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The manipulation of edge configurations and structures in atomically thin transition metal dichalcogenides (TMDs) for versatile functionalization has attracted intensive interest in recent years. The chemical vapor deposition (CVD) approach has shown promise for TMD edge engineering of atomic edge configurations (1H, 1T or 1T'-zigzag or armchair edges), as well as diverse edge morphologies (1D nanoribbons, 2D dendrites, 3D spirals, etc). These rich-edge TMD layers offer versatile candidates for probing the physical and chemical properties, and exploring new applications in electronics, optoelectronics, catalysis, sensing and quantum field. In this review, we present an overview of the current state-of-the-art in the manipulation of TMD atomic edges and edge-rich structures using CVD. We highlight the vast range of unique properties associated with these edge configurations and structures and provide insights into the opportunities afforded by such edge-functionalized crystals. The objective of this review is to motivate further research and development efforts in using CVD as a scalable approach to harness the benefits of such crystal-edge engineering.

This paper addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a Real-Time Compressed Sensing (RTCS) network designed to be lightweight and require only relatively few training samples for efficient and robust HSI reconstruction in the presence of the stripe effect and under noisy transmission conditions. The RTCS network features a simplified architecture that reduces the required training samples and allows for easy implementation on integer-8-based encoders, facilitating rapid compressed sensing for stripe-like HSI, which exactly matches the moderate design of miniaturized satellites on push broom scanning mechanism. This contrasts optimization-based models that demand high-precision floating-point operations, making them difficult to deploy on edge devices. Our encoder employs an integer-8-compatible linear projection for stripe-like HSI data transmission, ensuring real-time compressed sensing. Furthermore, based on the novel two-streamed architecture, an efficient HSI restoration decoder is proposed for the receiver side, allowing for edge-device reconstruction without needing a sophisticated central server. This is particularly crucial as an increasing number of miniaturized satellites necessitates significant computing resources on the ground station. Extensive experiments validate the superior performance of our approach, offering new and vital capabilities for existing miniaturized satellite systems., Comment: Accepted by TGRS 2024

In reality, images often exhibit multiple degradations, such as rain and fog at night (triple degradations). However, in many cases, individuals may not want to remove all degradations, for instance, a blurry lens revealing a beautiful snowy landscape (double degradations). In such scenarios, people may only desire to deblur. These situations and requirements shed light on a new challenge in image restoration, where a model must perceive and remove specific degradation types specified by human commands in images with multiple degradations. We term this task Referring Flexible Image Restoration (RFIR). To address this, we first construct a large-scale synthetic dataset called RFIR, comprising 153,423 samples with the degraded image, text prompt for specific degradation removal and restored image. RFIR consists of five basic degradation types: blur, rain, haze, low light and snow while six main sub-categories are included for varying degrees of degradation removal. To tackle the challenge, we propose a novel transformer-based multi-task model named TransRFIR, which simultaneously perceives degradation types in the degraded image and removes specific degradation upon text prompt. TransRFIR is based on two devised attention modules, Multi-Head Agent Self-Attention (MHASA) and Multi-Head Agent Cross Attention (MHACA), where MHASA and MHACA introduce the agent token and reach the linear complexity, achieving lower computation cost than vanilla self-attention and cross-attention and obtaining competitive performances. Our TransRFIR achieves state-of-the-art performances compared with other counterparts and is proven as an effective architecture for image restoration. We release our project at https://github.com/GuanRunwei/FIR-CP., Comment: 15 pages, 19 figures

Excitons are key to the optoelectronic applications of van der Waals semiconductors with the potential for versatile on-demand tuning of properties. Yet, their electrical manipulation is complicated by their inherent charge neutrality and the additional loss channels induced by electrical doping. We demonstrate the dynamic control of valley polarization in charged biexciton (quinton) states of monolayer tungsten disulfide, achieving up to a sixfold increase in the degree of circular polarization under off-resonant excitation. In contrast to the weak direct tuning of excitons typically observed using electrical gating, the quinton photoluminescence remains stable, even with increased scattering from electron doping. By exciting at the exciton resonances, we observed the reproducible non-monotonic switching of the charged state population as the electron doping is varied under gate bias, indicating a coherent interplay between neutral and charged exciton states.

