Miracle-dz/UniFAR-FTUs huggingface dataset:
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations

We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models. The motivation behind MIM-Refiner is rooted in the insight that optimal representations within MIM models generally reside in intermediate layers. Accordingly, MIM-Refiner leverages multiple contrastive heads that are connected to diverse intermediate layers. In each head, a modified nearest neighbor objective helps to construct respective semantic clusters. The refinement process is short but effective. Within a few epochs, we refine the features of MIM models from subpar to state-of-the-art, off-the-shelf features. Refining a ViT-H, pre-trained with data2vec 2.0 on ImageNet-1K, achieves new state-of-the-art results in linear probing (84.7%) and low-shot classification among models that are pre-trained on ImageNet-1K. In ImageNet-1K 1-shot classification, MIM-Refiner sets a new state-of-the-art of 64.2%, outperforming larger models that were trained on up to 2000x more data such as DINOv2-g, OpenCLIP-G and MAWS-6.5B. Project page: https://ml-jku.github. io/MIM-Refiner
Quantifying Systematic Uncertainties in Experimental Physics: An Approximation Method

In the domain of physics experiments, data fitting is a pivotal technique for extracting insights from both experimental and simulated datasets. This article presents an approximation method designed to estimate the systematic errors prevalent in data analyses. By applying our method to the Nab experiment, we compare our findings with simulation-derived results, thereby confirming the concordance of our approach with established simulation outcomes. This corroboration highlights the versatility of our method as a good tool for validating simulation results across various experimental contexts.
Hairy Vetch Becomes an Alternative Basal N Fertilizer in Low-input Fresh-market Tomato Production in a Plastic High Tunnel

In order to establish a low-input alternative production system based on cover crops, the effectiveness of hairy vetch ( Vicia villosa Roth; HV) incorporated as a basal nitrogen fertilizer and its effects on soil carbon (C) and nitrogen (N) content in fresh-market tomato production in plastic high tunnel was investigated in Sapporo (snow cover region), Japan. The treatments consisted of HV seeding rates (HV2, 2 kg/10 a; HV5, 5 kg/10 a) and ammonium sulfate (AS10, 10 kg N/10 a) applied as basal N fertilizers, and the Non HV (control) with no HV and ammonium sulfate. In all plots, 15 kg N/10 a were added before tomato transplanting as a controlled-release fertilizer (LPS100 41%−N) to maintain the tomato growth in the mid and late stages. HV aboveground biomass (dry weight) was 585 kg/10 a in HV2 and 719 kg/10 a in HV5. Nitrate in petiole sap was higher in HV2, HV5, and AS10 plots than in Non HV plots throughout the cultivation period with the exception of the first 2 weeks after transplanting (WAT). The Growth index (GI) was higher in HV2 (47971), HV5 (46285), and AS10 (43397) than in Non HV (39847) at 7 WAT, the beginning of the harvest. Higher marketable yields were found in tomatoes grown in HV2 (6.87 t/10 a), HV5 (6.91 t/10 a), and AS10 (6.08 t/10 a), compared with the Non HV (5.19 t/10 a). The HV plots had greater soil total and organic N than AS10 and Non HV, and slightly increased soil C after tomato production. From these results, the HV seeding rate of 2 kg/10 a will be enough to support the growth of tomatoes planted after HV production if HV grows steadily in a plastic high tunnel.
Propulsion of a three-sphere micro-robot in a porous medium

Microorganisms and synthetic microswimmers often encounter complex environments consisting of networks of obstacles embedded into viscous fluids. Such settings include biological media, such as mucus with filamentous networks, as well as environmental scenarios, including wet soil and aquifers. A fundamental question in studying their locomotion is how the impermeability of these porous media impact their propulsion performance compared with the case that in a purely viscous fluid. Previous studies showed that the additional resistance due to the embedded obstacles leads to an enhanced propulsion of different types of swimmers, including undulatory swimmers, helical swimmers, and squirmers. In this work we employ a canonical three-sphere swimmer model to probe the impact of propulsion in porous media. The Brinkman equation is utilized to model a sparse network of stationary obstacles embedded into an incompressible Newtonian liquid. We present both a far-field theory and numerical simulations to characterize the propulsion performance of the swimmer in such porous media. In contrast to enhanced propulsion observed in other swimmer models, our results reveal that both the propulsion speed and efficiency of the three-sphere swimmer are largely reduced by the impermeability of the porous medium. We attribute the substantial reduction in propulsion performance to the screened hydrodynamic interactions among the spheres due to the more rapid spatial decays of flows in Brinkman media. These results highlight how enhanced or hindered propulsion in porous media is largely dependent on individual propulsion mechanisms. The specific example and physical insights provided here may guide the design of synthetic microswimmers for effective locomotion in porous media in their potential biological and environmental applications.
Neural Network Approaches for Parameterized Optimal Control

