Reviewerly/exHarmony huggingface dataset:
Approximating Subset Sum Ratio faster than Subset Sum

Subset Sum Ratio is the following optimization problem: Given a set of $n$ positive numbers $I$, find disjoint subsets $X,Y \subseteq I$ minimizing the ratio $\max\{\Sigma(X)/\Sigma(Y),\Sigma(Y)/\Sigma(X)\}$, where $\Sigma(Z)$ denotes the sum of all elements of $Z$. Subset Sum Ratio is an optimization variant of the Equal Subset Sum problem. It was introduced by Woeginger and Yu in '92 and is known to admit an FPTAS [Bazgan, Santha, Tuza '98]. The best approximation schemes before this work had running time $O(n^4/\varepsilon)$ [Melissinos, Pagourtzis '18], $\tilde O(n^{2.3}/\varepsilon^{2.6})$ and $\tilde O(n^2/\varepsilon^3)$ [Alonistiotis et al. '22]. In this work, we present an improved approximation scheme for Subset Sum Ratio running in time $O(n / \varepsilon^{0.9386})$. Here we assume that the items are given in sorted order, otherwise we need an additional running time of $O(n \log n)$ for sorting. Our improved running time simultaneously improves the dependence on $n$ to linear and the dependence on $1/\varepsilon$ to sublinear. For comparison, the related Subset Sum problem admits an approximation scheme running in time $O(n/\varepsilon)$ [Gens, Levner '79]. If one would achieve an approximation scheme with running time $\tilde O(n / \varepsilon^{0.99})$ for Subset Sum, then one would falsify the Strong Exponential Time Hypothesis [Abboud, Bringmann, Hermelin, Shabtay '19] as well as the Min-Plus-Convolution Hypothesis [Bringmann, Nakos '21]. We thus establish that Subset Sum Ratio admits faster approximation schemes than Subset Sum. This comes as a surprise, since at any point in time before this work the best known approximation scheme for Subset Sum Ratio had a worse running time than the best known approximation scheme for Subset Sum.
Controllable Data Generation Via Iterative Data-Property Mutual Mappings

Deep generative models have been widely used for their ability to generate realistic data samples in various areas, such as images, molecules, text, and speech. One major goal of data generation is controllability, namely to generate new data with desired properties. Despite growing interest in the area of controllable generation, significant challenges still remain, including 1) disentangling desired properties with unrelated latent variables, 2) out-of-distribution property control, and 3) objective optimization for out-of-distribution property control. To address these challenges, in this paper, we propose a general framework to enhance VAE-based data generators with property controllability and ensure disentanglement. Our proposed objective can be optimized on both data seen and unseen in the training set. We propose a training procedure to train the objective in a semi-supervised manner by iteratively conducting mutual mappings between the data and properties. The proposed framework is implemented on four VAE-based controllable generators to evaluate its performance on property error, disentanglement, generation quality, and training time. The results indicate that our proposed framework enables more precise control over the properties of generated samples in a short training time, ensuring the disentanglement and keeping the validity of the generated samples.
Ferret: Refer and Ground Anything Anywhere at Any Granularity

We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of understanding spatial referring of any shape or granularity within an image and accurately grounding open-vocabulary descriptions. To unify referring and grounding in the LLM paradigm, Ferret employs a novel and powerful hybrid region representation that integrates discrete coordinates and continuous features jointly to represent a region in the image. To extract the continuous features of versatile regions, we propose a spatial-aware visual sampler, adept at handling varying sparsity across different shapes. Consequently, Ferret can accept diverse region inputs, such as points, bounding boxes, and free-form shapes. To bolster the desired capability of Ferret, we curate GRIT, a comprehensive refer-and-ground instruction tuning dataset including 1.1M samples that contain rich hierarchical spatial knowledge, with 95K hard negative data to promote model robustness. The resulting model not only achieves superior performance in classical referring and grounding tasks, but also greatly outperforms existing MLLMs in region-based and localization-demanded multimodal chatting. Our evaluations also reveal a significantly improved capability of describing image details and a remarkable alleviation in object hallucination. Code and data will be available at https://github.com/apple/ml-ferret
DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language Models

Watermarking techniques offer a promising way to secure data via embedding covert information into the data. A paramount challenge in the domain lies in preserving the distribution of original data during watermarking. Our research extends and refines existing watermarking framework, placing emphasis on the importance of a distribution-preserving (DiP) watermark. Contrary to the current strategies, our proposed DiPmark preserves the original token distribution during watermarking (stealthy), is detectable without access to the language model API or weights (efficient), and is robust to moderate changes of tokens (resilient). This is achieved by incorporating a novel reweight strategy, combined with a hash function that assigns unique \textit{i.i.d.} ciphers based on the context. The empirical benchmarks of our approach underscore its stealthiness, efficiency, and resilience, making it a robust solution for watermarking tasks that demand impeccable quality preservation.
To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing

NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by better understanding our past. We study factors that shape NLP as a field, including culture, incentives, and infrastructure by conducting long-form interviews with 26 NLP researchers of varying seniority, research area, institution, and social identity. Our interviewees identify cyclical patterns in the field, as well as new shifts without historical parallel, including changes in benchmark culture and software infrastructure. We complement this discussion with quantitative analysis of citation, authorship, and language use in the ACL Anthology over time. We conclude by discussing shared visions, concerns, and hopes for the future of NLP. We hope that this study of our field's past and present can prompt informed discussion of our community's implicit norms and more deliberate action to consciously shape the future.
Landslide Movement of Bendungan District Trenggalek Using an Artificial Neural Network

Landslide is one of the disasters that often occurs in Indonesia in the East Java Province, especially in Bendungan District, Trenggalek Regency. Analysis of landslide susceptibility in Bendungan District is needed to spatially locate the landslide occurrences. The purpose of this study was to predict landslide events using an artificial neural network. Rainfall, topography, physical soil properties, and land-use were used as the explanatory variables. An analytic hierarchy process approach was applied to determine the weight of the variables. The model satisfactorily classified the landslide hazards with an area under curve of 0.96. The northwest area of the Bendungan District was found to be a region at high risk with rainfall and soil texture as the most influential parts in triggering the landslides.
Volcanic ash &amp; health: New chemical and isotopic approach

Volcanic ash exposure may lead to significant risk.To date, damages on the respiratory and pulmonary system are the most evident toxic side effects although the causes of these symptoms remain unclear.Additionally, the effects on other organs remain largely under-explored, limiting our understanding of the longterm volcanic ash-related risk at the whole-body scale.The metallome i.e., metal concentrations and isotopic compositions within the body, is suspected to be affected by volcanic ash exposure, having thus the potential for capturing some specificities of ash toxicity.The way and extent the metallome is affected and how these chemical deregulations correlate with pathophysiological dysfunctions is however poorly understood.Here, we adopt a multidisciplinary approach combining high precision chemical analyses (major and trace element concentrations) and Cu-Zn isotope measurements coupled with histological and biochemical analyses in 9 organs and 2 biological fluids of mice (C57BL/6) exposed to volcanic ash.We show that after 1 month of exposure, mice develop important metal homeostasis deregulations characterized by generalized heavy metal accumulation in the body associated to a significant and organ-specific Cu and Zn isotopic divergence observed in liver, kidney, urine and blood.These chemical variations are associated with early testicular defects and hepatic impairments that may, after prolonged exposure, seriously affect fertility and favor the emergence of liver diseases.Altogether, these results show that volcanic ash is an important metalenriched reservoir being highly toxic for the organism.Cu-Zn isotope compositions are promising tools for monitoring the toxic effect of volcanic ash and identifying the main biological processes and vital functions vulnerable to long-term exposure.
Research on the application of artificial intelligence generated AI technology in new media art

With the rapid development of information society and computer science, new media art has not made a qualitative leap in the past decade. The reason is that in big data and machine learning, the rapid development of artificial intelligence technology has brought computer science up to a higher level, while art is still stuck in aesthetics, acoustics, vision, psychology and other aspects, lack of logic, intelligence and integration. Therefore, the new media art needs the support of the computer technology, especially the new technology.In the design stage of new media art, technologies such as artificial intelligence, machine learning and big data mining are integrated to improve the molding speed of new media art through mathematical modeling.Through machine learning, quickly understand user experience and user needs, further optimize new media art works, use artificial intelligence AI technology to simulate new media art, and simulate the whole process of user experience and interaction is the main content of this paper.This makes new media art become a typical application in the field of AICG (artificial intelligence production content). Generative AI will greatly reduce the marginal cost of creation and knowledge work, greatly improve labor productivity and economic value, and realize the generation of new media art original content at one tenth of the cost and thousands times production speed.
On the preconditioning of the primal form of TFOV-based image deblurring model

