Miracle-dz/UniFAR-FTUs huggingface dataset:
Deadlock Analysis and Resolution in Multi-Robot Systems: The Two Robot Case

Collision avoidance for multirobot systems is a well studied problem. Recently, control barrier functions (CBFs) have been proposed for synthesizing decentralized controllers that guarantee collision avoidance (safety) and goal stabilization (performance) for multiple robots. However, it has been noted in several works that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that causes the robots to come to a standstill before they reach their goals. In this paper, we analyze the incidence of deadlocks in a multirobot system that uses CBFs for goal stabilization and collision avoidance. Our analysis is formal, in that we demonstrate that system deadlock is indeed the result of a force-equilibrium on robots. We show how to interpret deadlock as a subset of the state space and prove that this set is non-empty, bounded, of measure zero and located on the boundary of the safe set. Based on this analysis, we develop a decentralized three-phase algorithm that uses feedback linearization to ensure that the robots provably exit the deadlock set and converge to their goals while avoiding collisions. We show simulation results and experimentally validate the deadlock resolution algorithm on Khepera-IV robots.
A dwarf–dwarf merger and dark matter core as a solution to the globular cluster problems in the Fornax dSph

The five globular clusters (GCs) of the Fornax dwarf spheroidal galaxy are puzzling for two reasons; the mass in GCs is high with respect to the galaxy’s old stellar mass and their survival and large distance (>1 kpc) is at odds with naive expectations of dynamical friction. We present here a semi-analytic model, simultaneously addressing both problems in a comprehensive evolutionary framework for Fornax. Key to the model is inclusion of: (1) hydrodynamical constraints on the GC formation locations, (2) self-consistent velocity distribution functions in the dynamical friction calculations, and (3) expansion of GC orbits due to a past dwarf–dwarf merger in the orbit integrations. The latter is crucial for reconciling the dynamical survival of the clusters and their chemical properties with respect to the Fornax field stars. We find that in order for four of the GCs to survive at their observed projected location, a dark matter core of size rc > 1.5 kpc and a dwarf merger with dynamical mass ratio of 1:5 ≤ η ≤ 1:2 with Fornax is required. We support the merger scenario by showing that aspects of the field star metallicity distribution function and anomalous chemical properties of GC5, are representative of a merging galaxy which is ∼1/3 less massive than Fornax. Together the chemical and dynamical models suggest a scenario where three in situ GCs in proto-Fornax were ejected to the outskirts during the merger, a GC4 formed during the merger at about 10 Gyr ago, with GC5 being brought in by the merging galaxy to Fornax.
The unusual broad-band X-ray spectral variability of NGC 1313 X-1 seen with XMM–Newton, Chandra, and NuSTAR

We present results from the major coordinated X-ray observing programme on the ULX NGC 1313 X-1 performed in 2017, combining XMM–Newton, Chandra, and NuSTAR, focusing on the evolution of the broad-band (∼0.3–30.0 keV) continuum emission. Clear and unusual spectral variability is observed, but this is markedly suppressed above ∼10–15 keV, qualitatively similar to the ULX Holmberg IX X-1. We model the multi-epoch data with two-component accretion disc models designed to approximate super-Eddington accretion, allowing for both a black hole and a neutron star accretor. With regards to the hotter disc component, the data trace out two distinct tracks in the luminosity–temperature plane, with larger emitting radii and lower temperatures seen at higher observed fluxes. Despite this apparent anticorrelation, each of these tracks individually shows a positive luminosity–temperature relation. Both are broadly consistent with L ∝ T4, as expected for blackbody emission with a constant area, and also with L ∝ T2, as may be expected for an advection-dominated disc around a black hole. We consider a variety of possibilities for this unusual behaviour. Scenarios in which the innermost flow is suddenly blocked from view by outer regions of the super-Eddington disc/wind can explain the luminosity–temperature behaviour, but are difficult to reconcile with the lack of strong variability at higher energies, assuming this emission arises from the most compact regions. Instead, we may be seeing evidence for further radial stratification of the accretion flow than is included in the simple models considered, with a combination of winds and advection resulting in the suppressed high-energy variability.
Spline Analysis of Biomarker Data Pooled from Multiple Matched/Nested Case–Control Studies

