Miracle-dz/UniFAR-FTUs huggingface dataset:
Mean‐strain 10‐node tetrahedron with energy‐sampling stabilization

In this study, a new mean‐strain 10‐node tetrahedral element is developed using energy‐sampling stabilization. The proposed 10‐node tetrahedron is composed of several four‐node linear tetrahedral elements, four tetrahedra in the corners and four tetrahedra that tile the central octahedron in three possible sets of four‐node linear tetrahedra, corresponding to three different choices for the internal diagonal. The assumed strains are calculated from mean ‘basis function gradients. ’ The energy‐sampling technique introduced previously for removing zero‐energy modes in the mean‐strain hexahedron is adapted for the present element: the stabilization energy is evaluated on the four‐corner tetrahedra. The proposed element naturally leads to a lumped‐mass matrix and does not have unphysical low‐energy vibration modes. For simplicity, we limit our developments to linear elasticity with compressible and nearly incompressible material. The numerical tests demonstrate that the present element performs well compared with the classical 10‐node tetrahedral elements for shell and plate structures, and nearly incompressible materials. Copyright © 2016 John Wiley & Sons, Ltd.
Long-term deprescription in chronic pain and opioid use disorder patients: Pharmacogenetic and sex differences

Abstract More than half of patients with opioid use disorder for chronic non-cancer pain (CNCP) reduced their dose through a progressive opioid withdrawal supported by a rotation to buprenorphine and/or tramadol. The aim of this research is to analyse the long-term effectiveness of opioid deprescription taking into account the impact of sex and pharmacogenetics on the inter-individual variability. A cross-sectional study was carried out from October 2019 to June 2020 on CNCP patients who had previously undergone an opioid deprescription (n = 119 patients). Demographic, clinical (pain, relief and adverse events) and therapeutic (analgesic use) outcomes were collected. Effectiveness (< 50 mg per day of morphine equivalent daily dose without any aberrant opioid use behaviour) and safety (number of side-effects) were analysed in relation to sex differences and pharmacogenetic markers impact [OPRM1 genotype (rs1799971) and CYP2D6 phenotypes]. Long-term opioid deprescription was achieved in 49 % of the patients with an increase in pain relief and a reduction of adverse events. CYP2D6 poor metabolizers showed the lowest long-term opioid doses. Here, women showed a higher degree of opioid deprescription, but increased use of tramadol and neuromodulators, as well as an increased number of adverse events. Long-term deprescription was successful in half of the cases. Understanding sex and gender interaction plus a genetic impact could help to design more individualized strategies for opioid deprescription.
On the number of limit cycles in a predator prey model with non-monotonic functional response

In this work we analyze a Gause type predator-prey model with a non-monotonic functional response and we show that it has two limit cycles encircling an unique singularity at the interior of the first quadrant, the innermost unstable and the outermost stable, completing the results obtained in previous paper [12, 17, 26, 28]. Moreover, using the Poisson bracket we give a proof, shorter than the ones found in the literature, for determining the type of a cusp point of a singularity at the first quadrant.
Schneller als das Licht?

Elektromagnetische Wellen breiten sich üblicherweise höchstens mit Lichtgeschwindigkeit aus. Experimente mit elektromagnetischen evaneszenten Moden, dem klassischen Analogon der wellenmechanischen Tunnellösungen, zeigen jedoch, daß sich Signale und Energien auch schneller als das Licht im Vakuum ausbreiten können.
Joint Reference and Relation Extraction from Legal Documents with Enhanced Decoder Input

Abstract This paper deals with an important task in legal text processing, namely reference and relation extraction from legal documents, which includes two subtasks: 1) reference extraction; 2) relation determination. Motivated by the fact that two subtasks are related and share common information, we propose a joint learning model that solves simultaneously both subtasks. Our model employs a Transformer-based encoder-decoder architecture with non-autoregressive decoding that allows relaxing the sequentiality of traditional seq2seq models and extracting references and relations in one inference step. We also propose a method to enrich the decoder input with learnable meaningful information and therefore, improve the model accuracy. Experimental results on a dataset consisting of 5031 legal documents in Vietnamese with 61,446 references show that our proposed model performs better results than several strong baselines and achieves an F1 score of 99.4% for the joint reference and relation extraction task.
Increasing Global Terrestrial Diurnal Temperature Range for 1980–2021

The 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment.
The 2016 Menyuan Earthquake: The Largest Self‐Arrested Crustal Earthquake Ever Observed

