Miracle-dz/UniFAR-FTUs huggingface dataset:
Balancing Learning Model Privacy, Fairness, and Accuracy With Early Stopping Criteria

As deep learning models mature, one of the most prescient questions we face is: what is the ideal tradeoff between accuracy, fairness, and privacy (AFP)? Unfortunately, both the privacy and the fairness of a model come at the cost of its accuracy. Hence, an efficient and effective means of fine-tuning the balance between this trinity of needs is critical. Motivated by some curious observations in privacy–accuracy tradeoffs with differentially private stochastic gradient descent (DP-SGD), where fair models sometimes result, we conjecture that fairness might be better managed as an indirect byproduct of this process. Hence, we conduct a series of analyses, both theoretical and empirical, on the impacts of implementing DP-SGD in deep neural network models through gradient clipping and noise addition. The results show that, in deep learning, the number of training epochs is central to striking a balance between AFP because DP-SGD makes the training less stable, providing the possibility of model updates at a low discrimination level without much loss in accuracy. Based on this observation, we designed two different early stopping criteria to help analysts choose the optimal epoch at which to stop training a model so as to achieve their ideal tradeoff. Extensive experiments show that our methods can achieve an ideal balance between AFP.
Memory Accuracy After 20 Years for Interviews About Child Maltreatment

When adults allege childhood victimization, their long-term memory comes under scrutiny. This scrutiny can extend to the adults’ memory of childhood interviews. The concerns raise important theoretical and applied issues regarding memory for long-past discussions of child maltreatment and trauma. In this longitudinal study, 104 adults, who as children (ages 3–15 years) were interviewed in child maltreatment investigations (Time 1), were questioned 20 years later (Time 2) about the Time 1 interviews. Verbatim documentation from Time 1 permitted scoring of memory accuracy. A subset of the participants (36%) reported no memory for the Time 1 interviews. Of the 64% who remembered being interviewed at Time 1, those who had been adolescents at Time 1 remembered the forensic interview discussion about abuse incidents better than discussion about general psychological issues. Adult trauma symptoms were associated with more accurate memory for interview content that directly concerned abuse experiences but not for non–abuse-specific information. Findings indicate that the veracity of adults’ long-term memory for clinical/forensic conversations about childhood maltreatment depends on age at interview, interview content, and traumatization factors. Implications are discussed.
Assessing coordination performance based on centrality in an e-mail communication network

AbstractBuilding design and construction require the collective effort of diverse project participants. The coordination performance of these project participants is important for effective management and needs to be assessed periodically. However, there is no uncomplicated quantitative way to measure coordination. Measuring coordination is cumbersome and time-consuming particularly during the project execution phase. This study proposes an easy procedure for monitoring the coordinative performance of project participants. The degree, betweenness, and closeness centrality measures of the project participants in a wayfinding signage project at a major airport construction project are calculated using social network analysis on the e-mail communication network between the participants. A centrality index is defined for each firm based on the average of these three centrality measures. The firm’s coordination score is also calculated based on content analysis of the sent and received e-mails between the part.
Abortion Safety and Use with Normally Prescribed Mifepristone in Canada.

