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Diffusion of native defects such as vacancies and their interactions with impurities are fundamental in semiconductor crystal growth, device processing, and long-term aging of equilibration and transient diffusion of vacancies are rarely investigated. We used aluminum-gallium oxide/gallium oxide superlattices (SLs) to detect and analyze transient diffusion of cation vacancies during annealing in O2 at 1000-1100 C. Using a novel finite difference scheme for the diffusion equation with time- and space-varying diffusion constant, we extract diffusion constants for Al, Fe, and cation vacancies under the given conditions, including the vacancy concentration dependence for Al. indicate that vacancies present in the substrate transiently diffuse through the SLs, interacting with Sn as it also diffuses. In the case of SLs grown on Sn-doped beta-gallium oxide substrates, gradients observed in the extent of Al diffusion indicate that vacancies present in the substrate transiently diffuse through the SLs, interacting with Sn as it also diffuses. In the case of SLs grown on (010) Fe-doped substrates, the Al diffusion is uniform through the SLs, indicating a depth-uniform concentration of vacancies. We find no evidence in either case for the introduction of gallium vacancies from the free surface at rates sufficient to affect Al diffusion down to ppm concentrations, which has important bearing on the validity of typically-made assumptions of vacancy equilibration. Additionally, we show that unintentional impurities in Sn-doped gallium oxide such as Fe, Ni, Mn, Cu, and Li also diffuse towards the surface and accumulate. Many of these likely have fast interstitial diffusion modes capable of destabilizing devices over time, thus highlighting the importance of controlling unintentional impurities in beta-gallium oxide wafers., Comment: 11 pages, 4 figures, references a supplimental which will be submitted seperately

In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success. However, as the foundation model for many downstream tasks, current MLLMs are composed of the well-known Transformer network, which has a less efficient quadratic computation complexity. To improve the efficiency of such basic models, we propose Cobra, a linear computational complexity MLLM. Specifically, Cobra integrates the efficient Mamba language model into the visual modality. Moreover, we explore and study various modal fusion schemes to create an effective multi-modal Mamba. Extensive experiments demonstrate that (1) Cobra achieves extremely competitive performance with current computationally efficient state-of-the-art methods, e.g., LLaVA-Phi, TinyLLaVA, and MobileVLM v2, and has faster speed due to Cobra's linear sequential modeling. (2) Interestingly, the results of closed-set challenging prediction benchmarks show that Cobra performs well in overcoming visual illusions and spatial relationship judgments. (3) Notably, Cobra even achieves comparable performance to LLaVA with about 43% of the number of parameters. We will make all codes of Cobra open-source and hope that the proposed method can facilitate future research on complexity problems in MLLM. Our project page is available at: https://sites.google.com/view/cobravlm., Comment: Update ablation results

As firms' pricing strategies increasingly rely on algorithms, two concerns have received much attention: algorithmic tacit collusion and price discrimination. This paper investigates the interaction between these two issues through simulations. In each period, a new buyer arrives with independently and identically distributed willingness to pay (WTP), and each firm, observing private signals about WTP, adopts Q-learning algorithms to set prices. We document two novel mechanisms that lead to collusive outcomes. Under asymmetric information, the algorithm with information advantage adopts a Bait-and-Restrained-Exploit strategy, surrendering profits on some signals by setting higher prices, while exploiting limited profits on the remaining signals by setting much lower prices. Under a symmetric information structure, competition on some signals facilitates convergence to supra-competitive prices on the remaining signals. Algorithms tend to collude more on signals with higher expected WTP. Both uncertainty and the lack of correlated signals exacerbate the degree of collusion, thereby reducing both consumer surplus and social welfare. A key implication is that the over-usage of data, both payoff-relevant and non-relevant, by AIs in competitive contexts will reduce the degree of collusion and consequently lead to a decline in industry profits.

Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve these goals. We propose an adaptive multi-delta coding as a spiking coding method to improve recognition accuracy. We propose two additive solvers for SNN, which can reduce inference energy consumption and amount of parameters significantly, and improve the robustness of temporal differences. In addition, we propose a linear action detection method TAD-LIF, which is suitable for SNNs. TAD-LIF is an improved LIF neuron that can detect transient-state gestures quickly and accurately. We collected two datasets from 20 subjects including 6 micro gestures. The collection devices are two designed lightweight consumer-level sEMG wristbands (3 and 8 electrode channels respectively). Compared to CNN, FCN, and normal SNN-based methods, the proposed SNN has higher recognition accuracy. The accuracy of the proposed SNN is 83.85% and 93.52% on the two datasets respectively. In addition, the inference latency of the proposed SNN is about 1% of CNN, the power consumption is about 0.1% of CNN, and the memory occupation is about 20% of CNN. The proposed methods can be used for precise, high-speed, and low-power micro-gesture recognition tasks, and are suitable for consumer-level intelligent wearable devices, which is a general way to achieve ubiquitous computing.

