Miracle-dz/UniFAR-FTUs huggingface dataset:
Tidally forced turbulence in planetary interiors

The turbulence generated in the liquid metal cores and subsurface oceans of planetary bodies may be due to the role of mechanical forcing through precession/nutation, libration, tidal forcing and collisions. Here, we model the response of an enclosed constant density fluid to tidal forcing by combining laboratory equatorial velocity measurements with selected high-resolution numerical simulations to show, for the first time, the generation of bulk filling turbulence. The transition to saturated turbulence is characterized by an elliptical instability that first excites primary inertial modes of the system, then secondary inertial modes forced by the primary inertial modes, and then bulk filling turbulence. The amplitude of this saturated turbulence scales with the body's elliptical distortion, U ∼ β, while a time- and radially averaged azimuthal zonal flow scales with β2. The results of the current tidal experiments are compared with recent studies of the libration-driven turbulent flows studied by Grannan et al. and Favier et al. Tides and libration correspond to two end-member types of geophysical mechanical forcings. For satellites dominated by tidal forcing, the ellipsoidal boundary enclosing the internal fluid layers is elastically deformed while, for librational forcing, the core-mantle boundary possesses an inherently rigid, frozen-in ellipsoidal shape. We find striking similarities between tidally and librationally driven flow transitions to bulk turbulence and zonal flows. This suggests a generic fluid response independent of the style of mechanical forcing. Since β ≲ 10−4 in planetary bodies, it is often argued that mechanically driven zonal velocities will be small. In contrast, our linear scaling for mechanically driven bulk turbulence, U ∼ β, suggests geophysically significant velocities that can play a significant role in planetary processes including tidal dissipation and magnetic field generation.
Antibiotic Stewardship in Dentistry: Evaluating the Impact of Outpatient Antibiotic Prophylaxis in a Tertiary Care Hospital Setting

Aim. To assess the antibiotic prophylaxis in the prescriptions of the patients attending the Dental Outpatient department in a Tertiary care hospital. Background. Antibiotics are most commonly used in outpatient setting when compared to inpatient setting in dental department. American Dental Association guidelines for antibiotic use for dental infections plays an important role in reducing prescribing for common conditions in dentistry. As there is no proper evidence regarding efficient use of antibiotic prophylaxis in dentistry, this study is aimed to address the prophylactic use of appropriate antibiotics among the outpatients in dental department. Methods. This Retrospective Observational study was conducted in the dental outpatient department in a tertiary care hospital for a month. The data regarding the Antibiotic prophylaxis along with underlying cardiac conditions were collected from records of the patients prescription and analysed using Simple statistics. Results. A total of 150 prescriptions (Male = 78 & Female = 72) are collected. Mean age was found to be 45.8 years (0 - 90). Most dental diagnosis are Scaling, Tooth extraction, Dental caries, Luting, Filling, Splint placing, Impacted tooth, Dental Abscess and Other Procedures. 88 prescriptions were found to have Antibiotic prophylaxis, where Amoxicillin is the most prescribed antibiotic. 2 days is the mostly prescribed duration of Antibiotic therapy. 13 Prescriptions had patients with underlying cardiac conditions who were prescribed with antibiotics prophylaxis. Conclusion. The antibiotic prescribing pattern in Dental outpatient department was assessed and it was found that 85% of patients were prescribed with antibiotics other than patients with underlying cardiac conditions. Keywords: Antibiotics, Antimicrobial stewardship, Prophylaxis, Prescribing patterns, Dental, Outpatients.
Tendencias cuánticas en empresas orientadas hacia el conocimiento: Análisis de caso de una experiencia costarricense. Quantic trends in knowledge-based companies: A case analysis of a Costa Rican experience

ResumenEl articulo desarrolla un debate sobre la aplicacion de los conceptos propios de los modelos tradicionales de la Ciencia Administrativa, a la sazon amparados bajo la llamada escuela “newtoniana”, vis a vis con aquellos mas contemporaneos y asertivos, cobijados bajo la optica de los novedosos enfoques administrativos de corte “cuantico” para el caso especifico de una empresa orientada hacia el conocimiento. La indagacion supone que una firma de esta naturaleza, y que es abordada como un analisis de caso en el entorno de negocios costarricense, deberia presentar una vision mas cercana a los enfoques mas recientes de la Administracion, precisamente porque, por su giro de negocios, los desafios ambientales y organizativos que enfrenta son muy importantes. Del analisis de la informacion que destila de sus directores de proyectos, hemos podido concluir que, pese a lo anterior, sus puntos de vista presentan tendencias que no son lo suficientemente contestes con los enfoques cuanticos, lo que representa un factor que le resta competitividad a la compania, asi como su capacidad de adaptacion y flexibilidad en el funcionamiento de sus operaciones. AbstractThis article presents a debate between the application of concepts related to the traditional model of administrative sciences, included within the so-called ‘newtonian’ school, and the application of more contemporary concepts, covered under the new ‘quantic’ administrative approaches, using the specific case of a knowledge-based enterprise. The inquiry supposes that a company of this nature, which is also taken as a case study in the Costa Rican business environment, should hold a closer position to the more recent administrative focuses, especially because its business orientation presents important administrative and environmental challenges. From the information collected from the project managers involved in this firm, we have concluded that their points of view show tendencies that differ from the quantic approaches, and this constitutes an element that diminishes the enterprise’s competitiveness, as well as its capacity to adapt, and the flexibility in the functioning of its operation.
Occurrence of aflatoxin B1, ochratoxin A and zearalenone in maize silage in the region of Vojvodina, Serbia

