weeks with a piece of paper duct taped to it that says “out of order.” It might even be useless all semester. These universities need to make a change, and they’re in a great position to do so, because they have the perfect clientele for an upgrade. Students are the best users for new laundry technology. College students do more laundry than anyone. There are only so many clothes they can bring with them from home. And every time their favorite shirt needs a wash, that’s another load. Every student today has a smartphone — or they should. So, why not capitalize on the ubiquity of those devices and go with a coinless, efficient system for laundry? Most students rarely have cash on them, let alone coins. People get change when they make purchases with cash, and students don’t make purchases with cash. They use university ID cards or they pay with their debit cards. Students expect to live in a tech-based world, and universities constantly advertise themselves as cutting edge. They claim to have the newest technology and do the most exciting research. Their students are primed to integrate tech into every part of their lives. Yet their laundry facilities are from 1995, if not earlier. You can’t deny the perks of the updated system. The mobile-based laundry app we’re developing at Washlava isn’t even comparable to the current situation. Students can use the app to reserve machines, lock them, pay for loads, and even learn how to do laundry on our newly-installed machines. Our goal is to take a chore and turn it into an experience — just like all the other experiences these universities are advertising. And it’s working. Our pilot program at the University of Florida in Gainesville was a huge success by every measure. Students loved how convenient the experience was. They knew if and when they could get a machine, they were notified when their laundry was done, and they could pay for it all with their phones. But it wasn’t just the students who loved the machines. The residence hall staff told us they made the laundry room more efficient, and the new machines were a draw on campus tours. Residence hall directors were actually fighting over who would get Washlava first, and the management company that operates the residence halls issued a letter recommendation for Washlava. Those are the reactions of people who’ve been dealing with a terrible product and experience for much too long. College students deserve better laundry, and when their universities finally realize this, we’re going to give it to them one campus at a time. By , Founder and CEO at Washlava. on . Todd Belveal Originally published Quora For more trending tech answers from , visit . Quora HackerNoon.com/quora

Laundry Room Logic: Updating (Extremely Outdated) Campus Technologies

**ZUG, Switzerland, November 21st, 2023/Chainwire/--**Future3 Campus, in partnership with The Open Network (TON) Foundation, has today announced the launch of The TON Bootcamp, an incubation program to foster mini-app development within TON’s Web3 ecosystem in Telegram. The 15 selected teams may receive support for integrating into TON’s Web3 ecosystem and stand a chance to get up to USD $500,000 in funding from Future3 Campus. Applications to be selected for The TON Bootcamp are open until December 18, 2023. The TON Bootcamp is a Web2.5 incubator, empowering developers to create mini-apps that bridge the Web2 experience with the decentralized benefits of Web3. The program will run for five months and offer selected participants a chance to receive significant funding for their projects. The Bootcamp is focused on supporting projects building real-world payment and gaming solutions to seamlessly integrate into TON's flourishing Web3 ecosystem in Telegram. Participants in the Bootcamp may take advantage of a spectrum of marketplace opportunities, including showcasing their projects on , enjoying the same visibility that has brought other TON-based applications success. Tapps.Center TON Foundation's team of seasoned blockchain professionals will offer hands-on mentorship and guidance, helping developers navigate the intricacies of integrating their solutions within Telegram's ecosystem. The selected projects may also receive the funding and support necessary to deploy their mini-apps in a system designed for the mass adoption of Web3 products. These resources include marketing amplification within TON’s ecosystem and priority access to an active audience of over 800 million monthly users on Telegram. The TON Bootcamp may also equip projects with mentorship from leaders at top blockchain investment institutions and office spaces in Hong Kong, Singapore, and Japan. Bootcamp participants may have the opportunity to present their products and services to an audience of entrepreneurs, industry leaders, and investors at Web3 Festival 2024’s demo day pitch and showcase events. The TON Bootcamp is an unprecedented opportunity for mini-app developers to transform their visions into reality. Builders worldwide are encouraged to seize this opportunity before applications close on December 18, 2023. Projects seeking to apply for the TON Bootcamp may click here: https://echo3.ucraft.site About The Open Network: The Open Network (TON) is a global, decentralized blockchain community focused on putting crypto in every pocket. By building the Web3 ecosystem in Telegram Messenger, TON's vision is to empower 800 million users to own their digital identity, data, and assets by 2028. Learn more at . https://ton.org/ About Future3 Campus The Future3 Campus is a Web3.0-oriented incubation platform powered by Wanxiang Blockchain Labs and HashKey Capital with a mission to promote and foster entrepreneurship and innovation in Web3.0. Focusing on Web3.0 Massive Adoption, DePIN and AI, the Future3 Campus kicks off a $50 million seed fund in Web3.0 incubation worldwide. For more information, please visit , or contact us at . http://www.future3.io/#/home contact@future3.io Contact TON Foundation ton@theagencypartnership.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