BACKGROUND In the United States, mifepristone is available for medical abortion (for use with misoprostol) only with Risk Evaluation and Mitigation Strategy (REMS) restrictions, despite an absence of evidence to support such restrictions. Mifepristone has been available in Canada with a normal prescription since November 2017. METHODS Using population-based administrative data from Ontario, Canada, we examined abortion use, safety, and effectiveness using an interrupted time-series analysis comparing trends in incidence before mifepristone was available (January 2012 through December 2016) with trends after its availability without restrictions (November 7, 2017, through March 15, 2020). RESULTS A total of 195,183 abortions were performed before mifepristone was available and 84,032 after its availability without restrictions. After the availability of mifepristone with a normal prescription, the abortion rate continued to decline, although more slowly than was expected on the basis of trends before mifepristone had been available (adjusted risk difference in time-series analysis, 1.2 per 1000 female residents between 15 and 49 years of age; 95% confidence interval [CI], 1.1 to 1.4), whereas the percentage of abortions provided as medical procedures increased from 2.2% to 31.4% (adjusted risk difference, 28.8 percentage points; 95% CI, 28.0 to 29.7). There were no material changes between the period before mifepristone was available and the nonrestricted period in the incidence of severe adverse events (0.03% vs. 0.04%; adjusted risk difference, 0.01 percentage points; 95% CI, -0.06 to 0.03), complications (0.74% vs. 0.69%; adjusted risk difference, 0.06 percentage points; 95% CI, -0.07 to 0.18), or ectopic pregnancy detected after abortion (0.15% vs. 0.22%; adjusted risk difference, -0.03 percentage points; 95% CI, -0.19 to 0.09). There was a small increase in ongoing intrauterine pregnancy continuing to delivery (adjusted risk difference, 0.08%; 95% CI, 0.04 to 0.10). CONCLUSIONS After mifepristone became available as a normal prescription, the abortion rate remained relatively stable, the proportion of abortions provided by medication increased rapidly, and adverse events and complications remained stable, as compared with the period when mifepristone was unavailable. (Funded by the Canadian Institutes of Health Research and the Women's Health Research Institute.).
Sensorimotor Integration of Active Knee Exoprostheses

Losing a limb is one of the most psychologically and physically devastating events possible in life and amputees have to adapt to the loss of their physical integrity. The major cause for amputation of the lower-limb in the aging industrial nations are dysvascular diseases often with comorbidity of diabetes. However, also trauma, malignancy, sepsis and congenital malformation are possible reasons for this life-altering surgery. Starting with postoperative rehabilitation, amputees have to learn how to live life with the support of a prosthetic leg. The aim is to prevent that limited mobility leads to limited independence and, as a consequence, to a reduced engagement in physical and social activities. Therefore, artificial limbs are required that adequately replace the biological counterpart. Substantial advances in actuation technology and control design have been underway during the past decades. However, the full potential of sophisticated prostheses can only be exploited if the artificial limb is seamlessly integrated into the sensory-motor system: the prosthesis must behave like the physiological leg and being perceived as such. The aim of this thesis is to foster the integration of actuated knee exoprostheses into the human sensory-motor system. For this purpose, two major challenges are tackled: replication of physiological joint impedance modulation and restoration of sensory feedback. To attain the first goal, it is fundamental to analyze and understand physiological joint impedance modulation during walking. Therefore, a potential approach is presented that utilizes brief Transient Endpoint Perturbations for System Identification and Parameter Estimation (TEP-SIPE) of joint impedance. The feasibility of TEP-SIPE is assessed in experiments with a two-segmented single-joint physical model and with two human subjects performing standing and walking on a dual-belt treadmill. The results suggest that TEP-SIPE can generate consistent estimates of, first, known joint properties and, second, ankle joint impedance estimates found in the literature. To realize the physiological impedance modulation in a prosthesis, in the next step, a bio-inspired adaptive impedance controller is implemented. On the one hand, the neuromusculoskeletal model that has been proved to be of value for TEP-SIPE is proposed as an alternative to the classical spring-damper virtual impedance. On the other hand, a human model-based adaptive controller is developed which can optionally be added on top of the variable impedance control. First, simulations reveal the properties of both control strategies. Second, the controllers are implemented on the active knee prosthesis ANGELAA and tested in two unimpaired and one amputee subject. The results suggest that the neuromusculoskeletal impedance model improves gait
Augmented Hopfield network for constrained generator scheduling

Many scheduling algorithms do not incorporate all the physical constraints of the problem. However, as their operational environments change many power systems are operated closer to physical limits and scheduling algorithms that consider all constraints are required. This paper presents an augmented Hopfield neural network scheduling algorithm that unifies the unit commitment and generation dispatch functions. This algorithm successfully considers ramp rate, transmission and fuel constraints in addition to the more common constraints. Results show that feasible solutions can be obtained in highly constrained circumstances.
Trust in the research community predicts intent to comply with COVID-19 prevention measures: An analysis of a large-scale international survey dataset

