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Bin Yang

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preprint2026arXiv

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existing MAS typically rely on pre-defined or pre-compiled communication topologies, which limits their flexibility and adaptability to dynamic task requirements. In this work, we propose Differentiable Mixture-of-Agents (DMoA), a self-evolving multi-agent framework that enables elastic and adaptive agent collaboration during inference. Instead of statically constructing workflows, DMoA dynamically routes and activates agents at each reasoning step, allowing the system to implicitly simulate diverse communication topologies and adapt to evolving demands. To achieve this, we design a differentiable, context-aware routing mechanism that leverages recurrent structures to incorporate historical and contextual information, producing sparse agent activations in a step-wise manner. Furthermore, we introduce predictive entropy as self-supervised signals to optimize the routing process, enabling efficient test-time adaptation without external annotations. Extensive experiments across 9 benchmarks demonstrate that DMoA achieves state-of-the-art performance while exhibiting strong efficiency, robustness, and ensembling capabilities.

preprint2024arXiv

Calibration-free online test-time adaptation for electroencephalography motor imagery decoding

Providing a promising pathway to link the human brain with external devices, Brain-Computer Interfaces (BCIs) have seen notable advancements in decoding capabilities, primarily driven by increasingly sophisticated techniques, especially deep learning. However, achieving high accuracy in real-world scenarios remains a challenge due to the distribution shift between sessions and subjects. In this paper we will explore the concept of online test-time adaptation (OTTA) to continuously adapt the model in an unsupervised fashion during inference time. Our approach guarantees the preservation of privacy by eliminating the requirement to access the source data during the adaptation process. Additionally, OTTA achieves calibration-free operation by not requiring any session- or subject-specific data. We will investigate the task of electroencephalography (EEG) motor imagery decoding using a lightweight architecture together with different OTTA techniques like alignment, adaptive batch normalization, and entropy minimization. We examine two datasets and three distinct data settings for a comprehensive analysis. Our adaptation methods produce state-of-the-art results, potentially instigating a shift in transfer learning for BCI decoding towards online adaptation.

preprint2024arXiv

Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation

Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model during test-time, leveraging the current test data. In real-world scenarios, test data streams are not always independent and identically distributed (i.i.d.). Instead, they are frequently temporally correlated, making them non-i.i.d. Many existing methods struggle to cope with this scenario. In response, we propose a diversity-aware and category-balanced buffer that can simulate an i.i.d. data stream, even in non-i.i.d. scenarios. Combined with a diversity and entropy-weighted entropy loss, we show that a stable adaptation is possible on a wide range of corruptions and natural domain shifts, based on ImageNet. We achieve state-of-the-art results on most considered benchmarks.

preprint2022arXiv

$(O,G)$-granular variable precision fuzzy rough sets based on overlap and grouping functions

Since Bustince et al. introduced the concepts of overlap and grouping functions, these two types of aggregation functions have attracted a lot of interest in both theory and applications. In this paper, the depiction of $(O,G)$-granular variable precision fuzzy rough sets ($(O,G)$-GVPFRSs for short) is first given based on overlap and grouping functions. Meanwhile, to work out the approximation operators efficiently, we give another expression of upper and lower approximation operators by means of fuzzy implications and co-implications. Furthermore, starting from the perspective of construction methods, $(O,G)$-GVPFRSs are represented under diverse fuzzy relations. Finally, some conclusions on the granular variable precision fuzzy rough sets (GVPFRSs for short) are extended to $(O,G)$-GVPFRSs under some additional conditions.

preprint2022arXiv

A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis

The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energy management, where anomaly detection is often essential to enable reliability and safety. Many recent studies target anomaly detection for time series data. Indeed, area of time series anomaly detection is characterized by diverse data, methods, and evaluation strategies, and comparisons in existing studies consider only part of this diversity, which makes it difficult to select the best method for a particular problem setting. To address this shortcoming, we introduce taxonomies for data, methods, and evaluation strategies, provide a comprehensive overview of unsupervised time series anomaly detection using the taxonomies, and systematically evaluate and compare state-of-the-art traditional as well as deep learning techniques. In the empirical study using nine publicly available datasets, we apply the most commonly-used performance evaluation metrics to typical methods under a fair implementation standard. Based on the structuring offered by the taxonomies, we report on empirical studies and provide guidelines, in the form of comparative tables, for choosing the methods most suitable for particular application settings. Finally, we propose research directions for this dynamic field.

preprint2022arXiv

A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning

In post-silicon validation, tuning is to find the values for the tuning knobs, potentially as a function of process parameters and/or known operating conditions. In this sense, an more efficient tuning requires identifying the most critical tuning knobs and process parameters in terms of a given figure-of-merit for a Device Under Test (DUT). This is often manually conducted by experienced experts. However, with increasingly complex chips, manual inspection on a large amount of raw variables has become more challenging. In this work, we leverage neural networks to efficiently select the most relevant variables and present a corresponding deep-learning-aided pipeline for efficient tuning.

preprint2022arXiv

An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions

Integrating different representations from complementary sensing modalities is crucial for robust scene interpretation in autonomous driving. While deep learning architectures that fuse vision and range data for 2D object detection have thrived in recent years, the corresponding modalities can degrade in adverse weather or lighting conditions, ultimately leading to a drop in performance. Although domain adaptation methods attempt to bridge the domain gap between source and target domains, they do not readily extend to heterogeneous data distributions. In this work, we propose an unsupervised domain adaptation framework, which adapts a 2D object detector for RGB and lidar sensors to one or more target domains featuring adverse weather conditions. Our proposed approach consists of three components. First, a data augmentation scheme that simulates weather distortions is devised to add domain confusion and prevent overfitting on the source data. Second, to promote cross-domain foreground object alignment, we leverage the complementary features of multiple modalities through a multi-scale entropy-weighted domain discriminator. Finally, we use carefully designed pretext tasks to learn a more robust representation of the target domain data. Experiments performed on the DENSE dataset show that our method can substantially alleviate the domain gap under the single-target domain adaptation (STDA) setting and the less explored yet more general multi-target domain adaptation (MTDA) setting.

preprint2022arXiv

Conditional Seq2Seq model for the time-dependent two-level system

We apply the deep learning neural network architecture to the two-level system in quantum optics to solve the time-dependent Schrodinger equation. By carefully designing the network structure and tuning parameters, above 90 percent accuracy in super long-term predictions can be achieved in the case of random electric fields, which indicates a promising new method to solve the time-dependent equation for two-level systems. By slightly modifying this network, we think that this method can solve the two- or three-dimensional time-dependent Schrodinger equation more efficiently than traditional approaches.

preprint2022arXiv

Conditional Variable Selection for Intelligent Test

Intelligent test requires efficient and effective analysis of high-dimensional data in a large scale. Traditionally, the analysis is often conducted by human experts, but it is not scalable in the era of big data. To tackle this challenge, variable selection has been recently introduced to intelligent test. However, in practice, we encounter scenarios where certain variables (e.g. some specific processing conditions for a device under test) must be maintained after variable selection. We call this conditional variable selection, which has not been well investigated for embedded or deep-learning-based variable selection methods. In this paper, we discuss a novel conditional variable selection framework that can select the most important candidate variables given a set of preselected variables.

preprint2022arXiv

Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer

In recent years, there has been tremendous progress in the field of semantic segmentation. However, one remaining challenging problem is that segmentation models do not generalize to unseen domains. To overcome this problem, one either has to label lots of data covering the whole variety of domains, which is often infeasible in practice, or apply unsupervised domain adaptation (UDA), only requiring labeled source data. In this work, we focus on UDA and additionally address the case of adapting not only to a single domain, but to a sequence of target domains. This requires mechanisms preventing the model from forgetting its previously learned knowledge. To adapt a segmentation model to a target domain, we follow the idea of utilizing light-weight style transfer to convert the style of labeled source images into the style of the target domain, while retaining the source content. To mitigate the distributional shift between the source and the target domain, the model is fine-tuned on the transferred source images in a second step. Existing light-weight style transfer approaches relying on adaptive instance normalization (AdaIN) or Fourier transformation still lack performance and do not substantially improve upon common data augmentation, such as color jittering. The reason for this is that these methods do not focus on region- or class-specific differences, but mainly capture the most salient style. Therefore, we propose a simple and light-weight framework that incorporates two class-conditional AdaIN layers. To extract the class-specific target moments needed for the transfer layers, we use unfiltered pseudo-labels, which we show to be an effective approximation compared to real labels. We extensively validate our approach (CACE) on a synthetic sequence and further propose a challenging sequence consisting of real domains. CACE outperforms existing methods visually and quantitatively.

preprint2022arXiv

Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey

Deep learning technologies have demonstrated remarkable effectiveness in a wide range of tasks, and deep learning holds the potential to advance a multitude of applications, including in edge computing, where deep models are deployed on edge devices to enable instant data processing and response. A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices. These characteristics make it difficult to build deep learning solutions that unleash the potential of edge devices while complying with their constraints. A promising approach to addressing this challenge is to automate the design of effective deep learning models that are lightweight, require only a little storage, and incur only low computational overheads. This survey offers comprehensive coverage of studies of design automation techniques for deep learning models targeting edge computing. It offers an overview and comparison of key metrics that are used commonly to quantify the proficiency of models in terms of effectiveness, lightness, and computational costs. The survey then proceeds to cover three categories of the state-of-the-art of deep model design automation techniques: automated neural architecture search, automated model compression, and joint automated design and compression. Finally, the survey covers open issues and directions for future research.

preprint2022arXiv

Evolution of barchan dune interactions investigated by a downscaled water tunnel experiment: the temporal characteristics and a soliton-like behavior

This paper reports a downscaled water tunnel experiment to study the temporal characteristics of a double dune interaction system and the new pattern of dune interaction when the initial mass ratio of the two dunes is large. These topics are useful for a comprehensive understanding of the dune interaction system but were rarely covered before. The turnover time scale under dune interaction is defined, and its time averaged value is found to have a nonmonotonic relationship with the initial mass ratio. A nonmonotonic relationship is also found between the convexity of the downstream dune tip and the initial mass ratio. The stationary points of the two nonmonotonic curves above correspond to the same dune interaction pattern named 'exchange-chasing', which is considered indispensable in the classification map of dune interactions. The upstream dune acts as an energy transmitter between fluid flow and the downstream dune. A soliton-like behavior occurs when the downstream dune enlarges, where a small dune is detached from the downstream dune tip and gets passed by the upstream dune approximately without mass exchange. The activity of such temporary soliton is found to be negatively related with the initial dune spacing and positively related with the initial mass ratio.

preprint2022arXiv

Fast solver for J2-perturbed Lambert problem using deep neural network

This paper presents a novel and fast solver for the J2-perturbed Lambert problem. The solver consists of an intelligent initial guess generator combined with a differential correction procedure. The intelligent initial guess generator is a deep neural network that is trained to correct the initial velocity vector coming from the solution of the unperturbed Lambert problem. The differential correction module takes the initial guess and uses a forward shooting procedure to further update the initial velocity and exactly meet the terminal conditions. Eight sample forms are analyzed and compared to find the optimum form to train the neural network on the J2-perturbed Lambert problem. The accuracy and performance of this novel approach will be demonstrated on a representative test case: the solution of a multi-revolution J2-perturbed Lambert problem in the Jupiter system. We will compare the performance of the proposed approach against a classical standard shooting method and a homotopy-based perturbed Lambert algorithm. It will be shown that, for a comparable level of accuracy, the proposed method is significantly faster than the other two.

