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Spin-orbit coupling plays an important role in both fundamental physics and technological applications. Precise estimation of the spin-orbit coupling is necessary for accurate designing across various physical setups such as solid state devices and quantum hardware. Here, we exploit quantum features in a 1D quantum wire for estimating the Rashba spin-orbit coupling with enhanced sensitivity beyond the capability of classical probes. The Heisenberg limited enhanced precision is achieved across a wide range of parameters and does not require fine tuning. Such advantage is directly related to the gap-closing nature of the probe across the entire relevant range of parameters. This provides clear advantage over conventional criticality-based quantum sensors in which quantum enhanced sensitivity can only be achieved through fine-tuning around the phase transition point. We have demonstrated quantum enhanced sensitivity for both single particle and interacting many-body probes. In addition to extending our results to thermal states and the multi-parameter scenario, we have provided an measurement basis to perform close to the ultimate precision., Comment: 6 pages, 5 figures. Comments are welcome

Discrete time crystals (DTC) exhibit a special non-equilibrium phase of matter in periodically driven many-body systems with spontaneous breaking of time translational symmetry. The presence of decoherence generally enhances thermalization and destroys the coherence required for the existence of DTC. In this letter, we devise a mechanism for establishing a stable DTC with period-doubling oscillations in an open quantum system that is governed by a properly tailored non-Hermitian Hamiltonian. We find a specific class of non-reciprocal couplings in our non-Hermitian dynamics which prevents thermalization through eigenstate ordering. Such choice of non-Hermitian dynamics, significantly enhances the stability of the DTC against imperfect pulses. Through a comprehensive analysis, we determine the phase diagram of the system in terms of pulse imperfection., Comment: Comments are welcome!

Language models (LMs) are widely used by an increasing number of users, underscoring the challenge of maintaining factuality across a broad range of topics. We first present VERIFY (Verification and Evidence RetrIeval for FactualitY evaluation), a pipeline to evaluate LMs' factuality in real-world user interactions. VERIFY considers the verifiability of LM-generated content and categorizes content units as supported, unsupported, or undecidable based on the retrieved evidence from the Web. Importantly, factuality judgment by VERIFY correlates better with human evaluations than existing methods. Using VERIFY, we identify "hallucination prompts" across diverse topics, i.e., those eliciting the highest rates of incorrect and inconclusive LM responses. These prompts form FactBench, a dataset of 1K prompts across 150 fine-grained topics. Our dataset captures emerging factuality challenges in real-world LM interactions and can be regularly updated with new prompts. We benchmark widely-used LMs from GPT, Gemini, and Llama3.1 family on FactBench, yielding the following key findings: (i) Proprietary models exhibit better factuality, with performance declining from Easy to Hard hallucination prompts. (ii) Llama3.1-405B-Instruct shows comparable or lower factual accuracy than Llama3.1-70B-Instruct across all evaluation methods due to its higher subjectivity that leads to more content labeled as undecidable. (iii) Gemini1.5-Pro shows a significantly higher refusal rate, with over-refusal in 25% of cases. Our code and data are publicly available at https://huggingface.co/spaces/launch/factbench., Comment: 25 pages, 10 figures

Quantum sensors based on critical many-body systems are known to exhibit enhanced sensing capability. Such enhancements typically scale algebraically with the probe size. Going beyond algebraic advantage and reaching exponential scaling has remained elusive when all the resources, such as the preparation time, are taken into account. In this work, we show that systems with first order quantum phase transitions can indeed achieve exponential scaling of sensitivity, thanks to their exponential energy gap closing. Remarkably, even after considering the preparation time using local adiabatic driving, the exponential scaling is sustained. Our results are demonstrated through comprehensive analysis of three paradigmatic models exhibiting first order phase transitions, namely Grover, $p$-spin, and biclique models. We show that this scaling survives moderate decoherence during state preparation and also can be optimally measured in experimentally available basis., Comment: 9 pages, 7 figures. Comments are welcome