In the present study, I explored the relationship between people's trust in different agents related to prevention of spread of COVID-19 and their compliance with pharmaceutical and non-pharmaceutical preventive measures. The COVIDiSTRESSII Global Survey dataset, which was collected from international samples, was analysed to examine the aforementioned relationship across different countries. For data-driven exploration, network analysis and Bayesian generalized linear model (GLM) analysis were performed. The result from network analysis demonstrated that trust in the scientific research community was most central in the network of trust and compliance. In addition, the outcome from Bayesian GLM analysis indicated that the same factor, trust in the scientific research community, was most fundamental in predicting participants' intent to comply with both pharmaceutical and non-pharmaceutical preventive measures. I briefly discussed the implications of the findings, the importance of trust in the scientific research community in explaining people's compliance with measure to prevent spread of COVID-19.
CONFLICT DETECTION IN AIR TRAFFIC CONTROL: A TASK ANALYSIS, A LITERATURE REVIEW, AND A NEED FOR FURTHER RESEARCH

A task analysis for a typical air traffic control conflict detection scenario is described in this paper. Two types of prediction components are identified: (1) a relative judgment (RJ) task with regard to which aircraft will reach the projected point of intersection first; and (2) a prediction-motion (PM) task estimating the time-to-collide if the aircraft will reach the intersection simultaneously. The paper also identified several alternative strategies that can be potentially used by controllers to perform the RJ and PM tasks. Following the task analysis, a literature review on factors influencing the RJ and PM performance reveals that although there are a number of studies on controller performance in conflict detection, almost no one has studied how the controller actually performs the RJ and PM tasks. Finally, a number of potential research questions are proposed with an aim to shed new light on the human attention/cognition mechanisms involved in the prediction tasks in air traffic control (ATC) and hopefully contribute to the knowledge base regarding motion prediction involving multiple objects or multiple tasks.
Enterovirus D68 epidemic, UK, 2018, was caused by subclades B3 and D1, predominantly in children and adults, respectively, with both subclades exhibiting extensive genetic diversity

Enterovirus D68 (EV-D68) has been recently identified in biennial epidemics coinciding with diagnoses of non-polio acute flaccid paralysis/myelitis (AFP/AFM). We investigated the prevalence, genetic relatedness and associated clinical features of EV-D68 in 194 known EV positive samples from late 2018, UK. EV-D68 was detected in 83 (58%) of the 143 EV positive samples. Sequencing and phylogenetic analysis revealed an extensive genetic diversity, split between subclades B3 (n=50) and D1 (n=33), suggesting epidemiologically unrelated infections. B3 predominated in children and younger adults, and D1 in older adults and the elderly (p=0.0009). Clinical presentation indicated causation or exacerbation of respiratory distress in 91.4% of EV-D68-positive individuals, principally: cough (75.3%), shortness of breath (56.8%), coryza (48.1%), wheeze (46.9%), supplemental oxygen required (46.9%) and fever (38.9%). Clinical features were not distinguished by subclade. Two cases of AFM were observed, one with EV-D68 detectable in the cerebrospinal fluid, otherwise neurological symptoms were rarely reported (n=4).
Weakly-Nonlinear, Long Internal Gravity Waves in Stratified Fluids of Finite Depth

This paper presents an analytical investigation of the propagation of a weakly-nonlinear, long internal gravity wave in a stratified medium of finite total depth. The governing equation is derived and shown to reduce to the KdV equation in the shallow-water limit and to the Benjamin/Ono equation in the deep-water limit. The equation is also shown to possess four conserved quantities, just as was the case for the deep-water waves considered by Ono. The equation suggests the existence of a steady-state waveform described by two parameters which degenerates into the one-parameter, steady-state waveforms discussed by Benjamin. A numerical approach using Fornberg's pseudospectral method is used to examine the solution. The results demonstrate the existence of a solitary wavelike steady-state solution with a solitonlike behavior. The effect of finite water depth on the waveform and the wave speed of the steady-state solitary waves is discussed.
Indirect estimation of turbulent mixing in western route of Indonesian throughflow