Future3 Campus And TON Foundation Announce Bootcamp For Mini-App Builders In TON's Ecosystem

authentic nCent team fist-bump Here at nCent Labs we spend a great deal of time thinking about how to unlock collective talent and appropriately attribute value within communities. These are the building blocks of efficient incentive markets. Why We Need nGage Campus Founders The field of incentive design is both broad and interdisciplinary, touching , , and even . However, applications of incentive to is an emergent field of thought inquiry. Making enough progress to support market growth will require a whole new generation of independent thinkers. These are mainly long term issues and questions. Our “meta” solution is ! Economics Sociology Computer Science Biology design blockchain nGage We are currently laser focused on growing a founder network on college campuses. The goals for this project are: To promote an entrepreneurial “founder” driven view of technical development of blockchains in general and incentive markets on blockchains in particular To build an nCent Global Campus network that will between students across the globe, and connect mentors with practitioners implementing incentive compatible designs in the market facilitate connections and knowledge transfer To to be more inclusive of diverse backgrounds and interdisciplinary viewpoints in guiding the development of the blockchain community expand the pipeline of talent How Does nGage Work? We will get back to you with an invitation. There are special prizes for referring your friends to apply! Apply to be a Founder here . This requires you and a co-founder and 10 total members. Everyone in the group will be able to recruit new members with a unique link from our campus nGage page. The only requirement for validated members at this time is that they should submit their email on the Campus Founder page, and follow the nCent Nation telegram group ( ) Recruit your friends to join your chapter. https://t.me/ncent Post a photo of your first meeting and we will send you a box of swag. We will also cover the cost of pizza and soda for your meeting (restrictions may apply). You will also be connected with our nCent Worldwide Campus Founders Network. This will have special offers and deals on many products and services, educational and curriculum content, special dev seminars and classes, early access to pre-release tools, opportunities to be a hub for grants and other airdrops on campus, and potentially tickets to nCent sponsored meetups and events across the Globe. We also have travel grants available to help select students to attend our annual conference, Receive Swag and nCent Global Campus Resources! nSpire.network whiteboarding in progress (on blackboard) Why are We So Excited About nGage? We are fired up to get student attention about the power of incentive markets and - we know there is an entrepreneurial hunger out there and we are eager to what the community will build with our tools and platform nCent Even if there is no short term economic payoff, we think getting smart college kids involved with our platform will be mutually beneficial. This is our way of “paying it forward” and we could see a world where that positive karma results in numerous internships, startups and other opportunities for these talented students down the road We want to have numerous “use cases” ready to go when our main net launches. (Some of our current use cases include , , , and even ). We cannot possibly do it all alone. The fervor and community spirit of young people in College is unmatched and unstoppable — we want to be inspired by YOU! employee referral programs volunteering projects online auctions dating apps Learn more by emailing me directly at kk@ncnt.io or visiting campus.ncent.io . To stay in the nCent loop, hear me tweet and join our international telegram channel . — PS the .edu/student email address that refers the most people to sign up on our website will get extra swag (we are currently weighing sunglasses, water bottles, or notebooks, but if the referral count is >50 will likely be all 3!)

