Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulating For-Hire Autonomous Vehicles for An Equitable Multimodal Transportation Network

This paper assesses the equity impacts of for-hire autonomous vehicles (AVs) and investigates regulatory policies that promote spatial and social equity in future autonomous mobility ecosystems. To this end, we consider a multimodal transportation network, where a ride-hailing platform operates a fleet of AVs to offer mobility-on-demand services in competition with a public transit agency that offers transit services on a transportation network. A game-theoretic model is developed to characterize the intimate interactions between the ride-hailing platform, the transit agency, and multiclass passengers with distinct income levels. An algorithm is proposed to compute the Nash equilibrium of the game and conduct an ex-post evaluation of the performance of the obtained solution. Based on the proposed framework, we evaluate the spatial and social equity in transport accessibility using the Theil index, and find that although the proliferation of for-hire AVs in the ride-hailing network improves overall accessibility, the benefits are not fairly distributed among distinct locations or population groups, implying that the deployment of AVs will enlarge the existing spatial and social inequity gaps in the transportation network if no regulatory intervention is in place. To address this concern, we investigate two regulatory policies that can improve transport equity: (a) a minimum service-level requirement on ride-hailing services, which improves the spatial equity in the transport network; (b) a subsidy on transit services by taxing ride-hailing services, which promotes the use of public transit and improves the spatial and social equity of the transport network. We show that the minimum service-level requirement entails a trade-off: as a higher minimum service level is imposed, the spatial inequity reduces, but the social inequity will be exacerbated. On the other hand .
The Determination of Sky View-Factors in Urban Environments Using Video Imagery

Abstract A technique is described whereby sky view-factors may be determined using a video camera equipped with a fish-eye lens. The video image is digitized and then analyzed to distinguish between “sky” and “non-sky” pixels. View-factors are calculated for each pixel and then summed for all “sky” pixels to yield a composite sky view-factor for the image. The technique is illustrated by applying it in three urban locations, all of which are characterized by high building densities (and hence complex skylines). The three images processed have sky, view-factors in the range 0.15 to 0.46 (as independently determined). It is shown that the present technique produces values in close agreement with these and appears quite robust when compared with calculations based on the work of Johnson and Watson.
Mixed Attention with Deep Supervision for Delineation of COVID Infection in Lung CT

The COVID-19 pandemic, with its multiple variants, has placed immense pressure on the global healthcare system. An early effective screening and grading become imperative towards optimizing the limited available resources of the medical facilities. Computed tomography (CT) provides a significant non-invasive screening mechanism for COVID-19 infection. An automated segmentation of the infected volumes in lung CT is expected to significantly aid in the diagnosis and care of patients. However, an accurate demarcation of lesions remains problematic due to their irregular structure and location(s) within the lung. A novel deep learning architecture, Mixed Attention Deeply Supervised Network (MiADS-Net), is proposed for delineating the infected regions of the lung from CT images. Incorporating dilated convolutions with varying dilation rates, into a mixed attention framework, allows capture of multi-scale features towards improved segmentation of lesions having different sizes and textures. Mixed attention helps prioritise relevant feature maps to be probed, along with those regions containing crucial information within these maps. Deep supervision facilitates discovery of robust and discriminatory characteristics in the hidden layers at shallower levels, while overcoming the vanishing gradient. This is followed by estimating the severity of the disease, based on the ratio of the area of infected region in each lung with respect to its entire volume. Experimental results, on three publicly available datasets, indicate that the MiADS-Net outperforms several state-of-the-art architectures in the COVID-19 lesion segmentation task; particularly in defining structures involving complex geometries.
Probing the blue axion with cosmic optical background anisotropies

A radiative decaying Big Bang relic with a mass $m_a\simeq 5-25 \,\rm eV$, which we dub"blue axion", can be probed with direct and indirect observations of the cosmic optical background (COB). The strongest bounds on blue-axion cold dark matter come from the Hubble Space Telescope (HST) measurements of COB anisotropies at $606$~nm. We suggest that new HST measurements at higher frequencies ($336$~nm and $438$~nm) can improve current constraints on the lifetime up to one order of magnitude, and we show that also thermally produced and hot relic blue axions can be competitively probed by COB anisotropies. We exclude the simple interpretation of the excess in the diffuse COB detected by the Long Range Reconnaissance Imager (LORRI) as photons produced by a decaying hot relic. Finally, we comment on the reach of upcoming line intensity mapping experiments, that could detect blue axions with a lifetime as large as $10^{29}\,\rm s$ or $10^{27}\,\rm s$ for the cold dark matter and the hot relic case, respectively.
Ae2I: A Double Autoencoder for Imputation of Missing Values

