Miracle-dz/UniFAR-FTUs huggingface dataset:
AdversarialWord Dilution as Text Data Augmentation in Low-Resource Regime

Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training. Despite the success, generating data augmentations as hard positive examples that may increase their effectiveness is under-explored. This paper proposes an Adversarial Word Dilution (AWD) method that can generate hard positive examples as text data augmentations to train the low-resource text classification model efficiently. Our idea of augmenting the text data is to dilute the embedding of strong positive words by weighted mixing with unknown-word embedding, making the augmented inputs hard to be recognized as positive by the classification model. We adversarially learn the dilution weights through a constrained min-max optimization process with the guidance of the labels. Empirical studies on three benchmark datasets show that AWD can generate more effective data augmentations and outperform the state-of-the-art text data augmentation methods. The additional analysis demonstrates that the data augmentations generated by AWD are interpretable and can flexibly extend to new examples without further training.
Spiking Network Initialisation and Firing Rate Collapse

In recent years, newly developed methods to train spiking neural networks (SNNs) have rendered them as a plausible alternative to Artificial Neural Networks (ANNs) in terms of accuracy, while at the same time being much more energy efficient at inference and potentially at training time. However, it is still unclear what constitutes a good initialisation for an SNN. We often use initialisation schemes developed for ANN training which are often inadequate and require manual tuning. In this paper, we attempt to tackle this issue by using techniques from the ANN initialisation literature as well as computational neuroscience results. We show that the problem of weight initialisation for ANNs is a more nuanced problem than it is for ANNs due to the spike-and-reset non-linearity of SNNs and the firing rate collapse problem. We firstly identify and propose several solutions to the firing rate collapse problem under different sets of assumptions which successfully solve the issue by leveraging classical random walk and Wiener processes results. Secondly, we devise a general strategy for SNN initialisation which combines variance propagation techniques from ANNs and different methods to obtain the expected firing rate and membrane potential distribution based on diffusion and shot-noise approximations. Altogether, we obtain theoretical results to solve the SNN initialisation which consider the membrane potential distribution in the presence of a threshold. Yet, to what extent can these methods be successfully applied to SNNs on real datasets remains an open question.
Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks

In this work we introduce a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.
State-Space Methods for Time Series Analysis: Theory, Applications and Software

Introduction Linear state-space models The multiple error model Single error models Model transformations Model decomposition Model combination Change of variables in the output Uses of these transformations Filtering and smoothing The conditional moments of a state-space model The Kalman filter Decomposition of the smoothed moments Smoothing for a general state-space model Smoothing for fixed-coefficients and single-error models Uncertainty of the smoothed estimates in a fixed-coefficients SEM Examples Likelihood computation for fixed-coefficients models Maximum likelihood estimation The likelihood for a non-stationary model The likelihood for a model with inputs Examples The likelihood of models with varying parameters Regression with time-varying parameters Periodic models The likelihood of models with GARCH errors Examples Subspace methods Theoretical foundations System order estimation Constrained estimation Multiplicative seasonal models Examples Signal extraction Input and error-related components Estimation of the deterministic components Decomposition of the stochastic component Structure of the method Examples The VARMAX representation of a state-space model Notation and previous results Obtaining the VARMAX form of a state-space model Practical applications and examples Aggregation and disaggregation of time series The effect of aggregation on a state-space model Observability in the aggregated model Specification of the high-frequency model Empirical example The cross-sectional extension: longitudinal and panel data Model formulation The Kalman filter The linear mixed model in state-space form Maximum likelihood estimation Missing data modifications Real data examples Appendix A: Some results in numerical algebra and linear systems Appendix B: Asymptotic properties of maximum likelihood estimates Appendix C: Software (E4) Appendix D: Downloading E4 and the examples in this book Bibliography
Single-input polarization-sensitive optical coherence tomography through a catheter.

