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The Majorana fermion offers fascinating possibilities such as non-Abelian statistics and non-local robust qubits, and hunting it is one of the most important topics in current condensed matter physics. Most of the efforts have been focused on the Majorana bound state at zero energy in terms of scanning tunneling spectroscopy searching for the quantized conductance. On the other hand, a chiral Majorana edge channel appears at the surface of a three-dimensional topological insulator when engineering an interface between proximity-induced superconductivity and ferromagnetism. Recent advances in microwave spectroscopy of topological edge states open a new avenue for observing signatures of such Majorana edge states through the local optical conductivity. As a guide to future experiments, we show how the local optical conductivity and density of states present distinct qualitative features depending on the symmetry of the superconductivity, that can be tuned via the magnetization and temperature. In particular, the presence of the Majorana edge state leads to a characteristic non-monotonic temperature dependence achieved by tuning the magnetization., Comment: Main text: 9 pages, 5 figures. Supplemental Information: 4 pages, 3 figures

We explore the potential primordial connection between the black hole merger events detected by LIGO and the nano-Hz stochastic gravitational wave background observed by pulsar timing arrays. We propose an innovative mechanism for the formation of primordial black holes, suggesting that the Poisson fluctuations within the domain wall network can give rise to horizon-sized overdense regions. Our results indicate a plausible common origin for gravitational wave observations in two different frequency bands, potentially linked to the annihilation of the domain wall network at the QCD scale, while accounting for the accretion effects on primordial black holes. Furthermore, we demonstrate that the bias potential induced by the QCD instanton effect may naturally facilitate the annihilation of the domain wall network during the QCD phase transition. Additionally, our scenario can yield the correct axion dark matter relic abundance, particularly if realized within the clockwork axion framework., Comment: 6 pages, 2 figures, 1 table

Domain walls are topological defects produced by the spontaneous symmetry-breaking of discrete symmetry during cosmological phase transitions. The horizon-size domain wall can significantly contribute to the energy density in the late-evolution stage. We propose that the density perturbations from the fluctuations in the number density of the horizon-size domain wall could collapse to form primordial black holes. This mechanism becomes effective when the domain wall energy density ratio to that of the radiation reaches about 0.1 in the radiation-dominated universe. We find that models with $Z_2$ symmetry are excluded for interpreting pulsar timing array observations on the nano-Hz gravitational wave background since this model's domain wall number density fluctuations could lead to an overabundance of the primordial black holes. Moreover, models with $N\sim 10$ domain walls also suffer strong constraints from the overabundance of primordial black holes., Comment: 39 pages, 11 figures, 4 tables

In recent years, we have observed a rapid advancement in speech language models (SpeechLLMs), catching up with humans' listening and reasoning abilities. SpeechLLMs have demonstrated impressive spoken dialog question-answering (SQA) performance in benchmarks like Gaokao, the English listening test of the college entrance exam in China, which seemingly requires understanding both the spoken content and voice characteristics of speakers in a conversation. However, after carefully examining Gaokao's questions, we find the correct answers to many questions can be inferred from the conversation transcript alone, i.e.\ without speaker segmentation and identification. Our evaluation of state-of-the-art models Qwen-Audio and WavLLM on both Gaokao and our proposed "What Do You Like?" dataset shows a significantly higher accuracy in these context-based questions than in identity-critical questions, which can only be answered reliably with correct speaker identification. The results and analysis suggest that when solving SQA, the current SpeechLLMs exhibit limited speaker awareness from the audio and behave similarly to an LLM reasoning from the conversation transcription without sound. We propose that tasks focused on identity-critical questions could offer a more accurate evaluation framework of SpeechLLMs in SQA., Comment: Accepted to IEEE SLT 2024

The detection of gravitational waves has ushered in a new era of observing the universe. Quantum resource advantages offer significant enhancements to the sensitivity of gravitational wave observatories. While squeezed states for ground-based gravitational wave detection have received marked attention, the generation of squeezed states suitable for mid-to-low-frequency detection has remained unexplored. To address the gap in squeezed state optical fields at ultra-low frequencies, we report on the first direct observation of a squeezed vacuum field until Fourier frequency of 4 millihertz with the quantum noise reduction of up to 8 dB, by the employment of a multiple noise suppression scheme. Our work provides quantum resources for future gravitational wave observatories, facilitating the development of quantum precision measurement., Comment: 17 pages,4 figures,1 table and 50 Refs. To be published in Light: Science & Applications

