Miracle-dz/UniFAR-FTUs huggingface dataset:
EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis

Introduction: Electroencephalogram (EEG) signals have gained significant popularity in various applications due to their rich information content. However, these signals are prone to contamination from various sources of artifacts, notably the electrooculogram (EOG) artifacts caused by eye movements. The most effective approach to mitigate EOG artifacts involves recording EOG signals simultaneously with EEG and employing blind source separation techniques, such as independent component analysis (ICA). Nevertheless, the availability of EOG recordings is not always feasible, particularly in pre-recorded datasets. Objective: In this paper, we present a novel methodology that combines a long short-term memory (LSTM)-based neural network with ICA to address the challenge of EOG artifact removal from contaminated EEG signals. Approach: Our approach aims to accomplish two primary objectives: 1) estimate the horizontal and vertical EOG signals from the contaminated EEG data, and 2) employ ICA to eliminate the estimated EOG signals from the EEG, thereby producing an artifact-free EEG signal. Main results: To evaluate the performance of our proposed method, we conducted experiments on a publicly available dataset comprising recordings from 27 participants. We employed well-established metrics such as mean squared error, mean absolute error, and mean error to assess the quality of our artifact removal technique. Significance: Furthermore, we compared the performance of our approach with two state-of-the-art deep learning-based methods reported in the literature, demonstrating the superior performance of our proposed methodology.
Exploring the Use of Generative AI in the Search for Extraterrestrial Intelligence (SETI)

The search for extraterrestrial intelligence (SETI) is a field that has long been within the domain of traditional signal processing techniques. However, with the advent of powerful generative AI models, such as GPT-3, we are now able to explore new ways of analyzing SETI data and potentially uncover previously hidden signals. In this work, we present a novel approach for using generative AI to analyze SETI data, with focus on data processing and machine learning techniques. Our proposed method uses a combination of deep learning and generative models to analyze radio telescope data, with the goal of identifying potential signals from extraterrestrial civilizations. We also discuss the challenges and limitations of using generative AI in SETI, as well as potential future directions for this research. Our findings suggest that generative AI has the potential to significantly improve the efficiency and effectiveness of the search for extraterrestrial intelligence, and we encourage further exploration of this approach in the SETI community. (Disclosure: For the purpose of demonstration, the abstract and title were generated by ChatGPT and slightly modified by the lead author.
Accelerating Continuous Integration with Parallel Batch Testing

Continuous integration at scale is costly but essential to software development. Various test optimization techniques including test selection and prioritization aim to reduce the cost. Test batching is an effective alternative, but overlooked technique. This study evaluates parallelization’s effect by adjusting machine count for test batching and introduces two novel approaches. We establish TestAll as a baseline to study the impact of parallelism and machine count on feedback time. We re-evaluate ConstantBatching and introduce DynamicBatching, which adapts batch size based on the remaining changes in the queue. We also propose TestCaseBatching, enabling new builds to join a batch before full test execution, thus speeding up continuous integration. Our evaluations utilize Ericsson’s results and 276 million test outcomes from open-source Chrome, assessing feedback time, execution reduction, and providing access to Chrome project scripts and data. The results reveal a non-linear impact of test parallelization on feedback time, as each test delay compounds across the entire test queue. ConstantBatching, with a batch size of 4, utilizes up to 72% fewer machines to maintain the actual average feedback time and provides a constant execution reduction of up to 75%. Similarly, DynamicBatching maintains the actual average feedback time with up to 91% fewer machines and exhibits variable execution reduction of up to 99%. TestCaseBatching holds the line of the actual average feedback time with up to 81% fewer machines and demonstrates variable execution reduction of up to 67%. We recommend practitioners use DynamicBatching and TestCaseBatching to reduce the required testing machines efficiently. Analyzing historical data to find the threshold where adding more machines has minimal impact on feedback time is also crucial for resource-effective testing.
STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction

This paper introduces a novel benchmark to study the impact and relationship of built environment elements on pedestrian collision prediction, intending to enhance environmental awareness in autonomous driving systems to prevent pedestrian injuries actively. We introduce a built environment detection task in large-scale panoramic images and a detection-based pedestrian collision frequency prediction task. We propose a baseline method that incorporates a collision prediction module into a state-of-the-art detection model to tackle both tasks simultaneously. Our experiments demonstrate a significant correlation between object detection of built environment elements and pedestrian collision frequency prediction. Our results are a stepping stone towards understanding the interdependencies between built environment conditions and pedestrian safety.
Influences of Displaying Permission-related Information on Web Single Sign-On Login Decisions