Intravascular polarimetry with catheter-based polarization-sensitive optical coherence tomography (PS-OCT) complements the high-resolution structural tomograms of OCT with morphological contrast available through polarimetry. Its clinical translation has been complicated by the need for modification of conventional OCT hardware to enable polarimetric measurements. Here, we present a signal processing method to reconstruct the polarization properties of tissue from measurements with a single input polarization state, bypassing the need for modulation or multiplexing of input states. Our method relies on a polarization symmetry intrinsic to round-trip measurements and uses the residual spectral variation of the polarization states incident on the tissue to avoid measurement ambiguities. We demonstrate depth-resolved birefringence and optic axis orientation maps reconstructed from in-vivo data of human coronary arteries. We validate our method through comparison with conventional dual-input state measurements and find a mean cumulative retardance error of 13.2deg without observable bias. The 95% limit of agreement between depth-resolved birefringence is 2.80 · 10-4, which is less than the agreement between two repeat pullbacks of conventional PS-OCT (3.14 · 10-4), indicating that the two methods can be used interchangeably. The hardware simplification arising from using a single input state may be decisive in realizing the potential of polarimetric measurements for assessing coronary atherosclerosis in clinical practice.
Quantum Geometrodynamics Revived II. Hilbert Space of Positive Definite Metrics

This paper represents the second in a series of works aimed at reinvigorating the quantum geometrodynamics program. Our approach introduces a lattice regularization of the hypersurface deformation algebra, such that each lattice site carries a set of canonical variables given by the components of the spatial metric and the corresponding conjugate momenta. In order to quantize this theory, we describe a representation of the canonical commutation relations that enforces the positivity of the operators $\hat q_{ab} s^a s^b$ for all choices of $s$. Moreover, symmetry of $\hat q_{ab}$ and $\hat p^{ab}$ is ensured. This reflects the physical requirement that the spatial metric should be a positive definite, symmetric tensor. To achieve this end, we resort to the Cholesky decomposition of the spatial metric into upper triangular matrices with positive diagonal entries. Moreover, our Hilbert space also carries a representation of the vielbein fields and naturally separates the physical and gauge degrees of freedom. Finally, we introduce a generalization of the Weyl quantization for our representation. We want to emphasize that our proposed methodology is amenable to applications in other fields of physics, particularly in scenarios where the configuration space is restricted by complicated relationships among the degrees of freedom.
Operationalizing content moderation "accuracy" in the Digital Services Act

The Digital Services Act, recently adopted by the EU, requires social media platforms to report the"accuracy"of their automated content moderation systems. The colloquial term is vague, or open-textured -- the literal accuracy (number of correct predictions divided by the total) is not suitable for problems with large class imbalance, and the ground truth and dataset to measure accuracy against is unspecified. Without further specification, the regulatory requirement allows for deficient reporting. In this interdisciplinary work, we operationalize"accuracy"reporting by refining legal concepts and relating them to technical implementation. We start by elucidating the legislative purpose of the Act to legally justify an interpretation of"accuracy"as precision and recall. These metrics remain informative in class imbalanced settings, and reflect the proportional balancing of Fundamental Rights of the EU Charter. We then focus on the estimation of recall, as its naive estimation can incur extremely high annotation costs and disproportionately interfere with the platform's right to conduct business. Through a simulation study, we show that recall can be efficiently estimated using stratified sampling with trained classifiers, and provide concrete recommendations for its application. Finally, we present a case study of recall reporting for a subset of Reddit under the Act. Based on the language in the Act, we identify a number of ways recall could be reported due to underspecification. We report on one possibility using our improved estimator, and discuss the implications and need for legal clarification.
Self-Consistent Study of the Superconducting Gap in the Strontium-doped Lanthanum Cuprate

This work is aimed at numerically investigating the behavior of the Fermi energy in Strontium-doped Lanthanum Cuprate, using a numerical zero temperature elastic scattering cross-section procedure in the unitary collision regime. The main task is to vary the zero temperature superconducting energy gap from its zero value in the normal state, to the highest value of 60 meV. We find that there are two different reduced phase space regimes for the first harmonic line node's order parameter. The first scenario considers that when the Fermi energy and the nearest hopping terms have the same order of magnitude, the physics can be described by a picture given by nonequilibrium statistical mechanics. A second scenario indicates, that when the Fermi energy parameter and the hopping term have different order of magnitude; the physical picture tends to be related to the nonrelativistic quantum mechanical degrees of freedom coming from quasi-stationary quantum energy levels, with a damping term seen in the probability density distribution function, that is described in the configuration space. Henceforth, it is concluded that the use of the zero temperature elastic scattering cross-section links the phase and configuration spaces through the inverse scattering lifetime, and helps to clarify the role of the degrees of freedom in Strontium-doped Lanthanum Cuprate. Finally, we think that the self-consistent numerical procedure with the reduced phase space, induces nonlocality in the inverse scattering lifetime.
BOSS -- Biomarker Optimal Segmentation System

