Miracle-dz/UniFAR-FTUs huggingface dataset:
A new integrative approach to assess aortic stenosis burden and predict objective functional improvement after TAVR

Background A non-negligible rate of patients undergoing transcatheter aortic valve replacement (TAVR) do not report symptomatic improvement or even die in the short-midterm. We sought to assess the degree of objective functional recovery after TAVR and its prognostic implications and to develop a predictive model. Methods In a cohort of patients undergoing TAVR, a prospective evaluation of clinical, anatomical, and physiological parameters was conducted before and after the procedure. These parameters were derived from echocardiography, non-invasive analysis of arterial pulse waves, and cardiac tomography. Objective functional improvement 6 months after TAVR was assessed using a 6-min walk test and nitro-terminal pro-brain natriuretic peptide (NT-proBNP) levels. The derived predictive model was prospectively validated in a different cohort. A clinical follow-up was conducted at 2 years. Results Among the 212 patients included, objective functional improvement was observed in 169 patients (80%) and subjective improvement in 187 (88%). Patients with objective functional improvement showed a much lower death rate at 2 years (9% vs. 31% p = 0.0002). Independent predictors of improvement were as follows: mean aortic gradient of ≥40 mmHg, augmentation index75 of ≥45%, the posterior wall thickness of ≤12 mm, and absence of atrial fibrillation. A simple integer-based point score was developed (GAPA score), which showed an area under the curve of 0.81 for the overall cohort and 0.78 for the low-gradient subgroup. In a validation cohort of 216 patients, these values were 0.75 and 0.76, respectively. Conclusion A total of 80% of patients experienced objective functional improvement after TAVR, showing a significantly lower 2-year mortality rate. A predictive score was built that showed a good discriminative performance in overall and low-gradient populations.
Advanced mask process modeling for 45-nm and 32-nm nodes

As tolerance requirements for the lithography process continue to shrink with each new technology node, the contributions of all process sequence steps to the critical dimension error budgets are being closely examined, including wafer exposure, resist processing, pattern etch, as well as the photomask process employed during the wafer exposure. Along with efforts to improve the mask manufacturing processes, the elimination of residual mask errors via pattern correction has gained renewed attention. The portfolio of correction tools for mask process effects is derived from well established techniques commonly used in optical proximity correction and in electron beam proximity effect compensation. The process component that is not well captured in the correction methods deployed in mask manufacturing today is etch. A mask process model to describe the process behavior and to capture the physical effects leading to deviation of the critical dimension from the target value represents the key component of model-based correction and verification. This paper presents the flow for generating mask process models that describe both shortrange and long-range mask process effects, including proximity loading effects from etching, pattern density loading effects, and across-mask process non-uniformity. The flow is illustrated with measurement data from real test masks. Application of models for both mask process correction and verification is discussed.
A study of confined Stark effect, hydrostatic pressure and temperature on nonlinear optical properties in 1D GaxAl1-xAs/GaAs/GaxAl1-xAs quantum dots under a finite square well potential

A BSTRACT In the present paper, investigations of nonlinear optical rectiﬁcation, absorption coefﬁcient and refractive index in a 1D Ga x Al 1 − x As/GaAs/Ga x Al 1 − x As quantum dots under a ﬁnite square well potential using simulation software such as COMSOL Multi-Physics and Matlab have been carried out in the presence of electric ﬁeld, hydrostatic pressure and temperature. Results show that the resonant peaks of ORC (optical rectiﬁcation coefﬁcient) exhibit a blue shift under increasing of the electric ﬁeld, while a red shift trailed by a blue shift is displayed under increasing of hydrostatic pressure and temperature. Similar trends take place for the refractive index as well as for the absorption coefﬁcient under changing of the electric ﬁeld, temperature and hydrostatic pressure. The attained theoretical results would pave a novel opportunity in designing, optimizing and applications of nonlinear opto-electronic devices by tuning the performance of the quantum dots and controlling some of their speciﬁc properties.
Drift–Alfvén vortices in dusty plasmas with non-zero ion-temperature effects