Web users are increasingly presented with multiple login options, including password-based login and common web single sign-on (SSO) login options such as"Login with Google"and"Login with Facebook". There has been little focus in previous studies on how users choose from a list of login options and how to better inform users about privacy issues in web SSO systems. In this paper, we conducted a 200-participant study to understand factors that influence participants' login decisions, and how they are affected by displaying permission differences across login options; permissions in SSO result in release of user personal information to third-party web sites through SSO identity providers. We compare and report on login decisions made by participants before and after viewing permission-related information, examine self-reported responses for reasons related to their login decisions, and report on the factors that motivated their choices. We find that usability preferences and inertia (habituation) were among the dominant factors influencing login decisions. After participants viewed permission-related information, many prioritised privacy over other factors, changing their login decisions to more privacy-friendly alternatives. Displaying permission-related information also influenced some participants to make tradeoffs between privacy and usability preferences.
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Additionally, different attribute settings of RL in RL-EA are discussed. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets to facilitate a quick comparative study. Finally, we analyze potential directions for future research.
Robust Core-Periphery Constrained Transformer for Domain Adaptation

Unsupervised domain adaptation (UDA) aims to learn transferable representation across domains. Recently a few UDA works have successfully applied Transformer-based methods and achieved state-of-the-art (SOTA) results. However, it remains challenging when there exists a large domain gap between the source and target domain. Inspired by humans' exceptional transferability abilities to adapt knowledge from familiar to uncharted domains, we try to apply the universally existing organizational structure in the human functional brain networks, i.e., the core-periphery principle to design the Transformer and improve its UDA performance. In this paper, we propose a novel brain-inspired robust core-periphery constrained transformer (RCCT) for unsupervised domain adaptation, which brings a large margin of performance improvement on various datasets. Specifically, in RCCT, the self-attention operation across image patches is rescheduled by an adaptively learned weighted graph with the Core-Periphery structure (CP graph), where the information communication and exchange between images patches are manipulated and controlled by the connection strength, i.e., edge weight of the learned weighted CP graph. Besides, since the data in domain adaptation tasks can be noisy, to improve the model robustness, we intentionally add perturbations to the patches in the latent space to ensure generating robust learned weighted core-periphery graphs. Extensive evaluations are conducted on several widely tested UDA benchmarks. Our proposed RCCT consistently performs best compared to existing works, including 88.3\% on Office-Home, 95.0\% on Office-31, 90.7\% on VisDA-2017, and 46.0\% on DomainNet.
Direct limits of large orbits and the Knaster continuum homeomorphism group

The main result is that the group $\textrm{Homeo} (K)$ of homeomorphisms of the universal Knaster continuum contains an open subgroup with a comeager conjugacy class. Actually, this open subgroup is the very natural subgroup consisting of degree-one homeomorphisms. We give a general fact about finding comeager orbits in Polish group actions which are approximated densely by direct limits of actions with comeager orbits. The main theorem comes as a result of this fact and some finer analysis of the conjugacy action of the group $\textrm{Homeo}_+[0,1]$.
Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology

Multivariate Time Series (MVTS) anomaly detection is a long-standing and challenging research topic that has attracted tremendous research effort from both industry and academia recently. However, a careful study of the literature makes us realize that 1) the community is active but not as organized as other sibling machine learning communities such as Computer Vision (CV) and Natural Language Processing (NLP), and 2) most proposed solutions are evaluated using either inappropriate or highly flawed protocols, with an apparent lack of scientific foundation. So flawed is one very popular protocol, the so-called point-adjust protocol, that a random guess can be shown to systematically outperform all algorithms developed so far. In this paper, we review and evaluate many recent algorithms using more robust protocols and discuss how a normally good protocol may have weaknesses in the context of MVTS anomaly detection and how to mitigate them. We also share our concerns about benchmark datasets, experiment design and evaluation methodology we observe in many works. Furthermore, we propose a simple, yet challenging, baseline based on Principal Components Analysis (PCA) that surprisingly outperforms many recent Deep Learning (DL) based approaches on popular benchmark datasets. The main objective of this work is to stimulate more effort towards important aspects of the research such as data, experiment design, evaluation methodology and result interpretability, instead of putting the highest weight on the design of increasingly more complex and"fancier"algorithms.
Exploring the length scales, timescales and chemistry of challenging materials (Part 1)

