Miracle-dz/UniFAR-FTUs huggingface dataset:
Aberration compensation in digital holographic reconstruction of microscopic objects

Abstract We present a digital method for the compensation of the aberrations appearing when a lensless hologram of a microscopic object is reconstructed. The digital hologram of the microscopic object is recorded on a relatively low resolution device (CCD sensor). An expansion of the hologram by interpolation of the recorded intensity is performed in order to increase the number of pixels. For digital reconstruction of the wavefronts the expanded hologram is multiplied by the reference wave followed by simulation of the diffraction using the Rayleigh-Sommerfeld propagation relation. Experimental results are presented.
The Normalized Cross Density Functional: A Framework to Quantify Statistical Dependence for Random Processes

This paper presents a novel approach to measuring statistical dependence between two random processes (r.p.) using a positive-definite function called the Normalized Cross Density (NCD). NCD is derived directly from the probability density functions of two r.p. and constructs a data-dependent Hilbert space, the Normalized Cross-Density Hilbert Space (NCD-HS). By Mercer's Theorem, the NCD norm can be decomposed into its eigenspectrum, which we name the Multivariate Statistical Dependence (MSD) measure, and their sum, the Total Dependence Measure (TSD). Hence, the NCD-HS eigenfunctions serve as a novel embedded feature space, suitable for quantifying r.p. statistical dependence. In order to apply NCD directly to r.p. realizations, we introduce an architecture with two multiple-output neural networks, a cost function, and an algorithm named the Functional Maximal Correlation Algorithm (FMCA). With FMCA, the two networks learn concurrently by approximating each other's outputs, extending the Alternating Conditional Expectation (ACE) for multivariate functions. We mathematically prove that FMCA learns the dominant eigenvalues and eigenfunctions of NCD directly from realizations. Preliminary results with synthetic data and medium-sized image datasets corroborate the theory. Different strategies for applying NCD are proposed and discussed, demonstrating the method's versatility and stability beyond supervised learning. Specifically, when the two r.p. are high-dimensional real-world images and a white uniform noise process, FMCA learns factorial codes, i.e., the occurrence of a code guarantees that a specific training set image was present, which is important for feature learning.
The Hybridization of Branch and Bound with Metaheuristics for Nonconvex Multiobjective Optimization

A hybrid framework combining the branch and bound method with multiobjective evolutionary algorithms is proposed for nonconvex multiobjective optimization. The hybridization exploits the complementary character of the two optimization strategies. A multiobjective evolutionary algorithm is intended for inducing tight lower and upper bounds during the branch and bound procedure. Tight bounds such as the ones derived in this way can reduce the number of subproblems that have to be solved. The branch and bound method guarantees the global convergence of the framework and improves the search capability of the multiobjective evolutionary algorithm. An implementation of the hybrid framework considering NSGA-II and MOEA/D-DE as multiobjective evolutionary algorithms is presented. Numerical experiments verify the hybrid algorithms benefit from synergy of the branch and bound method and multiobjective evolutionary algorithms.
Efficient Strategies for Graph Pattern Mining Algorithms on GPUs

Graph Pattern Mining (GPM) is an important, rapidly evolving, and computation demanding area. GPM computation relies on subgraph enumeration, which consists in extracting subgraphs that match a given property from an input graph. Graphics Processing Units (GPUs) have been an effective platform to accelerate applications in many areas. However, the irregularity of subgraph enumeration makes it challenging for efficient execution on GPU due to typical uncoalesced memory access, divergence, and load imbalance. Unfortunately, these aspects have not been fully addressed in previous work. Thus, this work proposes novel strategies to design and implement subgraph enumeration efficiently on GPU. We support a depth-first search style search (DFS-wide) that maximizes memory performance while providing enough parallelism to be exploited by the GPU, along with a warp-centric design that minimizes execution divergence and improves utilization of the computing capabilities. We also propose a low-cost load balancing layer to avoid idleness and redistribute work among thread warps in a GPU. Our strategies have been deployed in a system named DuMato, which provides a simple programming interface to allow efficient implementation of GPM algorithms. Our evaluation has shown that DuMato is often an order of magnitude faster than state-of-the-art GPM systems and can mine larger subgraphs (up to 12 vertices).
A systematic literature review on Security of Unmanned Aerial Vehicle Systems

