In the tropical, temperate forests it calls home, the velvet worm uses a projectile "slime" to capture its prey. When it's ejected from the worm, the slime transforms into a gel before solidifying into stiff fibers upon exposure to air.

Velvet worm slime could inspire sustainable synthetic materials, study finds

‘Cause my love is mine, all mine I love, my, my, mine Nothing in the world belongs to me But my love, mine, all mine, all mine Furthermore, Mitski’s choice of Patrick Hyland as the producer accentuates the song’s emotional resonance. The duo’s ...

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The 2023 study followed a similar protocol as the previous one: Participants read headlines and summaries of online New York Times articles while undergoing functional magnetic resonance imaging (fMRI), which allowed researchers to track activity in the ...

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Researchers have used methods from high-performance computing to significantly reduce the time it takes to reconstruct images acquired through four-dimensional magnetic resonance imaging (4D-MRI), a leading-edge,

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Resonance

resonance

Charles Dray is founder and CEO of Resonance Security, a full-spectrum cybersecurity suite of software and services that aims to integrate essential traditional and emerging offensive and defensive security practices into a single platform in order to protect companies and individuals against any and all threats that emerge, either from the existing internet or its future iterations. In this interview with Jillian Godsil, he shares his insights and experience running Resonance Security. What is your company in 2–5 words? Full spectrum effortless cybersecurity. Why is now the time for your company to exist? Cybersecurity threats continue to evolve and target larger audiences. Victims lose an estimated 714 Billion to cybercrime annually and it will increase with emerging technology threats. What do you love about your team, and why are you the ones to solve this problem? We focus on delivering an impactful experience we want organizations and individuals to be proud of using. Every single person on the team is highly passionate about our purpose, and we have the experience to back it. Our security team has a proven track record with defensive and offensive cybersecurity across institutions, small businesses, web3, and consumers. We’ve seen what’s out there and know we found the solution. If you weren’t building your startup, what would you be doing? I’d still be involved in cybersecurity and emerging tech, and I’d be thinking about another way to effectively make people safer from attacks which could ruin their lives and mental health. At the moment, how do you measure success? What are your metrics? I measure success through analyzing customer feedback, and reviewing how many customers end up being sales people. There’s a saying that says “your customer is your best sales person” and I believe it is spot on. In a few sentences, what do you offer to whom? Effortless, effective, and evolving cybersecurity software solutions for institutions, small businesses, web3, VIPs, and consumers. A solution for any circumstance, budget, and any technical level. What’s most exciting about your traction to date? We’ve enrolled customers in every vertical and received positive feedback from 100% of our customers since March of 2023! It’s an incredibly rewarding experience to see that the business model is working better then we could have ever imagined – I feel that giving each customer a human experience is a big part of it. Knowing they have a team of security experts on their side setting them up for success drives betterment on so many levels. Where do you think your growth will be next year? I see significant growth in the consumer, small business, and web3 segment in the next year. These segments are well served in the identity theft (for consumers) and testing market (for small businesses and web3), but are very exposed from an advanced full spectrum offensive and defensive security standpoint. It’s hard to build a team and software that can deliver those solutions truly effectively, so no one has really approached that market. For years, we have developed a playbook that effectively covers any audience. Our team of red team engineers and software builders are exclusive to Resonance and are driven by the passion to protect. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was back in March of 2023. It was a COSMWASM Rust code audit for a money transfer project. Our experience with emerging tech originates from our background in traditional cybersecurity, so projects are delighted to work with engineers that know how to tie together legacy technologies with emerging technologies and detect attack vectors of both technologies together. We reached nearly seven figures in Revenue our first year when we were in software beta mode. With the inflow of organizations and individuals starting to take security seriously we are on track to be at eight figures in the next year, and that’s mostly software revenue. What’s your biggest threat? That organizations and individuals don’t take cybersecurity seriously enough, and come to us later in a much less favorable situation after a hack rather than starting to take the proper actions today. It’s always sad to see how unwell people become after a major cybersecurity attack. Cybersecurity attacks make people loose trust in other people, and that’s never a good thing for mental health. Charles Dray is founder and CEO of Resonance Security, a full-spectrum cybersecurity suite of software and services that aims to integrate essential traditional and emerging offensive and defensive security practices into a single platform in order to protect companies and individuals against any and all threats that emerge, either from the existing internet or its future iterations. In this interview with Jillian Godsil, he shares his insights and experience running Resonance Security. What is your company in 2–5 words? What is your company in 2–5 words? Full spectrum effortless cybersecurity. Why is now the time for your company to exist? Why is now the time for your company to exist? Cybersecurity threats continue to evolve and target larger audiences. Victims lose an estimated 714 Billion to cybercrime annually and it will increase with emerging technology threats. What do you love about your team, and why are you the ones to solve this problem? What do you love about your team, and why are you the ones to solve this problem? We focus on delivering an impactful experience we want organizations and individuals to be proud of using. Every single person on the team is highly passionate about our purpose, and we have the experience to back it. Our security team has a proven track record with defensive and offensive cybersecurity across institutions, small businesses, web3, and consumers. We’ve seen what’s out there and know we found the solution. If you weren’t building your startup, what would you be doing? If you weren’t building your startup, what would you be doing? I’d still be involved in cybersecurity and emerging tech, and I’d be thinking about another way to effectively make people safer from attacks which could ruin their lives and mental health. At the moment, how do you measure success? What are your metrics? At the moment, how do you measure success? What are your metrics? I measure success through analyzing customer feedback, and reviewing how many customers end up being sales people. There’s a saying that says “your customer is your best sales person” and I believe it is spot on. In a few sentences, what do you offer to whom? In a few sentences, what do you offer to whom? Effortless, effective, and evolving cybersecurity software solutions for institutions, small businesses, web3, VIPs, and consumers. A solution for any circumstance, budget, and any technical level. What’s most exciting about your traction to date? What’s most exciting about your traction to date? We’ve enrolled customers in every vertical and received positive feedback from 100% of our customers since March of 2023! It’s an incredibly rewarding experience to see that the business model is working better then we could have ever imagined – I feel that giving each customer a human experience is a big part of it. Knowing they have a team of security experts on their side setting them up for success drives betterment on so many levels. Where do you think your growth will be next year? Where do you think your growth will be next year? I see significant growth in the consumer, small business, and web3 segment in the next year. These segments are well served in the identity theft (for consumers) and testing market (for small businesses and web3), but are very exposed from an advanced full spectrum offensive and defensive security standpoint. It’s hard to build a team and software that can deliver those solutions truly effectively, so no one has really approached that market. For years, we have developed a playbook that effectively covers any audience. Our team of red team engineers and software builders are exclusive to Resonance and are driven by the passion to protect. Tell us about your first paying customer and revenue expectations over the next year. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was back in March of 2023. It was a COSMWASM Rust code audit for a money transfer project. Our experience with emerging tech originates from our background in traditional cybersecurity, so projects are delighted to work with engineers that know how to tie together legacy technologies with emerging technologies and detect attack vectors of both technologies together. We reached nearly seven figures in Revenue our first year when we were in software beta mode. With the inflow of organizations and individuals starting to take security seriously we are on track to be at eight figures in the next year, and that’s mostly software revenue. What’s your biggest threat? What’s your biggest threat? That organizations and individuals don’t take cybersecurity seriously enough, and come to us later in a much less favorable situation after a hack rather than starting to take the proper actions today. It’s always sad to see how unwell people become after a major cybersecurity attack. Cybersecurity attacks make people loose trust in other people, and that’s never a good thing for mental health.