Reduced two-fluid equations governing the nonlinear dynamics of drift–Alfvén waves in dusty plasmas with non-zero ion temperature are derived. In the linear limit, we find a dispersion relation that shows the coupling between the ion-drift–Shukla–Varma mode, and electron-drift (magnetostatic) and (inertial or kinetic) Alfvén waves due to the finite collisionless electron skin depth or Larmorradius corrections. In contrast to the case of an electron–ion plasma, when the nonlinear drift–Alfvén vortices are weakly localized, i.e. decrease at infinity as r−1, the presence of the charged dust grains makes exponential localization possible. The physical meaning of such a localization is connected with the fact that charged massive dust granules provide an additional screening that results in stronger localization of the vortex. In several intermediate-β plasmas with 1 [Gt ] β [Gt ] me/mi (me,i is the electron or ion mass), the localization length approaches a minimum value when the vortex velocity is of the order of the ion diamagnetic drift. It then reaches the value ρiδ−1/4d where ρi is the ion Larmor radius and δd is the ratio of the dust to ion densities multiplied by the dust charge number Zd. In the case of very low plasma pressure, β [Lt ] me/mi, the vortex is localized with typical scale (λeρi)1/2δ−1/4d where λe is the electron skin depth. Our investigation can thus predict the velocities of coherent nonlinear structures in space plasmas.
Frequency Domain Causal Analysis Allows the Detection of Baroreflex Control Recovery in Patients Undergoing Surgical Aortic Valve Replacement After a Three-Months Follow-up

Surgical aortic valve replacement (SAVR) can impact on cardiovascular control as assessed via the analysis of heart period (HP) and systolic arterial pressure (SAP) variability. Frequency domain causality techniques allow to explore HP-SAP closed-loop relation in the typical frequency bands of the cardiovascular control. A frequency-domain causality analysis was applied to HP and SAP variability acquired from 58 patients (age: $65\pm 13\ yrs, 39$ males) before SAVR (PRE), within one-week post-surgery (POST) and after a three-month follow-up (POST3). Analyses were carried out at rest in supine position (REST) and during an active standing test (STAND). Causal squared coherence $(K^{2})$ analysis was performed along the baroreflex pathway from SAP to HP $(K^{2}_{SAP\rightarrow HP})$ and along the mechanical feedforward link from HP to SAP $(K^{2}_{HP\rightarrow SAP})$ in the low frequency (LF, 0.04-0.15 Hz) and high frequency $(HF,\ 0.15- 0.4\ Hz)$ bands. Findings suggested that baroreflex control was depressed just after SAVR but recovered after a three-month follow-up while mechanical feedforward link was not affected. Future studies will be aimed to investigate a longer follow-up and to link results on the occurrence of post-surgery adverse outcomes.
A Fusion of Handcrafted Feature-Based and Deep Learning Classifiers for Heart Murmur Detection

As part of George B. Moody Physionet Challenge 2022, our team Melbourne Kangas, proposed an algorithm for identifying abnormal heart sounds from paediatric phono-cardiograms (PCGs). We developed a Deep Learning (DL) approach and a handcrafted feature-based approach. The DL classifier was based on bidirectional long-short-term-memory and Mel-frequency cepstrum coefficients from raw PCG signals. The feature-based approach used non-negative matrix factorisation to denoise PCG signals and then extracted the features based on the whole and segmented recordings, followed by feature selection. A random under-sampling boosting classifier for murmur classification and robust boosting classifier for outcome classification were given the subset of features. The feature-based performed better than the DL classifiers on the validation set. The feature-based classifier received a weighted accuracy of 0.632 (29th out of 41 teams) and a challenge cost of 11,735 (3rd out of 39 teams) on the test set. Decision fusion of the two approaches decreased 10-fold cross-validation results.
Classification of audio signals using generalized spatial fuzzy clustering.

