Reviewerly/exHarmony huggingface dataset:
Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions

Recently, there has been increasing awareness of the tremendous opportunities inherent in quantum computing (QC). Specifically, the speed and efficiency of QC will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased QC research funding from national and regional governments and private firms. However, critical concerns regarding legal, political, and business-related policies germane to QC adoption exist. A few relevant studies are currently available, and those few focus heavily on the technical side of QC. Thus, this study offers a multidisciplinary review of QC, drawing on the expertise of scholars from a wide range of disciplines whose insights coalesce into a framework that simplifies the understanding of QC. This study offers a timely contribution to both practitioners and academia, as it explores use cases in business, addresses fundamental legal and political issues undermining QC's adoption, and highlights several research gaps critical to advancing knowledge within the field.
Tensor-network-assisted variational quantum algorithm

Near-term quantum devices generally suffer from shallow circuit depth and hence limited expressivity due to noise and decoherence. To address this, we propose tensor-network-assisted parametrized quantum circuits, which concatenate a classical tensor-network operator with a quantum circuit to effectively increase the circuit's expressivity without requiring a physically deeper circuit. We present a framework for tensor-network-assisted variational quantum algorithms that can solve quantum many-body problems using shallower quantum circuits. We demonstrate the efficiency of this approach by considering two examples of unitary matrix-product operators and unitary tree tensor networks, showing that they can both be implemented efficiently. Through numerical simulations, we show that the expressivity of these circuits is greatly enhanced with the assistance of tensor networks. We apply our method to two-dimensional Ising models and one-dimensional time-crystal Hamiltonian models with up to 16 qubits and demonstrate that our approach consistently outperforms conventional methods using shallow quantum circuits.
Cost-Effective Resource Allocation and Optimization Strategies for Multi-Cloud Environments

Abstract: As organizations increasingly adopt multi-cloud strategies to leverage the benefits of multiple cloud service providers, the need for effective cost optimization becomes paramount. This research paper aims to explore and propose innovative approaches for optimizing costs in multi-cloud environments. The study will investigate resource allocation, workload placement, and performance optimization techniques, taking into account factors such as workload variability, service-level agreements (SLAs), and pricing models across different cloud providers. The objective is to develop cost-effective strategies that balance performance and cost across a heterogeneous cloud ecosystem, ultimately helping organizations make informed decisions in their multi-cloud deployments. This research topic addresses the growing interest in multi-cloud architectures and the associated challenges in managing costs while ensuring high-quality service delivery. It provides a valuable contribution to the field of cloud computing and cost optimization, which is increasingly crucial for businesses and enterprises seeking to maximize the benefits of cloud technology.
Phase diagram of stochastic gradient descent in high-dimensional two-layer neural networks <sup>*</sup>

Abstract Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent. The picture can be radically different for narrow networks, which tend to get stuck in badly-generalizing local minima. Here we investigate the cross-over between these two regimes in the high-dimensional setting, and in particular investigate the connection between the so-called mean-field/hydrodynamic regime and the seminal approach of Saad &amp; Solla. Focusing on the case of Gaussian data, we study the interplay between the learning rate, the time scale, and the number of hidden units in the high-dimensional dynamics of stochastic gradient descent (SGD). Our work builds on a deterministic description of SGD in high-dimensions from statistical physics, which we extend and for which we provide rigorous convergence rates.
Specialty detection in the context of telemedicine in a highly imbalanced multi-class distribution

The Covid-19 pandemic has led to an increase in the awareness of and demand for telemedicine services, resulting in a need for automating the process and relying on machine learning (ML) to reduce the operational load. This research proposes a specialty detection classifier based on a machine learning model to automate the process of detecting the correct specialty for each question and routing it to the correct doctor. The study focuses on handling multiclass and highly imbalanced datasets for Arabic medical questions, comparing some oversampling techniques, developing a Deep Neural Network (DNN) model for specialty detection, and exploring the hidden business areas that rely on specialty detection such as customizing and personalizing the consultation flow for different specialties. The proposed module is deployed in both synchronous and asynchronous medical consultations to provide more real-time classification, minimize the doctor effort in addressing the correct specialty, and give the system more flexibility in customizing the medical consultation flow. The evaluation and assessment are based on accuracy, precision, recall, and F1-score. The experimental results suggest that combining multiple techniques, such as SMOTE and reweighing with keyword identification, is necessary to achieve improved performance in detecting rare classes in imbalanced multiclass datasets. By using these techniques, specialty detection models can more accurately detect rare classes in real-world scenarios where imbalanced data is common.
Belief Fisher–Shannon information plane: Properties, extensions, and applications to time series analysis