Abstract Silage made from the whole-plant maize is one of the most popular forages in Serbia. Consumption of maize silage by cows can be up to 30-35 kg/day. In Serbia in the few last years in the focus of the public and agriculture community were two mycotoxins, aflatoxin B1 and its metabolite aflatoxin M1 due to the outbreak of contaminated maize which affected the Balkans in 2012. Maize is regularly checked on the occurrence of aflatoxin B1, however forages are often neglected as a potential source of mycotoxins in the nutrition of dairy cattle. In this work, 48 samples of maize silage were analyzed for the occurrence of aflatoxin B1, ochratoxin A and zearalenone. Samples were collected from three regions (Bačka, Banat and Srem) in Vojvodina. In all samples, at least one mycotoxin above the limit of quantification was measured. Aflatoxin B1 was detected in 36 (75%) samples. In two samples from Banat, the concentration of aflatoxin B1 exceeded the maximum level (ML) set by Serbian regulation (30 µg/kg at moisture content of 12%). In seven samples, the concentration of aflatoxin B1 was above 20 µg/kg which is the EU regulated ML. Average concentration of ochratoxin A was 10.4 µg/kg, while the maximum measured value was 34.3 µg/kg. Maximum zearalenone content in all samples was 538 µg/kg while the average zearalenone concentration was 138 µg/kg. The results from this research point out that mycotoxin contaminated silage in the region of Vojvodina, Serbia can significantly contribute to daily intake of aflatoxin B1 in dairy cattle.
Speech Translation with Large Language Models: An Industrial Practice

Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM. By integrating the large language model (LLM) with a speech encoder and employing multi-task instruction tuning, LLM-ST can produce accurate timestamped transcriptions and translations, even from long audio inputs. Furthermore, our findings indicate that the implementation of Chain-of-Thought (CoT) prompting can yield advantages in the context of LLM-ST. Through rigorous experimentation on English and Chinese datasets, we showcase the exceptional performance of LLM-ST, establishing a new benchmark in the field of speech translation. Demo: https://speechtranslation.github. io/llm-st/.
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation

To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object's pose and shape. The status quo for in-hand perception primarily employs vision, and restricts to tracking a priori known objects. Moreover, visual occlusion of objects in-hand is imminent during manipulation, preventing current systems to push beyond tasks without occlusion. We combine vision and touch sensing on a multi-fingered hand to estimate an object's pose and shape during in-hand manipulation. Our method, NeuralFeels, encodes object geometry by learning a neural field online and jointly tracks it by optimizing a pose graph problem. We study multimodal in-hand perception in simulation and the real-world, interacting with different objects via a proprioception-driven policy. Our experiments show final reconstruction F-scores of $81$% and average pose drifts of $4.7\,\text{mm}$, further reduced to $2.3\,\text{mm}$ with known CAD models. Additionally, we observe that under heavy visual occlusion we can achieve up to $94$% improvements in tracking compared to vision-only methods. Our results demonstrate that touch, at the very least, refines and, at the very best, disambiguates visual estimates during in-hand manipulation. We release our evaluation dataset of 70 experiments, FeelSight, as a step towards benchmarking in this domain. Our neural representation driven by multimodal sensing can serve as a perception backbone towards advancing robot dexterity. Videos can be found on our project website https://suddhu.github. io/neural-feels/
Highly-efficient fiber to Si-waveguide free-form coupler for foundry-scale silicon photonics

As silicon photonics transitions from research to commercial deployment, packaging solutions that efficiently couple light into highly-compact and functional sub-micron silicon waveguides are imperative but remain challenging. The 220 nm silicon-on-insulator (SOI) platform, poised to enable large-scale integration, is the most widely adopted by foundries, resulting in established fabrication processes and extensive photonic component libraries. The development of a highly-efficient, scalable and broadband coupling scheme for this platform is therefore of paramount importance. Leveraging two-photon polymerization (TPP) and a deterministic free-form micro-optics design methodology based on the Fermat's principle, this work demonstrates an ultra-efficient and broadband 3-D coupler interface between standard SMF-28 single-mode fibers and silicon waveguides on the 220 nm SOI platform. The coupler achieves a low coupling loss of 0.8 dB for fundamental TE mode, along with 1-dB bandwidth exceeding 180 nm. The broadband operation enables diverse bandwidth-driven applications ranging from communications to spectroscopy. Furthermore, the 3-D free-form coupler also enables large tolerance to fiber misalignments and manufacturing variability, thereby relaxing packaging requirements towards cost reduction capitalizing on standard electronic packaging process flows.
Statistical learning theory and Occam's razor: The argument from empirical risk minimization