Conventional formulation of quantum sensing has been mostly developed in the context of local estimation, where the unknown parameter is roughly known. In contrast, global sensing, where the prior information is incomplete and the unknown parameter is only known to lie within a broad interval, is practically more engaging but has received far less theoretical attention. Available formulations of global sensing rely on adaptive Bayesian strategies or minimizing average uncertainty. These methods either rely on challenging adaptive measurements or provide unsaturable bounds. Here, we provide an operationally motivated approach to global sensing in the frequentist picture. Our scheme yields a saturable bound on average uncertainty and allows for optimizing the measurement as well as the probe preparation simultaneously. We illustrate the implications for Gaussian single-mode sensing tasks like thermometry and phase estimation by showing that the optimal measurement indeed changes from homodyne, for local sensing, towards heterodyne, for global sensing. Depending on the task, this transformation can be gradual or sudden., Comment: 4.5 pages + 3 figs, Comments/suggestions welcome

In this paper, we propose time and frequency synchronization techniques for the uplink of multiuser OTFS (MU-OTFS) in high-mobility scenarios. We introduce a spectrally efficient and practical pilot pattern where each user utilizes a pilot with a cyclic prefix (PCP) within a shared pilot region on the delay-Doppler plane. At the receiver, a bank of filters is deployed to separate the users' signals and accurately estimate their timing offsets (TOs) and carrier frequency offsets (CFOs). Our technique employs a threshold-based approach that provides precise TO estimates. Our proposed CFO estimation technique reduces the multi-dimensional maximum likelihood (ML) search problem into multiple one-dimensional search problems. Furthermore, we apply the Chebyshev polynomials of the first kind basis expansion model (CPF-BEM) to effectively handle the time-variations of the channel in obtaining the CFO estimates for all the users. Finally, we numerically investigate the error performance of our proposed synchronization technique in high mobility scenarios for the MU-OTFS uplink. Our simulation results confirm the efficacy of the proposed technique in estimating the TOs and CFOs which also leads to an improved channel estimation performance.

In this article, we investigate symmetric designs admitting a flag-transitive and point-primitive affine automorphism group. We prove that if an automorphism group $G$ of a symmetric $(v,k,\lambda)$ design with $\lambda$ prime is point-primitive of affine type, then $G=2^{6}{:}\mathrm{S}_{6}$ and $(v,k,\lambda)=(16,6,2)$, or $G$ is a subgroup of $\mathrm{A\Gamma L}_{1}(q)$ for some odd prime power $q$. In conclusion, we present a classification of flag-transitive and point-primitive symmetric designs with $\lambda$ prime, which says that such an incidence structure is a projective space $\mathrm{PG}(n,q)$, it has parameter set $(15,7,3)$, $(7, 4, 2)$, $(11, 5, 2)$, $(11, 6, 2)$, $(16,6,2)$ or $(45, 12, 3)$, or $v=p^d$ where $p$ is an odd prime and the automorphism group is a subgroup of $\mathrm{A\Gamma L}_{1}(q)$.

Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents significant ethical challenges related to bias and transparency. AI applications are hugely impacted by biases, presenting inconsistent and unreliable findings, leading to significant costs and consequences, highlighting and perpetuating inequalities and unequal access to resources. Hence, developing safe, reliable, ethical, and Trustworthy AI systems is essential. Our team of researchers working with Trustworthy and Responsible AI, part of the Transdisciplinary Scholarship Initiative within the University of Calgary, conducts research on Trustworthy and Responsible AI, including fairness, bias mitigation, reproducibility, generalization, interpretability, and authenticity. In this paper, we review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias. We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making, as well as guidelines to foster Responsible and Trustworthy AI models., Comment: 44 pages, 2 figures