Semiconductor double quantum dot (DQD) qubits coupled via superconducting microwave resonators provide a powerful means of long-range manipulation of the qubits' spin and charge degrees of freedom. Quantum gates can be implemented by parametrically driving the qubits while their transition frequencies are detuned from the resonator frequency. Long-range two-qubit CZ gates have been proposed for the DQD spin qubit within the rotating-wave approximation (RWA). Rapid gates demand strong coupling, but RWA breaks down when coupling strengths become significant relative to system frequencies. Therefore, understanding the detrimental impact of time-dependent terms ignored by RWA is critical for high-fidelity operation. Here, we go beyond RWA to study CZ gate fidelity for both DQD spin and charge qubits. We propose a novel parametric drive on the charge qubit that produces fewer time-dependent terms and show that it outperforms its spin counterpart. We find that drive amplitude - a parameter dropped in RWA - is critical for optimizing fidelity and map out high-fidelity regimes. Our results demonstrate the necessity of going beyond RWA in understanding how long-range gates can be realized in DQD qubits, with charge qubits offering considerable advantages in high-fidelity operation., Comment: 6 Pages, 3 Figures (Main text); 12 Pages, 1 Figure (Supplemental Material)

The development of single-platform qubits, predominant for most of the last few decades, has driven the progress of quantum information technologies but also highlighted the limitations of various platforms. Some inherent issues such as charge/spin noise in materials hinder certain platforms, while increased decoherence upon attempts to scale-up severely impact qubit quality and coupling on others. In addition, a universal solution for coherent information transfer between quantum systems remains lacking. By combining one or more qubit platforms, one could potentially create new hybrid platforms that might alleviate significant issues that current single platform qubits suffer from, and in some cases, even facilitate the conversion of static to flying qubits on the same hybrid platform. While nascent, this is an area of rising importance that could shed new light on robust and scalable qubit development and provide new impetus for research directions. Here, we define the requirements for hybrid systems with artificial atoms in solid state, exemplify them with systems that have been proposed or attempted, and conclude with our outlook for such hybrid quantum systems., Comment: 31 pages, 7 figures

Conductive ferroelectric domain walls (DWs) represent a promising topical system for the development of nanoelectronic components and devices. DWs show very different properties as compared to their bulk counterparts. Of central interest here is the domain wall current (DWC) of charged DWs in 5mol\% Mg-doped lithium niobate single crystals; in contrast to former works, we extend the DWC study here to temperatures as high as 400$^\circ$C. Both the temporal stability and the thermal activation energies of 90 - 160 meV are readily deduced from current-voltage sweeps as recorded over multiple heating cycles. Our experimental work is backed up by atomistic modelling of the DWC. The latter suggests that a large band bending renders head-to-head and tail-to-tail DWs semimetallic. These detailed investigations underline the potential to extend DWC-based nanoelectronic applications even into the so-far unexplored high-temperature regime., Comment: 5 pages, 4 figures; with included supplementary material (6 pages, 9 figures)

Pyrrhotite, Fe$_7$S$_8$, provides an example of exceptionally strong magnetoelastic coupling through pinning of ferromagnetic domains by ferroelastic twins. Using direct imaging of both magnetic and ferroelastic domains by magnetic force microscopy (MFM), the mechanism by which this coupling controls local magnetic switching behaviour of regions on the pyrrhotite surface is revealed, and leads to quantitative fitting of field dependent MFM phase shifts with bulk magnetometry data. It is shown that characteristic inflection points in the magnetometry data along certain direction, in particular $[\overline 120]^*_h$ of the hexagonal parent structure, are in fact caused by ferroelastic pinning of the magnetic moments.

