Miracle-dz/UniFAR-FTUs huggingface dataset:
Stephanostomum minankisi n. sp. (Digenea: Acanthocolpidae) from dusky flounder Syacium papillosum (Paralichthydae) from southern Gulf of Mexico: A new species without spines?

Abstract A new species of the genus Stephanostomum is described for the southeastern Gulf of Mexico based on morphological and nucleotide evidence. Stephanostomum minankisi n. sp. infects the intestine of the dusky flounder Syacium papillosum in the Yucatan Continental Shelf, Mexico (Yucatan Peninsula). Sequences of the 28S ribosomal gene were obtained and compared with available sequences of the other species and genera of the families Acanthocolpidae and Brachycladiidae from GenBank. A phylogenetic analysis was conducted, including 39 sequences, 26 of which represented 21 species and six genera of the family Acanthocolpidae. The new species is characterized by the absence of circumoral spines and spines on the tegument. Nonetheless, scanning electron microscopy consistently revealed the pits of 52 circumoral spines distributed in a double row with 26 spines each, and forebody spined. Other distinctive features of this species are testes in contact (sometimes overlapping), the vitellaria running along the body lateral fields to the mid-level of the cirrus-sac, pars prostatica and ejaculatory duct similar in length, and uroproct present. The phylogenetic tree showed that the three species found as parasites of dusky flounder (the new adult species and two in metacercaria stages) were grouped into two different clades. S. minankisi n. sp. was the sister species of Stephanostomum sp. 1 (Bt = 56) and formed a clade with S. tantabiddii, supported by high bootstrap values (100).
New perspectives on morphological and genetic variability of Corynosoma bullosum [Linstow, 1892] parasitizing southern elephant seals from the Antarctic Peninsula

Abstract Previous descriptions of Corynosoma bullosum (Linstow, 1892) show that specimens vary greatly in the proportions of different body structures, measurements of females and males, number of rows of hooks, and egg measurements, among other features. We redescribe this species from specimens found in southern elephant seal faeces from King George Island. We also provide a molecular characterization, in addition to 5.8S and internal transcribed spacer (ITS) existing sequences. We examined 41 elephant seals, and 30 adult acanthocephalans were found in 15 of them. The specimens were identified as belonging to the genus Corynosoma due to each having a tubular body with an inflated anterior part forming a thorny disk and the posterior part bearing somatic spines on the ventral surface, and genital spines surrounding the genital pore. Individual morphology corresponded to C. bullosum: large size, marked sexual dimorphism, and proboscis with 16–18 rows of spines with 11–15 spines per row. The molecular profile of three specimens of C. bullosum was analysed using 18S rDNA. We inferred phylogenetic relationships of the family Polymorphidae using maximum likelihood and Bayesian inference. We provide an updated morphological redescription for C. bullosum including electron microscopy photographs and molecular data. The 18S gene sequences showed low genetic variation and supported that C. bullosum is a sister to Corynosoma australe.
Marginal Modelling of Categorical Data from Crossover Experiments

Marginal models provide a useful framework for the analysis of crossover experiments when the response variable is categorical. In this paper we use the three-treatment, three-period crossover experiment with a binary outcome variable to demonstrate how marginal models can be used to perform a likelihood-based analysis of multiple-period crossover experiments. Other designs are discussed in less detail. Maximum likelihood estimation is performed using a constraint equation specification of the marginal model. Data from a crossover trial comparing treatments for primary dysmenorrhoea are used to demonstrate the utility of marginal models in analysing crossover data
Validation of the German version of the Short Health Scale – a brief, valid and reliable instrument to assess health-related quality of life in German-speaking patients with inflammatory bowel diseases