With the increasing use of multimedia data, the need for automatic classification and retrieval of certain kinds of audio data has become an important issue. In this paper, we propose an efficient method of audio signal segmentation and classification from audiovisual database. While conventional methods apply thresholding to audio features such as energy and zero‐crossing rate to detect the boundaries, causing misclassification for audio signals which contain certain audio effects such as fade‐in, fade‐out, and cross‐fade, the proposed algorithm, called general spatial fuzzy c‐means algorithm (GSFCM), solves the problem by taking into account the local spatial information which is weighted correspondingly to neighbor elements based on their distance attributes. GSFCM detects the boundaries between two different audio signals, classifies segments, and then extracts unique feature vectors. This results in the accurate detection and classification. Experiment results for the audio signal from TV news progra.
Validation of the Articulation Index

French and Steinberg proposed the basic concept and method of calculating the Articulation Index (AI) approximately 15 years ago. Although improvements and modifications of AI have been made over the years, it has not been generally accepted, perhaps because of insufficient evidence as to its validity. On the basis of studies reported in the literature and on new studies herein reported, it is shown that AI is a valid predictor of the intelligibility of speech under a wide variety of conditions of noise masking and speech distortion.
A Lightweight Unidimensional Deep Learning Model for Atrial Fibrillation Detection

Continuous rhythm monitoring using wearable devices is a potential tool for early identification of atrial fibril-lation (AF), the most frequent cardiac arrhythmia (with 0,51% worldwide prevalence, increasing with time), and is also a tool for remote monitoring patients after cardiac surgery. However, AF detection directly through wearable devices is limited by the computational complexity of the classifier model. In this work we propose a lightweight AF classifier model based on the VGG-11 architecture (Lite VGG-11), focusing on reducing the number of parameters and nu-merical operations. Using a low number of filters, depth-wise separable convolution, and global pooling, this model has only 20,454 parameters and needs 6.9 MFLOP to make an inference for an input of 10 seconds of the ECG leads I and II, sampled at 200 Hz. To test its effectiveness for AF detection we used the PhysioNet/CinC Challenge 2021 public dataset, stratifying the classes into sinus rhythm, AF, and other rhythms. After 10 Monte Carlo cross-validation splits, with 24,260 unbalanced samples for training and 1,536 balanced samples for validation and testing, the observed met-rics (mean±standard deviation) were: Se 94.1±0.1%; Sp 91.9±0.8%; F1-Score 89.50. 7±%; and AUC 96.1±0.6%.
Generalized Tag-Based Physical-Layer Authentication Under Frequency Selective Fading Channels

Physical Layer Authentication (PLA) in wireless systems has drawn much attention because it can provide information-theoretic security. However, the performance of the prior PLA schemes significantly declines under a frequency selective fading channel and the theoretical analysis of a PLA scheme becomes extremely challenging under a frequency selective fading channel as compared to that under a frequency flat fading channel. This paper addresses the problem of authenticating transmitters at the physical layer under a frequency selective fading channel. We propose two tag-based PLA schemes under a frequency selective fading channel. The first scheme is the Generalized Tag-based PLA (GT-PLA) scheme, where the negative effect caused by a frequency selective fading channel is compensated for. If the channel reciprocity holds, we will propose the second scheme to further improve the performance of the GT-PLA scheme, named the Adaptive Generalized Tag-based PLA (AGT-PLA) scheme. We provide the theoretical analysis of both proposed schemes over wireless fading channels in terms of robustness, security, and compatibility, and derive their closed-form expressions. Moreover, we implement the proposed schemes and conduct extensive performance comparisons. Our experimental results demonstrate that the GT-PLA scheme achieves higher robustness over the prior PLA schemes while the AGT-PLA scheme can further improve the performance of the GT-PLA scheme in terms of robustness, security, and compatibility.
A Theoretically Motivated Reduced Order Model for the Control of Dynamic Biped Locomotion

