Miracle-dz/UniFAR-FTUs huggingface dataset:
A critical analysis of model-based formal verification efforts within the automotive industry

There are a number of software-controlled features in today's automotive vehicles to meet the increasing number of requirements for comfort, safety, infotainment and reduced emissions. To meet the growing demands from such features, the software content is not only increasing rapidly, but also becoming increasingly distributed within the Electronic Control Units (ECUs), leading to the possibility of unwanted interactions and consequent loss of reliability. Therefore, the automotive software-based features have to be designed and verified using sophisticated tools and techniques.Formal methods-based techniques and tools have been used on various industrial designs over the past 6 years by one of the authors in development and applied research projects, collaborating with a number of automotive companies. The challenges faced in large projects are discussed in this context. A large number of industrial users have recounted their own experiences in user conferences and workshops, which will be summarized. The results of a short survey of real users will also be discussed in this context.
Astrocytic α2-Na+/K+ ATPase inhibition suppresses astrocyte reactivity and reduces neurodegeneration in a tauopathy mouse model

Alzheimer’s disease (AD) is the most dominant form of dementia characterized by the deposition of extracellular amyloid plaques and intracellular neurofibrillary tau tangles (NFTs). In addition to these pathologies, an emerging pathophysiological mechanism that influences AD is neuroinflammation. Astrocytes are a vital type of glial cell that contribute to neuroinflammation, and reactive astrocytes, or astrogliosis, are a well-known pathological feature of AD. However, the mechanisms by which astrocytes contribute to the neurodegenerative process in AD have not been fully elucidated. Here, we showed that astrocytic α2-Na+/K+ adenosine triphosphatase (α2-NKA) is elevated in postmortem human brain tissue from AD and progressive nuclear palsy, a primary tauopathy. The increased astrocytic α2-NKA was also recapitulated in a mouse model of tauopathy. Pharmacological inhibition of α2-NKA robustly suppressed neuroinflammation and reduced brain atrophy. In addition, α2-NKA knockdown in tauopathy mice halted the accumulation of tau pathology. We also demonstrated that α2-NKA promoted tauopathy, in part, by regulating the proinflammatory protein lipocalin-2 (Lcn2). Overexpression of Lcn2 in tauopathy mice increased tau pathology, and prolonged Lcn2 exposure to primary neurons promoted tau uptake in vitro. These studies collectively highlight the contribution of reactive astrocytes to tau pathogenesis in mice and define α2-NKA as a major regulator of astrocytic-dependent neuroinflammation. Description Astrocytic α2-Na+/K+ ATPase inhibition and knockdown in a model of tauopathy protects against neurodegeneration by attenuating astrocytic reactivity. Targeting the pump One of the cellular hallmark of Alzheimer’s disease and other tauopathies is the presence of reactive astrocytes associated with neuroinflammation and neurodegeneration. However, how astrocytes contribute to the pathophysiology of these disorders remain to be fully elucidated. Now, Mann et al. performed in vitro experiments in human tissue and in vivo studies in a mouse model and reported increased expression of the astrocytic α2-Na+/K+ ATPase (α2-NKA) in brain tissue from patients with tauopathies. In mice, pharmacological or genetic α2-NKA blockade reduced neurodegeneration by suppressing astrogliosis and reducing proinflammatory proteins including Lcn-2 which limited tau uptake. The results suggest that targeting astrocyte activation or inhibiting α2-NKA activity might be effective in reducing neurodegeneration and brain inflammation occurring in tauopathies.
Children Undergoing Laryngeal Surgery for Obstructive Sleep Apnea: NSQIP Analysis of Length of Stay, Readmissions, and Reoperations

