Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent Degradation Representation Constraint for Single Image Deraining

Since rain streaks show a variety of shapes and directions, learning the degradation representation is extremely challenging for single image deraining. Existing methods are mainly targeted at designing complicated modules to implicitly learn latent degradation representation from coupled rainy images. This way, it is hard to decouple the content-independent degradation representation due to the lack of explicit constraint, resulting in over- or under-enhancement problems. To tackle this issue, we propose a novel Latent Degradation Representation Constraint Network (LDRCNet) that consists of Direction-Aware Encoder (DAEncoder), UNet Deraining Network, and Multi-Scale Interaction Block (MSIBlock). Specifically, the DAEncoder is proposed to adaptively extract latent degradation representation by using the deformable convolutions to exploit the direction consistency of rain streaks. Next, a constraint loss is introduced to explicitly constraint the degradation representation learning during training. Last, we propose an MSIBlock to fuse with the learned degradation representation and decoder features of the deraining network for adaptive information interaction, which enables the deraining network to remove various complicated rainy patterns and reconstruct image details. Experimental results on synthetic and real datasets demonstrate that our method achieves new state-of-the-art performance.
Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations

The distribution and evolution of photospheric magnetic fields in sunspots, plages, and network, and variations in their relative flux content, play key roles in radial velocity (RV) fluctuations observed in Sun-as-a-star spectra. Differentiating and disentangling such magnetic contributions to RVs help in building models to account for stellar activity signals in high-precision RV exoplanet searches. In this work, as earlier authors, we employ high-resolution images of the solar magnetic field and continuum intensities from SDO/HMI to understand the activity contributions to RVs from HARPS-N solar observations. Using well-observed physical relationships between strengths and fluxes of photospheric magnetic fields, we show that the strong fields (spots, plages, and network) and the weak inter-network fields leave distinguishing features in their contributions to the RV variability. We also find that the fill factors and average unsigned magnetic fluxes of different features correlate differently with the RVs and hence warrant care in employing either of them as a proxy for RV variations. In addition, we examine disk-averaged UV intensities at 1600 and 1700 Å wavelength bands imaged by SDO/AIA and their performances as proxies for variations in different magnetic features. We find that the UV intensities provide a better measure of contributions of plage fields to RVs than the Ca ii H-K emission indices, especially during high activity levels when the latter tend to saturate.
Benne: A Modular and Self-Optimizing Algorithm for Data Stream Clustering

In various real-world applications, ranging from the Internet of Things (IoT) to social media and financial systems, data stream clustering is a critical operation. This paper introduces Benne, a modular and highly configurable data stream clustering algorithm designed to offer a nuanced balance between clustering accuracy and computational efficiency. Benne distinguishes itself by clearly demarcating four pivotal design dimensions: the summarizing data structure, the window model for handling data temporality, the outlier detection mechanism, and the refinement strategy for improving cluster quality. This clear separation not only facilitates a granular understanding of the impact of each design choice on the algorithm's performance but also enhances the algorithm's adaptability to a wide array of application contexts. We provide a comprehensive analysis of these design dimensions, elucidating the challenges and opportunities inherent to each. Furthermore, we conduct a rigorous performance evaluation of Benne, employing diverse configurations and benchmarking it against existing state-of-the-art data stream clustering algorithms. Our empirical results substantiate that Benne either matches or surpasses competing algorithms in terms of clustering accuracy, processing throughput, and adaptability to varying data stream characteristics. This establishes Benne as a valuable asset for both practitioners and researchers in the field of data stream mining.
Unsupervised Chunking with Hierarchical RNN

In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This paper introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a two-layer Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compositions. Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks. Experiments on the CoNLL-2000 dataset reveal a notable improvement over existing unsupervised methods, enhancing phrase F1 score by up to 6 percentage points. Further, finetuning with downstream tasks results in an additional performance improvement. Interestingly, we observe that the emergence of the chunking structure is transient during the neural model's downstream-task training. This study contributes to the advancement of unsupervised syntactic structure discovery and opens avenues for further research in linguistic theory.
Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Feature generation aims to generate new and meaningful features to create a discriminative representation space. A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities. We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation. To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach. We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross. git.
Comparison and equality of generalized $\psi$-estimators

We solve the comparison problem for generalized $\psi$-estimators introduced in Barczy and P\'ales (2022). Namely, we derive several necessary and sufficient conditions under which a generalized $\psi$-estimator less than or equal to another $\psi$-estimator for any sample. We also solve the corresponding equality problem for generalized $\psi$-estimators. For applications, we solve the two problems in question for Bajraktarevi\'c-type- and quasi-arithmetic-type estimators. We also apply our results for some known statistical estimators such as for empirical expectiles and Mathieu-type estimators and for solving likelihood equations in case of normal, a Beta-type, Gamma, Lomax (Pareto type II), lognormal and Laplace distributions.
Precise determination of ground-state hyperfine splitting and calculation of Zeeman coefficients for 171Yb+ microwave frequency standard