The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from other columns of the same row, or from the other rows of the same column. This paper introduces a double autoencoder for imputation ($Ae^2I$) that simultaneously and collaboratively uses both row-row relationship and column-column relationship to impute the missing values. Empirical tests on Movielens 1M dataset demonstrated that $Ae^2I$ outperforms the current state-of-the-art models for recommender systems by a significant margin.
Hammocks for non-domestic string algebras

We show that the order type of the simplest version of a hammock for string algebras lies in the class of finite description linear orders--the smallest class of linear orders containing $\mathbf 0$, $\mathbf 1$, and that is closed under isomorphisms, finite order sum, anti-lexicographic product with $\omega$ and $\omega^*$, and shuffle of finite subsets--using condensation (localization) of linear orders as a tool. We also introduce two finite subsets of the set of bands and use them to describe the location of left $\mathbb N$-strings in the completion of hammocks.
Risk-aware Vehicle Motion Planning Using Bayesian LSTM-Based Model Predictive Control

—Understanding the probabilistic traffic environment is a vital challenge for the motion planning of autonomous vehicles. To make feasible control decisions, forecasting future trajectories of adjacent cars is essential for intelligent vehicles to assess potential conflicts and react to reduce the risk. This paper first introduces a Bayesian Long Short-term Memory (BLSTM) model to learn human drivers’ behaviors and habits from their historical trajectory data. The model predicts the probability distribution of surrounding vehicles’ positions, which are used to estimate dynamic conflict risks. Next, a hybrid automaton is built to model the basic motions of a car, and the conflict risks are assessed for real-time state-space transitions based on environmental information. Finally, a BLSTM-based Model Predictive Control (MPC) is built to navigate vehicles through safe paths with the least predicted conflict risk. By merging BLSTM with MPC, the designed neural-based MPC overcomes the defect that traditional MPC is hard to model uncertain conflict risks. The simulation results show that our proposed BLSTM-based MPC performs better than human drivers because it can foresee potential conflicts and take action to avoid them.
Pose Metrics: a New Paradigm for Character Motion Edition

In animation, style can be considered as a distinctive layer over the content of a motion, allowing a character to achieve the same gesture in various ways. Editing existing animation to modify the style while keeping the same content is an interesting task, which can facilitate the re-use of animation data and cut down on production time. Existing animation edition methods either work directly on the motion data, providing precise but tedious tools, or manipulate semantic style categories, taking control away from the user. As a middle ground, we propose a new character motion edition paradigm allowing higher-level manipulations without sacrificing controllability. We describe the concept of pose metrics, objective value functions which can be used to edit animation, leaving the style interpretation up to the user. We then propose an edition pipeline to edit animation data using pose metrics.
Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis

Automatic lesion analysis is critical in skin cancer diagnosis and ensures effective treatment. The computer aided diagnosis of such skin cancer in dermoscopic images can significantly reduce the clinicians workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detection and accurate identification of skin lesions remain challenging. This research focuses on a two step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effectiveness of deep convolutional neural network based architectures by designing an encoder-decoder architecture for skin lesion segmentation and CNN based classification network. The proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union and Dice Similarity Coefficient. Our cascaded end to end deep learning based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation.
Thermodynamically consistent phase-field theory including nearest-neighbor pair correlations

Most of our current understanding of phase separation is based on ideas that disregard correlaions. Here we illuminate unexpected effects of correlations on the structure and thermodynamics of interfaces and in turn phase separation, which are decisive in systems with strong interactions. Evaluating the continuum limit of the Ising model on the Bethe-Guggenheim level, we derive a Cahn-Hilliard free energy that takes into account pair correlations. For a one-dimensional interface in a strip geometry these are shown to give rise to an effective interface broadening at interaction strengths near and above the thermal energy, which is verified in the Ising model. Interface broadening is the result of an entropy-driven interface delocalization, which is not accounted for in the widely adopted mean field theory. Pair correlations are required for thermodynamic consistency as they enforce a thermodynamically optimal local configuration of defects and profoundly affect nucleation and spinodal decomposition at strong coupling.
Fast hardware-aware matrix-free algorithm for higher-order finite-element discretized matrix multivector products on distributed systems