10 Questions for Cybersecurity Firm Resonance Founder Charles Dray

I spent the last year working on the new PHP framework to push this programming language further into the modern era. I firmly believe that the future of PHP lies in , Coroutines, and libraries like . Fibers Swoole The Bright Future of PHP Most people know PHP just as a website framework, but it's much more - it can serve as a communication hub in your infrastructure that interconnects all your services through REST API, GraphQL, WebSockets, gRPC, and more. It's effortless to prototype and write new features in PHP. PHP is also straightforward to extend; if necessary, replacing parts of your application with code written in a different language is simple. It's the perfect starting point when creating new applications. About Resonance Resonance is centered around this idea - to quickly start from the ground up in the asynchronous environment and be able to set up all the necessary services and APIs and then swap, replace, or expand all the application's components. Attributes First Everything is as modular as possible, with only minimal central configuration files - everything is configured through Attributes. For example: <?php

namespace App\HttpResponder;

use App\DatabaseEntity\BlogPostForDisplay;
use App\HttpRouteSymbol;
use Distantmagic\Resonance\Attribute\RespondsToHttp;
use Distantmagic\Resonance\Attribute\RouteParameter;
use Distantmagic\Resonance\Attribute\Singleton;
use Distantmagic\Resonance\HttpInterceptableInterface;
use Distantmagic\Resonance\HttpResponder;
use Distantmagic\Resonance\HttpResponder\HttpController;
use Distantmagic\Resonance\RequestMethod;
use Distantmagic\Resonance\SingletonCollection;
use Distantmagic\Resonance\TwigTemplate;

#[RespondsToHttp(
    method: RequestMethod::GET,
    pattern: '/blog/{blog_post_slug}',
    routeSymbol: HttpRouteSymbol::BlogPostShow,
)]
#[Singleton(collection: SingletonCollection::HttpResponder)]
final readonly class BlogPostShow extends HttpController
{
    public function handle(
        #[RouteParameter(from: 'blog_post_slug')]
        BlogPostForDisplay $blogPost,
    ): HttpInterceptableInterface {
        return new TwigTemplate('turbo/website/blog_post.twig', [
            'blog_post' => $blogPost,
        ]);
    }
} Asynchronous GraphQL in PHP You can build the entire GraphQL schema in PHP using attributes: <?php

use GraphQL\Type\Definition\ObjectType;
use GraphQL\Type\Definition\Type;
use Distantmagic\Resonance\Framework\Attribute\Singleton;
use Distantmagic\Resonance\Framework\SwooleFuture;

#[Singleton]
final class PingType extends ObjectType
{
    public function __construct()
    {
        parent::__construct([
            'name' => 'Ping',
            'description' => 'Test field',
            'fields' => [
                'message' => [
                    'type' => Type::string(),
                    'description' => 'Always responds with pong',
                    'resolve' => $this->resolveMessage(...),
                ],
            ],
        ]);
    }

    private function resolveMessage(): SwooleFuture
    {
        return new SwooleFuture(function () {
            sleep(1);

            return 'pong';
        });
    }
} This query is going to take 1 second (instead of 3), since the field is resolved asynchronously: query MultiPing() {
    ping1: ping { message }
    ping2: ping { message }
    ping3: ping { message }
} Promise-like Objects in PHP Swoole's coroutines are wrapped inside a SwooleFuture object, which provides easy access to asynchronous features: <?php

use Distantmagic\Resonance\SwooleFuture;

$future1 = new SwooleFuture(function (int $value) {
    assert($value === 1);

    return $value + 2;
});

$future2 = $future1->then(new SwooleFuture(function (int $value) {
    assert($value === 3);

    return $value + 4;
}));

assert($future2->resolve(1)->result === 7); Much more The project is free and open source. You can check the documentation here: https://resonance.distantmagic.com/docs/features/ GitHub repo: https://github.com/distantmagic/resonance Feel free to join the community: https://resonance.distantmagic.com/community/ Resonance is new, and it's my first open-source project of this magnitude. Currently, I am working on it full-time. If you try it out and Resonance will help you with your projects, I'd be the happiest man alive. If you find the project interesting please leave me a star on GitHub! Also published . here