Motivation: Precision medicine is a major trend in the future of medicine. It aims to provide tailored medical treatment and prevention strategies based on an individual's unique characteristics and needs. Biomarker is the primary source of patients' unique features used in precision medicine. We often need to investigate many cutoff values of a continuous biomarker to find the optimal one and test if it can help segment patients into two groups with significantly different clinical outcomes. This requires multiple testing adjustments on tests conducted on overlapped data. The permutation-based approach is often a preferred solution, since it does not suffer the limitations of state-of-art theoretical methods. However, permutation is computationally expensive and limits its application scenarios, such as web applications requiring a fast response or the analysis of genomic study requiring to repeat analysis many times on tens of thousands of genes. Results: We proposed a novel method BOSS, Biomarker Optimal Segmentation System, to solve this problem. In simulation studies, we found BOSS's statistical power and type I error control are both non-inferior to the permutation approach, and it is hundreds of times faster than permutation. To illustrate our method, we applied BOSS to real data and revealed potentially converging biomarkers that have referential importance in exploring synergy and target-matched therapies in lung adenocarcinoma. Availability: An R package, boss, is being developed and will be available on CRAN
Towards a methodology to consider the environmental impacts of digital agriculture

Agriculture affects global warming, while its yields are threatened by it. Information and communication technology (ICT) is often considered as a potential lever to mitigate this tension, through monitoring and process optimization. However, while agricultural ICT is actively promoted, its environmental impact appears to be overlooked. Possible rebound effects could put at stake its net expected benefits and hamper agriculture sustainability. By adapting environmental footprint assessment methods to digital agriculture context, this research aims at defining a methodology taking into account the environmental footprint of agricultural ICT systems and their required infrastructures. The expected contribution is to propose present and prospective models based on possible digitalization scenarios, in order to assess effects and consequences of different technological paths on agriculture sustainability, sufficiency and resilience. The final results could be useful to enlighten societal debates and political decisions.
Equity Protection Swaps: An New Type of Insurance for Superannuation

An equity protection swap (EPS) is a proposed new type of insurance product for superannuation accounts, which is largely based on an equity swap structure. It is a financial derivative, which is reminiscent of a total return swap but has also some features of the annuity insurance product known as the registered index-linked annuity (RILA). The buyer of an EPS agrees to share portfolio's gains with its provider if the realised return on the reference portfolio is above a predetermined positive level and obtain a partial protection if a loss on that portfolio occurs. Formally, a generic EPS consists of the protection and fee legs with different participation rates, which are negotiated by the provider of an EPS and its buyer. We first derive a general pricing formula for a standard EPS by obtaining the static hedging strategy based on traded European options. Next, we argue that it order to make the contract appealing to a typical holder of superannuation account, the provider should select an appropriate participation rate for the fee leg in relation to the protection level required by the holder so that fair premium for the contract at its inception date is null. We present numerical examples based on the market data to illustrate our theoretical findings and demonstrate the benefits of an EPS as an efficient portfolio's insurance tool for its holder.
Increasing Melanoma Diagnostic Confidence: Forcing the Convolutional Network to Learn from the Lesion

Deep learning implemented with convolutional network architectures can exceed specialists' diagnostic accuracy. However, whole-image deep learning trained on a given dataset may not generalize to other datasets. The problem arises because extra-lesional features - ruler marks, ink marks, and other melanoma correlates - may serve as information leaks. These extra-lesional features, discoverable by heat maps, degrade melanoma diagnostic performance and cause techniques learned on one data set to fail to generalize. We propose a novel technique to improve melanoma recognition by an EfficientNet model. The model trains the network to detect the lesion and learn features from the detected lesion. A generalizable elliptical segmentation model for lesions was developed, with an ellipse enclosing a lesion and the ellipse enclosed by an extended rectangle (bounding box). The minimal bounding box was extended by 20% to allow some background around the lesion. The publicly available International Skin Imaging Collaboration (ISIC) 2020 skin lesion image dataset was used to evaluate the effectiveness of the proposed method. Our test results show that the proposed method improved diagnostic accuracy by increasing the mean area under receiver operating characteristic curve (mean AUC) score from 0.9 to 0.922. Additionally, correctly diagnosed scores are also improved, providing better separation of scores, thereby increasing melanoma diagnostic confidence. The proposed lesion-focused convolutional technique warrants further study.
Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visions

