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Social media has a significant impact on people's lives. Hate speech on social media has emerged as one of society's most serious issues in recent years. Text and pictures are two forms of multimodal data that are distributed within articles. Unimodal analysis has been the primary emphasis of earlier approaches. Additionally, when doing multimodal analysis, researchers neglect to preserve the distinctive qualities associated with each modality. To address these shortcomings, the present article suggests a scalable architecture for multimodal hate content detection called transformer-based multilevel attention (STMA). This architecture consists of three main parts: a combined attention-based deep learning mechanism, a vision attention-mechanism encoder, and a caption attention-mechanism encoder. To identify hate content, each component uses various attention processes and handles multimodal data in a unique way. Several studies employing multiple assessment criteria on three hate speech datasets such as Hateful memes, MultiOff, and MMHS150K, validate the suggested architecture's efficacy. The outcomes demonstrate that on all three datasets, the suggested strategy performs better than the baseline approaches.

Social media, particularly Twitter, has seen a significant increase in incidents like trolling and hate speech. Thus, identifying hate speech is the need of the hour. This paper introduces a computational framework to curb the hate content on the web. Specifically, this study presents an exhaustive study of pre-processing approaches by studying the impact of changing the sequence of text pre-processing operations for the identification of hate content. The best-performing pre-processing sequence, when implemented with popular classification approaches like Support Vector Machine, Random Forest, Decision Tree, Logistic Regression and K-Neighbor provides a considerable boost in performance. Additionally, the best pre-processing sequence is used in conjunction with different ensemble methods, such as bagging, boosting and stacking to improve the performance further. Three publicly available benchmark datasets (WZ-LS, DT, and FOUNTA), were used to evaluate the proposed approach for hate speech identification. The proposed approach achieves a maximum accuracy of 95.14% highlighting the effectiveness of the unique pre-processing approach along with an ensemble classifier.

The rapid advancement of machine learning technologies raises questions about the security of machine learning models, with respect to both training-time (poisoning) and test-time (evasion, impersonation, and inversion) attacks. Models performing image-related tasks, e.g. detection, and classification, are vulnerable to adversarial attacks that can degrade their performance and produce undesirable outcomes. This paper introduces a novel technique for anomaly detection in images called 2DSig-Detect, which uses a 2D-signature-embedded semi-supervised framework rooted in rough path theory. We demonstrate our method in adversarial settings for training-time and test-time attacks, and benchmark our framework against other state of the art methods. Using 2DSig-Detect for anomaly detection, we show both superior performance and a reduction in the computation time to detect the presence of adversarial perturbations in images.

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It is being virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria and Medical Imaging and Signal Analysis Hub (MISAHUB) in collaboration with the 9th International Conference on Computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document describes the overview of the challenge, its registration and rules, submission format, and the description of the utilized datasets., Comment: 6 pages

MIRC-X and MYSTIC are six-telescope near-infrared beam (1.08-2.38 ${\mu}$m) combiners at the CHARA Array on Mt Wilson CA, USA. Ever since the commissioning of MIRC-X (J and H bands) in 2018 and MYSTIC (K bands) in 2021, they have been the most popular and over-subscribed instruments at the array. Observers have been able to image stellar objects with sensitivity down to 8.1 mag in H and 7.8 mag in K-band under the very best conditions. In 2022 MYSTIC was upgraded with a new ABCD mode using the VLTI/GRAVITY 4-beam integrated optics chip, with the goal of improving the sensitivity and calibration. The ABCD mode has been used to observe more than 20 T Tauri stars; however, the data pipeline is still being developed. Alongside software upgrades, we detail planned upgrades to both instruments in this paper. The main upgrades are: 1) Adding a motorized filter wheel to MIRC-X along with new high spectral resolution modes 2) Updating MIRC-X optics to allow for simultaneous 6T J+H observations 3) Removing the warm window between the spectrograph and the warm optics in MYSTIC 4) Adding a 6T ABCD mode to MIRC-X in collaboration with CHARA/SPICA 5) Updating the MIRC-X CRED-ONE camera funded by Prof. Kraus from U. Exeter 6) Carrying out science verification of the MIRC-X polarization mode 7) Developing new software for ABCD-mode data reduction and more efficient calibration routines. We expect these upgrades to not only improve the observing experience, but also increase the sensitivity by 0.4 mag in J+H-bands, and 1 mag in K-band., Comment: 11 pages, 9 figures, Submitted to SPIE in Yokohama 2024

