Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor map construction algorithm based on RGBD semantic segmentation

In the field of robotics and autonomous driving, vision or laser-based map construction has always been the main direction for solving mobile vehicle's perception and localization. The stereo camera is widely used in robot map construction because it can perceive both color information and depth information. Based on the RGBD semantic segmentation network, this paper proposes a map construction algorithm based on deep semantic segmentation. By using the pixel information of deep semantic segmentation, the missing part of the 3D point cloud is filled to construct an octomap. After experiments, on the datasets, the algorithm has achieved better results than only using the depth information, and after actual deployment, the algorithm has completed the construction of real-time indoor semantic maps on the robot.
On the redshift of the bright BL Lacertae object PKS 0048-097

Aims. We attempt to determine the spectroscopic redshifts of bright BL Lac objects, which have proven difficult to measure. Methods. We use the spectra acquired using the newly available spectrograph X-Shooter at the European Southern Observatory Very Large Telescope, which combines high resolution with a large wavelength range, to obtain ultraviolet to near-infrared spectra of BL Lacs. Results. Our observations of PKS 0048-097 detect three emission lines, which permit us to derive a redshift z = 0.635. Moreover, we discover a Mg II absorption system at z = 0.154 that is associated with a foreground spiral galaxy a 50 kpc of projected distance. Conclusions. The measured redshift allows us to discuss the optical beaming factor and the absorption of the high energy spectrum by the extragalactic background light.
Estimation of centroid sideslip angle and roll angle of multi-axle special vehicle based on neural network

Aiming at the problems of high test cost, nonlinear dynamics and uncertainty in the direct measurement of the side-slip angle and roll angle of the center of mass by the on-board sensors of multi-axis heavy-duty special vehicles, in order to realize the observation of steering instability under high maneuvering conditions, Neural Network (NN) is used to estimate the side-slip angle and roll angle of the vehicle center of mass. , and use the Trucksim model verified by real vehicle experiments to obtain the data set required by the neural network. The neural network estimates the sideslip angle and roll angle through easily measurable variables. Finally, the effectiveness and reliability of the algorithm are further verified by simulation experiments.
Deep learning based recyclable waste classification

Waste classification has attracted more and more attention in recent years, which is an important part of building an eco-friendly city. Traditional manual garbage classification has poor efficiency and accuracy. In this paper, based on deep learning, the garbage classification algorithm I-ResNet50 is proposed to improve the ResNet50 network, and the geometric transformation of the original data is performed. The test set results show that the I-ResNet50 algorithm can achieve a classification accuracy of 62.6%, which is a substantial improvement in accuracy compared with the original method.
In-situ data curation: a key to actionable AI at the edge

Machine learning (ML) algorithms have shown great potential in edge-computing environments, however, the literature mainly focuses on model inference only. We investigate how ML can be operationalized and how in-situ curation can improve the quality of edge applications, in the context of ML-assisted environmental surveys. We show that camera-enabled ML systems deployed on edge devices can enable scientists to perform real-time monitoring of species of interest or characterization of natural habitats. However, the benefit of this new technology is only as good as the quality and accuracy of the edge ML model inferences. In this demonstration, we show that with small additional time investment, domain scientists can manually curate ML model outputs and thus obtain highly reliable scientific insights, leading to more effective and scalable environmental surveys.
Design of a hand assisted exoskeleton robot

This paper introduces the simulation research and design of a finger assisted exoskeleton. The purpose of this project is to speed up the rehabilitation of patients with loss of human limb motor function caused by stroke and provide power-assisted exercise equipment. This paper designs a multi-degree of freedom finger exoskeleton system to assist patients in bending and stretching exercise. Through a series of repetitive training and treatment, it increases the possibility of restoring finger motor function. By analysing the basic structure of human finger system, the finger movement is divided into grasping and grasping, and the dynamics of finger exoskeleton system is calculated. The 3D modelling of finger exoskeleton is established by using SolidWorks software, and the DH parameter table and kinematics equation of the exoskeleton are calculated. Combined with MATLAB software, the trajectory planning and related index parameters are generated. Finally, the rationality of the theoretical model is verified by comparing the simulation data and theoretical calculation data, It provides important data and theoretical basis for later finger exoskeleton motion control and model manufacturing.
Tutti: coupling 5G RAN and mobile edge computing for latency-critical video analytics

