Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting

Crowd counting is a fundamental yet challenging task, which desires rich information to generate pixel-wise crowd density maps. However, most previous methods only used the limited information of RGB images and cannot well discover potential pedestrians in unconstrained scenarios. In this work, we find that incorporating optical and thermal information can greatly help to recognize pedestrians. To promote future researches in this field, we introduce a large-scale RGBT Crowd Counting (RGBT-CC) benchmark, which contains 2,030 pairs of RGB-thermal images with 138,389 annotated people. Furthermore, to facilitate the multimodal crowd counting, we propose a cross-modal collaborative representation learning framework, which consists of multiple modality-specific branches, a modality-shared branch, and an Information Aggregation-Distribution Module (IADM) to capture the complementary information of different modalities fully. Specifically, our IADM incorporates two collaborative information transfers to dynamically enhance the modality-shared and modality-specific representations with a dual information propagation mechanism. Extensive experiments conducted on the RGBT-CC benchmark demonstrate the effectiveness of our framework for RGBT crowd counting. Moreover, the proposed approach is universal for multimodal crowd counting and is also capable to achieve superior performance on the ShanghaiTechRGBD [22] dataset. Finally, our source code and benchmark have been released at http://lingboliu.com/RGBT_Crowd_Counting. html.
Electric dipole response of low-lying excitations in the two-neutron halo nucleus 
F29

The neutron-rich $^{28,29}$F isotopes have been recently studied via knockout and interaction cross-section measurements. The $2n$ halo in $^{29}$F has been linked to the occupancy of $pf$ intruder configurations. We investigate bound and continuum states in $^{29}$F, focusing on the $E1$ response of low-lying excitations and the effect of dipole couplings on nuclear reactions. $^{29}\text{F}$ ($^{27}\text{F}+n+n$) wave functions are built within the hyperspherical harmonics formalism, and reaction cross sections are calculated using the Glauber theory. Continuum states and $B(E1)$ transition probabilities are described in a pseudostate approach using the analytical THO basis. The corresponding structure form factors are used in CDCC calculations to describe low-energy scattering. Parity inversion in $^{28}$F leads to a $^{29}$F ground state characterized by 57.5% of $(p_{3/2})^2$ intruder components, a strong dineutron configuration, and an increase of the matter radius with respect to the core radius of $\Delta R=0.20$ fm. Glauber-model calculations for a carbon target at 240 MeV/nucleon provide a total reaction cross section of 1370 mb, in agreement with recent data. The model produces also a barely bound excited state corresponding to a quadrupole excitation. $B(E1)$ calculations into the continuum yield a total strength of 1.59 e$^2$fm$^2$ up to 6 MeV, and the $E1$ distribution exhibits a resonance at $\approx$ 0.85 MeV. Results using a standard shell-model order for $^{28}$F lead to a considerable reduction of the $B(E1)$ distribution. The four-body CDCC calculations for $^{29}\text{F}+^{120}\text{Sn}$ around the Coulomb barrier are dominated by dipole couplings, which totally cancel the Fresnel peak in the elastic cross section. These results are consistent with a two-neutron halo and may guide future experimental campaigns.
A β Cephei pulsator and a changing orbital inclination in the high-mass eclipsing binary system VV Orionis

We present an analysis of the high-mass eclipsing binary system VV Ori based on photometry from the TESS satellite. The primary star (B1 V, 9.5 $\, {\rm M}_\odot$) shows β Cephei pulsations and the secondary (B7 V, 3.8 $\, {\rm M}_\odot$) is possibly a slowly pulsating B star. We detect 51 significant oscillation frequencies, including two multiplets with separations equal to the orbital frequency, indicating that the pulsations are tidally perturbed. We analyse the TESS light curve and published radial velocities to determine the physical properties of the system. Both stars are only the second of their pulsation type with a precisely measured mass. The orbital inclination is also currently decreasing, likely due to gravitational interactions with a third body.
Battery Model Calibration with Deep Reinforcement Learning

Lithium-Ion (Li-I) batteries have recently become pervasive and are used in many physical assets. To enable a good prediction of the end of discharge of batteries, detailed electrochemical Li-I battery models have been developed. Their parameters are typically calibrated before they are taken into operation and are typically not re-calibrated during operation. However, since battery performance is affected by aging, the reality gap between the computational battery models and the real physical systems leads to inaccurate predictions. A supervised machine learning algorithm would require an extensive representative training dataset mapping the observation to the ground truth calibration parameters. This may be infeasible for many practical applications. In this paper, we implement a Reinforcement Learning-based framework for reliably and efficiently inferring calibration parameters of battery models. The framework enables real-time inference of the computational model parameters in order to compensate the reality-gap from the observations. Most importantly, the proposed methodology does not need any labeled data samples, (samples of observations and the ground truth calibration parameters). Furthermore, the framework does not require any information on the underlying physical model. The experimental results demonstrate that the proposed methodology is capable of inferring the model parameters with high accuracy and high robustness. While the achieved results are comparable to those obtained with supervised machine learning, they do not rely on the ground truth information during training.
MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics

