Reviewerly/exHarmony huggingface dataset:
Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images

Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.
Correcting spelling mistakes in Persian texts with rules and deep learning methods

This study aims to develop a system for automatically correcting spelling errors in Persian texts using two approaches: one that relies on rules and a common spelling mistake list and another that uses a deep neural network. The list of 700 common misspellings was compiled, and a database of 55,000 common Persian words was used to identify spelling errors in the rule-based approach. 112 rules were implemented for spelling correction, each providing suggested words for misspelled words. 2500 sentences were used for evaluation, with the word with the shortest Levenshtein distance selected for evaluation. In the deep learning approach, a deep encoder-decoder network that utilized long short-term memory (LSTM) with a word embedding layer was used as the base network, with FastText chosen as the word embedding layer. The base network was enhanced by adding convolutional and capsule layers. A database of 1.2 million sentences was created, with 800,000 for training, 200,000 for testing, and 200,000 for evaluation. The results showed that the network's performance with capsule and convolutional layers was similar to that of the base network. The network performed well in evaluation, achieving accuracy, precision, recall, F-measure, and bilingual evaluation understudy (Bleu) scores of 87%, 70%, 89%, 78%, and 84%, respectively.
Improved Ship Detection with YOLOv8 Enhanced with MobileViT and GSConv

In tasks that require ship detection and recognition, the irregular shapes of ships and complex backgrounds pose significant challenges. This paper presents an advanced extension of the YOLOv8 model to address these challenges. A lightweight visual transformer, MobileViTSF, is proposed and combined with the YOLOv8 model. To address the loss of semantic information that arises from inconsistent scales in the detection of small ships, a layer intended for the detection of small targets is introduced to lead to improved fusion of deep and shallow features. Furthermore, the traditional convolution (Conv) blocks are replaced with GSConv blocks, and a novel GSC2f block is designed for fewer model parameters and improved detection performance. Experiments on a benchmark dataset suggest that this new model can achieve significantly improved accuracy for ship detection with fewer model parameters and a reduced model size. A comparison with several other state-of-the-art methods shows that higher accuracy can be obtained for ship detection with this model. Moreover, this new model is suitable for edge computing devices, demonstrating practical application value.
Quantum Crosstalk Robust Quantum Control

The prevalence of quantum crosstalk in current quantum devices poses challenges for achieving high-fidelity quantum logic operations and reliable quantum processing. Through quantum control theory, we develop an analytical condition for achieving crosstalk-robust single-qubit control of multiqubit systems. We examine the effects of quantum crosstalk via a cumulant expansion and develop a condition to suppress the leading order contributions to the dynamics. The efficacy of the condition is illustrated in the domains of quantum state preservation and noise characterization through the development of crosstalk-robust dynamical decoupling and quantum noise spectroscopy (QNS) protocols. Using the IBM Quantum Experience, crosstalk-robust state preservation is demonstrated on 27 qubits, where up to a 3.5× improvement in coherence decay is observed for single-qubit product and multipartite entangled states. Through the use of noise injection, we demonstrate crosstalk-robust dephasing QNS on a seven qubit processor, where a 10^{4} improvement in reconstruction accuracy over alternative protocols is found. Together, these experiments highlight the significant impact the crosstalk suppression condition can have on improving multiqubit characterization and control on current quantum devices.
Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images

Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs). The null hypothesis asserts that SN has no effect on the accuracy of CNN-based IDC grading, while the alternative hypothesis suggests the contrary. We evaluated six SN techniques, with five templates selected as target images for the conventional SN techniques. We also utilized seven ImageNet pre-trained CNNs for IDC grading. The performance of models trained with and without SN was compared to discern the influence of SN on classification outcomes. The analysis unveiled a p-value of 0.11, indicating no statistically significant difference in Balanced Accuracy Scores between models trained with StainGAN-normalized images, achieving a score of 0.9196 (the best-performing SN technique), and models trained with non-normalized images, which scored 0.9308. As a result, we did not reject the null hypothesis, indicating that we found no evidence to support a significant discrepancy in effectiveness between stain-normalized and non-normalized datasets for IDC grading tasks. This study demonstrates that SN has a limited impact on IDC grading, challenging the assumption of performance enhancement through SN.
User Simulation for Evaluating Information Access Systems

With the emergence of various information access systems exhibiting increasing complexity, there is a critical need for sound and scalable means of automatic evaluation. To address this challenge, user simulation emerges as a promising solution. This half-day tutorial focuses on providing a thorough understanding of user simulation techniques designed specifically for evaluation purposes. We systematically review major research progress, covering both general frameworks for designing user simulators, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. We also highlight some important future research directions.
Event Knowledge in Large Language Models: The Gap Between the Impossible and the Unlikely

