Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchrony in psychotherapy: High physiological positive concordance predicts symptom reduction and negative concordance predicts symptom aggravation

Abstract Objective Therapeutic alliance is often considered as a predictor for therapeutic success. This study explored dyadic synchrony of skin conductance response (SCR) during naturalistic therapeutic interactions and investigated its potential as an objective biomarker for predicting therapy effectiveness. Methods In this proof‐of‐concept study, skin conductance from both dyad members was continuously measured via wristbands during psychotherapy. Patients and therapists completed post‐session reports capturing their subjective appraisal of therapeutic alliance. Additionally, patients completed symptom questionnaires. Each therapeutic dyad was recorded twice in a follow‐up design. The first session of the follow‐up group was assessed for physiological synchrony (Single Session Index (SSI)). Therapy outcome was captured by the difference between symptom severity scores over time. Results SCR synchrony significantly predicted the outcome variable of change in patients' global severity index (GSI). High positive SCR concordance was linked to a reduction in patients' GSI, while negative or small positive SSI values were linked to an increase in patients' GSI. Conclusion The results demonstrate the presence of SCR synchrony in clinical interactions. Skin conductance response synchrony was a significant predictor for change in patients' symptom severity index, emphasizing its potential as an objective biomarker in the context of evidence‐based psychotherapy.
A note on distributional equations in discounted risk processes

In this paper we give an account of the classical discounted risk processes and their limiting distributions. For the models considered, we set the Markov chains embedded in the continuous-time processes; we also set distributional equations for the limit distributions. Additionally, we mention some applications regarding ruin probabilities and optimal premium. 2010 Mathematics Subject Classication: 60K05.
The trajectory and break-up of a particle laden liquid jet in the centrifugal field

The breakup of a particle laden dilated viscous liquid jet, emitted from a cylindrical nozzle in a rotating cup is studied by means of linear stability analysis and experiments. The motion of the liquid jet is investigated by perturbation analysis, thus, it is decomposed into a time steady motion and time dependent oscillating disturbances. The equation of motion for the time steady flow are determined by balances of forces and mass. A method to solve the resulting under-determined axial equation of motion is shown. The time dependent motion of the oscillating disturbances is studied by stability analysis of a particle laden liquid jet. It is based on a balance of mass and impulse in the Eulerian formulation, hence each phase is treated as a continuum. The surrounding gas motion is also included in the model. Jet stability is also investigated experimentally. The curvature, breakup length and the resulting drops are analyzed using shadow imaging. The experimental findings are compared to the numerical model.
Fast and Slow Rhythms of Naturalistic Reading Revealed by Combined Eye-Tracking and Electroencephalography

Neural oscillations are thought to support speech and language processing. They may not only inherit acoustic rhythms, but might also impose endogenous rhythms onto processing. In support of this, we here report that human (both male and female) eye movements during naturalistic reading exhibit rhythmic patterns that show frequency-selective coherence with the EEG, in the absence of any stimulation rhythm. Periodicity was observed in two distinct frequency bands: First, word-locked saccades at 4-5 Hz display coherence with whole-head theta-band activity. Second, fixation durations fluctuate rhythmically at ∼1 Hz, in coherence with occipital delta-band activity. This latter effect was additionally phase-locked to sentence endings, suggesting a relationship with the formation of multi-word chunks. Together, eye movements during reading contain rhythmic patterns that occur in synchrony with oscillatory brain activity. This suggests that linguistic processing imposes preferred processing time scales onto reading, largely independent of actual physical rhythms in the stimulus. SIGNIFICANCE STATEMENT The sampling, grouping, and transmission of information are supported by rhythmic brain activity, so-called neural oscillations. In addition to sampling external stimuli, such rhythms may also be endogenous, affecting processing from the inside out. In particular, endogenous rhythms may impose their pace onto language processing. Studying this is challenging because speech contains physical rhythms that mask endogenous activity. To overcome this challenge, we turned to naturalistic reading, where text does not require the reader to sample in a specific rhythm. We observed rhythmic patterns of eye movements that are synchronized to brain activity as recorded with EEG. This rhythmicity is not imposed by the external stimulus, which indicates that rhythmic brain activity may serve as a pacemaker for language processing.
Management strategies for heavily calcified coronary stenoses: an EAPCI clinical consensus statement in collaboration with the EURO4C-PCR group.

