Miracle-dz/UniFAR-FTUs huggingface dataset:
A WN4 companion to BD +62 ◦ 2296 in Cas OB5 ⋆

I report observations of the triple system BD +62 ◦ 2296 showing that all its components are early-type stars, most likely physically related. The faintest component BD +62 ◦ 2296B is a hitherto uncatalogued Wolf-Rayet star. The brightest component, star A, is shown to be a seemingly normal B2.5Ia supergiant. Long-slit spectroscopy of BD +62 ◦ 2296B shows it to be a narrow-lined WN4 star. Given the spatial separation, the two objects are unlikely to form a physical binary. Spectra of the third visual component, BD +62 ◦ 2296C, allow its classiﬁcation as a B0III star. Such concentration of massive stars strongly suggests that BD +62 ◦ 2296 is in reality a very compact young open cluster in the area of the OB association Cas OB5.
InTeX: Interactive Text-to-texture Synthesis via Unified Depth-aware Inpainting

Text-to-texture synthesis has become a new frontier in 3D content creation thanks to the recent advances in text-to-image models. Existing methods primarily adopt a combination of pretrained depth-aware diffusion and inpainting models, yet they exhibit shortcomings such as 3D inconsistency and limited controllability. To address these challenges, we introduce InteX, a novel framework for interactive text-to-texture synthesis. 1) InteX includes a user-friendly interface that facilitates interaction and control throughout the synthesis process, enabling region-specific repainting and precise texture editing. 2) Additionally, we develop a unified depth-aware inpainting model that integrates depth information with inpainting cues, effectively mitigating 3D inconsistencies and improving generation speed. Through extensive experiments, our framework has proven to be both practical and effective in text-to-texture synthesis, paving the way for high-quality 3D content creation.
The Transcription Factor Serum Response Factor Stimulates Axon Regeneration through Cytoplasmic Localization and Cofilin Interaction

Axonal injury generates growth inert retraction bulbs with dynamic cytoskeletal properties that are severely compromised. Conversion of “frozen” retraction bulbs into actively progressing growth cones is a major aim in axon regeneration. Here we report that murine serum response factor (SRF), a gene regulator linked to the actin cytoskeleton, modulates growth cone actin dynamics during axon regeneration. In regeneration-competent facial motoneurons, Srf deletion inhibited axonal regeneration. In wild-type mice after nerve injury, SRF translocated from the nucleus to the cytoplasm, suggesting a cytoplasmic SRF function in axonal regeneration. Indeed, adenoviral overexpression of cytoplasmic SRF (SRF-ΔNLS-GFP) stimulated axonal sprouting and facial nerve regeneration in vivo. In primary central and peripheral neurons, SRF-ΔNLS-GFP stimulated neurite outgrowth, branch formation, and growth cone morphology. Furthermore, we uncovered a link between SRF and the actin-severing factor cofilin during axonal regeneration in vivo. Facial nerve axotomy increased the total cofilin abundance and also nuclear localization of phosphorylated cofilin in a subpopulation of lesioned motoneurons. This cytoplasmic-to-nucleus translocation of P-cofilin upon axotomy was reduced in motoneurons expressing SRF-ΔNLS-GFP. Finally, we demonstrate that cytoplasmic SRF and cofilin formed a reciprocal regulatory unit. Overexpression of cytoplasmic SRF reduced cofilin phosphorylation and vice versa: overexpression of cofilin inhibited SRF phosphorylation. Therefore, a regulatory loop consisting of SRF and cofilin might take part in reactivating actin dynamics in growth-inert retraction bulbs and facilitating axon regeneration.
Relation Between Ventricular Repolarization Duration and Cardiac Cycle Length During 24‐Hour Holter Recordings: Findings in Normal Patients and Patients With Long QT Syndrome