This themed issue explores the different length scales and timescales that determine the physics and chemistry of a variety of key materials, explored from the perspective of a wide range of disciplines, including physics, chemistry, materials science, Earth science and biochemistry. The topics discussed include catalysis, chemistry under extreme conditions, energy materials, amorphous and liquid structure, hybrid organic materials and biological materials. The issue is in two parts, with the present part exploring glassy and amorphous systems and materials at high pressure. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 1)'.
A review of abstraction methods towards verifying neural networks

Neural networks as a machine learning technique are increasingly deployed in various domains. Despite their performances and their continuous improvement, the deployment of neural networks in safety-critical systems, in particular for autonomous mobility, remains restricted. This is mainly due to the lack of (formal) specifications and verification methods and tools that allow for getting sufficient confidence in the behavior of the neural network-based functions. Recent years have seen neural network verification getting more attention; and many verification methods were proposed, yet the practical applicability of these methods to real-world neural network models remains limited. The main challenge of neural network verification methods is related to the computational complexity and the large size of neural networks pertaining to complex functions. As a consequence, applying abstraction methods for neural network verification purposes is seen as a promising mean to cope with such issues. The aim of abstraction is to build an abstract model by omitting some irrelevant details or some details that are not highly impacting w.r. t some considered features. Thus, the verification process is made faster and easier while preserving, to some extent, the relevant behavior regarding the properties to be examined on the original model. In this paper, we review both the abstraction techniques for activation functions and model size reduction approaches, with a particular focus on the latter. The review primarily discusses the application of abstraction techniques on feed-forward neural networks, and explores the potential for applying abstraction to other types of neural networks. Throughout the paper, we present the main idea of each approach, and then discuss their respective advantages and limitations in details. Finally, we provide some insights and guidelines to improve the discussed methods.
PVA-assisted metal transfer for vertical WSe2 photodiode with asymmetric van der Waals contacts

Abstract The vertical electronic and optoelectronic devices based on 2D materials have shown great advantages over lateral devices, such as higher current density, faster switch speed, and superior short-channel control. However, it is difficult to fabricate vertical device with conventional metal deposition methods due to the aggressive process usually results in damage to the contact region. Here, we develop a simple and effective metal transfer technique and fabricate p-type and n-type WSe2 transistors by using metals with different work functions and subsequently create a vertical WSe2 transistors with a 18-nm-thick channel, which retain good gate coupling effect. Furthermore, a vertical WSe2 photodiode is constructed with graphene and Pt as asymmetric van der Waals (vdW) contacts. The work-function difference between graphene and Pt generates a built-in electric filed, leading to a high current rectification over 105. Under 405 nm laser illumination, the device exhibits excellent self-powered photodetection properties, including a high responsivity of 0.28 A W−1, fast response speed of 24 μs, and large light on/off ratio exceeding 105 at zero bias, which surpass most of the vdW photodiodes. This work demonstrates that the metal transfer technique is a promising strategy for the construction of high-performance vertical optoelectronic devices.
Networks of Queer Care: The Privatization of Social Responsibility and the Politics of Queer Generosity

Dana L. Cloud, “Networks of Queer Care: The Privatization of Social Responsibility and the Politics of Queer Generosity,” QED: A Journal in GLBTQ Worldmaking 10, no. 1 (2023): 203–213. DOI: 10.14321/qed.10. 1.0203. ISSN 2327-1574. Copyright © 2023 The Author(s). All rights reserved. Care Work: Dreaming Disability Justice. By Leah Lakshmi PiepznaSamarsinha. Vancouver, Canada: Arsenal Pulp Press, 2018; $15.63 paper, $10.49 Kindle, $25.99 audio CD.
cGAS/STING and NLRP3 cooperatively activate CD8+ T cell-mediated anti-tumor immunity in colorectal cancer