Mobile edge computing (MEC), as a key ingredient of the 5G ecosystem, is envisioned to support demanding applications with stringent latency requirements. The basic idea is to deploy servers close to end-users, e.g., on the network edge-side instead of the remote cloud. While conceptually reasonable, we find that the operational 5G is not coordinated with MEC and thus suffers from intolerable long response latency. In this work, we propose Tutti, which couples 5G RAN and MEC at the user space to assure the performance of latency-critical video analytics. To enable such capacity, Tutti precisely customizes the application service demand by fusing instantaneous wireless dynamics from the 5G RAN and application-layer content changes from edge servers. Tutti then enforces a deadline-sensitive resource provision for meeting the application service demand by real-time interaction between 5G RAN and edge servers in a lightweight and standard-compatible way. We prototype and evaluate Tutti on a software-defined platform, which shows that Tutti reduces the response latency by an average of 61.69% compared with the existing 5G MEC system, as well as negligible interaction costs.
Design and simulation of a lower limb exoskeleton rehabilitation robot

In order to help patients with lower extremity rehabilitation training, this project aims to design a lower extremity-assisted and adjustable exoskeleton robot. First, the degree of freedom of each joint of the exoskeleton was determined according to the characteristics of human anatomy and bone connection. and the structural design of the normal size of the mechanism was determined by the average size of the bones. Secondly, the angle equation determined by the leg joint coordinates is established, and the angle parameters of different time periods in a gait cycle are obtained by observing the changes of the joint coordinates. The coordinate system of each joint is established by the DH parameter method. It provides a theoretical basis for animation simulation, structural design, kinematics analysis and control. Finally, the kinematics analysis and simulation are carried out in MATLAB; the overall structure and simulation animation of the robot are established by SOLIDWORKS. The PID method is used to construct the power-assisted control frame diagram of the robot. The results showed that the exoskeleton robot responds well to various kinematics and other properties of the lower limbs.
InFi: end-to-end learnable input filter for resource-efficient mobile-centric inference

Mobile-centric AI applications put forward high requirements for resource-efficiency of model inference. Input filtering is a promising approach to eliminate the redundancy in the input so as to reduce the cost of inference. Previous efforts have tailored effective solutions for many applications, but left two essential questions unanswered: (1) theoretical filterability of an inference workload to guide the application of input filtering techniques, thereby avoiding the trial-and-error cost for resource-constrained mobile applications; (2) robust discriminability of feature embedding to allow input filtering to be widely effective for diverse inference tasks and input content. To answer these questions, we first provide a generic formalization of the input filtering problem and theoretically compare the hypothesis complexity of inference models and their input filters to understand the optimization potential of applying input filtering. Then we propose the first end-to-end learnable input filtering framework that covers most state-of-the-art methods and surpasses them in feature embedding with robust discriminability. Based on our framework, we design and implement an input filtering system InFi supporting six input modalities. InFi is the first to support text and sensor signal inputs and model partitioning deployments widely adopted by under-resourced mobile systems. Comprehensive evaluations confirm our theoretical results and show that InFi outperforms strong baselines in applicability, accuracy, and efficiency, owing to its generality and end-to-end learnability. InFi can achieve 8.5X throughput and save 95% bandwidth, while keeping over 90% accuracy, for a video analytics app on mobile platforms.
Mobi2Sense: enabling wireless sensing under device motions

Besides the communication function, various RF signals such as WiFi and RFID have been actively exploited for sensing purposes recently. However, a missing component of existing RF sensing is sensing under device motions. This paper takes the first step to involve device mobility into the ecosystem of RF sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chips in recent years, we propose to integrate the accuracy of UWB sensing with device mobility to support truly ubiquitous RF sensing. This is a challenging task because the motion artifacts from RF devices can easily overwhelm the target motion, such as subtle chest displacement for respiration sensing. In this demo, we propose Mobi2Sense to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using a commodity UWB module. Comprehensive evaluation demonstrates that Mobi2Sense is able to "hear" music and "see" human respiration at high accuracy in the presence of device motions.
Thermal proteome profiling reveals Haemonchus orphan protein HCO_011565 as a target of the nematocidal small molecule UMW-868

Parasitic roundworms (nematodes) cause destructive diseases, and immense suffering in humans and other animals around the world. The control of these parasites relies heavily on anthelmintic therapy, but treatment failures and resistance to these drugs are widespread. As efforts to develop vaccines against parasitic nematodes have been largely unsuccessful, there is an increased focus on discovering new anthelmintic entities to combat drug resistant worms. Here, we employed thermal proteome profiling (TPP) to explore hit pharmacology and to support optimisation of a hit compound (UMW-868), identified in a high-throughput whole-worm, phenotypic screen. Using advanced structural prediction and docking tools, we inferred an entirely novel, parasite-specific target (HCO_011565) of this anthelmintic small molecule in the highly pathogenic, blood-feeding barber’s pole worm, and in other socioeconomically important parasitic nematodes. The “hit-to-target” workflow constructed here provides a unique prospect of accelerating the simultaneous discovery of novel anthelmintics and associated parasite-specific targets.
Effects of information sources on public preventive behaviors in health emergencies: Evidence from a digital epidemiologic study during the COVID-19 pandemic