In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions.
Efficient Numerical Algorithms for the Generalized Langevin Equation

We study the design and implementation of numerical methods to solve the generalized Langevin equation (GLE) focusing on canonical sampling properties of numerical integrators. For this purpose, we cast the GLE in an extended phase space formulation and derive a family of splitting methods which generalize existing Langevin dynamics integration methods. We show exponential convergence in law and the validity of a central limit theorem for the Markov chains obtained via these integration methods, and we show that the dynamics of a suggested integration scheme is consistent with asymptotic limits of the exact dynamics and can reproduce (in the short memory limit) a superconvergence property for the analogous splitting of underdamped Langevin dynamics. We then apply our proposed integration method to several model systems, including a Bayesian inference problem. We demonstrate in numerical experiments that our method outperforms other proposed GLE integration schemes in terms of the accuracy of sampling. Moreover, using a parameterization of the memory kernel in the GLE as proposed by Ceriotti et al [9], our experiments indicate that the obtained GLE-based sampling scheme outperforms state-of-the-art sampling schemes based on underdamped Langevin dynamics in terms of robustness and efficiency.
Extended HNCO, SiO, and HC3N Emission in 43 Southern Star-forming Regions

We have selected 43 southern massive star-forming regions to study the spatial distribution of HNCO 404–303, SiO 2–1, and HC3N 10–9 line emission and to investigate their spatial association with the dust emission. The morphology of HNCO 404–303 and HC3N 10–9 agrees well with the dust emission. HC3N 10–9 tends to originate from more compact regions than HNCO 404–303 and SiO 2–1. We divided our sources into three groups: those in the Central Molecular Zone (CMZ), those associated with bubbles (Bubble), and the remaining sources, which are termed “normal star-forming regions” (NMSFR). These three groups, subdivided into three different categories with respect to line widths, integrated intensities, and column densities, hint at the presence of different physical and chemical processes. We find that the dust temperature T d, and the abundance ratios N HNCO/N SiO and N HNCO/N HC3N show a decreasing trend toward the central dense regions of CMZ sources, while N HC3N/N SiO moves in the opposite direction. Moreover, a better agreement is found between T d and N HC3N/N SiO in Bubble and NMSFR category sources. Both outflow and inflow activities have been found in eight of the 16 bubble and NMSFR sources. The low outflow detection rate indicates either that in these sources the SiO 2–1 line wing emission is below our sensitivity limit or that the bulk of the SiO emission may be produced by the expansion of an H ii region or supernova remnant, which has pushed molecular gas away, forming a shock and yielding SiO.
Finding structure in sequences of real numbers via graph theory: a problem list

We investigate a method of generating a graph $G=(V,E)$ out of an ordered list of $n$ distinct real numbers $a_1, \dots, a_n$. These graphs can be used to test for the presence of interesting structure in the sequence. We describe sequences exhibiting intricate hidden structure that was discovered this way. Our list includes sequences of Deutsch, Erdős, Freud & Hegyvari, Recaman, Quet, Zabolotskiy and Zizka. Since our observations are mostly empirical, each sequence in the list is an open problem.
On-Road Motion Planning for Automated Vehicles at Ulm University

The Institute of Measurement, Control, and Microtechnology at Ulm University has investigated advanced driver assistance systems for decades and concentrated, in large part, on autonomous driving. It is well known that motion planning is a key technology for autonomous driving. It is, first and foremost, responsible for the safety of vehicle passengers as well as all surrounding traffic participants. However, a further task consists of providing smooth and comfortable driving behavior. In Ulm, we are grateful to have the opportunity to test our algorithms under real conditions in public traffic and diversified scenarios. In this article, we give readers an insight into our work, including the vehicle and test track, as well as related problems, challenges, and solutions. We describe the motion planning system and explain the implemented functionalities. Furthermore, we show how our vehicle moves through public road traffic and deals with challenging scenarios, e.g., driving through roundabouts and intersections.
Performance Analysis of Keypoint Detectors and Binary Descriptors Under Varying Degrees of Photometric and Geometric Transformations

