Miracle-dz/UniFAR-FTUs huggingface dataset:
SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds

As personal robots become increasingly accessible and affordable, their applications extend beyond large corporate warehouses and factories to operate in diverse, less controlled environments, where they interact with larger groups of people. In such contexts, ensuring not only safety and efficiency but also mitigating potential adverse psychological impacts on humans and adhering to unwritten social norms become paramount. In this research, we aim to address these challenges by developing a cutting-edge model capable of predicting pedestrian movements and interactions in crowded environments. To this end, we propose a novel approach called the Social Group Long Short-term Memory (SG-LSTM) model, which effectively captures the complexities of human group behavior and interactions within dense surroundings. By integrating social awareness into the LSTM architecture, our model achieves significantly enhanced trajectory predictions. The implementation of our SG-LSTM model empowers navigation algorithms to compute collision-free paths faster and with higher accuracy, particularly in complex and crowded scenarios. To foster further advancements in social navigation research, we contribute a substantial video dataset comprising labeled pedestrian groups, which we release to the broader research community. To thoroughly evaluate the performance of our approach, we conduct extensive experiments on multiple datasets, including ETH, Hotel, and MOT15. We compare various prediction approaches, such as LIN, LSTM, O-LSTM, and S-LSTM, and rigorously assess runtime performance.
Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets

There have been recent efforts to move to population-based structural health monitoring (PBSHM) systems. One area of PBSHM which has been recognised for potential development is the use of multi-task learning (MTL); algorithms which differ from traditional independent learning algorithms. Presented here is the use of the MTL, ''Joint Feature Selection with LASSO'', to provide automatic feature selection for a structural dataset. The classification task is to differentiate between the port and starboard side of a tailplane, for samples from two aircraft of the same model. The independent learner produced perfect F1 scores but had poor engineering insight; whereas the MTL results were interpretable, highlighting structural differences as opposed to differences in experimental set-up.
Continuous Function Structured in Multilayer Perceptron for Global Optimization

The gradient information of multilayer perceptron with a linear neuron is modified with functional derivative for the global minimum search benchmarking problems. From this approach, we show that the landscape of the gradient derived from given continuous function using functional derivative can be the MLP-like form with ax+b neurons. In this extent, the suggested algorithm improves the availability of the optimization process to deal all the parameters in the problem set simultaneously. The functionality of this method could be improved through intentionally designed convex function with Kullack-Liebler divergence applied to cost value as well.
Fast offset corrected in-memory training

In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner. To unleash the full potential of in-memory computing, it is desirable to accelerate the training as well as inference for large deep neural networks (DNNs). In the past, specialized in-memory training algorithms have been proposed that not only accelerate the forward and backward passes, but also establish tricks to update the weight in-memory and in parallel. However, the state-of-the-art algorithm (Tiki-Taka version 2 (TTv2)) still requires near perfect offset correction and suffers from potential biases that might occur due to programming and estimation inaccuracies, as well as longer-term instabilities of the device materials. Here we propose and describe two new and improved algorithms for in-memory computing (Chopped-TTv2 (c-TTv2) and Analog Gradient Accumulation with Dynamic reference (AGAD)), that retain the same runtime complexity but correct for any remaining offsets using choppers. These algorithms greatly relax the device requirements and thus expanding the scope of possible materials potentially employed for such fast in-memory DNN training.
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference

Large Language Models (LLMs) have sparked significant interest in their generative capabilities, leading to the development of various commercial applications. The high cost of using the models drives application builders to maximize the value of generation under a limited inference budget. This paper presents a study of optimizing inference hyperparameters such as the number of responses, temperature and max tokens, which significantly affects the utility/cost of text generation. We design a framework named EcoOptiGen which leverages economical hyperparameter optimization and cost-based pruning. Experiments with the GPT-3.5/GPT-4 models on a variety of tasks verify its effectiveness. EcoOptiGen is implemented in the `autogen' package of the FLAML library: \url{https://aka.ms/autogen}.
Scouting for dark showers at CMS and LHCb

We assess the capabilities of the CMS and LHCb searches for low-$p_T$ displaced dimuon pairs to discover hidden valley models, using a newly-developed benchmark model that realizes a range of dimuon vertex topologies. We show that the data scouting techniques used in these searches provide unique sensitivity and we make some additional suggestions to further extend the scope of future experimental searches.
DNBP: Differentiable Nonparametric Belief Propagation