The main power of quantum sensors is achieved when the probe is composed of several particles. In this situation, quantum features such as entanglement contribute in enhancing the precision of quantum sensors beyond the capacity of classical sensors. Originally, quantum sensing was formulated for non-interacting particles which are prepared in a special form of maximally entangled states. These probes are extremely sensitive to decoherence and any interaction between particles is detrimental to their performance. An alternative framework for quantum sensing has been developed exploiting quantum many-body systems, where the interaction between particles plays a crucial role. In this review, we investigate different aspects of the latter approach for quantum metrology and sensing. Many-body probes have been used in both equilibrium and non-equilibrium scenarios. Quantum criticality has been identified as a resource for achieving quantum enhanced sensitivity in both scenarios. In equilibrium, various types of criticalities, such as first order, second order, topological, and localization phase transitions have been exploited for sensing purposes. In non-equilibrium scenarios, quantum enhanced sensitivity has been discovered for Floquet, dissipative, and time crystal phase transitions. While each type of these criticalities has its own characteristics, the presence of one feature is crucial for achieving quantum enhanced sensitivity: the energy/quasi-energy gap closing. In non-equilibrium quantum sensing, time is another parameter which can affect the sensitivity of the probe. Typically, the sensitivity enhances as the probe evolves in time. In general, a more complete understanding of resources for non-equilibrium quantum sensors is now rapidly evolving. In this review, we provide an overview of recent progress in quantum metrology and sensing using many-body systems., Comment: Review article. Newly added Section in v2: Optimal control and machine learning strategies for quantum sensing. Several new references added. Feedback is highly appreciated!

Green hydrogen is a promising solution within carbon free energy systems with Sub-Saharan Africa being a possibly well-suited candidate for its production. However, green hydrogen in Sub-Saharan Africa is not yet investigated in detail. This work determines the green hydrogen cost-potential for green hydrogen within this region. Therefore, a potential analysis for PV, wind and hydropower, groundwater analysis, and energy systems optimization are conducted. The results are evaluated under local socio-economic factors. Results show that hydrogen costs start at 1.6 EUR/kg in Mauritania with a total potential of ~259 TWh/a under 2 EUR/kg in 2050. Two third of the regions experience groundwater limitations and need desalination at surplus costs of ~1% of hydrogen costs. Socio-economic analysis show, that green hydrogen deployment can be hindered along the Upper Guinea Coast and the African Great Lakes, driven by limited energy access, low labor costs in West Africa, and high labor potential in other regions.

Prompt engineering is an iterative procedure often requiring extensive manual effort to formulate suitable instructions for effectively directing large language models (LLMs) in specific tasks. Incorporating few-shot examples is a vital and effective approach to providing LLMs with precise instructions, leading to improved LLM performance. Nonetheless, identifying the most informative demonstrations for LLMs is labor-intensive, frequently entailing sifting through an extensive search space. In this demonstration, we showcase a human-in-the-loop tool called APE (Active Prompt Engineering) designed for refining prompts through active learning. Drawing inspiration from active learning, APE iteratively selects the most ambiguous examples for human feedback, which will be transformed into few-shot examples within the prompt. The demo recording can be found with the submission or be viewed at https://youtu.be/OwQ6MQx53-Y., Comment: 3 pages, Proceedings of the Fifth Workshop on Data Science with Human-in-the-Loop (DaSH 2024)

Entanglement has been extensively used to characterize the structure of strongly correlated many-body systems. Most of these analyses focus on either spatial properties of entanglement or its temporal behavior. Negativity, as an entanglement measure, quantifies entanglement between different non-complementary blocks of a many-body system. Here, we consider a combined spatial-temporal analysis of entanglement negativity in a strongly correlated many-body system to characterize complex formation of correlations through non-equilibrium dynamics of such systems. A bond defect is introduced through a local quench at one of the boundaries of a uniform Heisenberg spin chain. Using negativity and entanglement entropy, computed by the time-dependent density matrix renormalization group, we analyze the extension of entanglement in the model as a function of time. We find that an entanglement cloud is formed, detached from the boundary spin and composed of spins with which it is highly entangled. The cloud travels ballistically in the chain until it reaches the other end where it reflects back and the cycle repeats. The revival dynamics exhibits an intriguing contraction (expansion) of the cloud as it moves away from (towards) the boundary spin.