Objective: No national study to date has specifically evaluated the predictive variables associated with extended hospitalization and other postoperative complications following laryngeal surgery in children with obstructive sleep apnea (OSA). The goals of this study were to identify perioperative risk factors and provide a descriptive analysis of surgical outcomes in these children using the National Surgical Quality Improvement Program–Pediatrics (NSQIP-P) database. Methods: Patients aged 0 to 18 years who underwent laryngeal surgery with a postoperative diagnosis of OSA were queried via the 2014-2018 NSQIP-P database using Current Procedural Terminology code 31541. Variables collected included age, sex, ethnicity, body mass index (BMI), medical comorbidities, American Society of Anesthesiologists (ASA) physical classification, operative time, and concurrent procedures. Endpoints of interest were length of stay, unplanned reoperation, readmission, reintubation, and postoperative complications. Univariate and multivariate linear regression analyses were performed. Results: A total of 181 cases were identified (57.5% male and 42.5% female, mean age 4.36 years, range 14 days-17.7 years). Body mass index (P = .015, OR = 0.96), structural CNS abnormality (P = .034, OR = 1.95), preoperative oxygen supplementation (P = .043, OR = 1.28), operative time (P = .019, OR = 1.84, 95% CI = 1.28-2.54), and concurrent procedure (P < .001, OR = 2.21) were all independently associated with LOS. Postoperative complications had no significantly associated variables, with an overall low incidence of readmission (5.0%), reoperation (1.7%), and reintubation (1.1%). Conclusion: In this data set, children with OSA undergoing laryngeal surgery experienced minimal postoperative complications. Recognition of the factors associated with increased LOS could lead to improvement in the quality of care for children with OSA.
Tick borne relapsing fever - a systematic review and analysis of the literature

Tick borne relapsing fever (TBRF) is a zoonosis caused by various Borrelia species transmitted to humans by both soft-bodied and (more recently recognized) hard-bodied ticks. In recent years, molecular diagnostic techniques have allowed to extend our knowledge on the global epidemiological picture of this neglected disease. Nevertheless, due to the patchy occurrence of the disease and the lack of large clinical studies, the knowledge on several clinical aspects of the disease remains limited. In order to shed light on some of these aspects, we have systematically reviewed the literature on TBRF and summarized the existing data on epidemiology and clinical aspects of the disease. Publications were identified by using a predefined search strategy on electronic databases and a subsequent review of the reference lists of the obtained publications. All publications reporting patients with a confirmed diagnosis of TBRF published in English, French, Italian, German, and Hungarian were included. Maps showing the epidemiogeographic mosaic of the different TBRF Borrelia species were compiled and data on clinical aspects of TBRF were analysed. The epidemiogeographic mosaic of TBRF is complex and still continues to evolve. Ticks harbouring TBRF Borrelia have been reported worldwide, with the exception of Antarctica and Australia. Although only molecular diagnostic methods allow for species identification, microscopy remains the diagnostic gold standard in most clinical settings. The most suggestive symptom in TBRF is the eponymous relapsing fever (present in 100% of the cases). Thrombocytopenia is the most suggestive laboratory finding in TBRF. Neurological complications are frequent in TBRF. Treatment is with beta-lactams, tetracyclines or macrolids. The risk of Jarisch-Herxheimer reaction (JHR) appears to be lower in TBRF (19.3%) compared to louse-borne relapsing fever (LBRF) (55.8%). The overall case fatality rate of TBRF (6.5%) and LBRF (4–10.2%) appears to not differ. Unlike LBRF, where perinatal fatalities are primarily attributable to abortion, TBRF-related perinatal fatalities appear to primarily affect newborns.
Degenerative Cervical Myelopathy: Development and Natural History [AO Spine RECODE-DCM Research Priority Number 2]

Study Design: Narrative review. Objectives: To discuss the current understanding of the natural history of degenerative cervical myelopathy (DCM). Methods: Literature review summarizing current evidence pertaining to the natural history and risk factors of DCM. Results: DCM is a common condition in which progressive arthritic disease of the cervical spine leads to spinal cord compression resulting in a constellation of neurological symptoms, in particular upper extremity dysfunction and gait impairment. Anatomical factors including cord-canal mismatch, congenitally fused vertebrae and genetic factors may increase individuals’ risk for DCM development. Non-myelopathic spinal cord compression (NMSCC) is a common phenomenon with a prevalence of 24.2% in the healthy population, and 35.3% among individuals >60 years of age. Clinical radiculopathy and/or electrophysiological signs of cervical cord dysfunction appear to be risk factors for myelopathy development. Radiological progression of incidental Ossification of the Posterior Longitudinal Ligament (OPLL) is estimated at 18.3% over 81-months and development of myelopathy ranges between 0-61.5% (follow-up ranging from 40 to 124 months between studies) among studies. In patients with symptomatic DCM undergoing non-operative treatment, 20-62% will experience neurological deterioration within 3-6 years. Conclusion: Current estimates surrounding the natural history of DCM, particularly those individuals with mild or minimal impairment, lack precision. Clear predictors of clinical deterioration for those treated with non-operative care are yet to be identified. Future studies are needed on this topic to help improve treatment counseling and clinical prognostication.
Long-term cognitive impairments in kidney transplant recipients: impact on participation and quality of life

