Miracle-dz/UniFAR-FTUs huggingface dataset:
Random blisters on stickers: metrology through defects

Blisters are commonly observed when an adhesive sheet is carelessly deposited on a plate. Although such blisters are usually not desired for practical applications, we show through model experiments on angular blisters how material properties can be deduced from height profile measurements. In particular the typical curvature of the crests is found to be proportional to an elasto-capillary length that compares the bending stiffness of the sheet with the adhesion energy. In addition, the radius of the tip allows the estimation of the product of this length with the thickness of the sheet. The relevance of these results to realistic random configurations is finally confirmed.
Efficient Certified Defenses Against Patch Attacks on Image Classifiers

Adversarial patches pose a realistic threat model for physical world attacks on autonomous systems via their perception component. Autonomous systems in safety-critical domains such as automated driving should thus contain a fail-safe fallback component that combines certifiable robustness against patches with efficient inference while maintaining high performance on clean inputs. We propose BagCert, a novel combination of model architecture and certification procedure that allows efficient certification. We derive a loss that enables end-to-end optimization of certified robustness against patches of different sizes and locations. On CIFAR10, BagCert certifies 10.000 examples in 43 seconds on a single GPU and obtains 86% clean and 60% certified accuracy against 5x5 patches.
A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan–Vese segmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, the Chan–Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score, Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR) were calculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with an average Dice Score of 0.92 (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076, and MAE of 52.946), pointing to the high reliability of the proposed architecture.
Reliable Probabilistic Face Embeddings in the Wild

Probabilistic Face Embeddings (PFE) can improve face recognition performance in unconstrained scenarios by integrating data uncertainty into the feature representation. However, existing PFE methods tend to be over-conﬁdent in estimating uncertainty and is too slow to apply to large-scale face matching. This paper proposes a regularized probabilistic face embedding method to improve the robustness and speed of PFE. Speciﬁcally, the mutual likelihood score (MLS) metric used in PFE is simpliﬁed to speedup the matching of face feature pairs. Then, an output-constraint loss is proposed to penalize the variance of the uncertainty output, which can regularize the output of the neural network. In addition, an identiﬁcation preserving loss is proposed to improve the discriminative of the MLS metric, and a multi-layer feature fusion module is proposed to improve the neural network’s uncertainty estimation ability. Comprehensive experiments show that the proposed method can achieve comparable or better results in 8 benchmarks than the state-of-the-art methods, and can improve the performance of risk-controlled face recognition. The code of ProbFace is publicly available in GitHub (https://github.com/KaenChan/ProbFace).
Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion

Social media have become an integral part of our lives, expanding our interlinking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand, however, some serious negative implications of social media have been repeatedly highlighted in recent years, pointing at various threats to society and its more vulnerable members, such as teenagers, in particular, ranging from much-discussed problems such as digital addiction and polarization to manipulative influences of algorithms and further to more teenager-specific issues (e.g., body stereotyping). The impact of social media—both at an individual and societal level—is characterized by the complex interplay between the users' interactions and the intelligent components of the platform. Thus, users' understanding of social media mechanisms plays a determinant role. We thus propose a theoretical framework based on an adaptive “Social Media Virtual Companion” for educating and supporting an entire community, teenage students, to interact in social media environments in order to achieve desirable conditions, defined in terms of a community-specific and participatory designed measure of Collective Well-Being (CWB). This Companion combines automatic processing with expert intervention and guidance. The virtual Companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term by balancing the level of social media threats the users are exposed to, and in the long term by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. We put an emphasis on experts and educators in the educationally managed social media community of the Companion. They play five key roles: (a) use the Companion in classroom-based educational activities; (b) guide the definition of the CWB; (c) provide a hierarchical structure of learning strategies, objectives and activities that will support and contain the adaptive sequencing algorithms of the CWB-RS based on hierarchical reinforcement learning; (d) act as moderators of direct conflicts between the members of the community; and, finally, (e) monitor and address ethical and educational issues that are beyond the intelligent agent's competence and control. This framework offers a possible approach to understanding how to design social media systems and embedded educational interventions that favor a more healthy and positive society. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.
Advances in biofouling mitigation: A review