Detecting image correspondences by feature matching forms the basis of numerous computer vision applications. Several detectors and descriptors have been presented in the past, addressing the efficient generation of features from interest points (keypoints) in an image. In this paper, we investigate eight binary descriptors (AKAZE, BoostDesc, BRIEF, BRISK, FREAK, LATCH, LUCID, and ORB) and eight interest point detector (AGAST, AKAZE, BRISK, FAST, HarrisLapalce, KAZE, ORB, and StarDetector). We have decoupled the detection and description phase to analyze the interest point detectors and then evaluate the performance of the pairwise combination of different detectors and descriptors. We conducted experiments on a standard dataset and analyzed the comparative performance of each method under different image transformations. We observed that: (1) the FAST, AGAST, ORB detectors were faster and detected more keypoints, (2) the AKAZE and KAZE detectors performed better under photometric changes while ORB was more robust against geometric changes, (3) in general, descriptors performed better when paired with the KAZE and AKAZE detectors, (4) the BRIEF, LUCID, ORB descriptors were relatively faster, and (5) none of the descriptors did particularly well under geometric transformations, only BRISK, FREAK, and AKAZE showed reasonable resiliency.
The resistivity and thermopower of amorphous Mg-Zn alloys

The resistivity and thermopower of amorphous Mg-Zn alloys have been computed as a function of temperature and composition. The diffraction model incorporating the dynamical partial structure factors is applied. The effect of the electron mean free path is investigated. The authors find that the resistivity is well described by the model, and that the inclusion of the mean free path does not change the results considerably. In the case of thermopower the diffraction model turns out to be inadequate: it gives a composition dependence which is against the experimental evidence. This suggests that there exists another scattering mechanism, which is not accounted for by the diffraction model. The choice of the various parameters of the model is discussed.
Novel one-pot green synthesis of graphene in aqueous medium under microwave irradiation using a regenerative catalyst and the study of its electrochemical properties

In this work we report an economic, eco-friendly, high yielding and facile one-pot method for the large scale synthesis of few layer graphene (FLG) nanosheets directly from graphite in aqueous medium using a regenerative catalyst, sodium tungstate. This method is fast and makes use of environmental friendly chemicals and microwave radiation. The as-synthesized FLG nanosheets are characterized by field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) surface area analysis. Raman analysis indicates that the as-synthesized graphene is bilayered with a smaller domain size of 3.9 nm which is responsible for a higher specific surface area of FLG nanosheets (1103.62 m2 g−1). Moreover, XPS analysis of FLG nanosheets shows a high C : O ratio (∼9.6) which is the best among the graphene prepared from green chemicals. The electrochemical performance of as-synthesized FLG nanosheets is analysed by cyclic voltammetry (CV), chronopotentiometry and electrochemical impedance spectroscopy (EIS) in neat 1-ethyl-3-methylimidazolium tetrafluoroborate (EMIMBF4) electrolyte. The superior capacitive performance with large capacitance (219 F g−1), high energy density (83.56 W h kg−1) and excellent cyclability (3000 cycles) exhibited by these graphene nanosheets make them an excellent candidate for supercapacitor material.
Investigation of PVC plastisol tissue-mimicking phantoms for MR-and ultrasound-elastography.

Objective: Realistic tissue-mimicking phantoms are essential for the development, the investigation and the calibration of medical imaging techniques and protocols. Because it requires taking both mechanical and imaging properties into account, the development of robust, calibrated phantoms is a major challenge in elastography. Soft polyvinyl chloride gels in a liquid plasticizer (plastisol or PVCP) have been proposed as soft tissue-mimicking phantoms (TMP) for elasticity imaging. PVCP phantoms are relatively low-cost and can be easily stored over long time periods without any specific requirements. In this work, the preparation of a PVCP gel phantom for both MR and ultrasoundelastography is proposed and its acoustic, NMR and mechanical properties are studied.Material and methods: The acoustic and magnetic resonance imaging properties of PVCP are measured for different mass ratios between ultrasound speckle particles and PVCP solution, and between resin and plasticizer. The linear mechanical properties of plastisol samples are then investigated over time using not only indentation tests, but also MR and ultrasound-elastography clinical protocols. These properties are compared to typical values reported for biological soft tissues and to the values found in the literature for PVCP gels.Results and conclusions: After a period of two weeks, the mechanical properties of the plastisol samples measured with indentation testing are stable for at least the following 4 weeks (end of follow-up period 43 days after gelation-fusion). Neither the mechanical nor the NMR properties of plastisol gels were found to be affected by the addition of cellulose as acoustic speckle. Mechanical properties of the proposed gels were successfully characterized by clinical, commercially-available MR Elastography and sonoelastography protocols. PVCP with a mass ratio of ultrasound speckle particles of 0.6% to 0.8% and a mass ratio between resin and plasticizer between 50 and 70% appears as a good TMP candidate that can be used with both MR and ultrasound-based elastography methods.
Effect of Enzymatic Hydrolysis on Polylactic Acid Fabrics by Lipases from Different Origins

