Reviewerly/exHarmony huggingface dataset:
GIFTS: Introducing Agile Process and Product Development in an FYE Course

Abstract Great Ideas for Teaching Students (GIFTS): This paper describes two activities developed for the pilot offering of a new first-year experience course for all engineering and computing majors in our college. The course is multi-disciplinary, with hands-on projects from several different areas. The course introduces engineering and computing design principles and practices, with a particular focus on an agile methodology. The first activity is part of the team building phase of the course, and it is a kinesthetic activity where students develop a process that satisfies constraints and meets an objective. The activity involves several sprints wherein the students measure their results, reflect, and improve their processes. It is adapted from an industry activity using balls; we use balloons because they are more cost effective and easier to store. The second activity is a team-based, computing-themed project where the students prototype an app for a humanitarian organization. The teams choose their topic from several provided lists (UN Sustainable Goals, for example), collaborate to create user stories (“as a [persona], I want to [do something] so that [a goal is achieved]”), develop functionality and wireframe mockups, then use free app simulation software to create an interactive app. Both activities are completed within a 2-hour classroom session (one session apiece). The first activity is assessed through an individual reflection essay about the student’s experience in developing, refining, and participating in the process. The second activity is assessed via a team lab report that showcases the brainstorming process and end result.
Effect of nonlinearity induced by atomic switch in Ag/Ag2S nanoparticles on performance of in-materio reservoir computing

A random network of Ag/Ag2S nanoparticles (NPs) was used as a physical system in reservoir computing (RC) because the network has nonlinear and dynamical characteristics. Ag/Ag2S NPs were synthesized by the modified Brust–Schiffrin method. Atomic switching among the NPs caused nonlinear dynamical behavior of the random network. The Fourier transform of output signals indicated that the generated harmonics were far higher with a larger amplitude of the input sine wave because the atomic switching occurred only at high bias voltages. Higher accuracy was achieved in the Boolean logic RC task because of the nonlinearity originating from switching. These findings suggest that nonlinearity plays a fundamental role in the design and implementation of RC devices.
Exploring the Possibility of Deciphering Tolkāppiyam through Korean Grammar

Tolkāpiyam, the oldest known book written in Old Tamil, is believed to have been authored around 100 B.C. Although Tolkāpiyam deals with the grammar of Tamil, a language still spoken by Tamils today, this ancient text, along with other Old Tamil books, has remained largely undeciphered. The reason for this lies in the fact that the grammar of Old Tamil has not been fully comprehended. Previous attempts to decipher Tolkāpiyam have mostly involved making educated guesses about the meaning of a paragraph based on the meanings of individual words.&#x0D; In this paper, I reveal the grammatical system of Old Tamil, drawing from the grammar theory presented in Part 2 of Tolkāpiyam. Old Tamil, much like pre-medieval Korean, employs predicates as nouns. It features elements such as chosa (조사), verb endings, honorifics, and causative verb suffixes. Remarkably, Tolkāpiyam is structured following the Korean word order. The grammar of Old Tamil closely resembles that of modern Korean. As a demonstration that Koreans can decipher Tolkāpiyam without prior knowledge of ancient Tamil, relying on word meanings alone, I also provide a correct interpretation for several paragraphs in Part I (Phonology). These interpretations challenge previous misconceptions and affirm the validity of my paper's argument, highlighting the limitations of prior research.
Challenges and Opportunities for Foreign Language Teachers in the Era of Artificial Intelligence

The emergence of large-scale language models such as ChatGPT has presented unprecedented challenges and opportunities in foreign language education. On one hand, artificial intelligence poses a disruptive crisis; on the other hand, it offers new educational insights, methodologies, and resources. Faced with these challenges and opportunities, university foreign language teachers must seize the opportunity to integrate intelligent technology into their teaching practices to enhance their teaching abilities and the quality of talent development. This study aims to investigate approaches for integrating artificial intelligence into foreign language teaching at the university level to improve teaching effectiveness and talent development outcomes. Through literature analysis and related research, the study identifies best practices and strategies for utilizing artificial intelligence in university foreign language education. The findings suggest that integrating artificial intelligence into foreign language teaching at the university level can significantly enhance teaching effectiveness and talent development. Thus, it is recommended that university foreign language teachers actively explore and implement artificial intelligence technology to enhance the quality of foreign language education and student talent development
The preoperative nursing consultation: constraints and suggestions for operationalisation - a qualitative study