We consider numerical approaches for deterministic, finite-dimensional optimal control problems whose dynamics depend on unknown or uncertain parameters. We seek to amortize the solution over a set of relevant parameters in an offline stage to enable rapid decision-making and be able to react to changes in the parameter in the online stage. To tackle the curse of dimensionality arising when the state and/or parameter are high-dimensional, we represent the policy using neural networks. We compare two training paradigms: First, our model-based approach leverages the dynamics and definition of the objective function to learn the value function of the parameterized optimal control problem and obtain the policy using a feedback form. Second, we use actor-critic reinforcement learning to approximate the policy in a data-driven way. Using an example involving a two-dimensional convection-diffusion equation, which features high-dimensional state and parameter spaces, we investigate the accuracy and efficiency of both training paradigms. While both paradigms lead to a reasonable approximation of the policy, the model-based approach is more accurate and considerably reduces the number of PDE solves.
On Designing Features for Condition Monitoring of Rotating Machines

Various methods for designing input features have been proposed for fault recognition in rotating machines using one-dimensional raw sensor data. The available methods are complex, rely on empirical approaches, and may differ depending on the condition monitoring data used. Therefore, this article proposes a novel algorithm to design input features that unifies the feature extraction process for different time-series sensor data. This new insight for designing/extracting input features is obtained through the lens of histogram theory. The proposed algorithm extracts discriminative input features, which are suitable for a simple classifier to deep neural network-based classifiers. The designed input features are given as input to the classifier with end-to-end training in a single framework for machine conditions recognition. The proposed scheme has been validated through three real-time datasets: a) acoustic dataset, b) CWRU vibration dataset, and c) IMS vibration dataset. The real-time results and comparative study show the effectiveness of the proposed scheme for the prediction of the machine's health states.
Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length

Neural networks offer good approximation to many tasks but consistently fail to reach perfect generalization, even when theoretical work shows that such perfect solutions can be expressed by certain architectures. Using the task of formal language learning, we focus on one simple formal language and show that the theoretically correct solution is in fact not an optimum of commonly used objectives -- even with regularization techniques that according to common wisdom should lead to simple weights and good generalization (L1, L2) or other meta-heuristics (early-stopping, dropout). However, replacing standard targets with the Minimum Description Length objective (MDL) results in the correct solution being an optimum.
Analyzing the Impact of Computation in Adaptive Dynamic Programming for Stochastic LQR Problem

Adaptive dynamic programming (ADP) for stochastic linear quadratic regulation (LQR) demands the precise computation of stochastic integrals during policy iteration (PI). In a fully model-free problem setting, this computation can only be approximated by state samples collected at discrete time points using computational methods such as the canonical Euler-Maruyama method. Our research reveals a critical phenomenon: the sampling period can significantly impact control performance. This impact is due to the fact that computational errors introduced in each step of PI can significantly affect the algorithm's convergence behavior, which in turn influences the resulting control policy. We draw a parallel between PI and Newton's method applied to the Ricatti equation to elucidate how the computation impacts control. In this light, the computational error in each PI step manifests itself as an extra error term in each step of Newton's method, with its upper bound proportional to the computational error. Furthermore, we demonstrate that the convergence rate for ADP in stochastic LQR problems using the Euler-Maruyama method is O(h), with h being the sampling period. A sensorimotor control task finally validates these theoretical findings.
Interplay of competition and facilitation in grazing succession by migrant Serengeti herbivores

Competition, facilitation, and predation offer alternative explanations for successional patterns of migratory herbivores. However, these interactions are difficult to measure, leaving uncertainty about the mechanisms underlying body-size-dependent grazing—and even whether succession occurs at all. We used data from an 8-year camera-trap survey, GPS-collared herbivores, and fecal DNA metabarcoding to analyze the timing, arrival order, and interactions among migratory grazers in Serengeti National Park. Temporal grazing succession is characterized by a “push-pull” dynamic: Competitive grazing nudges zebra ahead of co-migrating wildebeest, whereas grass consumption by these large-bodied migrants attracts trailing, small-bodied gazelle that benefit from facilitation. “Natural experiments” involving intense wildfires and rainfall respectively disrupted and strengthened these effects. Our results highlight a balance between facilitative and competitive forces in co-regulating large-scale ungulate migrations. Editor’s summary The Serengeti migrations consist of millions of herbivores sharing food resources. For decades, there has been interest in understanding how these resources are divided up and what drives the division. Anderson et al. used ecological and animal movement data, fecal DNA, and “natural experiments” driven by rain and fire to characterize the divisions among the three main herbivores within the migration: zebra, wildebeest, and gazelle. They found that competition pushes zebra ahead of the other larger species, wildebeest. Herbivory by wildebeest then facilitates the development of newer growth, which gazelle take advantage of as they trail the other two. —Sacha Vignieri A camera-trap survey, GPS-collared herbivores, and fecal DNA analysis elucidate the timing, arrival order, and interactions of migratory grazers.
Delineating the shape of COPII coated membrane bud