Abstract To address the staircasing problem in deblurred images generated by a simple total variation (TV) based model, one approach is to use the total fractional-order variation (TFOV) image deblurring model. However, the discretization of the Euler–Lagrange equations for the TFOV-based model results in a nonlinear ill-conditioned system, which adversely influences the performance of computational methods like Krylov subspace algorithms (e.g., Generalized Minimal Residual, Conjugate Gradient). To address this challenge, three novel preconditioned matrices are proposed to improve the conditioning of the primal model when using the conjugate gradient method. These matrices are designed based on circulant approximations of the matrix associated with blurring kernel. Experimental evaluations demonstrate the effectiveness of the proposed preconditioned matrices in enhancing the convergence and accuracy of the conjugate gradient method for solving the primal form of the TFOV-based image deblurring model. The results highlight the importance of appropriate preconditioning strategies in achieving robust and high-quality image deblurring using the TFOV approach.
Formal Agricultural Credit Schemes and Economic Growth in Nigeria

This study examined the impact of formal agricultural credits on economic growth in Nigeria between 1981 and 2021. The specific objectives were to determine the effects of the agricultural credit guarantee scheme fund, bank loans and advances to agriculture, foreign aid to agriculture and the cost of funds on real gross domestic product (GDP) growth. The data required for the analysis were obtained from the Central Bank of Nigeria (CBN) Statistical Bulletin and the Organization for Economic Cooperation and Development (OECD) Statistics. The descriptive statistics, fully modified ordinary least squares (FM-OLS) and parsimonious error correction model (ECM) formed the basis for the data analysis. The unit root and Johansen cointegration tests results showed that the variables are all stationary at the first difference and cointegrated at the 5 per cent significance level. The long-run regression results showed that the agriculture credit guarantee scheme fund positively affected real GDP growth. The results further showed that bank loans and advances to the agriculture sector have a positive and significant effect on real GDP growth. It was found that 1 per cent increase in that bank loans and advances to the agriculture sector leads to 0.0378 per cent in real GDP growth. The effect of foreign aid to agriculture on real GDP growth was positive but insignificant at the 5 per cent significance level. Evidence of a positive and significant effect of the cost of funds on real GDP growth was established from the long-run results. The estimated coefficient showed that the real GDP growth increases by 0.00356 per cent due to a 1 per cent increase in the cost of funds. The short-run results showed that the coefficient (-0.5125) of the error correction term is negative and significant at the 5 per cent significance level, indicating that about 51.25 per cent distortion from the long-r
Quantum private function evaluation

Abstract Private function evaluation (PFE) is a task that aims to obtain the output of a function while keeping the function secret. So far its quantum analog has not yet been articulated. In this study, we initiate the study of quantum PFE (QPFE), the quantum analog of classical PFE. We give a formal definition of QPFE and present two schemes together with their security proofs. We then give an experimental demonstration of the scheme. Finally we apply QPFE to quantum copy protection to illustrate its usage.
Artificial intelligence approach for linking competences in nuclear field

Bridging traditional experts’ disciplinary boundaries is important for nuclear knowledge management systems. However, expert competences are often described in unstructured texts and require substantial human effort to link related competences across disciplines. The purpose of this research is to develop and evaluate a natural language processing approach, based on Latent Semantic Analysis, to enable the automatic linking of related competences across different disciplines and communities of practice. With datasets of unstructured texts as input training data, our results show that the algorithm can readily identify nuclear domain-specific semantic links between words and concepts. We discuss how our results can be utilized to generate a quantitative network of links between competences across disciplines, thus acting as an enabler for identifying and bridging communities of practice, in nuclear and beyond.
AI Framework for Non-Native PDF Comparison

Figuring out how a document has changed from one version to another isn’t always the simplest task. We encountered the problem of comparing two PDF documents, edited using different editing tools. When we tried to compare these PDFs, using existing comparison tools, comparison results were not sat- isfactory. After analysis, we found that, if documents had been edited using any other tool than acrobat(non-Native), then these tools were unable to detect the proper layout (para, header, footer, columns, tables etc.) of the document and therefore unable to sequence them in correct order resulting in false com- parison output. To overcome this problem, we tried latest developments in computer vision to detect the layout information of the document. Using lay- out information, contents were arranged in correct order and then compared. This resulted in better comparison output. Also, using AI for layout detection made it independent of how the document was created and edited. We built a complete framework which includes reading the information, detecting lay- out, arranging information, comparing it, and visualizing the differences. This Framework can be applied to build any document comparison tool irrespective of document type.
DIGITAL SAFETY IN SOCIAL NETWORKS: SOCIOLOGICAL SURVEY RESULTS AMONG STUDENTS OF VOCATIONAL EDUCATIONAL ORGANIZATIONS