ABSTRACT The attachment of microbial biomass on solid surfaces is a universal phenomenon that occurs in natural and engineering systems and is responsible for various types of biofouling. Biological fouling, also referred to as biofouling, remains one of the most critical problems toward the durable application of materials. The eradication of this problem using sustainable and environmentally friendly techniques is still of critical importance and is therefore receiving significant interest from researchers worldwide. The authors start with a definition of processes that lead to biofouling and their relevance to various industrial applications. The most relevant findings from past research are presented and particular emphasis is focused on present research, where recent developments in surface modification receive specific attention.
Extremal learning: extremizing the output of a neural network in regression problems

Neural networks allow us to model complex relationships between variables. We show how to efficiently find extrema of a trained neural network in regression problems. Finding the extremizing input of an approximated model is formulated as the training of an additional neural network with a loss function that minimizes when the extremizing input is achieved. We further show how to incorporate additional constraints on the input vector such as limiting the extrapolation of the extremizing input vector from the original training data set. An instructional example of this approach using TensorFlow is included.
Application of CFD to evaluate the pore morphology effect on nanofluid flooding for enhanced oil recovery

In this study a computational fluid dynamics (CFD) method has been developed to simulate the effect of pore morphology and its distribution in a 2D micromodel on the enhanced oil recovery factor of nanofluid flooding. Seven types of micromodel with different schematics and pore shapes were considered. SiO2 nanoparticles, dispersed in distilled water, were used for the preparation of the nanofluid and flooding operation. To generate the desirable porous media, the geometries of the micromodels were generated using the commercial grid generation tool, Gambit 2.3. Then, the momentum and mass transport equations were solved based on the finite volume method using the Fluent 6.3 software to investigate the displacement of oil at the pore scale. In order to better understand the nanoparticles’ effects and to confirm the validity of the CFD simulations, numerical results have been compared with the experimental data. The influences of some parameters such as heterogeneity of pores, connectivity of pores with or without throat line, tortuosity and pore shape on the enhanced oil recovery, breakthrough time and fluid trapping in the porous media were investigated. From the results, it has been found that random generation of pore distribution illustrates better results compared to homogeneous pore distribution. In addition, with the presence of nanoparticles in the injected fluid the number of fingers decreases. The fingering effect has the main effect on the oil recovery factor with a lower fingering effect having a higher recovery factor. So, in the homogeneous pattern the nanofluid flow in the porous media is uniform and symmetric. But in the random distribution model, the fluid flow is more realistic and similar to the fluid flow in reservoirs.
Stanley--Elder--Fine theorems for colored partitions

We give a new proof of a partition theorem popularly known as Elder’s theorem, but which is also credited to Stanley and Fine. We extend the theorem to the context of colored partitions (or prefabs). More specifically, we give analogous results for b-colored partitions, where each part occurs in b colors; for b-colored partitions with odd parts (or distinct parts); for partitions where the part k comes in k colors; and, overpartitions.
Open-World Semi-Supervised Learning

A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this assumption rarely holds for data in-the-wild, where instances belonging to novel classes may appear at testing time. Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data. In this novel setting, the goal is to solve the class distribution mismatch between labeled and unlabeled data, where at the test time every input instance either needs to be classified into one of the existing classes or a new unseen class needs to be initialized. To tackle this challenging problem, we propose ORCA, an end-to-end deep learning approach that introduces uncertainty adaptive margin mechanism to circumvent the bias towards seen classes caused by learning discriminative features for seen classes faster than for the novel classes. In this way, ORCA reduces the gap between intra-class variance of seen with respect to novel classes. Experiments on image classification datasets and a single-cell annotation dataset demonstrate that ORCA consistently outperforms alternative baselines, achieving 25% improvement on seen and 96% improvement on novel classes of the ImageNet dataset.
The two-sided exit problem for a random walk on $\mathbb{Z}$ and having infinite variance II

