2024-12-27 16:00:12
For technology startups, the rush to scale often overtakes the imperative of sustainable growth, leading to a perilous imbalance where hype overshadows substance.
\ This issue is not merely theoretical; the stories of companies like Theranos and WeWork illustrate the real-world consequences of building empires on unstable foundations. These narratives, while different in detail, share a common theme: the peril of prioritizing valuation over value, hype over substance.
\ Such cases underscore the challenging role of chief marketing officers (CMOs), who are often caught between the drive for rapid growth and the need for honest representation.
\ THE MIRAGE OF THE NEXT BIG THING
The tech startup ecosystem thrives on innovation and disruption, but it’s also rife with stories of companies that promised more than they could deliver. Theranos, for instance, became infamous for its false claims about revolutionizing blood testing, which were foundational to its fundraising and valuation reaching billions of dollars before its dramatic collapse. Similarly, WeWork, once valued at $47 billion, faced a drastic downturn when its overblown growth projections and erratic management practices could no longer sustain investor confidence.
\ These examples highlight the dangers of startups that scale based on misrepresentations, but these are only the big names that made the news. What about the other thousands of smaller start-ups taking in customer and investor money while still diluting their marketing with questionable representations. Not only do such practices lead to significant financial and reputational damage, but they also erode trust in the broader tech industry.
\ THE CMO’S CONUNDRUM
For CMOs in these environments, marketing strategy must navigate the fine line between promoting potential and misrepresenting capabilities. The role involves casting a product in the best light, but when the capabilities of the product are fundamentally misrepresented—either through exaggeration or by omission—the marketing narrative can quickly cross into unethical territory.
\ Navigating the precarious balance between aggressive marketing and ethical practices, CMOs in high-growth tech startups face significant challenges, often intensified by pressures from CEOs and other executives who might prioritize rapid growth over accuracy. A CMO must champion transparency, ensuring that all marketing materials accurately reflect what the product can and cannot do. This is critical not only for maintaining customer trust, but also for setting realistic expectations that align with actual product capabilities.
\ Furthermore, it’s essential for CMOs to ensure that marketing strategies remain customer-centric. This approach involves aligning marketing messages with true customer experiences and benefits, rather than overblown promises that can lead to customer dissatisfaction and damage to the company’s reputation. Compliance with regulatory standards is another critical area where CMOs must hold firm, even when pushed to bend the rules for the sake of hype. Adhering to advertising laws and guidelines not only avoids legal pitfalls, but also supports a reputation of reliability and trustworthiness.
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\ CMOs should also implement robust internal checks and balances to verify the accuracy of marketing claims before they go public. This ‘truth squad’ approach helps maintain a culture of integrity within the company, ensuring that the marketing department does not become a source of hyperbole that could later backfire.
\ Lastly, it’s crucial to embrace a long-term vision over short-term gains. This perspective helps CMOs resist the pressure to deliver immediate results that may rely on exaggerated claims, focusing instead on building sustainable growth and long-term brand loyalty.
\ MARKETING WITH ETHICS
The role of a CMO in a high-growth tech startup involves not just promoting the product, but preserving the integrity of the marketing message. While it may not make every start-up CEO happy to hear, this responsibility is paramount in an era where consumers are more informed and skeptical than ever before.
\ By focusing on ethical marketing and sustainable growth, startups can avoid the pitfalls of their over-hyped predecessors and build lasting trust and value. Ultimately, for the tech industry to continue thriving, it must ensure that its foundations are as robust as the visions it projects to the world.
2024-12-27 15:10:50
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2024-12-27 06:00:14
Protocol
Security Analysis
\ A. Codes
B. Proofs
Currently, many on-chain options trading platforms are available in the market. They price the options using an Automatic Market Maker (AMM). Lyra [10] stands as the preeminent decentralized options trading platform, commanding around a third of the market’s TVL, and employs an AMM with a Black76 [4] pricing model. However, its operations hinge on external data feeds from oracle, such as spot prices and implied volatility. Hegic [44] decentralizes the writers’ risk and employs a fixed pricing rate based on option expiry date and target prices, which leads to less accurate pricing.
