Miracle-dz/UniFAR-FTUs huggingface dataset:
kFW: A Frank-Wolfe style algorithm with stronger subproblem oracles

This paper proposes a new variant of Frank-Wolfe (FW), called $k$FW. Standard FW suffers from slow convergence: iterates often zig-zag as update directions oscillate around extreme points of the constraint set. The new variant, $k$FW, overcomes this problem by using two stronger subproblem oracles in each iteration. The first is a $k$ linear optimization oracle ($k$LOO) that computes the $k$ best update directions (rather than just one). The second is a $k$ direction search ($k$DS) that minimizes the objective over a constraint set represented by the $k$ best update directions and the previous iterate. When the problem solution admits a sparse representation, both oracles are easy to compute, and $k$FW converges quickly for smooth convex objectives and several interesting constraint sets: $k$FW achieves finite $\frac{4L_f^3D^4}{\gamma\delta^2}$ convergence on polytopes and group norm balls, and linear convergence on spectrahedra and nuclear norm balls. Numerical experiments validate the effectiveness of $k$FW and demonstrate an order-of-magnitude speedup over existing approaches.
Concept and the implementation of a tool to convert industry 4.0 environments modeled as FSM to an OpenAI Gym wrapper

Industry 4.0 systems have a high demand for optimization in their tasks, whether to minimize cost, maximize production, or even synchronize their actuators to finish or speed up the manufacture of a product. Those challenges make industrial environments a suitable scenario to apply all modern reinforcement learning (RL) concepts. The main difficulty, however, is the lack of that industrial environments. In this way, this work presents the concept and the implementation of a tool that allows us to convert any dynamic system modeled as an FSM to the open-source Gym wrapper. After that, it is possible to employ any RL methods to optimize any desired task. In the first tests of the proposed tool, we show traditional Q-learning and Deep Q-learning methods running over two simple environments.
An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment

Resource allocation is one of the major concern in cloud computing model. When several problems exists in rendering a useful resource allocator. In this research , a self adaptive resource allocation frame work based on machine learning is proposed for modelling and analysing the problem of multi-dimensional cloud resource. This novel self-adaptive resource allocation architecture consists of three stages, QoS prediction model, Improved Bat Algorithm (IBA) and Energy Efficient Model (EEM). The first one, the QoS prediction model, which depends on the same scale of system’s past events data, can attain a comparable accuracy with regard to QoS prediction. Secondly, an Energy Efficient Model, which is based on Modified Clonal Selection Algorithm (MCSA) is introduced for minimizing the energy depletion. Thirdly, a runtime decision-making algorithm that depends on improved bat algorithm can rapidly decide on a suitable function for resource allocation
New forms of structure in ecosystems revealed with the Kuramoto model

Ecological systems, as is often noted, are complex. Equally notable is the generalization that complex systems tend to be oscillatory, whether Huygens' simple patterns of pendulum entrainment or the twisted chaotic orbits of Lorenz’ convection rolls. The analytics of oscillators may thus provide insight into the structure of ecological systems. One of the most popular analytical tools for such study is the Kuramoto model of coupled oscillators. We apply this model as a stylized vision of the dynamics of a well-studied system of pests and their enemies, to ask whether its actual natural history is reflected in the dynamics of the qualitatively instantiated Kuramoto model. Emerging from the model is a series of synchrony groups generally corresponding to subnetworks of the natural system, with an overlying chimeric structure, depending on the strength of the inter-oscillator coupling. We conclude that the Kuramoto model presents a novel window through which interesting questions about the structure of ecological systems may emerge.
True to the Model or True to the Data?