The emergence of a vast repository of van der Waals (vdW) materials supporting polaritons - light coupled to matter excitations - offers a plethora of different possibilities to tailor electromagnetic waves at the subwavelength-scale. In particular, the development of twistoptics - the study of the optical properties of twisted stacks of vdW materials - allows the directional propagation of phonon polaritons (PhPs) along a single spatial direction, which has been coined as canalization. Here we demonstrate a complementary type of nanoscale unidirectional propagation that naturally emerges thanks to twistoptics: unidirectional ray polaritons (URPs). This natural phenomenon arises in two types of twisted hyperbolic stacks: homostructures of $\alpha$-MoO$_3$ and heterostructures of $\alpha$-MoO$_3$ and $\beta$-Ga$_2$O$_3$, each with very different thicknesses of its constituents. URPs are characterized by the absence of diffraction and the presence of a single phase of the propagating field. Importantly, we demonstrate that this ray behavior can be tuned by means of both relative twist angle and illumination frequency variations. Additionally, an unprecedented "pinwheel-like" propagation emerges at specific twist angles of the homostructure. We show that URPs emerge due to the twist between asymmetrically stacked biaxial slabs, while the shear effect in monoclinic $\beta$-Ga$_2$O$_3$ is of minor importance. Our findings demonstrate a natural way to excite unidirectional ray-like PhPs and offer a unique platform for controlling the propagation of PhPs at the nanoscale with many potential applications like nanoimaging, (bio)-sensing or polaritonic thermal management., Comment: 15 pages, 5 figures

Lithium niobate (LNO) and lithium tantalate (LTO) see widespread use in fundamental research and commercial technologies reaching from electronics over classical optics to integrated quantum communication. In recent years, the mixed crystal system lithium niobate tantalate (LNT) allows for the dedicate engineering of material properties by combining the advantages of the two parental materials LNO and LTO. Vibrational spectroscopies such as Raman spectroscopy or (Fourier transform) infrared spectroscopy are vital techniques to provide detailed insight into the material properties, which is central to the analysis and optimization of devices. In this work, we present a joint experimental-theoretical approach allowing to unambiguously assign the spectral features in the LNT material family through both Raman and IR spectroscopy, as well as to provide an in-depth explanation for the observed scattering efficiencies based on first-principles calculations. The phononic contribution to the static dielectric tensor is calculated from the experimental and theoretical data using the generalized Lyddane-Sachs-Teller relation and compared with the results of the first-principles calculations. The joint methodology can be readily expanded to other materials and serves, e.g., as the basis for studying the role of point defects or doping.

We report THz-pump / mid-infrared probe near-field studies on Si-doped GaAs-InGaAs core-shell nanowires utilizing THz radiation from the free-electron laser FELBE. Upon THz excitation of free carriers, we observe a red shift of the plasma resonance in both amplitude and phase spectra, which we attribute to the heating up of electrons in the conduction band. The simulation of heated electron distributions anticipates a significant electron population in both L- and X-valleys. The two-temperature model is utilized for a quantitative analysis of the dynamics of the electron gas temperature under THz pumping at various power levels., Comment: Manuscript + Supplementary Information

The perception of waterways based on human intent is significant for autonomous navigation and operations of Unmanned Surface Vehicles (USVs) in water environments. Inspired by visual grounding, we introduce WaterVG, the first visual grounding dataset designed for USV-based waterway perception based on human prompts. WaterVG encompasses prompts describing multiple targets, with annotations at the instance level including bounding boxes and masks. Notably, WaterVG includes 11,568 samples with 34,987 referred targets, whose prompts integrates both visual and radar characteristics. The pattern of text-guided two sensors equips a finer granularity of text prompts with visual and radar features of referred targets. Moreover, we propose a low-power visual grounding model, Potamoi, which is a multi-task model with a well-designed Phased Heterogeneous Modality Fusion (PHMF) mode, including Adaptive Radar Weighting (ARW) and Multi-Head Slim Cross Attention (MHSCA). Exactly, ARW extracts required radar features to fuse with vision for prompt alignment. MHSCA is an efficient fusion module with a remarkably small parameter count and FLOPs, elegantly fusing scenario context captured by two sensors with linguistic features, which performs expressively on visual grounding tasks. Comprehensive experiments and evaluations have been conducted on WaterVG, where our Potamoi archives state-of-the-art performances compared with counterparts., Comment: 10 pages, 10 figures