Abstract Background Health-related quality of life (hrQoL) may be the most important patient-reported outcome for patients with chronic disorders. The Short Health Scale (SHS) is a brief four-item instrument to assess hrQoL in patients with bowel disorders. This study examined the validity, reliability and sensitivity of the German translation of the SHS in a cohort of outpatients with inflammatory bowel diseases (IBD). Methods The study was preregistered in April 2021 ( https://doi.org/10.17605/OSF.IO/S82D9 ). Outpatients with IBD (n=225) in different stages of disease activity (as determined by the Harvey–Bradshaw index or partial Mayo score) completed the German SHS and the short Inflammatory Bowel Disease Questionnaire (sIBDQ) as an established measure of hrQoL to examine the convergent validity. To assess reliability, a subset of patients (n=30) in remission completed the same questionnaires after 4–8 weeks. Sensitivity to change was established from questionnaires of patients with either decreased (n=15) or increased (n=16) disease activity after 3–6 months. Results The internal consistency of the German SHS was high (Cronbach’s α=0.860). SHS total scores correlated strongly with sIBDQ scores (ρ=–0.760, p<0.001) and disease activity (ρ=0.590, p<0.001). Retest reliability was high (ρ=0.695, p<0.001). Sensitivity to change was statistically significant for patients with decreased (p=0.013) but not increased (p=0.134) disease activity. Conclusion The German version of the SHS is a valid and reliable tool to measure hrQoL in persons with IBD.
IR Imaging Using Arrays of SiO2 Micromechanical Detectors

In this letter, we describe the fabrication of an array of bimaterial detectors for infrared (IR) imaging that utilize SiO2 as a structural material. All the substrate material underneath the active area of each detector element was removed. Each detector element incorporates an optical resonant cavity layer in the IR absorbing region of the sensing element. The simplified microfabrication process requires only four photolithographic steps with no wet etching or sacrificial layers. The thermomechanical deflection sensitivity was 7.9 10-3 rad/K which corresponds to a noise equivalent temperature difference (NETD) of 2.9 mK. In the present work the array was used to capture IR images while operating at room temperature and atmospheric pressure and no need for vacuum packaging. The average measured NETD of our IR detector system was approximately 200 mK but some sensing elements exhibited an NETD of 50 mK.
Observations of 4.26 μm CO2 Auroral Emissions From AIRS Nadir Sounder Measurements

The Atmospheric Infrared Sounder (AIRS) instrument onboard the NASA Aqua satellite is used to observe aurora associated with the CO2 4.26 μm emission. These observations are due to non‐local thermodynamic equilibrium (NLTE) resulting from the vibrational excitation of CO2, which arises in the process of auroral energetic particle precipitation, as opposed to the dayside NLTE occurring due to solar radiation. The observations are confirmed to be associated with aurora using the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) limb measurements and the SuperMAG Electrojet (SME) index. The high spectral resolution and low noise associated with the AIRS instrument allows for the emission spectrum to be calculated and confirmed to arise from CO2. Our new NLTE index values derived from AIRS provide the ability to globally measure auroral events associated with CO2 with a spatial resolution on the order of ∼13.5 km.
How Long Can the Postseismic and Interseismic Phases of Great Subduction Earthquake Sustain? Toward an Integrated Earthquake‐Cycle Perspective

The transition of subduction systems from postseismic to interseismic phase is an important indicator of their stress state and readiness for subsequent events. However, understanding the dynamics of this transition of megathrust earthquakes is hindered by the short instrumental time compared to the centuries‐long recurrence interval (T). Here, with an updated earthquake‐cycle perspective of global spatiotemporally limited geodetic observations, we use generic viscoelastic earthquake‐cycle models to explore the evolution of time‐varying surface deformation. Self‐consistently, our models reproduce key observed features of the deformation cycle, suggesting common processes activated at different margins. The relative postseismic and interseismic durations are found dependent mainly nonlinearly on the ratio of T to mantle viscosity, with a plausible range of ∼0.2–0.4T at representative margins and minimum >0.55T, respectively. Our results thus provide a useful tool for understanding the similarities and differences of earthquake‐cycle deformation and guiding monitoring of possible future signals at world‐wide subduction zones.
Envisioning Equitable Speech Technologies for Black Older Adults

There is increasing concern that how researchers currently define and measure fairness is inadequate. Recent calls push to move beyond traditional concepts of fairness and consider related constructs through qualitative and community-based approaches, particularly for underrepresented communities most at-risk for AI harm. One in context, previous research has identified that voice technologies are unfair due to racial and age disparities. This paper uses voice technologies as a case study to unpack how Black older adults value and envision fair and equitable AI systems. We conducted design workshops and interviews with 16 Black older adults, exploring how participants envisioned voice technologies that better understand cultural context and mitigate cultural dissonance. Our findings identify tensions between what it means to have fair, inclusive, and representative voice technologies. This research raises questions about how and whether researchers can model cultural representation with large language models.
Robust Efficient License Plate and Character Detection System Based on Simplified CNN