Introducing Resonance, A New PHP Framework Built on Top of Swoole

“Why do these things keep happening?” I asked myself, staring blankly at the news headline. It was a hot Sunny afternoon, and Cointelegraph had just reported the of , which resulted in the loss of over $31 million. hacking Alphapo’s hot wallets Two days earlier, Curve’s liquidity pool platform, Conic Finance, was reportedly hacked for $3.2 million in eth. The recurring attacks on crypto exchanges and service providers are a major challenge for the crypto industry. However, cyber security experts understand why they occur and how they can be addressed. CEO , gave valuable insights into blockchain cybersecurity in this interview with Olayimika Oyebanji. Resonance Security Charles Dray It’s great to sit down with you, Charles. Can you tell us about yourself? First of all, thank you for taking the time to speak with me. I am a born and bred New Yorker who spent more than a decade working in traditional banking covering FX, eFX, and transactional banking for both individuals and institutional clients. After noticing continuous inefficiencies in transactional processing and KYC, I immersed myself in better understanding the blockchain space to figure out a way to solve issues my clients frequently experienced. In that research, I realized that there are a number of projects that would help with the inefficiencies I encountered in traditional banking, but the downside is that many of these projects were vulnerable to hacks, making them less appealing to potential clients. I realized that focusing on cybersecurity would help these projects build trust with the public, and open the door for a wider variety of end users to take advantage of blockchain efficiencies. What's your route to blockchain technology? I have always been a bit of a tech geek wanting to stay up to date with the newest gadgets and software. I dabbled a bit in cryptocurrencies out of curiosity and even created an NFT gallery in Decentraland. The more time I spent on these hobbies, the more opportunities I started to see what blockchain could accomplish. Not only could blockchain solve some of the ongoing issues with transactional processing and KYC I saw daily in the corporate banking world, but there was a slew of other use cases for blockchain that spanned industries far beyond banking. When I tried to explain these benefits to many of my colleagues, their responses were that it wasn’t secure enough to be a viable alternative, which got me looking more deeply into the cybersecurity side of the blockchain world. It was clear to me that in order to unlock blockchain opportunities, there was a lot of work to be done to secure these projects and to influence public opinion, which led me to opportunities in the Web3 cybersecurity space. Charles, we are dealing with a 4 billion dollar cybersecurity  problem. How did we get to this point? There are so many contributing factors leading to these high dollar vulnerabilities. Some of it comes down to the fact that blockchain is still evolving. As the space has built various chains and projects, we’re obviously thinking about functionality and safety, and as we’ve stress tested those systems some of their vulnerabilities become more apparent. Any time the space builds a new chain or functionality, we open the door to new hacking opportunities, so the proliferation of blockchains widens the vectors for attack, and that requires more attention paid to testing and auditing before going live. We’ll continue to see this as we incorporate other emerging technologies as well. On the other hand, if you look at the Web 2 space, it’s not as though that space is “hack free” either, although more time has been spent on identifying potential risks and solutions to mitigate them. Many of the vulnerabilities we see on the Web2 side also carry over to Web3. We spend a lot of time on education because people are often the biggest opportunity for vulnerability. Obviously, we hear a lot about bad actors in blockchain, but there are plenty of people in the space who are inadvertently jeopardizing projects by doing simple things like clicking a link in an unverified email or leaving their laptops unattended. At the end of the day, we can’t be naive about all the ways our security can potentially fail, and we need to hold projects accountable for being more proactive about their security strategies. What inspired the founding of Resonance Security? As I spent time talking to a huge variety of project founders in the space, two things became clear to me. The first is that security is not one size fits all and different projects had different needs when it came to cybersecurity. The second was that we were on the precipice of a number of new technologies, such as AI, machine learning, and quantum computing, being leveraged in the space that would increase the sophistication of cybersecurity attacks. For the industry to be prepared to meet these threats, we needed to be immersed in these technologies and we needed to have the flexibility to provide the right security mix to each client. This realization led me to form a team of seven all-star engineers and executives to start Resonance Security, with a focus on delivering a true cybersecurity concierge experience that covers best-in-class engagements with a cybersecurity dashboard made up of dozens of curated products and services. Is there a market gap you are trying to fill? The landscape in the industry is constantly changing, and it’s hard, even for the most informed people in the space, to stay up to date on all the options available to secure a project. We realized what was missing in the cybersecurity space was a digital Shepard or someone who has insider knowledge and can ensure projects are picking the best services and products for their specific needs, and even better, provide it all in one place. Our key objective is to make it effortless for projects to implement the appropriate cybersecurity measures for their organization. We take a personalized, holistic look at a project’s needs, identify their cybersecurity priorities, and provide them with a security roadmap. We have partnered with dozens of cybersecurity products spanning AI, forensics, social engineering, fuzzing tools, hardware, bug bounty programs, and more to ensure a strong security posture. If we don’t offer the cybersecurity product you are looking for, we will act as a concierge and interview products to find the right match for you. We want to be a top-tier service provider but also the add-on to any audit no matter where you’re getting your audit or what stage you’re at. Why are the routine cybersecurity practices that you recommend for users’ safety? There are many best practices, and it depends on the scope, audience, chain, and various other factors, but some tried and true things to consider as an individual is to Do your own research first. Understand the history of projects before you invest, always use 2fa, be aware of social engineering attacks, and generally just seek to know when it comes to cybersecurity and emerging tech. For developers, ongoing awareness of the different types of security vulnerabilities in web3 apps and how they tie into web2 components including webapps cloud and ci/cd are very important. The space is constantly changing, so this is a constant activity for our engineers, and if you’re developing in the space, you should make it your job to stay up to date. Following the best secure coding practices for the specific language you are working with, you should have extensive knowledge of the blockchains they are deploying the system on. There are so many more!! I can go on forever. Keep in mind that while these are some best practices, we believe that cybersecurity needs to be customized based on a variety of factors. We always suggest conducting multiple audits with different security companies as well as implementing a suite of tools for ongoing security. Organizations must educate their audiences regarding cybersecurity threats from all fronts; this not only includes auditing the technical stack, but also training staff members, and users. Security awareness and education should be a continuous narrative in every ecosystem, and even that is just a small piece of security that should resonate. What is the biggest challenge in blockchain cybersecurity? How can it be addressed? One of the biggest challenges I think we see in cybersecurity currently is not taking the topic of end-to-end cybersecurity seriously enough. There are a lot of check-box security reviews happening rather than truly taking an end-to-end look at how projects can be secure on all fronts. Smart contract audits are great, and I’m going to be the first one to tell you that you need more than one, but there are other aspects to security like front-end, back-end, and employee attack vectors that can destroy an organization. That’s why Resonance not only offers audits, pen tests, and education but our cybersecurity product dashboard as an add-on to any audit that can ensure you have the bespoke security you need. The other looming challenge is the impact of emerging tech and how we’ll need to respond to meet the new threats that arise from those technologies. My feeling is the best way to stay ahead of threats is to get immersed in the technologies. Understanding how they can be used and leveraging them to build our own tools is the best way for us to be prepared to meet this threat. Can you share your personal experience as the CEO of Resonance Security? One of the things I love the most about my job is the ability to talk to a wide variety of cybersecurity projects and products spanning every use case you can possibly imagine. It’s incredible to see the innovation happening in the space and seeing how invested people are in creating novel solutions to problems that have long existed. I’m always excited to get on a call with a new client and learn about what they’re doing. I am also incredibly lucky to have an amazing team at Resonance. I’m impressed daily by our engineers; not only are they auditors, but they are researchers, teachers, and innovators. Their insights help us to develop high-quality in-house tools, integrate emerging technologies into our audits and product offerings, and stay on top of trends in the space. Does addressing cyber security challenges require heavy funding? So much of what a project’s cybersecurity needs are will be unique to an individual project, so there is no one size fits all answer to this question. That being said, there are a variety of ways to help secure your project. Audits can be found at a variety of price points, and although all audits are not equal, having someone look at your code who was not part of its creation will help projects identify issues that may have been overlooked. We’re also at a place now where there are so many high-quality tools and products in the market that can help identify threats outside of an audit. Some of the products we work with currently cost less than your monthly coffee habit, and others are free for the basic functionality. Despite the fact that there are some lower-cost options in the space, I think it’s important for projects to start to understand what their specific security needs will be early on in their building process and make sure to budget accordingly for the security needed. This is not the place to cheap out because if you run into a problem down the road, it will cost ten times more to fix, and the reputational damage may not be fixable. Any parting words? We are at the stone age of the emerging technology era, and we've just discovered fire. It's important that we all be cognizant that everything will need to be tested, fine-tuned, and eventually converged to create a stronger whole. The best way for the community to learn and adapt quickly is for us to foster collaboration throughout the industry so we stay ahead of future threats and optimize the innovation from these new technologies.

