Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Car-Following Control Based on a Hybrid of Reinforcement Learning and Supervised Learning

With the development of intelligent vehicles, more research has focused on achieving human-like driving. As an important component of intelligent vehicle control, car-following control should ensure safety, tracking, comfort while considering the acceptance of human drivers. In this paper, we propose a car-following control strategy <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pi }_{ \boldsymbol {Hybrid}}$ </tex-math></inline-formula> based on a hybrid of reinforcement learning (RL) and supervised learning (SL). RL is used to achieve multi-objective collaborative optimization in car-following control, and SL is used to achieve human like car-following. Through the complementary advantages of the two learning methods, <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pi }_{Hybrid}$ </tex-math></inline-formula> can achieve high performance car-following while matching the personalized car-following characteristics of human drivers. RL is used as the main framework of <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pi }_{Hybrid}$ </tex-math></inline-formula>. In addition, the personalized car-following reference model (PCRM) of human drivers based on Gaussian mixture regression, and the motion uncertainty model of preceding vehicle (MUMPV) based on the sequence-to-sequence network are established and incorporated into the RL framework. PCRM can lead <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pi }_{Hybrid}$ </tex-math></inline-formula> to learn the different characteristics of human drivers, and improve the anthropomorphism of <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pi }_{Hybrid}$ </tex-math></inline-formula>; MUMPV enables <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pi }_{Hybrid}$ </tex-math></inline-formula> to consider the dynamic changes of the traffic environment and to become more robust. <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pi }_{Hybrid}$ </tex-math></inline-formula> is trained and tested on High D dataset, and the generalizability verification is based on the self-built real vehicle data collection platform. The results show that <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pi }_{Hybrid}$ </tex-math></inline-formula> can match human drivers’ personalized car-following characteristics and can outperform human drivers in safety, comfort, and tracking of the preceding vehicle.
Visually Analyzing Large Scale Graphs

Many datasets can be modeled as graphs, and graphs naturally capture the relations among entities in the datasets. Graph visualization is an effective way to help users understand the graph data. However a fundamental challenge for graph visualization is to visualize large scale graphs: most of the graph visualization methods usually run into the limitation of display device – that is at large graph scales one is often unable to represent nodes and edges effectively within the available pixel limits of modern displays. In this thesis, we propose a framework focusing on visually analyzing large-scale networks. Our framework has the following three components, and each component focuses on visually analyzing one particular aspect of a graph. The first component is to visually analyze dense subgraphs. We define and introduce the notion of a Triangle K-Core, a dense subgraph pattern and can be used as a proxy for identifying and extracting clique-like structure from large graphs. Based on this definition we first develop a localized algorithm for extracting Triangle K-Cores from large graphs. Subsequently we extend the simple algorithm to accommodate dynamic graphs (where edges can be dynamically added and deleted). Finally, we extend the basic definition to support various template pattern cliques with applications to network visualization and event detection on graphs and networks. Based on the Triangle K-Core motif definition, we propose a parallel method to detect the approximate cliques in large graphs. Our method first partitions the graph, and extends
Molecular Evolution of Histone Methylation Modification Families in the Plant Kingdom and Their Genome-Wide Analysis in Barley

In this study, based on the OneKP database and through comparative genetic analysis, we found that HMT and HDM may originate from Chromista and are highly conserved in green plants, and that during the evolution from algae to land plants, histone methylation modifications gradually became complex and diverse, which is more conducive to the adaptation of plants to complex and variable environments. We also characterized the number of members, genetic similarity, and phylogeny of HMT and HDM families in barley using the barley pangenome and the Tibetan Lasa Goumang genome. The results showed that HMT and HDM were highly conserved in the domestication of barley, but there were some differences in the Lasa Goumang SDG subfamily. Expression analysis showed that HvHMTs and HvHDMs were highly expressed in specific tissues and had complex expression patterns under multiple stress treatments. In summary, the amplification and variation of HMT and HDM facilitate plant adaptation to complex terrestrial environments, while they are highly conserved in barley and play an important role in barley growth and development with abiotic stresses. In brief, our findings provide a novel perspective on the origin and evolutionary history of plant HvHMTs and HvHDMs, and lay a foundation for further investigation of their functions in barley.
Sparse Model-Based Reconstruction in Dynamic Positron Emission Tomography