We present a differentiable approach to learn the probabilistic factors used for inference by a nonparametric belief propagation algorithm. Existing nonparametric belief propagation methods rely on domain-specific features encoded in the probabilistic factors of a graphical model. In this work, we replace each crafted factor with a differentiable neural network, enabling the factors to be learned using an efficient optimization routine from labeled data. By combining differentiable neural networks with an efficient belief propagation algorithm, our method learns to maintain a set of marginal posterior samples using end-to-end training. We evaluate our differentiable nonparametric belief propagation (DNBP) method on a set of articulated pose tracking tasks and compare performance with learned baselines. Results from these experiments demonstrate the effectiveness of using learned factors for tracking and suggest the practical advantage over hand-crafted approaches. The project webpage is available at: https://progress.eecs. umich.edu/projects/dnbp/ .
Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture

Dextrous in-hand manipulation with a multi-fingered robotic hand is a challenging task, esp. when performed with the hand oriented upside down, demanding permanent force-closure, and when no external sensors are used. For the task of reorienting an object to a given goal orientation (vs. infinitely spinning it around an axis), the lack of external sensors is an additional fundamental challenge as the state of the object has to be estimated all the time, e.g., to detect when the goal is reached. In this paper, we show that the task of reorienting a cube to any of the 24 possible goal orientations in a π/2-raster using the torque-controlled DLR-Hand II is possible. The task is learned in simulation using a modular deep reinforcement learning architecture: the actual policy has only a small observation time window of 0.5 s but gets the cube state as an explicit input which is estimated via a deep differentiable particle filter trained on data generated by running the policy. In simulation, we reach a success rate of 92% while applying significant domain randomization. Via zero-shot Sim2Real-transfer on the real robotic system, all 24 goal orientations can be reached with a high success rate. (Web: dlr-alr.github.io/dlr-tactile-manipulation)
Application of supervised learning models in the Chinese futures market

Based on the characteristics of the Chinese futures market, this paper builds a supervised learning model to predict the trend of futures prices and then designs a trading strategy based on the prediction results. The Precision, Recall and F1-score of the classification problem show that our model can meet the accuracy requirements for the classification of futures price movements in terms of test data. The backtest results show that our trading system has an upward trending return curve with low capital retracement.
Pulsars and physics

Twenty-five years of active pulsar research have yielded a quite unexpected harvest of physical effects. These range from pleasing corroborations of well-established physical principles in unfamiliar environments to direct verification of the general theory of relativity. Pulsars have the potential for advancing our understanding of high energy nuclear physics and relativistic plasma physics. They have also enlarged our horizons in atomic physics, quantum electrodynamics and solid state physics. As extremely compact sources of regularly pulsed, high brightness radio waves, they provide excellent probes of the interstellar medium and globular clusters. The large variety of connections between pulsars and physics is illustrated with examples taken from each of these areas.
An experimental study on the influence of scatter and beam hardening in x-ray CT for dimensional metrology

Scattered radiation and beam hardening introduce artefacts that degrade the quality of data in x-ray computed tomography (CT). It is unclear how these artefacts influence dimensional measurements evaluated from CT data. Understanding and quantifying the influence of these artefacts on dimensional measurements is required to evaluate the uncertainty of CT-based dimensional measurements. In this work the influence of scatter and beam hardening on dimensional measurements is investigated using the beam stop array scatter correction method and spectrum pre-filtration for the measurement of an object with internal and external cylindrical dimensional features. Scatter and beam hardening are found to influence dimensional measurements when evaluated using the ISO50 surface determination method. On the other hand, a gradient-based surface determination method is found to be robust to the influence of artefacts and leads to more accurate dimensional measurements than those evaluated using the ISO50 method. In addition to these observations the GUM method for evaluating standard measurement uncertainties is applied and the standard measurement uncertainty due to scatter and beam hardening is estimated.
The DRBEM solution of incompressible MHD flow equations

This paper presents a dual reciprocity boundary element method (DRBEM) formulation coupled with an implicit backward difference time integration scheme for the solution of the incompressible magnetohydrodynamic (MHD) flow equations. The governing equations are the coupled system of Navier‐Stokes equations and Maxwell's equations of electromagnetics through Ohm's law. We are concerned with a stream function‐vorticity‐magnetic induction‐current density formulation of the full MHD equations in 2D. The stream function and magnetic induction equations which are poisson‐type, are solved by using DRBEM with the fundamental solution of Laplace equation. In the DRBEM solution of the time‐dependent vorticity and current density equations all the terms apart from the Laplace term are treated as nonhomogeneities. The time derivatives are approximated by an implicit backward difference whereas the convective terms are approximated by radial basis functions. The applications are given for the MHD flow, in a square cavity and in a backward‐facing step. The numerical results for the square cavity problem in the presence of a magnetic field are visualized for several values of Reynolds, Hartmann and magnetic Reynolds numbers. The effect of each parameter is analyzed with the graphs presented in terms of stream function, vorticity, current density and magnetic induction contours. Then, we provide the solution of the step flow problem in terms of velocity field, vorticity, current density and magnetic field for increasing values of Hartmann number. Copyright © 2010 John Wiley & Sons, Ltd.
Performance of VATA64HDR16 ASIC for medical physics applications based on continuous crystals and SiPMs

