Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Free Triiodothyronine with Long-Term Prognosis in Hypertrophic cardiomyopathy patients with HFpEF: A Dual center research study

Abstract Objective: The objective of this study was to identify prognostic factors in Hypertrophic cardiomyopathy patients with HFpEF, with a particular focus on the role of FT3 levels. Research Design and Methods: We conducted a retrospective cohort study of 992 patients with HFpEF who were admitted to our two medical centers between 2009 and 2019. We collected demographic and clinical data, including FT3 levels, and conducted univariate and multivariate Cox analyses, KM survival curve analysis, and RSC curve analysis to identify prognostic factors and evaluate the non-linear predictive value of FT3. Results: We found that age, atrial fibrillation, and NT-proBNP levels were all significant prognostic factors in patients with heart failure. Additionally, FT3 levels were a significant independent predictor of all-cause mortality and cardiac transplantation. Patients with lower FT3 levels had worse long-term prognoses, and the critical value of FT3 was 2.885. Conclusions: Our findings suggest that FT3 levels are an important prognostic factor in patients with heart failure and should be considered when evaluating patient outcomes. Clinicians should monitor FT3 levels and consider interventions to maintain or improve thyroid function in patients with heart failure.
The evaluation of the quality of life in survivors of critical illness after discharge from Intensive Care Unit (ICU): A prospective cohort study

Introduction: Admission to the Intensive Care Unit (ICU) constitutes a substantial psychophysical burden for patients and their relatives. Individuals who are critically ill and receive care in the ICU frequently exhibit numerous physical and mental issues stemming from the primary ailment, its complications, and subsequent treatments. This study aimed to assess the quality of life (QoL) for ICU patients in the first and third month after ICU discharge and identify any issues arising from their ICU hospitalization. Material and methods: This was a prospective cohort study. The study participants were adult medical or surgical patients admitted to Intensive Care Units (ICUs) in three public hospitals in Attica in, Greece, from August 2020 to December 2021. The short form (SF)-36 was used to measure QoL. Data collection was performed through telephone interviews during the first and third month after ICU discharge. Results: The study included 43 patients, 34 men and nine women. The mean age was 59.63 years. The average value of the two main categories in the 1st month was: Physical health: at 53.72 and Mental health: at 69.03, while in the 3rd month, it was 62.42 and 72.81, respectively. The duration of mechanical ventilation, high-flow oxygen therapy, and spontaneous breathing in days seemed to be correlated with the Physical Functioning, Pain, and Limitation of the role due to Physical health subscales of SF-36, respectively (p-value <0.05). The total length of hospitalization seemed to have a statistically significant negative correlation with Physical Function and Physical Health subscales (p-value<0.05). Conclusions: An improvement in the QoL of patients was demonstrated three months after discharge from the ICU. Factors such as gender, cause of ICU admission, occurrence or non-occurrence of cardiac arrest, the performance of a tracheostomy, and type of ventilation support should always be considered when assessing the quality of life after ICU hospitalization.
The Szego Kernel of Certain Polynomial Models, and Heat Kernel Estimates for Schrodinger Operators with Reverse Holder Potentials

We present two different results on operator kernels, each in the context of its relationship to a class of CR manifolds M = { (z, w1, . . . , wn) ∈ C : Imwi = φi(Re z) } where n ≤ 2 and φi(x) is subharmonic for i = 1, . , n. Such models have proven useful for studying canonical operators such as the Szegő projection on weakly pseudoconvex domains of finite type in C, and may play a similar role in work on higher codimension CR manifolds in C. Our study in Part II concerns the Szegő kernel on M for which the φi are subharmonic nonharmonic polynomials. We wish to develop, for n = 2, an approach based on [36], Nagel’s estimation of the Szegő kernel through an explicit integral formula when n = 1. After a careful review of his methods and the related control geometry, we write out the analogous integral formula in codimension two. For the “degenerate” case of M ⊂ C with φ2(x) = a φ1(x) for a ∈ R, we prove a simple relationship between the Szegő kernel on M and the kernel on the codimension one CR manifold defined by φ1(x). Part III of the dissertation considers only n = 1. Identifying M with C × R with coordinates (x, y, t) and taking a partial Fourier transform in the y and t directions, ∂̄b on L (M) is transformed to a two parameter family of differential operators D̄ητ = ∂x − η + φ1τ on L(R). For τ > 0 we study D̄ητDητ and DητD̄ητ as real Schrödinger operators on L (R). Using Auscher and Ben Ali’s work [4] on reverse Hölder potentials, we obtain new upper bounds on the heat kernels associated to these operators for a large class of φ1(x). In fact, our estimates apply to the heat kernel of any Schrödinger operator on L(R) whose potential satisfies a reverse Hölder inequality. For Schrödinger operators with potentials in the supremum reverse Hölder class, we also prove heat kernel lower bounds derived from van den Berg’s estimates [55] on the Dirichlet Laplacian. Acknowledgements Dr. Andrew Raich has my lasting gratitude for the encouragement and guidance he has offered during my exploration of the ideas herein.
The Thermal Insulation Composites for Lithium-ion Batteries