BackgroundThe interval from the R wave to the maximum amplitude of the T wave (RTm) contains the heart rate dependency of ventricular repolarization. Methods and ResultsA computer algorithm was developed to quantify the RTm and preceding RR intervals for each of more than 50,000 beats on 24-hour ambulatory electrocardiographic (Holter) recordings to evaluate the dynamic relation between repolarization duration and cycle length. The relation of RTm to the preceding RR interval (RTm/RR slope) was determined by the best-fit linear regression equation between these two parameters. Eleven normal subjects and 16 patients with long QT syndrome (LQTS) were investigated. Six of the normal subjects had Holter recordings obtained before and after βblocker therapy. βBlockers were associated with a significant (p = 0.005) reduction in the RTm/RR slope from 0.13±0.02 to 0.10±0.02. The mean value of the RTmIRR slope was significantly (p = 0.003) larger in the LQTS patients (0.21±0.08) than in normal subjects (0.14±0.03). ConclusionsThese findings indicate that 1) quantification of the dynamic relation between ventricular repolarization and RR cycle length can be obtained on a large number of Holter-recorded heart beats; 2) βblockers reduce the RTm/RR slope in normal patients; and 3) LQTS patients have an exaggerated delay in repolarization at long RR cycle lengths.
A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection

Spacecraft operations are highly critical, demanding impeccable reliability and safety. Ensuring the optimal performance of a spacecraft requires the early detection and mitigation of anomalies, which could otherwise result in unit or mission failures. With the advent of deep learning, a surge of interest has been seen in leveraging these sophisticated algorithms for anomaly detection in space operations. This study aims to compare the efficacy of various deep learning architectures in detecting anomalies in spacecraft data. The deep learning models under investigation include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based architectures. Each of these models was trained and validated using a comprehensive dataset sourced from multiple spacecraft missions, encompassing diverse operational scenarios and anomaly types. Initial results indicate that while CNNs excel in identifying spatial patterns and may be effective for some classes of spacecraft data, LSTMs and RNNs show a marked proficiency in capturing temporal anomalies seen in time-series spacecraft telemetry. The Transformer-based architectures, given their ability to focus on both local and global contexts, have showcased promising results, especially in scenarios where anomalies are subtle and span over longer durations. Additionally, considerations such as computational efficiency, ease of deployment, and real-time processing capabilities were evaluated. While CNNs and LSTMs demonstrated a balance between accuracy and computational demands, Transformer architectures, though highly accurate, require significant computational resources. In conclusion, the choice of deep learning architecture for spacecraft anomaly detection is highly contingent on the nature of the data, the type of anomalies, and operational constraints.
MSLM-S2ST: A Multitask Speech Language Model for Textless Speech-to-Speech Translation with Speaker Style Preservation

There have been emerging research interest and advances in speech-to-speech translation (S2ST), translating utterances from one language to another. This work proposes Multitask Speech Language Model (MSLM), which is a decoder-only speech language model trained in a multitask setting. Without reliance on text training data, our model is able to support multilingual S2ST with speaker style preserved.
Align and Distill: Unifying and Improving Domain Adaptive Object Detection

Object detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, we identify systemic benchmarking pitfalls that call past results into question and hamper further progress: (a) Overestimation of performance due to underpowered baselines, (b) Inconsistent implementation practices preventing transparent comparisons of methods, and (c) Lack of generality due to outdated backbones and lack of diversity in benchmarks. We address these problems by introducing: (1) A unified benchmarking and implementation framework, Align and Distill (ALDI), enabling comparison of DAOD methods and supporting future development, (2) A fair and modern training and evaluation protocol for DAOD that addresses benchmarking pitfalls, (3) A new DAOD benchmark dataset, CFC-DAOD, enabling evaluation on diverse real-world data, and (4) A new method, ALDI++, that achieves state-of-the-art results by a large margin. ALDI++ outperforms the previous state-of-the-art by +3.5 AP50 on Cityscapes to Foggy Cityscapes, +5.7 AP50 on Sim10k to Cityscapes (where ours is the only method to outperform a fair baseline), and +2.0 AP50 on CFC Kenai to Channel. Our framework, dataset, and state-of-the-art method offer a critical reset for DAOD and provide a strong foundation for future research. Code and data are available: https://github.com/justinkay/aldi and https://github.com/visipedia/caltech-fish-counting.
Maidenhair Ferns, Adiantum, are Indeed Monophyletic and Sister to Shoestring Ferns, Vittarioids (Pteridaceae)