Teams can outperform individuals; could adding AI teammates further bolster performance of teams solving problems collaboratively? Collaborative problem solving (CPS) research commonly studies teams with two agents (human-human or human-AI), but team research literature finds that, for complex tasks, larger teams are more effective. Progress in studying collaboration with more than two agents, through textual records of team interactions, is hindered by a major data challenge: available CPS corpora are predominantly dyadic, and adapting pre-existing CPS tasks to more agents is non-trivial. We address this data challenge by developing a CPS task generator, CPS-TaskForge, that can produce environments for studying CPS under a wide array of conditions, and releasing a CPS task design checklist grounded in the theoretical PISA 2015 CPS framework to help facilitate the development of CPS corpora with more agents. CPS-TaskForge takes the form of a resource management (tower defense) game, and different CPS tasks can be studied by manipulating game design parameters. We conduct a case study with groups of 3-4 humans to validate production of diverse natural language CPS communication in a game instance produced by CPS-TaskForge. We discuss opportunities for advancing research in CPS (both with human-only and human-AI teams) using different task configurations. We will release data and code.

We study the Josephson effect in a superconductor/altermagnet/superconductor (S/AM/S) junction. We find anomalous phenomena including $0$-$\pi$ transition as well as multi-nodal current-phase relations. Similar to $d$-wave superconductor, $d$-wave altermagnet can support $\varphi$ junction where free energy minima locate neither $\varphi=0$ nor $\pm \pi$ with double degeneracy. These properties can be tunable by parameters, e.g., the exchange energy, the orientation of crystal axis, the length, and the applied gate voltage of altermagnet. These rich features lead to accessible functionality of S/AM/S junction., Comment: 6 pages, 4 figures

Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble prediction system (EPS) based on conventional NWP models is highly computationally expensive. ML models have emerged as valuable tools for deterministic weather forecasts, providing forecasts with significantly reduced computational requirements and even surpassing the forecast performance of traditional NWP models. However, challenges arise when applying ML models to ensemble forecasting. Recent ML models, such as GenCast and SEEDS model, rely on the ERA5 EDA or operational NWP ensemble members for forecast generation. Their spatial resolution is also considered too coarse for many applications. To overcome these limitations, we introduce FuXi-ENS, an advanced ML model designed to deliver 6-hourly global ensemble weather forecasts up to 15 days. This model runs at a significantly increased spatial resolution of 0.25\textdegree, incorporating 5 atmospheric variables at 13 pressure levels, along with 13 surface variables. By leveraging the inherent probabilistic nature of Variational AutoEncoder (VAE), FuXi-ENS optimizes a loss function that combines the CRPS and the KL divergence between the predicted and target distribution, facilitating the incorporation of flow-dependent perturbations in both initial conditions and forecast. This innovative approach makes FuXi-ENS an advancement over the traditional ones that use L1 loss combined with the KL loss in standard VAE models for ensemble weather forecasting. Results demonstrate that FuXi-ENS outperforms ensemble forecasts from the ECMWF, a world leading NWP model, in the CRPS of 98.1% of 360 variable and forecast lead time combinations. This achievement underscores the potential of the FuXi-ENS model to enhance ensemble weather forecasts, offering a promising direction for further development in this field.

We theoretically study the tunneling conductance of a junction consisting of a two-dimensional $p$-wave altermagnet (AM) and a superconductor (SC) for various pairing symmetries. The zero bias conductance peaks arising from the dispersionless surface Andreev bound states (SABSs) in $d_{xy}$-wave and $p_{x}$-wave superconductor junctions are insensitive against varying the altermagnetic spin-splitting strength $\alpha _{y}$. Moreover, for chiral $p$- or chiral $d$-wave SCs, zero bias conductance shows a non-monotonic change as a function of $\alpha_{y}$ indicating the existence of the dispersive SABSs. Our obtained results of tunneling spectroscopy based on a $p$-wave AM serve as an effective way for the identification of the pairing symmetries of unconventional superconductors. It is noted that our used Hamiltonian of AM is also available for persistent spin helix systems., Comment: 37 pages, 23 figures

Transformer-based NLP models are powerful but have high computational costs that limit deployment. Finetuned encoder-decoder models are popular in specialized domains and can outperform larger more generalized decoder-only models, such as GPT-4. We introduce a new configuration for encoder-decoder models that improves efficiency on structured output and decomposable tasks where multiple outputs are required for a single shared input. Our method, prompt-in-decoder (PiD), encodes the input once and decodes the output in parallel, boosting both training and inference efficiency by avoiding duplicate input encoding and increasing the operational intensity (ratio of numbers of arithmetic operation to memory access) of decoding process by sharing the input key-value cache. We achieve computation reduction that roughly scales with the number of subtasks, gaining up to 4.6x speed-up over state-of-the-art models for dialogue state tracking, summarization, and question-answering tasks, with comparable or better performance., Comment: 14 pages, 4 figures. https://github.com/boru-roylu/encode-once-and-decode-in-parallel

