Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Multi-Label Correlation in Label Distribution Learning

Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structure of label distribution to capture label correlation. However, recent studies disclosed that label distribution matrices are typically full-rank, posing challenges to those works exploiting low-rank label correlation. Note that multi-label is generally low-rank; low-rank label correlation is widely adopted in multi-label learning (MLL) literature. Inspired by that, we introduce an auxiliary MLL process in LDL and capture low-rank label correlation on that MLL rather than LDL. In such a way, low-rank label correlation is appropriately exploited in our LDL methods. We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL.
ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional Data

Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at osf.io/9k6jw, and source code at github.com/ManiVaultStudio.
Metric Space Spread, Intrinsic Dimension and the Manifold Hypothesis

The concepts of spread and spread dimension of a metric space were introduced by Willerton in the context of quantifying biodiversity of ecosystems. This paper develops practical applications of spread dimension in the context of machine learning and manifold learning; we show that the topological dimension of a Riemannian manifold can be accurately estimated by computing the spread dimension of a finite subset. These results are presented as the theoretical basis for a novel method of estimating the intrinsic dimension of data. The practical applications of this method are demonstrated with empirical computations using real and synthetic data.
Unsupervised Representation Learning for Time Series: A Review

Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enabling unsupervised representation learning is extremely crucial for time series data, due to its unique annotation bottleneck caused by its complex characteristics and lack of visual cues compared with other data modalities. In recent years, unsupervised representation learning techniques have advanced rapidly in various domains. However, there is a lack of systematic analysis of unsupervised representation learning approaches for time series. To fill the gap, we conduct a comprehensive literature review of existing rapidly evolving unsupervised representation learning approaches for time series. Moreover, we also develop a unified and standardized library, named ULTS (i.e., Unsupervised Learning for Time Series), to facilitate fast implementations and unified evaluations on various models. With ULTS, we empirically evaluate state-of-the-art approaches, especially the rapidly evolving contrastive learning methods, on 9 diverse real-world datasets. We further discuss practical considerations as well as open research challenges on unsupervised representation learning for time series to facilitate future research in this field.
Accelerating Grover Adaptive Search: Qubit and Gate Count Reduction Strategies with Higher-Order Formulations

Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this paper, we propose higher-order binary formulations that can simultaneously reduce the numbers of qubits and gates required for GAS. Specifically, we consider two novel strategies: one that reduces the number of gates through polynomial factorization, and the other that halves the order of the objective function, subsequently decreasing circuit runtime and implementation cost. Our analysis demonstrates that the proposed higher-order formulations improve the convergence performance of GAS by both reducing the search space size and the number of quantum gates. Our strategies are also beneficial for general combinatorial optimization problems using one-hot encoding.
UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles

This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset.
MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies

Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music.
Study on Microstructure and Coercivity of Thermally Processed Nd-Fe-B–type HDDR Material

Variation of intrinsic coercivity of the HDDR-treated Nd12.5Fe80. 8B6.4Ga0. 3 alloy after heating in various modes was investigated. Influence of vacuum degree and cooling after heating on the coercivity of the HDDR material was examined. The heat-treated HDDR material had consistently higher coercivity when it was quenched after heating compared to when slow-cooled. Higher coercivity in the quenched material was attributable to the grain boundary with lower Fe content. HDDR-treated material heated in high vacuum showed consistently higher coercivity than the material heated in lower vacuum, and this was attributed to less heavily oxidized surface. Reduced coercivity of the HDDR-treated material heated at moderate temperature was noticeably recovered at higher temperature, and this was attributed to lower Fe content in the grain boundary.
Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting

In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized loss function. Within this framework, the optimal neuron-interaction matrices turn out to be a class of matrices which correspond to Hebbian kernels revised by a reiterated unlearning protocol. Remarkably, the extent of such unlearning is proved to be related to the regularization hyperparameter of the loss function and to the training time. Thus, we can design strategies to avoid overfitting that are formulated in terms of regularization and early-stopping tuning. The generalization capabilities of these attractor networks are also investigated: analytical results are obtained for random synthetic datasets, next, the emerging picture is corroborated by numerical experiments that highlight the existence of several regimes (i.e., overfitting, failure and success) as the dataset parameters are varied.
Lexicon and Rule-based Word Lemmatization Approach for the Somali Language

Lemmatization is a Natural Language Processing (NLP) technique used to normalize text by changing morphological derivations of words to their root forms. It is used as a core pre-processing step in many NLP tasks including text indexing, information retrieval, and machine learning for NLP, among others. This paper pioneers the development of text lemmatization for the Somali language, a low-resource language with very limited or no prior effective adoption of NLP methods and datasets. We especially develop a lexicon and rule-based lemmatizer for Somali text, which is a starting point for a full-fledged Somali lemmatization system for various NLP tasks. With consideration of the language morphological rules, we have developed an initial lexicon of 1247 root words and 7173 derivationally related terms enriched with rules for lemmatizing words not present in the lexicon. We have tested the algorithm on 120 documents of various lengths including news articles, social media posts, and text messages. Our initial results demonstrate that the algorithm achieves an accuracy of 57\% for relatively long documents (e.g. full news articles), 60.57\% for news article extracts, and high accuracy of 95.87\% for short texts such as social media messages.
Accounting Measurement Revolution and Market Value