preprint2022arXiv

Federated Latent Class Regression for Hierarchical Data

Federated Learning (FL) allows a number of agents to participate in training a global machine learning model without disclosing locally stored data. Compared to traditional distributed learning, the heterogeneity (non-IID) of the agents slows down the convergence in FL. Furthermore, many datasets, being too noisy or too small, are easily overfitted by complex models, such as deep neural networks. Here, we consider the problem of using FL regression on noisy, hierarchical and tabular datasets in which user distributions are significantly different. Inspired by Latent Class Regression (LCR), we propose a novel probabilistic model, Hierarchical Latent Class Regression (HLCR), and its extension to Federated Learning, FEDHLCR. FEDHLCR consists of a mixture of linear regression models, allowing better accuracy than simple linear regression, while at the same time maintaining its analytical properties and avoiding overfitting. Our inference algorithm, being derived from Bayesian theory, provides strong convergence guarantees and good robustness to overfitting. Experimental results show that FEDHLCR offers fast convergence even in non-IID datasets.

preprint2022arXiv

Improving the estimation of directional area scattering factor (DASF) from canopy reflectance: theoretical basis and validation

Directional area scattering factor (DASF) is a critical canopy structural parameter for vegetation monitoring. It provides an efficient tool for decoupling of canopy structure and leaf optics from canopy reflectance. Current standard approach to estimate DASF from canopy bidirectional reflectance factor (BRF) is based on the assumption that in the weakly absorbing 710 to 790 nm spectral interval, leaf scattering does not change much with the concentration of dry matter and thus its variation can be neglected. This results in biased estimates of DASF and consequently leads to uncertainty in DASF-related applications. This study proposes a new approach to account for variations in concentrations of this biochemical constituent, which additionally uses the canopy BRF at 2260 nm. In silico analysis of the proposed approach suggests significant increase in accuracy over the standard technique by a relative root mean square error (rRMSE) of 49% and 34% for one- and three dimensional scenes, respectively. When compared with indoor multi-angular hyperspectral measurements reported in literature, the mean absolute error has reduced by 68% for needle leaf and 20% for broadleaf canopies. Thus, the proposed DASF estimation approach outperforms the current one and can be used more reliably in DASF-related applications, such as vegetation monitoring of functional traits, dynamics, and radiation budget.

preprint2022arXiv

Influence-aware Task Assignment in Spatial Crowdsourcing (Technical Report)

With the widespread diffusion of smartphones, Spatial Crowdsourcing (SC), which aims to assign spatial tasks to mobile workers, has drawn increasing attention in both academia and industry. One of the major issues is how to best assign tasks to workers. Given a worker and a task, the worker will choose to accept the task based on her affinity towards the task, and the worker can propagate the information of the task to attract more workers to perform it. These factors can be measured as worker-task influence. Since workers' affinities towards tasks are different and task issuers may ask workers who performed tasks to propagate the information of tasks to attract more workers to perform them, it is important to analyze worker-task influence when making assignments. We propose and solve a novel influence-aware task assignment problem in SC, where tasks are assigned to workers in a manner that achieves high worker-task influence. In particular, we aim to maximize the number of assigned tasks and worker-task influence. To solve the problem, we first determine workers' affinities towards tasks by identifying workers' historical task-performing patterns. Next, a Historical Acceptance approach is developed to measure workers' willingness of performing a task, i.e., the probability of workers visiting the location of the task when they are informed. Next, we propose a Random reverse reachable-based Propagation Optimization algorithm that exploits reverse reachable sets to calculate the probability of workers being informed about tasks in a social network. Based on worker-task influence derived from the above three factors, we propose three influence-aware task assignment algorithms that aim to maximize the number of assigned tasks and worker-task influence. Extensive experiments on two real-world datasets offer detailed insight into the effectiveness of our solutions.

preprint2022arXiv

Mathematical Analysis and Numerical Approximations of Density Functional Theory Models for Metallic Systems

In this paper, we investigate the energy minimization model of the ensemble Kohn-Sham density functional theory for metallic systems, in which a pseudo-eigenvalue matrix and a general smearing approach are involved. We study the invariance and the existence of the minimizer of the energy functional. We propose an adaptive double step size strategy and the corresponding preconditioned conjugate gradient methods for solving the energy minimization model. Under some mild but reasonable assumptions, we prove the global convergence of our algorithms. Numerical experiments show that our algorithms are efficient, especially for large scale metallic systems. In particular, our algorithms produce convergent numerical approximations for some metallic systems, for which the traditional self-consistent field iterations fail to converge.

preprint2022arXiv

MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption

An unresolved problem in Deep Learning is the ability of neural networks to cope with domain shifts during test-time, imposed by commonly fixing network parameters after training. Our proposed method Meta Test-Time Training (MT3), however, breaks this paradigm and enables adaption at test-time. We combine meta-learning, self-supervision and test-time training to learn to adapt to unseen test distributions. By minimizing the self-supervised loss, we learn task-specific model parameters for different tasks. A meta-model is optimized such that its adaption to the different task-specific models leads to higher performance on those tasks. During test-time a single unlabeled image is sufficient to adapt the meta-model parameters. This is achieved by minimizing only the self-supervised loss component resulting in a better prediction for that image. Our approach significantly improves the state-of-the-art results on the CIFAR-10-Corrupted image classification benchmark. Our implementation is available on GitHub.

preprint2022arXiv

On three types of $L$-fuzzy $β$-covering-based rough sets

In this paper, we mainly construct three types of $L$-fuzzy $β$-covering-based rough set models and study the axiom sets, matrix representations and interdependency of these three pairs of $L$-fuzzy $β$-covering-based rough approximation operators. Firstly, we propose three pairs of $L$-fuzzy $β$-covering-based rough approximation operators by introducing the concepts such as $β$-degree of intersection and $β$-subsethood degree, which are generalizations of degree of intersection and subsethood degree, respectively. And then, the axiom set for each of these $L$-fuzzy $β$-covering-based rough approximation operator is investigated. Thirdly, we give the matrix representations of three types of $L$-fuzzy $β$-covering-based rough approximation operators, which make it valid to calculate the $L$-fuzzy $β$-covering-based lower and upper rough approximation operators through operations on matrices. Finally, the interdependency of the three pairs of rough approximation operators based on $L$-fuzzy $β$-covering is studied by using the notion of reducible elements and independent elements. In other words, we present the necessary and sufficient conditions under which two $L$-fuzzy $β$-coverings can generate the same lower and upper rough approximation operations.

preprint2022arXiv

RetroGraph: Retrosynthetic Planning with Graph Search

Retrosynthetic planning, which aims to find a reaction pathway to synthesize a target molecule, plays an important role in chemistry and drug discovery. This task is usually modeled as a search problem. Recently, data-driven methods have attracted many research interests and shown promising results for retrosynthetic planning. We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e.g., AND-OR tree search, Monte Carlo tree search). Such redundancies make the search process inefficient. We propose a graph-based search policy that eliminates the redundant explorations of any intermediate molecules. As searching over a graph is more complicated than over a tree, we further adopt a graph neural network to guide the search over graphs. Meanwhile, our method can search a batch of targets together in the graph and remove the inter-target duplication in the tree-based search methods. Experimental results on two datasets demonstrate the effectiveness of our method. Especially on the widely used USPTO benchmark, we improve the search success rate to 99.47%, advancing previous state-of-the-art performance for 2.6 points.

preprint2022arXiv

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version

Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable to outliers and exhibit low explainability. To address these two limitations, we propose robust and explainable unsupervised autoencoder frameworks that decompose an input time series into a clean time series and an outlier time series using autoencoders. Improved explainability is achieved because clean time series are better explained with easy-to-understand patterns such as trends and periodicities. We provide insight into this by means of a post-hoc explainability analysis and empirical studies. In addition, since outliers are separated from clean time series iteratively, our approach offers improved robustness to outliers, which in turn improves accuracy. We evaluate our approach on five real-world datasets and report improvements over the state-of-the-art approaches in terms of robustness and explainability. This is an extended version of "Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection", to appear in IEEE ICDE 2022.

preprint2022arXiv

Some neighborhood-related fuzzy covering-based rough set models and their applications for decision making

Fuzzy rough set (FRS) has a great effect on data mining processes and the fuzzy logical operators play a key role in the development of FRS theory. In order to further generalize the FRS theory to more complicated data environments, we firstly propose four types of fuzzy neighborhood operators based on fuzzy covering by overlap functions and their implicators in this paper. Meanwhile, the derived fuzzy coverings from an original fuzzy covering are defined and the equalities among overlap function-based fuzzy neighborhood operators based on a finite fuzzy covering are also investigated. Secondly, we prove that new operators can be divided into seventeen groups according to equivalence relations, and the partial order relations among these seventeen classes of operators are discussed, as well. Go further, the comparisons with $ t$-norm-based fuzzy neighborhood operators given by D'eer et al. are also made and two types of neighborhood-related fuzzy covering-based rough set models, which are defined via different fuzzy neighborhood operators that are on the basis of diverse kinds of fuzzy logical operators proposed. Furthermore, the groupings and partially order relations are also discussed. Finally, a novel fuzzy TOPSIS methodology is put forward to solve a biosynthetic nanomaterials select issue, and the rationality and enforceability of our new approach is verified by comparing its results with nine different methods.

preprint2022arXiv

Some properties of fuzzy $t$-norm and vague $t$-norm

Rosenfeld defined a fuzzy subgroup of group $G$ as a fuzzy subset of $G$ with two special conditions attached\cite{Rosenfeld1971Fuzzysubgroups}. In this paper, we introduce the fuzzy $t$-norms and vague $t$-norms. The unit interval with a $t$-norm or a $t$-conorm is a special monoid, so we mainly talk about fuzzy subsets of monoids and vague monoids. Firstly, we generalize some properties of $t$-norm to fuzzy $t$-subnorm, so that we can analyze and classify the fuzzy $t$-norms. Further, we explore specific research on these properties of the vague $t$-norm. In addition, the concept of lattice is introduced, and then the present conclusions are extended to bounded lattices. Finally, we define the concept of fuzzy monoids by aggregate functions, uninorms, nullnorms and draw the relevant results.

preprint2022arXiv

The generalizations of fuzzy monoids and vague monoids

In this paper, we present the fuzzy monoids and vague monoids by using aggregation operators. The unit interval with a $t$-norm or a $t$-conorm is a special monoid, so we mainly talk about fuzzy subsets of monoids. Firstly, the classification of fuzzy sets based on some special aggregation operators is discussed. At the same time, we give two basic propositions about submonoids of $t$-norm and $t$-conorm. The fuzzification by uninorm and nullnorm are denoted and some properties can be drawn. Next, we briefly present fuzzy subsets on lattice. Finally, the vague monoids on aggregation operators are redefined and further consider the special cases of uninorms and nullnorms.

preprint2022arXiv

Thermal spin injection from a ferromagnet into graphene by transverse and longitudinal current

Graphene is a very promising material in spintronics due to both its high electric mobility and low intrinsic spin-obit coupling. Electronic spins can be injected from a ferromagnetic material through a tunnel contact into graphene owing to a spin relaxation length as high as 5μm. In recent years, a new approach creating spin current employed thermal effects and heat flow. Here, by applying transverse and longitudinal current to a grahene spin valve device, the interplay between the heat spin current and the charge spin current is investigated. The non-local spin voltage is enhanced by the thermal spin injection and the thermal spin voltage reaches a maximum close to the Dirac point which makes graphene a promising material for a future thermoelectric spin device due to its long spin lifetime and spin diffusion length.