Vision-Language Models (VLMs) have witnessed a surge in both research and real-world applications. However, as they are becoming increasingly prevalent, ensuring their robustness against adversarial attacks is paramount. This work systematically investigates the impact of model design choices on the adversarial robustness of VLMs against image-based attacks. Additionally, we introduce novel, cost-effective approaches to enhance robustness through prompt formatting. By rephrasing questions and suggesting potential adversarial perturbations, we demonstrate substantial improvements in model robustness against strong image-based attacks such as Auto-PGD. Our findings provide important guidelines for developing more robust VLMs, particularly for deployment in safety-critical environments.

For fast-tracking climate change response, green hydrogen is key for achieving greenhouse gas neutral energy systems. Especially Sub-Saharan Africa can benefit from it enabling an increased access to clean energy through utilizing its beneficial conditions for renewable energies. However, developing green hydrogen strategies for Sub-Saharan Africa requires highly detailed and consistent information ranging from technical, environmental, economic, and social dimensions, which is currently lacking in literature. Therefore, this paper provides a comprehensive novel approach embedding the required range of disciplines to analyze green hydrogen cost-potentials in Sub-Saharan Africa. This approach stretches from a dedicated land eligibility based on local preferences, a location specific renewable energy simulation, locally derived sustainable groundwater limitations under climate change, an optimization of local hydrogen energy systems, and a socio-economic indicator-based impact analysis. The capability of the approach is shown for case study regions in Sub-Saharan Africa highlighting the need for a unified, interdisciplinary approach.

We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when executed on real quantum hardware. This encompasses the development of algorithms for quantum circuit compilation and qubit mapping, crucial components for achieving optimal performance on quantum processors. In addition to the core framework, we introduce QuPack, a meticulously crafted quantum computing acceleration engine. QuPack significantly accelerates the simulation speed of MindSpore Quantum, particularly in variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and tensor network simulations, providing astonishing speed. This combination of cutting-edge technologies empowers researchers and practitioners to explore the frontiers of quantum computing with unprecedented efficiency and performance.

Calibrating language models (LMs) aligns their generation confidence with the actual likelihood of answer correctness, which can inform users about LMs' reliability and mitigate hallucinated content. However, prior calibration methods, such as self-consistency-based and logit-based approaches, are either limited in inference-time efficiency or fall short of providing informative signals. Moreover, simply filtering out low-confidence responses reduces the LM's helpfulness when the answers are correct. Therefore, effectively using calibration techniques to enhance an LM's factuality remains an unsolved challenge. In this paper, we first propose an activation-based calibration method, ActCab, which trains a linear layer on top of the LM's last-layer activations that can better capture the representations of knowledge. Built on top of ActCab, we further propose CoDec, a confidence-guided decoding strategy to elicit truthful answers with high confidence from LMs. By evaluating on five popular QA benchmarks, ActCab achieves superior calibration performance than all competitive baselines, e.g., by reducing the average expected calibration error (ECE) score by up to 39%. Further experiments on CoDec show consistent improvements in several LMs' factuality on challenging QA datasets, such as TruthfulQA, highlighting the value of confidence signals in enhancing factuality.

We investigate the sensing capacity of non-equilibrium dynamics in quantum systems exhibiting Bloch oscillations. By focusing on the resource efficiency of the probe, quantified by quantum Fisher information, we find different scaling behaviors in two different phases, namely localized and extended. Our results provide a quantitative ansatz for quantum Fisher information in terms of time, probe size, and the number of excitations. In the long-time regime, the quantum Fisher information is a quadratic function of time, touching the Heisenberg limit. The system size scaling drastically depends on the phase changing from super-Heisenberg scaling in the extended phase to size-independent behavior in the localized phase. Furthermore, increasing the number of excitations always enhances the precision of the probe, although, in the interacting systems the enhancement becomes less eminent than the non-interacting probes. This is due to the induced localization by increasing the interaction between the excitations.