Since the publication of the 2015 EAPCI consensus on rotational atherectomy, the number of percutaneous coronary interventions (PCI) performed in patients with severely calcified coronary artery disease has grown substantially. This has been prompted on one side by the clinical demand for the continuous increase in life expectancy, the sustained expansion of the primary PCI networks worldwide and the routine performance of revascularization procedures in elderly patients; on the other side, the availability of new and dedicated technologies such as orbital atherectomy and intravascular lithotripsy, as well as the optimization of the rotational atherectomy system, have increased operators' confidence in attempting more challenging PCI. This current EAPCI clinical consensus statement prepared in collaboration with the EURO4C-PCR group describes the comprehensive management of patients with heavily calcified coronary stenoses, starting with how to use non-invasive and invasive imaging to assess calcium burden and inform procedural planning. Objective and practical guidance is provided on the selection of the optimal interventional tool and technique based on the specific calcium morphology and anatomic location. Finally, the specific clinical implications of treating these patients are considered, including the prevention and management of complications, and the importance of adequate training and education.
3D-2D Distance Maps Conversion Enhances Classification of Craniosynostosis

Objective: Diagnosis of craniosynostosis using photogrammetric 3D surface scans is a promising radiation-free alternative to traditional computed tomography. We propose a 3D surface scan to 2D distance map conversion enabling the usage of the first convolutional neural networks (CNNs)-based classification of craniosynostosis. Benefits of using 2D images include preserving patient anonymity, enabling data augmentation during training, and a strong under-sampling of the 3D surface with good classification performance. Methods: The proposed distance maps sample 2D images from 3D surface scans using a coordinate transformation, ray casting, and distance extraction. We introduce a CNN-based classification pipeline and compare our classifier to alternative approaches on a dataset of 496 patients. We investigate into low-resolution sampling, data augmentation, and attribution mapping. Results: Resnet18 outperformed alternative classifiers on our dataset with an F1-score of 0.964 and an accuracy of 98.4%. Data augmentation on 2D distance maps increased performance for all classifiers. Under-sampling allowed 256-fold computation reduction during ray casting while retaining an F1-score of 0.92. Attribution maps showed high amplitudes on the frontal head. Conclusion: We demonstrated a versatile mapping approach to extract a 2D distance map from the 3D head geometry increasing classification performance, enabling data augmentation during training on 2D distance maps, and the usage of CNNs. We found that low-resolution images were sufficient for a good classification performance. Significance: Photogrammetric surface scans are a suitable craniosynostosis diagnosis tool for clinical practice. Domain transfer to computed tomography seems likely and can further contribute to reducing ionizing radiation exposure for infants.
Cross-Task Mental Workload Recognition Based on EEG Tensor Representation and Transfer Learning

The accurate evaluation of mental workload of operators in human machine systems is of great significance in ensuring the safety of operators and the correct execution of tasks. However, the effectiveness of EEG based cross-task mental workload evaluation are still unsatisfactory because of the different EEG response patterns in different tasks, which hindered its generalization in real scenario severely. To solve this problem, this paper proposed a feature construction method based on EEG tensor representation and transfer learning, which was verified in various task conditions. Specifically, four working memory load tasks with different types of information were designed firstly. The EEG signals of participants were collected synchronously during task execution. Then, the wavelet transform method was used to perform time-frequency analysis of multi-channel EEG signals, and three-way EEG tensor (time-frequency-channel) features were constructed. EEG tensor features from different tasks were transferred based on the criteria of feature distribution alignment and class-wise discrimination criteria. Finally, the support vector machine was used to construct a 3-class mental workload recognition model. Results showed that compared with the classical feature extraction methods, the proposed method can achieve higher accuracy in both within-task and cross-task mental workload evaluation (91.1% for within-task and 81.3% for cross-task). These results demonstrated that the EEG tensor representation and transfer learning method is feasible and effective for cross-task mental workload evaluation, which can provide theoretical basis and application reference for future researches.
Simulating American Bond Options in an HJM Framework

This paper develops a method called Markov Chain Approximation (MCA) to approximate the value of American bond options in a general multifactor HeathJarrow� Morton (HJM) framework. Our approach is based on the methodology of Barraquand and Martineau (1995), which was developed for processes which are Markovian in a finite number of state variables. We have extended their methodology to the HJM setting, which in general is only Markovian in an infinite number of state variables. The numerical results obtained from our MCA compare quite closely with closed form or tree solutions.
A Resolution of the Fisher Effect Puzzle: A Comparison

