Miracle-dz/UniFAR-FTUs huggingface dataset:
PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data Analysis

Over the years, Principal Component Analysis (PCA) has served as the baseline approach for dimensionality reduction in gene expression data analysis. Its primary objective is to identify a subset of disease-causing genes from a vast pool of thousands of genes. However, PCA possesses inherent limitations that hinder its interpretability, introduce class ambiguity, and fail to capture complex geometric structures in the data. Although these limitations have been partially addressed in the literature by incorporating various regularizers, such as graph Laplacian regularization, existing PCA based methods still face challenges related to multiscale analysis and capturing higher-order interactions in the data. To address these challenges, we propose a novel approach called Persistent Laplacian-enhanced Principal Component Analysis (PLPCA). PLPCA amalgamates the advantages of earlier regularized PCA methods with persistent spectral graph theory, specifically persistent Laplacians derived from algebraic topology. In contrast to graph Laplacians, persistent Laplacians enable multiscale analysis through filtration and can incorporate higher-order simplicial complexes to capture higher-order interactions in the data. We evaluate and validate the performance of PLPCA using ten benchmark microarray data sets that exhibit a wide range of dimensions and data imbalance ratios. Our extensive studies over these data sets demonstrate that PLPCA provides up to 12% improvement to the current state-of-the-art PCA models on five evaluation metrics for classification tasks after dimensionality reduction.
Between-Sample Relationship in Learning Tabular Data Using Graph and Attention Networks

Traditional machine learning assumes samples in tabular data to be independent and identically distributed (i.i.d). This assumption may miss useful information within and between sample relationships in representation learning. This paper relaxes the i.i. d assumption to learn tabular data representations by incorporating between-sample relationships for the first time using graph neural networks (GNN). We investigate our hypothesis using several GNNs and state-of-the-art (SOTA) deep attention models to learn the between-sample relationship on ten tabular data sets by comparing them to traditional machine learning methods. GNN methods show the best performance on tabular data with large feature-to-sample ratios. Our results reveal that attention-based GNN methods outperform traditional machine learning on five data sets and SOTA deep tabular learning methods on three data sets. Between-sample learning via GNN and deep attention methods yield the best classification accuracy on seven of the ten data sets. This suggests that the i.i. d assumption may not always hold for most tabular data sets.
Differentially private sliced inverse regression in the federated paradigm

Sliced inverse regression (SIR), which includes linear discriminant analysis (LDA) as a special case, is a popular and powerful dimension reduction tool. In this article, we extend SIR to address the challenges of decentralized data, prioritizing privacy and communication efficiency. Our approach, named as federated sliced inverse regression (FSIR), facilitates collaborative estimation of the sufficient dimension reduction subspace among multiple clients, solely sharing local estimates to protect sensitive datasets from exposure. To guard against potential adversary attacks, FSIR further employs diverse perturbation strategies, including a novel vectorized Gaussian mechanism that guarantees differential privacy at a low cost of statistical accuracy. Additionally, FSIR naturally incorporates a collaborative variable screening step, enabling effective handling of high-dimensional client data. Theoretical properties of FSIR are established for both low-dimensional and high-dimensional settings, supported by extensive numerical experiments and real data analysis.
An extension of the weighted geometric mean in unital $JB$-algebras

Let $\mathcal{A}$ be a unital $JB$-algebra and $A,~B\in\mathcal{A}$, we extend the weighted geometric mean $A\sharp_r B$, from $r\in [0,1]$ to $r\in (-1, 0)\cup(1, 2)$. We will notice that many results will be reversed when the domain of $r$ change from $[0,1]$ to $(-1,0)$ or $(1,2)$. We investigate some property of $A\sharp_r B$ for such quantities of $r$, such as we show that $A\sharp_r B$ is separately operator convex with respect to $A, B$. We also introduce the Heinz and Heron means for unital $JB$-algebras and give some famous inequalities involving them.
Continuity of a spatial gradient of a weak solution to a very singular parabolic equation involving anisotropic diffusivity