The study is aims to examine whether the adoption of accounting measurement of AIS/IFRS standards has resulted in more relevant financial information for decision making or not. This study examines the use of fair value measurement on value of financial instruments to capture if there is any effect on the market value of financial companies. Ohlson (1995) framework has been employed to investigate this relationship. The Jordanian financial sector was the sample for the study during the years from 2012 to 2016. Our study findings, based on the results of correlation and multiple regression analysis showed that, market value of Jordanian financial companies is significantly positively related to financial instruments measured by fair values. The final result assured that; the new accounting measurements methods such as fair values are value relevant for Jordanian financial companiesduring all period of the study.
Functional Shift-Induced Degenerate Transcritical Neimark–Sacker Bifurcation in a Discrete Hypercycle

In this paper, we investigate the impact of functional shifts in a time-discrete cross-catalytic system. We use the hypercycle model considering that one of the species shifts from a cooperator to a degrader. At the bifurcation caused by this functional shift, an invariant curve collapses to a point [Formula: see text] while, simultaneously, two fixed points collide with [Formula: see text] in a transcritical bifurcation. Moreover, all points of a line containing [Formula: see text] become fixed points at the bifurcation and only at the bifurcation in a degenerate scenario. We provide a complete analytical description of this degenerate bifurcation. As a result of our study, we prove the existence of the invariant curve arising from the transition to cooperation.
Impact of Sensing Range on Real-Time Adaptive Control of Signalized Intersections Using Vehicle Trajectory Information

Advanced signal control algorithms are anticipated with the increasing availability of vehicle speed and position data from vehicle-to-infrastructure communication and from sensors. This study examines the impact of the sensing range, meaning the distance from the intersection that such data can be obtained, on the quality of the signal control. Two advanced signal control methods, a Self-Organizing Algorithm (SOA) and Phase Allocation Algorithm (PAA), were implemented in simulation and tested to understand the impact of sensing ranges. SOA is based on fully-actuated control with an added secondary extension for vehicle platoons along the arterial. PAA uses dynamic programming to optimize phase sequences and phase duration within a planning horizon. Three different traffic scenarios were developed: symmetric, asymmetric, and balanced. In general, both algorithms exhibited improvements in performance as the sensing range was increased. Under the symmetric volume scenario, SOA converged at 1000 ft and PAA converged at 1500 ft (with vehicles traveling at 45mph). Under the asymmetrical and balanced volume scenarios, both algorithms outperformed conventional methods. Both algorithms performed better than coordinated-actuated control if the sensing range is 660 ft or higher. For low sensing ranges, SOA experiences similar delay compared to fully-actuated control with advance detector and PAA experienced more delay than coordinated-actuated control in symmetric and asymmetric scenario but performed better for asymmetric scenario. The results suggest that for the SOA algorithm, the sensing range may constrain the maximum allowable secondary extension, hence as the sensing range increases, the vehicular delay would decrease for arterial movements and increase for non-arterial movements. For PAA, arterial and non-arterial delay decreases with increase in sensing range until it converges.
Data-Driven Nonlinear TDOA for Accurate Source Localization in Complex Signal Dynamics

The complex and dynamic propagation of oscillations and waves is often triggered by sources at unknown locations. Accurate source localization enables the elimination of the rotor core in atrial fibrillation (AFib) as an effective treatment for such severe cardiac disorder; it also finds potential use in locating the spreading source in natural disasters such as forest fires and tsunamis. However, existing approaches such as time of arrival (TOA) and time difference of arrival (TDOA) do not yield accurate localization results since they tacitly assume a constant signal propagation speed whereas realistic propagation is often non-static and heterogeneous. In this paper, we develop a nonlinear TDOA (NTDOA) approach which utilizes observational data from various positions to jointly learn the propagation speed at different angles and distances as well as the location of the source itself. Through examples of simulating the complex dynamics of electrical signals along the surface of the heart and satellite imagery from forest fires and tsunamis, we show that with a small handful of measurements, NTDOA, as a data-driven approach, can successfully locate the spreading source, leading also to better forecasting of the speed and direction of subsequent propagation.
Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration

Utilizing radar sensing for assisting communication has attracted increasing interest thanks to its potential in dynamic environments. A particularly interesting problem for this approach appears in the vehicle-to-vehicle (V2V) millimeter wave and terahertz communication scenarios, where the narrow beams change with the movement of both vehicles. To address this problem, in this work, we develop a radar-aided beam-tracking framework, where a single initial beam and a set of radar measurements over a period of time are utilized to predict the future beams after this time duration. Within this framework, we develop two approaches with the combination of various degrees of radar signal processing and machine learning. To evaluate the feasibility of the solutions in a realistic scenario, we test their performance on a real-world V2V dataset. Our results indicated the importance of high angular resolution radar for this task and affirmed the potential of using radar for the V2V beam management problems.
Holographic bulk reconstruction with α 0 corrections