A variety of recent papers discuss the application of Shapley values, a concept for explaining coalitional games, for feature attribution in machine learning. However, the correct way to connect a machine learning model to a coalitional game has been a source of controversy. The two main approaches that have been proposed differ in the way that they condition on known features, using either (1) an interventional or (2) an observational conditional expectation. While previous work has argued that one of the two approaches is preferable in general, we argue that the choice is application dependent. Furthermore, we argue that the choice comes down to whether it is desirable to be true to the model or true to the data. We use linear models to investigate this choice. After deriving an efficient method for calculating observational conditional expectation Shapley values for linear models, we investigate how correlation in simulated data impacts the convergence of observational conditional expectation Shapley values. Finally, we present two real data examples that we consider to be representative of possible use cases for feature attribution -- (1) credit risk modeling and (2) biological discovery. We show how a different choice of value function performs better in each scenario, and how possible attributions are impacted by modeling choices.
Local Neighbor Propagation Embedding

Manifold Learning occupies a vital role in the field of nonlinear dimensionality reduction and its ideas also serve for other relevant methods. Graph-based methods such as Graph Convolutional Networks (GCN) show ideas in common with manifold learning, although they belong to different fields. Inspired by GCN, we introduce neighbor propagation into LLE and propose Local Neighbor Propagation Embedding (LNPE). With linear computational complexity increase compared with LLE, LNPE enhances the local connections and interactions between neighborhoods by extending $1$-hop neighbors into $n$-hop neighbors. The experimental results show that LNPE could obtain more faithful and robust embeddings with better topological and geometrical properties.
Communication and Computing Resource Optimization for Connected Autonomous Driving

Transportation system is facing a sharp disruption since the Connected Autonomous Vehicles (CAVs) can free people from driving and provide good driving experience with the aid of Vehicle-to-Vehicle (V2V) communications. Although CAVs bring benefits in terms of driving safety, vehicle string stability, and road traffic throughput, most existing work aims at improving only one of these performance metrics. However, these metrics may be mutually competitive, as they share the same communication and computing resource in a road segment. From the perspective of joint optimizing driving safety, vehicle string stability, and road traffic throughput, there is a big research gap to be filled on the resource management for connected autonomous driving. In this paper, we first explore the joint optimization on driving safety, vehicle string stability, and road traffic throughput by leveraging on the consensus Alternating Directions Method of Multipliers algorithm (ADMM). However, the limited communication bandwidth and on-board processing capacity incur the resource competition in CAVs. We next analyze the multiple tasks competition in the contention based medium access to attain the upper bound delay of V2V-related application offloading. An efficient sleeping multi-armed bandit tree-based algorithm is proposed to address the resource assignment problem. A series of simulation experiments are carried out to validate the performance of the proposed algorithms.
Contrasting geographic patterns of genetic variation for molecular markers vs. phenotypic traits in the energy grass Miscanthus sinensis

Species and hybrids of Miscanthus are a promising energy crop, but their outcrossing mating systems and perennial life cycles are serious challenges for breeding programs. One approach to accelerating the domestication of Miscanthus is to harness the tremendous genetic variation that is present within this genus using phenotypic data from extensive field trials, high‐density genotyping and sequencing technologies, and rapidly developing statistical methods of relating phenotype to genotype. The success of this approach, however, hinges on detailed knowledge about the population genetic structure of the germplasm used in the breeding program. We therefore used data for 120 single‐nucleotide polymorphism and 52 simple sequence repeat markers to depict patterns of putatively neutral population structure among 244 Miscanthus genotypes grown in a field trial near Aberystwyth (UK) and delineate a population of 145 M. sinensis genotypes that will be used for association mapping and genomic selection. Comparative multivariate analyses of molecular marker and phenotypic data for 17 traits related to phenology, morphology/biomass, and cell wall composition revealed significant geographic patterns in this population. A longitudinal cline accounted for a substantial proportion of molecular marker variation (R2 = 0.60, P = 3.4 × 10−15). In contrast, genetic variation for phenotypic traits tended to follow latitudinal and altitudinal gradients, with several traits appearing to have been affected by divergent selection (i.e., QST >> FST). These contrasting geographic trends are unusual relative to other plants and provide opportunities for powerful studies of phenotype–genotype associations and the evolutionary history of M. sinensis.
Dynamic Constraint-based Influence Framework and its Application in Stochastic Modeling of Load Balancing