We report precise measurement of the hyperfine splitting and calculation of the Zeeman coefficients of the $^{171}$Yb$^+$ ground state. The absolute hyperfine splitting frequency is measured using high-resolution laser-microwave double-resonance spectroscopy at 0.1 mHz level, and evaluated using more accurate Zeeman coefficients. These Zeeman coefficients are derived using Land\'{e} $g_J$ factors calculated by two atomic-structure methods, multiconfiguration Dirac-Hartree-Fock, and multireference configuration interaction. The cross-check of the two calculations ensures an accuracy of the Zeeman coefficients at $10^{-2}$ Hz/G$^2$ level. The results provided in this paper improve the accuracy and reliability of the second-order Zeeman shift correction, thus further improving the accuracy of the microwave frequency standards based on $^{171}$Yb$^+$. The high-precision hyperfine splitting and Zeeman coefficients could also support could also support further experiments to improve the constraints of fundamental constants through clock frequency comparison of the Yb$^+$ system.
Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts

This paper summarizes the results of experimenting with Universal Dependencies (UD) adaptation of an Unsupervised, Compositional and Recursive (UCR) rule-based approach for Sentiment Analysis (SA) submitted to the Shared Task at Rest-Mex 2023 (Team Olga/LyS-SALSA) (within the IberLEF 2023 conference). By using basic syntactic rules such as rules of modification and negation applied on words from sentiment dictionaries, our approach exploits some advantages of an unsupervised method for SA: (1) interpretability and explainability of SA, (2) robustness across datasets, languages and domains and (3) usability by non-experts in NLP. We compare our approach with other unsupervised approaches of SA that in contrast to our UCR rule-based approach use simple heuristic rules to deal with negation and modification. Our results show a considerable improvement over these approaches. We discuss future improvements of our results by using modality features as another shifting rule of polarity and word disambiguation techniques to identify the right sentiment words.
Neuromorphic Auditory Perception by Neural Spiketrum

Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intelligence, it needs to solve the challenges of the neuromorphic hardware architecture design of biological neural substrate and the hardware amicable algorithms with spike-based encoding and learning. Here we introduce a neural spike coding model termed spiketrum, to characterize and transform the time-varying analog signals, typically auditory signals, into computationally efficient spatiotemporal spike patterns. It minimizes the information loss occurring at the analog-to-spike transformation and possesses informational robustness to neural fluctuations and spike losses. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks. We further investigate the algorithm-hardware co-designs through a neuromorphic cochlear prototype which demonstrates that our approach can provide a systematic solution for spike-based artificial intelligence by fully exploiting its advantages with spike-based computation.
Sustainability assessment methodology oriented to soil-associated agricultural experiments

Summary A variety of established tools are available for agricultural sustainability assessment at global, regional, and farm geographical scales. However, no assessment has been reported in research literature to indicate their ability to provide insights about the most sustainable cropping system at plot level or experimental unit. Despite the environmental and social importance of soil in agricultural systems, many of the sustainability assessments use few or no indicators related to soil properties or processes. Hence, we propose a sustainability assessment methodology oriented to soil-associated agricultural experiments (SMAES) by defining its parameters through simulations and testing the methodology with real data from a fertilization tomato experiment with five treatments: chemical control (CR); organic control (OR); and organic:chemical ratios (OR) of 25:75, 50:50, and 75:25. The distance from the maximum, principal component analysis, and product of weighted indicator techniques were chosen for normalization, weighting, and aggregation in a single index process, respectively. Applying the SMAES methodology, the sustainability level of the treatments followed this sequence: CR (0.95) > O25:C75 (0.73) > O50:C50 (0.60) > O75:C25 (0.55) > OR (0.45). The proposed SMAES methodology allows soil researchers to define the best treatment through the interaction of the environmental, social, and economic dimensions of agricultural systems.
Combined morphologic-metabolic biomarkers from [18F]FDG-PET/CT stratify prognostic groups in low-risk NSCLC