Colorectal cancer (CRC) is a highly prevalent and deadly disease that is largely refractory to immunotherapy. The only CRC subset that responds to these therapies is characterized by prevalent microsatellite instability (MSI), extensive CD8+ T cell infiltration and high expression of innate immune signaling pathways. Endogenous activation of the cGAS/STING pathway is essential for the CD8+ T cell antitumor response in MSI CRCs, suggesting that activating it in other CRCs could boost immunotherapy response rates. We show that cGAS/STING signaling can be enhanced by costimulation of the NLRP3 inflammasome and that dual stimulation increases CD8+ T cell-mediated antitumor immunity in both MSI and non-MSI CRCs. The ability of NLRP3 to boost cGAS/STING signaling was specific and did not occur with activation of other innate immune pathways such as AIM2 or TLRs. Cooperativity between cGAS/STING and NLRP3 proceeded via a positive feedback loop that was inflammasome-independent and required early crosstalk between the signaling mediators and regulation of their gene expression. Notably, increased cGAS/STING signaling enhanced CD8+ T cell activation when in conjunction with anti-PD1 immunotherapy, suggesting that signaling via NLRP3 could further boost this response and render otherwise resistant CRC susceptible to immunotherapy. Significance Innate immune signaling pathways cooperatively regulate CD8+ T cell-mediated antitumor immunity in both hot and cold tumors. In addition to serving as predictive biomarkers, these pathways can be therapeutically targeted to increase response rates to immunotherapy while minimizing undesirable adverse events.
Impairment of the SKN-1A/NRF1 proteasome surveillance pathway triggers tissue-specific protective immune responses against distinct natural pathogens in C. elegans

Protein quality control pathways play important roles in resistance against pathogen infection. For example, the conserved transcription factor SKN-1/NRF upregulates proteostasis capacity after blockade of the proteasome, and also promotes resistance against bacterial infection in the nematode C. elegans. SKN-1/NRF has three isoforms, and the SKN-1A/NRF1 isoform in particular regulates proteasomal gene expression upon proteasome dysfunction as part of a conserved bounce-back response. We report here that, in contrast to the previously reported role of SKN-1 in promoting resistance against bacterial infection, loss-of-function mutants in skn-1a and its activating enzymes ddi-1 and png-1, show constitutive expression of immune response programmes against natural eukaryotic pathogens of C. elegans. These programmes are the Oomycete Recognition Response (ORR), which promotes resistance against oomycetes that infect through the epidermis, and the Intracellular Pathogen Response (IPR), which promotes resistance against intestine-infecting microsporidia. Consequently, skn-1a mutants show increased resistance to both oomycete and microsporidia infections. We also report that almost all ORR/IPR genes induced in common between these programmes are regulated by the proteasome and interestingly, specific ORR/IPR genes can be induced in distinct tissues depending on the exact trigger. Furthermore, we show that increasing proteasome function significantly reduces oomycete-mediated induction of multiple ORR markers. Altogether, our findings demonstrate that proteasome regulation keeps innate immune responses in check in a tissue-specific manner, against natural eukaryotic pathogens of the C. elegans epidermis and intestine.
Global performance monitoring from TanDEM-X quicklook data

The primary objective of the TanDEM-X mission is the generation of a global high resolution digital elevation model (DEM) with single-pass SAR interferometry. In order to globally monitor and optimize the interferometric performance, TanDEM-X quicklook data and quality indicators in the annotation files are used to generate large scale maps. With these maps, DEM performance limitations can be efficiently analyzed and improved solutions for future acquisitions can be found.
Creating Online Indigenous Language Courses as Decolonizing Praxis

Résumé:Le présent article traite d'un projet de recherche-action participative sur l'utilisation de la technologie, en particulier en ce qui concerne les cours de langues autochtones en ligne pour l'apprentissage et l'enseignement des langues autochtones. Cette recherche est menée par le partenariat NEȾOLṈE « un esprit, un peuple », l'organisme 7000 Languages, et deux partenaires autochtones qui ont créé des cours avec 7000 Languages : la Hase' Language Revitalization Society et le Prairies to Woodlands Indigenous Language Revitalization Circle (P2WILRC). Ensemble, les collaborateurs examinent a) comment les nations et les organisations autochtones créent et mettent en œuvre des cours d'apprentissage de leurs langues en ligne et b) quelles interventions issues de la communauté pourraient améliorer l'efficacité de ces efforts. Ils considèrent leur travail commun comme une praxis décolonisatrice et transformatrice visant le maintien du kwa wala, du michif et d'autres langues autochtones.Abstract:This article shares a participatory action research project about the use of technology, specifically online Indigenous language courses, to learn and teach Indigenous languages. The research collaborators are the NEȾOLṈE « one mind, one people », Partnership, 7000 Languages, and two Indigenous Partners who have created courses with 7000 Languages: the Hase' Language Revitalization Society and the Prairies to Woodlands Indigenous Language Revitalization Circle (P2WILRC). Together we consider (a) how Indigenous Nations and organizations create and implement online courses for their languages and (b) what community-led interventions could make these efforts more effective. We position our work together as decolonizing and transformative praxis toward the continuance of Kwawala, Michif, and other Indigenous languages.