In this paper, we study an affine connection approach to realizing nonholonomic mechanical systems mediated by viscous friction forces with large coefficients, viewed as a singular perturbation of the nonholonomic system. We show that the associated slow manifold is represented coordinate-free as the image of a section over the nonholonomic distribution. We propose a novel invariance condition based on covariant derivatives and prove that this condition is equivalent to the classical invariance condition based on time derivatives. Accordingly, we propose a novel recursive procedure to approximate the slow manifold based on the covariant derivatives of a formal power series expansion of the section. Using this recurrence relation, we derive, up to second order, approximations of the slip velocities residing in the slow manifold, as well as the associated approximated dynamics up to first order. Lastly, we illustrate our approach with a case study of a vertical rolling disk., Comment: 30 pages

We continue our study of exponent semigroups of rational matrices. Our main result is that the matricial dimension of a numerical semigroup is at most its multiplicity (the least generator), greatly improving upon the previous upper bound (the conductor). For many numerical semigroups, including all symmetric numerical semigroups, our upper bound is tight.

Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks in various domains. However, RAG based on open-source LLM for specialized domains has challenges of evaluating generated responses. A popular framework in the literature is the RAG Assessment (RAGAS), a publicly available library which uses LLMs for evaluation. One disadvantage of RAGAS is the lack of details of derivation of numerical value of the evaluation metrics. One of the outcomes of this work is a modified version of this package for few metrics (faithfulness, context relevance, answer relevance, answer correctness, answer similarity and factual correctness) through which we provide the intermediate outputs of the prompts by using any LLMs. Next, we analyse the expert evaluations of the output of the modified RAGAS package and observe the challenges of using it in the telecom domain. We also study the effect of the metrics under correct vs. wrong retrieval and observe that few of the metrics have higher values for correct retrieval. We also study for differences in metrics between base embeddings and those domain adapted via pre-training and fine-tuning. Finally, we comment on the suitability and challenges of using these metrics for in-the-wild telecom QA task., Comment: Accepted for publication in ICML 2024 Workshop on Foundation Models in the Wild

Cardiovascular disease is a major life-threatening condition that is commonly monitored using electrocardiogram (ECG) signals. However, these signals are often contaminated by various types of noise at different intensities, significantly interfering with downstream tasks. Therefore, denoising ECG signals and increasing the signal-to-noise ratio is crucial for cardiovascular monitoring. In this paper, we propose a deep learning method that combines a one-dimensional convolutional layer with transformer architecture for denoising ECG signals. The convolutional layer processes the ECG signal by various kernel/patch sizes and generates an embedding called multi-scale patch embedding. The embedding then is used as the input of a transformer network and enhances the capability of the transformer for denoising the ECG signal.

ESO's Very Large Telescope Interferometer has a history of record-breaking discoveries in astrophysics and significant advances in instrumentation. The next leap forward is its new visitor instrument, called Asgard. It comprises four natively collaborating instruments: HEIMDALLR, an instrument performing both fringe tracking and stellar interferometry simultaneously with the same optics, operating in the K band; Baldr, a Strehl optimizer in the H band; BIFROST, a spectroscopic combiner to study the formation processes and properties of stellar and planetary systems in the Y-J-H bands; and NOTT, a nulling interferometer dedicated to imaging nearby young planetary systems in the L band. The suite is in its integration phase in Europe and should be shipped to Paranal in 2025. In this article, we present details of the alignment and calibration unit, the observing modes, the integration plan, the software architecture, and the roadmap to completion of the project.