Current license plate recognition systems struggle with image noise reduction and license plate feature detecting processes. This paper presents an efficient and highly accurate license plate detection and character detection program based on the YOLO neural network, which is a simplified CNN-based neural network frame for robust image processing systems. Different than most approaches, the system we proposed simply requires a prioritized analysis of the dataset in order to evaluate potential noises inside images so that program implementations could be more effective and more targeted to design and optimize with YOLO neural network. With our presented system, the accuracy of license plate detection improves from 63% which is performed by traditional image processing methods to 90.3%.
Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias

Nowadays, Machine Learning (ML) systems are widely used in various businesses and are increasingly being adopted to make decisions that can significantly impact people’s lives. However, these decision-making systems rely on data-driven learning, which poses a risk of propagating the bias embedded in the data. Despite various attempts by the algorithmic fairness community to outline different types of bias in data and algorithms, there is still a limited understanding of how these biases relate to the fairness of ML-based decision-making systems. In addition, efforts to mitigate bias and unfairness are often agnostic to the specific type(s) of bias present in the data. This paper explores the nature of fundamental types of bias, discussing their relationship to moral and technical frameworks. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced throughout the entire modelling pipeline and possibly how to mitigate it. Our primary contribution is a framework for generating synthetic datasets with different forms of biases. We use our proposed synthetic data generator to perform experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. Furthermore, we provide initial insights into mitigating specific types of bias through post-processing techniques. The implementation of the synthetic data generator and experiments can be found at https://github.com/rcrupiISP/BiasOnDemand.
Trust-based Enrollment in a Group in a Distributed Setting

We investigate how trust can be defined and exploited in the problem of accepting a candidate for enrollment in a group in a distributed setting. A simulation in Python using the NetworkX package has the servers (nodes) implement the WedID protocol. Each node has a profile document with a FOAF graph connecting the server to its neighbors via the foaf:knows property. We assume that the FOAF graphs have been assembled into a network tying together the group. Enrollment relies heavily on two novel notions of trust: one is the trust one node has in another, which we take to come from several kinds of sources, and the other is the trustworthiness a node enjoys because of its centrality. If someone objects to the candidate, there is a vote, a novel procedure that weights a node's votes by its trustworthiness and allows votes based on reference weighted by trust in the referrer. Our main contributions are the procedure for accepting a candidate and the two notions of trust. Results to date include the simulation mentioned; thirty-five test runs provided reasonable results. Work in progress is an implementation of the procedures on a group of servers implementing the WebID protocol.
Bias as Boundary Object: Unpacking The Politics Of An Austerity Algorithm Using Bias Frameworks

Whether bias is an appropriate lens for analysis and critique remains a subject of debate among scholars. This paper contributes to this conversation by unpacking the use of bias in a critical analysis of a controversial austerity algorithm introduced by the Austrian public employment service in 2018. It was envisioned to classify the unemployed into three risk categories based on predicted prospects for re-employment. The system promised to increase efficiency and effectivity of counseling while objectifying a new austerity support measure allocation scheme. This approach was intended to cut spending for those deemed at highest risk of long term unemployment. Our in-depth analysis, based on internal documentation not available to the public, systematically traces and categorizes various problematic biases to illustrate harms to job seekers and challenge promises used to justify the adoption of the system. The classification is guided by a long-established bias framework for computer systems developed by Friedman and Nissenbaum, which provides three sensitizing basic categories. We identified in our analysis "technical biases," like issues around measurement, rigidity, and coarseness of variables, "emergent biases," such as disruptive events that change the labor market, and, finally, "preexisting biases," like the use of variables that act as proxies for inequality. Grounded in our case study, we argue that articulated biases can be strategically used as boundary objects to enable different actors to critically debate and challenge problematic systems without prior consensus building. We unpack benefits and risks of using bias classification frameworks to guide analysis. They have recently received increased scholarly attention and thereby may influence the identification and construction of biases. By comparing four bias frameworks and drawing on our case study, we illustrate how they are political by prioritizing certain aspects in analysis while disregarding others. Furthermore, we discuss how they vary in their granularity and how this can influence analysis. We also problematize how these frameworks tend to favor explanations for bias that center the algorithm instead of social structures. We discuss several recommendations to make bias analyses more emancipatory, arguing that biases should be seen as starting points for reflection on harmful impacts, questioning the framing imposed by the imagined “unbiased" center that the bias is supposed to distort, and seeking out deeper explanations and histories that also center bigger social structures, power dynamics, and marginalized perspectives. Finally, we reflect on the risk that these frameworks may stabilize problematic notions of bias, for example, when they become a standard or enshrined in law.
Weighted Matrix Factorization with Wilson Lower Bound Score