The 2016 Mw 5.9 Menyuan earthquake occurred near the Haiyuan fault system in the northeastern Tibetan plateau. Although some kinematic properties of this event have been investigated, more dynamic properties still need to be clarified. First, we use the empirical Green's function method to obtain this event's apparent source time functions (ASTFs), which present a bell shape with a duration of 3s and corresponding smooth spectra. We then conduct dynamic rupture simulations, which are well constrained by the seismic and geodetic observations, to explore more in‐depth source properties. The results show that the characteristics of ASTFs can be explained by the self‐arresting rupture model, which implies that the rupture propagation of this event may spontaneously terminate before reaching the barriers. This result suggests that the initial conditions when this earthquake nucleated may have controlled the rupture process. Our work contributes to understanding the stopping mechanism of some moderate earthquake ruptures.
On The Impact of Machine Learning Randomness on Group Fairness

Statistical measures for group fairness in machine learning reflect the gap in performance of algorithms across different groups. These measures, however, exhibit a high variance between different training instances, which makes them unreliable for empirical evaluation of fairness. What causes this high variance? We investigate the impact on group fairness of different sources of randomness in training neural networks. We show that the variance in group fairness measures is rooted in the high volatility of the learning process on under-represented groups. Further, we recognize the dominant source of randomness as the stochasticity of data order during training. Based on these findings, we show how one can control group-level accuracy (i.e., model fairness), with high efficiency and negligible impact on the model’s overall performance, by simply changing the data order for a single epoch.
Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness

Commensurate with the rise in algorithmic bias research, myriad algorithmic bias mitigation strategies have been proposed in the literature. Nonetheless, many voice concerns about the lack of transparency that accompanies mitigation methods and the paucity of mitigation methods that satisfy protocol and data limitations of practitioners. Influence functions from robust statistics provide a novel opportunity to overcome both issues. Previous work demonstrates the power of influence functions to improve fairness outcomes. This work proposes a novel family of fairness solutions, coined influential fairness (IF), that is human-understandable and also agnostic to the underlying machine learning model and choice of fairness metric. We conduct an investigation of practitioner profiles and design mitigation methods for practitioners whose limitations discourage them from utilizing existing bias mitigation methods.
Honor Ethics: The Challenge of Globalizing Value Alignment in AI

Some researchers have recognized that privileged communities dominate the discourse on AI Ethics, and other voices need to be heard. As such, we identify the current ethics milieu as arising from WEIRD (Western, Educated, Industrialized, Rich, Democratic) contexts, and aim to expand the discussion to non-WEIRD global communities, who are also stakeholders in global sociotechnical systems. We argue that accounting for honor, along with its values and related concepts, would better approximate a global ethical perspective. This complex concept already underlies some of the WEIRD discourse on AI ethics, but certain cultural forms of honor also bring overlooked issues and perspectives to light. We first describe honor according to recent empirical and philosophical scholarship. We then review “consensus” principles for AI ethics framed from an honor-based perspective, grounding comparisons and contrasts via example settings such as content moderation, job hiring, and genomics databases. A better appreciation of the marginalized concept of honor could, we hope, lead to more productive AI value alignment discussions, and to AI systems that better reflect the needs and values of users around the globe.
Study of Dielectric Properties and Ultrasonic Attenuation in KDP-Type Ferroelectrics

The soft mode dynamical model has been used to study dielectric properties and ultrasonic attenuation in KDP-type ferroelectric crystals. The model Hamiltonian proposed by Blinc and Zeks has been modified by considering lattice anharmonicity up to fourth-order. The correlations appearing in the dynamical equation have been evaluated using double-time thermal retarded Green’s functions method and Dyson’s equation. Without any decoupling, the higher order correlations, appearing in the dynamical equation, have been evaluated using the renormalized Hamiltonian. The expressions for collective frequencies, width, dielectric constant, ultrasonic attenuation, and tangent loss have been calculated. The dielectric properties and ultrasonic attenuation strongly depend on the relaxational mode behavior of stochastic motion of H2PO4 group in KDP-type ferroelectrics. By fitting model values of physical quantities, the temperature dependence of and for different value of four-body coupling coefficient and dielectric constant and loss tangent has been calculated. The calculated and observed results have been found in good agreement.
WiMeasure