Blockchain Cybersecurity: Exclusive Interview With Resonance Security CEO Charles Dray

I Over Relied on AI and Those Shortcuts Cost Me

لقد اعتمدت بشكل مفرط على الذكاء الاصطناعي وهذه الاختصارات كلفتني الكثير

Nayax AI ukaruw juk’amp atinisiyäta ukat uka atajos ukanakax nayaruw qullqix churasi

Прекалено разчитах на AI и тези преки пътища ми струваха

Přehnaně jsem se spoléhal na AI a ty zkratky mě stály

Jeg stolede for meget på AI, og de genveje kostede mig

Βασίστηκα υπερβολικά στην τεχνητή νοημοσύνη και αυτές οι συντομεύσεις μου κοστίζουν

Confié demasiado en la IA y esos atajos me costaron caro

Luotin liikaa tekoälyyn ja nuo pikakuvakkeet maksoivat minulle

AIに頼りすぎて、近道で損をした

Мен AIга таяндым жана бул жарлыктар мага кымбатка турду

Би хиймэл оюун ухаанд хэт найддаг байсан бөгөөд эдгээр товчлолууд надад үнэтэй байсан

Ndzi Titshege Ku tlula mpimo Hi AI naswona Swiphemu Sweswo Swi Ndzi Koxa

The Stanford Grad Who Forgot How To Think

Die Stanford-graad wat vergeet het hoe om te dink

خريج جامعة ستانفورد الذي نسي كيف يفكر

স্ট্যানফোর্ড গ্র্যাড যারা কিভাবে চিন্তা করতে ভুলে গেছেন

El graduado de Stanford que olvidó cómo pensar

Nola pentsatu ahaztu zuen Stanfordeko gradua

Lulusan Stanford yang Lupa Cara Berpikir

考える方法を忘れたスタンフォード卒業生

생각하는 법을 잊은 스탠포드 졸업생

Absolwent Stanfordu, który zapomniał, jak myśleć

Icyiciro cya Stanford Wibagiwe Gutekereza

The Stanford Grad Loyi A Riveleke Ndlela Yo Ehleketa

I-Stanford Grad Ekhohlwe Ukucabanga Kanjani

Authors:
(1) Diptarka Chakraborty, National University of Singapore, Singapore
(2) Sourav Chakraborty, Indian Statistical Institute, Kolkata;
(3) Gunjan Kumar, National University of Singapore, Singapore;
(4) Kuldeep S. Meel, University of Toronto, Toronto. Table of Links Abstract and 1 Introduction 2 Notations and Preliminaries 3 Related Work 4 An Efficient One-Round Adaptive Algorithm and 4.1 High-Level Overview 4.2 Algorithm Description 4.3 Technical Analysis 5 Conclusion 6 Acknowledgements and References A Missing Proofs B An O(log log n)-query fully adaptive algorithm Abstract 1 Introduction Evaluating different properties of an unknown object is a fundamental challenge in statistics. When dealing with large objects, it becomes essential to determine these properties by making only a limited number of queries to the object. In the case of unknown objects being probability distributions, the goal is to assess whether the input distribution(s) possess specific properties or deviate significantly (i.e., “ε-far” for some ε > 0) from meeting them. All of this needs to be accomplished while minimizing the number of queries made (also known as query complexity) to the distribution(s). Probability distributions are crucial subjects of study, and distribution testing has remained central to sublinear algorithms and modern data analysis since its introduction [GGR98, GR11, BFR+13]. Early investigations into distribution testing primarily utilized the SAMP query model, which only allows drawing samples from the given distribution(s). However, for testing many interesting properties, the SAMP model proves to be restrictive, as evidenced by strong polynomial (in domain size) lower bounds on the sample complexity. To overcome this limitation, several alternative query models have been proposed over the past decade. Among these models, the conditional sampling model (COND) [CFGM13, CRS14] has been extensively studied. This model permits drawing samples from the input distribution(s) conditioned on any arbitrary subset of the domain. Various distribution testing problems have been explored under the COND model [FJO+15, KT19, Nar21, CKM23] and certain variants of it like subcube conditioning model [BC18, CCK+21, CJLW21]. Moreover, the COND model and its variants have recently found applications in the areas like formal methods and machine learning (e.g., [CM19, MPC20, GJM22]). In this work, we study the equivalence testing problem, one of the most fundamental problems in distribution testing. In this problem, given query access to two (unknown) distributions P and Q, the objective is to decide whether they are equal or ε-far from each other in the total variation distance. For this problem, a query-optimal algorithm is already known in the COND model. However, the primary challenge with the query-efficient algorithm/tester in the COND model is its inherent sequential or adaptive nature. A tester is considered non-adaptive if it can generate all its queries based solely on the input parameter (in this case, the domain size) and its internal randomness, without relying on previous query responses. Non-adaptive testers are generally favored in practical situations because they can make multiple queries simultaneously. Exploring the balance between the degree of adaptivity and query complexity is a captivating area of research. This curiosity prompted [CG18] to delve deeper into adaptive testing, introducing a nuanced approach by permitting a limited number of adaptive stages or rounds. This multi-stage or bounded-round adaptivity concept finds resonance in various other problems, including group testing [DHH00, DMT13, EBE20], submodular function maximization [BS18, CQ19], compressed sensing and sparse recovery [NSWZ18, KP19], multi-armed bandits problem [AAAK17]. In this work, we initiate the study of bounded-round adaptivity in the context of the equivalence testing problem and provide a query-efficient one-round adaptive tester. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Diptarka Chakraborty, National University of Singapore, Singapore (2) Sourav Chakraborty, Indian Statistical Institute, Kolkata; (3) Gunjan Kumar, National University of Singapore, Singapore; (4) Kuldeep S. Meel, University of Toronto, Toronto. Authors: Authors: (1) Diptarka Chakraborty, National University of Singapore, Singapore (2) Sourav Chakraborty, Indian Statistical Institute, Kolkata; (3) Gunjan Kumar, National University of Singapore, Singapore; (4) Kuldeep S. Meel, University of Toronto, Toronto. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Notations and Preliminaries 2 Notations and Preliminaries 3 Related Work 3 Related Work 4 An Efficient One-Round Adaptive Algorithm and 4.1 High-Level Overview 4 An Efficient One-Round Adaptive Algorithm and 4.1 High-Level Overview 4.2 Algorithm Description 4.2 Algorithm Description 4.3 Technical Analysis 4.3 Technical Analysis 5 Conclusion 5 Conclusion 6 Acknowledgements and References 6 Acknowledgements and References A Missing Proofs A Missing Proofs B An O(log log n)-query fully adaptive algorithm B An O(log log n)-query fully adaptive algorithm Abstract 1 Introduction Evaluating different properties of an unknown object is a fundamental challenge in statistics. When dealing with large objects, it becomes essential to determine these properties by making only a limited number of queries to the object. In the case of unknown objects being probability distributions, the goal is to assess whether the input distribution(s) possess specific properties or deviate significantly (i.e., “ε-far” for some ε > 0) from meeting them. All of this needs to be accomplished while minimizing the number of queries made (also known as query complexity) to the distribution(s). Probability distributions are crucial subjects of study, and distribution testing has remained central to sublinear algorithms and modern data analysis since its introduction [GGR98, GR11, BFR+13]. Early investigations into distribution testing primarily utilized the SAMP query model, which only allows drawing samples from the given distribution(s). However, for testing many interesting properties, the SAMP model proves to be restrictive, as evidenced by strong polynomial (in domain size) lower bounds on the sample complexity. To overcome this limitation, several alternative query models have been proposed over the past decade. Among these models, the conditional sampling model (COND) [CFGM13, CRS14] has been extensively studied. This model permits drawing samples from the input distribution(s) conditioned on any arbitrary subset of the domain. Various distribution testing problems have been explored under the COND model [FJO+15, KT19, Nar21, CKM23] and certain variants of it like subcube conditioning model [BC18, CCK+21, CJLW21]. Moreover, the COND model and its variants have recently found applications in the areas like formal methods and machine learning (e.g., [CM19, MPC20, GJM22]). In this work, we study the equivalence testing problem, one of the most fundamental problems in distribution testing. In this problem, given query access to two (unknown) distributions P and Q, the objective is to decide whether they are equal or ε-far from each other in the total variation distance. For this problem, a query-optimal algorithm is already known in the COND model. However, the primary challenge with the query-efficient algorithm/tester in the COND model is its inherent sequential or adaptive nature. A tester is considered non-adaptive if it can generate all its queries based solely on the input parameter (in this case, the domain size) and its internal randomness, without relying on previous query responses. Non-adaptive testers are generally favored in practical situations because they can make multiple queries simultaneously. Exploring the balance between the degree of adaptivity and query complexity is a captivating area of research. This curiosity prompted [CG18] to delve deeper into adaptive testing, introducing a nuanced approach by permitting a limited number of adaptive stages or rounds. This multi-stage or bounded-round adaptivity concept finds resonance in various other problems, including group testing [DHH00, DMT13, EBE20], submodular function maximization [BS18, CQ19], compressed sensing and sparse recovery [NSWZ18, KP19], multi-armed bandits problem [AAAK17]. In this work, we initiate the study of bounded-round adaptivity in the context of the equivalence testing problem and provide a query-efficient one-round adaptive tester. 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

