Search

Pre-trained Language Models (PLMs) are integral to many modern natural language processing (NLP) systems. Although multilingual models cover a wide range of languages, they often grapple with challenges like high inference costs and a lack of diverse non-English training data. Arabic-specific PLMs are trained predominantly on modern standard Arabic, which compromises their performance on regional dialects. To tackle this, we construct an Arabic dialectal corpus comprising 3.4M sentences gathered from social media platforms. We utilize this corpus to expand the vocabulary and retrain a BERT-based model from scratch. Named AlcLaM, our model was trained using only 13 GB of text, which represents a fraction of the data used by existing models such as CAMeL, MARBERT, and ArBERT, compared to 7.8%, 10.2%, and 21.3%, respectively. Remarkably, AlcLaM demonstrates superior performance on a variety of Arabic NLP tasks despite the limited training data. AlcLaM is available at GitHub https://github.com/amurtadha/Alclam and HuggingFace https://huggingface.co/rahbi., Comment: Accepted by ArabicNLP 2024, presented in ACL 2024

Quantum digital signatures (QDS), which utilize correlated bit strings among sender and recipients, guarantee the authenticity, integrity and non-repudiation of classical messages based on quantum laws. Continuous-variable (CV) quantum protocol with heterodyne and homodyne measurement has obvious advantages of low-cost implementation and easy wavelength division multiplexing. However, security analyses in previous researches are limited to the proof against collective attacks in finite-size scenarios. Moreover, existing multi-bit CV QDS schemes have primarily focused on adapting single-bit protocols for simplicity of security proof, often sacrificing signature efficiency. Here, we introduce a CV QDS protocol designed to withstand general coherent attacks through the use of a cutting-edge fidelity test function, while achieving high signature efficiency by employing a refined one-time universal hashing signing technique. Our protocol is proved to be robust against finite-size effects and excess noise in quantum channels. In simulation, results demonstrate a significant reduction of over 6 orders of magnitude in signature length for a megabit message signing task compared to existing CV QDS protocols and this advantage expands as the message size grows. Our work offers a solution with enhanced security and efficiency, paving the way for large-scale deployment of CV QDS in future quantum networks., Comment: 18 pages, 8 figures

Satellite-to-ground quantum communication constitutes the cornerstone of the global quantum network, heralding the advent of the future of quantum information. Continuous-variable quantum key distribution is a strong candidate for space-ground quantum communication due to its simplicity, stability, and ease of implementation, especially for the robustness of space background light noise. Recently, the discrete-modulated continuous-variable protocol has garnered increased attention, owing to its lower implementation requirements, acceptable security key rate, and pronounced compatibility with extant infrastructures. Here, we derive key rates for discrete-modulated continuous-variable quantum key distribution protocols in free-space channel environments across various conditions through numerical simulation, revealing the viability of its application in satellite-to-ground communication., Comment: 12 pages, 6 figures

The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. This paper presents an overview of the current landscape of LLMs in healthcare, specifically focusing on their applications in analyzing and generating conversations for improved patient engagement. We showcase the power of LLMs in handling unstructured conversational data through four case studies: (1) analyzing mental health discussions on Reddit, (2) developing a personalized chatbot for cognitive engagement in seniors, (3) summarizing medical conversation datasets, and (4) designing an AI-powered patient engagement system. These case studies demonstrate how LLMs can effectively extract insights and summarizations from unstructured dialogues and engage patients in guided, goal-oriented conversations. Leveraging LLMs for conversational analysis and generation opens new doors for many patient-centered outcomes research opportunities. However, integrating LLMs into healthcare raises important ethical considerations regarding data privacy, bias, transparency, and regulatory compliance. We discuss best practices and guidelines for the responsible development and deployment of LLMs in healthcare settings. Realizing the full potential of LLMs in digital health will require close collaboration between the AI and healthcare professionals communities to address technical challenges and ensure these powerful tools' safety, efficacy, and equity., Comment: 10 pages, 6 figures, ICDH 2024 invited paper