Word co-occurrence patterns in language corpora contain a surprising amount of conceptual knowledge. Large language models (LLMs), trained to predict words in context, leverage these patterns to achieve impressive performance on diverse semantic tasks requiring world knowledge. An important but understudied question about LLMs' semantic abilities is whether they acquire generalized knowledge of common events. Here, we test whether five pretrained LLMs (from 2018's BERT to 2023's MPT) assign a higher likelihood to plausible descriptions of agent-patient interactions than to minimally different implausible versions of the same event. Using three curated sets of minimal sentence pairs (total n = 1215), we found that pretrained LLMs possess substantial event knowledge, outperforming other distributional language models. In particular, they almost always assign a higher likelihood to possible versus impossible events (The teacher bought the laptop vs. The laptop bought the teacher). However, LLMs show less consistent preferences for likely versus unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow-up analyses, we show that (i) LLM scores are driven by both plausibility and surface-level sentence features, (ii) LLM scores generalize well across syntactic variants (active vs. passive constructions) but less well across semantic variants (synonymous sentences), (iii) some LLM errors mirror human judgment ambiguity, and (iv) sentence plausibility serves as an organizing dimension in internal LLM representations. Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events.
Unsupervised Satellite Video Deep Intrinsic Decomposition Using Physical Prior Constraints

Satellite video intrinsic decomposition has emerged as a promising area of research with significant application potential. However, existing methods still have certain limitations that hinder their effectiveness in extracting high-quality intrinsic information from complex scenes, ensuring temporal stability of the reflectance component, and achieving computational efficiency. In this article, an unsupervised satellite video intrinsic decomposition network (USVIDNet) is proposed, which overcomes the limitations encountered by existing methods. The USVIDNet incorporates three loss functions based on physical priors: reconstruction loss, chromaticity consistency loss, and spatiotemporal reflectance similarity loss, which provide constraints to guide the intrinsic decomposition process, eliminating the dependence on ground truth intrinsic images required for supervised learning. The network is based on a U-Net architecture variant, consisting of an encoder and two decoders. The encoder captures essential features of the input satellite video, while the decoders focus on predicting two components: reflectance and shading. To enhance the processing efficiency of intrinsic decomposition, a novel initialization-decomposition mode is proposed by leveraging the invariant background characteristics of staring satellites. Experiments are conducted on six Jilin-1 satellite videos to assess the performance of the proposed method in terms of intrinsic component extraction and improved ability of satellite video applications. The experimental results demonstrate the superiority of the proposed method.
Baldur: Whole-Proof Generation and Repair with Large Language Models

Formally verifying software is a highly desirable but labor-intensive task. Recent work has developed methods to automate formal verification using proof assistants, such as Coq and Isabelle/HOL, e.g., by training a model to predict one proof step at a time and using that model to search through the space of possible proofs. This paper introduces a new method to automate formal verification: We use large language models, trained on natural language and code and fine-tuned on proofs, to generate whole proofs at once. We then demonstrate that a model fine-tuned to repair generated proofs further increasing proving power. This paper: (1) Demonstrates that whole-proof generation using transformers is possible and is as effective but more efficient than search-based techniques. (2) Demonstrates that giving the learned model additional context, such as a prior failed proof attempt and the ensuing error message, results in proof repair that further improves automated proof generation. (3) Establishes, together with prior work, a new state of the art for fully automated proof synthesis. We reify our method in a prototype, Baldur, and evaluate it on a benchmark of 6,336 Isabelle/HOL theorems and their proofs, empirically showing the effectiveness of whole-proof generation, repair, and added context. We also show that Baldur complements the state-of-the-art tool, Thor, by automatically generating proofs for an additional 8.7% of the theorems. Together, Baldur and Thor can prove 65.7% of the theorems fully automatically. This paper paves the way for new research into using large language models for automating formal verification.
High-capacity device-independent quantum secure direct communication based on hyper-encoding

Quantum secure direct communication (QSDC) can directly transmit secret messages through quantum channel without keys. Device-independent (DI) QSDC guarantees the message security relying only on the observation of the Bell-inequality violation, but not on any detailed description or trust of the devices’ inner workings. Compared with conventional QSDC, DI-QSDC has relatively low secret message capacity. To increase DI-QSDC’s secret messages capacity, we propose a high-capacity DI-QSDC protocol based on the hyper-encoding technique. The total message leakage rate of our DI-QSDC protocol only relies on the most robust degree of freedom. We provide the numerical simulation of its secret message capacity altered with the communication distance. Our work serves as an important step toward the further development of DI-QSDC systems.
Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation

The process of transforming input images into corresponding textual explanations stands as a crucial and complex endeavor within the domains of computer vision and natural language processing. In this paper, we propose an innovative ensemble approach that harnesses the capabilities of Contrastive Language-Image Pretraining (CLIP) models. Our ensemble framework encompasses two significant variations of the CLIP model, each meticulously designed to cater to specific nuances within the image-to-text transformation landscape. The first model introduces an elaborated architecture, featuring multiple layers with distinct learning rates, thereby amplifying its adeptness in capturing intricate relationships between images and text. The second model strategically exploits CLIP's inherent zero-shot learning potential to generate image-text embeddings, subsequently harnessed by a K-Nearest Neighbors (KNN) model. Through this KNN-based paradigm, the model facilitates image-to-text transformation by identifying closely related embeddings within the embedding space. Notably, our ensemble approach is rigorously evaluated, employing the cosine similarity metric to gauge the alignment between model-generated embeddings and ground truth representations. Comparative experiments vividly highlight the superiority of our ensemble strategy over standalone CLIP models. This study not only advances the state-of-the-art in image-to-text transformation but also accentuates the promising trajectory of ensemble learning in effectively addressing intricate multimodal tasks.
Learning to Predict Arbitrary Quantum Processes

We present an efficient machine-learning (ML) algorithm for predicting any unknown quantum process E over n qubits. For a wide range of distributions D on arbitrary n-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process E, with a small average error over input states drawn from D. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to 106 and system size up to 50 qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.Received 14 April 2023Revised 11 November 2023Accepted 15 November 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040337Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum information processingQuantum information theoryQuantum Information, Science & Technology
Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems

The complexity of managing multiagent systems (MASs) in autonomic computing can be mitigated using a self-adaptation approach, where systems are equipped to monitor and adjust themselves based on specific concerns. Communication in these systems is key given that in scenarios involving agent interaction, it enhances cooperation and reduces coordination challenges by enabling direct, clear information exchange. However, the tasks of boosting communication expressiveness within MASs and logically processing a multitude of variables in dynamic environments are still challenging. This paper presents a novel strategy: integrating large language models (LLMs) like GPT-based technologies into MASs to boost communication and agent autonomy. Our proposal encompasses the development of a novel LLM/GPT-based agent architecture, focusing not only on advanced conversation features but also on the reasoning and decision-making capacities of these models. This is grounded in the MAPE-K model, known for supporting system adaptability in dynamic environments. We illustrate our approach through a marketplace scenario. This work represents a paradigm shift in MAS self-adaptation, utilizing LLMs' capabilities and indicating further research opportunities to assess LLMs' applicability in more complex MAS scenarios. This could pave the way for more potent problem-solving capabilities and refined communication within MASs.
Sentiment Analysis in the Age of COVID-19: A Bibliometric Perspective

The global impact of the COVID-19 pandemic has been profound, placing significant challenges upon healthcare systems and the world economy. The pervasive presence of illness, uncertainty, and fear has markedly diminished overall life satisfaction. Consequently, sentiment analysis has gained substantial traction among scholars seeking to unravel the emotional and attitudinal dimensions of this crisis. This research endeavors to provide a bibliometric perspective, shedding light on the principal contributors to this emerging field. It seeks to spotlight the academic institutions associated with this research domain, along with identifying the most influential publications in terms of both paper volume and h-index metrics. To this end, we have meticulously curated a dataset comprising 646 papers sourced from the ISI Web of Science database, all centering on the theme of sentiment analysis during the COVID-19 pandemic. Our findings underscore a burgeoning interest exhibited by the academic community in this particular domain, evident in an astonishing annual growth rate of 153.49%. Furthermore, our analysis elucidates key keywords and collaborative networks within the authorship, offering valuable insights into the global proliferation of this thematic pursuit. In addition to this, our analysis encompasses an n-gram investigation across keywords, abstracts, titles, and keyword plus, complemented by an examination of the most frequently cited works. The results gleaned from these endeavors offer crucial perspectives, contribute to the identification of pertinent issues, and provide guidance for informed decision-making.
Hallucinations in Large Multilingual Translation Models

Abstract Hallucinated translations can severely undermine and raise safety issues when machine translation systems are deployed in the wild. Previous research on the topic focused on small bilingual models trained on high-resource languages, leaving a gap in our understanding of hallucinations in multilingual models across diverse translation scenarios. In this work, we fill this gap by conducting a comprehensive analysis—over 100 language pairs across various resource levels and going beyond English-centric directions—on both the M2M neural machine translation (NMT) models and GPT large language models (LLMs). Among several insights, we highlight that models struggle with hallucinations primarily in low-resource directions and when translating out of English, where, critically, they may reveal toxic patterns that can be traced back to the training data. We also find that LLMs produce qualitatively different hallucinations to those of NMT models. Finally, we show that hallucinations are hard to reverse by merely scaling models trained with the same data. However, employing more diverse models, trained on different data or with different procedures, as fallback systems can improve translation quality and virtually eliminate certain pathologies.
Review of large vision models and visual prompt engineering

Visual prompt engineering is a fundamental methodology in the field of visual and image artificial general intelligence. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.