Explore The Power of Bounded Adaptivity Through Equivalence Testing

They’re two of the world’s largest stocks by market capitalization, but could the might of Nvidia (NASDAQ:NVDA) and Microsoft (NASDAQ:MSFT) come under new strain in 2025 as fresh challenges emerge? The news that London-based investment firm Findlay Park, which counts $10.7 billion in assets under management, has been cutting its holdings of both Nvidia and Microsoft has raised eyebrows throughout US markets. Findlay Park’s pedigree for success can be found in the performance of its sole fund, the American Fund, which has outperformed 86% of its peers, making its decision to lower exposure to two of the world’s largest stocks particularly curious. The fund’s CEO Simon Pryke highlighted tepid earnings and growth expectations, as well as misaligned valuations with this slower rate of growth as a fundamental reason for trimming holdings in the two tech giants. But could we see the likes of Nvidia and Microsoft struggle to win over institutional investors in the new year? The future growth of the stocks will depend on managing hype and sustainable growth. Could Nvidia Struggle as Growth Slows? Being one of the ‘Magnificent Seven’ stocks, there’s little doubt about Nvidia’s pedigree, the stock has rallied amid the ongoing generative AI boom to become one of the world’s most valuable stocks. Nvidia’s Q3 2024 earnings reports were impressive, posting 94% year-over-year growth and sales of $35 billion. Nvidia’s data center business also posted growth of 112% thanks to increasing AI demand, while net profit doubled to $20 billion. However, there are concerns over Nvidia’s long-term growth prospects. The company’s forecasted revenue of $37.5 billion for the fourth quarter, slightly above analyst estimates of $37.09 billion, is healthy but far behind its previous quarterly growth rates. Should Nvidia’s fourth-quarter forecast be met, it indicates a year-to-year growth of around 70% from 2023. This would be impressive for most firms, but for a company that recorded 265% annual growth in the year-ago period? Not so much. Despite this, it’s worth looking at Nvidia’s advantageous position at the forefront of an AI market that’s ripe for growth. With Nvidia holding around 90% of the GPU market and 98% of the GPU training market, buying into NVDA is tantamount to betting on the growth of generative AI, which is a market forecasted to be worth $1.3 trillion by 2032. There’s also more hope that Nvidia can sustain its growth rates through innovations like the CUDA platform, which is a software framework designed to accelerate the capabilities of GPU software. “Forecasts suggest that Nvidia could continue its upward trajectory, potentially reaching a valuation of $10 trillion by 2030,” suggests Maxim Manturov, head of investment research at Freedom24. “This ambitious target reflects expectations of sustained growth in both the hardware and software segments, with CUDA playing a key role in expanding market reach and strengthening its presence. competitive advantage.” Can Microsoft Out-Innovate its Falling Market Share? Microsoft’s slipping grip on the global desktop market will be a cause for concern as Windows 11’s market share fell from 35.6% in October 2024 to 34.9% by December. Although Windows 10 made subtle gains, MSFT’s struggle to win new consumers for its more recent release could point to trouble ahead. In addition to these problems, the company has been hit with a class action lawsuit in the UK over the price of its software. If successful, Microsoft could be liable to pay a fine exceeding £1 billion ($1.27bn). MSFT has been making steady progress on Wall Street, posting growth of nearly 15% for the calendar year up to the beginning of December, but investors will want to see more ground covered in selling newer software to consumers. However, solace can be taken from Microsoft’s wins elsewhere. Although Windows 11 has struggled for dominance in the global desktop market, the system has achieved a new high market share of 53% throughout Steam’s gaming market. This highlights the firm’s long-term focus on building into the lucrative video game landscape, which is forecasted to grow at a CAGR of 13.1% towards 2030. Microsoft can also secure more Wall Street growth through its various environmental initiatives. Having committed $1 billion to its Climate Innovation Fund, the tech leader’s investments in climate change-focused technologies are likely to gain new resonance in the coming years as global warming continues to climb international priority lists. Already, Microsoft has provided 850 grants to organizations in 110 countries as part of its AI for Earth scheme, and these initiatives can be crucial to the stock’s sustainability credentials. What’s Next for NVDA and MSFT? The challenges facing Nvidia and Microsoft revolve around their ability to build on their impressive performance in recent years. The scale of the AI boom is such that retaining their seismic market share across key industries is essential, and this will require a commitment to R&D, and outpacing a growing network of competitors. However, with strong innovation pipelines and commitments to sustainability, both stocks appear set to maintain strong growth rates into the future. Determining the scale of this growth is likely to be the $1 trillion question for key institutional investors. They’re two of the world’s largest stocks by market capitalization, but could the might of Nvidia (NASDAQ:NVDA) and Microsoft (NASDAQ:MSFT) come under new strain in 2025 as fresh challenges