Miracle-dz/UniFAR-FTUs huggingface dataset:
SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations

Whilst significant research effort into adversarial examples (AE) has emerged in recent years, the main vector to realize these attacks in the real-world currently relies on static adversarial patches, which are limited in their conspicuousness and can not be modified once deployed. In this paper, we propose Short-Lived Adversarial Perturbations (SLAP), a novel technique that allows adversaries to realize robust, dynamic real-world AE from a distance. As we show in this paper, such attacks can be achieved using a light projector to shine a specifically crafted adversarial image in order to perturb real-world objects and transform them into AE. This allows the adversary greater control over the attack compared to adversarial patches: (i) projections can be dynamically turned on and off or modified at will, (ii) projections do not suffer from the locality constraint imposed by patches, making them harder to detect. We study the feasibility of SLAP in the self-driving scenario, targeting both object detector and traffic sign recognition tasks. We demonstrate that the proposed method generates AE that are robust to different environmental conditions for several networks and lighting conditions: we successfully cause misclassifications of state-of-the-art networks such as Yolov3 and Mask-RCNN with up to 98% success rate for a variety of angles and distances. Additionally, we demonstrate that AE generated with SLAP can bypass SentiNet, a recent AE detection method which relies on the fact that adversarial patches generate highly salient and localized areas in the input images.
A Distilled Model for Tracking and Tracker Fusion

Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we unify such goals by proposing a novel tracking methodology that takes advantage of other visual trackers, offline and online. A compact student model is trained via the marriage of knowledge distillation and reinforcement learning. The first allows to transfer and compress tracking knowledge of other trackers. The second enables the learning of evaluation measures which are then exploited online. After learning, the student can be ultimately used to build (i) a very fast single-shot tracker, (ii) a tracker with a simple and effective online adaptation mechanism, (iii) a tracker that performs fusion of other trackers. Extensive validation shows that the proposed algorithms compete with state-of-the-art trackers while running in real-time.
On Learning Semantic Representations for Million-Scale Free-Hand Sketches

In this paper, we study learning semantic representations for million-scale free-hand sketches. This is highly challenging due to the domain-unique traits of sketches, e.g., diverse, sparse, abstract, noisy. We propose a dual-branch CNNRNN network architecture to represent sketches, which simultaneously encodes both the static and temporal patterns of sketch strokes. Based on this architecture, we further explore learning the sketch-oriented semantic representations in two challenging yet practical settings, i.e., hashing retrieval and zero-shot recognition on million-scale sketches. Specifically, we use our dual-branch architecture as a universal representation framework to design two sketch-specific deep models: (i) We propose a deep hashing model for sketch retrieval, where a novel hashing loss is specifically designed to accommodate both the abstract and messy traits of sketches. (ii) We propose a deep embedding model for sketch zero-shot recognition, via collecting a large-scale edge-map dataset and proposing to extract a set of semantic vectors from edge-maps as the semantic knowledge for sketch zero-shot domain alignment. Both deep models are evaluated by comprehensive experiments on million-scale sketches and outperform the state-of-the-art competitors.
Chromatin proteins and their role in plant genetic transformation

Agrobacterium-mediated plant transformation is one of the most widely used technologies to introduce gene in plants. Plant-microbe interaction has been extensively studied in the last decade and several factors involved in this complex process such as nuclear import, T-DNA integration and expression have been identified. Despite a large amount of data available with regards to the bacterial factors involved in transformation, there are little known about host plant factors involved in this process. Moreover, the host plant responds to several cellular processes and the chromatin alteration is linked with the foreign DNA integration and finally with gene expression. Here, the current knowledge on the role of chromatin proteins during Agrobacterium-mediated plant transformation is briefly described.
Resource-Aware Protocols for Network Cost-Sharing Games

We study the extent to which decentralized cost-sharing protocols can achieve good price of anarchy (PoA) bounds in network cost-sharing games with nagents. We focus on the model of resource-aware protocols, where the designer has prior access to the network structure and can also increase the total cost of an edge (overcharging), and we study classes of games with concave or convex cost functions. We first consider concave cost functions and our main result is a cost-sharing protocol for symmetric games on directed acyclic graphs that achieves a PoA of 2+ε for some arbitrary small positive ε, which improves to 1+ε for games with at least two players. We also achieve a PoA of 1 for series-parallel graphs and show that no protocol can achieve a PoA better than Ω(√n) for multicast games. We then also consider convex cost functions and prove analogous results for series-parallel networks and multicast games, as well as a lower bound of Ω(√n) for the PoA on directed acyclic graphs without the use of overcharging.
Cross-lingual Inductive Transfer to Detect Offensive Language