ABSTRACT Background Cognitive impairment is often present shortly after transplantation in kidney transplant recipients (KTR). To date, it is unknown whether these impairments persist in the long term, to what extent they are associated with disease-related variables and whether they affect societal participation and quality of life (QoL) of KTR. Method This study was part of the TransplantLines Biobank & Cohort Study in the University Medical Center Groningen. A total of 131 KTR, with a mean age of 53.6 years (SD = 13.5) transplanted ≥1 year ago (M = 11.2 years, range 1–41.7 years), were included and compared with 306 healthy controls (HC). KTR and HC were well matched; there were no significant differences regarding age, sex and education. All participants were assessed with neuropsychological tests measuring memory, mental speed, attention and executive functioning, and with questionnaires examining societal participation and QoL. Results Compared with HC, KTR performed significantly worse on memory, mental speed and measures of executive functioning (all P-values <0.05). Moreover, 16% of KTR met the criteria for mild cognitive impairment (MCI), compared with 2.6% of the HC. MCI in KTR was not significantly correlated with age- and disease-related variables. Poorer cognitive functioning was significantly related to lower levels of societal participation and to lower QoL (all P-values <0.01). Conclusions This study shows long-term cognitive impairments in KTR, which are not related to disease-related variables. Neuropsychological assessment is important to timely signal these impairments, given their serious negative impact on societal participation and QoL.
Fast Dynamic Updates and Dynamic SpGEMM on MPI-Distributed Graphs

Sparse matrix multiplication (SpGEMM) is a fun-damental kernel used in many diverse application areas, both numerical and discrete. For example, many algebraic graph algorithms rely on SpGEMM in the tropical semiring to compute shortest paths in graphs. Recently, SpGEMM has received growing attention regarding implementations for specific (parallel) architectures. Yet, this concerns only the static problem, where both input matrices do not change. In many applications, however, matrices (or their corresponding graphs) change over time. Although recomputing from scratch is very expensive, we are not aware of any dynamic SpGEMM algorithms in the literature. In this paper, we thus propose a batch-dynamic algorithm for MPI-based parallel computing. Building on top of a distributed graph/matrix data structure that allows for fast updates, our dynamic SpGEMM reduces the communication volume signifi-cantly. It does so by exploiting that updates change far fewer matrix entries than there are non-zeros in the input operands. Our experiments with popular benchmark graphs show that our approach pays off. For batches of insertions or removals of matrix entries, our dynamic SpGEMM is substantially faster (sometimes by orders of magnitude) than the static algorithms in the state-of-the-art competitors CombBLAS, CTF and PETSc.
An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors

Purpose. In this study, we want to identify what are current possible causes for citing and referencing errors in scholarly literature to compare if something changed from the snapshot provided Sweetland in his 1989 paper. Design/methodology/approach. We analysed reference elements, i.e. bibliographic references, mentions, quotations, and respective in-text reference pointers, from 729 articles published in 147 journals across the 27 subject areas. Findings. The outcomes of our analysis pointed out that bibliographic errors have been perpetuated for decades and that their possible causes have increased, despite the encouraged use of technological facilities, i.e., the reference managers. Originality. As far as we know, our study is the best recent available analysis of errors in referencing and citing practices in the literature since Sweetland (1989).
Robust SVM Optimization in Banach spaces

We address the issue of binary classiﬁcation in Banach spaces in presence of uncertainty. We show that a number of results from classical support vector machines theory can be appropriately generalised to their robust counterpart in Banach spaces. These include the Representer Theorem, strong duality for the associated Optimization problem as well as their geometric interpretation. Furthermore, we propose a game theoretic interpretation by expressing a Nash equilibrium problem formulation for the more general problem of ﬁnding the closest points in two closed convex sets when the underlying space is reﬂexive and smooth.
Chord-Conditioned Melody Choralization with Controllable Harmonicity and Polyphonicity

Melody choralization, i.e. generating a four-part chorale based on a user-given melody, has long been closely associated with J.S. Bach chorales. Previous neural network-based systems rarely focus on chorale generation conditioned on a chord progression, and none of them realised controllable melody choralization. To enable neural networks to learn the general principles of counterpoint from Bach’s chorales, we ﬁrst design a music representation that encoded chord symbols for chord conditioning. We then propose DeepChoir, a melody choralization system, which can generate a four-part chorale for a given melody conditioned on a chord progression. Furthermore, with the improved density sampling, a user can control the extent of harmonicity and polyphonicity for the chorale generated by DeepChoir. Experimental results reveal the effectiveness of our data representation and the controllability of DeepChoir over harmonicity and polyphonicity. The code and generated samples (chorales, folk songs and a symphony) of Deep-Choir, and the dataset we use now are available at https://github.com/sander-wood/deepchoir.
Leading twist GTMDs at nonzero skewness and Wigner distributions in boost-invariant longitudinal position space