This thesis deals with the inverse problem of dynamic positron emission tomography, which we attempt to solve with variational methods. Based on kinetic modeling we express the unknown image as a linear combination of a large dictionary, which contains the given basis functions. Since only a few of them are necessary to express the data for each pixel, we motivate the usage of `1,∞-regularization to promote sparsity of the coefficients. In a more general approach we analyze, which conditions are necessary for exact recovery of the coefficients. The main difficulty is that the basis functions are extremely similar. Thus, most of the known conditions for exact recovery are not applicable in this case. We provide three different splitting algorithms, which demonstrate that the use of `1,∞-regularization is a reasonable approach. In order to analyze these algorithms, we finally test them on artificial data.
Space Efficient 3D Model Indexing

Abstract : We show that the set of 2D images produced by the point features of a rigid 3D model can be represented with two lines in two high-dimensional spaces. These lines are the lowest-dimensional representation possible. We use this result to build a system for representing in a hash table at compile time, all the images that groups of model features can produce. Then at run time a group of image features can access the table and find all model groups that could match it. This table is efficient in terms of space, and is built and accessed through analytic methods that account for the effect of sensing error. In real images, it reduces the set of potential matches by a factor of several thousand. We also use this representation of a model's images to analyze two other approaches to recognition: invariants, and non-accidental properties. These are properties of images that some models always produce, and all other models either never produce (invariants) or almost never produce (non-accidental properties). In several domains we determine when invariants exist. In general we show that there is an infinite set of non-accidental properties that are qualitatively similar. Object recognition, Non-accidental properties, Indexing, Hashing, Invariants, Space efficiency.
AI Marketplace: Serving Environment for AI Solutions Using Kubernetes

: Recent advances in the ﬁeld of artiﬁcial intelligence (AI) provide numerous potentials for industrial companies. However, the adoption of AI in practice is still left behind. One of the main reasons is a lack of knowledge about possible AI application areas by industry experts. The AI Marketplace addresses this problem by providing a platform for the cooperation between industry experts and AI developers. An essential function of this platform is a serving environment that allows AI developers to present their solution to industry experts. The solutions are packaged in a uniform way and made accessible to all platform members via the serving environment. In this paper, we present the conceptual design of this environment, its implementation using Amazon Web Services, and illustrate its application on two exemplary use cases.
Calibration Formula of Rheological Parameters of Bingham Fluid in Couette Rheometer

Bingham plastic (Bingham 1922) is a non-Newtonian fluid characterized with the yield stress. It has practical applications for the modeling in many fields, e.g., food engineering, polymer modeling, hydraulic engineering, and geological modeling. Different from Newtonian fluid, Bingham plastic behaves like a solid if the stress in fluid is less than yield stress, but likes a fluid if stress is greater than yield stress. To interpret the relationship between shear stress ij  and strain-rate tensor ij e , Bingham plastic (Prager 1961) can be expressed as
Pulse extraction under risk and a numerical forestry application

The problem of optimal pulse harvesting of a resource under risk is discussed. The fundamental importance of stationarity in the stochastic process is investigated and the limiting optimal stopping rule is derived. A numerical example from forestry is wed lo discuss the expected present value and the optimal slopping criterion as functions of time. Finally, the probability distribution of different optimal harvesting ages is calculated. In 1987, a publication will appear closely related to this one. It will contain a purely analytical derivation of the numerical results presented in this paper. Furthermore, it will contain a more general numerical optimization model, where any first order autoregressive price process and any price-age relationship can be used. Hence, this paper is a "popular" version of the final paper. Preliminary results show that the qualitative results discussed in this paper hold in very general cases.
Psychometric Properties of the Japanese Version of the Parental Acceptance and Action Questionnaire in Parents with Infants and Toddlers