Exploiting activation sparsity is a promising approach to significantly accelerating the inference process of large language models (LLMs) without compromising performance. However, activation sparsity is determined by activation functions, and commonly used ones like SwiGLU and GeGLU exhibit limited sparsity. Simply replacing these functions with ReLU fails to achieve sufficient sparsity. Moreover, inadequate training data can further increase the risk of performance degradation. To address these challenges, we propose a novel dReLU function, which is designed to improve LLM activation sparsity, along with a high-quality training data mixture ratio to facilitate effective sparsification. Additionally, we leverage sparse activation patterns within the Feed-Forward Network (FFN) experts of Mixture-of-Experts (MoE) models to further boost efficiency. By applying our neuron sparsification method to the Mistral and Mixtral models, only 2.5 billion and 4.3 billion parameters are activated per inference iteration, respectively, while achieving even more powerful model performance. Evaluation results demonstrate that this sparsity achieves a 2-5x decoding speedup. Remarkably, on mobile phones, our TurboSparse-Mixtral-47B achieves an inference speed of 11 tokens per second. Our models are available at \url{https://huggingface.co/PowerInfer}

Unstructured text in medical notes and dialogues contains rich information. Recent advancements in Large Language Models (LLMs) have demonstrated superior performance in question answering and summarization tasks on unstructured text data, outperforming traditional text analysis approaches. However, there is a lack of scientific studies in the literature that methodically evaluate and report on the performance of different LLMs, specifically for domain-specific data such as medical chart notes. We propose an evaluation approach to analyze the performance of open-source LLMs such as Llama2 and Mistral for medical summarization tasks, using GPT-4 as an assessor. Our innovative approach to quantitative evaluation of LLMs can enable quality control, support the selection of effective LLMs for specific tasks, and advance knowledge discovery in digital health.

Space-based gravitational wave detection is extremely sensitive to disturbances. The Keplerian configuration cannot accurately reflect the variations in spacecraft configuration. Planetary gravitational disturbances are one of the main sources. Numerical simulation is an effective method to investigate the impact of perturbation on spacecraft orbits. This study shows that, in the context of the Taiji project, Earth's gravity is an essential factor in the change in heliocentric formation configuration, contributing to the relative acceleration between spacecrafts in the order of $\mathcal O(10^{-6})\,{\rm m\cdot s^{-2}}$. Considering 00:00:00 on 27 October 2032 as the initial orbiting moment, under the influence of Earth's gravitational perturbation, the maximum relative change in armlengths and variation rates of armlengths for Taiji is $1.6\times 10^{5}\,{\rm km}$, $32\,{\rm m\cdot s^{-1}}$, respectively, compared with the unperturbed Keplerian orbit. Additionally, by considering the gravitational perturbations of Venus and Jupiter, the armlength and relative velocity for Taiji are reduced by $16.01\%$ and $17.45\%$, respectively, compared with when only considering that of Earth. The maximum amplitude of the formation motion indicator changes with the orbit entry time. Results show that the relative velocity increase between the spacecrafts is minimal when the initial orbital moment occurs in July. Moreover, the numerical simulation results are inconsistent when using different ephemerides. The differences between ephemerides DE440 and DE430 are smaller than those between DE440 and DE421., Comment: 18 pages, 11 figures

In recent years, the global unemployment rate has remained persistently high. Compounding this issue, the ageing population in China often encounters additional challenges in finding employment due to prevalent age discrimination in daily life. However, with the advent of social media, there has been a rise in the popularity of short videos and live-streams for recruiting ageing workers. To better understand the motivations of ageing job seekers to engage with these video-based recruitment methods and to explore the extent to which such platforms can empower them, we conducted an interview-based study with ageing job seekers who have had exposure to these short recruitment videos and live-streaming channels. Our findings reveal that these platforms can provide a job-seeking choice that is particularly friendly to ageing job seekers, effectively improving their disadvantaged situation., Comment: 14 pages, 3 figures; Accepted to ACM CHI 2024. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI'24)

We study the spin alignment of neutral rho mesons in a pion gas using spin kinetic or Boltzmann equations. The $\rho\pi\pi$ coupling is given by the chiral effective theory. The collision terms at the leading and next-to-leading order in spin Boltzmann equations are derived. The evolution of the spin density matrix of the neutral rho meson is simulated with different initial conditions. The numerical results show that the interaction of pions and neutral rho mesons creates very small spin alignment in the central rapidity region if there is no rho meson in the system at the initial time. Such a small spin alignment in the central rapidity region will decay rapidly toward zero in later time. If there are rho mesons with a sizable spin alignment at the initial time the spin alignment will also decrease rapidly. We also considered the effect on $\rho_{00}$ from the elliptic flow of pions in the blast wave model. With vanishing spin alignment at the initial time, the deviation of $\rho_{00}$ from 1/3 is positive but very small., Comment: RevTex 4, 17 pages, 12 figures

