Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcutaneous Carbon Dioxide Monitoring in Subjects With Acute Respiratory Failure and Severe Hypercapnia

BACKGROUND: Transcutaneous carbon dioxide (PtcCO2) monitoring is being used increasingly to assess acute respiratory failure. However, there are conflicting findings concerning its reliability when evaluating patients with high levels of PaCO2. Our study evaluates the accuracy of this method in subjects with respiratory failure according to the severity of hypercapnia. METHODS: We included subjects with respiratory failure, admitted to a respiratory intermediate care unit, who required arterial blood gas analysis. Simultaneously, PtcCO2 was measured using a digital monitor. Relations between PaCO2 and PtcCO2 were assessed by the Pearson correlation coefficient. Bland-Altman analysis was used to test data dispersion, and an analysis of variance test was used to compare the differences between PaCO2 and the corresponding PtcCO2 at different levels (level 1, <50 mm Hg; level 2, 50–60 mm Hg; level 3, >60 mm Hg). RESULTS: Eighty-one subjects were analyzed. The main diagnosis was COPD exacerbation (45%). PtcCO2 correlated well with PaCO2 (r2 = 0.93, P < .001). Bland-Altman analysis showed a mean PaCO2 − PtcCO2 difference of 4.9 ± 4.4 with 95% limits of agreement ranging from −3.6 to 13.4. The difference between variables increased in line with PaCO2 severity: level 1, 1.7 ± 3.2 mm Hg; level 2, 3.7 ± 2.8; level 3, 6.8 ± 4.7 (analysis of variance, P < .001). CONCLUSIONS: Our study showed an acceptable agreement of PtcCO2 monitoring with arterial blood gas analysis. However, we should consider that PtcCO2 underestimates PaCO2 levels, and its accuracy depends on the level of hypercapnia, so this method would not be suitable for acute patients with severe hypercapnia.
Informed Bayesian Inference for the A/B Test

Booming in business and a staple analysis in medical trials, the A/B test assesses the effect of an intervention or treatment by comparing its success rate with that of a control condition. Across many practical applications, it is desirable that (1) evidence can be obtained in favor of the null hypothesis that the treatment is ineffective; (2) evidence can be monitored as the data accumulate; (3) expert prior knowledge can be taken into account. Most existing approaches do not fulfill these desiderata. Here we describe a Bayesian A/B procedure based on Kass and Vaidyanathan (1992) that allows one to monitor the evidence for the hypotheses that the treatment has either a positive effect, a negative effect, or, crucially, no effect. Furthermore, this approach enables one to incorporate expert knowledge about the relative prior plausibility of the rival hypotheses and about the expected size of the effect, given that it is non-zero. To facilitate the wider adoption of this Bayesian procedure we developed the abtest package in R. We illustrate the package options and the associated statistical results with a synthetic example.
Zero-Shot Audio Classification Based On Class Label Embeddings

This paper proposes a zero-shot learning approach for audio classification based on the textual information about class labels without any audio samples from target classes. We propose an audio classification system built on the bilinear model, which takes audio feature embeddings and semantic class label embeddings as input, and measures the compatibility between an audio feature embedding and a class label embedding. We use VGGish to extract audio feature embeddings from audio recordings. We treat textual labels as semantic side information of audio classes, and use Word2Vec to generate class label embeddings. Results on the ESC-50 dataset show that the proposed system can perform zero-shot audio classification with small training dataset. It can achieve accuracy (26 % on average) better than random guess (10 %) on each audio category. Particularly, it reaches up to 39.7 % for the category of natural audio classes.
The Stokes complex for Virtual Elements in three dimensions

This paper has two objectives. On one side, we develop and test numerically divergence-free Virtual Elements in three dimensions, for variable “polynomial” order. These are the natural extension of the two-dimensional divergence-free VEM elements, with some modification that allows for a better computational efficiency. We test the element’s performance both for the Stokes and (diffusion dominated) Navier–Stokes equation. The second, and perhaps main, motivation is to show that our scheme, also in three dimensions, enjoys an underlying discrete Stokes complex structure. We build a pair of virtual discrete spaces based on general polytopal partitions, the first one being scalar and the second one being vector valued, such that when coupled with our velocity and pressure spaces, yield a discrete Stokes complex.
Genetic Variability and Molecular Evolution of the Human Respiratory Syncytial Virus Subgroup B Attachment G Protein