The Indonesian Throughflow (ITF) via its western path conveys mainly North Pacific water origin with Smax thermocline water and Smin intermediate water from its entry portal in Sangihe-Talaud arcs to the main outflow straits in Lombok, Ombai and Timor passage. Along its route, the throughflow water characteristics transforms significantly due to strong diapycnal mixing forced by internal tidal waves interaction along complex topography such as passages, sill, straits, and shallow islands chains. This paper reports a brief estimate of turbulent mixing profiles in Sangihe chains, and Makassar Strait. The CTD dataset are obtained from the year of maritime continent (YMC) Cruise in August 2019 on board the R.V. Baruna Jaya I. The Thorpe method is used to analysis dissipation energy ( ε ) and vertical diffusivity (Kz ) from CTD dataset. It is shown that the highest ε epsilon 5.87 × 10−7 Wkg −1 and Kz 4.42 × 10−3 m2s 1 are found in the Sangihe area. In Labani Channel and Dewakang Sill the averaged vertical diffusivity is much weaker at the order of 10−4 m 2s1. Thus, Sangihe Chains station have the highest values compared to other stations at depth 950-1000 meters.
MAGNETORHEOLOGICAL FLUID DAMPERS FOR ROTOR VIBRATION CONTROL

In this paper, magneto-rheological(MR) fluid with a rapid, reversible and dramatic change in its rheological properties by the application of an external magnetic field is used to develop controllable MR fluid dampers for rotating machinery. The basic operation principle of the MR fluid damper is first reviewed, then two MR fluid dampers, which are respectively based on shear operation mode and squeeze film operation mode, are presented. The effectiveness of these MR fluid dampers for attenuating and controlling the vibration of rotor systems are experimentally investigated. The existing problems in the MR fluid dampers are discussed. It is shown that the dynamic characteristics of the MR fluid dampers can be easily controlled by a magnetic coil with a low voltage (less than 50V) and that the MR fluid dampers are very effective for attenuating the vibration of rotor systems. It is possible to give the optimum support damping and stiffness for every vibration mode in rotor systems by controlling the applied current in the MR dampers, if they are not located at the main model nodes of rotor systems.
Comparison of quantum and classical methods for labels and patterns in Restricted Boltzmann Machines

Classification and data reconstruction using a restricted Boltzmann machine (RBM) is presented. RBM is an energy-based model which assigns low energy values to the configurations of interest. It is a generative model, once trained it can be used to produce samples from the target distribution. The D-Wave 2000Q is a quantum computer which has been used to exploit its quantum effect for machine learning. Bars-and-stripes (BAS) and cybersecurity (ISCX) datasets were used to train RBMs. The weights and biases of trained RBMs were used to map onto the D-Wave. Classification as well as image reconstruction were performed. Classification accuracy of both datasets indicates comparable performance using D-Wave’s adiabatic annealing and classical Gibb’s sampling.
Disentangling hand and tool processing: Distal effects of neuromodulation