An ancient and counterintuitive phenomenon know as the Mpemba effect (water can cool faster when initially heated up) showcases the critical role of initial conditions in relaxation processes. How to realize and utilize this effect for speeding up relaxation is an important but challenging task in purely quantum system till now. Here, we report the first experiment, as far as we know,about the strong Mpemba effect in a single trapped ion system in which an exponentially expedited relaxation in time is observed by preparing an optimal initial state with no excitation of the slowest decaying mode. Also, we find that the condition of realizing such effect coincides with the Liouvillian exceptional point, featuring the coalescence of both the eigenvalues and the eigenmodes of the system. Our work provides an efficient strategy to exponentially accelerate relaxations of quantum system to their stationary state, and suggests a link unexplored yet between the Mpemba effect and the non-Hermitian physics. It could open up the door to engineer a wide range of dissipative quantum systems by utilizing the anomalous Mpemba effect, for applications in quantum simulation and quantum information processing.

We give a brief overview on global polarization and spin alignment in heavy ion collisions. The current theoretical understandings on global polarization of hyperons and the global spin alignment of vector mesons are summarized., Comment: 10 pages, 2 figures, Proceeding for SPIN 2023

We present a calculation of the spin alignment for unflavored vector mesons in thermalized quark-gluon plasma based on the Kubo formula in linear response theory. This is achieved by expanding the system to the first order of the coupling constant and the spatial gradient. The effect strongly relies on the vector meson's spectral functions which are determined by the interaction and medium properties. The spectral functions are calculated for the one-quark-loop self-energy with meson-quark interaction. The numerical results show that the correction to the spin alignment from the thermal shear tensor is of the order $10^{-4}\sim10^{-5}$ for the chosen values of quark-meson coupling constant, if the magnitude of thermal shear tensor is $10^{-2}$., Comment: 22 pages, 5 figures

Cloud cover plays a pivotal role in assessing observational conditions for astronomical site-testing. Except for the fraction of observing time, its fragmentation also wields a significant influence on the quality of nighttime sky clarity. In this article, we introduce the function Gamma, designed to comprehensively capture both the fraction of available observing time and its continuity. Leveraging in situ measurement data gathered at the Muztagh-ata site between 2017 and 2021, we showcase the effectiveness of our approach. The statistical result illustrates that the Muztagh-ata site affords approximately 122 nights of absolute clear and 205 very good nights annually, corresponding to Gamma greater than or equal 0.9 and Gamma greater than or equal 0.36 respectively., Comment: 8pages, 5 figures

Recently, Sato et al. proposed an public verifiable blind quantum computation (BQC) protocol by inserting a third-party arbiter. However, it is not true public verifiable in a sense, because the arbiter is determined in advance and participates in the whole process. In this paper, a public verifiable protocol for measurement-only BQC is proposed. The fidelity between arbitrary states and the graph states of 2-colorable graphs is estimated by measuring the entanglement witnesses of the graph states,so as to verify the correctness of the prepared graph states. Compared with the previous protocol, our protocol is public verifiable in the true sense by allowing other random clients to execute the public verification. It also has greater advantages in the efficiency, where the number of local measurements is O(n^3*log {n}) and graph states' copies is O(n^2*log{n})., Comment: 17 pages, 5 figures

Artificial intelligence (AI) has shown great promise in revolutionizing the field of digital health by improving disease diagnosis, treatment, and prevention. This paper describes the Health Guardian platform, a non-commercial, scientific research-based platform developed by the IBM Digital Health team to rapidly translate AI research into cloud-based microservices. The platform can collect health-related data from various digital devices, including wearables and mobile applications. Its flexible architecture supports microservices that accept diverse data types such as text, audio, and video, expanding the range of digital health assessments and enabling holistic health evaluations by capturing voice, facial, and motion bio-signals. These microservices can be deployed to a clinical cohort specified through the Clinical Task Manager (CTM). The CTM then collects multi-modal, clinical data that can iteratively improve the accuracy of AI predictive models, discover new disease mechanisms, or identify novel biomarkers. This paper highlights three microservices with different input data types, including a text-based microservice for depression assessment, a video-based microservice for sit-to-stand mobility assessment, and a wearable-based microservice for functional mobility assessment. The CTM is also discussed as a tool to help design and set up clinical studies to unlock the full potential of the platform. Today, the Health Guardian platform is being leveraged in collaboration with research partners to optimize the development of AI models by utilizing a multitude of input sources. This approach streamlines research efforts, enhances efficiency, and facilitates the development and validation of digital health applications., Comment: 10 pages, 6 figures