preprint2022arXiv

Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version

Traffic time series forecasting is challenging due to complex spatio-temporal dynamics time series from different locations often have distinct patterns; and for the same time series, patterns may vary across time, where, for example, there exist certain periods across a day showing stronger temporal correlations. Although recent forecasting models, in particular deep learning based models, show promising results, they suffer from being spatio-temporal agnostic. Such spatio-temporal agnostic models employ a shared parameter space irrespective of the time series locations and the time periods and they assume that the temporal patterns are similar across locations and do not evolve across time, which may not always hold, thus leading to sub-optimal results. In this work, we propose a framework that aims at turning spatio-temporal agnostic models to spatio-temporal aware models. To do so, we encode time series from different locations into stochastic variables, from which we generate location-specific and time-varying model parameters to better capture the spatio-temporal dynamics. We show how to integrate the framework with canonical attentions to enable spatio-temporal aware attentions. Next, to compensate for the additional overhead introduced by the spatio-temporal aware model parameter generation process, we propose a novel window attention scheme, which helps reduce the complexity from quadratic to linear, making spatio-temporal aware attentions also have competitive efficiency. We show strong empirical evidence on four traffic time series datasets, where the proposed spatio-temporal aware attentions outperform state-of-the-art methods in term of accuracy and efficiency. This is an extended version of "Towards Spatio-Temporal Aware Traffic Time Series Forecasting", to appear in ICDE 2022 [1], including additional experimental results.

preprint2022arXiv

Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version

A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long sequence multivariate time series forecasting. While recent attention-based forecasting models show strong abilities in capturing long-term dependencies, they still suffer from two key limitations. First, canonical self attention has a quadratic complexity w.r.t. the input time series length, thus falling short in efficiency. Second, different variables' time series often have distinct temporal dynamics, which existing studies fail to capture, as they use the same model parameter space, e.g., projection matrices, for all variables' time series, thus falling short in accuracy. To ensure high efficiency and accuracy, we propose Triformer, a triangular, variable-specific attention. (i) Linear complexity: we introduce a novel patch attention with linear complexity. When stacking multiple layers of the patch attentions, a triangular structure is proposed such that the layer sizes shrink exponentially, thus maintaining linear complexity. (ii) Variable-specific parameters: we propose a light-weight method to enable distinct sets of model parameters for different variables' time series to enhance accuracy without compromising efficiency and memory usage. Strong empirical evidence on four datasets from multiple domains justifies our design choices, and it demonstrates that Triformer outperforms state-of-the-art methods w.r.t. both accuracy and efficiency. This is an extended version of "Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting", to appear in IJCAI 2022 [Cirstea et al., 2022a], including additional experimental results.

preprint2022arXiv

TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision

Nowadays, deep neural networks outperform humans in many tasks. However, if the input distribution drifts away from the one used in training, their performance drops significantly. Recently published research has shown that adapting the model parameters to the test sample can mitigate this performance degradation. In this paper, we therefore propose a novel modification of the self-supervised training algorithm SwAV that adds the ability to adapt to single test samples. Using the provided prototypes of SwAV and our derived test-time loss, we align the representation of unseen test samples with the self-supervised learned prototypes. We show the success of our method on the common benchmark dataset CIFAR10-C.

preprint2022arXiv

Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version

In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A temporal path(TP) that includes temporal information, e.g., departure time, into the path is fundamental to enable such applications. In this setting, it is essential to learn generic temporal path representations(TPRs) that consider spatial and temporal correlations simultaneously and that can be used in different applications, i.e., downstream tasks. Existing methods fail to achieve the goal since (i) supervised methods require large amounts of task-specific labels when training and thus fail to generalize the obtained TPRs to other tasks; (ii) through unsupervised methods can learn generic representations, they disregard the temporal aspect, leading to sub-optimal results. To contend with the limitations of existing solutions, we propose a Weakly-Supervised Contrastive (WSC) learning model. We first propose a temporal path encoder that encodes both the spatial and temporal information of a temporal path into a TPR. To train the encoder, we introduce weak labels that are easy and inexpensive to obtain and are relevant to different tasks, e.g., temporal labels indicating peak vs. off-peak hours from departure times. Based on the weak labels, we construct meaningful positive and negative temporal path samples by considering both spatial and temporal information, which facilities training the encoder using contrastive learning by pulling closer to the positive samples' representations while pushing away the negative samples' representations. To better guide contrastive learning, we propose a learning strategy based on Curriculum Learning such that the learning performs from easy to hard training instances. Experiments studies verify the effectiveness of the proposed method.

preprint2021arXiv

Anomaly Detection Based on Selection and Weighting in Latent Space

With the high requirements of automation in the era of Industry 4.0, anomaly detection plays an increasingly important role in higher safety and reliability in the production and manufacturing industry. Recently, autoencoders have been widely used as a backend algorithm for anomaly detection. Different techniques have been developed to improve the anomaly detection performance of autoencoders. Nonetheless, little attention has been paid to the latent representations learned by autoencoders. In this paper, we propose a novel selection-and-weighting-based anomaly detection framework called SWAD. In particular, the learned latent representations are individually selected and weighted. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD. On the benchmark datasets, the SWAD framework has reached comparable or even better performance than the state-of-the-art approaches.

preprint2021arXiv

Diverse Complexity Measures for Dataset Curation in Self-driving

Modern self-driving autonomy systems heavily rely on deep learning. As a consequence, their performance is influenced significantly by the quality and richness of the training data. Data collecting platforms can generate many hours of raw data in a daily basis, however, it is not feasible to label everything. It is thus of key importance to have a mechanism to identify "what to label". Active learning approaches identify examples to label, but their interestingness is tied to a fixed model performing a particular task. These assumptions are not valid in self-driving, where we have to solve a diverse set of tasks (i.e., perception, and motion forecasting) and our models evolve over time frequently. In this paper we introduce a novel approach and propose a new data selection method that exploits a diverse set of criteria that quantize interestingness of traffic scenes. Our experiments on a wide range of tasks and models show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.

preprint2021arXiv

End-to-end Interpretable Neural Motion Planner

In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.

preprint2021arXiv

On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks. In order to improve uncertainty estimation, we propose On-Manifold Adversarial Data Augmentation or OMADA, which specifically attempts to generate the most challenging examples by following an on-manifold adversarial attack path in the latent space of an autoencoder-based generative model that closely approximates decision boundaries between two or more classes. On a variety of datasets as well as on multiple diverse network architectures, OMADA consistently yields more accurate and better calibrated classifiers than baseline models, and outperforms competing approaches such as Mixup, as well as achieving similar performance to (at times better than) post-processing calibration methods such as temperature scaling. Variants of OMADA can employ different sampling schemes for ambiguous on-manifold examples based on the entropy of their estimated soft labels, which exhibit specific strengths for generalization, calibration of predicted uncertainty, or detection of out-of-distribution inputs.

preprint2021arXiv

SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving

Predicting future 3D LiDAR pointclouds is a challenging task that is useful in many applications in autonomous driving such as trajectory prediction, pose forecasting and decision making. In this work, we propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs), titled Stochastic LiDAR Prediction and Completion (SLPC). Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. Our contributions can thus be summarized as follows: we introduce the new task of predicting and completing depth maps from spatially sparse data, we present a sparse version of VRNNs and an effective self-supervised training method that does not require any labels. Experimental results illustrate the effectiveness of our framework in comparison to the state of the art methods in video prediction.

preprint2021arXiv

The appearance of a 'fresh' surface on 596 Scheila as a consequence of the 2010 impact event

Dust emission was detected on main-belt asteroid 596 Scheila in December 2010, and attributed to the collision of a few-tens-of-meters projectile on the surface of the asteroid. In such impact, the ejected material from the collided body is expected to mainly comes from its fresh, unweathered subsurface. Therefore, it is expected that the surface of 596 was partially or entirely refreshed during the 2010 impact. By combining spectra of 596 from the literature and our own observations, we show that the 2010 impact event resulted in a significant slope change in the near-infrared (0.8 to 2.5 μm) spectrum of the asteroid, from moderately red (T-type) before the impact to red (D-type) after the impact. This provides evidence that red carbonaceous asteroids become less red with time due to space weathering, in agreement with predictions derived from laboratory experiments on the primitive Tagish Lake meteorite, which is spectrally similar to 596. This discovery provides the very first telescopic confirmation of the expected weathering trend of asteroids spectrally analog to Tagish Lake and/or anhydrous chondritic porous interplanetary dust particles. Our results also suggest that the population of implanted objects from the outer solar system is much larger than previously estimated in the main-belt, but many of these objects are hidden below their space-weathered surface.

preprint2020arXiv

Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge Report

This technical report summarizes submissions and compiles from Actor-Action video classification challenge held as a final project in CSC 249/449 Machine Vision course (Spring 2020) at University of Rochester

preprint2020arXiv

DSDNet: Deep Structured self-Driving Network

In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network. Towards this goal, we develop a deep structured energy based model which considers the interactions between actors and produces socially consistent multimodal future predictions. Furthermore, DSDNet explicitly exploits the predicted future distributions of actors to plan a safe maneuver by using a structured planning cost. Our sample-based formulation allows us to overcome the difficulty in probabilistic inference of continuous random variables. Experiments on a number of large-scale self driving datasets demonstrate that our model significantly outperforms the state-of-the-art.

preprint2020arXiv

End-to-end Contextual Perception and Prediction with Interaction Transformer

In this paper, we tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. Towards this goal, we design a novel approach that explicitly takes into account the interactions between actors. To capture their spatial-temporal dependencies, we propose a recurrent neural network with a novel Transformer architecture, which we call the Interaction Transformer. Importantly, our model can be trained end-to-end, and runs in real-time. We validate our approach on two challenging real-world datasets: ATG4D and nuScenes. We show that our approach can outperform the state-of-the-art on both datasets. In particular, we significantly improve the social compliance between the estimated future trajectories, resulting in far fewer collisions between the predicted actors.

preprint2020arXiv

Force myography benchmark data for hand gesture recognition and transfer learning

Force myography has recently gained increasing attention for hand gesture recognition tasks. However, there is a lack of publicly available benchmark data, with most existing studies collecting their own data often with custom hardware and for varying sets of gestures. This limits the ability to compare various algorithms, as well as the possibility for research to be done without first needing to collect data oneself. We contribute to the advancement of this field by making accessible a benchmark dataset collected using a commercially available sensor setup from 20 persons covering 18 unique gestures, in the hope of allowing further comparison of results as well as easier entry into this field of research. We illustrate one use-case for such data, showing how we can improve gesture recognition accuracy by utilising transfer learning to incorporate data from multiple other persons. This also illustrates that the dataset can serve as a benchmark dataset to facilitate research on transfer learning algorithms.

preprint2020arXiv

Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies

Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automatic segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work we propose a 3D convolutional neural network (DCNet) to provide a robust and objective segmentation. In this retrospective study, we collected 1000 cases (66$\pm$ 13 years; 523 women) from the Tuebingen Family Study and from the German Center for Diabetes research (TUEF/DZD), as well as 300 cases (53$\pm$ 11 years; 152 women) from the German National Cohort (NAKO) database for model training, validation, and testing with a transfer learning between the cohorts. These datasets had variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners and imaging field strengths. The proposed DCNet was compared against a comparable 3D UNet segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds) and reliable adipose tissue segmentation can be obtained with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage 450x450x2000mm${}^3$). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician. Conclusion: Automatic adipose tissue segmentation is feasible in 3D whole-body MR data sets and is generalizable to different epidemiological cohort studies with the proposed DCNet.

preprint2020arXiv

Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes

Graph convolutional neural networks~(GCNs) have recently demonstrated promising results on graph-based semi-supervised classification, but little work has been done to explore their theoretical properties. Recently, several deep neural networks, e.g., fully connected and convolutional neural networks, with infinite hidden units have been proved to be equivalent to Gaussian processes~(GPs). To exploit both the powerful representational capacity of GCNs and the great expressive power of GPs, we investigate similar properties of infinitely wide GCNs. More specifically, we propose a GP regression model via GCNs~(GPGC) for graph-based semi-supervised learning. In the process, we formulate the kernel matrix computation of GPGC in an iterative analytical form. Finally, we derive a conditional distribution for the labels of unobserved nodes based on the graph structure, labels for the observed nodes, and the feature matrix of all the nodes. We conduct extensive experiments to evaluate the semi-supervised classification performance of GPGC and demonstrate that it outperforms other state-of-the-art methods by a clear margin on all the datasets while being efficient.

