Miracle-dz/UniFAR-FTUs huggingface dataset:
Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours

Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipeline. However, training accurate and reliable CNNs requires large fine-grain annotated datasets. To alleviate this, weakly-supervised methods can be used to obtain local information from global labels. This work proposes the use of localized perturbations as a weakly-supervised solution to extract segmentation masks of brain tumours from a pretrained 3D classification model. Furthermore, we propose a novel optimal perturbation method that exploits 3D superpixels to find the most relevant area for a given classification using a U-net architecture. Our method achieved a Dice similarity coefficient (DSC) of 0.44 when compared with expert annotations. When compared against Grad-CAM, our method outperformed both in visualization and localization ability of the tumour region, with Grad-CAM only achieving 0.11 average DSC.
Scaling approach to nuclear structure in high-energy heavy-ion collisions

In high-energy heavy-ion collisions, the initial condition of the produced quark-gluon plasma (QGP) and its evolution are sensitive to collective nuclear structure parameters describing the shape and radial profiles of the nuclei. We find a general scaling relation between these parameters and many experimental observables such as elliptic flow, triangular flow, and particle multiplicity distribution. In particular, the ratios of observables between two isobar systems depend only on the differences of these parameters, but not on the details of the final state interactions, hence offering a new way to constrain the QGP initial condition. Using this scaling relation, we show how the structure parameters of $^{96}_{44}$Ru and $^{96}_{40}$Zr conspire to produce the rich centrality dependences of these ratios, as measured by the STAR Collaboration. Our scaling approach demonstrates that isobar collisions are a precision tool to probe the initial condition of heavy-ion collisions, as well as the collective nuclear structures, including the neutron skin, of the atomic nuclei across energy scales.
Optimizing a solenoid for a Robocup kicker

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Baby universes, ensemble averages and factorizations with matters

We investigate 2D topological gravity theories with matter fields turned on. We compute correlators of boundary creation operators with extra matter insertions. We provide a systematic procedure to determine a set of α-states on which the correlation functions of the different boundary operators factorize. Our results suggest that the boundary dual is an ensemble average of theories with different numbers of replicas of a seed matter contribution that is represented by excitations on top of the gravity sector. The construction of the α-states and the subsequent computations in the Hilbert space point of view are most conveniently done when the bulk couplings of the matter fields are expanded in a basis of vortices, which can be thought of as a 1-dimensional analogue of the SD-branes. We further demonstrate the existence of null states and the conditions imposed by reflection positivity. Moreover, in the CGS model with only disk and cylinder topologies we map the baby universe creation and annihilation operators to the coupling constants in the one time SYK model, which allows us to match the wormhole and half-wormhole contributions in the CGS model explicitly to those identified in the SYK model. This explicitly realizes Coleman’s initial idea. Some details of the computations in a variety of concrete models are presented. ar X iv :2 11 1. 14 85 6v 1 [ he pth ] 2 9 N ov 2 02 1
Dimensionless heat transfer model to compress and analyze pulsed thermography data for NDT of materials

We develop a dimensionless heat transfer model to analyze pulsed thermography data for non-destructive testing (NDT) of materials. Simulated thermographic sequences are used in order to evaluate the performance of the inspection technique. Also, we inspect organic and inorganic samples, including a layered plate and two dental pieces, in search of internal defects and structural inhomogeneities. We detect cavities and the inner structure of the samples by means of reconstructed thermograms and a modified version of the differential absolute contrast (DAC). Moreover, we develop an effective data compression method that reduces a thermographic video with m frames of p × q pixels to two matrices of p × q elements. In this data reconstruction process, precision and compression ratio are independent parameters. Finally, we find that partial translucency of dental enamel, in infrared, permits imaging of the internal structure of a tooth. This inspection technique does not require a priori knowledge about a reference defect-free area.
Continuous Control With Ensemble Deep Deterministic Policy Gradients