Our aim in this paper is to initiate the study of exponent semigroups for rational matrices. We prove that every numerical semigroup is the exponent semigroup of some rational matrix. We also obtain lower bounds on the size of such matrices and discuss the related class of power-integral matrices., Comment: 12 pages

A plethora of sentence embedding models makes it challenging to choose one, especially for domains such as telecom, rich with specialized vocabulary. We evaluate multiple embeddings obtained from publicly available models and their domain-adapted variants, on both point retrieval accuracies as well as their (95\%) confidence intervals. We establish a systematic method to obtain thresholds for similarity scores for different embeddings. We observe that fine-tuning improves mean bootstrapped accuracies as well as tightens confidence intervals. The pre-training combined with fine-tuning makes confidence intervals even tighter. To understand these variations, we analyse and report significant correlations between the distributional overlap between top-$K$, correct and random sentence similarities with retrieval accuracies and similarity thresholds. Following current literature, we analyze if retrieval accuracy variations can be attributed to isotropy of embeddings. Our conclusions are that isotropy of embeddings (as measured by two independent state-of-the-art isotropy metric definitions) cannot be attributed to better retrieval performance. However, domain adaptation which improves retrieval accuracies also improves isotropy. We establish that domain adaptation moves domain specific embeddings further away from general domain embeddings., Comment: 10 pages, 3 figures, 4 tables

Numerous recent X-ray observations of coronal loops in both active regions (ARs) and solar flares have shown clearly that elemental abundances vary with time. Over the course of a flare, they have been found to move from coronal values towards photospheric values near the flare peak, before slowly returning to coronal values during the gradual phase. Coronal loop models typically assume that the elemental abundances are fixed, however. In this work, we introduce a time-variable abundance factor into the 0D ebtel++ code that models the changes due to chromospheric evaporation in order to understand how this affects coronal loop cooling. We find that for strong heating events ($\gtrsim$ 1 erg s$^{-1}$ cm$^{-3}$), the abundances quickly tend towards photospheric values. For smaller heating rates, the abundances fall somewhere between coronal and photospheric values, causing the loop to cool more quickly than the time-fixed photospheric cases (typical flare simulations) and more slowly than time-fixed coronal cases (typical AR simulations). This suggests heating rates in quiescent AR loops no larger than $\approx 0.1$ erg s$^{-1}$ cm$^{-3}$ to be consistent with recent measurements of abundance factors $f \gtrsim 2$., Comment: Accepted to ApJL. Comments and criticisms are more than welcome! The paper is fully reproducible, with scripts to run the simulations and generate the figures available on this Github repository: https://github.com/jwreep/ebtel_abundances

Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot generation of abstractive summaries for given articles. However, little is known about the robustness of such a process of zero-shot summarization. To bridge this gap, we propose relevance paraphrasing, a simple strategy that can be used to measure the robustness of LLMs as summarizers. The relevance paraphrasing approach identifies the most relevant sentences that contribute to generating an ideal summary, and then paraphrases these inputs to obtain a minimally perturbed dataset. Then, by evaluating model performance for summarization on both the original and perturbed datasets, we can assess the LLM's one aspect of robustness. We conduct extensive experiments with relevance paraphrasing on 4 diverse datasets, as well as 4 LLMs of different sizes (GPT-3.5-Turbo, Llama-2-13B, Mistral-7B, and Dolly-v2-7B). Our results indicate that LLMs are not consistent summarizers for the minimally perturbed articles, necessitating further improvements.