Machine translation focuses mainly on high-resource languages (HRLs), while low-resource languages (LRLs) like Taiwanese Hokkien are relatively under-explored. The study aims to address this gap by developing a dual translation model between Taiwanese Hokkien and both Traditional Mandarin Chinese and English. We employ a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage the orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin Chinese. Our comprehensive experiments involve translation tasks across various writing systems of Taiwanese Hokkien as well as between Taiwanese Hokkien and other HRLs. We find that the use of a limited monolingual corpus still further improves the model's Taiwanese Hokkien capabilities. We then utilize our translation model to standardize all Taiwanese Hokkien writing systems into Hokkien Han, resulting in further performance improvements. Additionally, we introduce an evaluation method incorporating back-translation and GPT-4 to ensure reliable translation quality assessment even for LRLs. The study contributes to narrowing the resource gap for Taiwanese Hokkien and empirically investigates the advantages and limitations of pre-training and fine-tuning based on LLaMA 2., Comment: Accepted by LREC-COLING 2024 as a long oral paper

The noise in absorption imaging of cold atoms significantly impacts measurement accuracy across a range of applications with ultracold atoms. It is crucial to adopt an approach that offers effective denoising capabilities without compromising the unique structure of the atoms. Here we introduce a novel image enhancement algorithm for cold atomic absorption imaging. The algorithm successfully suppresses background noise, enhancing image contrast significantly. Experimental results showcase that this approach can enhance the accuracy of cold atom particle number measurements by approximately tenfold, all while preserving essential information. Moreover, the method exhibits exceptional performance and robustness when confronted with fringe noise and multi-component imaging scenarios, offering high stability. Importantly, the optimization process is entirely automated, eliminating the need for manual parameter selection. The method is both compatible and practical, making it applicable across various absorption imaging fields.

A pico-second timing (PIST) front-end electronic chip has been developed using $55~\mathrm{nm}$ CMOS technology for future electron-positron collider experiments (namely Higgs factories). Extensive tests have been performed to evaluate the timing performance of a dedicated SiPM-readout system equipped with a PIST chip. The results show that the system timing resolution can achieve $45~\mathrm{ps}$ for SiPM signals at the minimum-ionizing particles (MIP) level ($200~\mathrm{p.e.}$) and better than $ 10~\mathrm{ps}$ for signals larger than $1200~\mathrm{p.e.}$, while the PIST intrinsic timing resolution is $4.76 \pm 0.60~\mathrm{ps}$. The PIST dynamic range has been further extended using the time-over-threshold (ToT) technique, which can cover the SiPM response spanning from $\mathrm{\sim 900~p.e.}$ to $~\mathrm{\sim 40000~p.e.}$.

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO, but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.

We explore the defect formation and universally critical dynamics in two-dimensional (2D) two-component Bose-Einstein condensates(BECs) subjected to two types of potential traps: a homogeneous trap and a harmonic trap.We focus on the non-equilibrium universal dynamics of the miscible-immiscible phase transition with both linear and nonlinear quenching types.Although there exists spatial independence of the critical point, we find that the inhomogeneity of trap doesn't affect the phase transition of system and the critical exponents can still be explained by the homogeneous Kibble-Zurek mechanism. By analyzing the Bogoliubov excitations, we establish a power-law relationship between the domain correlation length, the phase transition delay, and the quench time.Furthermore, through real-time simulations of phase transition dynamics, the formation of domain defects and the delay of phase transition in non-equilibrium dynamics are demonstrated, along with the corresponding universal scaling of correlation length and phase transition delay for various quench time and quench coefficients, which align well with our analytical predictions.Our study confirms that the universality class of two-component BECs remains unaffected by dimensionality, while the larger nonlinear coefficients effectively suppress non-adiabatic excitations, offering a novel perspective for addressing adiabatic evolution., Comment: 9 pages, 8 figures

The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fact that most data is protected from public release for privacy reasons. Problem-solving human dialogues in real applications can be much more complex than existing Wizard-of-Oz collections, preventing successful domain transfer. To support information extraction (IE) for a private call center dataset, we introduce a human-in-the-loop dialogue generation framework capable of synthesizing realistic dialogues. In IE experiments with auto insurance call center dialogues, we observe 25\% relative improvement in $F_1$ after augmenting a small set of real human conversations with synthetic data. We release code and our synthetic dataset to illustrate the complexity of real-world call center conversations and encourage development of complex dialogue datasets that are more representative of natural data.