In manufacturing settings, data collection and analysis are often a time-consuming, challenging, and costly process. It also hinders the use of advanced machine learning and data-driven methods which require a substantial amount of offline training data to generate good results. It is particularly challenging for small manufacturers who do not share the resources of a large enterprise. Recently, with the introduction of the Internet of Things (IoT), data can be collected in an integrated manner across the factory in real-time, sent to the cloud for advanced analysis, and used to update the machine learning model sequentially. Nevertheless, small manufacturers face two obstacles in reaping the benefits of IoT: they may be unable to afford or generate enough data to operate a private cloud, and they may be hesitant to share their raw data with a public cloud. Federated learning (FL) is an emerging concept of collaborative learning that can help small-scale industries address these issues and learn from each other without sacrificing their privacy. It can bring together diverse and geographically dispersed manufacturers under the same analytics umbrella to create a win-win situation. However, the widespread adoption of FL across multiple manufacturing organizations remains a significant challenge. This study aims to review the challenges and future directions of applying federated learning in the manufacturing industry, with a specific emphasis on the perspectives of Industry 4.0 and 5.0.
Non-Parametric Temporal Adaptation for Social Media Topic Classification

User-generated social media data is constantly changing as new trends influence online discussion and personal information is deleted due to privacy concerns. However, most current NLP models are static and rely on fixed training data, which means they are unable to adapt to temporal change -- both test distribution shift and deleted training data -- without frequent, costly re-training. In this paper, we study temporal adaptation through the task of longitudinal hashtag prediction and propose a non-parametric dense retrieval technique, which does not require re-training, as a simple but effective solution. In experiments on a newly collected, publicly available, year-long Twitter dataset exhibiting temporal distribution shift, our method improves by 64.12% over the best parametric baseline without any of its costly gradient-based updating. Our dense retrieval approach is also particularly well-suited to dynamically deleted user data in line with data privacy laws, with negligible computational cost and performance loss.
Rewards weaken cross-modal inhibition of return with visual targets

Previous studies have shown that rewards weaken visual inhibition of return (IOR). However, the specific mechanisms underlying the influence of rewards on cross-modal IOR remain unclear. Based on the Posner exogenous cue-target paradigm, the present study was conducted to investigate the effect of rewards on exogenous spatial cross-modal IOR in both visual cue with auditory target (VA) and auditory cue with visual target (AV) conditions. The results showed the following: in the AV condition, the IOR effect size in the high-reward condition was significantly lower than that in the low-reward condition. However, in the VA condition, there was no significant IOR in either the high- or low-reward condition and there was no significant difference between the two conditions. In other words, the use of rewards modulated exogenous spatial cross-modal IOR with visual targets; specifically, high rewards may have weakened IOR in the AV condition. Taken together, our study extended the effect of rewards on IOR to cross-modal attention conditions and demonstrated for the first time that higher motivation among individuals under high-reward conditions weakened the cross-modal IOR with visual targets. Moreover, the present study provided evidence for future research on the relationship between reward and attention.
Familial Whole Exome Sequencing Study of 30 Families With Early-Onset High Myopia

Purpose This study was conducted to investigate potential candidate pathogenic genes in early-onset high myopia (eoHM) in families with eoHM. Methods Whole-exome sequencing was performed on probands with eoHM to identify potential pathogenic genes. Sanger sequencing was used to verify the identified gene mutations causing eoHM in first-degree relatives of the proband. The identified mutations were screened out by bioinformatics analysis combined with segregation analysis. Results A total of 131 variant loci, involving 97 genes, were detected in the 30 families. A total of 28 genes (37 variants), which were carried by 24 families, were verified and analyzed by Sanger sequencing. We identified five genes and 10 loci associated with eoHM, which have not been reported in previous research. Hemizygous mutations in COL4A5, NYX, and CACNA1F were detected in this study. Inherited retinal disease-associated genes were found in 76.67% (23/30) of families. Genes that can be expressed in the retina in the Online Mendelian Inheritance in Man database were found in 33.33% (10/30) of families. Mutations in the genes associated with eoHM, including CCDC111, SLC39A5, P4HA2, CPSF1, P4HA2, and GRM6, were detected. The mutual correlation between candidate genes and phenotype of fundus photography was revealed in our study. The eoHM candidate gene mutation types contain five categories: missense mutations (78.38%), nonsense (8.11%), frameshift mutation (5.41%), classical splice site mutation (5.41%), and initiation codon mutation (2.70%). Conclusions Candidate genes carried by patients with eoHM are closely related to inherited retinal diseases. Genetic screening in children with eoHM facilitates the early identification and intervention of syndromic hereditary ocular disorders and certain hereditary ophthalmopathies.