preprint2020arXiv

Learning Lane Graph Representations for Motion Forecasting

We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we construct a lane graph from raw map data to explicitly preserve the map structure. To capture the complex topology and long range dependencies of the lane graph, we propose LaneGCN which extends graph convolutions with multiple adjacency matrices and along-lane dilation. To capture the complex interactions between actors and maps, we exploit a fusion network consisting of four types of interactions, actor-to-lane, lane-to-lane, lane-to-actor and actor-to-actor. Powered by LaneGCN and actor-map interactions, our model is able to predict accurate and realistic multi-modal trajectories. Our approach significantly outperforms the state-of-the-art on the large scale Argoverse motion forecasting benchmark.

preprint2020arXiv

LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World

We tackle the problem of producing realistic simulations of LiDAR point clouds, the sensor of preference for most self-driving vehicles. We argue that, by leveraging real data, we can simulate the complex world more realistically compared to employing virtual worlds built from CAD/procedural models. Towards this goal, we first build a large catalog of 3D static maps and 3D dynamic objects by driving around several cities with our self-driving fleet. We can then generate scenarios by selecting a scene from our catalog and "virtually" placing the self-driving vehicle (SDV) and a set of dynamic objects from the catalog in plausible locations in the scene. To produce realistic simulations, we develop a novel simulator that captures both the power of physics-based and learning-based simulation. We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds. We showcase LiDARsim's usefulness for perception algorithms-testing on long-tail events and end-to-end closed-loop evaluation on safety-critical scenarios.

preprint2020arXiv

Physically Realizable Adversarial Examples for LiDAR Object Detection

Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Despite the fact that this poses a security concern for the self-driving industry, there has been very little exploration in terms of 3D perception, as most adversarial attacks have only been applied to 2D flat images. In this paper, we address this issue and present a method to generate universal 3D adversarial objects to fool LiDAR detectors. In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%. We report attack results on a suite of detectors using various input representation of point clouds. We also conduct a pilot study on adversarial defense using data augmentation. This is one step closer towards safer self-driving under unseen conditions from limited training data.

preprint2020arXiv

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. Towards this goal we propose PnPNet, an end-to-end model that takes as input sequential sensor data, and outputs at each time step object tracks and their future trajectories. The key component is a novel tracking module that generates object tracks online from detections and exploits trajectory level features for motion forecasting. Specifically, the object tracks get updated at each time step by solving both the data association problem and the trajectory estimation problem. Importantly, the whole model is end-to-end trainable and benefits from joint optimization of all tasks. We validate PnPNet on two large-scale driving datasets, and show significant improvements over the state-of-the-art with better occlusion recovery and more accurate future prediction.

preprint2020arXiv

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the form of Doppler velocity. The main challenges of using Radar are the noise and measurement ambiguities which have been a struggle for existing simple input or output fusion methods. To better address this, we propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion, which learn from data to exploit both geometric and dynamic information of Radar data. RadarNet achieves state-of-the-art results on two large-scale real-world datasets in the tasks of object detection and velocity estimation. We further show that exploiting Radar improves the perception capabilities of detecting faraway objects and understanding the motion of dynamic objects.

preprint2020arXiv

SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks

The understanding of the surrounding environment plays a critical role in autonomous robotic systems, such as self-driving cars. Extensive research has been carried out concerning visual perception. Yet, to obtain a more complete perception of the environment, autonomous systems of the future should also take acoustic information into account. Recent sound event localization and detection (SELD) frameworks utilize convolutional recurrent neural networks (CRNNs). However, considering the recurrent nature of CRNNs, it becomes challenging to implement them efficiently on embedded hardware. Not only are their computations strenuous to parallelize, but they also require high memory bandwidth and large memory buffers. In this work, we develop a more robust and hardware-friendly novel architecture based on a temporal convolutional network(TCN). The proposed framework (SELD-TCN) outperforms the state-of-the-art SELDnet performance on four different datasets. Moreover, SELD-TCN achieves 4x faster training time per epoch and 40x faster inference time on an ordinary graphics processing unit (GPU).

preprint2020arXiv

Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction

We present a novel method for testing the safety of self-driving vehicles in simulation. We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. Instead, we directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing. Specifically, we use paired data in the form of ground truth labels and real perception and prediction outputs to train a model that predicts what the online system will produce. Importantly, the inputs to our system consists of high definition maps, bounding boxes, and trajectories, which can be easily sketched by a test engineer in a matter of minutes. This makes our approach a much more scalable solution. Quantitative results on two large-scale datasets demonstrate that we can realistically test motion planning using our simulations.

preprint2020arXiv

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently aggregating the information received from multiple nearby vehicles, we can observe the same scene from different viewpoints. This allows us to see through occlusions and detect actors at long range, where the observations are very sparse or non-existent. We also show that our approach of sending compressed deep feature map activations achieves high accuracy while satisfying communication bandwidth requirements.

preprint2019arXiv

Detection of CN gas in Interstellar Object 2I/Borisov

The detection of Interstellar Objects passing through the Solar System offers the promise of constraining the physical and chemical processes involved in planetary formation in other extrasolar systems. While the effect of outgassing by 1I/2017 U1 ('Oumuamua) was dynamically observed, no direct detection of the ejected material was made. The discovery of the active interstellar comet 2I/Borisov means spectroscopic investigations of the sublimated ices is possible for this object. We report the first detection of gas emitted by an interstellar comet via the near-UV emission of CN from 2I/Borisov at a heliocentric distance of $r$ = 2.7 au on 2019 September 20. The production rate was found to be Q(CN) = $(3.7\pm0.4)\times10^{24}$ s$^{-1}$, using a simple Haser model with an outflow velocity of 0.5 km s$^{-1}$. No other emission was detected, with an upper limit to the production rate of C$_2$ of $4\times10^{24}$ s$^{-1}$. The spectral reflectance slope of the dust coma over $3900$ Å $< λ< 6000$ Å\ is steeper than at longer wavelengths, as found for other comets. Broad band $R_c$ photometry on 2019 September 19 gave a dust production rate of $Afρ=143\pm10$ cm. Modelling of the observed gas and dust production rates constrains the nuclear radius to $0.7-3.3$ km assuming reasonable nuclear properties. Overall, we find the gas, dust and nuclear properties for the first active Interstellar Object are similar to normal Solar System comets.

preprint2019arXiv

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures. This limitation could be alleviated by leveraging information contained in images of the event posted on social media platforms, so-called "Volunteered Geographic Information (VGI)". To save the analyst from the need to inspect all images posted online manually, we propose to use content-based image retrieval with the possibility of relevance feedback for retrieving only relevant images of the event to be analyzed. To evaluate this approach, we introduce a new dataset of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution). We compare several image features and relevance feedback methods on that dataset, mixed with 97,085 distractor images, and are able to improve the precision among the top 100 retrieval results from 55% with the baseline retrieval to 87% after 5 rounds of feedback.

preprint2019arXiv

Isospin violating decay $D^*_s \to D_s π^0$ in chiral perturbation theory

We systematically calculate the isospin violating decay, $D_s^*\to D_sπ^0$, with the heavy meson chiral perturbation theory up to $\mathcal{O}(p^3)$ including the loop diagrams. The $\mathcal{O}(p^3)$ tree level amplitudes contain four undetermined LECs. We use two strategies to estimate them. With the nonanalytic dominance approximation, we get $Γ[D_s^\ast\to D_sπ^0]=(3.38\pm0.12)$ eV. With the naturalness assumption, we give a possible range of the isospin violating decay width, $[1.11-6.88]$ eV. We find that the contribution of the $\mathcal{O}(p^3)$ corrections might be significant.

preprint2019arXiv

Possible Molecular States Composed of Doubly Charmed Baryons with Couple-channel Effect

We systematically investigate the possible molecular states composed of (1) two spin-$\frac{3}{2}$ doubly charmed baryons, and (2) a pair of spin-$\frac{3}{2}$ and spin-$\frac{1}{2}$ doubly charmed baryons. The one-boson-exchange (OBE) model is used to describe the potential between two baryons. The channel mixing effect is considered for the systems with the same quantum number $(I(J^P))$ but different total spin ($S$) and orbital angular momenta ($L$). We also study the channel mixing effect among the systems composed of various doubly charmed baryons if they have the same quantum number. Many of the systems are good candidates of molecular states.

preprint2019arXiv

Searching for water ice in the coma of interstellar object 2I/Borisov

Interstellar Objects (ISO) passing through our Solar System offer a rare opportunity to probe the physical and chemical processes involved in solid body and planet formation in extrasolar systems. The main objective of our study is to search for diagnostic absorption features of water ice in the near infrared (NIR) spectrum of the second interstellar object 2I/2019 Q4 (Borisov) and compare its ice features to those of the Solar system icy objects. We observed 2I in the NIR on three separate occasions. The first observation was made on 2019 September 19 UT using the SpeX spectrograph at the 3-m IRTF and again on September 24 UT with the GNIRS spectrograph at the 8-m GEMINI telescope and the last observation was made on October 09 UT with IRTF. The spectra obtained from all three nights appear featureless. No absorption features associated with water ice are detected. Spectral modeling suggests that water grains, if present, comprise no more than 10% of the coma cross-section. The comet consistently exhibits a red D-type like spectrum with a spectral slope of about 6% per 100nm, which is similar to that of 1I/'Oumuamua and is comparable to Solar system comets.

preprint2019arXiv

Towards Adversarial Denoising of Radar Micro-Doppler Signatures

Generative Adversarial Networks (GANs) are considered the state-of-the-art in the field of image generation. They learn the joint distribution of the training data and attempt to generate new data samples in high dimensional space following the same distribution as the input. Recent improvements in GANs opened the field to many other computer vision applications based on improving and changing the characteristics of the input image to follow some given training requirements. In this paper, we propose a novel technique for the denoising and reconstruction of the micro-Doppler ($\boldsymbolμ$-D) spectra of walking humans based on GANs. Two sets of experiments were collected on 22 subjects walking on a treadmill at an intermediate velocity using a \unit[25]{GHz} CW radar. In one set, a clean $\boldsymbolμ$-D spectrum is collected for each subject by placing the radar at a close distance to the subject. In the other set, variations are introduced in the experiment setup to introduce different noise and clutter effects on the spectrum by changing the distance and placing reflective objects between the radar and the target. Synthetic paired noisy and noise-free spectra were used for training, while validation was carried out on the real noisy measured data. Finally, qualitative and quantitative comparison with other classical radar denoising approaches in the literature demonstrated the proposed GANs framework is better and more robust to different noise levels.

preprint2019arXiv

Unsupervised Medical Image Translation Using Cycle-MedGAN

Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. In this work, we propose a new unsupervised translation framework which is titled Cycle-MedGAN. The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images. Qualitative and quantitative comparisons against other unsupervised translation approaches demonstrate the performance of the proposed framework for PET-CT translation and MR motion correction.

preprint2018arXiv

A Machine-learning framework for automatic reference-free quality assessment in MRI

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest. Usually no reference image is available or difficult to define. Therefore, classical reference-based approaches are not applicable. Model observers mimicking the human observers (HO) can assist in this task. Thus, we propose a new machine-learning-based reference-free MR image quality assessment framework which is trained on HO-derived labels to assess MR image quality immediately after each acquisition. We include the concept of active learning and present an efficient blinded reading platform to reduce the effort in the HO labeling procedure. Derived image features and the applied classifiers (support-vector-machine, deep neural network) are investigated for a cohort of 250 patients. The MR image quality assessment framework can achieve a high test accuracy of 93.7$\%$ for estimating quality classes on a 5-point Likert-scale. The proposed MR image quality assessment framework is able to provide an accurate and efficient quality estimation which can be used as a prospective quality assurance including automatic acquisition adaptation or guided MR scanner operation, and/or as a retrospective quality assessment including support of diagnostic decisions or quality control in cohort studies.