This paper considers the epistemic justification for a simplicity preference in inductive inference that may be obtained from the machine learning framework of statistical learning theory. Uniting elements from both earlier arguments suggesting and rejecting such a justification, the paper spells out a qualified means-ends and model-relative justificatory argument, built on statistical learning theory's central mathematical learning guarantee for the method of empirical risk minimization.
Open-Source Reinforcement Learning Environments Implemented in MuJoCo with Franka Manipulator

This paper presents three open-source reinforcement learning environments developed on the MuJoCo physics engine with the Franka Emika Panda arm in MuJoCo Menagerie. Three representative tasks, push, slide, and pick-and-place, are implemented through the Gymnasium Robotics API, which inherits from the core of Gymnasium. Both the sparse binary and dense rewards are supported, and the observation space contains the keys of desired and achieved goals to follow the Multi-Goal Reinforcement Learning framework. Three different off-policy algorithms are used to validate the simulation attributes to ensure the fidelity of all tasks, and benchmark results are also given. Each environment and task are defined in a clean way, and the main parameters for modifying the environment are preserved to reflect the main difference. The repository, including all environments, is available at https://github.com/zichunxx/panda_mujoco_gym.
SMALL SATELLITE ATTITUDE CONTROL AND SIMULATION

Different attitude control strategies of a small satellite are presented in this paper as well as their simulation with the MATLAB® software. Firstly, the linear mathematical model of the satellite is derived for the gravity gradient (GG) control method, which represents a passive control design. Simulation results show that the response of the satellite to initial conditions is marginally stable. The second phase of the study focuses on the design of a control algorithm used to damp the satellite oscillations around its equilibrium position with a simple hardware setting added to the satellite. The mathematical model of the new system is developed and simulation about the roll and yaw axis are realized. A consequent amelioration in the satellite response can be observed.
Weighted least-squares approximation with determinantal point processes and generalized volume sampling

We consider the problem of approximating a function from $L^2$ by an element of a given $m$-dimensional space $V_m$, associated with some feature map $\varphi$, using evaluations of the function at random points $x_1,\dots,x_n$. After recalling some results on optimal weighted least-squares using independent and identically distributed points, we consider weighted least-squares using projection determinantal point processes (DPP) or volume sampling. These distributions introduce dependence between the points that promotes diversity in the selected features $\varphi(x_i)$. We first provide a generalized version of volume-rescaled sampling yielding quasi-optimality results in expectation with a number of samples $n = O(m\log(m))$, that means that the expected $L^2$ error is bounded by a constant times the best approximation error in $L^2$. Also, further assuming that the function is in some normed vector space $H$ continuously embedded in $L^2$, we further prove that the approximation is almost surely bounded by the best approximation error measured in the $H$-norm. This includes the cases of functions from $L^\infty$ or reproducing kernel Hilbert spaces. Finally, we present an alternative strategy consisting in using independent repetitions of projection DPP (or volume sampling), yielding similar error bounds as with i.i. d. or volume sampling, but in practice with a much lower number of samples. Numerical experiments illustrate the performance of the different strategies.
On a Verdier quotient of a derived category of a local ring

Let $R$ be a commutative noetherian local ring with residue field $k$. Denote by $\mathsf{D^b}(R)$ the bounded derived category of finitely generated $R$-modules. In this paper, we study the structure of the Verdier quotient $\mathsf{D^b}(R)/\mathsf{thick}(R\oplus k)$. We give necessary and sufficient conditions for it to admit an additive generator.
Turbulence spreading by the resonant wave-wave interactions: A fractional kinetics approach