\ In traditional markets, the price of options is determined by supply and demand. Devising an effective pricing model for options by AMM faces challenges due to the lack of accurate supply and demand modeling. Therefore, an order-book based decentralized exchange shows up. Aevo [2] is a high-performance, order-book based decentralized exchange, which closely resembles the traditional options market. However, its current implementation employs an off-chain orderbook coupled with on-chain settlement, which introduces a higher degree of centralized risk into the protocol. Opyn [30] provides users with the ability to sell European options by minting ERC20 tokens as the option. These tokens can be destroyed to exercise rights or transacted in the market. However, the system faces challenges due to high gas fees on Ethereum and a lack of necessary liquidity for exchanges. All of above on-chain protocols lack universality, most [2, 10, 30, 44] currently only support ETH and BTC options trading and lack the flexibility of customized pricing to meet the needs of the traders
\ A Hashed Timelock Contract (HTLC) can address the aforementioned issues by enabling two parties to create option contracts across two chains. These contracts lock assets, agreed upon by both parties, on two chains at a predetermined price. HTLCs [23, 29, 48] were originally designed for cross-chain atomic swaps. Subsequently, Han et al. [14] highlighted the optionality and fairness aspects for one party, demonstrating that an atomic crosschain swap is equivalent to a premium-free American call option. They estimate premiums with Cox-Ross-Rubinstein option pricing model [8]. They addresses the unfairness by incorporating a premium mechanism. In [47], the authors define a sore loser attack in cross-chain swaps and let participants escrow assets along with a negotiated option premium, which acts as compensation. Nadahalli et al. [26] separate the premium protocol from the collateral protocol, employs upfront communication of off-chain unspent transaction outputs as the option premium and collateral. [21, 42] introduce cross-chain atomic options, incorporating concepts such as the holder’s late margin deposit and early cancellation of the option. In [12], the authors introduce transferability of options. However, their approach requires long transfer times and does not support concurrent trading involving multiple buyers, which may lead to phantom bid attacks. An adversary can create multiple fake buyers who offer higher prices but fail to complete the transfer. Consequently, the option holder is unable to sell their position
\ None of these protocols eliminate the holder’s collateral in crosschain options. To eliminate the holder’s upfront collateral requirement, cross-chain transaction confirmation can be adopted to verify the collateral deposition on one chain when the option is exercised. This approach can employ cross-chain bridges. Some cross-chain bridges rely on external verification and introduces a trusted third party to facilitate message transmission. This approach is vulnerable to many attacks [40, 50], such as rug pulls [18], code vulnerabilities [28] and private key leakage [24]. Some bridges employ native verification and use light clients on both chains to verify proofs. This method requires complex smart contracts and incurs high verification and storage costs [9, 39, 46, 49]. The diversity and heterogeneity of blockchains significantly increase the time and cost of implementing a light client for each chain. An alternative is using Trusted Execution Environments (TEE) for cross-chain transactions [3, 41]. Those solutions are susceptible to both software and hardware vulnerabilities, including side-channel attacks, which present significant security risks [7, 20, 25, 36, 43].
\ Our proposed approach provides an efficient cross-chain option protocol by combining HTLC logic with a signature scheme. This combination facilitates the transfer of positions and replacement of hashlocks in option contracts. Our approach eliminates the need for option holders to provide upfront collateral. Instead of relying on cross-chain bridges, we achieve this through a distributed protocol design bolstered by economic incentives.
\
:::info Authors:
(1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]).
:::
:::info This paper is available on arxiv under CC BY 4.0 license.
:::
\
2024-12-27 05:30:17
Protocol
Security Analysis
\ A. Codes
B. Proofs
Implementation. We implemented, tested, and evaluated our proposed protocol, Efficient Cross-Chain Options without Upfront Holder Collateral, in Section 4.2.1. This was conducted within a simulated environment using the Ethereum Virtual Machine (EVM) (Remix VM Cancun) and the Solidity Compiler version 0.8.22. We employ the same signature algorithm as Ethereum, utilizing the secp256k1 curve and the Elliptic Curve Digital Signature Algorithm (ECDSA) [17]. Since EVM does not support direct on-chain verification of a public-private key pair, we implement the proof of the private key 𝑠𝑘 by signing a specific message with it. The corresponding codes are provided in Appendix A.
\ Expected Transfer Time Evaluation. We compared our work with that of Engel and Xue [12]. Assuming that the probability of an option being transferred and ultimately finalized within the current network is 𝑝, the total number of transfers is 𝑋. Then, in their protocol, 𝑋 follows a geometric distribution, i.e. 𝑋 ∼ 𝐺(𝑝). The relationship between the expected successful transfer time and the successful transfer probability of each phase is illustrated in Figure 3, where mutate lock phase in Engel and Xue’s protocol to corresponds to the reveal phase in our protocol. When a large number of malicious nodes exist in the current network, say, the finalization probability is 10%, the duration of the mutate lock phase and the consistency phase in their protocol becomes significantly prolonged, reaching 45Δ, which is approximately equivalent to 2 days in Bitcoin. By initiating the replace phase earlier and consolidating the mutate and consistency phases, we significantly reduce the duration of these phases.
\ Gas Consumption Evaluation. Figure 4 lists the gas consumption for contract deployment, option operations, and gas consumption in different phases, where gas price is 4.131 Gwei and ETH price is $2274.87 (Sep 7, 2024). We calculate the maximum gas used for each operation. In the transfer failure case, we only calculate the gas consumption of conforming parties.