With the growing use of social media and its availability, many instances of the use of offensive language have been observed across multiple languages and domains. This phenomenon has given rise to the growing need to detect the offensive language used in social media cross-lingually. In OffensEval 2020, the organizers have released the \textit{multilingual Offensive Language Identification Dataset} (mOLID), which contains tweets in five different languages, to detect offensive language. In this work, we introduce a cross-lingual inductive approach to identify the offensive language in tweets using the contextual word embedding \textit{XLM-RoBERTa} (XLM-R). We show that our model performs competitively on all five languages, obtaining the fourth position in the English task with an F1-score of $0.919$ and eighth position in the Turkish task with an F1-score of $0.781$. Further experimentation proves that our model works competitively in a zero-shot learning environment, and is extensible to other languages.
The κ-cookbook: a novel generalizing approach to unify κ-like distributions for plasma particle modelling

In the literature different so-called $\kappa$-distribution functions are discussed to fit and model the velocity (or energy) distributions of solar wind species, pickup ions or magnetospheric particles. Here we introduce a generalized (isotropic) $\kappa$-distribution as a "cookbook", which admits as special cases, or "recipes", all the other known versions of $\kappa$-models. A detailed analysis of the generalized distribution function is performed, providing general analytical expressions for the velocity moments, Debye length, and entropy, and pointing out a series of general requirements that plasma distribution functions should satisfy. From a contrasting analysis of the recipes found in the literature, we show that all of them lead to almost the same macroscopic parameters with a small standard deviation between them. However, one of these recipes called the regularized $\kappa$-distribution provides a functional alternative for macroscopic parameterization without any constraint for the power-law exponent $\kappa$.
Massive molecular gas reservoir around the central AGN in the CARLA J1103 + 3449 cluster at z = 1.44

Passive early-type galaxies dominate cluster cores at z ≲ 1.5. At higher redshift, cluster core galaxies are observed to have on-going star-formation, which is fueled by cold molecular gas. We measured the molecular gas reservoir of the central region around the radio-loud active galactic nucleus (AGN) in the cluster CARLA J1103 + 3449 at z = 1.44 using NOEMA. The AGN synchrotron emission dominates the continuum emission at 94.48 GHz, and we measured its flux at the AGN position and at the position of two radio jets. Combining our measurements with published results over the range 4.71–94.5 GHz, and assuming Ssynch ∝ ν−α, we obtain a flat spectral index of α = 0.14 ± 0.03 for the AGN core emission, and a steeper index of α = 1.43 ± 0.04 and α = 1.15 ± 0.04 at positions close to the western and eastern lobes, respectively. The total spectral index is α = 0.92 ± 0.02 over the range 73.8 MHz–94.5 GHz. We detect two CO(2–1) emission lines, both blueshifted with respect to the AGN. Their emission corresponds to two regions, ~17 kpc southeast and ~14 kpc southwest of the AGN, not associated with galaxies. In these two regions, we find a total massive molecular gas reservoir of Mgastot = 3.9 ± 0.4 × 1010 M⊙, which dominates (≳60%) the central total molecular gas reservoir. These results can be explained by massive cool gas flows in the center of the cluster. The AGN early-type host is not yet quenched; its star formation rate is consistent with being on the main sequence of star-forming galaxies in the field (star formation rate ~30–140 M⊙ yr−1), and the cluster core molecular gas reservoir is expected to feed the AGN and the host star formation before quiescence. The other confirmed cluster members show star formation rates at ~2σ below the field main sequence at similar redshifts and do not have molecular gas masses larger than galaxies of similar stellar mass in the field.
Run Distribution Over Flattened Partitions

The study of flattened partitions is an active area of current research. In this paper, our study unexpectedly leads us to the OEIS numbers A124324. We provide a new combinatorial interpretation of these numbers. A combinatorial bijection between flattened partitions over $[n+1]$ and the partitions of $[n]$ is also given in a separate section. We introduce the numbers $f_{n, k}$ which count the number of flattened partitions over $[n]$ having $k$ runs. We give recurrence relations defining them, as well as their exponential generating function in differential form. It should be appreciated if its closed form is established. We extend the results to flattened partitions where the first $s$ integers belong to different runs. Combinatorial proofs are given.
Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Image Classification

Generative Adversarial Networks (GANs) based semi-supervised learning (SSL) approaches are shown to improve classification performance by utilizing a large number of unlabeled samples in conjunction with limited labeled samples. However, their performance still lags behind the state-of-the-art non-GAN based SSL approaches. One main reason we identify is the lack of consistency in class probability predictions on the same image under local perturbations. This problem was addressed in the past in a generic setting using the label consistency regularization, which enforces the class probability predictions for an input image to be unchanged under various semantic-preserving perturbations. In this work, we incorporate the consistency regularization in the vanilla semi-GAN to address this critical limitation. In particular, we present a new composite consistency regularization method which, in spirit, combines two well-known consistency-based techniques -- Mean Teacher and Interpolation Consistency Training. We demonstrate the efficacy of our approach on two SSL image classification benchmark datasets, SVHN and CIFAR-10. Our experiments show that this new composite consistency regularization based semi-GAN significantly improves its performance and achieves new state-of-the-art performance among GAN-based SSL approaches.
How benign is benign overfitting?