A new reduced order model of a biped locomotion system and its theoretical basis are presented based upon the concept of local feedback. It has been reported from physiological studies that local feedback at each articular joint exists in human motor control, and the authors have already studied from the viewpoint of control engineering that local feedback makes the total system robust. A theorem is presented by which two dominant modes are obtained by the application of local feedback at each articular joint in the biped locomotion machine and it is shown that these two modes correspond to inverted-pendulum modes. The reduced order model derived by using these two modes contains the effect of the motion of body and swing leg which is indispensable for the control of biped locomotion. It is shown that this model can approximate very well the original higher model in almost all walking phases containing start-up and stopping motions.
Biological Flora of Britain and Ireland: Liparis loeselii

This account presents information on all aspects of the biology of Liparis loeselii (L.) Rich. (Fen Orchid) that are relevant to understanding its ecological characteristics and behaviour. The main topics are presented within the standard framework of the Biological Flora of Britain and Ireland: distribution, habitat, communities, responses to biotic factors, responses to environment, structure and physiology, phenology, floral and seed characters, herbivores and disease, and history and conservation. Liparis loeselii is a small terrestrial orchid that has a circumboreal distribution and is widespread in Europe and North America. Despite its wide distribution, the species is locally rare and has declined considerably in most of its range. In Britain, the species has a disjunct distribution and is now known to occur consistently at only six sites in eastern England and three in south Wales. It is absent from Ireland. Its most characteristic habitats in Britain are inland fens and coastal dune slacks, but outside Britain it can also be found in wet meadows, marshes, forested seep springs, at lake borders or on mats of floating peat. Populations of Liparis loeselii in dune slacks tend to be short‐lived, and can rapidly increase in size or decrease and disappear as environmental conditions change. The species does not tolerate high nutrient concentrations or low pH. It is susceptible to drought, which reduces seed germination, seedling recruitment and adult survival. Heavy predation by rabbits and rodents has been observed under drought conditions. Liparis loeselii reproduces both by sexual reproduction, and by vegetative propagation through the production of pseudobulbs. Although flowers are accessible to insects, entomophilous pollination is unusual, and most sexual reproduction is the result of selfing. Fruits ripen late in the growing season (mid‐October) and the dust‐like seeds are dispersed during winter by wind and water. Germination occurs during the following growing season and is supported by a wide variety of mycorrhizal fungi. Since the late 19th century Liparis loeselii has declined considerably in Britain and elsewhere in Europe, primarily due to habitat destruction and loss, natural succession, and habitat desiccation due to drainage. As a result, the species has been listed as endangered in the Bern Convention and the European Habitat Directive (92/43/EEC) and is the focus of intensive conservation efforts in many countries. Restoration of habitat by mowing, extensive grazing and peat removal, and the creation of new habitat by dune slack formation in dune systems and peat removal in fens may prolong population persistence and promote establishment of new populations.
Geometrical description of quantal state determination

Under the assumption that every quantal measurement may give data about the post-measurement state of the inspected ensemble, the problem of the state determination is reconsidered. It is shown that orthogonal decomposition of the set of complex, n*n, Hermitian matrices into the commutative subsets allows operators to be found such that post-measurement information on these observables allows a partial (in some cases total) determination of the pre-measurement state to be effected.
Interstellar Scintillation as a Cosmological Probe: Prospects and Challenges†

Abstract The discovery that interstellar scintillation (ISS) is suppressed for compact radio sources at z ≳ 2 has enabled ISS surveys to be used as cosmological probes. We discuss briefly the potential and challenges involved in such an undertaking, based on a dual-frequency survey of ISS carried out to determine the origin of this redshift dependence.
New, Low–Molecular Weight Chemical Compounds Inhibiting Biological Activity of Interleukin 15