In this paper, the high-efficiency thermal insulation composites were prepared and investigated, which are formed by the addition of hollow SiO2 microspheres, hollow glass microspheres, and hollow phenolic microspheres into addition-type liquid silicone rubber. The thermal conductivity of composites is as low as 0.054 W/(m·K), which is 70.65% lower than the matrix (the thermal conductivity of the matrix is 0.184 W/(m·K)), and the tensile strength is 1.003 Mpa, and the thermal decomposition temperature of the composites is 427 °C .
Design of An Intelligent Manipulator Controlled by Gestures Based on FPGA

With the development of science and technology, manipulators are increasingly used in industrial production and daily life. The manipulator with traditional control mode has high requirements for operators, complicated operation, and human-computer interaction is inconvenient. In this paper, an intelligent manipulator system based on gesture control is designed. FPGA is used as the control core of the system. We apply the improved Canny algorithm processing, which can automatically recognize gesture information. After judgment, the control signal is sent to the manipulator to control the manipulator to complete the specified action. Through experimental testing, the system can successfully recognize all kinds of gestures, and can well control the manipulator to complete all kinds of actions. The human-computer interaction is more real, which greatly improves the intelligence of the manipulator system and helps to improve the work efficiency.
Research on Wide-band Domain Current Sensing for Bushing End Screen Based on TMR

The transformer bushing is an important monitoring object of the substation online monitoring system. The end screen current of the transformer bushing is one of the important parameters to characterize the insulation state of the bushing, by collecting the full current of power frequency and partial discharge pulse current of the high-frequency domain. Based on the TMR array sensing principle, this paper studies the wide-band domain current sensing technology for the transformer bushing end screen current monitoring, and integrates it with the ground terminal of the bushing end screen to realize the sensing of the power frequency current and the high-frequency current of the bushing end screen. The detection bandwidth covers 50 Hz to 30 MHz. Power frequency current detection accuracy reaches 1%.
Profiling the effect of micronutrient levels on vital cardiac markers

Cardiovascular diseases (CVD) are among the most preventable chronic disorders accounting for about one-third of general mortality around the globe. Micronutrients have been shown to have a significant impact on cardiovascular health. Micronutrients have been looked at as the most adoptable lifestyle choice which could reduce the burden of disease around the world. In this context, it is important to study the levels of micronutrients and see their correlation to cardiac disease biomarkers. The present study has attempted to investigate the relationship between the diverse class of micronutrients and serum levels of the key lipids and lipoproteins. A retrospective analysis was carried out between the serum levels of micronutrients and vital cardiovascular markers. The study was carried out in a group of 358 individuals tested for the Cardio Health and Micronutrients Panel at Vibrant America Clinical Laboratory. The study population was categorized based on the serum concentration of lipids and lipoproteins into 3 groups Low Normal and High and the levels of micronutrients were compared among these groups. The results revealed a significant association of several cardiovascular markers with vitamins including Vit D, Vit E, Vit K, and minerals including zinc, iron, calcium, magnesium, and amino acids including leucine, isoleucine, and valine. Quantitative analysis by Pearsons correlation exhibited a negative correlation of asparagine with serum levels of cholesterol and LDL. Amino acids such as cysteine, isoleucine, and valine were found to have a significant negative correlation with HDL. A positive correlation was observed between valine and serum levels of LDL and Apo B. Vitamins such as Vit A, Vit D3, Vit E, and Vit K1 were found to have a strong positive correlation with levels of total cholesterol and triglycerides. The study summarizes micronutrients and modulation of several lipid markers which are critical for the management of cardiovascular diseases. Micronutrients such as vitamins B1, B3, asparagine, and glutamine have a strong positive association, and fat-soluble vitamins, and BCAA has a strong negative association with cardiovascular health
Microstructure predicts non-motor outcomes following Deep Brain Stimulation in Parkinson's disease

