Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of a Markovian Queuing Model for Autonomous Signal-Free Intersection

We consider a novel, analytical queuing model for vehicle coordination at signal-free intersections. Vehicles arrive at an intersection according to Poisson processes, and the crossing times are constants dependent of vehicle types. We use this model to quantitatively relate key operational parameters (vehicle speed/acceleration, inter-vehicle headway) to key performance metrics (throughput and delay) under the first-come-first-serve rule. We use the Foster-Lyapunov drift condition to obtain stability criteria and an upper bound for average time delay. Based on these results, we compare the efficiency of signal free intersections with conventional vehicles and with connected and autonomous vehicles. We also validate our results in Simulation of Urban Mobility (SUMO).
Recognition and Synthesis of Object Transport Motion

Deep learning typically requires vast numbers of training examples in order to be used successfully. Conversely, motion capture data is often expensive to generate, requiring specialist equipment, along with actors to generate the prescribed motions, meaning that motion capture datasets tend to be relatively small. Motion capture data does however provide a rich source of information that is becoming increasingly useful in a wide variety of applications, from gesture recognition in human-robot interaction, to data driven animation. This project illustrates how deep convolutional networks can be used, alongside specialized data augmentation techniques, on a small motion capture dataset to learn detailed information from sequences of a specific type of motion (object transport). The project shows how these same augmentation techniques can be scaled up for use in the more complex task of motion synthesis. By exploring recent developments in the concept of Generative Adversarial Models (GANs), specifically the Wasserstein GAN, this project outlines a model that is able to successfully generate lifelike object transportation motions, with the generated samples displaying varying styles and transport strategies.
Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions

Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to efficiently represent these entities. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Moreover, given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method even needs no additional learning. Experimental results on two KGC tasks with OOKG entities show that our method outperforms the previous methods by a large margin with higher efficiency.
Towards General Purpose and Geometry Preserving Single-View Depth Estimation

Single-view depth estimation plays a crucial role in scene understanding for AR applications and 3D modelling as it allows to retrieve the geometry of a scene. However, it is only possible if the inverse depth estimates are unbiased, i.e. they are either absolute or Up-to-Scale (UTS). In recent years, great progress has been made in general-purpose single-view depth estimation. Nevertheless, the latest general-purpose models were trained using ranking or on Up-to-Shift-Scale (UTSS) data. As a result, they provide UTSS predictions that cannot be used to reconstruct scene geometry. In this work, we strive to build a general-purpose single-view UTS depth estimation model. Following Ranftl et. al., we train our model on a mixture of datasets and test it on several previously unseen datasets. We show that our method outperforms previous state-of-the-art UTS models. We train several light-weight models following the proposed training scheme and prove that our ideas are applicable for computationally efficient depth estimation.
BC Cassiopeiae: First detection of IW Andromedae-type phenomenon among post-eruption novae

IW And-type dwarf novae are recently recognized group of cataclysmic variables which are characterized by a sequence of brightening from a standstill-like phase with damping oscillations often followed by a deep dip. We found that the supposed classical nova BC Cas which erupted in 1929 experienced a state of an IW And-type dwarf nova in 2018, 89 yr after the eruption. This finding suggests that high mass-transfer rate following the nova eruption is associated with the IW And-type phenomenon. The mass of the white dwarf inferred from the decline rate of the nova is considerably higher than the average mass of the white dwarfs in cataclysmic variables and the massive white dwarfs may be responsible for the manifestation of the IW And-type phenomenon.
Reducing the Frequency of Hallucinated Quantities in Abstractive Summaries

It is well-known that abstractive summaries are subject to hallucination—including material that is not supported by the original text. While summaries can be made hallucination-free by limiting them to general phrases, such summaries would fail to be very informative. Alternatively, one can try to avoid hallucinations by verifying that any specific entities in the summary appear in the original text in a similar context. This is the approach taken by our system, Herman. The system learns to recognize and verify quantity entities (dates, numbers, sums of money, etc.) in a beam-worth of abstractive summaries produced by state-of-the-art models, in order to up-rank those summaries whose quantity terms are supported by the original text. Experimental results demonstrate that the ROUGE scores of such up-ranked summaries have a higher Precision than summaries that have not been up-ranked, without a comparable loss in Recall, resulting in higher F1. Preliminary human evaluation of up-ranked vs. original summaries shows people’s preference for the former.
Wetting boundary conditions for multicomponent pseudopotential lattice Boltzmann