The growth of deep reinforcement learning (RL) has brought multiple exciting tools and methods to the field. This rapid expansion makes it important to understand the interplay between individual elements of the RL toolbox. We approach this task from an empirical perspective by conducting a study in the continuous control setting. We present multiple insights of fundamental nature, including: an average of multiple actors trained from the same data boosts performance; the existing methods are unstable across training runs, epochs of training, and evaluation runs; a commonly used additive action noise is not required for effective training; a strategy based on posterior sampling explores better than the approximated UCB combined with the weighted Bellman backup; the weighted Bellman backup alone cannot replace the clipped double Q-Learning; the critics' initialization plays the major role in ensemble-based actor-critic exploration. As a conclusion, we show how existing tools can be brought together in a novel way, giving rise to the Ensemble Deep Deterministic Policy Gradients (ED2) method, to yield state-of-the-art results on continuous control tasks from OpenAI Gym MuJoCo. From the practical side, ED2 is conceptually straightforward, easy to code, and does not require knowledge outside of the existing RL toolbox.
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution

With the development of Deep Neural Networks (DNNs), plenty of methods based on DNNs have been proposed for Single Image Super-Resolution (SISR). However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image. In this paper, we present a simple yet effective data augmentation method. We ﬁrst devise a heuristic metric to evaluate the informative importance of each patch pair. In order to reduce the computational cost for all patch pairs, we further propose to optimize the calculation of our metric by integral image, achieving about two orders of magnitude speedup. The training patch pairs are sampled according to their informative importance with our method. Extensive experiments show our sampling augmentation can consistently improve the convergence and boost the performance of various SISR architectures, including EDSR, RCAN, RDN, SRCNN and ESPCN across different scaling factors ( × 2, × 3, × 4). Code is available at https://github.com/littlepure2333/SamplingAug .
Managing Construction Safety and Health: Experience of New York State Department of Transportation

To construct and maintain transportation facilities in a cost-effective manner, management of a large state highway construction program must address health and safety risks to highway users and workers. Successful management of construction-site risks requires defined objectives, strong agency commitment, adherence to fundamental safety and health principles, and clearly defined procedures. Health and safety risks and associated costs inherent in highway construction are examined, and the New York State Department of Transportation’s construction safety and health program is described. These efforts are offered as a model to other large transportation agencies that may be considering adopting such an approach. Key elements in this program include clear-cut contractual requirements, adequate program staffing, an accident reporting system to identify critical concerns and track progress, formalized procedural manuals to provide operational guidance, quality assurance/ quality control procedures, and compliance measures and outreach efforts to the construction industry and other agencies. Problem areas have been identified where greater program focus can obtain results in terms of reduced losses. Initiatives have been directed toward falls, utility contacts, demolition safety, and work-zone traffic control.
Search of the Early O3 LIGO Data for Continuous Gravitational Waves from the Cassiopeia A and Vela Jr. Supernova Remnants

We present directed searches for continuous gravitational waves from the neutron stars in the Cassiopeia A (Cas A) and Vela Jr. supernova remnants. We carry out the searches in the LIGO data from the first six months of the third Advanced LIGO and Virgo observing run, using the Weave semi-coherent method, which sums matched-filter detection-statistic values over many time segments spanning the observation period. No gravitational wave signal is detected in the search band of 20--976 Hz for assumed source ages greater than 300 years for Cas A and greater than 700 years for Vela Jr. Estimates from simulated continuous wave signals indicate we achieve the most sensitive results to date across the explored parameter space volume, probing to strain magnitudes as low as ~$6.3\times10^{-26}$ for Cas A and ~$5.6\times10^{-26}$ for Vela Jr. at frequencies near 166 Hz at 95% efficiency.
Shearing effects on the phase coarsening of binary mixtures using the Active Model B

The phase separation of a two-dimensional active binary mixture is studied under the action of an applied shear through numerical simulations. It is highlighted how the strength of the external flow modifies the initial shape of growing domains. The activity is responsible for the formation of isolated droplets which affect both the coarsening dynamics and the morphology of the system. The characteristic dimensions of domains along the flow and the shear direction are modulated in time by oscillations whose amplitudes are reduced when the activity increases. This induces a broadening of the distribution functions of domain lengths with respect to the passive case due to the presence of dispersed droplets of different sizes.
Two-pion exchange as a leading-order contribution in chiral effective field theory