Background: Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an effective treatment for motor and non-motor symptoms in advanced Parkinson's disease (PD). However, considerable interindividual variability of outcomes exists. Neuroimaging based biomarkers, such as neurite orientation dispersion and density imaging (NODDI), a biophysical model based MRI-technique, have been proposed to predict clinical outcomes and therefore inform preoperative patient counselling. Objective: To detect microstructural properties of brain areas associated with short-term non-motor outcomes following STN-DBS in PD. Methods: In this prospective open-label study, 37 PD patients underwent diffusion MRI and comprehensive clinical assessments at preoperative baseline and 6-month follow-up. Neurite density index (NDI), orientation dispersion index (ODI), and fractional anisotropy (FA) were derived. Whole brain voxel-wise analysis assessed associations between microstructural metrics and non-motor outcomes corrected for multiple comparisons using a permutation-based approach. Results: Intact microstructure within specific areas including right insular cortex, right putamen, right cingulum, and bilateral corticospinal tract were associated with greater postoperative improvement of non-motor symptom burden. Furthermore, microstructural properties of distinct brain regions were associated with postoperative changes in sleep, attention/memory, and urinary symptoms. Conclusion: Microstructural properties of distinct brain areas predict non-motor outcomes in DBS for PD. Therefore, diffusion MRI can support preoperative patient counselling and treatment selection by identifying patients with above- or below-average non-motor responses.
Reference values for cut-point-free and traditional accelerometer metrics and associations with cardiorespiratory fitness: a cross-sectional study of healthy adults aged 20 to 89 years

Objectives: To compare the association between cardiorespiratory fitness (CRF) and cut-point-free accelerometer metrics (intensity gradient [IG] and average acceleration [AvAcc]) to that with traditional metrics in healthy adults aged 20 to 89 years and patients with heart failure, and 2) provide age-, sex-, and CRF-related reference values for healthy adults. Methods: In the COmPLETE study, 463 healthy adults and 67 patients with heart failure wore GENEActiv accelerometers on their non-dominant wrist and underwent cardiopulmonary exercise testing. Cut-point-free (IG: distribution of intensity of activity across the day; AvAcc: proxy of volume of activity) and traditional (moderate-to-vigorous and vigorous activity) metrics were generated. The 'rawacceleration' application was developed to translate findings into clinical practice. Results: IG and AvAcc yield complementary information on PA with both IG (p=0.009) and AvAcc (p<0.001) independently associated with CRF in healthy individuals. Only IG was independently associated with CRF in patients with heart failure (p=0.043). The best cut-point-free and cut-point-based model had similar predictive value for CRF in both cohorts. However, unlike traditional metrics, IG and AvAcc are comparable across populations and the most commonly used accelerometers. We produced age- and sex-specific reference values and percentile curves for IG, AvAcc, moderate-to-vigorous, and vigorous activity for healthy adults. Conclusions: IG and AvAcc are strongly associated with CRF and, thus, indirectly with the risk of non-communicable diseases and mortality in healthy adults and patients with heart failure. Our reference values enhance the utility of cut-point-free metrics and facilitate their interpretation. Trial registration: This study was registered on clinicaltrials.gov (NCT03986892).
Learning Trajectories are Generalization Indicators

This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient descent algorithms. Instead of concentrating solely on the generalization error of the DNN post-training, we present a novel perspective for analyzing generalization error by investigating the contribution of each update step to the change in generalization error. This perspective allows for a more direct comprehension of how the learning trajectory influences generalization error. Building upon this analysis, we propose a new generalization bound that incorporates more extensive trajectory information. Our proposed generalization bound depends on the complexity of learning trajectory and the ratio between the bias and diversity of training set. Experimental findings reveal that our method effectively captures the generalization error throughout the training process. Furthermore, our approach can also track changes in generalization error when adjustments are made to learning rates and label noise levels. These results demonstrate that learning trajectory information is a valuable indicator of a model's generalization capabilities.
Hydrodynamics in long-range interacting systems with center-of-mass conservation

In systems with a conserved density, the additional conservation of the center of mass (dipole moment) has been shown to slow down the associated hydrodynamics. At the same time, long-range interactions generally lead to faster transport and information propagation. Here, we explore the competition of these two effects and develop a hydrodynamic theory for long-range center-of-mass-conserving systems. We demonstrate that these systems can exhibit a rich dynamical phase diagram containing subdiffusive, diffusive, and superdiffusive behaviors, with continuously varying dynamical exponents. We corroborate our theory by studying quantum lattice models whose emergent hydrodynamics exhibit these phenomena.
Proximal Curriculum for Reinforcement Learning Agents

We consider the problem of curriculum design for reinforcement learning (RL) agents in contextual multi-task settings. Existing techniques on automatic curriculum design typically require domain-specific hyperparameter tuning or have limited theoretical underpinnings. To tackle these limitations, we design our curriculum strategy, ProCuRL, inspired by the pedagogical concept of Zone of Proximal Development (ZPD). ProCuRL captures the intuition that learning progress is maximized when picking tasks that are neither too hard nor too easy for the learner. We mathematically derive ProCuRL by analyzing two simple learning settings. We also present a practical variant of ProCuRL that can be directly integrated with deep RL frameworks with minimal hyperparameter tuning. Experimental results on a variety of domains demonstrate the effectiveness of our curriculum strategy over state-of-the-art baselines in accelerating the training process of deep RL agents.
Towards Generating Hop-constrained s-t Simple Path Graphs