preprint2016arXiv

Coverage Analysis of Heterogeneous Cellular Networks in Urban Areas

In this article, a network model incorporating both line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions is proposed to investigate impacts of blockages in urban areas on heterogeneous network coverage performance. Results show that co-existence of NLOS and LOS transmissions has a significant impact on network performance. We find in urban areas, that deploying more BSs in different tiers is better than merely deploying all BSs in the same tier in terms of coverage probability.

preprint2016arXiv

CRAFT Objects from Images

Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework and its fast versions. They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories. Despite that we are handling with two relatively easier tasks, they are not solved perfectly and there's still room for improvement. In this paper, we push the "divide and conquer" solution even further by dividing each task into two sub-tasks. We call the proposed method "CRAFT" (Cascade Region-proposal-network And FasT-rcnn), which tackles each task with a carefully designed network cascade. We show that the cascade structure helps in both tasks: in proposal generation, it provides more compact and better localized object proposals; in object classification, it reduces false positives (mainly between ambiguous categories) by capturing both inter- and intra-category variances. CRAFT achieves consistent and considerable improvement over the state-of-the-art on object detection benchmarks like PASCAL VOC 07/12 and ILSVRC.

preprint2016arXiv

Crafting GBD-Net for Object Detection

The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. Effective integration of local and contextual visual cues from these regions has become a fundamental problem in object detection. In this paper, we propose a gated bi-directional CNN (GBD-Net) to pass messages among features from different support regions during both feature learning and feature extraction. Such message passing can be implemented through convolution between neighboring support regions in two directions and can be conducted in various layers. Therefore, local and contextual visual patterns can validate the existence of each other by learning their nonlinear relationships and their close interactions are modeled in a more complex way. It is also shown that message passing is not always helpful but dependent on individual samples. Gated functions are therefore needed to control message transmission, whose on-or-offs are controlled by extra visual evidence from the input sample. The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO. This paper also shows the details of our approach in wining the ImageNet object detection challenge of 2016, with source code provided on \url{https://github.com/craftGBD/craftGBD}.

preprint2016arXiv

Distant activity of 67P/Churyumov-Gerasimenko in 2014: Ground-based results during the Rosetta pre-landing phase

As the ESA Rosetta mission approached, orbited, and sent a lander to comet 67P/Churyumov-Gerasimenko in 2014, a large campaign of ground-based observations also followed the comet. We constrain the total activity level of the comet by photometry and spectroscopy to place Rosetta results in context and to understand the large-scale structure of the comet's coma pre-perihelion. We performed observations using a number of telescopes, but concentrate on results from the 8m VLT and Gemini South telescopes in Chile. We use R-band imaging to measure the dust coma contribution to the comet's brightness and UV-visible spectroscopy to search for gas emissions, primarily using VLT/FORS. In addition we imaged the comet in near-infrared wavelengths (JHK) in late 2014 with Gemini-S/Flamingos 2. We find that the comet was already active in early 2014 at heliocentric distances beyond 4 au. The evolution of the total activity (measured by dust) followed previous predictions. No gas emissions were detected despite sensitive searches. The comet maintains a similar level of activity from orbit to orbit, and is in that sense predictable, meaning that Rosetta results correspond to typical behaviour for this comet. The gas production (for CN at least) is highly asymmetric with respect to perihelion, as our upper limits are below the measured production rates for similar distances post-perihelion in previous orbits.

preprint2016arXiv

Near-infrared thermal emission from near-Earth asteroids: Aspect-dependent variability

Here we explore a technique for constraining physical properties of near-Earth asteroids (NEAs) based on variability in thermal emission as a function of viewing aspect. We present case studies of the low albedo, near-Earth asteroids (285263) 1998 QE2 and (175706) 1996 FG3. The Near-Earth Asteroid Thermal Model (NEATM) is used to fit signatures of thermal emission in near-infrared (0.8 - 2.5 micron) spectral data. This analysis represents a systematic study of thermal variability in the near-IR as a function of phase angle. The observations of QE2 imply that carefully timed observations from multiple viewing geometries can be used to constrain physical properties like retrograde versus prograde pole orientation and thermal inertia. The FG3 results are more ambiguous with detected thermal variability possibly due to systematic issues with NEATM, an unexpected prograde rotation state, or a surface that is spectrally and thermally heterogenous. This study highlights the potential diagnostic importance of high phase angle thermal measurements on both sides of opposition. We find that the NEATM thermal beaming parameters derived from our near-IR data tend to be of order 10's of percent higher than parameters from ensemble analyses of longer wavelength data sets. However, a systematic comparison of NEATM applied to data in different wavelength regimes is needed to understand whether this offset is simply a reflection of small number statistics or an intrinsic limitation of NEATM when applied to near-IR data. With the small sample presented here, it remains unclear whether NEATM modeling at near-IR wavelengths can robustly determine physical properties like pole orientation and thermal inertia.

preprint2016arXiv

TorontoCity: Seeing the World with a Million Eyes

In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712.5 $km^2$ of land, 8439 $km$ of road and around 400,000 buildings. Our benchmark provides different perspectives of the world captured from airplanes, drones and cars driving around the city. Manually labeling such a large scale dataset is infeasible. Instead, we propose to utilize different sources of high-precision maps to create our ground truth. Towards this goal, we develop algorithms that allow us to align all data sources with the maps while requiring minimal human supervision. We have designed a wide variety of tasks including building height estimation (reconstruction), road centerline and curb extraction, building instance segmentation, building contour extraction (reorganization), semantic labeling and scene type classification (recognition). Our pilot study shows that most of these tasks are still difficult for modern convolutional neural networks.

preprint2015arXiv

A New Cell Association Scheme In Heterogeneous Networks

Cell association scheme determines which base station (BS) and mobile user (MU) should be associated with and also plays a significant role in determining the average data rate a MU can achieve in heterogeneous networks. However, the explosion of digital devices and the scarcity of spectra collectively force us to carefully re-design cell association scheme which was kind of taken for granted before. To address this, we develop a new cell association scheme in heterogeneous networks based on joint consideration of the signal-to-interference-plus-noise ratio (SINR) which a MU experiences and the traffic load of candidate BSs1. MUs and BSs in each tier are modeled as several independent Poisson point processes (PPPs) and all channels experience independently and identically distributed ( i.i.d.) Rayleigh fading. Data rate ratio and traffic load ratio distributions are derived to obtain the tier association probability and the average ergodic MU data rate. Through numerical results, We find that our proposed cell association scheme outperforms cell range expansion (CRE) association scheme. Moreover, results indicate that allocating small sized and high-density BSs will improve spectral efficiency if using our proposed cell association scheme in heterogeneous networks.

preprint2015arXiv

A possible Macronova in the late afterglow of the `long-short' burst GRB 060614

Long-duration ($>2$ s) $γ$-ray bursts that are believed to originate from the death of massive stars are expected to be accompanied by supernovae. GRB 060614, that lasted 102 s, lacks a supernova-like emission down to very stringent limits and its physical origin is still debated. Here we report the discovery of near-infrared bump that is significantly above the regular decaying afterglow. This red bump is inconsistent with even the weakest known supernova. However, it can arise from a Li-Paczyński macronova $-$ the radioactive decay of debris following a compact binary merger. If this interpretation is correct GRB 060614 arose from a compact binary merger rather than from the death of a massive star and it was a site of a significant production of heavy r-process elements. The significant ejected mass favors a black hole-neutron star merger but a double neutron star merger cannot be ruled out.

preprint2015arXiv

Barrier Coverage in Mobile Camera Sensor Networks with Grid-Based Deployment

Barrier coverage is a critical issue in wireless sensor networks for many practical applications,e.g., national border monitoring, security surveillance and intruder detection, etc. Its aim is to detect intruders that attempt to cross the protected region. In this paper, we study how to efficiently improve barrier coverage using mobile camera sensors, where camera sensors are deployed by a grid-based strategy.

preprint2015arXiv

Convolutional Channel Features

Deep learning methods are powerful tools but often suffer from expensive computation and limited flexibility. An alternative is to combine light-weight models with deep representations. As successful cases exist in several visual problems, a unified framework is absent. In this paper, we revisit two widely used approaches in computer vision, namely filtered channel features and Convolutional Neural Networks (CNN), and absorb merits from both by proposing an integrated method called Convolutional Channel Features (CCF). CCF transfers low-level features from pre-trained CNN models to feed the boosting forest model. With the combination of CNN features and boosting forest, CCF benefits from the richer capacity in feature representation compared with channel features, as well as lower cost in computation and storage compared with end-to-end CNN methods. We show that CCF serves as a good way of tailoring pre-trained CNN models to diverse tasks without fine-tuning the whole network to each task by achieving state-of-the-art performances in pedestrian detection, face detection, edge detection and object proposal generation.

preprint2015arXiv

Efficient and Accurate Path Cost Estimation Using Trajectory Data

Using the growing volumes of vehicle trajectory data, it becomes increasingly possible to capture time-varying and uncertain travel costs in a road network, including travel time and fuel consumption. The current paradigm represents a road network as a graph, assigns weights to the graph's edges by fragmenting trajectories into small pieces that fit the underlying edges, and then applies a routing algorithm to the resulting graph. We propose a new paradigm that targets more accurate and more efficient estimation of the costs of paths by associating weights with sub-paths in the road network. The paper provides a solution to a foundational problem in this paradigm, namely that of computing the time-varying cost distribution of a path. The solution consists of several steps. We first learn a set of random variables that capture the joint distributions of sub-paths that are covered by sufficient trajectories. Then, given a departure time and a path, we select an optimal subset of learned random variables such that the random variables' corresponding paths together cover the path. This enables accurate joint distribution estimation of the path, and by transferring the joint distribution into a marginal distribution, the travel cost distribution of the path is obtained. The use of multiple learned random variables contends with data sparseness, the use of multi-dimensional histograms enables compact representation of arbitrary joint distributions that fully capture the travel cost dependencies among the edges in paths. Empirical studies with substantial trajectory data from two different cities offer insight into the design properties of the proposed solution and suggest that the solution is effective in real-world settings.

preprint2015arXiv

Improving Signal to Noise in the Direct Imaging of Exoplanets and Circumstellar Disks

We present a new algorithm designed to improve the signal to noise ratio (SNR) of point and extended source detections in direct imaging data. The novel part of our method is that it finds the linear combination of the science images that best match counterpart images with signal removed from suspected source regions. The algorithm, based on the Locally Optimized Combination of Images (LOCI) method, is called Matched LOCI or MLOCI. We show using data obtained with the Gemini Planet Imager (GPI) and Near-Infrared Coronagraphic Imager (NICI) that the new algorithm can improve the SNR of point source detections by 30-400% over past methods. We also find no increase in false detections rates. No prior knowledge of candidate companion locations is required to use MLOCI. While non-blind applications may yield linear combinations of science images which seem to increase the SNR of true sources by a factor > 2, they can also yield false detections at high rates. This is a potential pitfall when trying to confirm marginal detections or to re-detect point sources found in previous epochs. Our findings are relevant to any method where the coefficients of the linear combination are considered tunable, e.g. LOCI and Principal Component Analysis (PCA). Thus we recommend that false detection rates be analyzed when using these techniques.

preprint2015arXiv

Multi-level Resistive Switching Characteristics of W/Co:TiO2/FTO Structures

In the present work, multi-level resistive switching (RS) in W/Co:TiO2/FTO structures induced by a multi-mixed mechanism was studied. It was found that the devices could be reproducibly programmed into three nonvolatile resistance states. And the directly switching between any resistance states was realized. This increases the operation speed and lowers the complexity of control circuit of multi-state nonvolatile memory.