Amyotrophic lateral sclerosis is a fatal disease that not only affects movement, speech, and breath but also cognition. Recent studies have focused on the use of language analysis techniques to detect ALS and infer scales for monitoring functional progression. In this paper, we focused on another important aspect, cognitive impairment, which affects 35-50% of the ALS population. In an effort to reach the ALS population, which frequently exhibits mobility limitations, we implemented the digital version of the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) test for the first time. This test which is designed to measure cognitive impairment was remotely performed by 56 participants from the EverythingALS Speech Study. As part of the study, participants (ALS and non-ALS) were asked to describe weekly one picture from a pool of many pictures with complex scenes displayed on their computer at home. We analyze the descriptions performed within +/- 60 days from the day the ECAS test was administered and extract different types of linguistic and acoustic features. We input those features into linear regression models to infer 5 ECAS sub-scores and the total score. Speech samples from the picture description are reliable enough to predict the ECAS subs-scores, achieving statistically significant Spearman correlation values between 0.32 and 0.51 for the model's performance using 10-fold cross-validation., Comment: conference paper

Driving is a daily routine for many individuals across the globe. This paper presents the configuration and methodologies used to transform a vehicle into a connected ecosystem capable of assessing driver physiology. We integrated an array of commercial sensors from the automotive and digital health sectors along with driver inputs from the vehicle itself. This amalgamation of sensors allows for meticulous recording of the external conditions and driving maneuvers. These data streams are processed to extract key parameters, providing insights into driver behavior in relation to their external environment and illuminating vital physiological responses. This innovative driver evaluation system holds the potential to amplify road safety. Moreover, when paired with data from conventional health settings, it may enhance early detection of health-related complications., Comment: 7 pages, 11 figures, 2023 IEEE International Conference on Digital Health (ICDH)

Conformal dimension of a metric space $X$, denoted by $\dim_C X$, is the infimum of the Hausdorff dimension among all its quasisymmetric images. If conformal dimension of $X$ is equal to its Hausdorff dimension, $X$ is said to be minimal for conformal dimension. In this paper we show that the graph of the one dimensional Brownian motion is almost surely minimal for conformal dimension. We also give many other examples of minimal sets for conformal dimension, which we call Bedford-McMullen type sets. In particular we show that Bedford-McMullen self-affine sets with uniform fibers are minimal for conformal dimension. The main technique in the proofs is the construction of ``rich families of minimal sets of conformal dimension one''. The latter concept is quantified using Fuglede's modulus of measures., Comment: 42 pages, 6 figures

Prewriting is the process of discovering and developing ideas before a first draft, which requires divergent thinking and often implies unstructured strategies such as diagramming, outlining, free-writing, etc. Although large language models (LLMs) have been demonstrated to be useful for a variety of tasks including creative writing, little is known about how users would collaborate with LLMs to support prewriting. The preferred collaborative role and initiative of LLMs during such a creativity process is also unclear. To investigate human-LLM collaboration patterns and dynamics during prewriting, we conducted a three-session qualitative study with 15 participants in two creative tasks: story writing and slogan writing. The findings indicated that during collaborative prewriting, there appears to be a three-stage iterative Human-AI Co-creativity process that includes Ideation, Illumination, and Implementation stages. This collaborative process champions the human in a dominant role, in addition to mixed and shifting levels of initiative that exist between humans and LLMs. This research also reports on collaboration breakdowns that occur during this process, user perceptions of using existing LLMs during Human-AI Co-creativity, and discusses design implications to support this co-creativity process., Comment: To appear at ACM CSCW 2024; Accepted to PACM HCI (CSCW); 25 pages, 2 figures