The implementation of boundary conditions is among the most challenging parts of modeling fluid flow through channels and complex media. Here, we show that the existing methods to deal with liquid‐wall interactions using multicomponent Lattice Boltzmann are accurate when the wall is aligned with the main axes of the lattice but fail otherwise. To solve this problem, we extend a strategy previously developed for multiphase models. As an example, we study the coalescence of two droplets on a curved surface in two dimensions. The strategy proposed here is of special relevance for binary flows in complex geometries.
Adaptive confidence thresholding for semi-supervised monocular depth estimation

Self-supervised monocular depth estimation has become an appealing solution to the lack of ground truth labels, but its reconstruction loss often produces over-smoothed results across object boundaries and is incapable of handling occlusion explicitly. In this paper, we propose a new approach to leverage pseudo ground truth depth maps of stereo images generated from pretrained stereo matching methods. Our method is comprised of three subnetworks; monocular depth network, confidence network, and threshold network. The confidence map of the pseudo ground truth depth map is first estimated to mitigate performance degeneration by inaccurate pseudo depth maps. To cope with the prediction error of the confidence map itself, we also propose to leverage the threshold network that learns the threshold {\tau} in an adaptive manner. The confidence map is thresholded via a differentiable soft-thresholding operator using this truncation boundary {\tau}. The pseudo depth labels filtered out by the thresholded confidence map are finally used to supervise the monocular depth network. To apply the proposed method to various training dataset, we introduce the network-wise training strategy that transfers the knowledge learned from one dataset to another. Experimental results demonstrate superior performance to state-of-the-art monocular depth estimation methods. Lastly, we exhibit that the threshold network can also be used to improve the performance of existing confidence estimation approaches.
Extreme on-line translation tolerance in human and machine object recognition

Visual translation tolerance refers to our capacity to recognize objects over a wide range of different retinal locations. Although translation is perhaps the simplest spatial transform that the visual system needs to cope with, the extent to which the human visual system can identify objects at previously unseen locations is unclear, with some studies reporting near complete invariance over 10° and other reporting zero invariance at 4° of visual angle. Similarly, there is confusion regarding the extent of translation tolerance in computational models of vision, as well as the degree of match between human and model performance. Here we report a series of eye-tracking studies (total N=70) demonstrating that novel objects trained at one retinal location can be recognized at high accuracy rates following translations up to 18°. We also show that standard deep convolutional networks (DCNNs) support our findings when pretrained to classify another set of stimuli across a range of locations, or when a Global Average Pooling (GAP) layer is added to produce larger receptive fields. Our findings provide a strong constraint for theories of human vision and help explain inconsistent findings previously reported with CNNs.
Market equilibrium in multi‐tier supply chain networks

We model multistage competition in supply chains by a sequential game theoretical model in a network. We show the uniqueness of equilibrium for series parallel networks and provide a linear time algorithm to compute the equilibrium. We study the impact of the network structure on the firms' payoffs, the total trade flow, and the overall social welfare.
High-energy factorization for the Drell-Yan process in 
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 collisions with new unintegrated PDFs

The formalism for uniform description of Drell-Yan transverse-momentum spectrum is presented in a framework of High-Energy Factorization, which smoothly interpolates between Collins-Soper-Sterman formalism at $|{\bf q}_T|\ll Q$ and usual Collinear Parton Model at $|{\bf q}_T|\sim Q\ll \sqrt{S}$. The new formula for deriving Unintegrated Parton Distribution Functions(UPDFs) from collinear ones is introduced, which leads to excellent description of the shape of $Z$-boson $|{\bf q}_T|$-spectrum at high energies up to $|{\bf q}_T|/\sqrt{S}\simeq 0.02$. Description of normalized $|{\bf q}_T|$-distributions at low energies is achieved via the fit of non-perturbative parameters of quark UPDFs. Reasonable description of angular distributions of leptons in the dilepton center-of-mass frame is also obtained with new UPDFs.
Accelerating Multi-Model Inference by Merging DNNs of Different Weights