preprint2015arXiv

Spatial Spectrum and Energy Efficiency of Random Cellular Networks

It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi tessellation (PVT) random cellular networks. To evaluate the user access the network, a Markov chain based wireless channel access model is first proposed for PVT random cellular networks. On that basis, the outage probability and blocking probability of PVT random cellular networks are derived, which can be computed numerically. Furthermore, taking into account the call arrival rate, the path loss exponent and the base station (BS) density in random cellular networks, spatial spectrum and energy efficiency models are proposed and analyzed for PVT random cellular networks. Numerical simulations are conducted to evaluate the network spectrum and energy efficiency in PVT random cellular networks.

preprint2014arXiv

Aggregate channel features for multi-view face detection

Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones. While many subsequences have improved the work with more powerful learning algorithms, the feature representation used for face detection still can't meet the demand for effectively and efficiently handling faces with large appearance variance in the wild. To solve this bottleneck, we borrow the concept of channel features to the face detection domain, which extends the image channel to diverse types like gradient magnitude and oriented gradient histograms and therefore encodes rich information in a simple form. We adopt a novel variant called aggregate channel features, make a full exploration of feature design, and discover a multi-scale version of features with better performance. To deal with poses of faces in the wild, we propose a multi-view detection approach featuring score re-ranking and detection adjustment. Following the learning pipelines in Viola-Jones framework, the multi-view face detector using aggregate channel features shows competitive performance against state-of-the-art algorithms on AFW and FDDB testsets, while runs at 42 FPS on VGA images.

preprint2014arXiv

Search for the Return of Activity in Active Asteroid 176P/LINEAR

We present the results of a search for the reactivation of active asteroid 176P/LINEAR during its 2011 perihelion passage using deep optical observations obtained before, during, and after that perihelion passage. Deep composite images of 176P constructed from data obtained between June 2011 and December 2011 show no visible signs of activity, while photometric measurements of the object during this period also show no significant brightness enhancements similar to that observed for 176P between November 2005 and December 2005 when it was previously observed to be active. An azimuthal search for dust emission likewise reveals no evidence for directed emission (i.e., a tail, as was previously observed for 176P), while a one-dimensional surface brightness profile analysis shows no indication of a spherically symmetric coma at any time in 2011. We conclude that 176P did not in fact exhibit activity in 2011, at least not on the level on which it exhibited activity in 2005, and suggest that this could be due to the devolatization or mantling of the active site responsible for its activity in 2005.

preprint2014arXiv

Spectrum and Energy Efficiency Evaluation of Two-Tier Femtocell networks With Partially Open Channels

Two-tier femtocell networks is an efficient communication architecture that significantly improves throughput in indoor environments with low power consumption. Traditionally, a femtocell network is usually configured to be either completely open or completely closed in that its channels are either made available to all users or used by its own users only. This may limit network flexibility and performance. It is desirable for owners of femtocell base stations if a femtocell can partially open its channels for external users access. In such scenarios, spectrum and energy efficiency becomes a critical issue in the design of femtocell network protocols and structure. In this paper, we conduct performance analysis for two-tier femtocell networks with partially open channels. In particular, we build a Markov chain to model the channel access in the femtocell network and then derive the performance metrics in terms of the blocking probabilities. Based on stationary state probabilities derived by Markov chain models, spectrum and energy efficiency are modeled and analyzed under different scenarios characterized by critical parameters, including number of femtocells in a macrocell, average number of users, and number of open channels in a femtocell. Numerical and Monte-Carlo (MC) simulation results indicate that the number of open channels in a femtocell has an adverse impact on the spectrum and energy efficiency of two-tier femtocell networks. Results in this paper provide guidelines for trading off spectrum and energy efficiency of two-tier femtocell networks by configuring different numbers of open channels in a femtocell.

preprint2013arXiv

Computer-Aided Derivation of Multi-scale Models: A Rewriting Framework

We introduce a framework for computer-aided derivation of multi-scale models. It relies on a combination of an asymptotic method used in the field of partial differential equations with term rewriting techniques coming from computer science. In our approach, a multi-scale model derivation is characterized by the features taken into account in the asymptotic analysis. Its formulation consists in a derivation of a reference model associated to an elementary nominal model, and in a set of transformations to apply to this proof until it takes into account the wanted features. In addition to the reference model proof, the framework includes first order rewriting principles designed for asymptotic model derivations, and second order rewriting principles dedicated to transformations of model derivations. We apply the method to generate a family of homogenized models for second order elliptic equations with periodic coefficients that could be posed in multi-dimensional domains, with possibly multi-domains and/or thin domains.

preprint2013arXiv

Connectivity for matroids based on rough sets

In mathematics and computer science, connectivity is one of the basic concepts of matroid theory: it asks for the minimum number of elements which need to be removed to disconnect the remaining nodes from each other. It is closely related to the theory of network flow problems. The connectivity of a matroid is an important measure of its robustness as a network. Therefore, it is very necessary to investigate the conditions under which a matroid is connected. In this paper, the connectivity for matroids is studied through relation-based rough sets. First, a symmetric and transitive relation is introduced from a general matroid and its properties are explored from the viewpoint of matroids. Moreover, through the relation introduced by a general matroid, an undirected graph is generalized. Specifically, the connection of the graph can be investigated by the relation-based rough sets. Second, we study the connectivity for matroids by means of relation-based rough sets and some conditions under which a general matroid is connected are presented. Finally, it is easy to prove that the connectivity for a general matroid with some special properties and its induced undirected graph is equivalent. These results show an important application of relation-based rough sets to matroids.

preprint2013arXiv

Direct and full-scale experimental verifications towards ground-satellite quantum key distribution

Quantum key distribution (QKD), provides the only intrinsically unconditional secure method for communication based on principle of quantum mechanics. Compared with fiber-based demonstrations-, free-space links could provide the most appealing solution for much larger distance. Despite of significant efforts, so far all realizations rely on stationary sites. Justifications are therefore extremely crucial for applications via a typical Low Earth Orbit Satellite (LEOS). To achieve direct and full-scale verifications, we demonstrate here three independent experiments with a decoy-state QKD system overcoming all the demanding conditions. The system is operated in a moving platform through a turntable, a floating platform through a hot-air balloon, and a huge loss channel, respectively, for substantiating performances under rapid motion, attitude change, vibration, random movement of satellites and in high-loss regime. The experiments cover expanded ranges for all the leading parameters of LEOS. Our results pave the way towards ground-satellite QKD and global quantum communication network.

preprint2013arXiv

Genetic Algorithm with Ensemble Learning for Detecting Community Structure in Complex Networks

Community detection in complex networks is a topic of considerable recent interest within the scientific community. For dealing with the problem that genetic algorithm are hardly applied to community detection, we propose a genetic algorithm with ensemble learning (GAEL) for detecting community structure in complex networks. GAEL replaces its traditional crossover operator with a multi-individual crossover operator based on ensemble learning. Therefore, GAEL can avoid the problems that are brought by traditional crossover operator which is only able to mix string blocks of different individuals, but not able to recombine clustering contexts of different individuals into new better ones. In addition, the local search strategy, which makes mutated node be placed into the community where most of its neighbors are, is used in mutation operator. At last, a Markov random walk based method is used to initialize population in this paper, and it can provide us a population of accurate and diverse clustering solutions. Those diverse and accurate individuals are suitable for ensemble learning based multi-individual crossover operator. The proposed GAEL is tested on both computer-generated and real-world networks, and compared with current representative algorithms for community detection in complex networks. Experimental results demonstrate that GAEL is highly effective at discovering community structure.

preprint2013arXiv

Main-Belt Comet P/2012 T1 (PANSTARRS)

We present initial results from observations and numerical analyses aimed at characterizing main-belt comet P/2012 T1 (PANSTARRS). Optical monitoring observations were made between October 2012 and February 2013 using the University of Hawaii 2.2 m telescope, the Keck I telescope, the Baade and Clay Magellan telescopes, Faulkes Telescope South, the Perkins Telescope at Lowell Observatory, and the Southern Astrophysical Research (SOAR) telescope. The object's intrinsic brightness approximately doubles from the time of its discovery in early October until mid-November and then decreases by ~60% between late December and early February, similar to photometric behavior exhibited by several other main-belt comets and unlike that exhibited by disrupted asteroid (596) Scheila. We also used Keck to conduct spectroscopic searches for CN emission as well as absorption at 0.7 microns that could indicate the presence of hydrated minerals, finding an upper limit CN production rate of QCN<1.5x10^23 mol/s, from which we infer a water production rate of QH2O<5x10^25 mol/s, and no evidence of the presence of hydrated minerals. Numerical simulations indicate that P/2012 T1 is largely dynamically stable for >100 Myr and is unlikely to be a recently implanted interloper from the outer solar system, while a search for potential asteroid family associations reveal that it is dynamically linked to the ~155 Myr-old Lixiaohua asteroid family.

preprint2013arXiv

Massive Parallelization of STED Nanoscopy Using Optical Lattices

Recent developments in stimulated emission depletion (STED) microscopy achieved nanometer scale resolution and showed great potential in live cell imaging. Yet, STED nanoscopy techniques are based on single point-scanning. This constitutes a drawback for wide field imaging, since the gain in spatial resolution requires dense pixelation and hence long recording times. Here we achieve massive parallelization of STED nanoscopy using wide-field excitation together with well-designed optical lattices for depletion and a fast camera for detection. Acquisition of large field of view super-resolved images requires scanning over a single unit cell of the optical lattice which can be as small as 290 nm*290nm. Interference STED (In-STED) images of 2.9 μm* 2.9 μm with resolution down to 70 nm are obtained at 12.5 frames per second. The development of this technique opens many prospects for fast wide-field nanoscopy.

preprint2013arXiv

New Repair strategy of Hadamard Minimum Storage Regenerating Code for Distributed Storage System

The newly presented $(k+2,k)$ Hadamard minimum storage regenerating (MSR) code is the first class of high rate storage code with optimal repair property for all single node failures. In this paper, we propose a new simple repair strategy, which can considerably reduces the computation load of the node repair in contrast to the original one.

preprint2013arXiv

Outgassing Behavior of C/2012 S1 (ISON) From September 2011 to June 2013

We report photometric observations for comet C/2012 S1 (ISON) obtained during the time period immediately after discovery (r=6.28 AU) until it moved into solar conjunction in mid-2013 June using the UH2.2m, and Gemini North 8-m telescopes on Mauna Kea, the Lowell 1.8m in Flagstaff, the Calar Alto 1.2m telescope in Spain, the VYSOS-5 telescopes on Mauna Loa Hawaii and data from the CARA network. Additional pre-discovery data from the Pan STARRS1 survey extends the light curve back to 2011 September 30 (r=9.4 AU). The images showed a similar tail morphology due to small micron sized particles throughout 2013. Observations at sub-mm wavelengths using the JCMT on 15 nights between 2013 March 9 (r=4.52 AU) and June 16 (r=3.35 AU) were used to search for CO and HCN rotation lines. No gas was detected, with upper limits for CO ranging between (3.5-4.5)E27 molec/s. Combined with published water production rate estimates we have generated ice sublimation models consistent with the photometric light curve. The inbound light curve is likely controlled by sublimation of CO2. At these distances water is not a strong contributor to the outgassing. We also infer that there was a long slow outburst of activity beginning in late 2011 peaking in mid-2013 January (r~5 AU) at which point the activity decreased again through 2013 June. We suggest that this outburst was driven by CO injecting large water ice grains into the coma. Observations as the comet came out of solar conjunction seem to confirm our models.