Inducing novel quantum phases and topologies in materials using intense light fields is a key objective of modern condensed matter physics, but nonetheless faces significant experimental challenges. Alternately, theory predicts that in the dense limit, excitons - collective excitations composed of Coulomb-bound electron-hole pairs - could also drive exotic quantum phenomena. However, the direct observation of these phenomena requires the resolution of electronic structure in momentum space in the presence of excitons, which became possible only recently. Here, using time- and angle-resolved photoemission spectroscopy of an atomically thin semiconductor in the presence of a high-density of resonantly and coherently photoexcited excitons, we observe the Bardeen-Cooper-Schrieffer (BCS) excitonic state - analogous to the Cooper pairs of superconductivity. We see the valence band transform from a conventional paraboloid into a Mexican-hat like Bogoliubov dispersion - a hallmark of the excitonic insulator phase; and we observe the recently predicted giant exciton-driven Floquet effects. Our work realizes the promise that intense bosonic fields, other than photons, can also drive novel quantum phenomena and phases in materials.

Code-switching (CS) refers to the switching of languages within a speech signal and results in language confusion for automatic speech recognition (ASR). To address language confusion, we propose the language alignment loss that performs frame-level language identification using pseudo language labels learned from the ASR decoder. This eliminates the need for frame-level language annotations. To further tackle the complex token alternatives for language modeling in bilingual scenarios, we propose to employ large language models via a generative error correction method. A linguistic hint that incorporates language information (derived from the proposed language alignment loss and decoded hypotheses) is introduced to guide the prompting of large language models. The proposed methods are evaluated on the SEAME dataset and data from the ASRU 2019 Mandarin-English code-switching speech recognition challenge. The incorporation of the proposed language alignment loss demonstrates a higher CS-ASR performance with only a negligible increase in the number of parameters on both datasets compared to the baseline model. This work also highlights the efficacy of language alignment loss in balancing primary-language-dominant bilingual data during training, with an 8.6% relative improvement on the ASRU dataset compared to the baseline model. Performance evaluation using large language models reveals the advantage of the linguistic hint by achieving 14.1% and 5.5% relative improvement on test sets of the ASRU and SEAME datasets, respectively., Comment: Manuscript submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing

The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework - Fairness-Aware Interpretable Modeling (FAIM), to improve model fairness without compromising performance, featuring an interactive interface to identify a "fairer" model from a set of high-performing models and promoting the integration of data-driven evidence and clinical expertise to enhance contextualized fairness. We demonstrated FAIM's value in reducing sex and race biases by predicting hospital admission with two real-world databases, MIMIC-IV-ED and SGH-ED. We show that for both datasets, FAIM models not only exhibited satisfactory discriminatory performance but also significantly mitigated biases as measured by well-established fairness metrics, outperforming commonly used bias-mitigation methods. Our approach demonstrates the feasibility of improving fairness without sacrificing performance and provides an a modeling mode that invites domain experts to engage, fostering a multidisciplinary effort toward tailored AI fairness.

Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely used for swift and efficient risk prediction. However, existing methods for constructing survival scores presume that data originates from a single source, posing privacy challenges in collaborations with multiple data owners. We propose a novel framework for building federated scoring systems for multi-site survival outcomes, ensuring both privacy and communication efficiency. We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States. Additionally, we independently developed local scores at each site. In testing datasets from each participant site, our proposed federated scoring system consistently outperformed all local models, evidenced by higher integrated area under the receiver operating characteristic curve (iAUC) values, with a maximum improvement of 11.6%. Additionally, the federated score's time-dependent AUC(t) values showed advantages over local scores, exhibiting narrower confidence intervals (CIs) across most time points. The model developed through our proposed method exhibits effective performance on each local site, signifying noteworthy implications for healthcare research. Sites participating in our proposed federated scoring model training gained benefits by acquiring survival models with enhanced prediction accuracy and efficiency. This study demonstrates the effectiveness of our privacy-preserving federated survival score generation framework and its applicability to real-world heterogeneous survival data.