We aimed to examine the reliability and validity of the Parental Acceptance and Action Questionnaire-Japanese version (PAAQ-J). We considered a total of 2000 mothers with infants and toddlers aged 0–3 years and evaluated their scores on the PAAQ-J Acceptance and Action Questionnaire-II (AAQ-II) and Hospital Anxiety and Depression Scale (HADS). We conducted an exploratory factor analysis, creating a PAAQ-J with 12 items and three factors (α = 0.80): Inaction-Behavior, Inaction-Cognition, and Unwillingness, with α of 0.84, 0.72 and 0.68, respectively. The test-retest reliability examination results showed that the interclass correlation coefficient was 0.49, with 95% CI between 0.44 and 0.54. The correlation coefficient of PAAQ-J was 0.57, 0.32, and 0.33 with AAQ-II, and HADS-depression and HADS-anxiety, respectively. PAAQ-J’s validity to adequately evaluate an individual’s avoidance of experiences regarding childcare and their psychological flexibility was proven. Since the original PAAQ was for 6–18-year-old children with anxiety symptoms, it is necessary to examine its reliability and validity not only for infants and toddlers, but also for parents of older children and adolescents in the future.
Relationship between Dance Performance Anxiety, Perfectionism Propensity, and Body Image of the Ballet Class Participants Since 2020

: This study focused on the relationship between obstacle factors and barriers that affect ballet class participants’ during the COVID-19 pandemic. During the pandemic, in higher education, students who majored in dance or are in the learning process had difficulties learning, practicing, and demonstrating due to the changing learning routine. Especially, ballet major students’ psychological and physical difficulties affected performance anxiety. This study aims to verify the between body image, perfectionism, and performance anxiety which could negatively influence learning and performance. Subjects who learned ballet at S, Y, and B universities participated in this study. There were 308 samples through purposive sampling. Perfectionism Propensity Scale, Multidimensional Body-self Relations Questionnaire, and Korean version Kenny Dance Performance Inventory were used as the measurement tools. The purposive quasi-experimental design was applied as the experimental research design. Additionally, multiple regression analysis was used for hypothesis verification (p<.05). As a result, weight control and appearance-oriented body image factor significantly affected personal and parental expectations of perfectionism propensity. In addition, weight control, and appearance-oriented body image significantly affected anxiety and intergenerational communication, depression and control, parental empathy and negative perception, and desperation and fear factors of dance performance anxiety. Health care and body satisfaction significantly affected concerns about the performance of dance performance anxiety. Moreover, personal and parental expectations and concerns and expectations about mistakes of perfectionism propensity significantly affected anxiety and intergenerational communication, concerns about performance, and anxiety about dance performance anxiety. In conclusion, Weight control and appearance were related to expectations and negative perceptions such as anxiety, depression, despair, fear, and communication with parents. In addition, the concern for the body and health was related to anxiety about performance. The expectations of themselves and their parents and concerns about performance were related to anxiety and communication.
Fourteen moment theory for granular gases