Unmanned aerial vehicles (UAVs) are becoming more common, and their operational range is expanding tremendously, making the security aspect of the inquiry essential. This study does a thorough assessment of the literature to determine the most common cyberattacks and the effects they have on UAV assaults on civilian targets. The STRIDE assault paradigm, the challenge they present, and the proper tools for the attack are used to categorize the cyber dangers discussed in this paper. Spoofing and denial of service assaults are the most prevalent types of UAV cyberattacks and have the best results. No attack style demands the employment of a hard-to-reach gadget, indicating that the security environment currently necessitates improvements to UAV use in civilian applications.
Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation

Language-conditioned policies allow robots to interpret and execute human instructions. Learning such policies requires a substantial investment with regards to time and compute resources. Still, the resulting controllers are highly device-specific and cannot easily be transferred to a robot with different morphology, capability, appearance or dynamics. In this paper, we propose a sample-efficient approach for training language-conditioned manipulation policies that allows for rapid transfer across different types of robots. By introducing a novel method, namely Hierarchical Modularity, and adopting supervised attention across multiple sub-modules, we bridge the divide between modular and end-to-end learning and enable the reuse of functional building blocks. In both simulated and real world robot manipulation experiments, we demonstrate that our method outperforms the current state-of-the-art methods and can transfer policies across 4 different robots in a sample-efficient manner. Finally, we show that the functionality of learned sub-modules is maintained beyond the training process and can be used to introspect the robot decision-making process. Code is available at https://github.com/ir-lab/ModAttn.
On the Sensitivity of Reward Inference to Misspecified Human Models

Inferring reward functions from human behavior is at the center of value alignment - aligning AI objectives with what we, humans, actually want. But doing so relies on models of how humans behave given their objectives. After decades of research in cognitive science, neuroscience, and behavioral economics, obtaining accurate human models remains an open research topic. This begs the question: how accurate do these models need to be in order for the reward inference to be accurate? On the one hand, if small errors in the model can lead to catastrophic error in inference, the entire framework of reward learning seems ill-fated, as we will never have perfect models of human behavior. On the other hand, if as our models improve, we can have a guarantee that reward accuracy also improves, this would show the benefit of more work on the modeling side. We study this question both theoretically and empirically. We do show that it is unfortunately possible to construct small adversarial biases in behavior that lead to arbitrarily large errors in the inferred reward. However, and arguably more importantly, we are also able to identify reasonable assumptions under which the reward inference error can be bounded linearly in the error in the human model. Finally, we verify our theoretical insights in discrete and continuous control tasks with simulated and human data.
The Platform for non-metallic pipes defects recognition. Design and Implementation

This paper describes a prototype software and hardware platform to provide support to ﬁeld operators during the inspection of surface defects of non-metallic pipes. Inspection is carried out by video ﬁlming defects created on the same surface in real-time using a ”smart” helmet device and other mobile devices. The work focuses on the detection and recognition of the defects which appears as colored iridescence of reﬂected light caused by the diﬀraction eﬀect arising from the presence of internal stresses in the inspected material. The platform allows you to carry out preliminary analysis directly on the device in oﬄine mode, and, if a connection to the network is established, the received data is transmitted to the server for post-processing to extract information about possible defects that were not detected at the previous stage. The paper presents a description of the stages of design, formal description, and implementation details of the platform. It also provides descriptions of the models used to recognize defects and examples of the result of the work.
A Comparative Performance Analysis of Explainable Machine Learning Models With And Without RFECV Feature Selection Technique Towards Ransomware Classification

Ransomware has emerged as one of the major global threats in recent days. The alarming increasing rate of ransomware attacks and new ransomware variants intrigue the researchers in this domain to constantly examine the distinguishing traits of ransomware and refine their detection or classification strategies. Among the broad range of different behavioral characteristics, the trait of Application Programming Interface (API) calls and network behaviors have been widely utilized as differentiating factors for ransomware detection, or classification. Although many of the prior approaches have shown promising results in detecting and classifying ransomware families utilizing these features without applying any feature selection techniques, feature selection, however, is one of the potential steps toward an efficient detection or classification Machine Learning model because it reduces the probability of overfitting by removing redundant data, improves the model's accuracy by eliminating irrelevant features, and therefore reduces training time. There have been a good number of feature selection techniques to date that are being used in different security scenarios to optimize the performance of the Machine Learning models. Hence, the aim of this study is to present the comparative performance analysis of widely utilized Supervised Machine Learning models with and without RFECV feature selection technique towards ransomware classification utilizing the API call and network traffic features. Thereby, this study provides insight into the efficiency of the RFECV feature selection technique in the case of ransomware classification which can be used by peers as a reference for future work in choosing the feature selection technique in this domain.
AuE-IPA: An AU Engagement Based Infant Pain Assessment Method