Curvature-generating proteins that direct membrane trafficking assemble on the surface of lipid bilayers to bud transport intermediates, which move protein and lipid cargoes from one cellular compartment to another. Our recent study on the COPII protein Sar1 showed that the inserted volume of the protein into the membrane determines the degree of membrane bending. However, it is unclear what controls the overall shape of the membrane bud once curvature induction has begun. In vitro experiments showed that excessive concentrations of Sar1 promoted the formation of membrane tubules from synthetic vesicles, while COPII-coated transport intermediates in cells are generally more spherical or lobed in shape. To understand the origin of these morphological dissimilarities, we employ atomistic, coarse-grained (CG), and continuum mesoscopic simulations of membranes in the presence of multiple curvature-generating proteins. We first demonstrate the membrane bending ability of amphipathic peptides derived from the amino terminus of Sar1, as a function of inter-peptide angle and concentration using an atomistic bicelle simulation protocol. Then, we employ CG (MARTINI) simulations to reveal that Sec23 and Sec24 control the relative spacing between Sar1 protomers and form the inner-coat unit through an attachment with Sar1. Finally, using Dynamical Triangulated Surface (DTS) simulations based on the Helfrich Hamiltonian we demonstrate that the uniform distribution of spacer molecules among curvature-generating proteins is crucial to the spherical budding of the membrane. Overall, we show that Sec23 and Sec24 act as a spacer to preserve a dispersed arrangement of Sar1 protomers and to help determine the overall shape of the membrane bud.
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation

Abstract Word-level AutoCompletion (WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neural network that maps the hidden vector of the input context into its corresponding label (i.e., the candidate target word is treated as a label). Since the context hidden vector itself does not take the label into account and it is projected to the label through a linear classifier, the model cannot sufficiently leverage valuable information from the source sentence as verified in our experiments, which eventually hinders its overall performance. To alleviate this issue, this work proposes an energy-based model for WLAC, which enables the context hidden vector to capture crucial information from the source sentence. Unfortunately, training and inference suffer from efficiency and effectiveness challenges, therefore we employ three simple yet effective strategies to put our model into practice. Experiments on four standard benchmarks demonstrate that our reranking-based approach achieves substantial improvements (about 6.07%) over the previous state-of-the-art model. Further analyses show that each strategy of our approach contributes to the final performance.1
Towards Semantic Trajectory Outlier Detection.

Only a few works in trajectory data mining have focused on outlier detection, and to the best of our knowledge, no works so far have made a deeper analysis to either understand or to give a meaning to the outliers. In this paper we present an algorithm to add meaning to trajectory outliers considering three main possible reasons for a detour: stops outside the standard route, events, and traffic jams in the standard path. We show with experiments on real data that the method correctly finds the different types of outliers.
Severe Gliosis Over a Closed Macular Hole Following Anterior Lens Capsular Flap Closure

Various management strategies, including the use of autologous and allogenic materials, are described for the management of persistent macular holes. An anterior lens capsular flap can be used, especially when cataract surgery is also planned, for a persistent full-thickness macular hole. We report a case of a gentleman in his 60s who underwent anterior lens capsular flap closure for a persistent macular hole. There was an improvement in visual acuity. However, he developed severe gliosis over the closed hole in the postoperative period. This could be due to the proliferation of residual epithelial cells in the lens capsule, micro damage to the retina, or an exaggerated inflammatory response to a foreign tissue placed over the retinal surface.
Model-Free Control of Magnetic Microrobotic Swarm for On-Demand Pattern Spreading

Introducing collective control strategies to microrobots provides a promising tool for enhancing the capability of individual microrobots and promoting the manipulation performance. However, controlling numerous micro-agents brings challenges to the precise control and deployment. Herein, an on-demand collective spreading strategy for magnetic particle microrobots is proposed. Under the actuation of the modulated magnetic field, the particle swarm can spread outward from the gathered state and reach a stable state. During the process, an iterative learning control (ILC)-based method is implemented to realize spreading area control in a biological fluid environment (e.g., chicken blood). The ILC-based control reduces the control uncertainty and non-linearity by real-time connecting the input parameters to the spreading area. The proposed method is validated through experiments and analysis, demonstrating the effectiveness of the collective control method in adjusting the swarm state of the particle swarm. Results show that the specific spreading area of the microrobotic swarm can be controlled with the experimental error less than 1%, demonstrating an effective strategy for collectively controlling the microrobotic swarm in targeted delivery and manipulation tasks.
Révision systématique du genre Microthrissa Boulenger, 1902 des eaux douces africaines ( Pisces, Clupeidae )