Introduction The preoperative nursing consultation can be seen as a privileged moment for transmitting information to the person in a perioperative situation, to better prepare them for surgery and promote their collaboration in perioperative care. As this procedure is not carried out regularly, it is therefore important to carry out research into the reasons why it is not done. Objective To learn about the constraints to the operationalisation of the preoperative nursing consultation, through the perception of operating theatre nurses, as well as to identify their suggestions for overcoming the constraints, and to analyse the strategies pointed out by the nurses to prioritise this consultation. Method An exploratory, descriptive study of a qualitative nature. Data collection: focus group interview using a semi-structured interview script, supplemented at the end with an individual questionnaire containing socio-professional data and two open questions. Participants: purposive sample - three groups of nurses from three operating theatres in a central hospital in central Portugal. Content analysis was carried out according to Bardin's framework. The study considered ethical principles and scientific integrity. Results It was possible to identify the constraints pointed out by the nurses: methodology for operationalisation, still undefined; shortage of available time; late bed allocation; compromised privacy when carrying out the consultation; lack of recognition by hierarchical structures; difficulties in adequate physical space; unpredictable prolongation of intraoperative activities; distant residence of the person in a perioperative situation, and deficient human resources. The following suggestions/strategies were identified to overcome the constraints: allocation of more human resources; appropriate physical space; the need for greater interdisciplinary coordination and to create visibility. Conclusion The preoperative nursing consultation is an autonomous procedure that nurses are motivated to make operational, overcoming the constraints they point out and prioritising their suggestions.
Quantized Approximately Orthogonal Recurrent Neural Networks

Orthogonal recurrent neural networks (ORNNs) are an appealing option for learning tasks involving time series with long-term dependencies, thanks to their simplicity and computational stability. However, these networks often require a substantial number of parameters to perform well, which can be prohibitive in power-constrained environments, such as compact devices. One approach to address this issue is neural network quantization. The construction of such networks remains an open problem, acknowledged for its inherent instability.In this paper, we explore the quantization of the recurrent and input weight matrices in ORNNs, leading to Quantized approximately Orthogonal RNNs (QORNNs). We investigate one post-training quantization (PTQ) strategy and three quantization-aware training (QAT) algorithms that incorporate orthogonal constraints and quantized weights. Empirical results demonstrate the advantages of employing QAT over PTQ. The most efficient model achieves results similar to state-of-the-art full-precision ORNN and LSTM on a variety of standard benchmarks, even with 3-bits quantization.
Random features models: a way to study the success of naive imputation

Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce a large bias for prediction purposes, as the imputed input may strongly differ from the true underlying data. However, recent works suggest that this bias is low in the context of high-dimensional linear predictors when data is supposed to be missing completely at random (MCAR). This paper completes the picture for linear predictors by confirming the intuition that the bias is negligible and that surprisingly naive imputation also remains relevant in very low dimension.To this aim, we consider a unique underlying random features model, which offers a rigorous framework for studying predictive performances, whilst the dimension of the observed features varies.Building on these theoretical results, we establish finite-sample bounds on stochastic gradient (SGD) predictors applied to zero-imputed data, a strategy particularly well suited for large-scale learning.If the MCAR assumption appears to be strong, we show that similar favorable behaviors occur for more complex missing data scenarios.
Combining additivity and active subspaces for high-dimensional Gaussian process modeling

Gaussian processes are a widely embraced technique for regression and classification due to their good prediction accuracy, analytical tractability and built-in capabilities for uncertainty quantification. However, they suffer from the curse of dimensionality whenever the number of variables increases. This challenge is generally addressed by assuming additional structure in theproblem, the preferred options being either additivity or low intrinsic dimensionality. Our contribution for high-dimensional Gaussian process modeling is to combine them with a multi-fidelity strategy, showcasing the advantages through experiments on synthetic functions and datasets.
Decoding the Impact of Neighboring Amino Acid on MS Intensity Output through Deep Learning

Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the link from sequence to intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low MS1 response. The model could predict MS1 response with a mean average percentage error of ~11% and a Pearson correlation coefficient of ~0.68.
Electronic Learning Practice MOODLE -LESSON

Here in this work are discussed practical&nbsp; aspects of Electronic Learning. Moodle LMS (Learning Management System), which is&nbsp; an open teaching system and spreads free of charge,&nbsp; is chosen&nbsp; as a research tool. The main pedagogical and technological aspects of the system are presented here. Some special explanations are needed for its installation&nbsp; on the local internet space; Here are presented recommendations for a special activity of the teaching course, namely&nbsp; for planning and constructing&nbsp; stages of an adaptive “ lecture” -Lesson. Here are also presented&nbsp; a teacher’s and a student’s positions in the local server&nbsp; environment of the Moodle-LMS. Moreover, in this work is discussed the advantage of using the practical model&nbsp; of the “Lesson” in the ” Moodle auditorium” compared to having it in a traditional auditorium. All these are discussed considering both - students’ active involvement in the studying process, as well as for their independent working process under non-synchronous, local conditions. The instructions, which are presented in this work, enclose additional&nbsp; presentational resource links prepared and shared in&nbsp; Cloud Storage by the author. This allows a reader to become more familiar with details of the essential information.
GenFormer: A Deep-Learning-Based Approach for Generating Multivariate  Stochastic Processes

Stochastic generators are essential to produce synthetic realizations that preserve target statistical properties. We propose GenFormer, a stochastic generator for spatio-temporal multivariate stochastic processes. It is constructed using a Transformer-based deep learning model that learns a mapping between a Markov state sequence and time series values. The synthetic data generated by the GenFormer model preserves the target marginal distributions and approximately captures other desired statistical properties even in challenging applications involving a large number of spatial locations and a long simulation horizon. The GenFormer model is applied to simulate synthetic wind speed data at various stations in Florida to calculate exceedance probabilities for risk management.
GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross  Appearance-Edge Learning

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative cross appearance-edge learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an appearance-edge cross-attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at \url{https://github.com/Jenine-321/GenFace
Understanding Time Series Anomaly State Detection through One-Class  Classification

For a long time, research on time series anomaly detection has mainly focused on finding outliers within a given time series. Admittedly, this is consistent with some practical problems, but in other practical application scenarios, people are concerned about: assuming a standard time series is given, how to judge whether another test time series deviates from the standard time series, which is more similar to the problem discussed in one-class classification (OCC). Therefore, in this article, we try to re-understand and define the time series anomaly detection problem through OCC, which we call 'time series anomaly state detection problem'. We first use stochastic processes and hypothesis testing to strictly define the 'time series anomaly state detection problem', and its corresponding anomalies. Then, we use the time series classification dataset to construct an artificial dataset corresponding to the problem. We compile 38 anomaly detection algorithms and correct some of the algorithms to adapt to handle this problem. Finally, through a large number of experiments, we fairly compare the actual performance of various time series anomaly detection algorithms, providing insights and directions for future research by researchers.
Learning General Parameterized Policies for Infinite Horizon Average  Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm

This paper explores the realm of infinite horizon average reward Constrained Markov Decision Processes (CMDP). To the best of our knowledge, this work is the first to delve into the regret and constraint violation analysis of average reward CMDPs with a general policy parametrization. To address this challenge, we propose a primal dual based policy gradient algorithm that adeptly manages the constraints while ensuring a low regret guarantee toward achieving a global optimal policy. In particular, we demonstrate that our proposed algorithm achieves $\tilde{\mathcal{O}}({T}^{3/4})$ objective regret and $\tilde{\mathcal{O}}({T}^{3/4})$ constraint violation bounds.
Locally-Adaptive Quantization for Streaming Vector Search

