Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiocarbon Chronology of the DSDDP Core at the Deepest Floor of the Dead Sea

Abstract This study establishes the chronological framework of the sedimentary sequence deposited Dead Sea, ICDP 5017-1, Radiocarbon chronology during the past 50 ka at the deepest part of the Dead Sea (the ICDP 5017-1 site), which was recovered by the Dead Sea Deep Drilling Project (DSDDP) under the auspices of the International Continental Scientific Drilling Program (ICDP). The age-depth model is constructed using 38 14C dates of terrestrial plant remains in a composite 150-m-long profile, generated by anchoring 32 marker layers identified in five cores. The sedimentary records at the ICDP 5017-1 site fills gaps in those obtained from the exposed sections at the high margins of the lake, particularly in times of lake-level retreat, and allows for a high-resolution comparison between the lake’s margins and deepest floor.
Recommendations for healthy aging as documented by health professionals: a summative content analysis of health records

Abstract Aim: To identify what type of recommendations were recorded in older adults’ health records by health professionals during preventive home visits. Background: To promote health and prevent ill health, health professionals can give support and recommendations to older adults. The preventive home visit for older adults is one example of an intervention where health professionals such as nurses, social workers, and assistant nurses can give recommendations. By exploring what recommendations are recorded and within what areas, we can also gain knowledge about areas where provision of recommendations seems lacking. This knowledge would provide health professionals with guidance in their counseling with the older adult. Methods: Records from preventive home visits (n = 596; mean age 78.71) were qualitatively and quantitatively analyzed. Findings: The most frequently recorded recommendations were related to physical or mental illness, falls, and then nutrition. The results showed that recommendations could be sorted into ten sub-categories related to physical or mental illness, falls, nutrition, physical activity, preparation for the future, social participation, finances, getting help from others, municipal services, and security at home. These ten sub-categories were classified into the International Classification of Functioning, Disability, and Health categories body functions & structure (including one sub-category), activity (including four sub-categories), participation (including three sub-categories), and environmental factors (including two sub-categories). From the results, we could conclude that the major focus was on risk prevention and less focus was on health promotion. Thus, the visitor’s recommendations most likely mirror the older adult’s explicit needs ‘here and now’ to a great extent. However, health visitors also need to focus on intrinsic capacities to promote health. Besides recommendations relating to the person’s intrinsic capacities, environmental aspects should be focused upon, to improve healthy aging.
ARDS-Diagnostik und -Therapie nach der Coronapandemie – alles beim Alten?

Die massiven wissenschaftlichen Aktivitäten zur Bewältigung der Coronapandemie haben für bestimmte intensivmedizinisch relevante Syndrome neue Blickwinkel und Perspektiven eröffnet. Neben der Sepsis betrifft dies das akute Lungenversagen als schwerste Manifestation der COVID-19-Infektion. In diesem Beitrag soll untersucht werden, ob sich daraus bereits neue Empfehlungen zur Diagnostik und Therapie des ARDS im Allgemeinen ableiten lassen.
Soil macroaggregates: The hotspots driving emission or mitigation of greenhouse gases according to the management system adopted