Recent studies have found that pain in infancy has a significant impact on infant development, including psychological problems, possible brain injury, and pain sensitivity in adulthood. However, due to the lack of specialists and the fact that infants are unable to express verbally their experience of pain, it is difficult to assess infant pain. Most existing infant pain assessment systems directly apply adult methods to infants ignoring the differences between infant expressions and adult expressions. Meanwhile, as the study of facial action coding system continues to advance, the use of action units (AUs) opens up new possibilities for expression recognition and pain assessment. In this paper, a novel AuE-IPA method is proposed for assessing infant pain by leveraging different engagement levels of AUs. First, different engagement levels of AUs in infant pain are revealed, by analyzing the class activation map of an end-to-end pain assessment model. The intensities of top-engaged AUs are then used in a regression model for achieving automatic infant pain assessment. The model proposed is trained and experimented on YouTube Immunization dataset, YouTube Blood Test dataset, and iCOPEVid dataset. The experimental results show that our AuE-IPA method is more applicable to infants and possesses stronger generalization ability than end-to-end assessment model and the classic PSPI metric.
Transient inverse energy cascade in free surface turbulence

We study the statistics of free-surface turbulence at large Reynolds numbers produced by direct numerical simulations in a fluid layer at different thickness with fixed characteristic forcing scale. We observe the production of a transient inverse cascade, with a duration which depends on the thickness of the layer, followed by a transition to three-dimensional turbulence initially produced close to the bottom, no-slip boundary. By switching off the forcing, we study the decaying turbulent regime and we find that it cannot be described by an exponential law. Our results show that boundary conditions play a fundamental role in the nature of turbulence produced in thin layers and give limits on the conditions to produce a two-dimensional phenomenology.
Spin Dynamics of Complex Oxides, Bismuth-Antimony Alloys, and Bismuth Chalcogenides

This thesis predicts that two types of material families could be a solution to the challenges in spintronics: complex oxides and bismuth based materials. We derive a general approach for constructing an effective spin-orbit Hamiltonian, which applies to all nonmagnetic materials. We also verify this formalism through comparisons with other approaches for III-V semiconductors. Its general applicability is illustrated by deriving the spin-orbit interaction and predicting spin lifetimes for strained SrTiO$_3$ and a two-dimensional electron gas (such as at the LaAlO$_3$/SrTiO$_3$ interface). Our results suggest robust spin coherence and spin transport properties in SrTiO$_3$ related materials. In the second part, we calculate intrinsic spin Hall conductivities for Bi$_{1-x}$Sb$_x$ semimetals with strong spin-orbit couplings, from the Kubo formula and using Berry curvatures evaluated from a tight-binding Hamiltonian. Nearly crossing bands with strong spin-orbit interaction generate giant spin Hall conductivities in these materials, ranging from 474 ($\hbar/e)( \Omega^{-1}cm^{-1}$) for bismuth to 96($\hbar/e)( \Omega^{-1}cm^{-1}$) for antimony; the value for bismuth is more than twice that of platinum. The large spin Hall conductivities persist for alloy compositions corresponding to a three-dimensional topological insulator state, such as Bi$_{0.83}$Sb$_{0.17}$. The spin Hall conductivity could be changed by a factor of 5 for doped Bi, or for Bi$_{0.83}$Sb$_{0.17}$, by changing the chemical potential, suggesting the potential for doping or voltage tuned spin Hall current. We also calculate intrinsic spin Hall conductivities of Bi$_2$Se$_3$ and Bi$_2$Te$_3$ topological insulators from an effective tight-binding Hamiltonian. We conclude that bismuth-antimony alloys and bismuth chalcogenides are primary candidates for efficiently generating spin currents through the spin Hall effect.
Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization

Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making. Assessing the quality of uncertainty estimates is challenging as ground truth for uncertainty estimates is not available. Ideally, in a well-calibrated model, uncertainty estimates should perfectly correlate with model error. We propose a novel error aligned uncertainty optimization method and introduce a trainable loss function to guide the models to yield good quality uncertainty estimates aligning with the model error. Our approach targets continuous structured prediction and regression tasks, and is evaluated on multiple datasets including a large-scale vehicle motion prediction task involving real-world distributional shifts. We demonstrate that our method improves average displacement error by 1.69% and 4.69%, and the uncertainty correlation with model error by 17.22% and 19.13% as quantified by Pearson correlation coefficient on two state-of-the-art baselines.
Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes

Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper circumvents this challenge by formulating a graph neural network (GNN)-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The results obtained using the IEEE 118-bus system indicate that the GNN-based state estimator outperforms both the model-based linear state estimator and a data-driven deep neural network-based state estimator in the presence of non-Gaussian measurement noise and topology changes, respectively.
Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models through Virtual Particles

Coarse-grained (CG) models parametrized using atomistic reference data, i.e., “bottom up” CG models, have proven useful in the study of biomolecules and other soft matter. However, the construction of highly accurate, low resolution CG models of biomolecules remains challenging. We demonstrate in this work how virtual particles, CG sites with no atomistic correspondence, can be incorporated into CG models within the context of relative entropy minimization (REM) as latent variables. The methodology presented, variational derivative relative entropy minimization (VD-REM), enables optimization of virtual particle interactions through a gradient descent algorithm aided by machine learning. We apply this methodology to the challenging case of a solvent-free CG model of a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) lipid bilayer and demonstrate that introduction of virtual particles captures solvent-mediated behavior and higher-order correlations which REM alone cannot capture in a more standard CG model based only on the mapping of collections of atoms to the CG sites.
ViMA – The Spinning Rotor Gauge to Measure the Viscosity of Tritium Between 77 and 300 K

Experimental values for the viscosity of the radioactive hydrogen isotope tritium (T$_2$) are currently unavailable in literature. The value of this material property over a wide temperature range is of interest for applications in the field of fusion, neutrino physics, as well as to test ab initio calculations. As a radioactive gas, tritium requires careful experiment design to ensure safe and environmental contamination free measurements. In this contribution, we present a spinning rotor gauge based, tritium compatible design of a gas viscosity measurement apparatus (ViMA) capable of covering the temperature range from 80 K to 300 K.
The integration of machine learning into automated test generation: A systematic mapping study

Machine learning (ML) may enable effective automated test generation. We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges in this intersection by performing. We perform a systematic mapping study on a sample of 124 publications. ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property‐based, and expected output oracles. Supervised learning—often based on neural networks—and reinforcement learning—often based on Q‐learning—are common, and some publications also employ unsupervised or semi‐supervised learning. (Semi‐/Un‐)Supervised approaches are evaluated using both traditional testing metrics and ML‐related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. The work‐to‐date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed—and how they are applied—benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.
An SLR on Edge Computing Security and possible threat protection

Mobile and Internet of Things devices are generating enormous amounts of multi-modal data due to their exponential growth and accessibility. As a result, these data sources must be directly analyzed in real time at the network edge rather than relying on the cloud. Significant processing power at the network's edge has made it possible to gather data and make decisions prior to data being sent to the cloud. Moreover, security problems have significantly towered as a result of the rapid expansion of mobile devices, Internet of Things (IoT) devices, and various network points. It's much harder than ever to guarantee the privacy of sensitive data, including customer information. This systematic literature review depicts the fact that new technologies are a great weapon to fight with the attack and threats to the edge computing security.
Energy Dissipation due to Capillary Interactions: Hydrophobicity Maps in Force Microscopy

The energy dissipation process involved in the formation and rupture of a nanometer-sized capillary-condensed water bridge is theoretically analyzed. With the help of numerical simulations, the dissipation contrast in amplitude-modulated atomic force microscopy is shown to be a result of a nontrivial interplay between the energy dissipated in each rupture process and the bistable motion of the cantilever. In the repulsive high amplitude regime, the dissipated power is a function of the tip and sample contact angles being independent of the elastic properties of the system. Working in this regime, energy dissipation images in air can be regarded as surface hydrophobicity maps.
Violent crime and incentives in the long-run: evidence from England and Wales

This study uses recent advances in time-series econometrics to investigate the non-stationarity and co-integration properties of violent crime series in England and Wales. In particular, we estimate the long-run impact of economic conditions, beer consumption and various deterrents on different categories of recorded violent crime. The results suggest that a long-run causal model exists for only minor crimes of violence, with beer consumption being a predominant factor.