Standardized DNN models that have been proved to perform well on machine learning tasks are widely used and often adopted as-is to solve downstream tasks, forming the transfer learning paradigm. However, when serving multiple instances of such DNN models from a cluster of GPU servers, existing techniques to improve GPU utilization such as batching are inapplicable because models often do not share weights due to fine-tuning. We propose NetFuse, a technique of merging multiple DNN models that share the same architecture but have different weights and different inputs. NetFuse is made possible by replacing operations with more general counterparts that allow a set of weights to be associated with only a certain set of inputs. Experiments on ResNet-50, ResNeXt-50, BERT, and XLNet show that NetFuse can speed up DNN inference time up to 3.6x on a NVIDIA V100 GPU, and up to 3.0x on a TITAN Xp GPU when merging 32 model instances, while only using up a small additional amount of GPU memory.
XTE: Explainable Text Entailment

Text entailment, the task of determining whether a piece of text logically follows from another piece of text, is a key component in NLP, providing input for many semantic applications such as question answering, text summarization, information extraction, and machine translation, among others. Entailment scenarios can range from a simple syntactic variation to more complex semantic relationships between pieces of text, but most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. Furthermore, for entailments requiring world knowledge, most systems still work as a "black box", providing a yes/no answer that does not explain the underlying reasoning process. In this work, we introduce XTE - Explainable Text Entailment - a novel composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically. Also, if a semantic matching is involved, we make the answer interpretable, using external knowledge bases composed of structured lexical definitions to generate natural language justifications that explain the semantic relationship holding between the pieces of text. Besides outperforming well-established entailment algorithms, our composite approach gives an important step towards Explainable AI, allowing the inference model interpretation, making the semantic reasoning process explicit and understandable.
A Continuous-Time Nesterov Accelerated Gradient Method for Centralized and Distributed Online Convex Optimization

This paper studies the online convex optimization problem by using an Online Continuous-Time Nesterov Accelerated Gradient method (OCT-NAG). We show that the continuous-time dynamics generated by the online version of the Bregman Lagrangian achieves a constant static regret $\frac{c}{\sigma}$ independent of $T$, provided that some boundedness assumptions on the objective functions and optimal solutions hold. To the best of the authors' knowledge, this is the lowest static regret in the literature (lower than $O(\text{log}(T))$). We further show that under the same assumptions, the dynamic regret of the algorithm is $O(T)$, which is comparable with the existing methods. Simulation results validate the effectiveness and efficiency of the method. Furthermore, the simulation shows that the algorithm performs well in terms of the dynamic regret for some specific scaling conditions. In addition, we consider the application of the proposed online optimization method in distributed online optimization problems, and show that the proposed algorithm achieves an $O(\sqrt{T})$ static regret, which is comparable with the existing distributed online optimization methods. Different from these methods, the proposed method requires neither the gradient boundedness assumption nor the compact constraint set assumption, which allows different objective functions and different optimization problems with those in the literature. A comparable dynamic regret is obtained. Simulation results show the effectiveness and efficiency of the distributed algorithm.
Observation of blackbody radiation enhanced superradiance in ultracold Rydberg gases

An ensemble of excited atoms can synchronize emission of light collectively in a process known as superradiance when its characteristic size is smaller than the wavelength of emitted photons. The underlying superradiance depends strongly on electromagnetic (photon) fields surrounding the atomic ensemble. High mode densities of microwave photons from 300 K blackbody radiation (BBR) significantly enhance decay rates of Rydberg states to neighbouring states, enabling superradiance that is not possible with bare vacuum induced spontaneous decay. Here we report observations of the superradiance of ultracold Rydberg atoms embedded in a bath of room-temperature photons. The temporal evolution of the Rydberg |nD⟩ to |(n + 1)P⟩ superradiant decay of Cs atoms (n the principal quantum number) is measured directly in free space. Theoretical simulations confirm the BBR enhanced superradiance in large Rydberg ensembles. We demonstrate that the van der Waals interactions between Rydberg atoms change the superradiant dynamics and modify the scaling of the superradiance. In the presence of static electric fields, we find that the superradiance becomes slow, potentially due to many-body interaction induced dephasing. Our study provides insights into many-body dynamics of interacting atoms coupled to thermal BBR, and might open a route to the design of blackbody thermometry at microwave frequencies via collective, dissipative photon-atom interactions.
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network