Introducing nGage: the nCent Labs Worldwide Campus Founder’s Program

I usually don’t write blogs on my personal experiences but this was one such experience that I feel like sharing with you all because it is really worth it. Ever since I entered engineering college, I was really excited about gaining a lot of practical knowledge apart from the regular learnings from the engineering curriculum. One fine day I was chatting with one of my friends & I told him about my keen desire to learn about startups and gain some practical experience. I also mentioned my love for meeting different people and listening to their stories. He suggested that I attend some workshops by and also intern with them if possible.So I spoke to , Founder of HelloMeets about interning with them. At that point she was hiring interns who could work for full time but being a college student that wasn’t possible for me. HelloMeets Sahiba Sethi Then one day, I was going through their website and I found the campus Ambassador Programme there. It really got me interested. They were offering the following to their campus ambassadors:- — Ambassadors could attend a few Meetups for free — Certificates — Opportunity to network with different people I immediately filled the form & the very next day received a call for an interview. After the interview I got selected as the Campus Ambassador. From sharing events on Social Media to handling an event on my own An interesting fact is that I used to just share events on social media and never attended a single meet up until one day when Sahiba texted me saying that I should attend the next meetup on . Those days as it is I was learning about social media marketing. So, I attended the event and it turned out to be really helpful for me. HelloMeets Content-based Marketing by Nalini Kher Also, I was the youngest one present in that workshop. Initially, I felt awkward, but when I interacted with all the attendees, I found them to be very interesting. This meetup boosted my confidence to talk to and network with people. Some key learnings from that event (October 2016)- I got to learn more about content marketing and speaker also gave us a sneak peak into marketing trends of 2016 We had a hands-on workshop session where I designed a campaign for Sheroes which was appreciated by all Then my exams came and I again got non-serious about HelloMeets. And honestly saying that was the time I missed some really awesome meetups like the one on Intermediate Photoshop & Wordpress Website Development. After that Sahiba arranged a Campus Ambassador Meet, where we discussed about an upcoming event — Hello Christmas Party (the only free event that HelloMeets does for its existing customers). It was a pool side party at Hotel Ashoka with food and live music. I got to meet and network with many entrepreneurs. That party had everything I loved and from that point, I started talking to Sahiba on a regular basis to learn anything that I could. Entry to the Christmas party was free for all the campus ambassadors. Once I asked her if I could volunteer at one of the Hiring Events. She agreed and asked me to come and manage the event. As she was going to be in Bangalore, I along with (Marketing, )& (who was a volunteer for the events that time) handled the event. It was a great experience for me in terms of learning how to talk to the startup founders, how to keep everyone happy and work with new people in a team. Ayushi Mohindra HelloMeets Vaibhav Gupta Then I got another chance to coordinate a hiring event. From there onwards I started my journey as Event Coordinator at HelloMeets. I am happy that I am connected with HelloMeets that believes in overall development of everyone, be it a campus ambassador or a full-time employee. Few months ago I saw the post on Facebook about an AI workshop by HelloMeets and I told Sahiba that I was very keen to attend it. She gave me the responsibility to manage the whole event on my own. That day due to some reason the workshop didn’t go well. We were not able to meet the expectations of the attendees. Learnings- I learned about a great topic like AI and that too for free !! :D I learned how to handle a situation where somethings go wrong People actually complained about their money being wasted as it was an expensive workshop. After that what Sahiba did was really surprising to me, she returned everyone’s money. I asked her why she did that and she said: “Money is important but when we ourselves know that we were not able to provide value to our customers then it is unethical to earn in that situation.” Some of my personal favorite meetups are the one on Product Management by Lakshay Pandey and Conversational UX by Neha Sharma. Reasons why I liked these meetups so much- Conversational UX- Learnt of a trendy way of UX designing and why conversations are important Product management- learnt how products are built and what all goes into it Then came our 2 weekends boot camp on UX designing. For this event, I managed and coordinated with the speakers and attendees on all the 4 days. Learnings- Being punctual- As we had this event in the morning I was supposed to reach even before the speakers!! It was quite a task for me. Handling the backend operations of an event Learning about User experiencing and why it plays such an important role in Design Getting feedbacks from the attendees and speakers This bootcamp was one of the best events organised by us with great speakers , , , and an amazing audience. Avisek Bhattacharya Ikshat Jain Anulal VS Riken Patel For details of UX bootcamp, you can read my blogs on the entire bootcamp event coming soon…. Now I feel that I can manage & organise workshops like these.I am also planning a startup related to workshop management for music, art, poetryn and Sahiba is advising and helping me build it. It has almost been a year at Hellomeets and now Sahiba has asked me to be the Community Head for Delhi. She is planning to expand to other cities and needs to focus on that so I have been given this new responsibility. It is a huge achievement for me that I am going to be the Community Head for Delhi very soon!! HelloMeets Blog credits: Naman Goel To join us as a Campus Ambassador, sign up here :) Some of our upcoming meetups: Investors & Founders Meetup — 8th July — Bengaluru GST-Opportunities & Challenges for Startups — 8th July — Delhi Augmented Reality Workshop — 8th July — Delhi The Art of Mobile App Testing — 16th July — Delhi