Components connected over a network influence each other and interact in various ways. Examples of such systems are networks of computing nodes, which the nodes interact by exchanging workload, for instance, for load balancing purposes. In this paper, we first study the Influence Model, a networked Markov chain framework, for modeling network interactions and discuss two key limitations of this model, which cause it to fall short in modeling constraint-based and dynamic interactions in networks. Next, we propose the Dynamic and Constraint-based Influence Model (DCIM) to alleviate the limitations. The DCIM extends the application of the Influence Model to more general network interaction scenarios. In this paper, the proposed DCIM is successfully applied to stochastic modeling of load balancing in networks of computing nodes allowing for prediction of the load distribution in the system, which is a novel application for the Influence Model. The DCIM is further used to identify the optimum workload distribution policy for load balancing in networked computing systems.
G2D: Generate to Detect Anomaly

In this paper, we propose a novel method for irregularity detection. Previous researches solve this problem as a One-Class Classification (OCC) task where they train a reference model on all of the available samples. Then, they consider a test sample as an anomaly if it has a diversion from the reference model. Generative Adversarial Networks (GANs) have achieved the most promising results for OCC while implementing and training such networks, especially for the OCC task, is a cumbersome and computationally expensive procedure. To cope with the mentioned challenges, we present a simple but effective method to solve the irregularity detection as a binary classification task in order to make the implementation easier along with improving the detection performance. We learn two deep neural networks (generator and discriminator) in a GAN-style setting on merely the normal samples. During training, the generator gradually becomes an expert to generate samples which are similar to the normal ones. In the training phase, when the generator fails to produce normal data (in the early stages of learning and also prior to the complete convergence), it can be considered as an irregularity generator. In this way, we simultaneously generate the irregular samples. Afterward, we train a binary classifier on the generated anomalous samples along with the normal instances in order to be capable of detecting irregularities. The proposed framework applies to different related applications of outlier and anomaly detection in images and videos, respectively. The results confirm that our proposed method is superior to the baseline and state-of-the-art solutions.
Generative Damage Learning for Concrete Aging Detection using Auto-flight Images

In order to monitor the state of large-scale infrastructures, image acquisition by autonomous flight drones is efficient for stable angle and high-quality images. Supervised learning requires a large data set consisting of images and annotation labels. It takes a long time to accumulate images, including identifying the damaged regions of interest (ROIs). In recent years, unsupervised deep learning approaches such as generative adversarial networks (GANs) for anomaly detection algorithms have progressed. When a damaged image is a generator input, it tends to reverse from the damaged state to the healthy state generated image. Using the distance of distribution between the real damaged image and the generated reverse aging healthy state fake image, it is possible to detect the concrete damage automatically from unsupervised learning. This paper proposes an anomaly detection method using unpaired image-to-image translation mapping from damaged images to reverse aging fakes that approximates healthy conditions. We apply our method to field studies, and we examine the usefulness of our method for health monitoring of concrete damage.
Small universal E-taster for rapid comparative analysis of liquids

An original method and a device for obtaining fingerprints of liquids are proposed. According to this method, a fingerprint is a dynamic parameter - an oscillogram of the electrical impedance of a quartz sensor on which a drop of the studied liquid dries. The design of the device and the principle of its operation are described. The method can be used for a quick and inexpensive assessment of the degree of compliance of a liquid with the standard. Moreover, the degree of the difference between liquids of different compositions is expressed quantitatively. The device is a compact console for a laptop. The method is implemented in the range of room temperatures and humidity. The work of the E-taster is demonstrated by comparing the fingerprints of spirits, wines of different types and brands and solutions of milk powder of different concentrations.
A Comprehensive Performance Analysis for mm-Wave Massive MIMO Hybrid Beamforming under PA Nonlinearities