Let F be a distribution function on the integer lattice Z and S = (Sn) the random walk with step distribution F . Suppose S is oscillatory and denote by Ua(x) and ua(x) the renewal function and sequence, respectively, of the strictly ascending ladder height process associated with S. Putting A(x) = ∫ x 0 [1−F (t)−F (−t)]dt, H(x) = 1−F (x)+F (−x) we suppose A(x)/ ( xH(x) ) → −∞ (x → ∞). Under some additional regularity condition on the positive tail of F , we show that ua(x) ∼ Ua(x)[1− F (x)]/|A(x)| as x → ∞ and uniformly for 0 ≤ x ≤ R, as R → ∞ P [S leaves [0, R] on its upper side |S0 = x] ∼ c A(x)ua(x), where c = ∑∞ n=1 P [Sn > S0; Sk < S0 for 0 < k < n] and the regularity condition is satisfied at least if S is recurrent, lim sup[1 − F (x)]/F (−x) < 1, and x[1 − F (x)]/L(x) (x ≥ 1) is bounded away from zero and infinity for some slowly varying function L. We also give some asymptotic estimates of the probability that S visits R before entering the negative half-line for asymptotically stable walks and obtain asymptotic behaviour of the probability that R is ever hit by S conditioned to avoid the negative half-line forever.
Constrained Ensemble Langevin Monte Carlo

The classical Langevin Monte Carlo method looks for samples from a target distribution by descending the samples along the gradient of the target distribution. The method enjoys a fast convergence rate. However, the numerical cost is sometimes high because each iteration requires the computation of a gradient. One approach to eliminate the gradient computation is to employ the concept of "ensemble. " A large number of particles are evolved together so the neighboring particles provide gradient information to each other. In this article, we discuss two algorithms that integrate the ensemble feature into LMC, and the associated properties.In particular, we find that if one directly surrogates the gradient using the ensemble approximation, the algorithm, termed Ensemble Langevin Monte Carlo, is unstable due to a high variance term. If the gradients are replaced by the ensemble approximations only in a constrained manner, to protect from the unstable points, the algorithm, termed Constrained Ensemble Langevin Monte Carlo, resembles the classical LMC up to an ensemble error but removes most of the gradient computation.
Point-set Distances for Learning Representations of 3D Point Clouds

Learning an effective representation of 3D point clouds requires a good metric to measure the discrepancy between two 3D point sets, which is non-trivial due to their irregularity. Most of the previous works resort to using the Chamfer discrepancy or Earth Mover’s distance, but those metrics are either ineffective in measuring the differences between point clouds or computationally expensive. In this paper, we conduct a systematic study with extensive experiments on distance metrics for 3D point clouds. From this study, we propose to use sliced Wasserstein distance and its variants for learning representations of 3D point clouds. In addition, we introduce a new algorithm to estimate sliced Wasserstein distance that guarantees that the estimated value is close enough to the true one. Experiments show that the sliced Wasserstein distance and its variants allow the neural network to learn a more efficient representation compared to the Chamfer discrepancy. We demonstrate the efficiency of the sliced Wasserstein metric and its variants on several tasks in 3D computer vision including training a point cloud autoencoder, generative modeling, transfer learning, and point cloud registration.
Two-body problem in a multiband lattice and the role of quantum geometry