Influence functions offer a robust framework for assessing the impact of each training data sample on model predictions, serving as a prominent tool in data-centric learning. Despite their widespread use in various tasks, the strong convexity assumption on the model and the computational cost associated with calculating the inverse of the Hessian matrix pose constraints, particularly when analyzing large deep models. This paper focuses on a classical data-centric scenario--trimming detrimental samples--and addresses both challenges within a unified framework. Specifically, we establish an equivalence transformation between identifying detrimental training samples via influence functions and outlier gradient detection. This transformation not only presents a straightforward and Hessian-free formulation but also provides profound insights into the role of the gradient in sample impact. Moreover, it relaxes the convexity assumption of influence functions, extending their applicability to non-convex deep models. Through systematic empirical evaluations, we first validate the correctness of our proposed outlier gradient analysis on synthetic datasets and then demonstrate its effectiveness in detecting mislabeled samples in vision models, selecting data samples for improving performance of transformer models for natural language processing, and identifying influential samples for fine-tuned Large Language Models.

Vortex fiber nulling (VFN) is a technique for detecting and characterizing faint companions at small separations from their host star. A near-infrared ($\sim2.3 \mu$m) VFN demonstrator mode was deployed on the Keck Planet Imager and Characterizer (KPIC) instrument at the Keck Observatory and presented earlier. In this paper, we present the first VFN companion detections. Three targets, HIP 21543 Ab, HIP 94666 Ab, and HIP 50319 B, were detected with host-companion flux ratios between 70 and 430 at and within one diffraction beamwidth ($\lambda/D$). We complement the spectra from KPIC VFN with flux ratio and position measurements from the CHARA Array to validate the VFN results and provide a more complete characterization of the targets. This paper reports the first direct detection of these three M dwarf companions, yielding their first spectra and flux ratios. Our observations provide measurements of bulk properties such as effective temperatures, radial velocities, and v$\sin{i}$, and verify the accuracy of the published orbits. These detections corroborate earlier predictions of the KPIC VFN performance, demonstrating that the instrument mode is ready for science observations., Comment: 13 pages, 2 figures; Accepted to ApJ Letters

Polarization, declining trust, and wavering support for democratic norms are pressing threats to U.S. democracy. Exposure to verified and quality news may lower individual susceptibility to these threats and make citizens more resilient to misinformation, populism, and hyperpartisan rhetoric. This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a large-scale two-week long field experiment (from 1/19/2023 to 2/3/2023) on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing two hardcoded elements: a URL to the topic-relevant section of quality news organization and an encouragement to follow its Twitter account. To further test differential effects by gender of the bots, treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our over-time intervention enhances the following of news media organization, the sharing and the liking of news content and the tweeting about politics and the liking of political content. We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control. Most of these results, however, were small in magnitude and confined to the already politically interested Twitter users, as indicated by their pre-treatment tweeting about politics. These findings have implications for social media and news organizations, and also offer direction for future work on how Large Language Models and other computational interventions can effectively enhance individual on-platform engagement with quality news and public affairs.

An expansion of cross-sectional area directly impacts the mass flow along a coronal loop, and significantly alters the radiative and hydrodynamic evolution of that loop as a result. Previous studies have found that an area expansion from chromosphere to corona significantly lengthens the cooling time of the corona, and appears to suppress draining from the corona. In this work, we examine the fluid dynamics to understand how the mass flow rate, the energy balance, and the cooling and draining timescales are affected by a non-uniform area. We find that in loops with moderate or large expansion (cross-sectional area expansion factors of 2, 3, 10, 30, 100 from photosphere to apex), impulsive heating, for either direct thermal heating or electron beam heating, induces a steady flow into the corona, so that the coronal density continues to rise during the cooling phase, whereas a uniform loop drains during the cooling phase. The induced upflow carries energy into the corona, balancing the losses from thermal conduction, and continues until thermal conduction weakens enough so that it can no longer support the radiative losses of the transition region (TR). As a result, the plasma cools primarily radiatively until the onset of catastrophic collapse. The speed and duration of the induced upflow both increase in proportion to the rate of area expansion. We argue that observations of blue-shifted spectral lines, therefore, could place a constraint on a loop's area expansion., Comment: Resubmitted to ApJ. Comments and criticisms are welcomed!

