Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring ML testing in practice – Lessons learned from an interactive rapid review with Axis Communications

There is a growing interest in industry and academia in machine learning (ML) testing. We believe that industry and academia need to learn together to produce rigorous and relevant knowledge. In this study, we initiate a collaboration between stakeholders from one case company, one research institute, and one university. To establish a common view of the problem domain, we applied an interactive rapid review of the state of the art. Four researchers from Lund University and RISE Research Institutes and four practitioners from Axis Communications reviewed a set of 180 primary studies on ML testing. We developed a taxonomy for the communication around ML testing challenges and results and identified a list of 12 review questions relevant for Axis Communications. The three most important questions (data testing, metrics for assessment, and test generation) were mapped to the literature, and an in-depth analysis of the 35 primary studies matching the most important question (data testing) was made. A final set of the five best matches were analysed and we reflect on the criteria for applicability and relevance for the industry. The taxonomies are helpful for communication but not final. Furthermore, there was no perfect match to the case company’s investigated review question (data testing). However, we extracted relevant approaches from the five studies on a conceptual level to support later context-specific improvements. We found the interactive rapid review approach useful for triggering and aligning communication between the different stakeholders.
Multi-scale Speaker Diarization with Dynamic Scale Weighting

Speaker diarization systems are challenged by a trade-off between the temporal resolution and the ﬁdelity of the speaker representation. By obtaining a superior temporal resolution with an enhanced accuracy, a multi-scale approach is a way to cope with such a trade-off. In this paper, we propose a more advanced multi-scale diarization system based on a multi-scale diarization decoder. There are two main contributions in this study that signiﬁcantly improve the diarization performance. First, we use multi-scale clustering as an initialization to estimate the number of speakers and obtain the average speaker representation vector for each speaker and each scale. Next, we propose the use of 1-D convolutional neural networks that dynamically de-termine the importance of each scale at each time step. To handle a variable number of speakers and overlapping speech, the proposed system can estimate the number of existing speakers. Our proposed system achieves a state-of-art performance on the CALLHOME and AMI MixHeadset datasets, with 3.92% and 1.05% diarization error rates, respectively.
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in autonomous driving to allow a vehicle to act safely in its 3D environment. The best performing methods in 3D semantic segmentation or object detection rely on a large amount of annotated data. Yet annotating 3D Lidar data for these tasks is tedious and costly. In this context, we propose a self-supervised pretraining method for 3D perception models that is tailored to autonomous driving data. Specifically, we leverage the availability of synchronized and calibrated image and Lidar sensors in autonomous driving setups for distilling self-supervised pre-trained image representations into 3D models. Hence, our method does not require any point cloud nor image annotations. The keyingredient of our method is the use of superpixels which are used to pool 3D point features and 2D pixel features in visually similar regions. We then train a 3D network on the self-supervised task of matching these pooled point features with the corresponding pooled image pixel features. The advantages of contrasting regions obtained by superpixels are that: (1) grouping together pixels and points of visually coherent regions leads to a more meaningful contrastive task that produces features well adapted to 3D semantic segmentation and 3D object detection; (2) all the different regions have the same weight in the contrastive loss regardless of the number of 3D points sampled in these regions; (3) it mitigates the noise produced by incorrect matching of points and pixels due to occlusions between the different sensors. Extensive experiments on autonomous driving datasets demonstrate the ability of our image-to-Lidar distillation strategy to produce 3D representations that transfer well on semantic segmentation and object detection tasks.
Ordered community detection in directed networks

We develop a method to infer community structure in directed networks where the groups are ordered in a latent one-dimensional hierarchy that determines the preferred edge direction. Our nonparametric Bayesian approach is based on a modification of the stochastic block model (SBM), which can take advantage of rank alignment and coherence to produce parsimonious descriptions of networks that combine ordered hierarchies with arbitrary mixing patterns between groups. Since our model also includes directed degree correction, we can use it to distinguish nonlocal hierarchical structure from local in- and out-degree imbalance-thus, removing a source of conflation present in most ranking methods. We also demonstrate how we can reliably compare with the results obtained with the unordered SBM variant to determine whether a hierarchical ordering is statistically warranted in the first place. We illustrate the application of our method on a wide variety of empirical networks across several domains.
Neural Pipeline for Zero-Shot Data-to-Text Generation

In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise. We examine how to avoid finetuning pretrained language models (PLMs) on D2T generation datasets while still taking advantage of surface realization capabilities of PLMs. Inspired by pipeline approaches, we propose to generate text by transforming single-item descriptions with a sequence of modules trained on general-domain text-based operations: ordering, aggregation, and paragraph compression. We train PLMs for performing these operations on a synthetic corpus WikiFluent which we build from English Wikipedia. Our experiments on two major triple-to-text datasets—WebNLG and E2E—show that our approach enables D2T generation from RDF triples in zero-shot settings.
SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network