The neural processing within a brain region that responds to more than one object category can be separated by looking at the horizontal modulations established by that region, which suggests that local representations can be affected by connections to distal areas, in a category-specific way. Here we first wanted to test whether by applying transcranial direct current stimulation (tDCS) to a region thatre sponds both to hands and tools (posterior middle temporal gyrus; pMTG), while participants performed either a hand- or tool-related training task, we would be able to specifically target the trained category, and thereby dissociate the overlapping neural processing. Second, we wanted to see if these effects were limited to the target area or extended to distal but functionally connected brain areas. After each combined tDCS and training session, participants therefore viewed images of tools, hands, and animals, in an fMRI scanner. Using multivoxel pattern analysis, we found that tDCS stimulation to pMTG indeed improved the classification accuracy between tools vs. animals, but only when combined with a tool training task (not a hand training task). However, surprisingly, tDCS stimulation to pMTG also improved the classification accuracy between hands vs. animals when combined with a tool training task (not a hand training task). Our findings suggest that overlapping but functionally-specific networks can be separated by using a category-specific training task together with tDCS - a strategy that can be applied more broadly to other cognitive domains using tDCS - and demonstrates the importance of horizontal modulations in objectcategory representations.
Phosphorylation dynamics in a flg22-induced, heterotrimeric G-protein dependent signaling network in Arabidopsis thaliana reveals a candidate PP2A phosphatase involved in AtRGS1 trafficking

flg22 is a 22-amino peptide released from bacterial flagellin, a Microbe-Associated Molecular Pattern (MAMP) that is recognized by the plant cell as a signal indicating that bacteria are present. On its own, flg22 initiates a rapid increase in cytoplasmic calcium, extracellular reactive oxygen species, and activation of a Mitogen Activated Protein Kinase (MAPK) cascade, all of which are activated within 15 minutes after the cell perceives flg22. Here we show a massive change in protein abundance and phosphorylation state of the Arabidopsis root cell proteome within this 15-minute duration in wildtype and a mutant deficient in G-protein coupled signaling. Integration of phosphoproteome with protein-protein interactome data followed by network topology analyses discovered that many of the flg22-induced phosphoproteome changes fall on proteins that comprise the G protein interactome and on the most highly populated hubs of the immunity network. Approximately 95% of the phosphorylation changes in the G-protein interactome depend on a functional heterotrimeric G protein complex, some occur on proteins that interact directly with components of G-coupled signal transduction. One of these is ATBα, a substrate-recognition sub-unit of the PP2A Ser/Thr phosphatase and an interactor to Arabidopsis thaliana REGULATOR OF G SIGNALING 1 protein (AtRGS1), a 7-transmembrane spanning modulator of the nucleotide-binding state of the core G protein complex. AtRGS1 is phosphorylated by BAK1, a component of the flg22 receptor, to initiate AtRGS1 endocytosis. A null mutation of ATBα confers high basal endocytosis of AtRGS1, suggesting sustained phosphorylated status. Loss of ATBα confers traits associated with loss of AtRGS1. Because the basal level of AtRGS1 is lower in the atbα null mutant in a proteasome-dependent manner, we propose that phosphorylation-dependent endocytosis of AtRGS1 is part of a mechanism to degrade AtRGS1 which then sustains activation of the G protein complex. Thus, the role of ATBα is now established as a central component of phosphorylation-dependent regulation of system dynamics in innate immunity.
PIPP: Improving peptide identity propagation using neural networks

Peptide identity propagation (PIP) can substantially reduce missing values in label-free mass spectrometry quantification by transferring peptides identified by tandem mass (MS/MS) spectra in one run to experimentally related runs where the peptides are not identified by MS/MS. The existing frameworks for matching identifications between runs perform peak tracing and propagation based on similarity of precursor features using only a limited number of dimensions available in MS1 data. These approaches do not produce accompanying confidence estimates and hence cannot filter probable false positive identity transfers. We introduce an embedding based PIP that uses a higher dimensional representation of MS1 measurements that is optimized to capture peptide identities using deep neural networks. We developed a propagation framework that works entirely on MaxQuant results. Current PIP workflows typically perform propagation mainly using two feature dimensions, and rely on deterministic tolerances for identification transfer. Our framework overcomes both these limitations while additionally assigning probabilities to each transferred identity. The proposed embedding approach enables quantification of the empirical false discovery rate (FDR) for peptide identification, while also increasing depth of coverage through coembedding the runs from the experiment with experimental libraries. In published datasets with technical and biological variability, we demonstrate that our method reduces missing values in MaxQuant results, maintains high quantification precision and accuracy, and low false transfer rate.