Pion exchange is the central ingredient to nucleon-nucleon interactions used in nuclear structure calculations, and one pion exchange (OPE) enters at leading order in chiral eﬀective ﬁeld theory. In the 2 S +1 L J = 1 S 0 partial wave, however, OPE and a contact term needed for proper renormalization fail to produce the qualitative, and quantitative, features of the scattering phase shifts. Cutoﬀ variation also revealed a surprisingly low breakdown momentum Λ b ≈ 330 MeV in this partial wave. Here we show that potentials consisting of OPE, two pion exchange (TPE), and a single contact address these problems and yield accurate and renormalization group (RG) invariant phase shifts in the 1 S 0 partial wave. We demonstrate that a leading-order potential with TPE can be systematically improved by adding a contact quadratic in momenta. For momentum cutoﬀs Λ (cid:46) 500 MeV, the removal of relevant physics from TPE loops needs to be compensated by additional contacts to keep RG invariance. Inclusion of the ∆ isobar degree of freedom in the potential does not change the strong contributions of TPE.
Privacy-Preserving Serverless Edge Learning with Decentralized Small Data

In the last decade, data-driven algorithms outperformed traditional optimization-based algorithms in many research areas, such as computer vision, natural language processing, etc. However, extensive data usages bring a new challenge or even threat to deep learning algorithms, i.e., privacy-preserving. Distributed training strategies have recently become a promising approach to ensure data privacy when training deep models. This paper extends conventional serverless platforms with serverless edge learning architectures and provides an efficient distributed training framework from the networking perspective. This framework dynamically orchestrates available resources among heterogeneous physical units to efficiently fulfill deep learning objectives. The design jointly considers learning task requests and underlying infrastructure heterogeneity, including last-mile transmissions, computation abilities of mobile devices, edge and cloud computing centers, and devices battery status. Furthermore, to significantly reduce distributed training overheads, small-scale data training is proposed by integrating with a general, simple data classifier. This low-load enhancement can seamlessly work with various distributed deep models to improve communications and computation efficiencies during the training phase. Finally, open challenges and future research directions encourage the research community to develop efficient distributed deep learning techniques.
Dynamics of quarks and gauge fields in the lowest-energy states in QCD and QED

The dynamics of quarks and gauge fields in the lowest energy states in QCD and QED interactions is studied by compactifying the (3+1)D space-time to the (1+1)D space-time with cylindrical symmetry and by combining Schwinger's longitudinal confinement in (1+1)D with Polyakov's transverse confinement in (2+1)D. Using the action integral, we separate out the transverse and longitudinal degrees of freedom. By solving the derived transverse and longitudinal equations, we study the QCD and QED collective excitations. In addition to the well known QCD low-energy states, we find stable collective QED excitations showing up as massive QED-confined mesons, in support of previous studies. In particular, the masses of the recently observed X17 particle at about 17 MeV and the E38 particle at about 38 MeV are calculated in the developed approach, in good agreement with experimental results.
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering

Traffic optimization challenges, such as load balancing, flow scheduling, and improving packet delivery time, are difficult online decision-making problems in wide area networks (WAN). Complex heuristics are needed for instance to find optimal paths that improve packet delivery time and minimize interruptions which may be caused by link failures or congestion. The recent success of reinforcement learning (RL) algorithms can provide useful solutions to build better robust systems that learn from experience in model-free settings. In this work, we consider a path optimization problem, specifically for packet routing, in large complex networks. We develop and evaluate a model-free approach, applying multi-agent meta reinforcement learning (MAMRL) that can determine the next-hop of each packet to get it delivered to its destination with minimum time overall. Specifically, we propose to leverage and compare deep policy optimization RL algorithms for enabling distributed model-free control in communication networks and present a novel meta-learning-based framework, MAMRL, for enabling quick adaptation to topology changes. To evaluate the proposed framework, we simulate with various WAN topologies. Our extensive packet-level simulation results show that compared to classical shortest path and traditional reinforcement learning approaches, MAMRL significantly reduces the average packet delivery time even when network demand increases; and compared to a non-meta deep policy optimization algorithm, our results show the reduction of packet loss in much fewer episodes when link failures occur while offering comparable average packet delivery time.
Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence

Defining multivariate generalizations of the classical univariate ranks has been a long-standing open problem in statistics. Optimal transport has been shown to offer a solution by transporting data points to grid approximating a reference measure (Chernozhukov et al., 2017; Hallin, 2017; Hallin et al., 2021a). We take up this new perspective to develop and study multivariate analogues of popular correlations measures including the sign covariance, Kendall’s tau and Spearman’s rho. Our tests are genuinely distribution-free, hence valid irrespective of the actual (absolutely continuous) distributions of the observations. We present asymptotic distribution theory for these new statistics, providing asymptotic approximations to critical values to be used for testing independence as well as an analysis of power of the resulting tests. Interestingly, we are able to establish a multivariate elliptical Chernoff–Savage property, which guarantees that, under ellipticity, our nonparametric tests of independence when compared to Gaussian procedures enjoy an asymptotic relative efficiency of one or larger. Hence, the nonparametric tests constitute a safe replacement for procedures based on multivariate Gaussianity. Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany; e-mail: hongjian.shi@tum.de Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany; e-mail: mathias.drton@tum.de ECARES and Department of Mathematics, Université Libre de Bruxelles, Brussels, Belgium; email: mhallin@ulb.ac. be Department of Statistics, University of Washington, Seattle, WA 98195, USA; e-mail: fanghan@uw.edu 1
New Datasets for Dynamic Malware Classification