The association of no-tillage (NT) and legume cover crops has shown positive results to organic carbon (SOC) accumulation in subtropical soils. However, soil dynamics of powerful greenhouse gases (GHG) such as nitrous oxide (N2O) and methane (CH4) are not fully understood at a microscale under these systems. Thus, our objective was to evaluate net cumulative emissions (NCE, in CO2 equivalent [eq.]) of GHG considering fluxes of N2O and CH4 and SOC accumulation in three soil aggregates classes. The soil was sampled in a 30-year experiment in a subtropical Acrisol under conventional tillage (CT) and NT, combined with cropping systems with or without legume cover crops. Large (9.51 to 2 mm) and small (2 to 0.25 mm) macroaggregates and microaggregates (<0.25 mm), obtained by wet sieving, were incubated under 400 g kg−1 (aerobic condition) and 700 g kg−1 (anaerobic condition) of volumetric water for six months. Under anaerobic conditions, NCE were positive due to the high emission of N2O and CH4, making all the soil aggregates a GHG source to the atmosphere. Under aerobic conditions, NT and legume cover crops greatly contributed to reducing NCE: for each kilogram of C accumulated in macroaggregates, greater C from the atmosphere was taken up by NT than CT (−69.4 versus 57.1 mg CO2eq. kg−1 SOC) and by cropping systems with than without legume cover crops (74.7 versus 51.8 mg CO2eq. kg−1 SOC). Management systems did not impact GHG emissions of soil microaggregates. By containing labile organic matter, soil macroaggregates acted as hotspots of soil GHG emission/mitigation. Despite favoring N2O emission, NT and legume cover crop lead to GHG mitigation by promoting CH4 uptake and C accumulation in soil macroaggregates.
Recurrent severe hypernatraemia in a young patient: a disconnect between osmoreceptor function and drinking behaviour

Chronic hypernatraemia is a rare clinical entity. In the younger population, hypernatraemia is often a consequence of failure to generate thirst in response to osmotic stimuli. We report the case of a male patient admitted with severe hypernatraemia (plasma sodium 175 mmol/L) on return from holidays. His urine was maximally concentrated at 894 mOsm/kg—suggestive of normal vasopressin reserve. MRI of the brain showed a large extra-axial cyst, with preservation of the posterior pituitary bright spot. Formal osmoregulatory studies demonstrated normal osmoregulated vasopressin secretion and normal thirst, but no appropriate drinking behaviour. This patient illustrates a unique pathophysiological disconnect between thirst appreciation and the central drive to drink, in the context of normal osmoregulatory function. It is likely that this disconnect is related to the patient’s large intracranial cyst. The management challenge is to maintain appropriate fluid intake in order to prevent recurrent severe hypernatraemia.
Effect of extracorporeal shock wave combined with Kinesio taping on upper limb function during individuals with biceps brachii tendinopathy：protocol for a double-blind, randomised controlled trial

Introduction Long head of biceps brachii tendinopathy (LHBT) is characterised by persistent pain and disability of shoulder joint, impairing patients’ quality of life. Extracorporeal shock wave therapy (ESWT) is a non-invasive treatment, which promotes tissue regeneration and repair. However, ESWT has a side effect that often causes short-term pain and swelling in the treatment area. It is known that the effects of Kinesio taping (KT) on relieving swelling and pain. Due to insufficient clinical evidence from current limited studies, this randomised controlled study aims to explore the effects of ESWT combined with KT on upper limb function during individuals with LHBT. Methods and analysis A 2×2 factorial design, double-blind, randomised controlled trial will be conducted. A total of 144 participants will be randomly allocated into one of four groups (KT+ESWT, KT+sham ESWT, sham KT+ESWT or sham KT+sham ESWT) to participate in a 4-week treatment programme. Measurements will be taken at pretreatment (baseline), immediately after treatment and 6 weeks after treatment. The primary endpoint will be the Constant-Murley score (CMS), the secondary endpoints will include the pain Numerical Rating Scale, range of motion, pressure pain threshold and soft tissue hardness of biceps, speed test and global rating of change. Repeated measures analysis of variance will be used to compare differences among the effects of different interventions. Ethics and dissemination Ethics approval was obtained from the Ethics Committee of the Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. In addition to international conference reports, findings will be disseminated through international publications in peer-reviewed journals. Trial registration number ChiCTR2100051324.
Convex SGD: Generalization Without Early Stopping

We consider the generalization error associated with stochastic gradient descent on a smooth convex function over a compact set. We show the first bound on the generalization error that vanishes when the number of iterations $T$ and the dataset size $n$ go to zero at arbitrary rates; our bound scales as $\tilde{O}(1/\sqrt{T} + 1/\sqrt{n})$ with step-size $\alpha_t = 1/\sqrt{t}$. In particular, strong convexity is not needed for stochastic gradient descent to generalize well.
Grimoire is All You Need for Enhancing Large Language Models