Chronic overproduction of IL–15 contributes to the pathogenesis of numerous inflammatory and autoimmune disorders. Experimental methods used to reduce the cytokine activity show promise as potential therapeutic approaches to modify IL–15 signaling and alleviate the development and progression of IL–15–related diseases. We previously demonstrated that an efficient reduction of IL–15 activity can be obtained by selective blocking of the specific, high affinity subunit alpha of the IL–15 receptor (IL–15Rα) with small–molecule inhibitors. In this study, we determined the structure–activity relationship of currently known IL–15Rα inhibitors in order to define the critical structural features required for their activity. To validate our predictions, we designed, analyzed in silico, and assessed in vitro function of 16 new potential IL–15Rα inhibitors. All newly synthesized molecules were benzoic acid derivatives with favorable ADME properties and they efficiently reduced IL–15 dependent peripheral blood mononuclear cells (PBMCs) proliferation, as well as TNF–α and IL–17 secretion. The rational design of IL–15 inhibitors may propel the identification of potential lead molecules for the development of safe and effective therapeutic agents.
The fundamental axioms of structural health monitoring

Based on the extensive literature that has developed on structural health monitoring over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or gen eral principles, have emerged. The intention of this paper is to explicitly state and justify these axioms. In so doing, it is hoped that two subsequent goals are facilitated. First, the statement of such axioms will give new researchers in the field a starting point that alleviates the need to review the vast amounts of literature in this field. Second, the authors hope to stimulate discussion and thought within the community regarding these axioms.
Deep Reinforcement Learning Reward Function Design for Autonomous Driving in Lane-Free Traffic

Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the notion of lanes, and consider the whole lateral space within the road boundaries. This constitutes an entirely different problem domain for autonomous driving compared to lane-based traffic, as there is no leader vehicle or lane-changing operation. Therefore, the observations of the vehicles need to properly accommodate the lane-free environment without carrying over bias from lane-based approaches. The recent successes of deep reinforcement learning (DRL) for lane-based approaches, along with emerging work for lane-free traffic environments, render DRL for lane-free traffic an interesting endeavor to investigate. In this paper, we provide an extensive look at the DRL formulation, focusing on the reward function of a lane-free autonomous driving agent. Our main interest is designing an effective reward function, as the reward model is crucial in determining the overall efficiency of the resulting policy. Specifically, we construct different components of reward functions tied to the environment at various levels of information. Then, we combine and collate the aforementioned components, and focus on attaining a reward function that results in a policy that manages to both reduce the collisions among vehicles and address their requirement of maintaining a desired speed. Additionally, we employ two popular DRL algorithms—namely, deep Q-networks (enhanced with some commonly used extensions), and deep deterministic policy gradient (DDPG), which results in better policies. Our experiments provide a thorough investigative study on the effectiveness of different combinations among the various reward components we propose, and confirm that our DRL-employing autonomous vehicle is able to gradually learn effective policies in environments with varying levels of difficulty, especially when all of the proposed rewards components are properly combined.
Integrated Detection and Imaging Algorithm for Radar Sparse Targets via CFAR-ADMM

Most research on sparsity-driven synthetic aperture radar (SAR) imaging has been carried out in $\ell _{1}$ -norm regularization and considers that the SAR image contains only targets and noise, which ignores the clutter and seriously degrades classical algorithms. To address this problem, we propose an integrated detection and imaging algorithm for radar sparse targets with constant false alarm rate (CFAR) regularization by alternating direction method of multipliers (ADMM), called CFAR-ADMM, and we further introduce total variation (TV) regularization and propose the more robust CFAR-TV-ADMM. First, a more complete echo signal model, which considers targets, the clutter, and the noise simultaneously, is established. Then, inspired by the CFAR detection, a novel regularization with sparse target awareness is proposed. The proposed regularization can obtain the statistical characteristics of clutter and noise region by region, and distinguish whether the current cell contains the target effectively and accurately. Benefiting from this novel regularization, CFAR-ADMM and TV-CFAR-ADMM can not only realize the sparse imaging but also detect sparse targets simultaneously, which can reduce the propagation error caused by cascading processing and improve the solution accuracy. Finally, the proposed algorithm is verified by simulation data results, phase transition analysis, and real data experiments.