Abstract This study measured the effect of general pre-treatment on PLA fabrics to confirm the benefits of enzymaticprocessing on PLA fabrics in the textile industry as well as evaluated the hydrolytic activities of three lipases.The effects of lipase hydrolysis were analyzed through moisture regain, dyeing ability, tensile strength, andsurface morphology. As a result, PLA fibers were easily damaged by a low concentration of sodium hydroxideand a low treatment temperature. The optimal treatment conditions of Lipase from Candida cylindracea werepH 8.0, 40 o C, and 1,000 U. The optimal treatment conditions for Lipase from Candida rugosa were pH 7.2,37 o C, and 1,000 U. The optimal treatment conditions for Lipase from Porcine pancreas were pH 8.0, 37 o C,and 2,000 U. The moisture regain and dyeing ability of PLA fabrics increased and the tensile strength of PLAfabrics decreased. The results of surface morphology revealed that there were some cracks due to hydrolysison the surface of the fiber.Key words
Experimental 3-dimensional tracking of the dynamics of a single electron in the Fermilab Integrable Optics Test Accelerator (IOTA)

We present the results of experimental studies on the transverse and longitudinal dynamics of a single electron in the IOTA storage ring. IOTA is a flexible machine dedicated to beam physics experiments with electrons and protons. A method was developed to reliably inject and circulate a controlled number of electrons in the ring. A key beam diagnostic system is the set of sensitive high-resolution digital cameras for the detection of synchrotron light emitted by the electrons. With 60–130 electrons in the machine, we measured beam lifetime and derived an absolute calibration of the optical system. At exposure times of 0.5 s, the cameras were sensitive to individual electrons. Camera images were used to reconstruct the time evolution of oscillation amplitudes of a single electron in all 3 degrees of freedom. The evolution of amplitudes directly showed the interplay between synchrotron-radiation damping, quantum excitations, and scattering with the residual gas. From the distribution of measured single-electron oscillation amplitudes, we deduced transverse emittances, momentum spread, damping times, and beam energy. Estimates of residual-gas density and composition were calculated from the measured distributions of vertical scattering angles. Combining scattering and lifetime data, we also provide an estimate of the aperture of the ring. To our knowledge, this is the first time that the dynamics of a single electron are tracked in all three dimensions with digital cameras in a storage ring.
Theoretical investigations of the structures and binding energies of Ben and Mgn(n=3–5) clusters

We have determined the equilibrium geometries and binding energies of Be and Mg trimers, tetramers and pentamers using single and double excitation coupled cluster (CCSD) and complete active space self‐consistent‐field (CASSCF) multireference configuration interaction (MRCI) wave functions in conjunction with extended atomic basis sets. Our best estimates of the cluster binding energies are 24, 83, and 110 kcal/mol for Be3, Be4, and Be5; and 9, 31, and 41 kcal/mol for Mg3, Mg4, Mg5, respectively. A comparison of the MRCI and CCSD results shows that even the best single‐reference approach (limited to single and double excitations) is not capable of quantitative accuracy in determining the binding energies of Be and Mg clusters.
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments

Robot navigation is a task where reinforcement learning approaches are still unable to compete with traditional path planning. State-of-the-art methods differ in small ways, and do not all provide reproducible, openly available implementations. This makes comparing methods a challenge. Recent research has shown that unsupervised learning methods can scale impressively, and be leveraged to solve difficult problems. In this work, we design ways in which unsupervised learning can be used to assist reinforcement learning for robot navigation. We train two end-to-end, and 18 unsupervised-learning-based architectures, and compare them, along with existing approaches, in unseen test cases. We demonstrate our approach working on a real life robot. Our results show that unsupervised learning methods are competitive with end-to-end methods. We also highlight the importance of various components such as input representation, predictive unsupervised learning, and latent features. We make all our models publicly available, as well as training and testing environments, and tools 1. This release also includes OpenAI-gym-compatible environments designed to emulate the training conditions described by other papers, with as much fidelity as possible. Our hope is that this helps in bringing together the field of RL for robot navigation, and allows meaningful comparisons across state-of-the-art methods.