Introduction It has been approved that information sources would affect public behaviors. However, due to the outbreak of COVID-19, this influence was enhanced and showed a distinctive pattern among different populations, which has been less noticed before. We aimed to investigate the potential roles of different information sources in COVID-19 preventive behaviors of different publics. Methods A cross-sectional online survey with 11,190 participants from 33 province-level regions in China was conducted during the COVID-19 pandemic. Sociodemographic characteristics, COVID-19 preventive behaviors, and information sources for COVID-19-related information were assessed. A mixed linear model was used to analyze risk factors of COVID-19 preventive behaviors. The effects of different information sources on COVID-19 prevention behaviors of different publics were analyzed. Results Generally, the Chinese public had good COVID-19 preventive behaviors, and the top three COVID-19 preventive behaviors with the higher action rate were avoiding eat bushmeat (76.1%), a healthy diet (74.8%), and avoiding contact with people with symptoms of respiratory diseases (73.0%). About information sources, 12320 telephone (National Public Health Hotline) (−0.62, 95% CI: −0.94 to −0.31) and acquaintances consulting (−1.00, 95% CI: −1.31 to −0.69) were negatively associated with COVID-19 preventive behaviors, while internet resources, family doctors, hospitals, and community health centers were positively associated with COVID-19 preventive behaviors (1.00 vs. 0.47 vs. 0.46 vs. 0.33, P < 0.05). For older adults, accessing to COVID-19-related information through family doctors and community health centers were positively associated with COVID-19 preventive behaviors. For the non-educated, family doctors and community health centers had positive effects on their COVID-19 preventive behaviors. Family doctors and internet resources were positively associated with COVID-19 preventive behaviors among those earning 5,000 yuans and above. The effects of family doctors, hospitals, and internet resources were higher for COVID-19 preventive behaviors of urban publics than for rural publics. Finally, the effect of internet resources on COVID-19 preventive behaviors of females was lower than males. Conclusions Obtaining COVID-19-related information through internet resources had the most significant effect on COVID-19 preventive behaviors, but was not significant among publics with old age, low education, low income, and living in rural area.
A nomogram for predicting screw loosening after single-level posterior lumbar interbody fusion utilizing cortical bone trajectory screw: A minimum 2-year follow-up study

Purpose This study aims to investigate the risk factors for screw loosening after single-level posterior lumbar interbody fusion (PLIF) utilizing cortical bone trajectory (CBT) screw and establish a nomogram for predicting screw loosening. Methods A total of 79 patients (316 screws) who underwent single-level PLIF with CBT screw were included in the study. Preoperative, postoperative, and final follow-up demographic data, surgical data, and radiographic parameters were documented and analyzed to identify risk factors, and a predictive nomogram was established for screw loosening. The nomogram was assessed by concordance index (C-index), calibration plot, decision curve analysis (DCA), and internal validation. Results The incidence of screw loosening was 26.6% in 79 patients and 11.4% in 316 screws. Multifactorial regression analysis confirmed that fixed to S1 (FS1, OR = 3.82, 95% CI 1.12–12.71, P = 0.029), the coronal angle of the screw (CA, OR = 1.07, 95% CI 1.01–1.14, P = 0.039), and cortical bone contacted layers (CBCLs, OR = 0.17, 95% CI 0.10–0.29, P < 0.001) were risk factors and incorporated in the nomogram for predicting screw loosening after single-level PLIF with a CBT screw. The C-index of the nomogram was 0.877 (95% CI 0.818–0.936), which demonstrated good predictive accuracy. The calibration plot indicated an acceptable calibration of the nomogram that also had a positive benefit in guiding treatment decisions. Conclusion FS1, CA, and CBCLs are identified to be significant risk factors for screw loosening after single-level PLIF with the CBT technique. The nomogram we have established can be used to predict screw loosening and contribute to surgical decisions.
Cross-domain adaptive transfer reinforcement learning based on state-action correspondence