In-context Learning (ICL) is one of the key methods for enhancing the performance of large language models on specific tasks by providing a set of few-shot examples. However, the ICL capability of different types of models shows significant variation due to factors such as model architecture, volume of learning data, and the size of parameters. Generally, the larger the model's parameter size and the more extensive the learning data, the stronger its ICL capability. In this paper, we propose a method SLEICL that involves learning from examples using strong language models and then summarizing and transferring these learned skills to weak language models for inference and application. This ensures the stability and effectiveness of ICL. Compared to directly enabling weak language models to learn from prompt examples, SLEICL reduces the difficulty of ICL for these models. Our experiments, conducted on up to eight datasets with five language models, demonstrate that weak language models achieve consistent improvement over their own zero-shot or few-shot capabilities using the SLEICL method. Some weak language models even surpass the performance of GPT4-1106-preview (zero-shot) with the aid of SLEICL.
Singular effect of linkage on long term genetic gain in the infinitesimal model

During the founding of the field of quantitative genetics, Fisher formulated in 1918 his infinitesimal model that provided a novel mathematical framework to describe the Mendelian transmission of quantitative traits. If the infinitely-many genes in that model are assumed to segregate independently during reproduction, corresponding to having no linkage, directional selection asymptotically leads to a constant genetic gain at each generation. However, in reality genes are subject to linkage because they lie on chromosomes and thus segregate in a correlated way. Various approximations have been used in the past to study that more realistic case of the infinitesimal model and they lead to the prediction that the asymptotic gain per generation is modestly decreased. Here we provide an exact solution to the problem where genes lie on continuous chromosomes. Surprisingly, the consequences of genetic linkage are in fact rather singular, changing the nature of the long term gain per generation: the asymptotic gain vanishes rather than being simply decreased. Nevertheless, the rate at which the per-generation gain tends to zero is slow enough for the total gain, accumulated over generations, to be unbounded.
Manifold-based Shapley for SAR Recognization Network Explanation

Explainable artificial intelligence (XAI) holds immense significance in enhancing the deep neural network's transparency and credibility, particularly in some risky and high-cost scenarios, like synthetic aperture radar (SAR). Shapley is a game-based explanation technique with robust mathematical foundations. However, Shapley assumes that model's features are independent, rendering Shapley explanation invalid for high dimensional models. This study introduces a manifold-based Shapley method by projecting high-dimensional features into low-dimensional manifold features and subsequently obtaining Fusion-Shap, which aims at (1) addressing the issue of erroneous explanations encountered by traditional Shap; (2) resolving the challenge of interpretability that traditional Shap faces in complex scenarios.
STAIR: Spatial-Temporal Reasoning with Auditable Intermediate Results for Video Question Answering

Recently we have witnessed the rapid development of video question answering models. However, most models can only handle simple videos in terms of temporal reasoning, and their performance tends to drop when answering temporal-reasoning questions on long and informative videos. To tackle this problem we propose STAIR, a Spatial-Temporal Reasoning model with Auditable Intermediate Results for video question answering. STAIR is a neural module network, which contains a program generator to decompose a given question into a hierarchical combination of several sub-tasks, and a set of lightweight neural modules to complete each of these sub-tasks. Though neural module networks are already widely studied on image-text tasks, applying them to videos is a non-trivial task, as reasoning on videos requires different abilities. In this paper, we define a set of basic video-text sub-tasks for video question answering and design a set of lightweight modules to complete them. Different from most prior works, modules of STAIR return intermediate outputs specific to their intentions instead of always returning attention maps, which makes it easier to interpret and collaborate with pre-trained models. We also introduce intermediate supervision to make these intermediate outputs more accurate. We conduct extensive experiments on several video question answering datasets under various settings to show STAIR's performance, explainability, compatibility with pre-trained models, and applicability when program annotations are not available. Code: https://github.com/yellow-binary-tree/STAIR
Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification

In multimodal land cover classification (MLCC), a common challenge is the redundancy in data distribution, where irrelevant information from multiple modalities can hinder the effective integration of their unique features. To tackle this, we introduce the Multimodal Informative Vit (MIVit), a system with an innovative information aggregate-distributing mechanism. This approach redefines redundancy levels and integrates performance-aware elements into the fused representation, facilitating the learning of semantics in both forward and backward directions. MIVit stands out by significantly reducing redundancy in the empirical distribution of each modality's separate and fused features. It employs oriented attention fusion (OAF) for extracting shallow local features across modalities in horizontal and vertical dimensions, and a Transformer feature extractor for extracting deep global features through long-range attention. We also propose an information aggregation constraint (IAC) based on mutual information, designed to remove redundant information and preserve complementary information within embedded features. Additionally, the information distribution flow (IDF) in MIVit enhances performance-awareness by distributing global classification information across different modalities' feature maps. This architecture also addresses missing modality challenges with lightweight independent modality classifiers, reducing the computational load typically associated with Transformers. Our results show that MIVit's bidirectional aggregate-distributing mechanism between modalities is highly effective, achieving an average overall accuracy of 95.56% across three multimodal datasets. This performance surpasses current state-of-the-art methods in MLCC. The code for MIVit is accessible at https://github.com/icey-zhang/MIViT.
BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning

Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasis on transitioning from local to global, resulting in the neglect of interactions between different contexts. To tackle this issue, we propose a parallel context learning strategy that involves acquiring bilateral consensus for the two-view correspondence pruning task. In our approach, we design a distinctive self-attention block to capture global context and parallel process it with the established local context learning module, which enables us to simultaneously capture both local and global consensuses. By combining these local and global consensuses, we derive the required bilateral consensus. We also design a recalibration block, reducing the influence of erroneous consensus information and enhancing the robustness of the model. The culmination of our efforts is the Bilateral Consensus Learning Network (BCLNet), which efficiently estimates camera pose and identifies inliers (true correspondences). Extensive experiments results demonstrate that our network not only surpasses state-of-the-art methods on benchmark datasets but also showcases robust generalization abilities across various feature extraction techniques. Noteworthily, BCLNet obtains significant improvement gains over the second best method on unknown outdoor dataset, and obviously accelerates model training speed.
The role of resonator neuron in the dynamics of two coupled integrator and resonator neurons of different types of excitability

In this manuscript, a silent resonator neuron is coupled with a spiking integrator neuron through the gap junction, when the coupled neurons are of different types of excitability and none of the coupled neurons exhibit mixed mode oscillations and bursting oscillations intrinsically. By using dynamical systems theory (e.g. the bifurcation theory), all the observed oscillation patterns and the transition mechanisms between them are investigated, when one of the coupling strengths is fixed and the other is varied. It is noticeable that, there is an interval in the parameter space, for the parameter values within which the coupled system is multi-stable. This multistability corresponds to the coexistence of mixed mode oscillations, bursting oscillations and subthreshold oscillations of the resonator neuron. In addition, some interval in the parameter space is introduced such that, for the values of the coupling strength within which the resonator neuron is in tonic spiking mode, while for the values of the coupling strength outside which the resonator neuron exhibits subthreshold oscillations. It is also verified that the final synchronization of the coupled neurons actually corresponds to the synchronization of tonic spiking oscillations of the integrator neuron and one-bursting oscillations of the resonator neuron.
Rabphilin-3A negatively regulates neuropeptide release, through its SNAP25 interaction