We consider weak solutions to very singular parabolic equations involving both one-Laplace-type operators, which have anisotropic diffusivity, and p-Laplace-type operators with 2n n`2 ă p ă 8, where n ě 2 denotes the space dimension. This equation becomes no longer uniformly parabolic near a facet, the place where a spatial gradient vanishes. The aim of this paper is to prove that spatial derivatives of weak solutions are continuous even across facets. This is possible by showing local a priori Hölder continuity of gradients suitably truncated near facets. To give a rigorous proof, we consider an approximation parabolic problem, and appeal to standard methods including De Giorgi’s truncation and comparisons to Dirichlet heat flows.
A Survey on Explainable Artificial Intelligence for Cybersecurity

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.
Stochastic approach for modeling soft fingers with creep behavior

Soft robots have high adaptability and safety due to their softness and are therefore widely used in human society. However, the controllability of soft robots to perform dexterous behaviors is insufficient when considering soft robots as alternative laborers for humans. Model-based control methods are effective for achieving dexterous behaviors. To build a suitable control model, problems based on specific properties, such as creep behavior and variable motions, must be addressed. In this paper, a lumped parameterized model for soft fingers with viscoelastic joints is established to address creep behavior. The parameters are expressed as distributions, which allows the model to account for motion variability. Furthermore, stochastic analyzes are performed based on the parameter distributions. The model results are consistent with the experimental results, and the model enables the investigation of the effects of various parameters related to robot variability. GRAPHICAL ABSTRACT
Concentration quenching of noncoherent excitation in solutions

Delocalization of excitation in a system of disordered but uniformly distributed centers, its quenching by alien admixtures, and self-quenching by cross relaxation by pairs of identical particles are considered. It is shown that the direct transfer of excitation from an initial center to its neighbors becomes sequential and transforms into diffusion. However, quenching is seldom diffusive. As a rule, it follows a singly-acting hopping mechanism. Different theories of this mechanism are discussed and compared. The transformation from the diffusive to the hopping mechanism is described, and criteria for both are examined. Theoretical considerations are illustrated by applications to the kinetics and rates of concentration quenching of electronic, vibrational, and spin excitations in solid and liquid solutions. This is based on data on the selective and nonselective luminescence of rare-earth ions, picosecond absorption by dye molecules, and the decay of the electron spin echo signal due to hydrogen atoms in irradiated hosts.
An Ontological-Semantic Framework for Text Analysis

for Concrete 6.9% 4/4 4/4 TOTAL 100% 37/59 = 62.7% 59/59 = 100% Table 9A. Metonymy Training Results on Data Set 1 (Static Weight Assignment) Data Set 1 (Collected examples) % of Total # Correct: Manual Weights # Correct: Trained Weights
Matching in Planar Graphs

The perfect matching problem is a well studied problem in the field of parallel algorithms. It has a close relation with complexity theory. There exist RNC algorithms to construct a perfect matching in a given graph [MVV87, KUW86], but no NC algorithm is known for it. In this work we are particularly interested in planar graphs. The problem of counting the number of perfect matchings, which intuitively should be harder than constructing a perfect matching, can be solved in NC for planar graphs [Kas67, Vaz89]. This leads us to believe that there should be an NC algorithm to construct a perfect matching in a planar graph. Our attempt here is to get such an algorithm. We look at three main ideas for constructing a perfect matching in a bipartite planar graph in NC due to Miller and Naor, Mahajan et al. and Kulkarni et al. [MN95, MV00, DKR08]. We try to generalize the latter two to the general planar case. The first algorithm due to Mahajan et al. [MV00] involves traversing the matching polytope and reaching one of its vertices. The main problem in generalizing this approach seems to be a large number of bounding hyperplanes of the matching polytope. The second approach uses a known RNC algorithm, due to Mulmuley et al. [MVV87], which finds a perfect matching in a general graph. Kulkarni et al. [DKR08] and Korwar [Kor09] derandomized the algorithm for bipartite planar graphs. We give some ideas for the derandomization in the general planar case. We also discuss a similarity between the two algorithms given by Miller and Naor [MN95] and Korwar [Kor09]. Though we did not get any NC algorithm to find a matching in a planar graph, we hope the ideas presented here would help in getting one. Dedicated to IIT Kanpur which showed me a glimpse of the Truth
Achieving Sustainable Transportation from the Perspective of Road Accessibility and Regional Correlations of County Seats in Hungary

Abstract Road traffic is a sub-sector, which has implications for almost all economic activity through transportation, and directly affects the economy and social processes of a region or country. The conscious and sustainable accessibility of the capital and county seats is particularly important in terms of goods distribution, labour mobility and access to services. Necessary research was conducted using Google Maps’ 2022 summer data and Hansen‘s gravity model. The study identifies county seats that require development and facilitates the mapping of regional relationships between the capital and the county seats.
Mass, Heat, and Freshwater Transport From Transoceanic Sections in the Atlantic Ocean at 30°S and 24.5°N: Single Sections Versus Box Models?