Retrieving the most similar vector embeddings to a given query among a massive collection of vectors has long been a key component of countless real-world applications. The recently introduced Retrieval-Augmented Generation is one of the most prominent examples. For many of these applications, the database evolves over time by inserting new data and removing outdated data. In these cases, the retrieval problem is known as streaming similarity search. While Locally-Adaptive Vector Quantization (LVQ), a highly efficient vector compression method, yields state-of-the-art search performance for non-evolving databases, its usefulness in the streaming setting has not been yet established. In this work, we study LVQ in streaming similarity search. In support of our evaluation, we introduce two improvements of LVQ: Turbo LVQ and multi-means LVQ that boost its search performance by up to 28% and 27%, respectively. Our studies show that LVQ and its new variants enable blazing fast vector search, outperforming its closest competitor by up to 9.4x for identically distributed data and by up to 8.8x under the challenging scenario of data distribution shifts (i.e., where the statistical distribution of the data changes over time). We release our contributions as part of Scalable Vector Search, an open-source library for high-performance similarity search.
Nonlinear subspace clustering by functional link neural networks

Nonlinear subspace clustering based on a feed-forward neural network has been demonstrated to provide better clustering accuracy than some advanced subspace clustering algorithms. While this approach demonstrates impressive outcomes, it involves a balance between effectiveness and computational cost. In this study, we employ a functional link neural network to transform data samples into a nonlinear domain. Subsequently, we acquire a self-representation matrix through a learning mechanism that builds upon the mapped samples. As the functional link neural network is a single-layer neural network, our proposed method achieves high computational efficiency while ensuring desirable clustering performance. By incorporating the local similarity regularization to enhance the grouping effect, our proposed method further improves the quality of the clustering results. Additionally, we introduce a convex combination subspace clustering scheme, which combining a linear subspace clustering method with the functional link neural network subspace clustering approach. This combination approach allows for a dynamic balance between linear and nonlinear representations. Extensive experiments confirm the advancement of our methods. The source code will be released on https://lshi91.github.io/ soon.
Prediction modelling with many correlated and zero-inflated predictors:  assessing a nonnegative garrote approach

Building prediction models from mass-spectrometry data is challenging due to the abundance of correlated features with varying degrees of zero-inflation, leading to a common interest in reducing the features to a concise predictor set with good predictive performance. In this study, we formally established and examined regularized regression approaches, designed to address zero-inflated and correlated predictors. In particular, we describe a novel two-stage regularized regression approach (ridge-garrote) explicitly modelling zero-inflated predictors using two component variables, comprising a ridge estimator in the first stage and subsequently applying a nonnegative garrote estimator in the second stage. We contrasted ridge-garrote with one-stage methods (ridge, lasso) and other two-stage regularized regression approaches (lasso-ridge, ridge-lasso) for zero-inflated predictors. We assessed the predictive performance and predictor selection properties of these methods in a comparative simulation study and a real-data case study to predict kidney function using peptidomic features derived from mass-spectrometry. In the simulation study, the predictive performance of all assessed approaches was comparable, yet the ridge-garrote approach consistently selected more parsimonious models compared to its competitors in most scenarios. While lasso-ridge achieved higher predictive accuracy than its competitors, it exhibited high variability in the number of selected predictors. Ridge-lasso exhibited slightly superior predictive accuracy than ridge-garrote but at the expense of selecting more noise predictors. Overall, ridge emerged as a favourable option when variable selection is not a primary concern, while ridge-garrote demonstrated notable practical utility in selecting a parsimonious set of predictors, with only minimal compromise in predictive accuracy.
Training Implicit Networks for Image Deblurring using Jacobian-Free  Backpropagation

Recent efforts in applying implicit networks to solve inverse problems in imaging have achieved competitive or even superior results when compared to feedforward networks. These implicit networks only require constant memory during backpropagation, regardless of the number of layers. However, they are not necessarily easy to train. Gradient calculations are computationally expensive because they require backpropagating through a fixed point. In particular, this process requires solving a large linear system whose size is determined by the number of features in the fixed point iteration. This paper explores a recently proposed method, Jacobian-free Backpropagation (JFB), a backpropagation scheme that circumvents such calculation, in the context of image deblurring problems. Our results show that JFB is comparable against fine-tuned optimization schemes, state-of-the-art (SOTA) feedforward networks, and existing implicit networks at a reduced computational cost.