The recent Pulsar Timing Arrays (PTAs) nano-Hz gravitational wave (GW) background signal can be naturally induced by the annihilation of domain walls (DWs) formed at a symmetry-breaking scale $f\simeq 200$~TeV in the clockwork axion framework. Based on our first successful and precise prediction, we for the first time suggest that the recent PTA observations strongly support the novel mechanism of the QCD instanton-induced DW annihilation in the clockwork axion framework. We also for the first time discover a novel correlation between dark matter (DM) relic abundance and nano-Hz GW background, which in turn indicates a natural connection between the axion decay constant and the symmetry-breaking scale in the clockwork framework. We find that the GW signal has a peak $h^2\Omega_{\rm GW}\simeq 10^{-6.6}-10^{-6.1}$ at about 50~nHz, which is definite and testable for future PTA data at frequencies $\gtrsim 25$~nHz and CMB-S4 experiment. We also propose various phenomena that may appear in PTAs and future GW interferometers., Comment: 5+8 pages, 3+3 figures, 3 tables, accepted for publication in PRD Lett

Atomic clocks are crucial for science and technology, but their sensitivity is often restricted by the standard quantum limit. To surpass this limit, correlations between particles or interrogation times must be leveraged. Although the sensitivity can be enhanced to the Heisenberg limit using quantum entanglement, it remains unclear whether the scaling of sensitivity with total interrogation time can achieve the Heisenberg scaling. Here, we design an adaptive Bayesian quantum frequency estimation protocol that approaches the Heisenberg scaling and experimentally demonstrate its validity with a cold-atom coherent-population-trapping (CPT) clock. In further, we achieve robust and high-precision closed-loop locking of the cold-atom CPT clock by utilizing our Bayesian quantum frequency estimation protocol. In comparison to the conventional proportional-integral-differential locking, our Bayesian locking scheme not only yields an improvement of 5.1(4) dB in fractional frequency stability, but also exhibits better robustness against technical noises. Our findings not only provide a robust and high-precision approach to lock atomic clocks, but also hold promising applications in various interferometry-based quantum sensors, such as quantum magnetometers and atomic interferometers., Comment: 20 pages, 13 figures

Quantum lock-in amplifier aims to extract an alternating signal within strong noise background by using quantum strategy. However, as the target signal usually has an unknown initial phase, we can't obtain the complete information of its amplitude, frequency and phase in a single lock-in measurement. Here, to overcome this challenge, we give a general protocol for achieving a quantum double lock-in amplifier and illustrate its realization. In analog to a classical double lock-in amplifier, our protocol is accomplished via two quantum mixers under orthogonal pulse sequences. The two orthogonal pulse sequences act the roles of two orthogonal reference signals in a classical double lock-in amplifier. Combining the output signals, the complete characteristics of the target signal can be obtained. As an example, we illustrate the realization of our quantum double lock-in amplifier via a five-level double-$\Lambda$ coherent population trapping system with $^{87}$Rb atoms, in which each $\Lambda$ structure acts as a quantum mixer and the two applied dynamical decoupling sequences take the roles of two orthogonal reference signals. Our numerical calculations show that the quantum double lock-in amplifier is robust against experimental imperfections, such as finite pulse length and stochastic noise. Our study opens an avenue for extracting complete characteristics of an alternating signal within strong noise background, which is beneficial for developing practical quantum sensing technologies.

Coherent population trapping (CPT) is a multi-level quantum coherence phenomenon of promising applications in atomic clocks and magnetometers. Particularly, multi-pulse CPT-Ramsey interferometry is a powerful tool for improving the performance of CPT atomic clocks. Most studies on multi-pulse CPT-Ramsey interferometry consider periodic pulse sequence and time-independent detuning. However, to further improve the accuracy and precision, one may modify the spectrum symmetry which involves pulse sequence with time-dependent detuning or phase shift. Here, we theoretically analyze the multi-pulse CPT-Ramsey interferometry under arbitrary pulse sequences of time-dependent detuning and obtain a general analytical formula. Using our formula, we analyze the popular CPT-Ramsey interferometry schemes such as two-pulse symmetric and antisymmetric spectroscopy, and multi-pulse symmetric and antisymmetric spectroscopy. Moreover, we quantitatively obtain the influences of pulse width, pulse period, pulse number, and Rabi frequency under periodic pulses. Our theoretical results can guide the experimental design to improve the performance of atomic clocks via multi-pulse CPT-Ramsey interferometry., Comment: 9 pages, 6 figures

To achieve Bose-Einstein condensation, one may implement evaporative cooling by dynamically regulating the power of laser beams forming the optical dipole trap. We propose and experimentally demonstrate a protocol of Bayesian optimization of Bose-Einstein condensation via the evaporative cooling model. Applying this protocol, pure Bose-Einstein condensate of 87Rb with 2.4X10e4 atoms can be produced via evaporative cooling from the initial stage when the number of atoms is 6.0X10e5 at a temperature of 12{\mu}K. In comparison with Bayesian optimization via blackbox experiment, our protocol only needs a few experiments required to verify some close-to-optimal curves for optical dipole trap laser powers, therefore it greatly saves experimental resources., Comment: 8 pages, 5 figures