The Atlantic meridional overturning circulation (AMOC) is central to the climate of the Atlantic by redistributing mass, heat, and freshwater. Hydrographic sections help monitor its strength at different latitudes, and inverse box models provide estimates of AMOC, heat, and freshwater transports. We have used all available hydrographic zonal sections at 24.5°N and 30°S over the last 30 years to conclude that single section inverse models agree with monthly outputs from an ocean general circulation model at the time of the cruise. In contrast, inverse models using multiple sections at different latitudes and times of the year for each of the last three decades are more consistent with decadal averages from the same model. Therefore, solutions of inverse models with single sections are affected by aliasing and represent the state of the ocean at the time of cruise. However, aliasing is greatly reduced when using multiple sections to assess low‐frequency variability.
On the Praxes and Politics of AI Speech Emotion Recognition

There is no scientific consensus on what is meant by “emotion” – researchers have examined various phenomena spanning brain modes, feelings, sensations, and cognitive structures, among others, in their study of emotional experiences. For the purposes of developing an AI speech emotion recognition (SER) system, however, emotion must be defined, bounded, and instantiated as ground truth in the training data. This means practical choices must be made in which particular emotional ontologies are prioritized over others in the construction of SER datasets. In this paper, I explore these tensions around fairness, accountability, and transparency by analyzing open-source datasets used for SER applications along with their accompanying methodology papers. Specifically, I critique the centrality of discrete emotion theory in SER applications as a contestable emotional framework that is invoked primarily for its practical utility and alignment – as opposed to scientific rigor – with machine learning epistemologies. In so doing, I also shed light on the role of the dataset creators as emotional designers in their attempt to produce, elicit, record, and index emotional expressions for the purposes of crafting SER training datasets. Ultimately, by further querying SER through the aperture of Critical Disability Studies, I use this empirical work to examine the sociopolitical stakes of SER as a normative and regulatory technology that siphons emotion into a broader agenda of capitalistic productivity in the context of call center optimization.
Legal Taxonomies of Machine Bias: Revisiting Direct Discrimination

Previous literature on ‘fair’ machine learning has appealed to legal frameworks of discrimination law to motivate a variety of discrimination and fairness metrics and de-biasing measures. Such work typically applies the US doctrine of disparate impact rather than the alternative of disparate treatment, and scholars of EU law have largely followed along similar lines, addressing algorithmic bias as a form of indirect rather than direct discrimination. In recent work, we have argued that such focus is unduly narrow in the context of European law: certain forms of algorithmic bias will constitute direct discrimination [1]. In this paper, we explore the ramifications of this argument for existing taxonomies of machine bias and algorithmic fairness, how existing fairness metrics might need to be adapted, and potentially new measures may need to be introduced. We outline how the mappings between fairness measures and discrimination definitions implied hitherto may need to be revised and revisited.
Semi-Supervised Learning for Wearable-based Momentary Stress Detection in the Wild

Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since stress annotation usually relies on self-reports during the study, a limited amount of labeled data can be an obstacle to developing accurate and generalized stress-predicting models. On the other hand, the sensors can continuously capture signals without annotations. This work investigates leveraging unlabeled wearable sensor data for stress detection in the wild. We propose a two-stage semi-supervised learning framework that leverages wearable sensor data to help with stress detection. The proposed structure consists of an auto-encoder pre-training method for learning information from unlabeled data and the consistency regularization approach to enhance the robustness of the model. Besides, we propose a novel active sampling method for selecting unlabeled samples to avoid introducing redundant information to the model. We validate these methods using two datasets with physiological signals and stress labels collected in the wild, as well as four human activity recognition (HAR) datasets to evaluate the generality of the proposed method. Our approach demonstrated competitive results for stress detection, improving stress classification performance by approximately 7% to 10% on the stress detection datasets compared to the baseline supervised learning models. Furthermore, the ablation study we conducted for the HAR tasks supported the effectiveness of our methods. Our approach showed comparable performance to state-of-the-art semi-supervised learning methods for both stress detection and HAR tasks.
DCNN-based Polyps Segmentation using Colonoscopy images