ABSTRACT Human respiratory syncytial virus (HRSV) is the most important cause of acute respiratory disease in infants. Two major subgroups (A and B) have been identified based on antigenic differences in the attachment G protein. Antigenic variation between and within the subgroups may contribute to reinfections with these viruses by evading the host immune responses. To investigate the circulation patterns and mechanisms by which HRSV-B viruses evolve, we analyzed the G protein genetic variability of subgroup B sequences isolated over a 45-year period, including 196 Belgian strains obtained over 22 epidemic seasons (1982 to 2004). Our study revealed that the HRSV-B evolutionary rate (1.95 × 10−3 nucleotide substitutions/site/year) is similar to that previously estimated for HRSV-A (1.83 × 10−3 nucleotide substitutions/site/year). However, natural HRSV-B isolates appear to accommodate more drastic changes in their attachment G proteins. The most recent common ancestor of the currently circulating subgroup B strains was estimated to date back to around the year 1949. The divergence between the two major subgroups was calculated to have occurred approximately 350 years ago. Furthermore, we have identified 12 positively selected sites in the G protein ectodomain, suggesting that immune-driven selective pressure operates in certain codon positions. HRSV-A and -B strains have similar phylodynamic patterns: both subgroups are characterized by global spatiotemporal strain dynamics, where the high infectiousness of HRSV permits the rapid geographic spread of novel strain variants.
Real Differences between OT and CRDT in Building Co-Editing Systems and Real World Applications

OT (Operational Transformation) was invented for supporting real-time co-editors in the late 1980s and has evolved to become a core technique used in today's working co-editors and adopted in major industrial products. CRDT (Commutative Replicated Data Type) for co-editors was first proposed around 2006, under the name of WOOT (WithOut Operational Transformation). Follow-up CRDT variations are commonly labeled as "post-OT" techniques and have made broad claims of superiority over OT solutions, in terms of correctness, time and space complexity, simplicity, etc. Over one decade later, however, OT remains the choice for building the vast majority of co-editors, whereas CRDT is rarely found in working co-editors. Why? To seek truth from facts, we set out to conduct a comprehensive and critical review of representative OT and CRDT solutions and working co-editors based on them. From this work, we have made important discoveries about OT and CRDT, and revealed facts and evidences that refute CRDT claims over OT on all accounts. We present our discoveries in three related and complementary articles. In prior two articles, we have revealed the similarities of OT and CRDT in following the same general transformation approach in co-editors, and their real differences in correctness and complexity. In this article, we examine the role of building working co-editors in shaping OT and CRDT research and solutions, and consequential differences in the choice between OT and CRDT in real world co-editors and industry products. In particular, we review the evolution of co-editors from research vehicles to real world applications, and discuss representative OT-based co-editors and alternative approaches in industry products and open source projects. Moreover, we evaluate CRDT-based co-editors in relation to published CRDT solutions, and clarify some myths surrounding "peer-to-peer" co-editing.
Continuous and Resource Managed Regression Testing: An Industrial Use Case

Regression testing is an important part of quality control in both software and embedded products, where hardware is involved. It is also one of the most expensive and time consuming part of the product cycle. To improve the cost effectiveness of the development cycle and the regression testing, we use test case prioritisation and selection techniques to run more important test cases earlier in the testing process. In this paper, we consider a functional test case prioritisation with an access only to the version control of the codebase and regression history. Prioritisation is used to aid our test case selection, where we have chosen 5-25 (0.4%-2.0% of 1254) test cases to validate our method. The selection technique together with other prioritisation methods allows us to shape the current static, retest-all regression testing into a more resource managed regression testing framework. This framework will serve the agile way of working better and will allow us to allocate testing resources more wisely. This is a joint work with a large international Finnish company in an embedded industrial domain.
Wave modes excited by photospheric p-modes and mode conversion in a multi-loop system

Context. Waves are ubiquitous in the solar corona and there are indications that they are excited by photospheric p-modes. However, it is unclear how p-modes in coronal loops are converted to sausage modes and transverse (kink) modes, which are observed in the corona. Aims. We aim to investigate how those wave modes are excited in the lower corona by photospheric acoustic waves. Methods. We built 3D magnetohydrostatic loop systems with multiple inclinations spanning from the photosphere to the lower corona. We then simulated these atmospheres with the MANCHA code, in which we perturb the equilibrium with a p-mode driver at the bottom of the domain. By splitting the velocity perturbation into components longitudinal, normal, and azimuthal to the magnetic flux surfaces we can study wave behavior. Results. In vertical flux tubes, we find that deformed fast sausage surface waves and slow sausage body waves are excited. In inclined flux tubes fast kink surface waves, slow sausage body waves, and either a fast sausage surface wave or a plane wave are excited. In addition, we calculate a wave conversion factor (0 ≤ C ≤ 1) from acoustic to magnetic wave behavior by taking the ratio of the mean magnetic energy flux to the sum of the mean magnetic and acoustic energy flux and compare it to a commonly used theoretical conversion factor. We find that between magnetic field inclinations of 10° to 30° those two methods lie within 40%. For smaller inclinations the absolute deviation is smaller than 0.1.
Outcome Analysis of Carotid Artery Occlusion