This paper introduce the belief Fisher–Shannon (BFS) information plane based on basic probability assignment concept. Moreover, upper and lower bounds of BFS information are also presented. In addition, BFS information plane is extended to belief Fisher–Rényi and fractional belief Fisher–Shannon ones, whose are based on Generalized Rényi and fractional Deng entropies, respectively. This study is exemplified by a simulation study of Logistic, Chebyshev, and Hénon chaotic map time series; and two applications related to fish condition factor and ozone pollutant time series. Time series were discretized using Freedman–Diaconis rule as a histogram estimator. Results indicate that proposed information planes based on histogram estimator provides a more efficient method with respect to parametric and non-parametric methods, such as one based on kernel density function. Therefore, our findings suggest that proposed information planes could be an appropriate tool to better investigate the complex dynamics of these signals.
TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records

Deep learning transformer-based models using longitudinal electronic health records (EHRs) have shown a great success in prediction of clinical diseases or outcomes. Pretraining on a large dataset can help such models map the input space better and boost their performance on relevant tasks through finetuning with limited data. In this study, we present TransformEHR, a generative encoder-decoder model with transformer that is pretrained using a new pretraining objective-predicting all diseases and outcomes of a patient at a future visit from previous visits. TransformEHR's encoder-decoder framework, paired with the novel pretraining objective, helps it achieve the new state-of-the-art performance on multiple clinical prediction tasks. Comparing with the previous model, TransformEHR improves area under the precision-recall curve by 2% (p < 0.001) for pancreatic cancer onset and by 24% (p = 0.007) for intentional self-harm in patients with post-traumatic stress disorder. The high performance in predicting intentional self-harm shows the potential of TransformEHR in building effective clinical intervention systems. TransformEHR is also generalizable and can be easily finetuned for clinical prediction tasks with limited data.
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

We present in this paper a novel denoising training method to speed up DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds GT bounding boxes with noises into the Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to faster convergence. Our method is universal and can be easily plugged into any DETR-like method by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$+1.9$</tex-math></inline-formula> AP) under the same setting and achieves 46.0 AP and 49.5 AP trained for 12 and 50 epochs with the ResNet-50 backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50\%$</tex-math></inline-formula> training epochs. We also demonstrate the effectiveness of denoising training in CNN-based detectors (Faster R-CNN), segmentation models (Mask2Former, Mask DINO), and more DETR-based models (DETR, Anchor DETR, Deformable DETR). Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IDEA-Research/DN-DETR</uri> .
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology

Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have been recently proposed for universal use in segmentation tasks. SAM shows remarkable promise in instance segmentation on natural images. However, the applicability of SAM to computational pathology tasks is limited due to the following factors: (1) lack of comprehensive pathology datasets used in SAM training and (2) the design of SAM is not inherently optimized for semantic segmentation tasks. In this work, we adapt SAM for semantic segmentation by first introducing trainable class prompts, followed by further enhancements through the incorporation of a pathology encoder, specifically a pathology foundation model. Our framework, SAM-Path enhances SAM’s ability to conduct semantic segmentation in digital pathology without human input prompts. Through extensive experiments on two public pathology datasets, the BCSS and the CRAG datasets, we demonstrate that the fine-tuning with trainable class prompts outperforms vanilla SAM with manual prompts by 27.52% in Dice score and 71.63% in IOU. On these two datasets, the proposed additional pathology foundation model further achieves a relative improvement of 5.07% to 5.12% in Dice score and 4.50% to 8.48% in IOU.
Studying User Perceptible Misalignment in Simulated Dynamic Facial Projection Mapping

High-speed dynamic facial projection mapping (DFPM) is an advanced technology that aims to create perceptual changes in facial appearance by overlapping images based on facial position and shape. Compared to traditional monitor-based augmented reality systems, DFPM offers a higher level of immersion because users can directly observe digital content on their faces. However, DFPM suffers from misalignment issues owing to a slight temporal delay from sensing to projection, which reduces the level of immersion. To the best of our knowledge, no previous study has established the necessary latency requirements to avoid perceptible misalignment and achieve an immersive experience. Furthermore, conventional DFPM works followed latency requirements that were not reported for the DFPM scenario. Therefore, this study measured the latency that provided a just-noticeable difference (JND) in DFPM under different facial motion conditions, using the weighted up-down two-alternative forced-choice method. The results showed that user-perceptible misalignment was influenced by facial motion types and their velocities. Additionally, it was found that an average latency of 3.87 ms was necessary to avoid perceptible misalignment in the DFPM system when the translation speed was 0.5 m/s, which contradicts the commonly held belief regarding the required latency threshold.
TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties

Despite the purported multilingual proficiency of instruction-finetuned large language models (LLMs) such as ChatGPT and Bard, the linguistic inclusivity of these models remains insufficiently explored. Considering this constraint, we present a thorough assessment of Bard and ChatGPT (encompassing both GPT-3.5 and GPT-4) regarding their machine translation proficiencies across ten varieties of Arabic. Our evaluation covers diverse Arabic varieties such as Classical Arabic (CA), Modern Standard Arabic (MSA), and several country-level dialectal variants. Our analysis indicates that LLMs may encounter challenges with dialects for which minimal public datasets exist, but on average are better translators of dialects than existing commercial systems. On CA and MSA, instruction-tuned LLMs, however, trail behind commercial systems such as Google Translate. Finally, we undertake a human-centric study to scrutinize the efficacy of the relatively recent model, Bard, in following human instructions during translation tasks. Our analysis reveals a circumscribed capability of Bard in aligning with human instructions in translation contexts. Collectively, our findings underscore that prevailing LLMs remain far from inclusive, with only limited ability to cater for the linguistic and cultural intricacies of diverse communities.
Enhancing Code-Switching for Cross-lingual SLU: A Unified View of Semantic and Grammatical Coherence

Despite the success of spoken language understanding (SLU) in high-resource languages, achieving similar performance in low-resource settings, such as zero-shot scenarios, remains challenging due to limited labeled training data. To improve zero-shot cross-lingual SLU, recent studies have explored code-switched sentences containing tokens from multiple languages. However, vanilla code-switched sentences often lack semantic and grammatical coherence. We ascribe this lack to two issues: (1) randomly replacing code-switched tokens with equal probability and (2) disregarding token-level dependency within each language. To tackle these issues, in this paper, we propose a novel method termed SoGo, for zero-shot cross-lingual SLU. First, we use a saliency-based substitution approach to extract keywords as substitution options. Then, we introduce a novel token-level alignment strategy that considers the similarity between the context and the code-switched tokens, ensuring grammatical coherence in code-switched sentences. Extensive experiments and analyses demonstrate the superior performance of SoGo across nine languages on MultiATIS++.
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Bo Qiao, Jue Zhang, Mohit Garg, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track. 2023.
“Are Your Explanations Reliable?” Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack

LIME has emerged as one of the most commonly referenced tools in explainable AI (XAI) frameworks that is integrated into critical machine learning applications (e.g., healthcare and finance). However, its stability remains little explored, especially in the context of text data, due to the unique text-space constraints. To address these challenges, in this paper, we first evaluate the inherent instability of LIME on text data to establish a baseline, and then propose a novel algorithm XAIFooler to perturb text inputs and manipulate explanations that casts investigation on the stability of LIME as a text perturbation optimization problem. XAIFooler conforms to the constraints to preserve text semantics and original prediction with small perturbations, and introduces Rank-biased Overlap (RBO) as a key part to guide the optimization of XAIFooler that satisfies all the requirements for explanation similarity measure. Extensive experiments on real-world text datasets demonstrate that XAIFooler significantly outperforms all baselines by large margins in its ability to manipulate LIME’s explanations with high semantic preservability.
Interactive Story Visualization with Multiple Characters

Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images. Most previous works endeavor to meet these requirements by fitting a text-to-image (T2I) model on a set of videos in the same style and with the same characters, e.g., the FlintstonesSV dataset. However, the learned T2I models typically struggle to adapt to new characters, scenes, and styles, and often lack the flexibility to revise the layout of the synthesized images. This paper proposes a system for generic interactive story visualization, capable of handling multiple novel characters and supporting the editing of layout and local structure. It is developed by leveraging the prior knowledge of large language and T2I models, trained on massive corpora. The system comprises four interconnected components: story-to-prompt generation (S2P), text-to-layout generation (T2L), controllable text-to-image generation (C-T2I), and image-to-video animation (I2V). First, the S2P module converts concise story information into detailed prompts required for subsequent stages. Next, T2L generates diverse and reasonable layouts based on the prompts, offering users the ability to adjust and refine the layout to their preferences. The core component, C-T2I, enables the creation of images guided by layouts, sketches, and actor-specific identifiers to maintain consistency and detail across visualizations. Finally, I2V enriches the visualization process by animating the generated images. Extensive experiments and a user study are conducted to validate the effectiveness and flexibility of interactive editing of the proposed system.
Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://github.com/bridgeqiqi/DREAM.
Exploring the Impact of ChatGPT Literacy on User Satisfaction: The Mediating Role of User Motivations

The introduction of chat generative pretrained transformer (ChatGPT), the fastest growing large language model, has changed the landscape of artificial intelligence–human interaction in everyday life. As the social influence of ChatGPT increases, competencies in it become important life skills. This study aims to explore the determinants of ChatGPT user satisfaction to provide practical implications by suggesting a significant independent variable and mediators between the independent variable and user satisfaction. To this end, this study recruited 822 college students with prior experience using ChatGPT (407 males and 415 females) and conducted an online survey. We tested the effects of ChatGPT literacy on user satisfaction and the mediating roles of different motives (i.e., information and knowledge acquisition and entertainment and leisure) in the relationship between ChatGPT literacy and user satisfaction. The results suggest that ChatGPT literacy significantly increases user satisfaction and that information and knowledge acquisition and entertainment and leisure partially mediate the relationship between the effect of ChatGPT literacy and user satisfaction. The results may have implications for large language model developers and practitioners, such as educators.