Multi-view learning aims to combine multiple features to achieve more comprehensive descriptions of data. Most previous works assume that multiple views are strictly aligned. However, real-world multi-view data may contain low-quality conflictive instances, which show conflictive information in different views. Previous methods for this problem mainly focus on eliminating the conflictive data instances by removing them or replacing conflictive views. Nevertheless, real-world applications usually require making decisions for conflictive instances rather than only eliminating them. To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data. We develop an Evidential Conflictive Multi-view Learning (ECML) method for this problem. ECML first learns view-specific evidence, which could be termed as the amount of support to each category collected from data. Then, we can construct view-specific opinions consisting of decision results and reliability. In the multi-view fusion stage, we propose a conflictive opinion aggregation strategy and theoretically prove this strategy can exactly model the relation of multi-view common and view-specific reliabilities. Experiments performed on 6 datasets verify the effectiveness of ECML., Comment: 9 pages and to be appeared in AAAI2024

Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particularly when dealing with complex operations. To alleviate the burden associated with writing spreadsheet formulas, this paper introduces a novel benchmark task called NL2Formula, with the aim to generate executable formulas that are grounded on a spreadsheet table, given a Natural Language (NL) query as input. To accomplish this, we construct a comprehensive dataset consisting of 70,799 paired NL queries and corresponding spreadsheet formulas, covering 21,670 tables and 37 types of formula functions. We realize the NL2Formula task by providing a sequence-to-sequence baseline implementation called fCoder. Experimental results validate the effectiveness of fCoder, demonstrating its superior performance compared to the baseline models. Furthermore, we also compare fCoder with an initial GPT-3.5 model (i.e., text-davinci-003). Lastly, through in-depth error analysis, we identify potential challenges in the NL2Formula task and advocate for further investigation., Comment: To appear at EACL 2024

Many studies have shown that human languages tend to optimize for lower complexity and increased communication efficiency. Syntactic dependency distance, which measures the linear distance between dependent words, is often considered a key indicator of language processing difficulty and working memory load. The current paper looks at diachronic trends in syntactic language change in both English and German, using corpora of parliamentary debates from the last c. 160 years. We base our observations on five dependency parsers, including the widely used Stanford CoreNLP as well as 4 newer alternatives. Our analysis of syntactic language change goes beyond linear dependency distance and explores 15 metrics relevant to dependency distance minimization (DDM) and/or based on tree graph properties, such as the tree height and degree variance. Even though we have evidence that recent parsers trained on modern treebanks are not heavily affected by data 'noise' such as spelling changes and OCR errors in our historic data, we find that results of syntactic language change are sensitive to the parsers involved, which is a caution against using a single parser for evaluating syntactic language change as done in previous work. We also show that syntactic language change over the time period investigated is largely similar between English and German for the different metrics explored: only 4% of cases we examine yield opposite conclusions regarding upwards and downtrends of syntactic metrics across German and English. We also show that changes in syntactic measures seem to be more frequent at the tails of sentence length distributions. To our best knowledge, ours is the most comprehensive analysis of syntactic language change using modern NLP technology in recent corpora of English and German., Comment: Updated to the current version

Magnetic-array-type current sensors have garnered increasing popularity owing to their notable advantages, including broadband functionality, a large dynamic range, cost-effectiveness, and compact dimensions. However, the susceptibility of the measurement error of one or more magnetic measurement units (MMUs) within the current sensor to drift significantly from the nominal value due to environmental factors poses a potential threat to the measurement accuracy of the current sensor. In light of the need to ensure sustained measurement accuracy over the long term, this paper proposes an innovative self-healing approach rooted in cyber-physics correlation. This approach aims to identify MMUs exhibiting abnormal measurement errors, allowing for the exclusive utilization of the remaining unaffected MMUs in the current measurement process. To achieve this, principal component analysis (PCA) is employed to discern the primary component, arising from fluctuations of the measured current, from the residual component, attributed to the drift in measurement error. This analysis is conducted by scrutinizing the measured data obtained from the MMUs. Subsequently, the squared prediction error (SPE) statistic (also called $Q$ statistic) is deployed to individually identify any MMU displaying abnormal behavior. The experimental results demonstrate the successful online identification of abnormal MMUs without the need for a standard magnetic field sensor. By eliminating the contributions from the identified abnormal MMUs, the accuracy of the current measurement is effectively preserved., Comment: 11 pages, 10 figures