emerge? The news that London-based investment firm Findlay Park, which counts $10.7 billion in assets under management, has been cutting its holdings of both Nvidia and Microsoft has raised eyebrows throughout US markets. Findlay Park’s pedigree for success can be found in the performance of its sole fund, the American Fund, which has outperformed 86% of its peers, making its decision to lower exposure to two of the world’s largest stocks particularly curious. The fund’s CEO Simon Pryke highlighted tepid earnings and growth expectations , as well as misaligned valuations with this slower rate of growth as a fundamental reason for trimming holdings in the two tech giants. tepid earnings and growth expectations tepid earnings and growth expectations But could we see the likes of Nvidia and Microsoft struggle to win over institutional investors in the new year? The future growth of the stocks will depend on managing hype and sustainable growth. likes of Nvidia Could Nvidia Struggle as Growth Slows? Being one of the ‘ Magnificent Seven ’ stocks, there’s little doubt about Nvidia’s pedigree, the stock has rallied amid the ongoing generative AI boom to become one of the world’s most valuable stocks. Nvidia’s Q3 2024 earnings reports were impressive, posting 94% year-over-year growth and sales of $35 billion. Magnificent Seven Magnificent Seven Nvidia’s data center business also posted growth of 112% thanks to increasing AI demand, while net profit doubled to $20 billion. However, there are concerns over Nvidia’s long-term growth prospects. The company’s forecasted revenue of $37.5 billion for the fourth quarter, slightly above analyst estimates of $37.09 billion, is healthy but far behind its previous quarterly growth rates. Should Nvidia’s fourth-quarter forecast be met, it indicates a year-to-year growth of around 70% from 2023. This would be impressive for most firms, but for a company that recorded 265% annual growth in the year-ago period? Not so much. year-to-year growth of around 70% year-to-year growth of around 70% Despite this, it’s worth looking at Nvidia’s advantageous position at the forefront of an AI market that’s ripe for growth. With Nvidia holding around 90% of the GPU market and 98% of the GPU training market, buying into NVDA is tantamount to betting on the growth of generative AI, which is a market forecasted to be worth $1.3 trillion by 2032 . 90% of the GPU market 90% of the GPU market worth $1.3 trillion by 2032 worth $1.3 trillion by 2032 There’s also more hope that Nvidia can sustain its growth rates through innovations like the CUDA platform, which is a software framework designed to accelerate the capabilities of GPU software. “Forecasts suggest that Nvidia could continue its upward trajectory, potentially reaching a valuation of $10 trillion by 2030,” suggests Maxim Manturov, head of investment research at Freedom24. “This ambitious target reflects expectations of sustained growth in both the hardware and software segments, with CUDA playing a key role in expanding market reach and strengthening its presence. competitive advantage.” Can Microsoft Out-Innovate its Falling Market Share? Microsoft’s slipping grip on the global desktop market will be a cause for concern as Windows 11’s market share fell from 35.6% in October 2024 to 34.9% by December. Although Windows 10 made subtle gains, MSFT’s struggle to win new consumers for its more recent release could point to trouble ahead. market share fell from 35.6% market share fell from 35.6% In addition to these problems, the company has been hit with a class action lawsuit in the UK over the price of its software. If successful, Microsoft could be liable to pay a fine exceeding £1 billion ($1.27bn). MSFT has been making steady progress on Wall Street , posting growth of nearly 15% for the calendar year up to the beginning of December, but investors will want to see more ground covered in selling newer software to consumers. MSFT has been making steady progress on Wall Street However, solace can be taken from Microsoft’s wins elsewhere. Although Windows 11 has struggled for dominance in the global desktop market, the system has achieved a new high market share of 53% throughout Steam’s gaming market. This highlights the firm’s long-term focus on building into the lucrative video game landscape, which is forecasted to grow at a CAGR of 13.1% towards 2030. market share of 53% market share of 53% Microsoft can also secure more Wall Street growth through its various environmental initiatives. Having committed $1 billion to its Climate Innovation Fund, the tech leader’s investments in climate change-focused technologies are likely to gain new resonance in the coming years as global warming continues to climb international priority lists. Already, Microsoft has provided 850 grants to organizations in 110 countries as part of its AI for Earth scheme, and these initiatives can be crucial to the stock’s sustainability credentials. What’s Next for NVDA and MSFT? The challenges facing Nvidia and Microsoft revolve around their ability to build on their impressive performance in recent years. The scale of the AI boom is such that retaining their seismic market share across key industries is essential, and this will require a commitment to R&D, and outpacing a growing network of competitors. However, with strong innovation pipelines and commitments to sustainability, both stocks appear set to maintain strong growth rates into the future. Determining the scale of this growth is likely to be the $1 trillion question for key institutional investors.