Abstract Across the tree of life, molecular phylogenetic studies often reveal surprising relationships between taxa with radically different morphologies that have long obscured their close affiliations. A spectacular botanical example is Rafflesia, a holoparasite that produces the largest flowers in the world, but that evolved from tiny-flowered ancestors within the Euphorbiaceae. Outside of parasitic lineages, such abrupt transformations are rarely seen. One exception involves the “maidenhair ferns” (Adiantum), which are quintessential ferns: beautifully dissected, terrestrial, and shade loving. The closely related “shoestring ferns” (vittarioids), in contrast, have an extremely simplified morphology, are canopy-dwelling epiphytes, and exhibit greatly accelerated rates of molecular evolution. While Adiantum and the vittarioids together have been shown to form a robust monophyletic group (adiantoids), there remain unanswered questions regarding the monophyly of Adiantum and the evolutionary history of the vittarioids. Here we review recent phylogenetic evidence suggesting support for the monophyly of Adiantum, and analyze new plastid data to confirm this result. We find that Adiantum is monophyletic and sister to the vittarioids. With this robust phylogenetic framework established for the broadest relationships in the adiantoid clade, we can now focus on understanding the evolutionary processes associated with the extreme morphological, ecological, and genetic transitions that took place within this lineage.
Mining unstructured processes: An exploratory study on a distance learning domain

Modern techniques widely applied in data mining, including computational intelligence and machine learning, have been fairly neglected in process mining. We conducted an exploratory study to use artificial neural networks to extract knowledge from an unstructured process in the distance learning domain. We discuss some possible benefits and limitations regarding the mining of unstructured processes. Results suggest that applying either classical process mining or modern data mining techniques would result in significant benefits for this domain. Our work helps to guide new studies related to the application of modern mining techniques in process mining.
Epistatic Genetic Effects among Alzheimer’s Candidate Genes

Background Novel risk variants for late-onset Alzheimer’s disease (AD) have been identified and replicated in genome-wide association studies. Recent work has begun to address the relationship between these risk variants and biomarkers of AD, though results have been mixed. The aim of the current study was to characterize single marker and epistatic genetic effects between the top candidate Single Nucleotide Polymorphisms (SNPs) in relation to amyloid deposition. Methods We used a combined dataset across ADNI-1 and ADNI-2, and looked within each dataset separately to validate identified genetic effects. Amyloid was quantified using data acquired by Positron Emission Tomography (PET) with 18F-AV-45. Results Two SNP-SNP interactions reached significance when correcting for multiple comparisons, BIN1 (rs7561528, rs744373) x PICALM (rs7851179). Carrying the minor allele in BIN1 was related to higher levels of amyloid deposition, however only in non-carriers of the protective PICALM minor allele. Conclusions Our results support previous research suggesting these candidate SNPs do not show single marker associations with amyloid pathology. However, we provide evidence for a novel interaction between PICALM and BIN1 in relation to amyloid deposition. Risk related to the BIN1 minor allele appears to be mitigated in the presence of the PICALM protective variant. In that way, variance in amyloid plaque burden can be better classified within the context of a complex genetic background. Efforts to model cumulative risk for AD should explicitly account for this epistatic effect, and future studies should explicitly test for such effects whenever statistically feasible.
Pharmacokinetics of sodium tungstate in rat and dog: a population approach.