It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, that is, they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. These are used in exploration to allow solutions to be found more thoroughly and in relation to a chosen solution than at random. TLSO, PLSO, and LLSO stand for Tournament Logarithmic Snake Optimizer, Proportional Logarithmic Snake Optimizer, and Linear Order Logarithmic Snake Optimizer, respectively. A number of 22 reference medical datasets were used in experiments. The findings indicate that, among 86 % of the datasets, TLSO attained the best accuracy, and among 82 % of the datasets, the best feature reduction. In terms of the standard deviation, the TLSO also attained noteworthy reliability and stability. On the basis of running duration, it is, nonetheless, quite effective.

Addressing the challenges of processing massive graphs, which are prevalent in diverse fields such as social, biological, and technical networks, we introduce HeiStreamE and FreightE, two innovative (buffered) streaming algorithms designed for efficient edge partitioning of large-scale graphs. HeiStreamE utilizes an adapted Split-and-Connect graph model and a Fennel-based multilevel partitioning scheme, while FreightE partitions a hypergraph representation of the input graph. Besides ensuring superior solution quality, these approaches also overcome the limitations of existing algorithms by maintaining linear dependency on the graph size in both time and memory complexity with no dependence on the number of blocks of partition. Our comprehensive experimental analysis demonstrates that HeiStreamE outperforms current streaming algorithms and the re-streaming algorithm 2PS in partitioning quality (replication factor), and is more memory-efficient for real-world networks where the number of edges is far greater than the number of vertices. Further, FreightE is shown to produce fast and efficient partitions, particularly for higher numbers of partition blocks.

In the absence of labeled target data, unsupervised domain adaptation approaches seek to align the marginal distributions of the source and target domains in order to train a classifier for the target. Unsupervised domain alignment procedures are category-agnostic and end up misaligning the categories. We address this problem by deploying a pretrained network to determine accurate labels for the target domain using a multi-stage pseudo-label refinement procedure. The filters are based on the confidence, distance (conformity), and consistency of the pseudo labels. Our results on multiple datasets demonstrate the effectiveness of our simple procedure in comparison with complex state-of-the-art techniques., Comment: 8 pages + 3 pages of references

Social identities play an important role in the dynamics of human societies, and it can be argued that some sense of identification with a larger cause or idea plays a critical role in making humans act responsibly. Often social activists strive to get populations to identify with some cause or notion -- like green energy, diversity, etc. in order to bring about desired social changes. We explore the problem of designing computational models for social identities in the context of autonomous AI agents. For this, we propose an agent model that enables agents to identify with certain notions and show how this affects collective outcomes. We also contrast between associations of identity with rational preferences. The proposed model is simulated in an application context of urban mobility, where we show how changes in social identity affect mobility patterns and collective outcomes.

Allocation games are zero-sum games that model the distribution of resources among multiple agents. In this paper, we explore the interplay between an \textit{subjective identity} and its impact on notions of fairness in allocation. The sense of identity in agents is known to lead to responsible decision-making in non-cooperative, non-zero-sum games like Prisoners' Dilemma, and is a desirable feature to add into agent models. However, when it comes to allocation, the sense of identity can be shown to exacerbate inequities in allocation, giving no rational incentive for agents to act fairly towards one another. This lead us to introduce a sense of fairness as an innate characteristic of autonomous agency. For this, we implement the well-known Ultimatum Game between two agents, where their sense of identity association and their sense of fairness are both varied. We study the points at which agents find it no longer rational to identify with the other agent, and uphold their sense of fairness, and vice versa. Such a study also helps us discern the subtle difference between responsibility and fairness when it comes to autonomous agency.

We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied previously in the literature. Position bias captures the tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. Through numerous experiments on four diverse real-world datasets, we study position bias in multiple LLM models such as GPT 3.5-Turbo, Llama-2, and Dolly-v2, as well as state-of-the-art pretrained encoder-decoder abstractive summarization models such as Pegasus and BART. Our findings lead to novel insights and discussion on performance and position bias of models for zero-shot summarization tasks., Comment: Accepted to NAACL 2024 Main Conference