Lithium niobate and lithium tantalate are among the most widespread materials for nonlinear, integrated photonics. Mixed crystals with arbitrary Nb-Ta ratios provide a new degree of freedom to tune materials properties, such as the birefringence, but also leverage the advantages of the singular compounds, for example, by combining the thermal stability of lithium tantalate with the larger nonlinear or piezoelectric constants of lithium niobate. Periodic poling is the prerequisite for any nonlinear optical application. For mixed crystals this has been challenging so far due to the lack of homogeneous, mono-domain crystals, which severely inhibit domain growth and nucleation. In this work we demonstrate that surface-near ($< 1$~$\mu$m depth) periodic poling on x-cut lithium niobate tantalate mixed crystals can be achieved via electric field poling and lithographically structured electrodes. We find that naturally occurring head-to-head or tail-to-tail domain walls in the as-grown crystal inhibit domain inversion at a larger scale. However, periodic poling is possible, if the gap size between the poling electrodes is of the same order of magnitude or smaller than the average size of naturally occurring domains. This work provides the basis for the nonlinear optical application of lithium niobate tantalate mixed crystals.

Piezoresponse Force Microscopy (PFM) is one of the most widespread methods for investigating and visualizing ferroelectric domain structures down to the nanometer length scale. PFM makes use of the direct coupling of the piezoelectric response to the crystal lattice, and hence is most often applied to spatially map the 3-dimensional (3D) near-surface domain distribution of any polar or ferroic sample. Nonetheless, since most samples investigated by PFM are at least semiconducting or fully insulating, the electric ac field emerging from the conductive scanning force microscopy (SFM) tip, penetrates the sample, and hence may also couple to polar features that are deeply buried into the bulk of the sample under investigation. Thus, in the work presented here, we experimentally and theoretically explore the contrast and depth resolution capabilities of PFM, by analyzing the dependence of several key parameters. These key parameters include the depth of the buried feature, i.e. here a domain wall (DW), as well as PFM-relevant technical parameters such as the tip radius, the PFM drive voltage and frequency, and the signal-to-noise ratio. The theoretical predictions are experimentally verified using x-cut periodically-poled lithium niobate single crystals that are specially prepared into wedge-shaped samples, in order to allow the buried feature, here the DW, to be `positioned' at any depth into the bulk. This inspection essentially contributes to the fundamental understanding in PFM contrast analysis, and to the reconstruction of 3D domain structures down to a 1-$\mu$m-penetration depth into the sample.

Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analysis techniques, from traditional statistical models to state-of-the-art machine learning algorithms, support healthcare intervention and policy decisions. However, there remains ongoing discussion about their comparative performance. We conducted a comparative study of several survival analysis methods, including Cox proportional hazards (CoxPH), stepwise CoxPH, elastic net penalized Cox model, Random Survival Forests (RSF), Gradient Boosting machine (GBM) learning, AutoScore-Survival, DeepSurv, time-dependent Cox model based on neural network (CoxTime), and DeepHit survival neural network. We applied the concordance index (C-index) for model goodness-of-fit, and integral Brier scores (IBS) for calibration, and considered the model interpretability. As a case study, we performed a retrospective analysis of patients admitted through the emergency department of a tertiary hospital from 2017 to 2019, predicting 90-day all-cause mortality based on patient demographics, clinicopathological features, and historical data. The results of the C-index indicate that deep learning achieved comparable performance, with DeepSurv producing the best discrimination (DeepSurv: 0.893; CoxTime: 0.892; DeepHit: 0.891). The calibration of DeepSurv (IBS: 0.041) performed the best, followed by RSF (IBS: 0.042) and GBM (IBS: 0.0421), all using the full variables. Moreover, AutoScore-Survival, using a minimal variable subset, is easy to interpret, and can achieve good discrimination and calibration (C-index: 0.867; IBS: 0.044). While all models were satisfactory, DeepSurv exhibited the best discrimination and calibration. In addition, AutoScore-Survival offers a more parsimonious model and excellent interpretability.