Abstract Aim The aim of this study was to derive prognostic parameters from 2-[ 18 F]fluoro-2-deoxy-D-glucose ([ 18 F]FDG-PET/CT) in patients with low-risk NSCLC and determine their prognostic value. Methods 81 (21 female, mean age 66 a) therapy-naive patients that underwent [18F]FDG-PET/CT before histologic confirmation of NSCLC with stadium I and II between 2008–2016 were included. A mean follow-up time of 58 months (13–176), overall and progression free survival (OS, PFS) were registered. A volume of interest for the primary tumor was defined on PET and CT images. Parameters SUV max , PET-solidity, PET-circularity, and CT-volume were analyzed. To evaluate the prognostic value of each parameter for OS, a minimum p-value approach was used to define cutoff values, survival analysis, and log-rank tests were performed, including subgroup analysis for combinations of parameters. Results Mean OS was 58±28 months. Poor OS was associated with a tumor CT-volume >14.3 cm 3 (p=0.02, HR=7.0, CI 2.7–17.7), higher SUV max values >12.2 (p=0.003; HR=3.0, CI 1.3–6.7) and PET-solidity >0.919 (p=0.004; HR=3.0, CI 1.0–8.9). Combined parameter analysis revealed worse prognosis in larger volume/high SUV max tumors compared to larger volume/lower SUV max (p=0.028; HR=2.5, CI 1.1–5.5), high PET-solidity/low volume (p=0.01; HR=2.4, CI 0.8–6.6) and low SUV max /high PET-solidity (p=0.02, HR=4.0, CI 0.8–19.0). Conclusion Even in this group of low-risk NSCLC patients, we identified a subgroup with a significantly worse prognosis by combining morphologic-metabolic biomarkers from [18F]FDG-PET/CT. The combination of SUV max and CT-volume performed best. Based on these preliminary data, future prospective studies to validate this combined morphologic-metabolic imaging biomarker for potential therapeutic decisions seem promising.
Laryngeal widening and adequate ventilation by expiratory pressure load training improve aerobic capacity in COPD: a randomised controlled trial

Rationale Despite strategies acting on peripheral airway obstruction in chronic obstructive pulmonary disease (COPD), exercise intolerance remains inadequately improved. We hypothesised that laryngeal narrowing is a potential treatment target of expiratory pressure load training (EPT) to improve exercise intolerance in COPD. Methods The effect of 3-month EPT was assessed in 47 patients with COPD divided into Global Initiative for Chronic Obstructive Lung Disease (GOLD) mild-to-moderate (I–II) and severe-to-very severe (III–IV), randomly allocating 1:1 to EPT or control groups. The primary outcome was endurance time in the constant work rate exercise test in GOLD III–IV patients. Results Compared with controls, EPT increased: (1) endurance time, with estimated treatment effect: +703 (95% CI: 379 to 1031) s, p=0.0008 (GOLD I–II); +390 (95% CI: 205 to 574) s, p=0.0006 (GOLD III–IV); (2) peak oxygen uptake (p=0.0086 in GOLD I–II; p=0.0004 in GOLD III–IV); (3) glottic dilatation ratio at maximum collapse on laryngoscopy in the submaximal exercise (p=0.0062 in GOLD I–II; p=0.0001 in GOLD III–IV); and (4) the inflection point of expiratory tidal volume relative to minute ventilation during the incremental exercise (p=0.0015 in GOLD I–II; p=0.0075 in GOLD III–IV). Across GOLD grades, the responses of glottic dilatation ratio at maximum collapse and the expiratory tidal volume at the inflection point were selected as more influential variables correlating with the improvement in peak oxygen uptake and endurance time, respectively. Conclusion These results show that EPT improved aerobic capacity and endurance time with larger laryngeal widening and adequate ventilation despite advanced COPD. Trial registration number UMIN000041250.
PUFFFIN: A novel, ultra-bright, customisable, single-plasmid system for labelling cell neighbourhoods

Cell communication orchestrates development, homeostasis, and disease. Understanding these processes depends on effective methods to study how cells influence their neighbours. Here, we present Positive Ultra-bright Fluorescent Fusion For Identifying Neighbours (PUFFFIN), a novel cell neighbour-labelling system based upon secretion and uptake of a positively supercharged fluorescent protein s36GFP. To achieve sensitive labelling, we amplified the fluorescent signal by fusing s36GFP to the ultra-bright protein mNeonGreen. Secretor cells transfer s36GFP-mNeonGreen to neighbours while retaining a nuclear mCherry, making it straightforward to unambiguously identify, isolate, and profile neighbours of secretor cells. Transfer of the s36GFP-mNeonGreen fusion occurs within minutes, enabling even relatively transient interactions to be captured or tracked over time with live-imaging or flow cytometry. To enhance flexibility of use, we incorporated HaloTag technology to facilitate colour- of-choice labelling. To further increase the range of applications, we engineered a customisable single-plasmid construct composed of interchangeable components with option to incorporate any additional transgene. This allows users to manipulate the properties of a cell, while at the same time applying a fluorescent label to the surrounding cells. PUFFFIN offers a simple, sensitive, customisable approach to profile non-cell-autonomous responses to natural or induced changes in cell identity or behaviour in mammalian cells. Graphical abstract
Extended field observations of cirrus clouds using a ground-based cloud observing system