Spiking Neural Networks (SNNs) have piqued researchers’ interest because of their capacity to process temporal information and low power consumption. However, current state-of-the-art methods lim-ited their biological plausibility and performance because their neurons are generally built on the simple Leaky-Integrate-and-Fire (LIF) model. Due to the high level of dynamic complexity, modern neuron models have seldom been implemented in SNN practice. In this study, we adopt the Phase Plane Analysis (PPA) technique, a technique often utilized in neurodynamics field, to integrate a recent neuron model, namely, the Izhikevich neuron. Based on the findings in the advancement of neuroscience, the Izhikevich neuron model can be biologically plausible while maintaining comparable computational cost with LIF neurons. By uti-lizing the adopted PPA, we have accomplished putting neurons built with the modified Izhikevich model into SNN practice, dubbed as the Standardized Izhikevich Tonic (SIT) neuron. For performance, we evaluate the suggested technique for image classification tasks in self-built LIF-and-SIT-consisted SNNs, named Hybrid Neural Network (HNN) on static MNIST, Fashion-MNIST, CIFAR-10 datasets and neuromorphic N-MNIST, CIFAR10-DVS, and DVS128 Gesture datasets. The experi-mental results indicate that the suggested method achieves comparable accuracy while exhibiting more biologically realistic behaviors on nearly all test datasets, demonstrating the efficiency of this novel strategy in bridging the gap between neurodynamics and SNN practice.
Reevaluate In Vitro CYP3A Index Reactions of Benzodiazepines and Steroids between Humans and Dogs

Cytochrome P450 3A (CYP3A), the most important class of drug-metabolizing enzymes, participates in the metabolism of half of clinically used drugs. The CYP3A index reactions of dogs, one of the most widely used preclinical nonrodent species, are still poorly understood. This work evaluated the activity and selectivity of 10 CYP3A index reactions, including midazolam (MDZ) 1'- and 4-hydroxylation, alprazolam (APZ) and triazolam (TRZ) α- and 4-hydroxylation, testosterone (T) 6β-hydroxylation, lithocholate (LCA) 6α-hydroxylation, deoxycholate (DCA) 1β- and 5β-hydroxylation, with quantitative reaction phenotyping and kinetic analysis in human and canine recombinant CYP enzymes (rCYPs). In human studies, all reactions are reconfirmed as mixed index reactions of CYP3A with minor contributions from non-CYP3A isoforms. In canine studies, all reactions are also primarily catalyzed by CYP3A12 with lower contributions from CYP3A26. However, the canine CYP2B11 appreciably contributes to the hydroxylation of benzodiazepines except for APZ 4-hydroxylation. The canine CYP3A isoforms have lower activity than human isoforms toward T 6β-hydroxylation and LCA 6α-hydroxylation and both substrates undergo non-CYP3A catalyzed side reactions. DCA 1β- and 5β-hydroxylation are validated as the CYP3A index reactions in both humans and dogs with limited non-CYP3A contributions and side reactions. In conclusion, this work provides a comprehensive overview for the selectivity and activity of in vitro CYP3A index reactions in humans and dogs. The validated CYP3A index reactions between humans and dogs may benefit future practices in drug metabolism and drug interaction studies. SIGNIFICANCE STATEMENT Dogs are one of the most important nonrodent animals with limited studies of cytochrome P450 enzymes than humans. This work provides the most comprehensive quantitative data to date for the selectivity and activity of CYP3A index reactions in humans and dogs. The canine CYP2B11 was found to appreciably contribute to hydroxylation of midazolam, alprazolam and triazolam, the well-known probes for human CYP3A. Deoxycholate 1β- and 5β-hydroxylation are validated as the CYP3A index reactions in both humans and dogs.
Fabrication of solid-state nanopores

Nanopores are valuable single-molecule sensing tools that have been widely applied to the detection of DNA, RNA, proteins, viruses, glycans, etc. The prominent sensing platform is helping to improve our health-related quality of life and accelerate the rapid realization of precision medicine. Solid-state nanopores have made rapid progress in the past decades due to their flexible size, structure and compatibility with semiconductor fabrication processes. With the development of semiconductor fabrication techniques, materials science and surface chemistry, nanopore preparation and modification technologies have made great breakthroughs. To date, various solid-state nanopore materials, processing technologies, and modification methods are available to us. In the review, we outline the recent advances in nanopores fabrication and analyze the virtues and limitations of various membrane materials and nanopores drilling techniques.
Alterations in blood glucose concentration in wild rodents, Holochilus sciureus, naturally infected with Schistosoma mansoni