Journey From Campus Ambassador to Community Head, Delhi @HelloMeets

Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing: Conclusion, and References

Authors:
(1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA (x1yu@ucsd.edu);
(2) Anthony Thomas, University of California San Diego, La Jolla, California, USA (ahthomas@ucsd.edu);
(3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico (ivannia.gomez@cetys.edu.mx);
(4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA (l8gutierrez@ucsd.edu);
(5) Tajana Šimunić Rosing, University of California San Diego, La Jolla, USA (tajana@ucsd.edu). Table of Links Abstract and 1. Introduction 2 Related Work 3 Background on HDC 4 Problem Definition 5 LifeDH 6 Variants of LifeHD 7 Evaluation of LifeHD 8 Evaluation of LifeHD semi and LifeHDa 9 Discussions and Future Works 10 Conclusion, Acknowledgments, and References 9 DISCUSSIONS AND FUTURE WORKS Problem Scale. One limitation of LifeHD is the relative small problem scale (e.g., the image size of CIFAR-100 is restricted to 32x32) due to the essential difficulty of unsupervised lifelong learning problem, including single-pass non-iid data and no supervision. For the same reason, there remains a disparity in accuracy between unsupervised lifelong learning and fully supervised NNs, as substantiated by prior research [13, 54]. In order to scale LifeHD to more challenging applications such as self-driving vehicles, one possible direction is to leverage the pretrained foundation model as a frozen feature extractor in the HDnn framework, which we leave for future investigation. Hyperparameter Tuning. While we recognize that hyperparameters can influence the performance of LifeHD, such an issue is not exclusive to LifeHD, but has persistently been a challenge in machine learning research [7]. In LifeHD, the impact of hyperparameters can be mitigated through pre-deployment evaluation and component co-design. For example, encoding parameters such as 𝑄, 𝑃 can be tuned on similar health monitoring data sources prior to deployment. Meanwhile, the component of cluster HVs merging can increase LifeHD’s resiliency to the novelty detection threshold 𝛾, as a higher quantity of novel clusters can be merged in later stage of learning. Limitations of HDC. HDC serves as the fundamental core of LifeHD. While HDC shows promise with its notable lightweight design, it is burdened by several limitations that remain active areas of research. First, for complex datasets like audio and images, HDC requires a pretrained feature extractor (the HDnn encoding) which may not exist for certain applications. Moreover, akin to any other architecture, HDC vectors face capacity limitations determined by the dimension of HD space, encoding method, and potential noise levels in the input data [56]. Due to these factors, careful evaluation and sometimes manual feature engineering are required to successfully deploy HDC for new applications. Future Works. Although LifeHD focuses on single-device lifelong learning for classification tasks, the method can be extended for other types of tasks and learning settings, such as federated learning and reinforcement learning. We leave the investigation of these topics for future work. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA (x1yu@ucsd.edu); (2) Anthony Thomas, University of California San Diego, La Jolla, California, USA (ahthomas@ucsd.edu); (3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico (ivannia.gomez@cetys.edu.mx); (4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA (l8gutierrez@ucsd.edu); (5) Tajana Šimunić Rosing, University of California San Diego, La Jolla, USA (tajana@ucsd.edu). Authors: Authors: (1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA (x1yu@ucsd.edu); (2) Anthony Thomas, University of California San Diego, La Jolla, California, USA (ahthomas@ucsd.edu); (3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico (ivannia.gomez@cetys.edu.mx); (4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA (l8gutierrez@ucsd.edu); (5) Tajana Šimunić Rosing, University of California San Diego, La Jolla, USA (tajana@ucsd.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Background on HDC 3 Background on HDC 4 Problem Definition 4 Problem Definition 5 LifeDH 5 LifeDH 6 Variants of LifeHD 6 Variants of LifeHD 7 Evaluation of LifeHD 7 Evaluation of LifeHD 8 Evaluation of LifeHD semi and LifeHDa 8 Evaluation of LifeHD semi and LifeHDa 9 Discussions and Future Works 9 Discussions and Future Works 10 Conclusion, Acknowledgments, and References 10 Conclusion, Acknowledgments, and References 9 DISCUSSIONS AND FUTURE WORKS Problem Scale. One limitation of LifeHD is the relative small problem scale (e.g., the image size of CIFAR-100 is restricted to 32x32) due to the essential difficulty of unsupervised lifelong learning problem, including single-pass non-iid data and no supervision. For the same reason, there remains a disparity in accuracy between unsupervised lifelong learning and fully supervised NNs, as substantiated by prior research [13, 54]. In order to scale LifeHD to more challenging applications such as self-driving vehicles, one possible direction is to leverage the pretrained foundation model as a frozen feature extractor in the HDnn framework, which we leave for future investigation. Problem Scale. Hyperparameter Tuning. While we recognize that hyperparameters can influence the performance of LifeHD, such an issue is not exclusive to LifeHD, but has persistently been a challenge in machine learning research [7]. In LifeHD, the impact of hyperparameters can be mitigated through pre-deployment evaluation and component co-design. For example, encoding parameters such as 𝑄, 𝑃 can be tuned on similar health monitoring data sources prior to deployment. Meanwhile, the component of cluster HVs merging can increase LifeHD’s resiliency to the novelty detection threshold 𝛾, as a higher quantity of novel clusters can be merged in later stage of learning. Hyperparameter Tuning. Limitations of HDC. HDC serves as the fundamental core of LifeHD. While HDC shows promise with its notable lightweight design, it is burdened by several limitations that remain active areas of research. First, for complex datasets like audio and images, HDC requires a pretrained feature extractor (the HDnn encoding) which may not exist for certain applications. Moreover, akin to any other architecture, HDC vectors face capacity limitations determined by the dimension of HD space, encoding method, and potential noise levels in the input data [56]. Due to these factors, careful evaluation and sometimes manual feature engineering are required to successfully deploy HDC for new applications. Limitations of HDC. Future Works. Although LifeHD focuses on single-device lifelong learning for classification tasks, the method can be extended for other types of tasks and learning settings, such as federated learning and reinforcement learning. We leave the investigation of these topics for future work. Future Works. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing: Discussions and Future Works