Tech Giants Nvidia and Microsoft Face Fresh Challenges as Investment Firms Think Twice About Holding

Технологичните гиганти Nvidia и Microsoft са изправени пред нови предизвикателства, тъй като инвестиционните дружества обмислят два пъти да задържат

Techničtí giganti Nvidia a Microsoft čelí novým výzvám, protože investiční společnosti dvakrát přemýšlejí o holdingu

Tech Giants Nvidia og Microsoft står over for nye udfordringer, mens investeringsselskaber tænker to gange om at holde

Die Technologiegiganten Nvidia und Microsoft stehen vor neuen Herausforderungen, da Investmentfirmen zweimal überlegen, ob sie ihre Aktien halten sollen.

Los gigantes tecnológicos Nvidia y Microsoft enfrentan nuevos desafíos mientras las firmas de inversión piensan dos veces antes de mantener su posición

Les géants de la technologie Nvidia et Microsoft font face à de nouveaux défis alors que les sociétés d'investissement réfléchissent à deux fois avant de détenir

I giganti della tecnologia Nvidia e Microsoft affrontano nuove sfide mentre le società di investimento ci pensano due volte prima di detenere azioni

Techgiganten Nvidia en Microsoft staan voor nieuwe uitdagingen, aangezien investeringsmaatschappijen twee keer nadenken over het aanhouden van

Giganci technologiczni Nvidia i Microsoft stają przed nowymi wyzwaniami, ponieważ firmy inwestycyjne dwa razy zastanawiają się nad posiadaniem

Giganții tehnologici Nvidia și Microsoft se confruntă cu noi provocări, deoarece firmele de investiții se gândesc de două ori la holding

Technologickí giganti Nvidia a Microsoft čelia novým výzvam, keďže investičné spoločnosti dvakrát premýšľajú o holdingu

Techjättarna Nvidia och Microsoft står inför nya utmaningar när värdepappersföretag tänker två gånger på innehav

Is Artificial Intelligence Turning Human Learning a Thing of the Past?

Advancing the Detection of Ultralight Vector Dark Matter through Novel Analytical Strategies