Abstract The present study aimed to evaluate the changes in peripheral blood glucose concentrations induced by Schistosoma mansoni infection in Holochilus sciureus rodents, a wild reservoir of the parasite. Glucose concentration was measured in the plasma of blood samples using a colorimetric enzymatic test. Biological parameters and S. mansoni burden in each rodent were also verified and correlated with glucose concentrations. A total of 76 H. sciureus were captured, out of which 20 (26%) were infected with S. mansoni (n=13 males and n=7 females). Although the parasite burden was comparable between the sexes, blood glucose concentration was lower in infected males and almost unchanged in females. Furthermore, histopathological data revealed that male rodents had a greater hepatic granulomatous inflammatory reaction than females. In addition, we also confirmed that the weight and total length of the analyzed animals had no effect on glucose levels. Therefore, natural infection with S. mansoni in H. sciureus may have a lower impact on glycemic homeostasis in females, which will help us understand the role of these rodents as reservoirs of S. mansoni.
An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models

This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from input-output data collected from the plant, and can be given a state-space representation with known measurable states made by past input and output variables, so that a state observer is not required. In the training phase, the Incremental Input-to-State Stability (δISS) property can be forced when consistent with the behavior of the plant. The δISS property is then leveraged to augment the model with an explicit integral action on the output tracking error, which allows to achieve offset-free tracking capabilities to the designed control scheme. The proposed control architecture is numerically tested on a water heating system and the achieved results are compared to those scored by another popular offset-free MPC method, showing that the proposed scheme attains remarkable performances even in presence of disturbances acting on the plant.
Near-optimality for infinite-horizon restless bandits with many arms

Restless bandits are an important class of problems with applications in recommender systems, active learning, revenue management and other areas. We consider infinite-horizon discounted restless bandits with many arms where a fixed proportion of arms may be pulled in each period and where arms share a finite state space. Although an average-case-optimal policy can be computed via stochastic dynamic programming, the computation required grows exponentially with the number of arms $N$. Thus, it is important to find scalable policies that can be computed efficiently for large $N$ and that are near optimal in this regime, in the sense that the optimality gap (i.e. the loss of expected performance against an optimal policy) per arm vanishes for large $N$. However, the most popular approach, the Whittle index, requires a hard-to-verify indexability condition to be well-defined and another hard-to-verify condition to guarantee a $o(N)$ optimality gap. We present a method resolving these difficulties. By replacing a global Lagrange multiplier used by the Whittle index with a sequence of Lagrangian multipliers, one per time period up to a finite truncation point, we derive a class of policies, called fluid-balance policies, that have a $O(\sqrt{N})$ optimality gap. Unlike the Whittle index, fluid-balance policies do not require indexability to be well-defined and their $O(\sqrt{N})$ optimality gap bound holds universally without sufficient conditions. We also demonstrate empirically that fluid-balance policies provide state-of-the-art performance on specific problems.
Inflammation in the early phase after kidney transplantation is associated with increased long‐term all‐cause mortality

In the general population, low‐grade inflammation has been established as a risk factor for all‐cause mortality. We hypothesized that an inflammatory milieu beyond the time of recovery from the surgical trauma could be associated with increased long‐term mortality in kidney transplant recipients (KTRs). This cohort study included 1044 KTRs. Median follow‐up time post‐engraftment was 10.3 years. Inflammation was assessed 10 weeks after transplantation by different composite inflammation scores based on 21 biomarkers. We constructed an overall inflammation score and five pathway‐specific inflammation scores (fibrogenesis, vascular inflammation, metabolic inflammation, growth/angiogenesis, leukocyte activation). Mortality was assessed with Cox regression models adjusted for traditional risk factors. A total of 312 (29.9%) patients died during the follow‐up period. The hazard ratio (HR) for death was 4.71 (95% CI: 2.85–7.81, p < .001) for patients in the highest quartile of the overall inflammation score and HRs 2.35–2.54 (95% CI: 1.40–3.96, 1.52–4.22, p = .001) for patients in the intermediate groups. The results were persistent when the score was analyzed as a continuous variable (HR 1.046, 95% CI: 1.033–1.056, p < .001). All pathway‐specific analyses showed the same pattern with HRs ranging from 1.19 to 2.70. In conclusion, we found a strong and consistent association between low‐grade systemic inflammation 10 weeks after kidney transplantation and long‐term mortality.
We will all age and one day be older adults ourselves: College students’ reflections on facilitating compassionate presence (CP) sessions for older adults