preprint2013arXiv

Rough matroids based on coverings

The introduction of covering-based rough sets has made a substantial contribution to the classical rough sets. However, many vital problems in rough sets, including attribution reduction, are NP-hard and therefore the algorithms for solving them are usually greedy. Matroid, as a generalization of linear independence in vector spaces, it has a variety of applications in many fields such as algorithm design and combinatorial optimization. An excellent introduction to the topic of rough matroids is due to Zhu and Wang. On the basis of their work, we study the rough matroids based on coverings in this paper. First, we investigate some properties of the definable sets with respect to a covering. Specifically, it is interesting that the set of all definable sets with respect to a covering, equipped with the binary relation of inclusion $\subseteq$, constructs a lattice. Second, we propose the rough matroids based on coverings, which are a generalization of the rough matroids based on relations. Finally, some properties of rough matroids based on coverings are explored. Moreover, an equivalent formulation of rough matroids based on coverings is presented. These interesting and important results exhibit many potential connections between rough sets and matroids.

preprint2013arXiv

Uncorrelated Volatile Behavior During the 2011 Apparition of Comet C/2009 P1 Garradd

The High Resolution Instrument Infrared Spectrometer (HRI-IR) onboard the Deep Impact Flyby spacecraft detected H2O, CO2, and CO in the coma of the dynamically young Oort cloud comet C/2009 P1 (Garradd) post-perihelion at a heliocentric distance of 2 AU. Production rates were derived for the parent volatiles, QH2O = 4.6e28, QCO2 = 3.9e27, and QCO = 2.9e28 molecules s-1, and are consistent with the trends seen by other observers and within the error bars of measurements acquired during a similar time period. When compiled with other observations of the dominant volatiles of Garradd, unexpected behavior was seen in the release of CO. The H2O outgassing of Garradd, increasing and peaking pre-perihelion and then steadily decreasing, is more typical than that of CO, which monotonically increased throughout the entire apparition. Due to the temporal asymmetry in volatile release, Garradd exhibited the highest CO to H2O abundance ratio ever observed for any comet inside the water snow line at 60 percent during the HRI-IR observations. Also, the HRI-IR made the only direct measurement of CO2, giving a typical cometary abundance ratio of CO2 to H2O of 8 percent but, with only one measurement, no sense of how it varied with orbital position.

preprint2013arXiv

Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version

We are witnessing increasing interests in the effective use of road networks. For example, to enable effective vehicle routing, weighted-graph models of transportation networks are used, where the weight of an edge captures some cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions or travel time. It is a precondition to using a graph model for routing that all edges have weights. Weights that capture travel times and GHG emissions can be extracted from GPS trajectory data collected from the network. However, GPS trajectory data typically lack the coverage needed to assign weights to all edges. This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost. A general framework is proposed to solve the problem. Specifically, the problem is modeled as a regression problem and solved by minimizing a judiciously designed objective function that takes into account the topology of the road network. In particular, the use of weighted PageRank values of edges is explored for assigning appropriate weights to all edges, and the property of directional adjacency of edges is also taken into account to assign weights. Empirical studies with weights capturing travel time and GHG emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark) offer insight into the design properties of the proposed techniques and offer evidence that the techniques are effective.

preprint2012arXiv

Are Large Trojan Asteroids Salty? An Observational, Theoretical, and Experimental Study

With a total mass similar to the main asteroid belt, the Jovian Trojan asteroids are a major feature in the solar system. Based upon the thermal infrared spectra of the largest Trojans obtained with the Spitzer space telescope, Emery et al. (2006) suggested that the surfaces of these Trojans may consist of fine-grained silicates suspended in a transparent matrix. To explore the transparent matrix hypothesis, we adopted a modified radiative transfer model to fit the Trojan spectra simultaneously both in the near and the thermal infrared regions. Our model shows that the Trojan spectra over a wide wavelength range can be consistently explained by fine grained silicates (1-5 wt.%) and highly absorbing material (e.g. carbon or iron, 2-10 wt.%) suspended in a transparent matrix. The matrix is consistent with a deposit of salt on the surfaces of the large Trojans. However, this consistency is not an actual detection of salt and other alternatives may still be possible. We suggest that early in the Solar System history, short-lived radionuclides heated ice-rich Trojans and caused melting, internal circulation of water and dissolution of soluble materials. Briny water volcanism were facilitated by internal volatiles and a possibly global sill of frozen brine was formed beneath the cold primitive crust. The frozen brine layer was likely to be evacuated by impact erosions and evaporation of the exposed brines eventually left a lag deposit of salt. Over the Solar System's history, fine dust from comets or impacts contaminated and colored these salty surfaces of the Trojans to produce the spectral properties observed today.

preprint2012arXiv

Design, fabrication and measurement of a novel cooling arm for fusion energy source

The issues of energy and environment are the main constraint of sustainable development in worldwide. Nuclear energy source is one important optional choice for long term sustainable development. The nuclear energy consists of fusion energy and fission energy. Compared with fission, inertial confinement fusion (ICF) is a kind of clean fusion energy and can generate large energy and little environmental pollution. ICF mainly consists of peripheral driver unit and target. The cooling arm is an important component of the target, which cools the hohlraum to maintain the required temperature and positions the thermal-mechanical package (TMP) assembly. This paper mainly investigates the cooling arm, including the structural design, the verticality of sidewall and the mechanical properties. The TMP assembly is uniformly clamped in its radial when using (111) crystal orientation silicon to fabricate cooling arm. The finite element method is used to design the structure of cooling arm with 16 clamping arms, and the MEMS technologies are employed to fabricate the micro-size cooling arm structure with high vertical sidewall. Finally, the mechanical test of cooling arm is taken, and the result can meet the requirement of positioning TMP assembly.

preprint2012arXiv

Discovery of Main-Belt Comet P/2006 VW139 by Pan-STARRS1

Main belt asteroid (300163) 2006 VW139 (later designated P/2006 VW139) was discovered to exhibit comet-like activity by the Pan-STARRS1 survey telescope using automated point-spread-function analyses performed by PS1's Moving Object Processing System. Deep follow-up observations show both a short (\sim 10") antisolar dust tail and a longer (\sim 60") dust trail aligned with the object's orbit plane, similar to the morphology observed for another main-belt comet, P/2010 R2 (La Sagra), and other well-established comets, implying the action of a long-lived, sublimation-driven emission event. Photometry showing the brightness of the near-nucleus coma remaining constant over \sim 30 days provides further evidence for this object's cometary nature, suggesting it is in fact a main-belt comet, and not a disrupted asteroid. A spectroscopic search for CN emission was unsuccessful, though we find an upper limit CN production rate of Q_CN < 1.3x10^24 mol/s, from which we infer a water production rate of Q_H2O < 10^26 mol/s. We also find an approximately linear optical spectral slope of 7.2%/1000A, similar to other cometary dust comae. Numerical simulations indicate that P/2006 VW139 is dynamically stable for > 100 Myr, while a search for a potential asteroid family around the object reveals a cluster of 24 asteroids within a cutoff distance of 68 m/s. At 70 m/s, this cluster merges with the Themis family, suggesting that it could be similar to the Beagle family to which another main-belt comet, 133P/Elst-Pizarro, belongs.

preprint2012arXiv

Features and heterogeneities in growing network models

Many complex networks from the World-Wide-Web to biological networks are growing taking into account the heterogeneous features of the nodes. The feature of a node might be a discrete quantity such as a classification of a URL document as personal page, thematic website, news, blog, search engine, social network, ect. or the classification of a gene in a functional module. Moreover the feature of a node can be a continuous variable such as the position of a node in the embedding space. In order to account for these properties, in this paper we provide a generalization of growing network models with preferential attachment that includes the effect of heterogeneous features of the nodes. The main effect of heterogeneity is the emergence of an "effective fitness" for each class of nodes, determining the rate at which nodes acquire new links. The degree distribution exhibits a multiscaling behaviour analogous to the the fitness model. This property is robust with respect to variations in the model, as long as links are assigned through effective preferential attachment. Beyond the degree distribution, in this paper we give a full characterization of the other relevant properties of the model. We evaluate the clustering coefficient and show that it disappears for large network size, a property shared with the Barabási-Albert model. Negative degree correlations are also present in the studied class of models, along with non-trivial mixing patterns among features. We therefore conclude that both small clustering coefficients and disassortative mixing are outcomes of the preferential attachment mechanism in general growing networks.

preprint2012arXiv

Matroidal structure of generalized rough sets based on symmetric and transitive relations

Rough sets are efficient for data pre-process in data mining. Lower and upper approximations are two core concepts of rough sets. This paper studies generalized rough sets based on symmetric and transitive relations from the operator-oriented view by matroidal approaches. We firstly construct a matroidal structure of generalized rough sets based on symmetric and transitive relations, and provide an approach to study the matroid induced by a symmetric and transitive relation. Secondly, this paper establishes a close relationship between matroids and generalized rough sets. Approximation quality and roughness of generalized rough sets can be computed by the circuit of matroid theory. At last, a symmetric and transitive relation can be constructed by a matroid with some special properties.

preprint2012arXiv

Observational and Dynamical Characterization of Main-Belt Comet P/2010 R2 (La Sagra)

We present observations of comet-like main-belt object P/2010 R2 (La Sagra) obtained by Pan-STARRS 1 and the Faulkes Telescope-North on Haleakala in Hawaii, the University of Hawaii 2.2 m, Gemini-North, and Keck I telescopes on Mauna Kea, the Danish 1.54 m telescope at La Silla, and the Isaac Newton Telescope on La Palma. An antisolar dust tail is observed from August 2010 through February 2011, while a dust trail aligned with the object's orbit plane is also observed from December 2010 through August 2011. Assuming typical phase darkening behavior, P/La Sagra is seen to increase in brightness by >1 mag between August 2010 and December 2010, suggesting that dust production is ongoing over this period. These results strongly suggest that the observed activity is cometary in nature (i.e., driven by the sublimation of volatile material), and that P/La Sagra is therefore the most recent main-belt comet to be discovered. We find an approximate absolute magnitude for the nucleus of H_R=17.9+/-0.2 mag, corresponding to a nucleus radius of ~0.7 km, assuming an albedo of p=0.05. Using optical spectroscopy, we find no evidence of sublimation products (i.e., gas emission), finding an upper limit CN production rate of Q_CN<6x10^23 mol/s, from which we infer an H2O production rate of Q_H2O<10^26 mol/s. Numerical simulations indicate that P/La Sagra is dynamically stable for >100 Myr, suggesting that it is likely native to its current location and that its composition is likely representative of other objects in the same region of the main belt, though the relatively close proximity of the 13:6 mean-motion resonance with Jupiter and the (3,-2,-1) three-body mean-motion resonance with Jupiter and Saturn mean that dynamical instability on larger timescales cannot be ruled out.

preprint2012arXiv

Quasinormal modes for asymptotic safe black holes

Under the hypothesis of asymptotic safety of gravity, the static, spherically symmetric black hole solutions in the infrared limit are corrected by non-perturbative effects. Specifically, the metric is modified by the running of gravitational couplings. In this work, we investigate the effects of this correction to the quasinormal modes (QNMs) of a test scalar field propagating in this kind of black hole background analytically and numerically. It is found that although the quasi-period frequencies and the damping of oscillations are respectively enhanced and weakened by the quantum correction term, the stability of the black hole remains.

preprint2012arXiv

The New Finite Temperature Schrodinger Equation

Implant the thoughtway of thermostatistics in quantum mechanics, set up the new finite temperature Schrödinger equation, define the pure-state free energy, and revise the microscopic entropy introduced by Wu, et al.

preprint2011arXiv

A modified model of non-locally corrected gravity: Cosmology and the Newtonian limit

A modified form of non-locally corrected theory of gravity is investigated in the context of cosmology and the Newtonian limit. This form of non-local correction to classic Einstein-Hilbert action can be locally represented by a triple-scalar-tensor theory in which one of the scalar degree of freedom is phantom-like and the other two are quintessence-like. We show that there exists a stable de Sitter solution for the cosmological dynamics if a suitable form of potential function $V(ϕ)$ (or equivalently, $f(R)$) is selected. However, no matter what a potential function is selected, there is always an early time repeller solution corresponding to a radiation dominated universe. Besides, the equations for linear scalar perturbations are presented and it is shown that the form of potential function $V(ϕ)$ is stringently constrained by the Solar system test, although the post-Newtonian parameter $γ$ is not directly affected by this function.