Ensuring that online discussions are civil and productive is a major challenge for social media platforms. Such platforms usually rely both on users and on automated detection tools to flag inappropriate arguments of other users, which moderators then review. However, this kind of post-hoc moderation is expensive and time-consuming, and moderators are often overwhelmed by the amount and severity of flagged content. Instead, a promising alternative is to prevent negative behavior during content creation. This paper studies how inappropriate language in arguments can be computationally mitigated. We propose a reinforcement learning-based rewriting approach that balances content preservation and appropriateness based on existing classifiers, prompting an instruction-finetuned large language model (LLM) as our initial policy. Unlike related style transfer tasks, rewriting inappropriate arguments allows deleting and adding content permanently. It is therefore tackled on document level rather than sentence level. We evaluate different weighting schemes for the reward function in both absolute and relative human assessment studies. Systematic experiments on non-parallel data provide evidence that our approach can mitigate the inappropriateness of arguments while largely preserving their content. It significantly outperforms competitive baselines, including few-shot learning, prompting, and humans.

Current quantum simulators are primarily qubit-based, making them naturally suitable for simulating 2-level quantum systems. However, many systems in nature are inherently $d$-level, including higher spins, bosons, vibrational modes, and itinerant electrons. To simulate $d$-level systems on qubit-based quantum simulators, an encoding method is required to map the $d$-level system onto a qubit basis. Such mapping may introduce illegitimate states in the Hilbert space which makes the simulation more sophisticated. In this paper, we develop a systematic method to address the illegitimate states. In addition, we compare two different mappings, namely binary and symmetry encoding methods, and compare their performance through variational simulation of the ground state and time evolution of various many-body systems. While binary encoding is very efficient with respect to the number of qubits it cannot easily incorporate the symmetries of the original Hamiltonian in its circuit design. On the other hand, the symmetry encoding facilitates the implementation of symmetries in the circuit design, though it comes with an overhead for the number of qubits. Our analysis shows that the symmetry encoding significantly outperforms the binary encoding, despite requiring extra qubits. Their advantage is indicated by requiring fewer two-qubit gates, converging faster, and being far more resilient to Barren plateaus. We have performed variational ground state simulations of spin-1, spin-3/2, and bosonic systems as well as variational time evolution of spin-1 systems. Our proposal can be implemented on existing quantum simulators and its potential is extendable to a broad class of physical models., Comment: 15 pages, 8 figures, 2 tables

Discrete time crystals are a special phase of matter in which time translational symmetry is broken through a periodic driving pulse. Here, we first propose and characterize an effective mechanism to generate a stable discrete time crystal phase in a disorder-free many-body system with indefinite persistent oscillations even in finite-size systems. Then we explore the sensing capability of this system to measure the spin exchange coupling. The results show strong quantum-enhanced sensitivity throughout the time crystal phase. As the spin exchange coupling varies, the system goes through a sharp phase transition and enters a non-time crystal phase in which the performance of the probe considerably decreases. We characterize this phase transition as a second-order type and determine its critical properties through a comprehensive finite-size scaling analysis. The performance of our probe is independent of the initial states and may even benefit from imperfections in the driving pulse., Comment: Comments are welcome!

Large language models (LLMs) can generate long-form and coherent text, yet they often hallucinate facts, which undermines their reliability. To mitigate this issue, inference-time methods steer LLM representations toward the "truthful directions" previously learned for truth elicitation. However, applying these truthful directions with the same intensity fails to generalize across different query contexts. We propose LITO, a Learnable Intervention method for Truthfulness Optimization that automatically identifies the optimal intervention intensity tailored to each specific context. LITO explores a sequence of model generations based on increasing levels of intervention intensities. It selects the most accurate response or refuses to answer when the predictions are highly uncertain. Experiments on multiple LLMs and question-answering datasets demonstrate that LITO improves truthfulness while preserving task accuracy. The adaptive nature of LITO counters the limitations of one-size-fits-all intervention methods, maximizing truthfulness by reflecting the model's internal knowledge only when it is confident. Our code is available at https://github.com/launchnlp/LITO., Comment: ACL 2024 Findings (Long paper)

Stark systems in which a linear gradient field is applied across a many-body system have recently been harnessed for quantum sensing. Here, we explore sensing capacity of Stark models, in both single-particle and many-body interacting systems, for estimating the strength of both linear and nonlinear Stark fields. The problem naturally lies in the context of multi-parameter estimation. We determine the phase diagram of the system in terms of both linear and nonlinear gradient fields showing how the extended phase turns into a localized one as the Stark fields increase. We also characterize the properties of the phase transition, including critical exponents, through a comprehesive finite-size scaling analysis. Interestingly, our results show that the estimation of both the linear and the nonlinear fields can achieve super-Heisenberg scaling. In fact, the scaling exponent of the sensing precision is directly proportional to the nonlinearity exponent which shows that nonlinearity enhances the estimation precision. Finally, we show that even after considering the cost of the preparation time the sensing precision still reveals super-Heisenberg scaling., Comment: 10 pages, 6 figures. Comments are welcome!