This paper attempts a resolution of the Fisher effect puzzle in terms of estimator choice. Using both short-term and long-term interest rates for 14 OECD countries, we find ample evidence supporting the existence of a long-run Fisher effect in which interest rates move oneto- one with inflation. Our results suggest that the reason why the Fisher effect has not found support internationally lies on the estimation method. When the hypothesis of a unit coefficient relating interest rates to expected inflation is tested within the Autoregressive Distributed Lag (ADL) framework, which is invariant to the integration properties of the data, the Fisher effect easily survives the empirical evidence. Similar, but less robust, results are reached on the grounds of the Pre-Whitened Fully Modified Least Squares (PW-FMLS) or the Johansen’s (JOH) estimators.
Myeloid Cell Derived IL1β Contributes to Pulmonary Vascular Remodeling in Heart Failure with Preserved Ejection Fraction

Background Pulmonary hypertension (PH) in heart failure with preserved ejection fraction (HFpEF) is a common and highly morbid syndrome, but mechanisms driving PH-HFpEF are not well understood. We sought to determine whether a well-accepted murine model of HFpEF also displays features of PH in HFpEF, and we sought to identify pathways that might drive early remodeling of the pulmonary vasculature in HFpEF. Methods Eight week old male and female C57/BL6J mice were given either L-NAME and high fat diet (HFD) or control water/diet for 2,5, and 12 weeks. Bulk RNA sequencing and single cell RNA sequencing was performed to identify early and cell-specific pathways that might regulate pulmonary vascular remodeling in PH-HFpEF. Finally, clodronate liposome and IL1β antibody treatments were utilized to deplete macrophages or IL1β, respectively, to assess their impact on pulmonary vascular remodeling in HFpEF. Results Mice given L-NAME/HFD developed PH, small vessel muscularization, and right heart dysfunction after 2 weeks of treatment. Inflammation-related gene ontologies were over-represented in bulk RNA sequencing analysis of whole lungs, with an increase in CD68+ cells in both murine and human PH-HFpEF lungs. Cytokine profiling of mouse lung and plasma showed an increase in IL1β, which was confirmed in plasma from patients with HFpEF. Single cell sequencing of mouse lungs also showed an increase in M1-like, pro-inflammatory populations of Ccr2+ monocytes and macrophages, and transcript expression of IL1β was primarily restricted to myeloid-type cells. Finally, clodronate liposome treatment prevented the development of PH in L-NAME/HFD treated mice, and IL1β antibody treatment also attenuated PH in L-NAME/HFD treated mice. Conclusions Our study demonstrated that a well-accepted model of HFpEF recapitulates features of pulmonary vascular remodeling commonly seen in patients with HFpEF, and we identified myeloid cell derived IL1β as an important contributor to PH in HFpEF.
A gene silencing pipeline to interrogate human cDC1 and pDC development and functions

Type 1 conventional dendritic cells (cDC1s) and plasmacytoid dendritic cells (pDCs) are thought to be critical for anti-tumor or antiviral immunity. In vitro differentiation systems have unlocked the ability to produce large numbers of these cells. However, a method is lacking to systematically identify the cell-intrinsic factors controlling their differentiation and functions that remain therefore poorly understood, in contrast to the situation in mice. Here, we developed a workflow for efficient gene silencing and its tracing in human cDC1s/pDCs generated in vitro. As proof of concept, we confirmed the key role of IRF8 in their development, and of IRF7/MyD88 in human pDC production of interferons-α/λ. We found that SAMHD1 and RAB7B promote human cDC1 differentiation, while SEPT3 promotes human pDC differentiation. We also found that PPT1 and RAB5 are required for optimal differentiation of pDCs and cDC1s. Finally, we identified BCL11A, PPT1 and RAB7 as novel HIV-1 restriction factors in cDC1s/pDCs. This approach will enable broader genetic screens to advance our understanding of human cDC1s/pDCs and harness them against viral infections or cancer.
Control Problems for 2-D Electromagnetic Wave Scattering Model

We consider the control problem for two-dimensional model of the electromagnetic field, which describes the scattering TM-polarized electromagnetic waves in an infinite homogeneous medium containing permeable dielectric obstacle with a covered for masking boundary. The role of control function is played by index of refraction of the dielectric obstacle. We prove the solvability of the control problem, derive the optimality system, which describes the necessary conditions of an extremum and develop an efficient numerical algorithm for the solution of the control problem.
Parametric Estimation of the Orientation of Textured