preprint2011arXiv

A Near-Infrared Search for Silicates in Jovian Trojan Asteroids

We obtained near-infrared (0.8 - 2.5 micron) spectra of seven Jovian Trojan asteroids that have been formerly reported to show silicate-like absorption features near 1 micron. Our sample includes the Trojan (1172) Aneas, which is one of three Trojans known to possess a comet-like 10 micron emission feature, indicative of fine-grained silicates. Our observations show that all seven Trojans appear featureless in high signal-to-noise ratio spectra. The simultaneous absence of the 1 micron band and presence of the 10 micron emission can be understood if the silicates on (1172) Aneas are iron-poor. In addition, we present near infrared observations of five optically grey Trojans, including three objects from the collisionally produced Eurybates family. The five grey Trojans appear featureless in the near infrared with no diagnostic absorption features. The near infrared spectrum of Eurybates can be best fitted with the spectrum of a CM2 carbonaceous chondrite, which hints that the C-type Eurybates family members may have experienced aqueous alteration.

preprint2011arXiv

Near Infrared Observations of Comet-Like Asteroid (596) Scheila

steroid (596) Scheila was reported to exhibit a cometary appearance and an increase in brightness on UT 2010 December 10.4. We used the IRCS spectrograph on the 8-m Subaru telescope to obtain medium-resolution spectra of Scheila in the HK-band (1.4 - 2.5$μ$m) and low-resolution spectra in the KL-band (2.0 - 4.0$μ$m) on UT 2010 December 13 and 14. In addition, we obtained low-resolution spectroscopy using the SpeX spectrograph on the 3-m NASA Infrared Telescope Facility (IRTF) telescope on UT 2011 January 04 and 05. The spectrum of Scheila shows a consistent red slope from 0.8 to 4.0$μ$m with no apparent absorption features, resembling spectra of D-type asteroids. An intimate mixing model suggests that the amount of water ice that might be present on the surface of Scheila is no more than a few percent. The spectrum of the Tagish Lake chondrite matches the asteroid's spectrum at shorter wavelengths ($λ< 2.5 μ$m), but no hydration features are observed at longer wavelengths on Scheila. Our analysis corroborates other studies suggesting that the comet-like activity of Scheila is likely not caused by the sublimation of water ice. The dust coma and tail may be results of a recent impact event.

preprint2011arXiv

Optical and Dynamical Characterization of Comet-Like Main-Belt Asteroid (596) Scheila

We present observations and a dynamical analysis of the comet-like main-belt object, (596) Scheila. V-band photometry obtained on UT 2010 December 12 indicates that Scheila's dust cloud has a scattering cross-section ~1.4 times larger than that of the nucleus, corresponding to a dust mass of M_d~3x10^7 kg. V-R color measurements indicate that both the nucleus and dust are redder than the Sun, with no significant color differences between the dust cloud's northern and southern plumes. We also undertake an ultimately unsuccessful search for CN emission, where we find CN and H2O production rates of Q(CN) < 9x10^23 s^-1 and Q(H2O) < 10^27 s^{-1}. Numerical simulations indicate that Scheila is dynamically stable for >100 Myr, suggesting that it is likely native to its current location. We also find that it does not belong to a dynamical asteroid family of any significance. We consider sublimation-driven scenarios that could produce the appearance of multiple plumes of dust emission, but reject them as being physically implausible. Instead, we concur with previous studies that the unusual morphology of Scheila's dust cloud is most simply explained by a single oblique impact, meaning this object is likely not a main-belt comet, but is instead the second disrupted asteroid after P/2010 A2 (LINEAR) to be discovered.

preprint2010arXiv

Experimental Quantum Error-Free Transmission

Error-free transmission (EFT) of quantum information is a crucial ingredient in quantum communication network. To overcome the unavoidable decoherence in noisy channel, to date, many efforts have focused on faithfully transmitting one state by consuming large numbers of synchronized ancillary states. However, huge demands of quantum resources are hard to meet with current technology, thus restrict practical applications. Here we propose and demonstrate an economical method of reliably transmitting quantum information. An arbitrary unknown quantum state is converted into time bins deterministically in terms of its own polarization using a modified Franson interferometer. Any arisen noise in channel will induce an associated error to the reference frame of the time bins, which can be utilized to reject errors and recover the initial state. By virtue of state-independent feature, our method can be applied to entanglement distribution. After passing through 0.8 km randomly twisted optical fiber, the entanglement still survives and is verified. Our approach significantly simplifies the implementation of quantum EFT and enables a general quantum state even entanglement to be protected by feedback, thus can be used as a basic building block in practical long-distance quantum communication network.

preprint2010arXiv

Identification of Magnetite in B-type Asteroids

Spectrally blue (B-type) asteroids are rare, with the second discovered asteroid Pallas being the largest and most famous example. We conducted a focused, infrared spectroscopic survey of B-type asteroids to search for water-related features in these objects. Our results show that the negative optical spectral slope of some B-type asteroids is due to the presence of a broad absorption band centered near 1.0 micron. The 1-micron band can be matched in position and shape using magnetite (Fe3O4), which is an important indicator of past aqueous alteration in the parent body. Furthermore, our observations of B-type asteroid (335) Roberta in the 3-micron region reveal an absorption feature centered at 2.9 micron, which is consistent with the absorption due to phyllosilicates (another hydration product) observed in CI chondrites. The new observations suggest that at least some B-type asteroids are likely to have incorporated significant amounts of water ice and to have experienced intensive aqueous alteration.

preprint2010arXiv

Methane, ammonia, and their irradiation products at the surface of an intermediate-size KBO? A portrait of Plutino (90482) Orcus

Orcus is an intermediate-size 1000km-scale Kuiper Belt Object in 3:2 mean-motion resonance with Neptune, in an orbit very similar to that of Pluto. We present visible and near-infrared photometry and spectroscopy obtained with the Keck 10m-telescope and the Gemini 8m-telescope . We confirm the unambiguous detection of crystalline water ice as well as absorption in the 2.2μm region. Both in the visible and near-infrared Orcus' spectral properties appear to be homogeneous over time (and probably rotation) at the resolution available. From Hapke radiative transfer models involving intimate mixtures of various ices we find for the first time that ammonium (NH+4) and traces of ethane (C2 H6), which are most probably solar irradiation products of ammonia and methane, and a mixture of methane and ammonia (diluted or not) are the best candidates to improve the description of the data with respect to a simple water ice mixture (Haumea type surface). The possible more subtle structure of the 2.2μm band(s) should be investigated thoroughly in the future for Orcus and other intermediate size Plutinos to better understand the methane and ammonia chemistry at work, if any. We investigated the thermal history of Orcus with a new 3D thermal evolution model. Simulations over 4.5 x109 yrs with an input 10% porosity, bulk composition of 23% amorphous water ice and 77% dust, and cold accretion show that even with the action of long-lived radiogenic elements only, Orcus should have a melted core and most probably suffered a cryovolcanic event in its history which brought large amounts of crystalline ice to the surface. The presence of ammonia in the interior would strengthen the melting process. The crystalline water ice possibly brought to the surface by a past cryovolcanic event sbe detectable after several billion years despite the irradiation eects, as demonstrated by recent laboratory experiments.

preprint2010arXiv

Modified holographic dark energy in DGP brane world

In this paper, the cosmological dynamics of a modified holographic dark energy which is derived from the UV/IR duality by considering the black hole mass in higher dimensions as UV cutoff, is investigated in Dvali-Gabadaze-Porrati (DGP) brane world model. We choose Hubble horizon and future event horizon as IR cutoff respectively. And the two branches of the DGP model are both taken into account. When Hubble horizon is considered as IR cutoff, the modified holographic dark energy (HDE) behaves like an effect dark energy that modification of gravity in pure DGP brane world model acts and it can drive the expansion of the universe speed up at late time in $ε=-1$ branch which in pure DGP model can not undergo an accelerating phase. When future event horizon acts as IR cutoff, the equation of state parameter of the modified HDE can cross the phantom divide.

Bin Yang

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101 published item(s)

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

Calibration-free online test-time adaptation for electroencephalography motor imagery decoding

Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation

$(O,G)$-granular variable precision fuzzy rough sets based on overlap and grouping functions

A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis

A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning

An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions

Conditional Seq2Seq model for the time-dependent two-level system

Conditional Variable Selection for Intelligent Test

Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer

Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey

Evolution of barchan dune interactions investigated by a downscaled water tunnel experiment: the temporal characteristics and a soliton-like behavior

Fast solver for J2-perturbed Lambert problem using deep neural network

Federated Latent Class Regression for Hierarchical Data

Improving the estimation of directional area scattering factor (DASF) from canopy reflectance: theoretical basis and validation

Influence-aware Task Assignment in Spatial Crowdsourcing (Technical Report)

Mathematical Analysis and Numerical Approximations of Density Functional Theory Models for Metallic Systems

MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption

On three types of $L$-fuzzy $β$-covering-based rough sets

RetroGraph: Retrosynthetic Planning with Graph Search

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version

Some neighborhood-related fuzzy covering-based rough set models and their applications for decision making

Some properties of fuzzy $t$-norm and vague $t$-norm

The generalizations of fuzzy monoids and vague monoids

Thermal spin injection from a ferromagnet into graphene by transverse and longitudinal current

Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version

Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version

TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision

Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version

Anomaly Detection Based on Selection and Weighting in Latent Space

Diverse Complexity Measures for Dataset Curation in Self-driving

End-to-end Interpretable Neural Motion Planner

On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving

The appearance of a 'fresh' surface on 596 Scheila as a consequence of the 2010 impact event

Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge Report

DSDNet: Deep Structured self-Driving Network

End-to-end Contextual Perception and Prediction with Interaction Transformer

Force myography benchmark data for hand gesture recognition and transfer learning

Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies

Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes

Learning Lane Graph Representations for Motion Forecasting

LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World

Physically Realizable Adversarial Examples for LiDAR Object Detection

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks

Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

Detection of CN gas in Interstellar Object 2I/Borisov

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

Isospin violating decay $D^*_s \to D_s π^0$ in chiral perturbation theory

Possible Molecular States Composed of Doubly Charmed Baryons with Couple-channel Effect

Searching for water ice in the coma of interstellar object 2I/Borisov

Towards Adversarial Denoising of Radar Micro-Doppler Signatures

Unsupervised Medical Image Translation Using Cycle-MedGAN

A Machine-learning framework for automatic reference-free quality assessment in MRI

Coverage Analysis of Heterogeneous Cellular Networks in Urban Areas

CRAFT Objects from Images

Crafting GBD-Net for Object Detection

Distant activity of 67P/Churyumov-Gerasimenko in 2014: Ground-based results during the Rosetta pre-landing phase

Near-infrared thermal emission from near-Earth asteroids: Aspect-dependent variability

TorontoCity: Seeing the World with a Million Eyes

A New Cell Association Scheme In Heterogeneous Networks

A possible Macronova in the late afterglow of the `long-short' burst GRB 060614

Barrier Coverage in Mobile Camera Sensor Networks with Grid-Based Deployment

Convolutional Channel Features

Efficient and Accurate Path Cost Estimation Using Trajectory Data

Improving Signal to Noise in the Direct Imaging of Exoplanets and Circumstellar Disks

Multi-level Resistive Switching Characteristics of W/Co:TiO2/FTO Structures

Spatial Spectrum and Energy Efficiency of Random Cellular Networks

Aggregate channel features for multi-view face detection

Search for the Return of Activity in Active Asteroid 176P/LINEAR

Spectrum and Energy Efficiency Evaluation of Two-Tier Femtocell networks With Partially Open Channels