Simple Summary This paper presents methods to pool continuous biomarker measurements from multiple studies to estimate the dose–response curves that allow for the nonlinear association between biomarker values and disease risks in matched/nested case–control studies. The approach can be easily applied to pooling projects of cancer studies and user-friendly software for implementing the method can be found on the corresponding author’s website. Abstract Pooling biomarker data across multiple studies enables researchers to obtain precise estimates of the association between biomarker measurements and disease risks due to increased sample sizes. However, biomarker measurements often vary significantly across different assays and laboratories; therefore, calibration of the local laboratory measurements to a reference laboratory is necessary before pooling data. We propose two methods for estimating the dose–response curves that allow for a nonlinear association between the continuous biomarker measurements and log relative risk in pooling projects of matched/nested case–control studies. Our methods are based on full calibration and internalized calibration methods. The full calibration method uses calibrated biomarker measurements for all subjects, even for people with reference laboratory measurements, while the internalized calibration method uses the reference laboratory measurements when available and otherwise uses the calibrated biomarker measurements. We conducted simulation studies to compare these methods, as well as a naive method, where data are pooled without calibration. Our simulation and theoretical results suggest that, in estimating the dose–response curves for biomarker-disease relationships, the internalized and full calibration methods perform substantially better than the naive method, and the full calibration approach is the preferred method for calibrating biomarker measurements. We apply our methods in a pooling project of nested case–control studies to estimate the association of circulating Vitamin D levels with risk of colorectal cancer.
Propagation of active nematic-isotropic interfaces on substrates.

Motivated by results for the propagation of active-passive interfaces of bacterial Serratia marcescens swarms [Nat. Commun. , 2018, 9, 5373], we used a hydrodynamic multiphase model to investigate the propagation of interfaces of active nematics on substrates. We characterized the active nematic phase of the model through the calculation of the spatial and temporal auto correlation functions and the energy spectrum and discussed its description of the statistical dynamics of the swarms reported in the experiment. We then studied the propagation of circular and flat active-passive interfaces. We found that the closing time of the circular passive domain decays quadratically with the activity and that the structure factor of the flat interface is similar to that reported for the swarms, with an activity dependent exponent. Finally, the effect of the substrate friction was investigated. We found an activity dependent threshold, above which the turbulent active nematic forms isolated islands that shrink until the system becomes isotropic and below which the active nematic expands, with a well defined propagating interface. We also found that the interface becomes static in the presence of a friction gradient.
Deep Representations for Cross-spectral Ocular Biometrics

One of the major challenges in ocular biometrics is the cross-spectral scenario, i.e. how to match images acquired in different wavelengths. This study designs and extensively evaluates cross-spectral ocular verification methods using well known deep learning representations based on the iris and periocular regions. Using as inputs, the bounding boxes of non-normalised iris-periocular regions, the authors fine-tune convolutional neural network models, originally trained for face recognition. On the basis of the experiments carried out in two publicly available cross-spectral ocular databases, they report results for intra-spectral and cross-spectral scenarios, with the best performance being observed when fusing ResNet-50 deep representations from both the periocular and iris regions. When compared to the state of the art, they observed that the proposed solution consistently reduces the equal error rate values by 90%/93%/96% and 61%/77%/83% on the cross-spectral scenario and in the PolyU bi-spectral and cross-eye-cross-spectral datasets. Finally, they evaluate the effect that the `deepness' factor of feature representations has in recognition effectiveness, and based on a subjective analysis of the most problematic pairwise comparisons - they point out further directions for this field of research.
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available. To leverage and adapt the label information from source domain, most existing methods employ a feature extracting function and match the marginal distributions of source and target domains in a shared feature space. In this paper, from the perspective of information theory, we show that representation matching is actually an insufficient constraint on the feature space for obtaining a model with good generalization performance in target domain. We then propose variational bottleneck domain adaptation (VBDA), a new domain adaptation method which improves feature transferability by explicitly enforcing the feature extractor to ignore the task-irrelevant factors and focus on the information that is essential to the task of interest for both source and target domains. Extensive experimental results demonstrate that VBDA significantly outperforms state-of-the-art methods across three domain adaptation benchmark datasets.
Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation

It is expensive to generate real-life image labels and there is a domain gap between real-life and simulated images, hence a model trained on the latter cannot adapt to the former. Solving this can totally eliminate the need for labeling real-life datasets completely. Class balanced self-training is one of the existing techniques that attempt to reduce the domain gap. Moreover, augmenting RGB with flow maps has improved performance in simple semantic segmentation and geometry is preserved across domains. Hence, by augmenting images with dense optical flow map, domain adaptation in semantic segmentation can be improved.
Localized Compression: Applying Convolutional Neural Networks to Compressed Images