We consider the two-body problem in a periodic potential, and study the bound-state dispersion of a spin- ↑ fermion that is interacting with a spin- ↓ fermion through a short-range attractive interaction. Based on a variational approach, we obtain the exact solution of the dispersion in the form of a set of self-consistency equations, and apply it to tight-binding Hamiltonians with onsite interactions. We pay special attention to the bipartite lattices with a two-point basis that exhibit time-reversal symmetry, and show that the lowest-energy bound states disperse quadratically with momentum, whose eﬀective-mass tensor is partially controlled by the quantum metric tensor of the underlying Bloch states. In particular, we apply our theory to the Mielke checkerboard lattice, and study the special role played by the interband processes in producing a ﬁnite eﬀective mass for the bound states in a non-isolated ﬂat band.
Eddy memory as an explanation of intraseasonal periodic behaviour in baroclinic eddies

The baroclinic annular mode (BAM) is a leading‐order mode of the eddy kinetic energy in the Southern Hemisphere exhibiting oscillatory behaviour at intraseasonal time‐scales. The oscillation mechanism has been linked to transient eddy–mean flow interactions which remain poorly understood. Here we demonstrate that the finite memory effect in eddy‐heat flux dependence on the large‐scale flow can explain the origin of the BAM's oscillatory behaviour. We represent the eddy memory effect by a delayed integral kernel that leads to a generalized Langevin equation for the planetary‐scale heat equation. Using a mathematical framework for the interactions between planetary‐ and synoptic‐scale motions, we derive a reduced dynamical model of the BAM – a stochastically forced oscillator with a period proportional to the geometric mean between the eddy memory time‐scale and the diffusive eddy equilibration time‐scale. Our model provides a formal justification for the previously proposed phenomenological model of the BAM and could be used to explicitly diagnose the memory kernel and improve our understanding of transient eddy–mean flow interactions in the atmosphere.
β-hairpin folding simulations in atomistic detail using an implicit solvent model1

Abstract We have used distributed computing techniques and a supercluster of thousands of computer processors to study folding of the C-terminal β-hairpin from protein G in atomistic detail using the GB/SA implicit solvent model at 300 K. We have simulated a total of nearly 38 μs of folding time and obtained eight complete and independent folding trajectories. Starting from an extended state, we observe relaxation to an unfolded state characterized by non-specific, temporary hydrogen bonding. This is followed by the appearance of interactions between hydrophobic residues that stabilize a bent intermediate. Final formation of the complete hydrophobic core occurs cooperatively at the same time that the final hydrogen bonding pattern appears. The folded hairpin structures we observe all contain a closely packed hydrophobic core and proper β-sheet backbone dihedral angles, but they differ in backbone hydrogen bonding pattern. We show that this is consistent with the existing experimental data on the hairpin alone in solution. Our analysis also reveals short-lived semi-helical intermediates which define a thermodynamic trap. Our results are consistent with a three-state mechanism with a single rate-limiting step in which a varying final hydrogen bond pattern is apparent, and semi-helical off-pathway intermediates may appear early in the folding process. We include details of the ensemble dynamics methodology and a discussion of our achievements using this new computational device for studying dynamics at the atomic level.
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models

Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be generated and spread through Open-Source Intelligence (OSINT) communities or on the Web to effect a data poisoning attack on these systems. Adversaries can use fake CTI examples as training input to subvert cyber defense systems, forcing their models to learn incorrect inputs to serve the attackers' malicious needs. In this paper, we show how to automatically generate fake CTI text descriptions using transformers. Given an initial prompt sentence, a public language model like GPT-2 with fine-tuning can generate plausible CTI text that can mislead cyber-defense systems. We use the generated fake CTI text to perform a data poisoning attack on a Cybersecurity Knowledge Graph (CKG) and a cybersecurity corpus. The attack introduced adverse impacts such as returning incorrect reasoning outputs, representation poisoning, and corruption of other dependent AI-based cyber defense systems. We evaluate with traditional approaches and conduct a human evaluation study with cyber-security professionals and threat hunters. Based on the study, professional threat hunters were equally likely to consider our fake generated CTI and authentic CTI as true.
Bacteriophage classification for assembled contigs using graph convolutional network

Abstract Motivation Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data. Results In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools. Availability and implementation The source code of PhaGCN is available via: https://github.com/KennthShang/PhaGCN.