Neuropeptides and neurotrophins are stored in and released from dense-core vesicles (DCVs). While DCVs and synaptic vesicles (SVs) share fundamental SNARE/SM proteins for exocytosis, a detailed understanding of DCV exocytosis remains elusive. We recently identified the RAB3-RIM1 pathway to be essential for DCV-, but not SV exocytosis, highlighting a significant distinction between the SV– and DCV secretory pathways. Whether RIM1 is the only RAB3 effector that is essential for DCV release is currently unknown. In this study, we characterized the role of rabphilin-3A (RPH3A), a known downstream effector of RAB3A, in the DCV secretory pathway using RPH3A-deficient hippocampal mouse neurons. RPH3A did not travel with DCVs, but remained stationary at synapses. In the absence of RPH3A, the number of DCV exocytosis events was 3-fold higher than in wildtype (WT) neurons. RPH3A lost its punctate distribution when it was unable to bind RAB3A. However, this was not rate limiting, as expressing a mutant RPH3A that was unable to bind RAB3A restored exocytosis to WT levels, but not when RPH3A was unable to bind SNAP25. In addition, RPH3A null neurons had longer neurites, which was partly restored when ablating all regulated secretion with tetanus neurotoxin. Taken together, we conclude that RPH3A negatively regulates DCV exocytosis, potentially also affecting neuron size. Furthermore, RAB3A interaction is required for the synaptic enrichment of RPH3A, but not for limiting DCV exocytosis. Instead the interaction of RPH3A with SNAP25 is relevant for inhibiting DCV exocytosis.
WEBDial, a Multi-domain, Multitask Statistical Dialogue Framework with RDF

Typically available dialogue frameworks have adopted a semantic representation based on dialogue-acts and slot-value pairs. Despite its simplicity, this representation has disadvantages such as the lack of expressivity, scalability and explainability. We present WEBDial: a dialogue framework that relies on a graph formalism by using RDF triples instead of slot-value pairs. We describe its overall architecture and the graph-based semantic representation. We show its applicability from simple to complex applications, by varying the complexity of domains and tasks: from single domain and tasks to multiple domains and complex tasks.
Mapping ground 3-D displacement with GPS and PS-InSAR networking in the Pingtung area,southwestern Taiwan,China

It′s promising to map ground 3-D displacement along the north,east and vertical directions combining accurate GPS observations and dense PS-InSAR measurement.This paper computes accurately the orbital azimuth of SAR satellites and analyzes the geometric transformation relationship between InSAR derived deformation in LOS direction and GPS observables.The model is employed to convert InSAR displacement into vertical deformation based on the accurate horizontal displacement from GPS.The selected PS targets and GPS stations are combined to establish a ground deformation monitoring network.The parameter adjustment method is used to estimate vertical displacement of PS.The algorithms are tested and validated using ERS-1/2 SAR images over the Pingtung area and GPS observations from 48 stations in southwestern Taiwan.The horizontal displacement in the Pingtung area is clearly derived from GPS observables;the annual velocity is 30～50 mm/a moving towards the west and the azimuth presents a counter-clockwise deviation towards the southwest.The vertical displacement field is derived from the GPS and PS-InSAR networking with the southern Pingtung plain subsiding at a rate of-10～-15 mm/a,the northern plain and Kaohsiung area uplifting at +5～+10 mm/a.
Nonlinear solution of classical three-wave interaction via finite dimensional quantum model

The quantum three-wave interaction, the lowest order nonlinear interaction in plasma physics, describes energy-momentum transfer between three resonant waves in the quantum regime. We describe how it may also act as a finite-degree-of-freedom approximation to the classical three-wave interaction in certain circumstances. By promoting the field variables to operators, we quantize the classical system, show that the quantum system has more free parameters than the classical system, and explain how these parameters may be selected to optimize either initial or long-term correspondence. We then numerically compare the long-time quantum/classical correspondence far from the fixed point dynamics. We discuss the Poincare recurrence of the system and the mitigation of quantum scrambling.