A fourteen moment theory for a granular gas is developed within the framework of the Boltzmann equation where the full contracted moment of fourth order is added to the thirteen moments of mass density, velocity, pressure tensor and heat flux vector. The spatially homogeneous solutions of the fourteen moment theory implied into a time decay of the temperature field which follows closely Haff's law, besides the more accentuated time decays of the pressure deviator, heat flux vector and fourth moment. The requirement that the fourth moment remains constant in time inferred into its identification with the coefficient $a_2$ in the Chapman-Enskog solution of the Boltzmann equation. The laws of Navier-Stokes and Fourier are obtained by restricting to a five field theory and using a method akin to the Maxwellian procedure. The dependence of the heat flux vector on the gradient of the particle number density was obtained thanks to the inclusion of the forth moment. The analysis of the dynamic behavior of small local disturbances from the spatially homogeneous solutions caused by spontaneous internal fluctuations is performed by considering a thirteen field theory and it is shown that for the longitudinal disturbances there exist one hydrodynamic and four kinetic modes, while for the transverse disturbances one hydrodynamic and two kinetic modes are present.
Dynamic diffuse optical tomography for assessing changes of breast tumors during neoadjuvant chemotherapy

We have developed a dynamic diffuse optical tomography imaging system that is capable of 3D imaging of both breasts simultaneously. In an ongoing study subjects receiving neoadjuvant chemotherapy are imaged at 6 time points throughout their 5-month treatment. At each time point the subjects preform a breath hold to observe the hemodynamic effects in the breasts. For each session the percent change of various hemodynamic parameters during the breath hold is determined. Preliminary results from show statistically significant differences in washout rates and deoxyhemoglobin changes at the 2-week imaging point between subjects that respond and do not respond to treatment.
Design of slot radiating RF planar sensor for homogeneity check of dielectric discontinuity

In this paper, a 2.8‐GHz RF planar sensor with radiating slots is proposed for effective checking of discontinuity in homogeneous dielectric samples. The slots etched in the ground plane reduce the effective area for sample placement, which increases in the case of a conventional ring at the desired frequency. Several forms of discontinuity are analyzed and measured with the help of varying geometrical configurations of Teflon, wood, and polyethylene samples with varying permittivity. It is observed that due to the uniform distribution of fields among all the radiating slots any sort of configurational discontinuity is detected by the sensor in terms of the shift in resonant frequency and S21 transmission notch. The simulations are carried out using CST‐MWS and the equivalent model is developed using ADS. The numerical analysis is carried out using Origin software and the measurement is carried out using Taconic TLY‐5 substrate by photolithographic etching.
Croissance et ensemble nodal de fonctions propres du laplacien sur des surfaces

We prove a result, announced by F. Nazarov, L. Polterovich and M. Sodin, that exhibits a relation between the average local growth of a Laplace eigenfunction on a closed surface and the global size of its nodal set. More precisely, we provide a lower and an upper bound to the Hausdorff measure of the nodal set in terms of the expected value of the growth exponent of an eigenfunction on disks of wavelength like radius. Combined with Yau’s conjecture, the result implies that the average local growth of an eigenfunction on such disks is bounded by constants in the semi-classical limit. We also obtain results that link the size of the nodal set to the growth of solutions of planar Schrödinger equations with small potential. Author : Guillaume Roy-Fortin. 2.
Semantic-Context Graph Network for Point-Based 3D Object Detection

Point-based indoor 3D object detection has received increasing attention with the large demand for augmented reality, autonomous driving, and robot technology in the industry. However, the detection precision suffers from inputs with semantic ambiguity, i.e., shape symmetries, occlusion, and texture missing, which would lead that different objects appearing similar from different viewpoints and then confusing the detection model. Typical point-based detectors relieve this problem via learning proposal representations with both geometric and semantic information, while the entangled representation may cause a reduction in both semantic and spatial discrimination. In this paper, we focus on alleviating the confusion from entanglement and then enhancing the proposal representation by considering the proposal’s semantics and the context in one scene. A semantic-context graph network (SCGNet) is proposed, which mainly includes two modules: a category-aware proposal recoding module (CAPR) and a proposal context aggregation module (PCAg). To produce semantically clear features from entanglement representation, the CAPR module learns a high-level semantic embedding for each category to extract discriminative semantic clues. In view of further enhancing the proposal representation and leveraging the semantic clues, the PCAg module builds a graph to mine the most relevant context in the scene. With few bells and whistles, the SCGNet achieves SOTA performance and obtains consistent gains when applying to different backbones (0.9% ~ 2.4% on ScanNet V2 and 1.6% ~ 2.2% on SUN RGB-D for mAP@0.25). Code is available at https://github.com/dsw-jlurgzn/SCGNet.
Feynman rules for any spin. II. Massless particles