Detectors based on Silicon Photomultipliers (SiPMs) coupled to continuous crystals are being tested in medical physics applications due to their potential high resolution and sensitivity. To cope with the high granularity required for a very good spatial resolution, SiPM matrices with a large amount of elements are needed. To be able to read the information coming from each individual channel, dedicated ASICs are employed. The VATA64HDR16 ASIC is a 64-channel, charge-sensitive amplifier that converts the collected charge into a proportional current or voltage signal. A complete assessment of the suitability of that ASIC for medical physics applications based on continuous crystals and SiPMs has been carried out. The input charge range is linear from 0−2 pC up to 55 pC. The energy resolution obtained at 511 keV is 10% FWHM with a LaBr3 crystal and 16% FWHM with a LYSO crystal. A coincidence timing resolution of 24 ns FWHM is obtained with two LYSO crystals.
Dimensions of Observability*

The concept of observability of entities in physical science is typically analysed in terms of the nature and significance of a dichotomy between observables and unobservables. In the present work, however, this categorization is resisted and observability is analysed in a descriptive way in terms of the information which one can receive through interaction with objects in the world. The account of interaction and the transfer of information is done using applicable scientific theories. In this way, the question of observability of scientific entities is put to science itself. The result is a demonstration that observability has many dimensions, some more epistemically significant than others.
Unsteady flow over a submerged source with low Froude number

In the low-Froude number limit, free-surface gravity waves caused by flow past a submerged obstacle have amplitude that is exponentially small. Consequently, these cannot be represented using an asymptotic series expansion. Previous studies have considered linearized steady flow past a submerged source in infinite-depth fluids, in which exponential asymptotics were used to determine the behaviour of downstream longitudinal and transverse free-surface gravity waves. Here, unsteady flow past a submerged source in an infinite-depth fluid is investigated, with the free surface taken to be initially waveless. The source is taken to be weak, and the flow is linearized about the undisturbed solution. Exponential asymptotics are applied to determine the wave behaviour on the free surface in terms of the two-dimensional plan-view, in order to show how the free surface waves evolve over time and eventually tend to the steady solution.
Distinguishing Between Binary-Source and Planetary Microlensing Perturbations

A planetary microlensing event is characterized by a short-lived perturbation to the standard microlensing curve. Planetary perturbations typically last from a few hours to a day, have maximum amplitudes, δmax, of ±5%-20% of the standard curve, and come in two classes: major and minor image perturbations. There exist a subset of binary-source events that can reproduce the main features of major image perturbations, which are likely to represent more than half of all planetary events, and thus masquerade as planetary events. These events require a binary source with a small flux ratio, ϵ ~ 10-2 to 10-4, and a small impact parameter for the fainter source, β2 ≲ ϵ/δmax. The detection probability of events of this type is ~β2, and can be as high as ~30%; this is comparable to planetary detection rates. Thus a sample of planetary-like perturbations could be seriously contaminated by binary-source events, and there exists the possibility that completely meaningless physical parameters would be derived for any given major image perturbation. Here I derive analytic expressions for a binary-source event in the extreme flux-ratio limit and use these to demonstrate the basic degeneracy between binary-source and planet perturbations. I describe how the degeneracy can be broken by dense and accurate sampling of the perturbation, optical/infrared photometry, or spectroscopic measurements.
A 5.57 hour modulation of the X-ray flux from 4U 1822-37

The X-ray flux of 4U 1822-37 is shown to be modulated with the 5.57 hr period of its optical counterpart. The X-ray light curve is two component, with a smooth sinusoidal-like 25% semiamplitude modulation and a 30 minute dip of approximately 0.2 in phase following the other minimum; the dip center occurs 0.04 in phase after the optical minimum. The X-ray spectrum is a relatively flat power law up to 17 keV, above which it steepens; iron emission is detected at 6.7 keV with a 4 keV FWHM and an equivalent width of 1100 eV. An excess below 2 keV is consistent with either a 0.25 keV thermal component of 350 eV equivalent width iron L emission. Modeling the eclipse for a 0.5-0.7 solar radius companion gives a system inclination of 70-79 deg and a spherical cloud radius of 0.2-0.3 solar radius. Models for the long-term modulation are considered, and a comparison of the properties of 4U 1822-37 with those of Cyg X-3 shows that they are similar systems.
Schmerzen nach Verwendung eines Chitosan-Neurotubes bei Medianusverletzung