The zeta ($\zeta$) potential is a pivotal metric for characterizing the electric field topology within an electric double layer - an important phenomenon on phase interface. It underpins critical processes in diverse realms such as chemistry, biomedical engineering, and micro/nanofluidics. Yet, local measurement of $\zeta$ potential at the interface has historically presented challenges, leading researchers to simplify a chemically homogenized surface with a uniform $\zeta$ potential. In the current investigation, we present evidence that, within a microchannel, the spatial distribution of $\zeta$ potential across a chemically homogeneous solid-liquid interface can become two-dimensional (2D) under an imposed flow regime, as disclosed by a state-of-art fluorescence photobleaching electrochemistry analyzer (FLEA) technique. The $\zeta$ potential' s propensity to become increasingly negative downstream, presents an approximately symmetric, V-shaped pattern in the spanwise orientation. Intriguingly, and of notable significance to chemistry and engineering, this 2D $\zeta$ potential framework was found to electrokinetically induce swirling flows in tens of nanometers, aligning with the streamwise axis, bearing a remarkable resemblance to the well-documented hairpin vortices in turbulent boundary layers. Our findings gesture towards a novel perspective on the genesis of vortex structures in nanoscale. Additionally, the FLEA technique emerges as a potent tool for discerning $\zeta$ potential at a local scale with high resolution, potentially accelerating the evolution and applications of novel surface material.

Despite the predominance of contextualized embeddings in NLP, approaches to detect semantic change relying on these embeddings and clustering methods underperform simpler counterparts based on static word embeddings. This stems from the poor quality of the clustering methods to produce sense clusters -- which struggle to capture word senses, especially those with low frequency. This issue hinders the next step in examining how changes in word senses in one language influence another. To address this issue, we propose a graph-based clustering approach to capture nuanced changes in both high- and low-frequency word senses across time and languages, including the acquisition and loss of these senses over time. Our experimental results show that our approach substantially surpasses previous approaches in the SemEval2020 binary classification task across four languages. Moreover, we showcase the ability of our approach as a versatile visualization tool to detect semantic changes in both intra-language and inter-language setups. We make our code and data publicly available., Comment: EACL2024 Camera Ready (20 pages)

We have analyzed the total intensity, spectral index, linear polarization, and RM distributions at pc scale for the quasar 1604+159. The source was observed in 2002 and 2020 with the VLBA. Combining the MOJAVE results, we studied the evolution of the magnetic field. We detected a core-jet structure. The jet extends to a distance of ~25 mas. The jet shape varies slightly with time. We divided the source structure into the central region and the jet region. In the jet region, we find the polarized emission varies with time. The flatter spectral index values and EVPA direction indicate the possible existence of shocks, contributing to the variation. In the central region, the derived core shift index k_r values indicate that the core in 2002 is close to the equipartition case while deviating from it in 2020. The measured magnetic field strength in 2020 is two orders of magnitude lower than that in 2002. We detected transverse RM gradients, evidence of a helical magnetic field, in the core. At 15 GHz, in the place close to the jet base, the polarization direction changes significantly with time from perpendicular to parallel to the jet direction. The evolution of RM and magnetic field structure are potential reasons for the observed polarization change. The core |RM| in 2020 increases with frequency following a power law with index a = 2.7, suggesting a fast electron density fall-off in the medium with distance from the jet base., Comment: 24 pages, 14 figures, accepted for publication in ApJ

The detection of dark matter is a challenging problem in modern physics. The ultralight scalar and axion dark matter could induce the oscillation of the nuclear charge radii and then oscillate the atomic transition frequency by interacting with standard model particles. We compute the differential phase shift caused by the scalar and axion dark matter in a pair of separated atom interferometers and give the proposed constraints on the scalar dark matter coupling parameters $d_g$ and $d_{\hat{m}}$ as well as the axion dark matter coupling parameter $1/f_a$. Our results are expected to improve the current detection level and complement with other experiments., Comment: 5 pages, 3 figures

We propose a novel approach to nonlinear functional regression, called the Mapping-to-Parameter function model, which addresses complex and nonlinear functional regression problems in parameter space by employing any supervised learning technique. Central to this model is the mapping of function data from an infinite-dimensional function space to a finite-dimensional parameter space. This is accomplished by concurrently approximating multiple functions with a common set of B-spline basis functions by any chosen order, with their knot distribution determined by the Iterative Local Placement Algorithm, a newly proposed free knot placement algorithm. In contrast to the conventional equidistant knot placement strategy that uniformly distributes knot locations based on a predefined number of knots, our proposed algorithms determine knot location according to the local complexity of the input or output functions. The performance of our knot placement algorithms is shown to be robust in both single-function approximation and multiple-function approximation contexts. Furthermore, the effectiveness and advantage of the proposed prediction model in handling both function-on-scalar regression and function-on-function regression problems are demonstrated through several real data applications, in comparison with four groups of state-of-the-art methods.