The Feynman rules are derived for massless particles of arbitrary spin j. The rules are the same as those presented in an earlier article for m>0, provided that we let m → 0 in propagators and wave functions, and provided that we keep to the (2j+l)-component formalism [with fields of the (j,0) or (0,j) type] or the 2(2j+l)-component formalism [with (j,0) ⊕ (0,J) fields]. But there are other field types which cannot be constructed for m = 0; these include the (j/2,j/2) tensor fields, and in particular the vector potential for j = 1. This restriction arises from the non-semi-simple structure of the little group for m = 0. Some other subjects discussed include: T, C, and P for massless particles and fields; the extent to which chirality conservation implies zero physical mass; and the Feynman rules for massive particles in the helicity formalism. Our approach is based on the assumption that the S matrix is Lorentz invariant, and makes no use of Lagrangians or the canonical formalism.
Precipitation and Evolution Sensitivity in Simulated Deep Convective Storms: Comparisons between Liquid-Only and Simple Ice and Liquid Phase Microphysics*

Abstract Weisman and Klemp suggested that their liquid-only, deep convective storm experiments should be repeated with a liquid-ice microphysics scheme to determine if the solutions are qualitatively the same. Using a three-dimensional, nonhydrostatic cloud model, such results are compared between three microphysics schemes: the “Kessler” liquid-only scheme (used by Weisman and Klemp), a Lin–Farley–Orville-like scheme with liquid and ice parameterization (Li), and the same Lin–Farley–Orville-like microphysics scheme but with only liquid processes turned on (Lr). Convection is simulated using a single thermodynamic profile and a variety of shear profiles. The shear profiles are represented by five idealized half-circle wind hodographs with arc lengths (Us) of 20, 25, 30, 40, and 50 m s−1. The precipitation, cold pool characteristics, and storm evolution produced by the different schemes are compared. The Kessler scheme produces similar accumulated precipitation over 2 h compared to Lr for all shear regimes.
Polypharmacy, Potentially Inappropriate Medications, and Drug-to-Drug Interactions in Patients with Chronic Myeloproliferative Neoplasms

Polypharmacy, potentially inappropriate medications (PIMs), and drug-to-drug interactions (DDIs) are highly prevalent in the elderly and may have adverse effects on health-related outcomes. Their occurrence and clinical and prognostic associations in patients with chronic myeloproliferative neoplasms (MPN) are unknown. We retrospectively evaluated polypharmacy, PIMs, and DDIs in a cohort of 124 MPN patients (essential thrombocythemia, ET = 63, polycythemia vera, PV = 44, myelofibrosis = 9, MPN unclassifiable = 8) from a single community hematology practice. There were 761 drug prescriptions with a median of five prescribed medications per patient. Polypharmacy, at least one PIM (calculated for persons >60 years of age, n = 101), and at least one DDI were recorded in 76 (61.3%), 46 (45.5%), and 77 (62.1%) of patients, respectively. Seventy-four (59.6%) and twenty-one (16.9%) patients had at least one C or at least one D interaction, respectively. Among other associations, polypharmacy and DDIs were associated with older age, management of disease-related symptoms, osteoarthritis/osteoporosis, and different CV disorders. In multivariate analyses adjusted for clinically meaningful parameters, both polypharmacy and DDIs were significantly associated with inferior overall survival (OS) and time to thrombosis (TTT), whereas PIMs had no significant associations with neither OS nor TTT. There were no associations with bleeding or transformation risks. Polypharmacy, DDIs, and PIMs are very frequent among MPN patients and may have important clinical associations.