With the ever-increasing growth of online recruitment data, job-resume matching has become an important task to automatically match jobs with suitable resumes. This task is typically casted as a supervised text matching problem. Supervised learning is powerful when the labeled data is sufficient. However, on online recruitment platforms, job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms. To alleviate these problems, in this paper, we propose a novel multi-view co-teaching network from sparse interaction data for job-resume matching. Our network consists of two major components, namely text-based matching model and relation-based matching model. The two parts capture semantic compatibility in two different views, and complement each other. In order to ad- dress the challenges from sparse and noisy data, we design two specific strategies to combine the two components. First, two com- ponents share the learned parameters or representations, so that the original representations of each component can be enhanced. More importantly, we adopt a co-teaching mechanism to reduce the influence of noise in training data. The core idea is to let the two components help each other by selecting more reliable training instances. The two strategies focus on representation enhancement and data enhancement, respectively. Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data. Experiment results have demonstrated that our model is able to outperform state-of-the-art methods for job-resume matching.
Techniques to Improve Q&A Accuracy with Transformer-based models on Large Complex Documents

This paper discusses the effectiveness of various text processing techniques, their combinations, and encodings to achieve a reduction of complexity and size in a given text corpus. The simplified text corpus is sent to BERT (or similar transformer based models) for question and answering and can produce more relevant responses to user queries. This paper takes a scientific approach to determine the benefits and effectiveness of various techniques and concludes a best-fit combination that produces a statistically significant improvement in accuracy.
Beneficial Perturbations Network for Defending Adversarial Examples

Adversarial training, in which a network is trained on both adversarial and clean examples, is one of the most trusted defense methods against adversarial attacks. However, there are three major practical difficulties in implementing and deploying this method - expensive in terms of running memory and computation costs; accuracy trade-off between clean and adversarial examples; cannot foresee all adversarial attacks at training time. Here, we present a new solution to ease these three difficulties - Beneficial perturbation Networks (BPN). BPN generates and leverages beneficial perturbations (somewhat opposite to well-known adversarial perturbations) as biases within the parameter space of the network, to neutralize the effects of adversarial perturbations on data samples. Thus, BPN can effectively defend against adversarial examples. Compared to adversarial training, we demonstrate that BPN can significantly reduce the required running memory and computation costs, by generating beneficial perturbations through recycling of the gradients computed from training on clean examples. In addition, BPN can alleviate the accuracy trade-off difficulty and the difficulty of foreseeing multiple attacks, by improving the generalization of the network, thanks to increased diversity of the training set achieved through neutralization between adversarial and beneficial perturbations.
Phase-Diffusion Equations for the Anisotropic Complex Ginzburg-Landau Equation

The anisotropic complex Ginzburg-Landau equation (ACGLE) describes slow modulations of patterns in anisotropic spatially extended systems near oscillatory (Hopf) instabilities with zero wavenumbers. Traveling wave solutions to the ACGLE become unstable near Benjamin-Feir-Newell instabilities. We determine two instability conditions in parameter space and study codimension-one (-two) bifurcations that occur if one (two) of the conditions is (are) met. We derive anisotropic Kuramoto-Sivashinsky-type equations that govern the phase of the complex solutions to the ACGLE and generate solutions to the ACGLE from solutions of the phase equations.
Metrics for Multi-Touch Input Technologies

Multi-touch input technologies are becoming popular with the increased interest in touchscreen- and touchpad-based devices. A great deal of work has been done on different multi-touch technologies, and researchers and practitioners are frequently coming up with new ones. However, it is almost impossible to compare such technologies due to the absence of multi-touch performance metrics. Designers usually use their own methods to report their techniques' performances. Moreover, multi-touch interaction was never modeled. That makes it impossible for designers to predict the performance of a new technology before developing it, costing them valuable time, effort, and money. This article discusses the necessity of having dedicated performance metrics and prediction model for multi-touch technologies, and ways of approaching that.
Parameter Experimental Analysis of the Reservoirs Observers using Echo State Network Approach

Dynamical systems has a variety of applications for the new information generated during the time. Many phenomenons like physical, chemical or social are not static, then an analysis over the time is necessary. In this work, an experimental analysis of parameters of the model Echo State Network is performed and the influence of the kind of Complex Network is explored to understand the influence on the performance. The experiments are performed using the Rossler attractor.
Isospectrality of Margulis-Smilga spacetimes for irreducible representations of real split semisimple Lie groups

In this article we show that, under certain conditions, equality of the Margulis-Smilga invariant spectra of two Margulis-Smilga spacetimes induce an automorphism of the ambient affine Lie group. In particular, we show that equality of the Margulis-Smilga invariant spectra of two Margulis-Smilga spacetimes, coming from the adjoint representation of a real split simple algebraic Lie group $\mathsf{G}$ with trivial center and Lie algebra $\mathfrak{g}$, induce an automorphism of the affine group $\mathsf{G}\ltimes\mathfrak{g}$.