This report aims to propose an improvement to the recommendation system based on collaborative filtering with implicit feedback. Because existing weighted matrix factorization weights all unknown ratings equally, this does not make much sense in reality. To enhance the precision and dependability of weighted matrix factorization, this report suggests combining weighted matrix factorization and Wilson lower bound scores to dynamically alter the weights of unknown ratings, which is more accurate than setting equal weights for all unknown ratings. Thus, exposure bias can be minimized more effectively.
The thermal properties and thermoelectric performance of γ-graphyne nanoribbons

The thermal properties and thermoelectric performance of one-dimensional armchair and zigzag γ-graphyne nanoribbons (γ-GYNRs) are theoretically investigated in the present study. We found that the pristine γ-GYNRs hold lower phononic thermal conductance and better figure of merit (ZT) than graphene nanorribons. The maximal ZT values for the armchair and zigzag γ-GYNRs are 0.93 and 0.61, respectively. By introducing 14C atoms, the thermoelectric conversion efficiencies of γ-GYNRs are greatly enhanced, thus the isotope effect can significantly improve the thermoelectric properties of the systems. More importantly, under a relatively low temperature, the maximal ZT of a defective zigzag γ-GYNR is as high as 2.12, indicating that γ-GYNRs are promising materials for constructing excellent thermoelectric nanodevices.
Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

AI systems are adopted in numerous domains due to their increasingly strong predictive performance. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time-consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our work highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other’s work and produce generalizable scientific knowledge. We also hope this work will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.
Touch-and-Heal

Affective touch plays an important role in human-robot interaction. However, it is challenging for robots to perceive various natural human tactile gestures accurately, and feedback human intentions properly. In this paper, we propose a data-driven affective computing system based on a biomimetic quadruped robot with large-format, high-density flexible pressure sensors, which can mimic the natural tactile interaction between humans and pet dogs. We collect 208-minute videos from 26 participates and construct a dataset of 1212 human gestures-dog actions interaction sequences. The dataset is manually annotated with an 81-tactile-gesture vocabulary and a 44-corresponding-dog-reaction vocabulary, which are constructed through literature, questionnaire, and video observation. Then, we propose a deep learning algorithm pipeline with a gesture classification algorithm based on ResNet and an action prediction algorithm based on Transformer, which achieve the classification accuracy of 99.1% and the 1-gram BLEU score of 0.87 respectively. Finally, we conduct a field study to evaluate the emotion regulation effects through tactile affective interaction, and compare it with voice interaction. The results show that our system with tactile interaction plays a significant role in alleviating user anxiety, stimulating user excitement and improving the acceptability of robotic dogs.
An Empirical Analysis of Racial Categories in the Algorithmic Fairness Literature

Recent work in algorithmic fairness has highlighted the challenge of defining racial categories for the purposes of anti-discrimination. These challenges are not new but have previously fallen to the state, which enacts race through government statistics, policies, and evidentiary standards in anti-discrimination law. Drawing on the history of state race-making, we examine how longstanding questions about the nature of race and discrimination appear within the algorithmic fairness literature. Through a content analysis of 60 papers published at FAccT between 2018 and 2020, we analyze how race is conceptualized and formalized in algorithmic fairness frameworks. We note that differing notions of race are adopted inconsistently, at times even within a single analysis. We also explore the institutional influences and values associated with these choices. While we find that categories used in algorithmic fairness work often echo legal frameworks, we demonstrate that values from academic computer science play an equally important role in the construction of racial categories. Finally, we examine the reasoning behind different operationalizations of race, finding that few papers explicitly describe their choices and even fewer justify them. We argue that the construction of racial categories is a value-laden process with significant social and political consequences for the project of algorithmic fairness. The widespread lack of justification around the operationalization of race reflects institutional norms that allow these political decisions to remain obscured within the backstage of knowledge production.