The outcome of carotid artery occlusion was studied through the retrospective identification of 115 affected patients. The majority were white (77%) males (61%) with multiple atherogenic risk factors and suffering ipsilateral stroke (57%). Those patients presenting with stroke were not distinguished by demographic features, risk factors, lipid profile, medical regimen, or subsequent mortality when compared with those without. Overall, 36 patients (31%) required contralateral carotid endarterectomy (CEA), with one (2.8%) perioperative stroke, whereas 4 (3%) underwent ipsilateral external CEA without incident. With 81% follow-up (mean 3.9 years), the overall mortality of the group was 46%; the annualized risk of ipsilateral stroke was 1.6%. This study documents a significant risk of stroke and contralateral occlusive disease with ipsilateral carotid artery occlusion, which cannot be reliably predicted by clinical criteria. Duplex surveillance is valuable, but flow velocity measurements alone may be misleading. Surgical endarterectomy can be performed with an acceptable rate of perioperative stroke.
Distributed topology control algorithm for multihop wireless networks

We present a network initialization algorithm for wireless networks with distributed intelligence. Each node (agent) has only local, incomplete knowledge and it must make local decisions to meet a predefined global objective. Our objective is to use power control to establish a topology based on the relative neighborhood graph which has good overall performance in terms of power usage, low interference, and reliability.
Towards Big Data Processing in IoT: Network Management for Online Edge Data Processing

Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, edge data can be partly processed at the edge. In this paper, a MEC-based big data analysis network is discussed, where distributed raw data are collected and processed by edge servers. The edge servers are supposed to split out a large sum of redundant data and transmit extracted information to the center cloud for further analysis. However, for consideration of the limited edge computation capability, part of the raw data may be directly transmitted to the cloud. To manage limited resources in an online manner, we propose an algorithm based on Lyapunov optimization, which jointly optimizes the policy involving edge processor frequency, transmission power and bandwidth allocation. The algorithm aims at stabilizing data processing delay while saving energy without knowing probability distributions of data sources. The proposed network management algorithm may contribute to big data processing in future IoT.
Learning to Control in Metric Space with Optimal Regret

We study online reinforcement learning for finite-horizon deterministic control systems with arbitrary state and action spaces. Suppose the transition dynamics and reward function is unknown, but the state and action space is endowed with a metric that characterizes the proximity between different states and actions. We provide a surprisingly simple upper-confidence reinforcement learning algorithm that uses a function approximation oracle to estimate optimistic Q functions from experiences. We show that the regret of the algorithm after K episodes is $o(DLK)^{\frac{d}{d+1}}H$ where D is the diameter of the state-action space, L is a smoothness parameter, and d is the doubling dimension of the state-action space with respect to the given metric. We also establish a near-matching regret lower bound. The proposed method can be adapted to work for more structured transition systems, including the finite-state case and the case where value functions are linear combinations of features, where the method also achieve the optimal regret.
Neuronal Substrate of Eating Disorders

Eating disorders are devastating and life-threatening psychiatric diseases. Although clinical and experimental investigations have significantly progressed in discovering the neuronal causes of eating disorders, the exact neuronal and molecular mechanisms of the development and maintenance of these pathologies are not fully understood. The complexity of the neuronal substrate of eating disorders hampers progress in revealing the precise mechanisms. The present review describes the current knowledge on the implication of the neuronal systems that regulate food intake, stress, emotions, and reward in eating disorders. The current data based on clinical and experimental research strongly suggest that these systems are interconnected and a misbalance in one system leads to altered activity in other food-related regulatory networks.
Optimizing the Size and Configuration of Combinatorial Libraries

This paper addresses a major issue in library design, namely how to efficiently optimize the library size (number of products) and configuration (number of reagents at each position) simultaneously with other properties such as diversity, cost, and drug-like physicochemical property profiles. These objectives are often in competition, for example, minimizing the number of reactants while simultaneously maximizing diversity, and thus present difficulties for traditional optimization methods such as genetic algorithms and simulated annealing. Here, a multiobjective genetic algorithm (MOGA) is used to vary library size and configuration simultaneously with other library properties. The result is a family of solutions that explores the tradeoffs in the objectives. This is achieved without the need to assign relative weights to the objectives. The user is then able to make an informed choice on an appropriate compromise solution. The method has been applied to two different virtual libraries: a two-component aminothiazole library and a four-component benzodiazepine library.
DeepCheck: A Non-intrusive Control-flow Integrity Checking based on Deep Learning