—Nowadays, malware and malware incidents are increasing daily, even with various anti-viruses systems and malware detection or classiﬁcation methodologies. Many static, dynamic, and hybrid techniques have been presented to detect malware and classify them into malware families. Dynamic and hybrid malware classiﬁcation methods have advantages over static malware classiﬁcation methods by being highly efﬁcient. Since it is difﬁcult to mask malware behavior while executing than its underlying code in static malware classiﬁcation, machine learning techniques have been the main focus of the security experts to detect malware and determine their families dynamically. The rapid increase of malware also brings the necessity of recent and updated datasets of malicious software. We introduce two new, updated datasets in this work: One with 9,795 samples obtained and compiled from VirusSamples and the one with 14,616 samples from VirusShare. This paper also analyzes multi-class malware classiﬁcation performance of the balanced and imbalanced version of these two datasets by using Histogram-based gradient boosting, Random Forest, Support Vector Machine, and XGBoost models with API call-based dynamic malware classiﬁcation. Results show that Support Vector Machine, achieves the highest score of 94% in the imbalanced VirusSample dataset, whereas the same model has 91% accuracy in the balanced VirusSample dataset. While XGBoost, one of the most common gradient boosting-based models, achieves the highest score of 90% and 80%. in both versions of the VirusShare dataset. This paper also presents the baseline results of VirusShare and VirusSample datasets by using the four most widely known machine learning techniques in dynamic malware classiﬁcation literature. We believe that these two datasets and baseline results enable researchers in this ﬁeld to test and validate their methods and approaches.
Model-Free μ Synthesis via Adversarial Reinforcement Learning

Motivated by the recent empirical success of policy-based reinforcement learning (RL), there has been a research trend studying the performance of policy-based RL methods on standard control benchmark problems. In this paper, we examine the effectiveness of policy-based RL methods on an important robust control problem, namely μ synthesis. We build a connection between robust adversarial RL and μ synthesis, and develop a model-free version of the well-known DK-iteration for solving state-feedback μ synthesis with static D-scaling. In the proposed algorithm, the K step mimics the classical central path algorithm via incorporating a recently-developed double-loop adversarial RL method as a subroutine, and the D step is based on model-free finite difference approximation. Extensive numerical study is also presented to demonstrate the utility of our proposed model-free algorithm. Our study sheds new light on the connections between adversarial RL and robust control.
Direct numerical simulation of turbulent mass transfer at the surface of an open channel flow

Abstract We present direct numerical simulation results of turbulent open channel flow at bulk Reynolds numbers up to 12 000, coupled with (passive) scalar transport at Schmidt numbers up to 200. Care is taken to capture the very large-scale motions which appear already for relatively modest Reynolds numbers. The transfer velocity at the flat, free surface is found to scale with the Schmidt number to the power ‘$-1/2$’, in accordance with previous studies and theoretical predictions for uncontaminated surfaces. The scaling of the transfer velocity with Reynolds number is found to vary, depending on the Reynolds number definition used. To compare the present results with those obtained in other systems, we define a turbulent Reynolds number at the edge of the surface-influenced layer. This allows us to probe the two-regime model of Theofanous et al. (Intl J. Heat Mass Transfer, vol. 19, 1976, pp. 613–624), which is found to correctly predict that small-scale vortices significantly affect the mass transfer for turbulent Reynolds numbers larger than 500. It is further established that the root mean square of the surface divergence is, on average, proportional to the mean transfer velocity. However, the spatial correlation between instantaneous surface divergence and transfer velocity tends to decrease with increasing Schmidt number and increase with increasing Reynolds number. The latter is shown to be caused by an enhancement of the correlation in high-speed regions, which in turn is linked to the spatial distribution of surface-parallel vortices.