This paper is concerned with the processes of spatial propagation and penetration of turbulence from the regions where it is locally excited into initially laminar regions. The phenomenon has come to be known as"turbulence spreading"and witnessed a renewed attention in the literature recently. Here, we propose a comprehensive theory of turbulence spreading based on fractional kinetics. We argue that the use of fractional-derivative equations permits a general approach focussing on fundamentals of the spreading process regardless of a specific turbulence model and/or specific instability type. The starting point is the Hamiltonian of the resonant wave-wave interactions, from which a family of scaling laws for the asymptotic spreading is derived. Both three- and four-wave interactions are considered. The results span from a subdiffusive spreading in the parameter range of weak chaos to avalanche propagation in regimes with population inversion. Attention is paid to how non-ergodicity introduces weak mixing, memory and intermittency into spreading dynamics, and how the properties of non-Markovianity and nonlocality emerge from the presence of islands of regular dynamics in phase space. Also we resolve an existing question concerning turbulence spillover into gap regions, where the instability growth is locally suppressed, and show that the spillover occurs through exponential (Anderson like) localization in case of four-wave interactions and through an algebraic (weak) localization in case of triad interactions. In the latter case an inverse-cubic behavior of the spillover function is found. Wherever relevant, we contrast our findings against the available observational and numerical evidence, and we also commit ourselves to establish connections with the models of turbulence spreading proposed previously.
Self-diffusion coefficient as a function of the thermodynamic factor

Much effort has been put into developing theories for dense fluids, as a result of these efforts many theories work for a certain type of particle or in a certain concentration regime. Rosenfeld proposed a dependence of the self-diffusion coefficient on the excess entropy. Our proposal is similar to Rosenfeld's in that it also attempts to describe diffusion in terms of a thermodynamic function but, instead of the excess entropy, we use the thermodynamic factor, or the excess chemical potential. Simulations were taken for hard spheres and our model was fitted with two free parameters. Simulations were then carried out for a Lennard Jones gas and our model correctly described the new data with the value of the free parameters that we had obtained for hard spheres. This is a feature of our model that we wish to emphasize, since the usual situation is that parameters have to be re-adjusted for different interaction potentials. An experimental xenon self-diffusion data set was used as an example where the model can be applied, especially in the high-density regime.
Rethinking of Feature Interaction for Multi-task Learning on Dense Prediction

Existing works generally adopt the encoder-decoder structure for Multi-task Dense Prediction, where the encoder extracts the task-generic features, and multiple decoders generate task-specific features for predictions. We observe that low-level representations with rich details and high-level representations with abundant task information are not both involved in the multi-task interaction process. Additionally, low-quality and low-efficiency issues also exist in current multi-task learning architectures. In this work, we propose to learn a comprehensive intermediate feature globally from both task-generic and task-specific features, we reveal an important fact that this intermediate feature, namely the bridge feature, is a good solution to the above issues. Based on this, we propose a novel Bridge-Feature-Centirc Interaction (BRFI) method. A Bridge Feature Extractor (BFE) is designed for the generation of strong bridge features and Task Pattern Propagation (TPP) is applied to ensure high-quality task interaction participants. Then a Task-Feature Refiner (TFR) is developed to refine final task predictions with the well-learned knowledge from the bridge features. Extensive experiments are conducted on NYUD-v2 and PASCAL Context benchmarks, and the superior performance shows the proposed architecture is effective and powerful in promoting different dense prediction tasks simultaneously.
FPGA Implementation of Machine Learning Hardware Accelerator for Mobile Applications of Brain-Computer Interface

Mobile application of brain-computer interface (BCI) system is of great significance. This paper first analyzes machine learning algorithms commonly used in BCI, and extracts QR decomposition as the core step of algorithm integration to design hardware accelerators. Secondly, using Givens Rotation (GR) as implementation strategy of QR decomposition (QRD). Finally, High Level Synthesis (HLS) method is used to synthesize and implement IP cores of QRD in FPGA. QRD based IP cores are designed with floating point and fixed point for 8×8 and 62×62 matrix. Experiments show that resource consumption by fixed point implementation reduce by 40% compared to floating point implementation and speed of operations increase by more than one time. Finally, based on QRD based IP cores, matrix inversion IP cores are designed and implemented, which is at least 3 times faster than ARM Cortex-M3 platform.
SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection

Current 3D object detection methods for indoor scenes mainly follow the voting-and-grouping strategy to generate proposals. However, most methods utilize instance-agnostic groupings, such as ball query, leading to inconsistent semantic information and inaccurate regression of the proposals. To this end, we propose a novel superpoint grouping network for indoor anchor-free one-stage 3D object detection. Specifically, we first adopt an unsupervised manner to partition raw point clouds into superpoints, areas with semantic consistency and spatial similarity. Then, we design a geometry-aware voting module that adapts to the centerness in anchor-free detection by constraining the spatial relationship between superpoints and object centers. Next, we present a superpoint-based grouping module to explore the consistent representation within proposals. This module includes a superpoint attention layer to learn feature interaction between neighboring superpoints, and a superpoint-voxel fusion layer to propagate the superpoint-level information to the voxel level. Finally, we employ effective multiple matching to capitalize on the dynamic receptive fields of proposals based on superpoints during the training. Experimental results demonstrate our method achieves state-of-the-art performance on ScanNet V2, SUN RGB-D, and S3DIS datasets in the indoor one-stage 3D object detection. Source code is available at https://github.com/zyrant/SPGroup3D.