Falldarstellung Die Erstvorstellung der 29-jährigen Patientin ohne relevante internistische Vorerkrankungen erfolgte dreieinhalb Jahre nach einem Stolpersturz in eine Glasscheibe. Hierbei zog sie sich eine Schnittverletzung beugeseits distal am rechten Unterarm zu. Bei der Erstversorgung wurde lediglich die durchtrennte Flexor-carpi-radialis-Sehne adressiert. Die vollständige Durchtrennung des N. medianus, der langen Daumenbeugesehne und der oberflächlichen Beugesehne des Mittelfingers (FDS III) wurden erst nach zehn Wochen erkannt, als die Patientin nach der Ruhigstellung bei der ergotherapeutischen Beübung eine persistierende Asensibilität im Medianusgebiet einhergehend mit Neuromschmerzen sowie eine Bewegungseinschränkung des Daumens beklagte.
Toxicity of Propylene Glycol Extract of Propolis on Central Nervous System and Liver in Pregnant and Neonatal Rats

Abstract Background Propolis has become one of the most preferred supplements due to its beneficial biological properties. Organic (water and vegetable oils) and chemical (ethyl alcohol, propylene glycol, and glycerol) solvents are used for propolis extraction. However, the effects of these chemicals on health should be taken into account. Objectives In this study, the effects of propolis extracts on health were evaluated. Methods 32 pregnant Wistar albino rats and 64 neonatal/young adults were given three different extractions of propolis (propylene glycol, water, and olive oil). Histopathological analyses were performed on the liver and brain, and blood samples were taken from the hearts of rats. Results Histopathological scoring showed that the intensity of pycnotic hepatocyte, sinusoidal dilatation, and bleeding was high in liver samples of pregnant and baby rats given propylene glycol extract of propolis (p<0.05). Propylene glycol extract caused dilatation of blood vessels and apoptosis of neurons in brain tissue. The histopathological score was significantly lower in liver and brain tissues of rats treated with water and olive oil extract compared to propylene propolis groups (p<0.05). Liver enzyme levels in the blood increased in propylene propolis rats (p<0.05). Conclusion Histopathological changes and biochemical alterations may indicate that propylene glycol extracts of propolis are more toxic than olive oil and water extracts. Therefore, olive oil and water extracts of propolis are more reliable than propylene glycol extract in pregnant and infant rats.
Sur la symétrie dans les phénomènes physiques, symétrie d'un champ électrique et d'un champ magnétique

Je pense qu’il y aurait interet a introduire dans l’etude des phenomenes physiques les considerations sur la symetrie familiere aux cristallographes. Un corps isotrope, par exemple, peut etre anime d’un mouvement rectiligne ou de rotation ; liquide, il peut etre le siege de mouvements tourbillonnaires ; solide, il peut etre comprime ou tordu ; il peut se trouver dans un champ electrique ou magnetique ; il peut etre traverse par un courant electrique ou calorifique ; il peut etre parcouru par un rayon de lumiere naturelle ou polarisee rectilignement, circulairement, elliptiquement, etc. Dans chaque cas, une certaine dissymetrie caracteristique est necessaire en chaque point du corps. Les dissymetries seront encore plus complexes, si l’on suppose que plusieurs de ces phenomenes coexistent dans un meme milieu ou si ces phenomenes se produisent dans un milieu cristallise qui possede deja, de par sa constitution, une certaine dissymetrie. Les physiciens utilisent souvent les conditions donnees par la symetrie, mais negligent generalement de definir la symetrie dans un phenomene, parce que, assez souvent, les conditions de symetrie sont simples et presque evidentes a priori.
Application of the CZ-Globio Model in Beskydy Protected Landscape Area

Abstract Spatially oriented simulation models have not yet been applied to the territory of Beskydy Protected Landscape Area (PLA) to assess the state of biodiversity at a local scale. The CZ-GLOBIO model, which is adapted to the conditions of the Czech Republic, was used as a tool to assess habitat degradation using four selected drivers. The aim of the article is to apply the CZ-GLOBIO model for biodiversity status assessment in Beskydy PLA at the biotope level using detailed habitat data. The result of the application of the model is the evaluation of the state of biodiversity and the risk of its degradation using the Mean Species Abundance (MSA) index. Values are obtained for each segment as well as the average value for the entire territory. The results of biodiversity modelling are available by five maps and five tables with output Mean Species Abundance (MSA) values. Understanding the spatial distribution of the resulting MSA values contributes to the landscape-level habitat assessment of Beskydy PLA. This can serve as a basis for further policy decisions in the environmental field.
Reactive Planning for Coordinated Handover of an Autonomous Aerial Manipulator

In this paper, we present a coordinated and reactive human-aware motion planner for performing a handover task by an autonomous aerial manipulator (AAM). We present a method to determine the final state of the AAM for a handover task based on the current state of the human and the surrounding obstacles. We consider the visual field of the human and the effort to turn the head and see the AAM as well as the discomfort caused to the human. We apply these social constraints together with the kinematic constraints of the AAM to determine its coordinated motion along the trajectory.