Graphs have been widely used in real-world applications, in which investigating relations between vertices is an important task. In this paper, we study the problem of generating the k-hop-constrained s-t simple path graph, i.e., the subgraph consisting of all simple paths from vertex s to vertex t of length no larger than k. To our best knowledge, we are the first to formalize this problem and prove its NP-hardness on directed graphs. To tackle this challenging problem, we propose an efficient algorithm namedEVE, which exploits the paradigm of edge-wise examination rather than exhaustively enumerating all paths. Powered by essential vertices appearing in all simple paths between vertex pairs,EVE distinguishes the edges that are definitely (or not) contained in the desired simple path graph, producing a tight upper-bound graph in the time cost O(k2|E|). Each remaining undetermined edge is further verified to deliver the exact answer. Extensive experiments are conducted on 15 real networks. The results show thatEVE significantly outperforms all baselines by several orders of magnitude. Moreover, by takingEVE as a built-in block, state-of-the-art for hop-constrained simple path enumeration can be accelerated by up to an order of magnitude.
A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation

With increasing complexity of modern communication systems, machine learning algorithms have become a focal point of research. However, performance demands have tightened in parallel to complexity. For some of the key applications targeted by future wireless, such as the medical field, strict and reliable performance guarantees are essential, but vanilla machine learning methods have been shown to struggle with these types of requirements. Therefore, the question is raised whether these methods can be extended to better deal with the demands imposed by such applications. In this paper, we look at a combinatorial resource allocation challenge with rare, significant events which must be handled properly. We propose to treat this as a multi-task learning problem, select two methods from this domain, Elastic Weight Consolidation and Gradient Episodic Memory, and integrate them into a vanilla actor-critic scheduler. We compare their performance in dealing with Black Swan Events with the state-of-the-art of augmenting the training data distribution and report that the multi-task approach proves highly effective.
Overview of the TREC 2022 NeuCLIR Track

This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval. The main task in this year's track was ad hoc ranked retrieval of Chinese, Persian, or Russian newswire documents using queries expressed in English. Topics were developed using standard TREC processes, except that topics developed by an annotator for one language were assessed by a different annotator when evaluating that topic on a different language. There were 172 total runs submitted by twelve teams.
Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11

With increasing demand for and adoption of virtual assistants, recent work has investigated ways to accelerate bot schema design through the automatic induction of intents or the induction of slots and dialogue states. However, a lack of dedicated benchmarks and standardized evaluation has made progress difficult to track and comparisons between systems difficult to make. This challenge track, held as part of the Eleventh Dialog Systems Technology Challenge, introduces a benchmark that aims to evaluate methods for the automatic induction of customer intents in a realistic setting of customer service interactions between human agents and customers. We propose two subtasks for progressively tackling the automatic induction of intents and corresponding evaluation methodologies. We then present three datasets suitable for evaluating the tasks and propose simple baselines. Finally, we summarize the submissions and results of the challenge track, for which we received submissions from 34 teams.
Quantum-optimal information encoding using noisy passive linear optics

The amount of information that a noisy channel can transmit has been one of the primary subjects of interest in information theory. In this work we consider a practically-motivated family of optical quantum channels that can be implemented without an external energy source. We optimize the Holevo information over procedures that encode information in attenuations and phase-shifts applied by these channels on a resource state of finite energy. It is shown that for any given input state and environment temperature, the maximum Holevo information can be achieved by an encoding procedure that uniformly distributes the channel's phase-shift parameter. Moreover for large families of input states, any maximizing encoding scheme has a finite number of channel attenuation values, simplifying the codewords to a finite number of rings around the origin in the output phase space. The above results and numerical evidence suggests that this property holds for all resource states. Our results are directly applicable to the quantum reading of an optical memory in the presence of environmental thermal noise.
KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis

This paper describes the system entered by the author to the SemEval-2023 Task 12: Sentiment analysis for African languages. The system focuses on the Kinyarwanda language and uses a language-specific model. Kinyarwanda morphology is modeled in a two tier transformer architecture and the transformer model is pre-trained on a large text corpus using multi-task masked morphology prediction. The model is deployed on an experimental platform that allows users to experiment with the pre-trained language model fine-tuning without the need to write machine learning code. Our final submission to the shared task achieves second ranking out of 34 teams in the competition, achieving 72.50% weighted F1 score. Our analysis of the evaluation results highlights challenges in achieving high accuracy on the task and identifies areas for improvement.