\ Notably, as shown in Figure 4a, compared to Engel and Xue’s protocol, the gas consumption for the holder transfer process has significantly decreased from 714,867 to 510,857 gas (a reduction of approximately 28.5% for successful transfers). For failed transfers, the gas consumption decreases from 330,350 gas to 248,388 gas (a reduction of approximately 3.4%). The gas consumption of the transferring writer also decreases to a similar level. The gas consumption for exercising an option increases from 96,916 to 145,337, while the gas consumption for abandonment decreased. This is because, during an exercise, Alice needs to deposit funds and Bob must fulfill the request by revealing the exercise secret. In contrast, for an abandonment, Bob only needs to perform a refund operation.
\ Figure 4b illustrates the gas consumption of a successful transfer across different phases. In our protocol, the reveal phase only requires the seller to reveal a signature in one contract, significantly reducing gas consumption compared to the mutate and replace/revert phases, lowering the gas for the holder and the writer to 123,158 and 123,435 gas, respectively. However, gas usage in the consistency phase is higher than that of their protocol, as we verify signatures in both contracts to ensure consistency.
\ The gas consumption for contract deployment in our protocol is generally higher compared to Engel and Xue’s protocol due to additional security measures and DAPS support. For instance, the deployment costs for 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝐴 and 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝐵 in our protocol are 2,549,610 and 2,220,156 gas, respectively. Nonetheless, this is acceptable, as deploying a transferable HTLC contract in Engle and Xue’s protocol consumes around 2.0M gas, while our protocol adds more secure operations and reduces the cost of option transfer.
\
\
:::info Authors:
(1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]).
:::
:::info This paper is available on arxiv under CC BY 4.0 license.
:::
\
2024-12-27 05:00:17
Protocol
Security Analysis
\ A. Codes
B. Proofs
In addition to the properties of the protocol during the option transfer process, here we explore the properties of option contracts.
\ • Option correctness: If both the holder and writer are conforming, either the exercise does not occur, the holder does not lose their collateral and the writer does not lose their collateral and guarantee; or upon completion of the exercise, they will each receive the other’s collateral, and the writer will reclaim their guarantee.
\ • Exercisablity: During the transfer of the writer position, the option holder can exercise the option without experiencing any delays or obstructions.
\ • Failure compensation: If the holder initiates the exercise before expiration, he will either successfully exercise the option or receive the pre-agreed compensation guarantee.
\ Theorem 6. Protocol 4.2 satisfies option correctness: If both the Alice and Bob are conforming, then if Alice does not exercise the right, Alice doesn’t lose the 𝐴𝑠𝑠𝑒𝑡𝐴 and Bob doesn’t lose the 𝐴𝑠𝑠𝑒𝑡𝐺 and 𝐴𝑠𝑠𝑒𝑡𝐵; or if Alice exercise the right, then Alice will receive 𝐴𝑠𝑠𝑒𝑡𝐵 and Bob will receive 𝐴𝑠𝑠𝑒𝑡𝐴 and 𝐴𝑠𝑠𝑒𝑡𝐺 .
\ Theorem 7. Protocol 4.2 satisfies exercisablity: During the transfer from Bob to Dave, the option remains active, allowing Alice to exercise the option without any delays.
\ Theorem 8. Protocol 4.2 satisfies failure compensation: Before expiration, Alice can exercise the option successfully, or if the exercise fails, she is compensated with the guarantee deposited by Bob.
\ The proofs are included in the Appendix Section B.2.
\
:::info Authors:
(1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]).
:::
:::info This paper is available on arxiv under CC BY 4.0 license.
:::
\
2024-12-27 05:00:13
2. Preliminaries and 2.1. Blind deconvolution
2.2. Quadratic neural networks
3.1. Time domain quadratic convolutional filter
3.2. Superiority of cyclic features extraction by QCNN
3.3. Frequency domain linear filter with envelope spectrum objective function
3.4. Integral optimization with uncertainty-aware weighing scheme
4. Computational experiments
4.1. Experimental configurations
4.3. Case study 2: JNU dataset
4.4. Case study 3: HIT dataset
5. Computational experiments
5.2. Classification results on various noise conditions
5.3. Employing ClassBD to deep learning classifiers
5.4. Employing ClassBD to machine learning classifiers
5.5. Feature extraction ability of quadratic and conventional networks
5.6. Comparison of ClassBD filters
Previously, we have theoretically demonstrated that quadratic networks possess superior cyclostationary feature extraction ability to conventional networks. It is also necessary to validate the performance in practice. Therefore, we construct two time-domain filters using quadratic convolutional layers and conventional convolutional layers with an identical structure and then evaluate their feature-extraction performance on the JNU dataset subjected to -10 dB noise.
\ The signals are analyzed using the Fast-SC method [84]. The results, as depicted in Figure 8, clearly demonstrate that the quadratic network outperforms in terms of feature extraction capability. The bright lines in the spectral coherence, highlight the quadratic network can extract cyclic frequency across high and low frequency bands. Despite the severe attenuation of the signal amplitude due to the noise, the quadratic network effectively recovers the cyclic frequency of the signal. Remarkably, the amplitude of the initial few cyclic frequencies is even higher than that of the raw signal.
\
\
:::info Authors:
(1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China;
(3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China;
(4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China.
:::
\
:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
:::
\