In this paper, we develop a framework to investigate the performances of different hybrid beamforming architectures for massive multiple input multiple output (MIMO) systems impaired by power amplifier (PA) nonlinearities. Indirect learning architecture based on feedback after anti-beamforming is adopted in design of digital pre-distortion (DPD) in order to compensate the nonlinear distortion caused by PA. In addition, we propose a novel analog beamformer design for partially connected architecture based on generalized eigen-beamformer (GEB) approach. In literature, the effects of nonlinear PA's on the out-of-band (OOB) radiation and achieved signal-to-interfence-plus-noise ratio (SINR) are investigated. However, these studies are limitted to fully digital or partially connected hybrid beamforming architectures while deploying Bussgang decompostion on a PA basis without considering the array architecture type in performance analysis. In this study, we derived an analytical bit-error-rate (BER) expression based on spatio-temporal Bussgang decompostion in matrix form, and mismatched decoding capacity via Generalized Mutual Information (GMI) is obtained under PA nonlinearity for different hybrid Massive MIMO architectures. Analytical results show that the nonlinear distortion significantly affects the system performance, and DPD can reduce these effects to some extend. Finally, obtained analytical BER expression is verified via numerical results.
On the stringy Hodge numbers of mirrors of quasi-smooth Calabi-Yau hypersurfaces

Mirrors $X^{\vee}$ of quasi-smooth Calabi-Yau hypersurfaces $X$ in weighted projective spaces ${\Bbb P}(w_0, \ldots, w_d)$ can be obtained as Calabi-Yau compactifications of non-degenerate affine toric hypersurfaces defined by Laurent polynomials whose Newton polytope is the lattice simplex spanned by $d+1$ lattice vectors $v_i$ satisfying the relation $\sum_i w_i v_i =0$. In this paper, we compute the stringy $E$-function of mirrors $X^\vee$ and compare it with the Vafa's orbifold $E$-function of quasi-smooth Calabi-Yau hypersurfaces $X$. As a result, we prove the equalities of Hodge numbers $h^{p,q}_{\rm str}(X^{\vee}) = h^{d-1-p,q}_{\rm orb}(X)$ for all $p, q$ and $d$ as it is expected in mirror symmetry.
Surface-based 3D Deep Learning Framework for Segmentation of Intracranial Aneurysms from TOF-MRA Images

Segmentation of intracranial aneurysms is an important task in medical diagnosis and surgical planning. Volume-based deep learning frameworks have been proposed for this task; however, they are not effective. In this study, we propose a surface-based deep learning framework that achieves higher performance by leveraging human intervention. First, the user semi-automatically generates a surface representation of the principal brain arteries model from time-of-flight magnetic resonance angiography images. The system then samples 3D vessel surface fragments from the entire brain artery model and classifies the surface fragments into those with and without aneurysms using the point-based deep learning network (PointNet++). Next, the system applies surface segmentation (SO-Net) to the surface fragments containing aneurysms. We conduct a head-to-head comparison of segmentation performance by counting voxels between the proposed surface-based framework and existing pixel-based framework, and our framework achieved a much higher dice similarity coefficient score (72%) than the existing one (46%).
Deep Involutive Generative Models for Neural MCMC

We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC. An involutive generative model represents a probability kernel $G(\phi \mapsto \phi')$ as an involutive (i.e., self-inverting) deterministic function $f(\phi, \pi)$ on an enlarged state space containing auxiliary variables $\pi$. We show how to make these models volume preserving, and how to use deep volume-preserving involutive generative models to make valid Metropolis-Hastings updates based on an auxiliary variable scheme with an easy-to-calculate acceptance ratio. We prove that deep involutive generative models and their volume-preserving special case are universal approximators for probability kernels. This result implies that with enough network capacity and training time, they can be used to learn arbitrarily complex MCMC updates. We define a loss function and optimization algorithm for training parameters given simulated data. We also provide initial experiments showing that Involutive Neural MCMC can efficiently explore multi-modal distributions that are intractable for Hybrid Monte Carlo, and can converge faster than A-NICE-MC, a recently introduced neural MCMC technique.
Non-Markovian decoherence dynamics of the hybrid quantum system with a cavity strongly coupling to a spin ensemble: a master equation approach