In the past few years, a large range of wireless signals such as WiFi, RFID, UWB and Millimeter Wave were utilized for sensing purposes. Among these wireless sensing modalities, WiFi sensing attracts a lot of attention owing to the pervasiveness of WiFi infrastructure in our surrounding environments. While WiFi sensing has achieved a great success in capturing the target's motion information ranging from coarse-grained activities and gestures to fine-grained vital signs, it still has difficulties in precisely obtaining the target size owing to the low frequency and small bandwidth of WiFi signals. Even Millimeter Wave radar can only achieve a very coarse-grained size measurement. High precision object size sensing requires using RF signals in the extremely high-frequency band (e.g., Terahertz band). In this paper, we utilize low-frequency WiFi signals to achieve accurate object size measurement without requiring any learning or training. The key insight is that when an object moves between a pair of WiFi transceivers, the WiFi CSI variations contain singular points (i.e., singularities) and we observe an exciting opportunity of employing the number of singularities to measure the object size. In this work, we model the relationship between the object size and the number of singularities when an object moves near the LoS path, which lays the theoretical foundation for the proposed system to work. By addressing multiple challenges, for the first time, we make WiFi-based object size measurement work on commodity WiFi cards and achieve a surprisingly low median error of 2.6 mm. We believe this work is an important missing piece of WiFi sensing and opens the door to size measurement using low-cost low-frequency RF signals.
Going public: the role of public participation approaches in commercial AI labs

In recent years, discussions of responsible AI practices have seen growing support for ‘participatory AI’ approaches, intended to involve members of the public in the design and development of AI systems. Prior research has identified a lack of standardised methods or approaches for how to use participatory approaches in the AI development process. At present, there is a dearth of evidence on attitudes to and approaches for participation in the sites driving major AI developments: commercial AI labs. Through 12 semi-structured interviews with industry practitioners and subject-matter experts, this paper explores how commercial AI labs understand participatory AI approaches and the obstacles they have faced implementing these practices in the development of AI systems and research. We find that while interviewees view participation as a normative project that helps achieve ‘societally beneficial’ AI systems, practitioners face numerous barriers to embedding participatory approaches in their companies: participation is expensive and resource intensive, it is ‘atomised’ within companies, there is concern about exploitation, there is no incentive to be transparent about its adoption, and it is complicated by a lack of clear context. These barriers result in a piecemeal approach to participation that confers no decision-making power to participants and has little ongoing impact for AI labs. This paper’s contribution is to provide novel empirical research on the implementation of public participation in commercial AI labs, and shed light on the current challenges of using participatory approaches in this context.
Hail in Southwestern France. I: Hailfall Characteristics and Hailstrom Environment

Abstract In the context of an experiment of hail prevention in the Aquitaine region, 12 869 reports of damaging hail have been compiled during 29 years in an area of 88 980 km2. These observations, together with crop insurance data, have led to a unique hail climatology. The data presented in this paper concern the geographical distribution of hail damage, the yearly, 5-day and hourly frequencies of hailfall, and the distributions of hailstone size and of hailfall duration. Most of these data are well explained by the fact that hail in the surveyed area is the result of almost any rather severe thunderstorm: large hail, however, is produced by a few isolated long-lived hailstorms traveling downwind of the central part of the Pyrenees with the strong upper level winds. Study of the mean characteristics of 30 of the most severe storms which have damaged the Aquitaine in the last three decades leads to the following description: a typical long-traveling hailstorm moves at 15 m s−1 for 1.5 h, dropping a hail .
The Slow Violence of Surveillance Capitalism: How Online Behavioral Advertising Harms People

People’s negative reactions to online behavioral advertising (OBA) are well-documented. However, past work has primarily focused on cataloguing these reactions and exploring how to change them, rather than understanding the ways these negative reactions affect people’s lived experiences. Drawing upon scholarship on socio-technical harms in human-computer interaction and computer-supported cooperative work, we investigate and categorize the different ways people report having been harmed by OBA. Through an online survey with 420 participants, we identified four key harms arising from OBA: psychological distress, loss of autonomy, constriction of user behavior, and algorithmic marginalization and traumatization. We next discuss the “slow violence” inflicted by OBA and the normalization of people’s affective discomfort with OBA, and how the two can present an opportunity for researchers to re-conceptualize OBA—and the invasive data practices it entails—as not just abstractly concerning to people, but as actively harmful.
Understanding College Level Student Learning of Basic Programming at an Open Access Institution

In this study, we present our findings with regard to teaching basic programming concepts at an Open Access Institution. The goal was to gather insight into how to best introduce programming to our students. We found that our students should start with block coding, as a means to slowly and easily introduce coding concepts. Then they are ready for text-based programming skills. This flow allows them to understand and be able to apply basic computing concepts and enjoy the process of learning. Also, introducing block coding should be done via a fun game or activity to entice them to want to learn programming.
Towards Efficient Emotion Self-report Collection Using Human-AI Collaboration