The article examines the issues of digital safety in social networks among students in a vocational educational organization:media threats they face, problems and ways to protect personal pages. The purpose of the study is to study the subjective idea of digital safety in social networks of students of a vocational educational organization. The sociological online survey of vocational educational organization students in the Chelyabinsk region was used as a research method (the chronological scope of the survey is from January 16 to January 31, 2023). Thus, the material of the study was the results of a sociological survey (N=16169). The results of the survey regarding the popularity of social networks are fully correlated with the results of large domestic sociological surveys – most often over the past year, vocational educational organization students used VKontakte and Telegram; in general, a student’s page on a social network allows you to get a fairly complete picture of a person and his activities; students on these resources most often encounter deception and fraud; hacking a personal page, insults and threats in private messages, as well as creating a clone page; respondents refer to effective ways to protect their account and their identity: the absence of contacts on the personal page, the timely exit from accounts on other people’s devices, closing the page for those who are not friends, as well as limiting the placement of personal information on the page and the lack of geolocation. The authors of the article draw the conclusion about the insufficient competence of students in matters of digital security and the need for regular preventive measures to improve the overall level of media literacy and media security of students.
Sentiment Analysis for Online Learning using The Lexicon-Based Method and The Support Vector Machine Algorithm

The pros and cons regarding online learning has been a hot topic in society, both on social media and in the real world. Indonesian netizens still post opinions about online learning on social media such as Twitter. This study aims to analyze public comments to determine whether the trend of the comments is positive, negative, or neutral. The classification of netizen opinions is called sentiment analysis. This study applies 2 ways of carrying out sentiment analysis. The first stage employs the SVM algorithm with data labeling automatically obtained from the Emprit Academy drone portal while the second stage is still using the SVM algorithm but the data labeling with lexicon-based method. The results of this study are comparisons of labels obtained automatically from the Emprit Academy drone portal and labeling using lexicon based. The SVM algorithm obtains an accuracy of 90%, while the use of lexicon-based increases the accuracy value by 5% to 95%. It can be concluded that labeling data using a lexicon-based method can improve the accuracy of the SVM algorithm.
Introduction to Special Issue on Multilingual Text Analysis

Amsterdam University Press is a leading publisher of academic books, journals and textbooks in the Humanities and Social Sciences. Our aim is to make current research available to scholars, students, innovators, and the general public. AUP stands for scholarly excellence, global presence, and engagement with the international academic community.
ITSMatch: Improved Safe Semi-Supervised Text Classification Under Class Distribution Mismatch

Deep semi-supervised learning (SSL) brings deep learning from lab with expensive label data costs to real-world commercial application. Today, deep SSL is being universally applied in various artificial intelligence commercial technologies. However, there may be a distribution mismatch between labeled and unlabeled datasets in practical application, which is a key issue that degrades deep SSL performance. Some recent studies deal with out-of-distribution (OOD) data by directly removing or uniformly reducing weights, which ignore potential value of OOD data. To address the issue, we propose ITSMatch, a simple, safe and effective SSL method to process text classification by recycling OOD data near labeled domain to fully utilize data information. Specifically, a weighted adversarial domain adaptation is applied to OOD data to project it into the space of labeled and in-distribution (ID) data, and its recover ability is accurately quantified by the transferable score. ITSMatch unifies mainstream methods, including pseudo-labels generation and consistency regularization on unlabeled data and its augmented data. Besides, we also perform metric learning on labeled data and ID data with pseudo-labels to fully acquire sample space features. Experiment results on the AG News and Yelp datasets demonstrate that our ITSMatch method performs better than the baseline methods including TextSMatch, MixText, UDA, and BERT. This method of semi-supervised text classification can be applied to the analysis of product reviews on e-commerce platforms to improve customers’ online shopping experience.
3D Facial Priors Guided Local-Global Motion Collaboration Transforms for One-shot Talking-Head Video Synthesis

This paper presents a method that takes a sequence of audio, pose source video, and one reference image as input to generate natural head spatial movement talking face video. Based on the powerful 3D Morphable Model(3DMM), we propose a novel framework comprising two modules: Audio Map Expression Net and Local-Global Motion Collaboration Transforms Render Net. To make the audio map expression network predict more precise 3D facial expression parameters, we propose to use the shape constraint loss and the lip-sync constraint loss as additional constraints. The proposed Local-Global Motion Collaboration Transforms Render Net aims to improve generalization capability in the presence of apparent head motion by utilizing cross-modal images (3D reconstruction face and realistic face) as input to predict the residual motion of pose-aware dynamic content. Our work demonstrates the ability of Convolutional Neural Networks to utilize cross-modal images as input for simultaneous estimation of pose-aware dynamic content and local residual motion through self-supervised learning. Finally, we propose a multi-scale feature adaptive denormalization (FADE) U-net network architecture to exploit coarse-to-fine feature representations to improve the generated image quality. Experimental results demonstrate the effectiveness compared to other state-of-the-art methods.