Authors:
(1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA (x1yu@ucsd.edu);
(2) Anthony Thomas, University of California San Diego, La Jolla, California, USA (ahthomas@ucsd.edu);
(3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico (ivannia.gomez@cetys.edu.mx);
(4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA (l8gutierrez@ucsd.edu);
(5) Tajana Šimunić Rosing, University of California San Diego, La Jolla, USA (tajana@ucsd.edu). Table of Links Abstract and 1. Introduction 2 Related Work 3 Background on HDC 4 Problem Definition 5 LifeDH 6 Variants of LifeHD 7 Evaluation of LifeHD 8 Evaluation of LifeHD semi and LifeHDa 9 Discussions and Future Works 10 Conclusion, Acknowledgments, and References 8 EVALUATION OF LIFEHDsemi AND LIFEHDa In this section, we compare LifeHDsemi and LifeHDa, our proposed extensions from LifeHD, with existing designs that are similar. Performance of LifeHDsemi. To evaluate LifeHDsemi in a lowlabel scenario, we compare it with SemiHD [22], which is the stateof-the-art HDC method for semi-supervised learning. We adapt SemiHD [22] for single-pass settings, introducing a pseudolabel assignment threshold. When the cosine similarity of an unlabeled sample to the nearest class hypervector surpasses the threshold, we assign that class as its pseudolabel. The sample is then employed to update the class hypervector in SemiHD. We explore various threshold values and choose the optimal result for comparison. Fig. 15 (a) compares LifeHDsemi and SemiHD [22] on ESC-50 and CIFAR100 across various labeling ratios 𝑟 < 0.01. The advantages of LifeHDsemi are most prominent when labels are limited, the weakly supervised scenario is LifeHDsemi’s major focus. LifeHDsemi improves ACC by up to 10.25% and 3.6% on ESC-50 and CIFAR-100 respectively. This outcome arises from the unsupervised nature of LifeHD, allowing it to autonomously organize prominent cluster HVs, especially when all samples from a class lack labels. As the labeling ratio increases, LifeHDsemi’s advantage over SemiHD diminishes, because more labels bolster SemiHD’s performance. Performance of LifeHDa. LifeHDa provides an interface to trade minimal performance loss for efficiency gains, by adaptively pruning out the insignificant dimensions. We compare LifeHDa with previous HDC works employing a fixed mask throughout training [25], and the results are presented in Fig. 15 (b) for CIFAR100, including ACC and training latency per batch on RPi 4B. Fixed masks negatively impact HDC learning, especially with smaller dimensions. Such masks fail to adapt to new hypervectors in classincremental streams, where less significant dimensions may become crucial later in training. LifeHDa addresses this issue by adjusting the mask upon novelty detection, leading to a degradation of only 0.71% in ACC and 4.5x efficiency gain compared to the complete LifeHD, using only 20% of the full HD dimension of LifeHD. The overhead of adaptively adjusting the mask is negligible when novelty detection occurs infrequently. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA (x1yu@ucsd.edu); (2) Anthony Thomas, University of California San Diego, La Jolla, California, USA (ahthomas@ucsd.edu); (3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico (ivannia.gomez@cetys.edu.mx); (4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA (l8gutierrez@ucsd.edu); (5) Tajana Šimunić Rosing, University of California San Diego, La Jolla, USA (tajana@ucsd.edu). Authors: Authors: (1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA (x1yu@ucsd.edu); (2) Anthony Thomas, University of California San Diego, La Jolla, California, USA (ahthomas@ucsd.edu); (3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico (ivannia.gomez@cetys.edu.mx); (4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA (l8gutierrez@ucsd.edu); (5) Tajana Šimunić Rosing, University of California San Diego, La Jolla, USA (tajana@ucsd.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Background on HDC 3 Background on HDC 4 Problem Definition 4 Problem Definition 5 LifeDH 5 LifeDH 6 Variants of LifeHD 6 Variants of LifeHD 7 Evaluation of LifeHD 7 Evaluation of LifeHD 8 Evaluation of LifeHD semi and LifeHDa 8 Evaluation of LifeHD semi and LifeHDa 9 Discussions and Future Works 9 Discussions and Future Works 10 Conclusion, Acknowledgments, and References 10 Conclusion, Acknowledgments, and References 8 EVALUATION OF LIFEHDsemi AND LIFEHDa In this section, we compare LifeHDsemi and LifeHDa, our proposed extensions from LifeHD, with existing designs that are similar. Performance of LifeHDsemi. To evaluate LifeHDsemi in a lowlabel scenario, we compare it with SemiHD [22], which is the stateof-the-art HDC method for semi-supervised learning. We adapt SemiHD [22] for single-pass settings, introducing a pseudolabel assignment threshold. When the cosine similarity of an unlabeled sample to the nearest class hypervector surpasses the threshold, we assign that class as its pseudolabel. The sample is then employed to update the class hypervector in SemiHD. We explore various threshold values and choose the optimal result for comparison. Fig. 15 (a) compares LifeHDsemi and SemiHD [22] on ESC-50 and CIFAR100 across various labeling ratios 𝑟 < 0.01. The advantages of LifeHDsemi are most prominent when labels are limited, the weakly supervised scenario is LifeHDsemi’s major focus. LifeHDsemi improves ACC by up to 10.25% and 3.6% on ESC-50 and CIFAR-100 respectively. This outcome arises from the unsupervised nature of LifeHD, allowing it to autonomously organize prominent cluster HVs, especially when all samples from a class lack labels. As the labeling ratio increases, LifeHDsemi’s advantage over SemiHD diminishes, because more labels bolster SemiHD’s performance. Performance of LifeHDsemi. Performance of LifeHDa. LifeHDa provides an interface to trade minimal performance loss for efficiency gains, by adaptively pruning out the insignificant dimensions. We compare LifeHDa with previous HDC works employing a fixed mask throughout training [25], and the results are presented in Fig. 15 (b) for CIFAR100, including ACC and training latency per batch on RPi 4B. Fixed masks negatively impact HDC learning, especially with smaller dimensions. Such masks fail to adapt to new hypervectors in classincremental streams, where less significant dimensions may become crucial later in training. LifeHDa addresses this issue by adjusting the mask upon novelty detection, leading to a degradation of only 0.71% in ACC and 4.5x efficiency gain compared to the complete LifeHD, using only 20% of the full HD dimension of LifeHD. The overhead of adaptively adjusting the mask is negligible when novelty detection occurs infrequently. Performance of LifeHDa. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

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