Tanmay Maji, ∗ Chandan Mondal, 3, † and Daekyoung Kang ‡ Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) and Institute of Modern Physics, Fudan University, Shanghai, China 200433 Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China (Dated: February 18, 2022)
Limits on Counting Triangles using Bipartite Independent Set Queries

Beame et al. [ITCS 2018 & TALG 2021] introduced and used the Bipartite Independent Set ( BIS ) and Independent Set ( IS ) oracle access to an unknown, simple, unweighted and undirected graph and solved the edge estimation problem. The introduction of this oracle set forth a series of works in a short span of time that either solved open questions mentioned by Beame et al. or were generalizations of their work as in Dell and Lapinskas [STOC 2018], Dell, Lapinskas and Meeks [SODA 2020], Bhattacharya et al. [ISAAC 2019 & Theory Comput. Syst. 2021], and Chen et al. [SODA 2020]. Edge estimation using BIS can be done using polylogarithmic queries, while IS queries need sub-linear but more than polylogarithmic queries. Chen et al. improved Beame et al.’s upper bound result for edge estimation using IS and also showed an almost matching lower bound. Beame et al. in their introductory work asked a few open questions out of which one was on estimating structures of higher order than edges, like triangles and cliques, using BIS queries. Motivated by this question, we completely resolve the query complexity of estimating triangles using BIS oracle. While doing so, we prove a lower bound for an even stronger query oracle called Edge Emptiness ( EE ) oracle, recently introduced by Assadi, Chakrabarty and Khanna [ESA 2021] to test graph connectivity.
FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

This paper presents FAMIE, a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction. FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a long time between annotation batches due to the time-consuming nature of model training and data selection at each AL iteration. This hinders the engagement, productivity, and efficiency of annotators. Based on the idea of using a small proxy network for fast data selection, we introduce a novel knowledge distillation mechanism to synchronize the proxy network with the main large model (i.e., BERT-based) to ensure the appropriateness of the selected annotation examples for the main model. Our AL framework can support multiple languages. The experiments demonstrate the advantages of FAMIE in terms of competitive performance and time efficiency for sequence labeling with AL. We publicly release our code (https://github.com/nlp-uoregon/famie) and demo website (http://nlp.uoregon.edu:9000/). A demo video for FAMIE is provided at: https://youtu.be/I2i8n_jAyrY
QuerTCI: A Tool Integrating GitHub Issue Querying with Comment Classification

Empirical Software Engineering (ESE) researchers study (open-source) project issues and the comments and threads within to discover—among others—challenges developers face when incorporating new technologies, platforms, and programming language constructs. However, such threads accumulate, becoming unwieldy and hindering any insight researchers may gain. While existing approaches alleviate this burden by classifying issue thread comments, there is a gap between searching popular open-source software repositories (e.g., those on GitHub) for issues containing particular keywords and feeding the results into a classification model. This pa-per demonstrates a research infrastructure tool called QuerTCI that bridges this gap by integrating the GitHub issue comment search API with the classification models found in existing approaches. Using queries, ESE researchers can retrieve GitHub issues containing particular keywords, e.g., those related to a specific programming language construct, and, subsequently, classify the discussions occurring in those issues. We hope that ESE researchers can use our tool to uncover challenges related to particular technologies using specific keywords through popular open-source repositories more seamlessly than previously possible. A tool demonstration video may be found at: https://youtu.be/fADKSxn0QUk.
OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines

Objective. To establish an open framework for developing plan optimization models for knowledge-based planning (KBP). Approach. Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. Main results. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50–0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P < 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model. Significance. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.
Synthetic Data for Unsupervised Polyp Segmentation

Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annotations. We address these problems by producing realistic synthetic images using a combination of 3D technologies and generative adversarial networks. We use zero annotations from medical professionals in our pipeline. Our fully unsupervised method achieves promising results on five real polyp segmentation datasets. As a part of this study we release Synth-Colon, an entirely synthetic dataset that includes 20000 realistic colon images and additional details about depth and 3D geometry: https://enric1994.github. io/synth-colon