Recent hardware-aware matrix-free algorithms for higher-order finite-element (FE) discretized matrix-vector multiplications reduce floating point operations and data access costs compared to traditional sparse matrix approaches. This work proposes efficient matrix-free algorithms for evaluating FE discretized matrix-multivector products on both multi-node CPU and GPU architectures. We address a critical gap in existing matrix-free implementations, which are well suited only for the action of FE discretized matrices on a single vector. We employ batched evaluation strategies, with the batchsize tailored to underlying hardware architectures, leading to better data locality and enabling further parallelization. On CPUs, we utilize even-odd decomposition, SIMD vectorization, and overlapping computation and communication strategies. On GPUs, we employ strategies to overlap compute and data movement in conjunction with GPU shared memory, constant memory, and kernel fusion to reduce data accesses. Our implementation outperforms the baselines for Helmholtz operator action, achieving up to 1.4x improvement on one CPU node and up to 2.8x on one GPU node, while reaching up to 4.4x and 1.5x improvement on multiple nodes for CPUs ($\sim 3000$ cores) and GPUs ($\sim$ 25 GPUs), respectively. We further benchmark the performance of the proposed implementation for solving a model eigenvalue problem for 1024 smallest eigenvalue-eigenvector pairs by employing the Chebyshev Filtered Subspace Iteration method, achieving up to 1.5x improvement on one CPU node and up to 2.2x on one GPU node while reaching up to 3.0x and 1.4x improvement on multinode CPUs ($\sim 3000$ cores) and GPUs ($\sim$ 25 GPUs), respectively.
Quantitative genetic-by-soil microbiome interactions in a perennial grass affect functional traits

Plants interact with diverse microbiomes that can impact plant growth and performance. Recent studies highlight the potential beneficial aspects of plant microbiomes, including the possibility that microbes facilitate the process of local adaptation in their host plants. Microbially mediated local adaptation in plants occurs when local host genotypes have higher fitness than foreign genotypes because of their affiliation with locally beneficial microbes. Here, plant adaptation results from genetic interactions of the host with locally beneficial microbes (e.g. host genotype-by-microbiome interactions). We used a recombinant inbred line (RIL) mapping population derived from upland and lowland ecotypes of the diploid C4 perennial bunch grass Panicum hallii to explore quantitative genetic responses to soil microbiomes focusing on functional root and shoot traits involved in ecotypic divergence. We show that the growth and development of ecotypes and their trait divergence depends on soil microbiomes. Moreover, we find that the genetic architecture is modified by soil microbiomes, revealing important plant genotype-by-microbiome interactions for quantitative traits. We detected a number of quantitative trait loci (QTL) that interact with the soil microbiome. Our results highlight the importance of microbial interactions in ecotypic divergence and trait genetic architecture in C4 perennial grasses.
An explicit solution of the inverse periodic problem for Hill's equation

Let the periodic spectrum of the Hill’s operator ${{ - d^2 } / {dx^2 + p(x)}}$ have n nonzero gaps. We give explicit formulas for the isospectral manifold of operators ${{ - d^2 } / {dx^2 + q(x)}}$ having the same spectrum. This allows us to realize the isospectral manifold explicitly as a torus. What makes this possible is an explicit solution of the flow \[ \left. {\frac{d}{{dt}}q = \frac{d}{{dx}}\frac{\partial }{{\partial q(x)}}\Delta (\lambda ,q)} \right|_{\lambda = \mu _n (q)} \] introduced by McKean and Trubowitz, where $\Delta $ is the discriminant and $\mu _n (q)$ is the nth Dirichlet eigenvalue. The general case (in which there are an infinite number of nonzero gaps) is handled by a limiting process.
First-Hand Impressions: Charting and Predicting User Impressions of Robot Hands

Designing robotic hands has been an active area of research and innovation in the last decade. However, little is known about how people perceive robot hands and react to being touched by them. To inform hand design for social robots, we created a database of 73 robot hands and ran two user studies. In the first study, 160 online users rated the hands in our database. Variations in user ratings mostly centered on the perceived Comfortableness, Interestingness, and Industrialness of the hands. In a second lab-based study, users evaluated seven physical hands and had similar ratings to results from the online study. Furthermore, we did not find a significant difference in user ratings before and after the users were touched by the hands. We provide regression models that can predict user ratings from the hand features (e.g., number of fingers) and an online interface for using our robot hand database and predictive models.
AIS-Based Vessel Traffic Flow Prediction Using Combined EMD-LSTM Method

The accurate prediction of vessel traffic flow is an essential problem of marine intelligent transportation systems. Existing approaches for predicting vessel traffic flow focus primarily on the trend of historical traffic flow, ignoring the influence of randomness in traffic flow on the prediction of vessel traffic flow. To achieve high precision vessel traffic flow prediction, this study introduced a vessel traffic flow prediction approach based on empirical mode decomposition (EMD) and long-short term memory network (LSTM). Specifically, this paper firstly extracts the traffic flow of vessel traffic by using automatic identification system (AIS); Secondly, in an attempt to reduce the influence of randomness in traffic flow prediction approach, in this study, the vessel traffic flow is decomposed using the EMD algorithm and the Intrinsic mode functions (IMF) of the change in vessel traffic flow are extracted; Then, the LSTM approach is applied to predict multiple IMFs of vessel traffic flow, and the results are superimposed to obtain accurate vessel traffic flow results; Finally, in this paper, we conduct experiments on a huge quantity of AIS data, and the experimental results show the superior performance of the proposed method in vessel traffic flow prediction.
jpf-logic