Despite the impressive success achieved in various domains, deep reinforcement learning (DRL) is still faced with the sample inefficiency problem. Transfer learning (TL), which leverages prior knowledge from different but related tasks to accelerate the target task learning, has emerged as a promising direction to improve RL efficiency. The majority of prior work considers TL across tasks with the same state-action spaces, while transferring across domains with different state-action spaces is relatively unexplored. Furthermore, such existing cross-domain transfer approaches only en-able transfer from a single source policy, leaving open the important question of how to best transfer from multiple source policies. This paper proposes a novel framework called Cross-domain Adaptive Transfer (CAT) to accelerate DRL. CAT learns the state-action correspondence from each source task to the target task and adaptively transfers knowledge from multiple source task policies to the target policy. CAT can be easily combined with existing DRL algorithms and experimental results show that CAT significantly accelerates learning and outperforms other cross-domain transfer methods on multiple continuous action control tasks. The code for this project are released, under the project page of https://github.com/TJU-DRL-LAB/transfer-and-multi-task-reinforcement-learning.
Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling

Bayesian networks tend to be considered as transparent and interpretable, but for big and dense networks they become harder to understand. This is the case of non-stationary, and more generally time-varying dynamic Bayesian networks, as the relations change over time and cannot be represented with a single template model. We introduce methods to explain how the model evolves qualitatively over time, and quantify these changes. In addition, we offer a functional implementation for time-varying dynamic Bayesian networks that includes our explainability proposals and some extensions that are targeted to simplify the networks in the specific field of climate sciences
On early extinction and the effect of travelling in the SIR model

We consider a population protocol version of the SIR model. In every round, an individual is chosen uniformly at random. If the individual is susceptible, then it becomes infected w.p. βI t /N , where I t is the number of infections at time t and N is the total number of individuals. If the individual is infected, then it recovers w.p. γ , whereas, if the individual is already recovered, nothing happens. We prove sharp bounds on the probability of the disease becoming pandemic vs extinguishing early (dying out quickly). The probability of extinguishing early, Pr [ E ext ] , is typically neglected in prior work since most use (deterministic) differential equations. Leveraging on this, using Pr [ E ext ] , we proceed by bounding the expected size of the population that contracts the disease E [ R ∞ ] . Prior work only calculated E (cid:2) R ∞ | E ext (cid:3) , or obtained non-closed form solutions. We then study the two-country model also accounting for the role of Pr [ E ext ] . We assume that both countries have different infection rates β ( i ) , but share the same recovery rate γ . In this model, each round has two steps: First, an individual is chosen u.a. r. and travels w.p. p travel to the other country. Afterwards, the process continues as before with the respective infection rates. Finally, using simulations, we characterise the influence of p travel on the total number of infections. Our simulations show that, depending on the β ( i ) , increasing p travel can decrease or increase the expected total number of infections E [ R ∞ ] .
Laplace approximated Gaussian process state-space models

Gaussian process state-space models describe time series data in a probabilistic and non-parametric manner using a Gaussian process transition function. As inference is intractable, recent methods use variational inference and either rely on simpli-fying independence assumptions on the approximate posterior or learn the temporal states itera-tively. The latter hampers optimization since the posterior over the presence can only be learned once the posterior governing the past has converged. We present a novel inference scheme that applies stochastic variational inference for the Gaussian process posterior and the Laplace approximation on the temporal states. This approach respects the conditional dependencies in the model and, through the Laplace approximation, treats the temporal states jointly, thereby avoiding their sequential learning. Our method is computationally efficient and leads to better calibrated predictions compared to state-of-the art alternatives on syn-thetic data and on a range of benchmark datasets.
Proportional allocation of indivisible resources under ordinal and uncertain preferences

We study a fair resource allocation problem with in-divisible items. The agents’ preferences over items are assumed to be ordinal and have uncertainties. We adopt stochastic dominance proportionality as our fairness notion and study a sequence of problems related to ﬁnding allocations that are fair with a high probability. We provide complexity analysis for each problem and efﬁcient algorithms for some problems. Finally, we propose several heuristic algorithms to ﬁnd an allocation that is fair with the highest probability. We thoroughly evaluate the performance of the algorithms on both synthetic and real datasets.
Sublinear time algorithms for greedy selection in high dimensions

Greedy selection is a widely used idea for solving many machine learning problems. But greedy selection algorithms often have high complexities and thus may be prohibitive for large-scale data. In this paper, we consider two fundamental optimization problems in machine learning: k center clustering and convex hull approximation, where they both can be solved via greedy selection. We propose sublinear time algorithms for them through combining the strategies of randomization and greedy selection. Our results are similar in spirit to the linear time stochastic greedy selection algorithms for submodular maximization [Mirzasoleiman et al., AAAI 2015, Hassidim and Singer, ICML 2017], but with several important differences. Our runtimes are independent of the number of input data items n . In particular, our runtime for k -center clustering signiﬁcantly improves upon that of the uniform sampling approach, especially when the dimensionality is high. Our sublinear algorithms can also reduce the computational complexities for various applications, such as data selection and compression, active learning, and topic modeling, etc .