This paper presents a parametric solution to the problem of estimating the orientation in space of a planar textured surface, from a single, noisy, observed image of it. The coordinate transformation from surface to image coordinates, due to the perspective projection, transforms each homogeneous sinusoidal component of the surface texture into a sinusoid whose frequency is a function of location. The functional dependence of the sinusoid phase in location is uniquely determined by the tilt and slant an- gles of the surface. Using the phase differencing algorithm we fit a polynomial phase model to a sinusoidal component of the observed texture. Assuming the estimated polynomial coefficients are the coefficients of a Taylor series expansion of the phase, we establish a linear recursive relation between the model parameters and the unknown slant and tilt. A linear least squares solution of the resulting system provides the slant and tilt estimates. To improve accuracy, an iterative refinement procedure is applied in a small neighborhood of these estimates. The performance of the proposed algorithms is evaluated by applying them to images of different planar surfaces, and by comparing their statistical performance with the Cramer-Rao bound. The combined two-stage algorithm is shown to produce estimates that are close to the bound.
APPROXIMATE SPECTRAL LEARNING USING NYSTROM METHOD

Constrained clustering algorithms as an input have a data set and constraints which inform it whether to put two items in the same cluster or not. Spectral clustering algorithms compute cluster assignments from eigenvectors of a matrix that is computed from the data set. In this paper, we study the class of constrained spectral clustering algorithms that incorporate constraints by modifying the graph adjacency matrix. The proposed algorithm combines Nystrom method with the existing spectral learning algorithm to produce a linear (in the number of vertices) time algorithm. We tested our algorithm on real world data sets and we demonstrated that it shows better results on some data sets than the original algorithm. In the end, we propose an algorithm for constrained multi view data clustering.
The Impact of Carbon Emission Trading Policy on Enterprise ESG Performance: Evidence from China

The carbon emission trading system profoundly impacts enterprises’ sustainable development as an important market incentive environmental regulation tool. Through data collected from Chinese A-share listed enterprises in Shanghai and Shenzhen from 2011 to 2019 and Bloomberg ESG score data, this paper empirically analyses the impact of carbon emission trading policy on enterprise ESG performance and its channel mechanism using the difference-in-difference (DID) method. Results of this study indicate that carbon emission trading policy improves enterprise ESG performance significantly, and robustness tests confirm these findings. Carbon emission trading policy can encourage enterprises to enhance their R&D investments and promote internal controls, ultimately enhancing their ESG performance. Additionally, carbon emission trading policy positively impacts ESG performance in low-carbon enterprises, enterprises where the CEO is separated from the company, enterprises with a high degree of digital transformation, and enterprises receiving high government subsidies. This paper extends our research into the economic implications of carbon emission trading policy, enriching the literature on market-based environmental regulation policies’ impact on enterprise ESG performance. With respect to governments’ use of carbon emission trading to regulate enterprises environmentally, this paper provides theoretical guidance. It has significant practical implications for improving enterprise ESG performance and sustainability.
Effects of using adjunct model of content-based instruction on students’ technical report writing performance

Abstract This study aimed to investigate effects of adjunct model of content-based instruction on EFL students’ technical report writing performance. A quasi-experimental design was employed to attain the main objective of the study. It included sixty-four mechanical engineering students in the control group (31) and experimental group (33). The experimental group participants were taught technical report writing skills using adjunct content-based instruction, and the control group participants were taught the same lesson through the conventional method for eight weeks. Writing tests and interviews were employed to collect the data. One-way MANOVA was applied to analyze the data collected using the tools. The findings revealed that the students participated in the adjunct EFL program outperformed the ones who were instructed through the conventional approach. They improved their skills of writing task achievement, cohesion and coherence, lexical resource, and grammatical range and accuracy significantly. The result also showed that the experimental group students had positive perceptions about adjunct content-based instruction in learning technical report writing. Based on the findings, it was recommended that adjunct content-based instruction could be incorporated into teaching technical report writing at colleges and universities.
Multilevel‐in‐width training for deep neural network regression

A common challenge in regression is that for many problems, the degrees of freedom required for a high‐quality solution also allows for overfitting. Regularization is a class of strategies that seek to restrict the range of possible solutions so as to discourage overfitting while still enabling good solutions, and different regularization strategies impose different types of restrictions. In this paper, we present a multilevel regularization strategy that constructs and trains a hierarchy of neural networks, each of which has layers that are wider versions of the previous network's layers. We draw intuition and techniques from the field of Algebraic Multigrid (AMG), traditionally used for solving linear and nonlinear systems of equations, and specifically adapt the Full Approximation Scheme (FAS) for nonlinear systems of equations to the problem of deep learning. Training through V‐cycles then encourage the neural networks to build a hierarchical understanding of the problem. We refer to this approach as multilevel‐in‐width to distinguish from prior multilevel works which hierarchically alter the depth of neural networks. The resulting approach is a highly flexible framework that can be applied to a variety of layer types, which we demonstrate with both fully connected and convolutional layers. We experimentally show with PDE regression problems that our multilevel training approach is an effective regularizer, improving the generalize performance of the neural networks studied.