ABSTRACT College students are likely to hold negative ageist views and attitudes toward older people. The compassionate presence (CP) intervention could be a promising approach to reduce ageist attitudes that younger adults have of older adults. College students were trained virtually in deep, active, and compassionate listening and were matched with older adults living in assisted-living facilities. The partners met virtually for an hour once per week. Students completed weekly reflections throughout the ten-week intervention. The weekly reflections revealed that students had preconceived notions about older adults before the CP training. After the CP training and throughout the intervention, we noted a positive shift in perception, knowledge, and attitudes toward older adults and aging. Students built meaningful social bonds with older adults, developed strong communication and interpersonal skills, and acquired research and core leadership skills needed for future careers and professional practice.
Vulvovaginal atrophy management with a laser: the placebo effect or the conditioning Pavlov reflex

Abstract Postmenopausal vulvovaginal pain and atrophy require appropriate and sensitive outcomes that correlate with the genital discomfort, symptoms and expected changes. In some studies, hormone and laser treatments do not detect benefits due to the fact that appropriate measuring tools were not used. While some studies have demonstrated placebo effects for both therapeutic approaches, others support that sham-intervention could be a therapeutic by creating a conditioning reflex. However, obtained results are directly related with the quality of outcomes: you get what you measure. It is imperative to follow the Core Outcomes in Menopause global initiative.
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling

This work presents a new resource for borrowing identification and analyzes the performance and errors of several models on this task. We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings—words from one language that are introduced into another without orthographic adaptation—and use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and topic-varied than previous corpora available for this task. Our results show that a BiLSTM-CRF model fed with subword embeddings along with either Transformer-based embeddings pretrained on codeswitched data or a combination of contextualized word embeddings outperforms results obtained by a multilingual BERT-based model.
Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects

Recent research has advanced two degree-of-freedom (DoF), simultaneous, independent and proportional control of hand-wrist prostheses using surface electromyogram signals from remnant muscles as the control input. We evaluated two such regression-based controllers, along with conventional, sequential two-site control with co-contraction mode switching (SeqCon), in box-block, refined-clothespin and door-knob tasks, on 10 able-bodied and 4 limb-absent subjects. Subjects operated a commercial hand and wrist using a socket bypass harness. One 2-DoF controller (DirCon) related the intuitive hand actions of open-close and pronation-supination to the associated prosthesis hand-wrist actions, respectively. The other (MapCon) mapped myoelectrically more distinct, but less intuitive, actions of wrist flexion-extension and ulnar-radial deviation. Each 2-DoF controller was calibrated from separate 90 s calibration contractions. SeqCon performed better statistically than MapCon in the predominantly 1-DoF box-block task (>20 blocks/minute vs. 8–18 blocks/minute, on average). In this task, SeqCon likely benefited from an ability to easily focus on 1-DoF and not inadvertently trigger co-contraction for mode switching. The remaining two tasks require 2-DoFs, and both 2-DoF controllers each performed better (factor of 2–4) than SeqCon. We also compared the use of 12 vs. 6 optimally-selected EMG electrodes as inputs, finding no statistical difference. Overall, we provide further evidence of the benefits of regression-based EMG prosthesis control of 2-DoFs in the hand-wrist.
Longitudinal Fairness with Censorship

Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic. Most assume availability of the class label, which is impractical in many real-world applications such as precision medicine, actuarial analysis and recidivism prediction. Here we consider fairness in longitudinal right-censored environments, where the time to event might be unknown, resulting in censorship of the class label and inapplicability of existing fairness studies. We devise applicable fairness measures, propose a debiasing algorithm, and provide necessary theoretical constructs to bridge fairness with and without censorship for these important and socially-sensitive tasks. Our experiments on four censored datasets confirm the utility of our approach.
An I/O-Efficient Disk-based Graph System for Scalable Second-Order Random Walk of Large Graphs

Random walk is widely used in many graph analysis tasks, especially the first-order random walk. However, as a simplification of real-world problems, the first-order random walk is poor at modeling higher-order structures in the data. Recently, second-order random walk-based applications (e.g., Node2vec, Second-order PageRank) have become attractive. Due to the complexity of the second-order random walk models and memory limitations, it is not scalable to run second-order random walk-based applications on a single machine. Existing disk-based graph systems are only friendly to the first-order random walk models and suffer from expensive disk I/Os when executing the second-order random walks. This paper introduces an I/O-efficient disk-based graph system for the scalable second-order random walk of large graphs, called GraSorw. First, to eliminate massive light vertex I/Os, we develop a bi-block execution engine that converts random I/Os into sequential I/Os by applying a new triangular bi-block scheduling strategy, the bucket-based walk management, and the skewed walk storage. Second, to improve the I/O utilization, we design a learning-based block loading model to leverage the advantages of the full-load and on-demand load methods. Finally, we conducted extensive experiments on six large real datasets as well as several synthetic datasets. The empirical results demonstrate that the end-to-end time cost of popular tasks in GraSorw is reduced by more than one order of magnitude compared to the existing disk-based graph systems.