The evolution of synoptic-scale dynamics associated with a middle and upper tropospheric cloud event that occurred on 26 November 1991 is examined. The case under consideration occurred during the FIRE CIRRUS-II Intensive Field Observing Period held in Coffeyville, KS during Nov. and Dec., 1991. Using data from the wind profiler demonstration network and a temporally and spatially augmented radiosonde array, emphasis is given to explaining the evolution of the kinematically-derived ageostrophic vertical circulations and correlating the circulation with the forcing of an extensively sampled cloud field. This is facilitated by decomposing the horizontal divergence into its component parts through a natural coordinate representation of the flow. Ageostrophic vertical circulations are inferred and compared to the circulation forcing arising from geostrophic confluence and shearing deformation derived from the Sawyer-Eliassen Equation. It is found that a thermodynamically indirect vertical circulation existed in association with a jet streak exit region. The circulation was displaced to the cyclonic side of the jet axis due to the orientation of the jet exit between a deepening diffluent trough and building ridge. The cloud line formed in the ascending branch of the vertical circulation with the most concentrated cloud development occurring in conjunction with the maximum large-scale vertical motion. The relationship between the large scale dynamics and the pammeterization of middle and upper tropospheric clouds in large-scale models is discussed and an example of ice water contents derived from a parameterization forced by the diagnosed vertical motions and observed water vapor contents is presented.
Cyclophilin D induces necrotic core formation by promoting mitochondria-mediated macrophage apoptosis in advanced atherosclerotic lesions

Background In advanced atherosclerotic lesions, apoptotic cell death of plaque macrophages results in necrotic core formation and plaque vulnerability. Cyclophilin D (CypD) is a mitochondria-specific cyclophilin involved in the process of cell death after organ ischemia-reperfusion. However, the role of CypD in atherosclerosis, especially in necrotic core formation, is unknown. Methods To clarify the specific role of CypD, apolipoprotein-E/CypD-double knockout (ApoE-/-CypD-/-) mice were generated. These mice were fed a high-fat diet containing 0.15% cholesterol for 24 weeks to accelerate atherosclerotic lesion development. Results The deletion of CypD decreased the necrotic core size, accompanied by a reduction of macrophage apoptosis compared to control ApoE-/- mice. In RAW264.7 cells treated with endoplasmic reticulum stress inducer thapsigargin, the release of cytochrome c to the cytosol was attenuated by siRNA-mediated knockdown of CypD. Ly-6Chigh inflammatory monocytes in the peripheral blood leukocytes and mRNA expression of Il1b in the aorta were decreased by the deletion of CypD. In contrast, siRNA-mediated knockdown of CypD did not significantly decrease Il1b nor Ccl2 mRNA expression in RAW264.7 cells treated with LPS and IFN-γ, suggesting that inhibition of inflammation in vivo is likely due to decreased cell death in the atherosclerotic lesions rather than a direct action of CypD deletion on the macrophage. Conclusions This is the first report showing that CypD induces macrophage death and promotes necrotic core formation in advanced atherosclerotic lesions. CypD could be a novel therapeutic target for treating atherosclerotic vascular diseases.
Using a Flipped Classroom to Improve Student Analysis and Feedback to Peers in The Reciprocal Style of Teaching

Abstract Quality physical education programs target student learning outcomes in all three learning domains. One instructional strategy that has the potential to reach learning outcomes and national content standards is the reciprocal style of teaching. Research examining the reciprocal style of teaching has shown a plethora of desirable learning outcomes such as, motor skill improvement, increase cognition about critical skill elements, and the ability to foster a more inclusive environment can be achieved. However, there is evidence suggesting that peer student observers struggle with diagnosing skill-error and providing accurate feedback related to performance. A flipped classroom can address these issues by affording students with online learning experiences that help improve their skill analysis and feedback skills prior to class. Physical education teachers can implement a flipped classroom with the intention of improving the reciprocal style of teaching by following four simple steps: (1) develop content; (2) embed formative assessments; (3) upload the content; and (4) review student responses. Physical education teachers will find many benefits to this approach, including less pressure to execute a long demonstration that addresses psychomotor, cognitive, and affective outcomes, and an increase in class time that can afford students more opportunities to respond as the doer and observer resulting in an increase in student learning.