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar 4.1 Recasting Generalised Limits onto B − L Dark Matter To recast our limits on β shown in Fig. 4 into one on the parameter of interest for this model, the gauge coupling strength gB−L, we must define four quantities. Firstly, we must make clear what the quantity A is for this experiment, which will depend on both the model and the signal of interest for this sensor. Secondly, we must choose an observation time, Tobs. Thirdly, we must elaborate on what the concrete noise profile, σ(ω), for this type of experiment is. Lastly, and least importantly following from our discussion above, we must choose a latitude for the experiment. Once these are known, Eq. (2.8) can be re-arranged for gB−L, giving us our model- and experiment-specific 90% CL limit. For B − L coupled dark photon dark matter, the relevant signal is a differential acceleration. This is given by Eq. (2.6) in the time domain, with a now concrete choice for A, Here, gB−L is the gauge coupling strength of the model, ∆ij is the differential B −L charge per nucleon between materials i and j, given by For our observation time, we take Tobs = 10T⊕ to be firmly in the regime where the three peaks can be resolved. For light cavities, which may only be able to remain coherent over the scale of hours rather than days, such a runtime is optimistic. However, since our aim here is merely to showcase how our method can be used concretely and compare to the works of Refs. [36, 38], we do not see this as an issue. Our strategy is general and can be applied to any axial sensor and vector-like DM model, and we have settled on this configuration only for the sake of argument; other sensor technologies, such a magnetically levitated sensors, do not have this issue. Moreover, multiple cavities or data-stacking techniques can be employed to mitigate this issue. Our choices for all of the above parameters except for the laser power, which we expand on below, are summarised in Table 1. These are in keeping with the choices made in Refs. [36, 38]. The choice of where to tune the laser power is critical to give us competitive limits for a wide range of dark matter masses. We have found that we can achieve excellent limits for low dark matter masses, which are of most relevance to our work, by tuning the laser power such that the back-action and shot-noise components are minimised at low We note that this choice of power tuning is different from the strategies usually employed in other studies. Typically, the laser power is tuned so that the measurement-added noise is either minimised on resonance or, as LIGO implements it, well above resonance [88]. However, we found that both of these choices detriments the limit that we can draw at low masses, increasing the background at low frequencies beyond the thermal noise floor. For these other strategies to be beneficial at both the respective frequency targets and at low frequencies, the thermal noise would have to be significantly lower than that achieved with our choice of sensor mass, quality factor, and bath temperature. In the treatment of our backgrounds, we have neglected seismic noise, which can become important at frequencies below ∼ 10 Hz. Since we require the use of two materials to measure the differential acceleration peculiar to B − L DM, we can envision constructing two sensors: one with a moveable mirror made of material i and another of material j. By subtracting their signals, we both isolate the differential acceleration signature and remove backgrounds common to both—this includes the seismic noise component [63] We show our limits in Fig. 5 for three choices of resonance frequency: 1 Hz, 10 Hz and 100 Hz. For all but the last of these frequencies, we are able to exclude new regions of the UDLM B − L parameter space, best excluded by the fifth-force satellite experiment MICROSCOPE [89] and torsion-balance experiment E¨ot-Wash [90]. This showcases the power of such sensors in searching for this DM candidate, as was first pointed out in Ref. [63]. In a single-peak analysis focusing solely on the Compton peak, our limits would be weakened by approximately a factor of 2, as can be seen from Fig. 4. This difference becomes more dramatic for sensors located closer to the equator. We note that σ(ω), though mostly slowly varying in the frequency width ∆ω = ω⊕, does exhibit a large gradient around the resonance frequency of the cavity. In the neighbourhood of this frequency, our assumption that σ(ω) does not vary greatly in the above range is incorrect, and a more careful analysis in which all three peaks take on different noise levels would have to be conducted for more representative limits. However, we do not expect that our limits would differ greatly from our calculation and, at any rate, would still smoothly join to the regimes on either side of the resonance frequency, where our assumption holds. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar 4.1 Recasting Generalised Limits onto B − L Dark Matter To recast our limits on β shown in Fig. 4 into one on the parameter of interest for this model, the gauge coupling strength gB−L, we must define four quantities. Firstly, we must make clear what the quantity A is for this experiment, which will depend on both the model and the signal of interest for this sensor. Secondly, we must choose an observation time, Tobs. Thirdly, we must elaborate on what the concrete noise profile, σ(ω), for this type of experiment is. Lastly, and least importantly following from our discussion above, we must choose a latitude for the experiment. Once these are known, Eq. (2.8) can be re-arranged for gB−L, giving us our model- and experiment-specific 90% CL limit. For B − L coupled dark photon dark matter, the relevant signal is a differential acceleration. This is given by Eq. (2.6) in the time domain, with a now concrete choice for A, Here, gB−L is the gauge coupling strength of the model, ∆ij is the differential B −L charge per nucleon between materials i and j, given by For our observation time, we take Tobs = 10T⊕ to be firmly in the regime where the three peaks can be resolved. For light cavities, which may only be able to remain coherent over the scale of hours rather than days, such a runtime is optimistic. However, since our aim here is merely to showcase how our method can be used concretely and compare to the works of Refs. [36, 38], we do not see this as an issue. Our strategy is general and can be applied to any axial sensor and vector-like DM model, and we have settled on this configuration only for the sake of argument; other sensor technologies, such a magnetically levitated sensors, do not have this issue. Moreover, multiple cavities or data-stacking techniques can be employed to mitigate this issue. Our choices for all of the above parameters except for the laser power, which we expand on below, are summarised in Table 1. These are in keeping with the choices made in Refs. [36, 38]. The choice of where to tune the laser power is critical to give us competitive limits for a wide range of dark matter masses. We have found that we can achieve excellent limits for low dark matter masses, which are of most relevance to our work, by tuning the laser power such that the back-action and shot-noise components are minimised at low We note that this choice of power tuning is different from the strategies usually employed in other studies. Typically, the laser power is tuned so that the measurement-added noise is either minimised on resonance or, as LIGO implements it, well above resonance [88]. However, we found that both of these choices detriments the limit that we can draw at low masses, increasing the background at low frequencies beyond the thermal noise floor. For these other strategies to be beneficial at both the respective frequency targets and at low frequencies, the thermal noise would have to be significantly lower than that achieved with our choice of sensor mass, quality factor, and bath temperature. In the treatment of our backgrounds, we have neglected seismic noise, which can become important at frequencies below ∼ 10 Hz. Since we require the use of two materials to measure the differential acceleration peculiar to B − L DM, we can envision constructing two sensors: one with a moveable mirror made of material i and another of material j. By subtracting their signals, we both isolate the differential acceleration signature and remove backgrounds common to both—this includes the seismic noise component [63] We show our limits in Fig. 5 for three choices of resonance frequency: 1 Hz, 10 Hz and 100 Hz. For all but the last of these frequencies, we are able to exclude new regions of the UDLM B − L parameter space, best excluded by the fifth-force satellite experiment MICROSCOPE [89] and torsion-balance experiment E¨ot-Wash [90]. This showcases the power of such sensors in searching for this DM candidate, as was first pointed out in Ref. [63]. In a single-peak analysis focusing solely on the Compton peak, our limits would be weakened by approximately a factor of 2, as can be seen from Fig. 4. This difference becomes more dramatic for sensors located closer to the equator. We note that σ(ω), though mostly slowly varying in the frequency width ∆ω = ω⊕, does exhibit a large gradient around the resonance frequency of the cavity. In the neighbourhood of this frequency, our assumption that σ(ω) does not vary greatly in the above range is incorrect, and a more careful analysis in which all three peaks take on different noise levels would have to be conducted for more representative limits. However, we do not expect that our limits would differ greatly from our calculation and, at any rate, would still smoothly join to the regimes on either side of the resonance frequency, where our assumption holds. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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