Based on the recent experiments on the hybrid quantum system of a superconducting microwave cavity coupling strongly to an inhomogeneous broadening spin ensemble under an external driving field, we use the exact master equation approach to investigate its non-Markovian decoherence dynamics. Here the spin ensemble is made by negatively charged nitrogen-vacancy (NV) defects in diamond. Our exact master equation theory for open systems depicts the experimental decoherence results and reveals the mechanism how the decoherence induced by the inhomogeneous broadening is suppressed in the strong-coupling regime. Moreover, we show how the spectral hole burning generates localized states to further suppress the cavity decoherence. We also investigate the two-time correlations in this system to further show how quantum fluctuations manifest quantum memory.
Passive Wi-Fi Monitoring in Public Transport: A case study in the Madeira Island

Transportation has become of evermore importance in the last years, affecting people's satisfaction and significantly impacting their quality of life. In this paper we present a low-cost infrastructure to collect passive Wi-Fi probes with the aim of monitoring, optimizing and personalizing public transport, towards a more sustainable mobility. We developed an embedded system deployed in 19 public transportation vehicles using passive Wi-Fi data. This data is analyzed on a per-vehicle and per-stop basis and compared against ground truth data (ticketing), while also using a method of estimating passenger exits, detecting peak loads on vehicles, and origin destination habits. As such, we argue that this data enables route optimization and provides local authorities and tourism boards with a tool to monitor and optimize the management of routes and transportation, identify and prevent accessibility issues, with the aim of improving the services offered to citizens and tourists, towards a more sustainable mobility.
Lookahead-Bounded Q-Learning

We introduce the lookahead-bounded Q-learning (LBQL) algorithm, a new, provably convergent variant of Q-learning that seeks to improve the performance of standard Q-learning in stochastic environments through the use of ``lookahead'' upper and lower bounds. To do this, LBQL employs previously collected experience and each iteration's state-action values as dual feasible penalties to construct a sequence of sampled information relaxation problems. The solutions to these problems provide estimated upper and lower bounds on the optimal value, which we track via stochastic approximation. These quantities are then used to constrain the iterates to stay within the bounds at every iteration. Numerical experiments on benchmark problems show that LBQL exhibits faster convergence and more robustness to hyperparameters when compared to standard Q-learning and several related techniques. Our approach is particularly appealing in problems that require expensive simulations or real-world interactions.
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings

Our goal is to enable machine learning systems to be trained interactively. This requires models that perform well and train quickly, without large amounts of hand-labeled data. We take a step forward in this direction by borrowing from weak supervision (WS), wherein models can be trained with noisy sources of signal instead of hand-labeled data. But WS relies on training downstream deep networks to extrapolate to unseen data points, which can take hours or days. Pre-trained embeddings can remove this requirement. We do not use the embeddings as features as in transfer learning (TL), which requires fine-tuning for high performance, but instead use them to define a distance function on the data and extend WS source votes to nearby points. Theoretically, we provide a series of results studying how performance scales with changes in source coverage, source accuracy, and the Lipschitzness of label distributions in the embedding space, and compare this rate to standard WS without extension and TL without fine-tuning. On six benchmark NLP and video tasks, our method outperforms WS without extension by 4.1 points, TL without fine-tuning by 12.8 points, and traditionally-supervised deep networks by 13.1 points, and comes within 0.7 points of state-of-the-art weakly-supervised deep networks-all while training in less than half a second.