Emotion-aware services are increasingly used in different applications such as gaming, mental health tracking, video conferencing, and online tutoring. The core of such services is usually a machine learning model that automatically infers its user's emotions based on different biological indicators (e.g., physiological signals and facial expressions). However, such machine learning models often require a large number of emotion annotations or ground truth labels, which are typically collected as manual self-reports by conducting long-term user studies, commonly known as Experience Sampling Method (ESM). Responding to repetitive ESM probes for self-reports is time-consuming and fatigue-inducing. The burden of repetitive self-report collection leads to users responding arbitrarily or dropping out from the studies, compromising the model performance. To counter this issue, we, in this paper, propose a Human-AI Collaborative Emotion self-report collection framework, HACE, that reduces the self-report collection effort significantly. HACE encompasses an active learner, bootstrapped with a few emotion self-reports (as seed samples), and enables the learner to query for only not-so-confident instances to retrain the learner to predict the emotion self-reports more efficiently. We evaluated the framework in a smartphone keyboard-based emotion self-report collection scenario by performing a 3-week in-the-wild study (N = 32). The evaluation of HACE on this dataset (≈11,000 typing sessions corresponding to more than 200 hours of typing data) demonstrates that it requires 46% fewer self-reports than the baselines to train the emotion self-report detection model and yet outperforms the baselines with an average self-report detection F-score of 85%. These findings demonstrate the possibility of adopting such a human-AI collaborative approach to reduce emotion self-report collection efforts.
Beydere 3: a new early Miocene small mammal assemblage from western Anatolia, Turkey

ABSTRACT The new micromammal site of Beydere 3 represents a typical Anatolian MN3 fauna in the high diversification and abundance of Eumyarion. Notably, two Eumyarion species are new; combined, they make up almost half of the assemblage. While Eumyarion beyderensis sp. nov. shows simple morphology in upper molars, Eumyarion aegeaniensis sp. nov. shows a more complex lophs pattern. In addition, Beydere 3 yielded nine species of rodents and an ochotonid: the hamsters Cricetodon kasapligili, Mirrabella crenulata, Megacricetodon hellenicus, Democricetodon doukasi, Vallaris zappai and Eumyarion sp. , the dormice Glirulus ekremi and Glis sp. , the beaver Steneofiber eseri, the squirrel Palaeosciurus fissurae and Ochotonidae indet. The fauna is significant in that it represents the first common occurrence of Megacricetodon in Anatolia. Eumyarion and Megacricetodon are both dominant groups which may indicate a signal of environmental change, but still closer to wet conditions. This, in combination with the new species of Eumyarion, suggests that Beydere 3 represents a time slice which was previously not recorded in Anatolia. Radiometric dating of ashes overlying the section indicates that the age of the locality is older than 18.21 (± 0.19) Ma.
RETRACTED ARTICLE: Elemental Analysis of Crustaceans by Inductively Coupled Plasma–Mass Spectrometry and Direct Mercury Analysis

We, the Editor, the Authors, and Publisher, have retracted the following article: Nargis Jamila, In Ming Hwang, Yu Min Park, Ga Hyun Lee, Ji Yeon Choi, Naeem Khan, Min Ja Cho, Kyung Su Park & Kyong Su Kim “Elemental Analysis of Crustaceans by Inductively Coupled Plasma—Mass Spectrometry and Direct Mercury Analysis. ” Analytical Letters (2017), https://dx.doi. org/10.1080/00032719.2017. 1296851 The authors have requested the retraction of their article due to variations in the concentration of the arsenic identified at the data analysis stage, which affected the reported results of the paper and conclusion. Upon realizing this error, the authors brought the issue to the journal Editor and Publisher immediately. There are no concerns regarding research or publishing conduct and the authors have had already resubmitted an updated paper, which has been accepted and published as https://dx.doi. org/10.1080/00032719.2021. 1895188 We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as RETRACTED.
One-step grown multi-walled carbon nanotubes with Ni filling and decoration

A single step approach for the synthesis of multi-walled carbon nanotubes filled with Ni nanowires (Ni-MWCNTs) and decorated with Ni nanoparticles has been illustrated. The MWCNTs are grown by a PECVD-sputtering hybrid process at the low temperature of 450 °C having an average diameter of 55 ± 6 nm and length of 1.35 ± 0.08 µm. Thin Ni films of the thickness 10 nm have been used, which act as a catalyst as well as a source material for the filling of MWCNTs with Ni nanowires, whereas sputtering of Ni is the source of decorated Ni particles. This process facilitates the growth of aligned MWCNTs filled with Ni nanowires and also decorated with Ni nanoparticles on the walls. Magnetic properties of the Ni filled and decorated MWCNTs are measured using a vibrating sample magnetometer. Magnetic hysteresis loops of Ni containing MWCNTs show ferromagnetic behavior. These Ni-MWCNTs shows coercivity of 135 Oe, which is significantly greater than that of the bulk Ni at room temperature. The magnetic property measurement reveals that the coercivity of the as grown MWCNTs is dependent on the size and content of Ni. Thus, a novel method has been demonstrated for the synthesis of ferromagnetic Ni-MWCNT which has potential applications in various fields.