CyberShake (CS) is a high-performance computing workflow for Probabilistic Seismic Hazard Assessment (PSHA) developed by the Statewide California Earthquake Center. Here, we employ CS to generate a set of 2103 fault ruptures and simulate the corresponding two horizontal velocity components time histories of ground motion (GM) on a 5-km grid of 625 stations in Southwest Iceland (SI). The ruptures were defined on a new synthetic time-independent 500-year catalog consisting of 223 hypothetical finite-fault sources of 5-7, generated using a new physics-based bookshelf fault system model in the SI transform zone. This fault system model and rupture realizations enable the CS time-independent physics-based approach to PSHA in the region. The study aims to migrate CS to SI and validate its kinematic fault rupture, anelastic wave propagation and ground motion simulations. Toward this goal, we use CS to generate multiple finite-fault rupture variations for each hypothetical fault. CS exploits seismic reciprocity for wave propagation by computing Strain Green Tensors along fault planes, which in turn are convolved with rupture models to generate GM seismograms. For each GM recording station, every adjoint simulation uses a 0-1 Hz Gaussian point source polarized along one horizontal grid direction. Comparison of the results in the form of rotation-invariant synthetic pseudo-acceleration spectral response values at 2, 3 and 5 sec periods are in very good agreement with the Icelandic strong-motion dataset, and a suite of new empirical Bayesian ground motion prediction equations (GMPEs). The vast majority of the CS results fall within one standard deviation of the mean GMPE predictions, previously estimated for the area. Importantly, at large magnitudes for which no data exists in Iceland, the CS dataset may play an important role in constraining the GMPEs for future applications., Comment: 23 pages and 7 figures

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT; various organs such as the brain, prostate, kidney, and pancreas; and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks., Comment: initial technical report

Measuring the temperature of a quantum system is an essential task in almost all aspects of quantum technologies. Theoretically, an optimal strategy for thermometry requires measuring energy which demands full accessibility over the entire system as well as complex entangled measurement basis. In this paper, we take a different approach and show that single qubit sequential measurements in the computational basis not only allows precise thermometry of a many-body system but may also achieve precision beyond the theoretical bound, avoiding demanding energy measurements at equilibrium. To obtain such precision, the time between the two subsequent measurements should be smaller than the thermalization time so that the probe never thermalizes. Therefore, the non-equilibrium dynamics of the system continuously imprint information about temperature in the state of the probe. This allows the sequential measurement scheme to reach precision beyond the accuracy achievable by complex energy measurements on equilibrium probes., Comment: 9 pages main text, 8 figures. Feedback is welcome!

This paper compares orthogonal time frequency space (OTFS) modulation and single-carrier frequency division multiple access (SC-FDMA). It shows that these are equivalent except for a set of linear phase shifts, applied to the transmit/receive data symbols, which can be absorbed into the channel. Through mathematical and numerical analysis, it is confirmed that SC-FDMA is in fact a delay-Doppler domain multiplexing technique that can achieve the same performance gains as those of OTFS in time-varying wireless environments. This is a promising result as SC-FDMA is already a part of the current wireless standards. The derivations in this paper also shed light on the time-frequency resources used by the delay-Doppler domain data symbols with the fine granularity of delay and Doppler spacings. While comparing the detection performance of the two waveforms, a timing offset (TO) estimation technique with orders of magnitude higher accuracy than the existing solutions in the literature is proposed. From multiple access viewpoint, the underlying tile structures in the time-frequency domain for OTFS and SC-FDMA are discussed. Finally, multiuser input-output relationships for both waveforms in the uplink are derived.