Colorectal polyps, which are associated with colorectal cancer, can be detected using a colonoscopy. Using the data from colonoscopy images to segment polyps is crucial in medical practice because it provides critical data for identification and surgery. However, precise segmentation of polyps is difficult due to the following factors: the polyp-bordering mucosa boundary is not sharp, and polyps of the same type differ in texture, size, and color. We propose to use the DeepLabV3+ architecture for image segmentation for medical purposes by examining its segmentation results on colonoscopy images from the datasets Kvasir and CVC-ClinicDB. DeepLabV3+ generates an F1-score of 0.865 for CVC-ClinicDB on an NVIDIA A100 class Cloud-Based GPU. The model is divided into the following parts: an encoder that performs separable convolution on the input map and a decoder that up-samples the data provided by the encoder using transpose convolution. Our approach significantly enhances segmentation accuracy and offers a number of benefits with respect to generality and real-time segmentation efficiency, according to evaluations done quantitatively and qualitatively on the two datasets.
Fictional and fictionalised religions as heritage? Reflections on the object of critical heritage studies

ABSTRACT This article attempts to make a connection between two fields, that usually operate in separate intellectual contexts: critical research on heritage as a social practice and the study of historical and fantasy fiction, concentrating on renditions of the Middle Ages. Both the construction of heritage and the creation of a fictional narrative are ways of processing of the past, although, at first sight, opposites. Claiming a site as heritage and therefore as determining a community’s identity elides the distance between past and present, whereas the authors of historical and fantasy fiction are keen to stress the exotic nature of the Middle Ages. Yet, the article argues that these practices operate on a sliding scale and asks the question whether the telling of a fictional tale about the Middle Ages might be a heritage practice in itself.
MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data

Motivation Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein. Results We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein’s PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes. Availability The source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.
Warming and altered precipitation independently and interactively suppress alpine soil microbial growth in a decadal-long experiment

Warming and precipitation anomalies affect terrestrial carbon balance partly through altering microbial eco-physiological processes (e.g., growth and death) in soil. However, little is known about how such processes responds to simultaneous regime shifts in temperature and precipitation. We used the 18O-water quantitative stable isotope probing approach to estimate bacterial growth in alpine meadow soils of the Tibetan Plateau after a decade of warming and altered precipitation manipulation. Our results showed that the growth of major taxa was suppressed by the single and combined effects of temperature and precipitation, eliciting 40-90% of growth reduction of whole community. The antagonistic interactions of warming and altered precipitation on population growth were common (~70% taxa), represented by the weak antagonistic interactions of warming and drought, and the neutralizing effects of warming and wet. The members in Solirubrobacter and Pseudonocardia genera had high growth rates under changed climate regimes. These results are important to understand and predict the soil microbial dynamics in alpine meadow ecosystems suffering from multiple climate change factors.
Genomes of historical specimens reveal multiple invasions of LTR retrotransposons in Drosophila melanogaster populations during the 19th century

Transposable element invasions have a profound impact on the evolution of genomes and phenotype. It is thus an important open question on how often such TE invasions occur. Based on strains sampled at different times, previous work showed that four different TE families invaded D. melanogaster during the 20th century. Here we utilize the genomes of historical specimens to extend this work by another 100 years. We found that the LTR retrotransposons Blood, Opus and 412 spread in D. melanogaster in the 19th century. These invasions were found to constitute second waves, as degraded fragments were found for all three TEs. We show that two of the three invasions, Opus and 412, led to pronounced geographic heterogeneity, likely due to founder effects during the invasions. Finally, we identified species from the simulans complex as the likely origin of the TEs. In total, seven TE families invaded D. melanogaster during the last 200 years, increasing the genome size by 1.2 Mbp. Based on the analysis of strains and specimens sampled at different times, we provide a detailed timeline of TE invasions, making D. melanogaster the first organism where we could infer the invasion history of TEs during the last two centuries.