We explore a scheme for entanglement generation and optimization in giant atoms by coupling them to finite one-dimensional arrays of spins that behave as cavities. We find that high values for the concurrence can be achieved in small-sized cavities, being the generation time very short. When exciting the system by external means, optimal concurrence is obtained for very weak drivings. We also analyze the effect of disorder in these systems, showing that although the average concurrence decreases with disorder, high concurrences can still be obtained even in scenarios presenting strong disorder. This result leads us to propose an optimization procedure in which by engineering the on-site energies or hoppings in the cavity, concurrences close to 1 can be reached within an extremely short period of time.

Ferroelectric domain wall conductivity (DWC) is an intriguing functional property, that can be controlled through external stimuli such as electric and mechanical fields. Optical-field control, as a non-invasive flexible handle, has rarely been applied so far, but significantly expands the possibility for both tuning and probing DWC. On the one hand, as known from Second-Harmonic, Raman, and CARS micro-spectroscopy, the optical in-and-out approach delivers parameters on the DW distribution, the DW inclination, and probes the DW vibrational modes; on the other hand, photons might be applied also to directly generate charge carriers within the DW, hence acting as a functional and spectrally tunable probe to deduce the integral or local absorption properties and bandgaps of conductive DWs. Here, we report on such an optoelectronic approach by investigating the photo-induced DWC (PI-DWC) in DWs of the model system lithium niobate, a material that is well known for hosting conductive DWs. We compare three different crystals containing different numbers of domain walls: (A) none, (B) one, and (C) many conductive DWs. All samples are inspected for their current-voltage (I-V) behavior (i) in darkness, and (ii) for different illumination wavelengths swept from 500 nm down to 310 nm. All samples show their maximum PI-DWC at 310 nm, i.e., at the optical bandgap of lithium niobate; moreover, sample (C) reaches PI-DWCs of several $\mu$A. Interestingly, a noticeable PI-DWC is also observed for sub-bandgap illumination, i.e., wavelengths as high as 500 nm, hinting towards the existence and decisive role of electronic in-gap states that contribute to the electronic transport along DWs. Finally, conductive atomic force microscopy (c-AFM) investigations under illumination proved that the PI-DWC is confined to the DW area, and does not originate from photo-induced bulk conductivity., Comment: 18 pages, 3 figures, and Supplementary Material (with 10 pages, 4 figures)

Recently, Denoising Diffusion Probabilistic Models (DDPMs) have attained leading performances across a diverse range of generative tasks. However, in the field of speech synthesis, although DDPMs exhibit impressive performance, their long training duration and substantial inference costs hinder practical deployment. Existing approaches primarily focus on enhancing inference speed, while approaches to accelerate training a key factor in the costs associated with adding or customizing voices often necessitate complex modifications to the model, compromising their universal applicability. To address the aforementioned challenges, we propose an inquiry: is it possible to enhance the training/inference speed and performance of DDPMs by modifying the speech signal itself? In this paper, we double the training and inference speed of Speech DDPMs by simply redirecting the generative target to the wavelet domain. This method not only achieves comparable or superior performance to the original model in speech synthesis tasks but also demonstrates its versatility. By investigating and utilizing different wavelet bases, our approach proves effective not just in speech synthesis, but also in speech enhancement.

Owing to ring-strain, cyclic ketones exhibit complex excited-state dynamics with multiple competing photochemical channels active on the ultrafast timescale. While the excited-state dynamics of cyclobutanone after $\pi^{\ast}\leftarrow n$ excitation into the lowest-energy excited singlet state (S$_1$) has been extensively studied, the dynamics following 3$s\leftarrow n$ excitation into the higher-lying singlet Rydberg (S$_2$) state are less well understood. Herein, we couple quantum and excited-state trajectory surface-hopping molecular dynamics simulations to study the relaxation of cyclobutanone following 3s$\leftarrow n$ excitation and to predict the ultrafast electron diffraction scattering signal that we anticipate to arise from the relaxation dynamics that we observe. Our simulations indicate that relaxation from the initially-populated singlet Rydberg state occurs on the hundreds-of-femtosecond to picosecond timescale consistent with the symmetry-forbidden nature of the state-to-state transition involved. Once cyclobutanone has relaxed non-radiatively to the electronic ground state (S$_0$), the vibrationally hot molecules have sufficient energy to form multiple fragmentory products on the electronic ground-state surface including C$_2$H$_4$ + CH$_2$CO (C2; 20%), and C$_3$H$_6$ + CO (C3; 2.5%). We discuss the limitations of our simulations, how these may influence the outcome of the excited-state dynamics we observe, and -- ultimately -- the predictive power of the simulated experimental observable.

Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inference. These techniques struggle to fully exploit the rich information in the diverse N-best hypotheses, making them less optimal for translation tasks that require a single, high-quality output sequence. In this paper, we propose a new generative paradigm for translation tasks, namely "GenTranslate", which builds upon LLMs to generate better results from the diverse translation versions in N-best list. Leveraging the rich linguistic knowledge and strong reasoning abilities of LLMs, our new paradigm can integrate the rich information in N-best candidates to generate a higher-quality translation result. Furthermore, to support LLM finetuning, we build and release a HypoTranslate dataset that contains over 592K hypotheses-translation pairs in 11 languages. Experiments on various speech and machine translation benchmarks (e.g., FLEURS, CoVoST-2, WMT) demonstrate that our GenTranslate significantly outperforms the state-of-the-art model., Comment: 18 pages, Accepted by ACL 2024. This work is open sourced at: https://github.com/YUCHEN005/GenTranslate

Despite over a decade of intense research efforts, the full potential of two-dimensional transition metal dichalcogenides continues to be limited by major challenges. The lack of compatible and scalable dielectric materials and integration techniques restrict device performances and their commercial applications Conventional dielectric integration techniques for bulk semiconductors are difficult to adapt for atomically thin two-dimensional materials. This review provides a brief introduction into various common and emerging dielectric synthesis and integration techniques and discusses their applicability for 2D transition metal dichalcogenides. Dielectric integration for various applications is reviewed in subsequent sections including nanoelectronics, optoelectronics, flexible electronics, valleytronics, biosensing, quantum information processing, and quantum sensing. For each application, we introduce basic device working principles, discuss the specific dielectric requirements, review current progress, present key challenges, and offer insights into future prospects and opportunities.

Spins confined to point defects in atomically-thin semiconductors constitute well-defined atomic-scale quantum systems that are being explored as single photon emitters and spin qubits. Here, we investigate the in-gap electronic structure of individual sulphur vacancies in molybdenum disulphide (MoS2) monolayers using resonant tunneling scanning probe spectroscopy in the Coulomb blockade regime. Spectroscopic mapping of defect wavefunctions reveals an interplay of local symmetry breaking by a charge-state dependent Jahn-Teller lattice distortion that, when combined with strong (~100 meV) spin-orbit coupling, leads to a locking of an unpaired spin-1/2 magnetic moment to the lattice at low temperature, susceptible to lattice strain. Our results provide new insights into spin and electronic structure of vacancy induced in-gap states towards their application as electrically and optically addressable quantum systems.

Assembling two-dimensional van der Waals layered materials into heterostructures is an exciting development that sparked the discovery of rich correlated electronic phenomena and offers possibilities for designer device applications. However, resist residue from fabrication processes is a major limitation. Resulting disordered interfaces degrade device performance and mask underlying transport physics. Conventional cleaning processes are inefficient and can cause material and device damage. Here, we show that thermal scanning probe based cleaning can effectively eliminate resist residue to recover pristine material surfaces. Our technique is compatible at both the material- and device-level, and we demonstrate the significant improvement in the electrical performance of 2D WS2 transistors. We also demonstrate the cleaning of van der Waals heterostructures to achieve interfaces with low disorder. This enables the electrical formation and control of quantum dots that can be tuned from macroscopic current flow to the single-electron tunnelling regime. Such material processing advances are crucial for constructing high-quality vdW heterostructures that are important platforms for fundamental studies and building blocks for quantum and nano-electronics applications., Comment: 4 Figures