Recent innovations in digital technology offer significant opportunities for older adults to engage in meaningful activities. To investigate older adults' perceptions of using existing and emerging technologies for meaningful activities, we conducted three participatory design workshops and follow-up interviews with adults aged over 65. The workshops encompassed discussions on existing technologies for meaningful activities, demonstrations of emerging technologies such as VR, AR, and AI, and design activities including prototyping and storyboarding. Our findings show that while participants had diverse interpretations of meaningful activities, they sought to use technologies to support continuity in the pursuit of these activities. Specifically, participants highlighted the importance of safe aging at home, which provides a pathway for meaningful activities in later life. We further discuss participants' discerning attitudes when assessing the use of different technologies for meaningful activities and several values and attributes they desire when envisioning future technologies, including simplicity, positivity, proactivity, and integration.

Time series prediction (TSP) has been widely used in various fields, such as life sciences and finance, to forecast future trends based on historical data. However, to date, there has been relatively little research conducted on the TSP for quantum physics. In this paper, we explore the potential application of TSP in forecasting the dynamical evolution of open quantum systems. We employ deep learning techniques to train a TSP model and evaluate its performance by comparison with exact solution. We use the ratio of the prediction step length and the sequence length to define short and long-term forecasting. Our results show that the trained model has the ability to effectively capture the inherent characteristics of time series for both short-term and long-term forecasting. Accurate predictions for different coupling intensities and initial states are obtained. Furthermore, we use our method to train another model and find that it can successfully predict the steady state of the system. These findings suggests that TSP is a valuable tool for the prediction of the dynamics in open quantum systems.

The jet composition of GRB plays an important role in understanding the energy dissipation and radiation mechanisms in GRB physics, but it is poorly constrained from the observational data. Recently, an interesting long-duration GRB 230307A with redshift $z=$0.065 has attracted great attention. The lack of detected thermal emission and mini-structure of prompt emission lightcurve of this burst suggest that the outflow is Poynting-flux-dominated and point towards the ICMART model. In this paper, we invoke two independent methods to investigate the jet composition of GRB 230307A. The high magnetization parameter ($\sigma>7$ or ever large) for$R_0=10^{10}$ cm that is used to suppress thermal component, strongly suggests that a significant fraction of the outflow energy is likely in a Poynting flux entrained with the baryonic matter. Moreover, it is found that the radiation efficiency of this burst for typical values $\epsilon_e=0.1$ and $\epsilon_B=0.01$ can reach as high as $~50\%$ which disfavors the internal shock model, but is consistent with ICMART model. Finally, a possible unified picture to produce GRB 230307A originated from a compact star merger is also discussed., Comment: 6 pages, 2 figures, 1 table, updated references, and matched with the published verison

We establish various Hardy inequalities involving the distance function from submanifolds of Riemannian manifolds, where the natural weights are expressed in terms of bounds of the mean curvature of the submanifold and sectional/Ricci curvature of the ambient Riemannian manifold. Our approach is based on subtle Heintze-Karcher-type Laplace comparisons of the distance function and on a D'Ambrosio-Dipierro-type weak divergence formula for suitable vector fields, providing Barbatis-Filippas-Tertikas-type Hardy inequalities in the curved setting. Under very mild assumptions, we also establish the sharpness and non-existence of extremal functions within the Hardy inequalities and - depending on the geometry of the ambient manifold - their extensibility to various function spaces. Several examples are provided by showing the applicability of our approach; in particular, well-known Hardy inequalities appear as limit cases of our new inequalities., Comment: 40 pages

We investigate the single photon scattering and bound states in a coupled resonator waveguide (CRW) which couples to a topological giant atom (TGA) via two distant sites. Here, the TGA is constructed by a one dimensional Su-Schrieffer-Heeger (SSH) chain with finite length. By modulating the topological phase of the TGA, the incident photon in the CRW can be completely reflected or transmitted, and is therefore beneficial to design the coherent photonic device. Meanwhile, we also achieve two pairs of bound states locating respectively above and blow the continuum. Whether the gap is open or closed depends on the boundary condition of the TGA. Therefore, the combination of the topology and the interference provides us an exciting opportunity to manipulate the photonic state in the context of waveguide quantum electrodynamics., Comment: 7 Pages, 6 Figures, Comments are welcomed