Le genre Microthrissa a ete revise a partir de caractkres morphologiques et osteologiques de 461 exemplaires, y compris les specimens types de toutes les esptkes nominales. Le genre Poecilothrissa est synonyme du genre Mic.rothrissa, au sein duquel il a ete reconnu comme un sous-genre distinct. Cinq especes valides ont ete retenues. Deux especes nominales ont ete mises en synonymie. Enfin, Miwothrissa normanae a kte rapporte au genre Pellonula, et mis en synonymie avec P. leonensis.
Variación temporal de la diversidad de estafi línidos (Coleoptera: Staphylinidae) nocturnos en un bosque tropical caducifolio de México Temporal variation of the diversity of nocturnal staphylinids (Coleoptera: Staphylinidae) in a tropical dry forest in Mexico

A study of the temporal variation of the nocturnal staphylinids from Sierra de Huautla, Morelos, Mexico is presented. The fi eldwork was carried out between November 1995 and October 1996, with monthly light trap samplings used as collection method. A total of 14 886 individuals were collected, belonging to 91 species, 49 genera, 15 tribes and 9 subfamilies. The subfamilies with the greatest number of individuals and species were Paederinae, Aleocharinae and Oxytelinae. The distribution of individuals per species was heterogeneous, with few species very abundant and many with only a few individuals. There were seasonal differences in richness and abundance and the highest number of species and individuals were recorded during the rainy season. The abundance and richness were related with precipitation, but not with temperature.
Tardive dyskinesia in Asia— current clinical practice and the role of neurologists in the care pathway

Tardive dyskinesia (TD) is a movement disorder that can arise as a side effect of treatment with dopamine receptor-blocking agents (DRBAs), including antipsychotic drugs (APDs) used to manage psychotic illnesses. Second-generation APDs (SGAs) are often preferred to first-generation drugs due to their lower propensity to cause TD, however many SGAs-treated patients still develop the condition. Although TD is a global health concern, evidence regarding the occurrence of TD and how it is managed in Asian countries is currently limited. This article reports the results of a systematic review of the published literature on TD focusing on its prevalence, types of patients, knowledge of the condition, causative factors, and usual treatment pathways in clinical practice in Asian countries. Epidemiological data suggest that the prevalence of TD is increasing globally due to an overall rise in APD use, contributing factors being polypharmacy with multiple APDs, the use of higher than necessary doses, and off-label use for non-psychotic indications. Although exact prevalence figures for TD in Asian countries are difficult to define, there is a similar pattern of rising APD use which will result in increasing numbers of TD patients in this region. These issues need to be addressed and strategies developed to minimize TD risk and manage this disabling condition which impacts patients' quality of life and daily functioning. To date, both research into TD has been predominantly psychiatry focused and the perspectives from neurologists regarding the clinical management of this challenging condition are scarce. However, neurologists have an essential role in managing the movement disorders manifestations that characterize TD. Optimum management of TD, therefore, should ideally involve collaboration between psychiatrists and neurologists in joint care pathways, wherever practical. Collaborative pathways are proposed in this article, and the challenges that will need to be addressed in Asian countries to improve the care of people with TD are highlighted, with a focus on the neurologist's viewpoint and the implications for the management of TD globally.
Antioxidant Status in STZ-induced diabetic rats treated with Vanadium pentoxide nanoparticles

Antioxidant status determines the susceptibility of tissues to the oxidative stress associated with diabetes and its complications; hence in the present study antioxidant status was explored in streptozotocin-induced diabetic rats treated with vanadium in the form of vanadium pentoxide nanoparticles. Vanadium pentoxide and vanadium pentoxide nanoparticles at the dose rate of 5mg/kg were administered orally in STZ (50mg/Kg) induced diabetic rats for 30 days and glimepiride (reference drug) was administered orally at the dose rate of 800 ìg/kg body weight. Vanadium pentoxide nanoparticles significantly reduced the blood glucose levels than the diabetic control and other treatment group of rats. On exploration of antioxidant status in liver, kidney and pancreas tissues, vanadium in the form of vanadium pentoxide nanoparticles outperformed the vanadium pentoxide by increasing the activities of the enzymes catalase, superoxide dismutase and glutathione peroxidase, and the concentration of reduced glutathione and by decreasing the lipid peroxide levels. The present study also showed that the restoration of antioxidant status by vanadium pentoxide nanoparticles is comparable with that of the reference drug. It can be concluded that vanadium pentoxide nanoparticles, due to its superior control over hyperglycemia and antioxidant properties, outperformed the vanadium pentoxide treatment in enhancing the antioxidant status in diabetic rats.