We report the results of radioactivity assays and heat leak calculations for a range of common cryogenic materials, considered for use in the QUEST-DMC superfluid 3He dark matter detector. The bolometer, instrumented with nanomechanical resonators, will be sensitive to energy deposits from dark matter interactions. Events from radioactive decays and cosmic rays constitute a significant background and must be precisely modelled, using a combination of material screening and Monte Carlo simulations. However, the results presented here are of wider interest for experiments and quantum devices sensitive to minute heat leaks and spurious events, thus we present heat leak per unit mass or surface area for every material studied. This can inform material choices for other experiments, especially if underground operation is considered where the radiogenic backgrounds will dominate even at shallow depths.

The 2019 edition of Stack Overflow developer survey highlights that, for the first time, Python outperformed Java in terms of popularity. The gap between Python and Java further widened in the 2020 edition of the survey. Unfortunately, despite the rapid increase in Python's popularity, there are not many testing and debugging tools that are designed for Python. This is in stark contrast with the abundance of testing and debugging tools for Java. Thus, there is a need to push research on tools that can help Python developers. One factor that contributed to the rapid growth of Java testing and debugging tools is the availability of benchmarks. A popular benchmark is the Defects4J benchmark; its initial version contained 357 real bugs from 5 real-world Java programs. Each bug comes with a test suite that can expose the bug. Defects4J has been used by hundreds of testing and debugging studies and has helped to push the frontier of research in these directions. In this project, inspired by Defects4J, we create another benchmark database and tool that contain 493 real bugs from 17 real-world Python programs. We hope our benchmark can help catalyze future work on testing and debugging tools that work on Python programs.

Audio-visual deepfake detection scrutinizes manipulations in public video using complementary multimodal cues. Current methods, which train on fused multimodal data for multimodal targets face challenges due to uncertainties and inconsistencies in learned representations caused by independent modality manipulations in deepfake videos. To address this, we propose cross-modality and within-modality regularization to preserve modality distinctions during multimodal representation learning. Our approach includes an audio-visual transformer module for modality correspondence and a cross-modality regularization module to align paired audio-visual signals, preserving modality distinctions. Simultaneously, a within-modality regularization module refines unimodal representations with modality-specific targets to retain modal-specific details. Experimental results on the public audio-visual dataset, FakeAVCeleb, demonstrate the effectiveness and competitiveness of our approach., Comment: Accepted by ICASSP 2024

The symmetry-breaking first-order phase transition between superfluid phases $^3$He-A and $^3$He-B can be triggered extrinsically by ionising radiation or heterogeneous nucleation arising from the details of the sample cell construction. However, the role of potential homogeneous intrinsic nucleation mechanisms remains elusive. Discovering and resolving the intrinsic processes may have cosmological consequences, since an analogous first-order phase transition, and the production of gravitational waves, has been predicted for the very early stages of the expanding Universe in many extensions of the Standard Model of particle physics. Here we introduce a new approach for probing the phase transition in superfluid $^3$He. The setup consists of a novel stepped-height nanofluidic sample container with close to atomically smooth walls. The $^3$He is confined in five tiny nanofabricated volumes and assayed non-invasively by NMR. Tuning of the state of $^3$He by confinement is used to isolate each of these five volumes so that the phase transitions in them can occur independently and free from any obvious sources of heterogeneous nucleation. The small volumes also ensure that the transitions triggered by ionising radiation are strongly suppressed. Here we present the preliminary measurements using this setup, showing both strong supercooling of $^3$He-A and superheating of $^3$He-B, with stochastic processes dominating the phase transitions between the two. The objective is to study the nucleation as a function of temperature and pressure over the full phase diagram, to both better test the proposed extrinsic mechanisms and seek potential parallel intrinsic mechanisms., Comment: 18 pages, 6 figures, part of the Proceedings of the International Conference on Quantum Fluids and Solids 2023. This is a copy of the version of record of this article, first published in Journal of Low Temperature Physics, available online at Publisher's website: https://doi.org/10.1007/s10909-024-03146-6