Shubho R. Roy and Debajyoti Sarkar Department of Physics, Indian Institute of Technology, Hyderabad, Kandi, Sangareddy 502285 Medak, Telengana, India Racah Inst. of Physics Hebrew, University of Jerusalem, Jerusalem 91904, Israel Arnold Sommerfeld Center, Ludwig-Maximilians-University, Theresienstr. 37, 80333 München, Germany Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), Föhringer Ring 6, D-80805 München, Germany (Received 8 August 2017; published 23 October 2017) We outline a holographic recipe to reconstruct α0 corrections to anti–de Sitter (AdS) (quantum) gravity from an underlying CFT in the strictly planar limit (N → ∞). Assuming that the boundary CFT can be solved in principle to all orders of the ’t Hooft coupling λ, for scalar primary operators, the λ−1 expansion of the conformal dimensions can be mapped to higher curvature corrections of the dual bulk scalar field action. Furthermore, for the metric perturbations in the bulk, the AdS=CFT operator-field isomorphism forces these corrections to be of the Lovelock type. We demonstrate this by reconstructing the coefficient of the leading Lovelock correction, also known as the Gauss-Bonnet term in a bulk AdS gravity action using the expression of stress-tensor two-point function up to subleading order in λ−1.
Dynamic 2 D-barcodes for multi-device Web session migration including mobile phones

Springer London, Personal and Ubiquitous Computing Published online: 2 April 2009, doi:10.1007/s00779-009-0228-5 Abstract This paper introduces a novel Web architecture that supports session migration in multi-device Web applications, particularly the case when a user starts a Web session on a computer and wishes to continue on a mobile phone. The proposed solution for transferring the needed session identifiers across devices is to dynamically generate pictures of 2D-barcodes containing a Web address and a session ID in an encoded form. 2D-barcodes are a cheap, fast and robust approach to the problem. They are widely known and used in Japan, and are spreading in other countries. Variations on the topic are covered in the article, including a possible migration from a mobile device to a computer (opposite direction), and between two or more mobile phones (possibly back and forth). The results show that this HCI approach is inexpensive, efficient, and works with most camera-phones on the market; the author does see any other mature technique with such assets.
Wasserstein Collaborative Filtering for Item Cold-start Recommendation

Item cold-start recommendation, which predicts user preference on new items that have no user interaction records, is an important problem in recommender systems. In this paper, we model the disparity between user preferences on warm items (those having interaction record) and that on cold-start items using the Wasserstein distance. On this basis, we propose Wasserstein Collaborative Filtering (WCF), which predicts user preference on cold-start items by minimizing the Wasserstein distance under user embedding constraint. Our analysis shows that minimizing the Wasserstein distance ensures that users sharing similar tastes on warm items also have similar preferences on cold-start items. Experimental results show that WCF consistently outperform the state-of-the-art methods in recommendation quality, usually by a large margin.
Expert Systems with Applications

http://dx.doi. org/10.1016/j.eswa. 2014.05. 054 0957-4174/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel. : +52 012222315058. E-mail address: julio.hernandez. t@ccc.inaoep. mx (J. Hernández). 1 Although the idea is inspired on multidimensional classification, the wo directly related to recent work in non-hierarchical multidimensional class e.g. Read, Pfahringer, Holmes, and Frank (2009). Julio Hernández ⇑, L. Enrique Sucar, Eduardo F. Morales
Foot and shin in terms of Anatomy Trains

The aim of this article is to make orthopedists familiar with the conception of Anatomy Trains, a new way to perceive a unit of movement a muscle is. According to this conception, we do not analyze functions of a single muscle but the myofascial continuity. Following the rule that “everything is joined with everything” in each organism, muscles and fascial structures that join them form entire chains of connections running along the body. From a clinical point of view, it seems of paramount importance not only because a disorder of one structure influences all the others in the given belt but also because a structure far from the location of symptoms can be a source of dysfunctions.
Secure Consistency Verification for Untrusted Cloud Storage by Public Blockchains Regular Paper

This work presents ContractChecker, a Blockchain-based security protocol for verifying the storage consistency between mutually distrusting cloud provider and clients. Unlike existing approaches, the ContractChecker uniquely delegates log auditing to the Blockchain, and has the advantages in reducing client cost and lowering requirements on client availability, lending itself to emerging security applications. New attacks are proposed that exploits the limitation of practical Blockchain systems including write unavailability, contract race conditions, and Blockchain forks. ContractChecker leverages proposed countermeasures to close the attack vectors and ensure the correctness of consistency assertion under attacks. We build a consistency verification service based on ContractChecker which charges service fee for running a smart contract on Ethereum. We evaluate the client cost of ContractChecker which is lower than the prior state-of-the-art by orders of magnitude. While our techniques improve the cost-effectiveness of Blockchain, the current system is practical mainly for low-frequency data storage.