We address the challenge of applying existing convolutional neural network (CNN) architectures to compressed images. Existing CNN architectures represent images as a matrix of pixel intensities with a specified dimension; this desired dimension is achieved by downgrading or cropping. Downgrading and cropping are attractive in that the result is also an image; however, an algorithm producing an alternative "compressed" representation could yield better classification performance. This compression algorithm need not be reversible, but must be compatible with the CNN's operations. This problem is thus the counterpart of the well-studied problem of applying compressed CNNs to uncompressed images, which has attracted great interest as CNNs are deployed to size-, weight-, and power- (SWaP)-limited devices. We introduce Localized Compression, a generalization of downgrading in which the original image is divided into blocks and each block is compressed to a smaller size using either sampling- or random-matrix-based techniques. By aligning the size of the compressed blocks with the size of the CNN's convolutional region, localized compression can be made compatible with any CNN architecture. Our experimental results show that Localized Compression results in classification accuracy approximately 1-2% higher than is achieved by downgrading to the equivalent resolution.
Self-Expiring Data Capsule using Trusted Execution Environment

Data privacy is unarguably of extreme importance. Nonetheless, there exist various daunting challenges to safe-guarding data privacy. These challenges stem from the fact that data owners have little control over their data once it has transgressed their local storage and been managed by third parties whose trustworthiness is questionable at times. Our work seeks to enhance data privacy by constructing a self-expiring data capsule. Sensitive data is encapsulated into a capsule which is associated with an access policy an expiring condition. The former indicates eligibility of functions that can access the data, and the latter dictates when the data should become inaccessible to anyone, including the previously eligible functions. Access to the data capsule, as well as its dismantling once the expiring condition is met, are governed by a committee of independent and mutually distrusting nodes. The pivotal contribution of our work is an integration of hardware primitive, state machine replication and threshold secret sharing in the design of the self-expiring data encapsulation framework. We implement the proposed framework in a system called TEEKAP. Our empirical experiments conducted on a realistic deployment setting with the access control committee spanning across four geographical regions reveal that TEEKAP can process access requests at scale with sub-second latency.
Deep-Learning-Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks

In this article, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs), and unmanned aerial vehicles (UAVs), all with the mobile edge cloud installed to enable user equipments (UEs) or Internet of Things (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real time, while considering the dynamic environment. To this end, we propose a hybrid deep-learning-based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LS-FCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Second, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed-integer nonlinear programming (MINLP) problems and generate the sample data sets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Third, a DNN with the scheduling layer is introduced to provide the user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from the traditional DNN predictor, we only input one UE’s information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN.
Deep Active Learning: Unified and Principled Method for Query and Training

In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning. We are providing theoretical insights from the intuition of modeling the interactive procedure in active learning as distribution matching, by adopting the Wasserstein distance. As a consequence, we derived a new training loss from the theoretical analysis, which is decomposed into optimizing deep neural network parameters and batch query selection through alternative optimization. In addition, the loss for training a deep neural network is naturally formulated as a min-max optimization problem through leveraging the unlabeled data information. Moreover, the proposed principles also indicate an explicit uncertainty-diversity trade-off in the query batch selection. Finally, we evaluate our proposed method on different benchmarks, consistently showing better empirical performances and a better time-efficient query strategy compared to the baselines.
Multi-Scale RCNN Model for Financial Time-series Classification

Financial time-series classification (FTC) is extremely valuable for investment management. In past decades, it draws a lot of attention from a wide extent of research areas, especially Artificial Intelligence (AI). Existing researches majorly focused on exploring the effects of the Multi-Scale (MS) property or the Temporal Dependency (TD) within financial time-series. Unfortunately, most previous researches fail to combine these two properties effectively and often fall short of accuracy and profitability. To effectively combine and utilize both properties of financial time-series, we propose a Multi-Scale Temporal Dependent Recurrent Convolutional Neural Network (MSTD-RCNN) for FTC. In the proposed method, the MS features are simultaneously extracted by convolutional units to precisely describe the state of the financial market. Moreover, the TD and complementary across different scales are captured through a Recurrent Neural Network. The proposed method is evaluated on three financial time-series datasets which source from the Chinese stock market. Extensive experimental results indicate that our model achieves the state-of-the-art performance in trend classification and simulated trading, compared with classical and advanced baseline models.
The interplay of insulating and superconducting orders in magic-angle graphene bilayers