Sodium tungstate has been found to correct hyperglycemia in insulin- and noninsulin-dependent models of diabetes when administered in drinking fluid with a low degree of toxicity; thus, it provides a potential treatment for diabetes. In the present report, pharmacokinetic studies with sodium tungstate were carried out in the Sprague-Dawley rat and beagle dog. This drug was administered either i.v. (8.97 mg/kg in rat; 25 and 50 mg/kg in dog) or orally in the form of solution (35.9 and 107.7 mg/kg in rat; 25 and 50 mg/kg in dog). Tungsten was quantified using an inductively coupled plasma method. Pharmacokinetic parameters were estimated using a population approach. Sodium tungstate followed first order kinetics, and plasma concentration-versus-time data were adequately described by a two-compartment model. In rat, bioavailability was high (92%), whereas it was lower in dog (approximately 65%). The total volume of distribution expressed by unit of body weight was much higher when the animal was smaller (0.46 l/kg in rat versus 0.23 l/kg in dog). The total body clearance normalized by weight, 0.19 l/h/kg in rat versus 0.043 l/h/kg in dog, changed as for the volume of distribution. The elimination half-life was two times higher in dog (approximately 4 h) than in rat (approximately 1.7 h). In the range of 35.9 to 107.7 mg/kg after oral administration in rat and 25 to 50 mg/kg after oral and i.v. administration in dog, tungsten plasma concentrations increased in proportion to dose.
Stochastic Halpern iteration in normed spaces and applications to reinforcement learning

We analyze the oracle complexity of the stochastic Halpern iteration with variance reduction, where we aim to approximate fixed-points of nonexpansive and contractive operators in a normed finite-dimensional space. We show that if the underlying stochastic oracle is with uniformly bounded variance, our method exhibits an overall oracle complexity of $\tilde{O}(\varepsilon^{-5})$, improving recent rates established for the stochastic Krasnoselskii-Mann iteration. Also, we establish a lower bound of $\Omega(\varepsilon^{-3})$, which applies to a wide range of algorithms, including all averaged iterations even with minibatching. Using a suitable modification of our approach, we derive a $O(\varepsilon^{-2}(1-\gamma)^{-3})$ complexity bound in the case in which the operator is a $\gamma$-contraction. As an application, we propose new synchronous algorithms for average reward and discounted reward Markov decision processes. In particular, for the average reward, our method improves on the best-known sample complexity.
Dual-Energy Cone-Beam CT Using Two Complementary Limited-Angle Scans with A Projection-Consistent Diffusion Model

Background: Dual-energy imaging on cone-beam CT (CBCT) scanners has great potential in different clinical applications, including image-guided surgery and adaptive proton therapy. However, the clinical practice of dual-energy CBCT (DE-CBCT) has been hindered by the requirement of sophisticated hardware components. Purpose: In this work, we aim to propose a practical solution for single-scan dual-energy imaging on current CBCT scanners without hardware modifications, using two complementary limited-angle scans with a projection-consistent diffusion model. Methods: Our approach has two major components: data acquisition using two complementary limited-angle scans, and dual-energy projections restoration with subsequent FDK reconstruction. Two complementary scans at different kVps are performed in a single rotation by switching the tube voltage at the middle of the source trajectory, acquiring the mixed-spectra projection in a single CBCT scan. Full-sampled dual-energy projections are then restored by a projection-consistent diffusion model in a slice-by-slice manner, followed by the DE-CBCT reconstruction using the FDK algorithm. Results: The proposed method was evaluated in a simulation study of digital abdomen phantoms and a study of real rat data. In the simulation study, the proposed method produced DE-CBCT images at a mean absolute error (MAE) of 20 HU. In the small-animal study, reconstructed DE-CBCT images using the proposed method gave an MAE of 25 HU. Conclusion: This study demonstrates the feasibility of DE-CBCT imaging using two complementary limited-angle scans with a projection-consistent diffusion model in both half-fan and short scans. The proposed method may allow quantitative applications of DE-CBCT and enable DE-CBCT-based adaptive proton therapy.
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention

Motivated by the challenge of seamless cross-dataset transfer in EEG signal processing, this article presents an exploratory study on the use of Joint Embedding Predictive Architectures (JEPAs). In recent years, self-supervised learning has emerged as a promising approach for transfer learning in various domains. However, its application to EEG signals remains largely unexplored. In this article, we introduce Signal-JEPA for representing EEG recordings which includes a novel domain-specific spatial block masking strategy and three novel architectures for downstream classification. The study is conducted on a 54~subjects dataset and the downstream performance of the models is evaluated on three different BCI paradigms: motor imagery, ERP and SSVEP. Our study provides preliminary evidence for the potential of JEPAs in EEG signal encoding. Notably, our results highlight the importance of spatial filtering for accurate downstream classification and reveal an influence of the length of the pre-training examples but not of the mask size on the downstream performance.
Poor lymphocyte recovery following CD34‐selected autologous peripheral blood stem cell transplantation for non‐Hodgkin's lymphoma

Positive selection of CD34+ cells in autologous grafts, designed to deplete tumour cells, also results in T‐cell depletion. To assess the reconstitution of the different lymphocyte subsets and of the T‐cell repertoire diversity following autologous transplantation of selected CD34+ peripheral blood stem cells (PBSC), we analysed sequential blood samples in eight patients autografted for advanced B‐cell non‐Hodgkin's lymphoma in a phase I–II pilot study. Although natural killer cell recovery was rapid, T‐ and B‐cell recovery was delayed with a median of 110/μl CD4+, 175/μl CD8+ T cells and 45/μl B cells at 12 months post‐transplant. The naive CD45RA+ T‐cell compartment was profoundly deficient up to 12 months for both CD4+ and CD8+ subsets. A transient expansion of memory CD8+CD45RO+ T cells consisting of an increased percentage of CD57+CD28− cells occurred within the first 3 months post‐transplant, but the memory CD4+CD45RO+ T cells remained far below the normal value. The CD8+CD28+ T‐cell subset did not recover. Using multiplex PCR analysis of the T‐cell receptor γ locus, we found that the repertoire diversity improved at 12 months after being poor and oligoclonal during the first 3 months post‐transplant. As shown by monoplex PCRγ analysis of every VJ combination, despite T‐cell depletion of the graft, mature T cells were carried over with the selected CD34+ PBSC and contributed to the T‐cell recovery after transplantation.
Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures

Neural network architectures for image demosaicing have been become more and more complex. This results in long training periods of such deep networks and the size of the networks is huge. These two factors prevent practical implementation and usage of the networks in real-time platforms, which generally only have limited resources. This study investigates the effectiveness of neural network architectures in hyperspectral image demosaicing. We introduce a range of network models and modifications, and compare them with classical interpolation methods and existing reference network approaches. The aim is to identify robust and efficient performing network architectures. Our evaluation is conducted on two datasets,"SimpleData"and"SimRealData,"representing different degrees of realism in multispectral filter array (MSFA) data. The results indicate that our networks outperform or match reference models in both datasets demonstrating exceptional performance. Notably, our approach focuses on achieving correct spectral reconstruction rather than just visual appeal, and this emphasis is supported by quantitative and qualitative assessments. Furthermore, our findings suggest that efficient demosaicing solutions, which require fewer parameters, are essential for practical applications. This research contributes valuable insights into hyperspectral imaging and its potential applications in various fields, including medical imaging.
A Practical Guide to Statistical Distances for Evaluating Generative Models in Science

Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes. How do we evaluate the samples these models generate? This work aims to provide an accessible entry point to understanding popular notions of statistical distances, requiring only foundational knowledge in mathematics and statistics. We focus on four commonly used notions of statistical distances representing different methodologies: Using low-dimensional projections (Sliced-Wasserstein; SW), obtaining a distance using classifiers (Classifier Two-Sample Tests; C2ST), using embeddings through kernels (Maximum Mean Discrepancy; MMD), or neural networks (Fr\'echet Inception Distance; FID). We highlight the intuition behind each distance and explain their merits, scalability, complexity, and pitfalls. To demonstrate how these distances are used in practice, we evaluate generative models from different scientific domains, namely a model of decision making and a model generating medical images. We showcase that distinct distances can give different results on similar data. Through this guide, we aim to help researchers to use, interpret, and evaluate statistical distances for generative models in science.