We present jpf-logic, an extension of the model checker Java PathFinder (JPF). Our extension jpf-logic provides a framework to check properties expressed in temporal logics such as linear temporal logic (LTL) and computation tree logic (CTL). To support a logic in our framework, we (1) implement a parser for the logic, (2) develop a hierarchy of classes that represent the abstract syntax of the logic and implement a transformation from parse trees of formulas to the corresponding abstract syntax trees, and (3) implement a model checking algorithm that takes as input an abstract syntax tree of a formula and a partial transition system. The latter represents a model of the Java application. All three components have been implemented for CTL. The first two have been implemented for LTL.
Defect Classification Method of X-ray Images Based on Improved U-Net

X-ray nondestructive testing means are widely used in the inspection process of internal defects of parts. In practical inspection, defects are generally determined and rated by manual inspection based on X-ray images, which is inefficient and cannot meet the requirements of high-volume automatic inspection. This paper proposes an automatic defect classification model based on an improved U-Net. First, a classifier is added behind the encoder of U-Net. The encoder and classifier are connected in series to form the main branch of the model to complete the classification task. Second, the decoder of U-Net is improved by adding an attention module. The encoder and the improved decoder form the auxiliary branch of the model, which completes the segmentation task. The model proposed in this paper has two advantages. First, for small defects in images, the segmentation-based auxiliary task enables the model to focus on these small targets during the learning process and learn features with more representational power. Second, the introduction of an attention mechanism in the decoder can suppress the interference information, retain the effective location information, focus on the learning of defective regions, adjust the different feature mapping weights, and improve the performance of the model. The method has been validated on the self-built dataset and the dataset collected from X-ray inspection industrial sites and compared with typical image classification methods. The results show that the proposed method in this paper has higher defect recognition accuracy than other classical deep network models and can effectively identify multiple types of defect features while providing assurance and reference for the quality and safety performance of the parts to be inspected.
Point Cloud Classification Method for Transmission Towers based on CAA-PointNet

In the filed of smart grid, the accurate classification of transmission towers is one of the hot research topics. However, to extract the different features of different towers in the process of classification is still a difficult task, in this paper a point cloud classification method for towers based on CAA-PointNet is proposed. Using PointNet as the basic framework, multi-scale local neighborhood is generated by sampling and grouping, and combined with the channel-wise affinity attention to enhance the the differential feature weight between the categories, so as to achieve accurate classification of towers. This method has good classification results for five different categories of tower point cloud data sets, with the overall accuracy of 95.0% and the average accuracy of 94.2%.
A Beamspace-Based Sparse Estimation Method for Array Signal

In this paper, the problem of direction of arrival (DOA) estimation with sparse methods for array processing is concerned with the observation domain aspect, and an estimation method named beamspace-based sparse (BSE) is proposed. In BSE method, the beam space energy of the array signal is observed and modeled as the weighted sum of the signal energy of each azimuth beam pattern sequences of the conventional beamforming (CBF). BSE constructs a solution architecture for joint -norm minimization and quadratic constraint linear programming (QCLP) of noise power. Based on the estimation of noise background power under Gaussian noise conditions, a parameter selection method is derived, which can be quickly solved by the convex programming method. BSE has higher azimuth resolution and a lower false alarm rate when compared to sparse estimation methods based on other observation domains. It also performs well in coherent environments.
BOD-tree: An One-Dimensional Balanced Indexing Algorithm

The rapid growth oftrajectory data has prompted researchers to develop multiple large trajectory data management systems. One of the fundamental requirements of all these systems, regardless of their architecture, is to partition data efficiently between machines. In the typical query operations of tracks, the query on ID is a frequent operation of track query, such as ID time range query, ID space range query, etc. A widely used ID indexing technique is to reuse an existing search tree, such as a Kd-tree, by building a temporary tree for the input samples and using its leaf nodes as partition boundaries. However, we show in this paper that this approach has significant limitations. To overcome these limitations, we propose a new indexing, BOD-tree, which inherits the main features of the Kd-tree and can also partition the dataset into multiple balanced splits. We test the method on real datasets, and extensive experiments show that our algorithm can improve resource usage efficiency.