The recent discovery of superconducting and correlated insulating states coexisting in magic-angle twisted bilayer graphene (MAG) at commensurate fillings have raised fascinating questions about the interplay of these orders. Tuning electron-electron inter-actions in MAG would shed light on the individual roles of these phases and their intri-cate relationship. Here we report on a new method to control electronic interactions in a correlated system, by varying the separation of a metallic screening layer in ultra-close proximity to a MAG layer. We observe that correlated insulators disappear when the separation becomes smaller than the extent of Wannier orbitals, w 0.5 tesla, exhibiting Chern bands with quan-tized Hall resistance of h/2e2 consistent with orbital magnetism. Our study establishes direct and independent control of the insulating and superconducting phases, pointing to their possibly distinct microscopic origin. The re-examination of the often-assumed mother-child relation between these phases, triggered by these observations, illustrates a new technique to directly probe microscopic mechanisms of anomalous superconductivity in MAG and other strongly-correlated systems.
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning

In sequence to sequence learning, the self-attention mechanism proves to be highly effective, and achieves significant improvements in many tasks. However, the self-attention mechanism is not without its own flaws. Although self-attention can model extremely long dependencies, the attention in deep layers tends to overconcentrate on a single token, leading to insufficient use of local information and difficultly in representing long sequences. In this work, we explore parallel multi-scale representation learning on sequence data, striving to capture both long-range and short-range language structures. To this end, we propose the Parallel MUlti-Scale attEntion (MUSE) and MUSE-simple. MUSE-simple contains the basic idea of parallel multi-scale sequence representation learning, and it encodes the sequence in parallel, in terms of different scales with the help from self-attention, and pointwise transformation. MUSE builds on MUSE-simple and explores combining convolution and self-attention for learning sequence representations from more different scales. We focus on machine translation and the proposed approach achieves substantial performance improvements over Transformer, especially on long sequences. More importantly, we find that although conceptually simple, its success in practice requires intricate considerations, and the multi-scale attention must build on unified semantic space. Under common setting, the proposed model achieves substantial performance and outperforms all previous models on three main machine translation tasks. In addition, MUSE has potential for accelerating inference due to its parallelism. Code will be available at this https URL
An Adaptive Load Balancer For Graph Analytical Applications on GPUs

Load-balancing among the threads of a GPU for graph analytics workloads is difficult because of the irregular nature of graph applications and the high variability in vertex degrees, particularly in power-law graphs. We describe a novel load balancing scheme to address this problem. Our scheme is implemented in the IrGL compiler to allow users to generate efficient load balanced code for a GPU from high-level sequential programs. We evaluated several graph analytics applications on up to 16 distributed GPUs using IrGL to compile the code and the Gluon substrate for inter-GPU communication. Our experiments show that this scheme can achieve an average speed-up of 2.2x on inputs that suffer from severe load imbalance problems when previous state-of-the-art load-balancing schemes are used.
The Linear Algebra Mapping Problem

We observe a disconnect between the developers and the end users of linear algebra libraries. On the one hand, the numerical linear algebra and the high-performance communities invest significant effort in the development and optimization of highly sophisticated numerical kernels and libraries, aiming at the maximum exploitation of both the properties of the input matrices, and the architectural features of the target computing platform. On the other hand, end users are progressively less likely to go through the error-prone and time consuming process of directly using said libraries by writing their code in C or Fortran; instead, languages and libraries such as Matlab, Julia, Eigen and Armadillo, which offer a higher level of abstraction, are becoming more and more popular. Users are given the opportunity to code matrix computations with a syntax that closely resembles the mathematical description; it is then a compiler or an interpreter that internally maps the input program to lower level kernels, as provided by libraries such as BLAS and LAPACK. Unfortunately, our experience suggests that in terms of performance, this translation is typically vastly suboptimal. In this paper, we first introduce the Linear Algebra Mapping Problem, and then investigate how effectively a benchmark of test problems is solved by popular high-level programming languages. Specifically, we consider Matlab, Octave, Julia, R, Armadillo (C++), Eigen (C++), and NumPy (Python); the benchmark is meant to test both standard compiler optimizations such as common subexpression elimination and loop-invariant code motion, as well as linear algebra specific optimizations such as optimal parenthesization of a matrix product and kernel selection for matrices with properties. The aim of this study is to give concrete guidelines for the development of languages and libraries that support linear algebra computations.