Code reuse attack (CRA) is a powerful attack that reuses existing codes to hijack the program control flow. Control flow integrity (CFI) is one of the most popular mechanisms to prevent against CRAs. However, current CFI techniques are difficult to be deployed in real applications due to suffering several issues such as modifying binaries or compiler, extending instruction set architectures (ISA) and incurring unacceptable runtime overhead. To address these issues, we propose the first deep learning-based CFI technique, named DeepCheck, where the control flow graph (CFG) is split into chains for deep neural network (DNN) training. Then the integrity features of CFG can be learned by DNN to detect abnormal control flows. DeepCheck does not interrupt the application and hence incurs zero runtime overhead. Experimental results on Adobe Flash Player, Nginx, Proftpd and Firefox show that the average detection accuracy of DeepCheck is as high as 98.9%. In addition, 64 ROP exploits created by ROPGadget and Ropper are used to further test the effectiveness, which shows that the detection success rate reaches 100%.
Determination of helper T‐cell precursor frequencies against non‐haemopoietic cells: comparison of co‐stimulation provided by anti‐CD28 antibody versus the cellular ligand B7‐1

Helper T‐cell precursor frequency assays (HTLp‐assays) are commonly used in transplantation to examine the frequency of T cells reactive against donor or host alloantigens. In these assays, peripheral blood mononuclear cells (PBMCs) are most often used as stimulator cells. However, cells targeted after transplantation do not always belong to the haematopoietic lineage and may express different alloantigens, especially minor histocompatibility antigens (mHags). Non‐haematopoietic cells lack expression of the B7 co‐stimulatory molecules needed to activate primary T cells that can be supplied by anti‐CD28 (αCD28) antibodies or transfection with B7‐1 coding sequences. At present, it is not known how these two ways of supplied co‐stimulation compare in HTLp assays. B7‐1‐transfected A431 keratinocytes (A431B7‐1) induced higher proliferative responses in allogeneic primary T cells and more interleukin (IL) 2 production than that induced by A431 cells plus αCD28, whereas the kinetics of proliferation and IL‐2 production were similar. Neither cross‐linking of αCD28 bound to T cells nor prevention of IL‐2 resorption by the anti‐IL‐2 receptor resulted in improved proliferation or IL‐2 production. Results of HTLp assays indicated that A431B7‐1 activated on average 7·5 times more alloreactive IL‐2‐producing T cells than A431 cells plus αCD28. We conclude that primary T‐cell alloresponses against major histocompatibility complexes (MHCs) and mHags expressed on non‐haematopoietic cells can be measured in HTLp assays using supplied co‐stimulation, although αCD28 yields an intrinsic underestimation of actual frequencies.
Learning With Learned Loss Function: Speech Enhancement With Quality-Net to Improve Perceptual Evaluation of Speech Quality

Utilizing a human-perception-related objective function to train a speech enhancement model has become a popular topic recently. The main reason is that the conventional mean squared error (MSE) loss cannot represent auditory perception well. One of the typical human-perception-related metrics, which is the perceptual evaluation of speech quality (PESQ), has been proven to provide a high correlation to the quality scores rated by humans. Owing to its complex and non-differentiable properties, however, the PESQ function may not be used to optimize speech enhancement models directly. In this study, we propose optimizing the enhancement model with an approximated PESQ function, which is differentiable and learned from the training data. The experimental results show that the learned surrogate function can guide the enhancement model to further boost the PESQ score (increase of 0.18 points compared to the results trained with MSE loss) and maintain the speech intelligibility.
M2H-GAN: A GAN-based Mapping from Machine to Human Transcripts for Speech Understanding

Deep learning is at the core of recent spoken language understanding (SLU) related tasks. More precisely, deep neural networks (DNNs) drastically increased the performances of SLU systems, and numerous architectures have been proposed. In the real-life context of theme identification of telephone conversations, it is common to hold both a human, manual (TRS) and an automatically transcribed (ASR) versions of the conversations. Nonetheless, and due to production constraints, only the ASR transcripts are considered to build automatic classifiers. TRS transcripts are only used to measure the performances of ASR systems. Moreover, the recent performances in term of classification accuracy, obtained by DNN related systems are close to the performances reached by humans, and it becomes difficult to further increase the performances by only considering the ASR transcripts. This paper proposes to distillates the TRS knowledge available during the training phase within the ASR representation, by using a new generative adversarial network called M2H-GAN to generate a TRS-like version of an ASR document, to improve the theme identification performances.