We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models. When trained with SGD, deep neural networks essentially achieve zero training error, even in the presence of label noise, while also exhibiting good generalization on natural test data, something referred to as benign overfitting [2, 10]. However, these models are vulnerable to adversarial attacks. We identify label noise as one of the causes for adversarial vulnerability, and provide theoretical and empirical evidence in support of this. Surprisingly, we find several instances of label noise in datasets such as MNIST and CIFAR, and that robustly trained models incur training error on some of these, i.e. they don't fit the noise. However, removing noisy labels alone does not suffice to achieve adversarial robustness. Standard training procedures bias neural networks towards learning "simple" classification boundaries, which may be less robust than more complex ones. We observe that adversarial training does produce more complex decision boundaries. We conjecture that in part the need for complex decision boundaries arises from sub-optimal representation learning. By means of simple toy examples, we show theoretically how the choice of representation can drastically affect adversarial robustness.
Effect of fruit maturation on sugar and organic acid composition in two blueberries (Vaccinium arctostaphylos and V. myrtillus) native to Turkey

Abstract Some organic acids and sugars in two blueberry species from north‐east Anatolia, Turkey, were studied. The fruits of the blueberry species (Vaccinium arctostaphylos L. and V. myrtillus L.) were collected at three stages of maturity (immature, mid ripe, and ripe), and the organic acids and sugars were studied by gas liquid chromatography (GLC) and verified by gas liquid chromatography‐mass spectrometry (GLC‐MS). The soluble sugars fructose, glucose, and sucrose, and the sugar alcohol inositol were identified and quantified. Fructose and glucose represented the major sugars in the fruits. Quinic and citric acids were found to be the major organic acids in both blueberry species. The levels of these acids were significantly different (P = 0.05) at all three stages of fruit maturation. The level of malic acid increased gradually in both species, whereas the levels of citric and quinic acids decreased rapidly. The total acid level in the fruits of the two species was also significantly different. The results of this study can be used to compare the two species with other Vaccinium species.
Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding

The observation of sparse trainable sub-networks within over-parametrized networks - also known as Lottery Tickets (LTs) - has prompted inquiries around their trainability, scaling, uniqueness, and generalization properties. Across 28 combinations of image classification tasks and architectures, we discover differences in the connectivity structure of LTs found through different iterative pruning techniques, thus disproving their uniqueness and connecting emergent mask structure to the choice of pruning. In addition, we propose a consensus-based method for generating refined lottery tickets. This lottery ticket denoising procedure, based on the principle that parameters that always go unpruned across different tasks more reliably identify important sub-networks, is capable of selecting a meaningful portion of the architecture in an embarrassingly parallel way, while quickly discarding extra parameters without the need for further pruning iterations. We successfully train these sub-networks to performance comparable to that of ordinary lottery tickets.
Collaboration in a Vast Setting

This chapter discusses how truly mobile occupational groups relate to locations in a vast working area when collaborating with each other. It brings forth two ethnographic studies on mobile professionals working on the road. Their work setting has predominantly been described from a perspective where they are isolated in the driver’s seat. However, seeing that the environment in which they drive through constitutes their workplace, the chapter examines their relation to the surroundings when performing their tasks. The empirical data illustrates the importance of mutual understanding of locations to successfully perform collaborative tasks. For example, coordinates supplied by a GPS receiver are not sufficient in the performance of their tasks. It is rather the mutual understanding of locations, being in proximity or other visual clues that are of importance. The authors argue the need for a detailed understanding 252 Normark & Esbjörnsson Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. regarding the use of locations to succeed in the development of future mobile position-based services.
Measuring gravitational-wave higher-order multipoles

We investigate the observability of higher harmonics in gravitational wave signals emitted during the coalescence of binary black holes. We decompose each mode into an overall amplitude, dependent upon the masses and spins of the system, and an orientation-dependent term, dependent upon the inclination and polarization of the source. Using this decomposition, we investigate the significance of higher modes over the parameter space and show that the $\ell = 3$, $m = 3$ mode is most significant across much of the sensitive band of ground-based interferometric detectors, with the $\ell = 4$, $m = 4$ having a significant contribution at high masses. We introduce the higher mode signal-to-noise ratio (SNR), and show that a simple threshold on this SNR can be used as a criterion for observation of higher harmonics. Finally, we investigate observability in a population of binaries and observe that higher harmonics will only be observable in a few percent of binaries, typically those with unequal masses and viewed close to edge-on.
Higher differentiability results for solutions to a class of non-autonomous obstacle problems with sub-quadratic growth conditions

Abstract We establish some higher differentiability results of integer and fractional order for solutions to non-autonomous obstacle problems of the form min⁡{∫Ωf⁢(x,D⁢v⁢(x)):v∈𝒦ψ⁢(Ω)},\min\biggl{\{}\int_{\Omega}f(x,Dv(x)):v\in\mathcal{K}_{\psi}(\Omega)\biggr{\}}, where the function f satisfies p-growth conditions with respect to the gradient variable, for 1<p<2{1<p<2}, and 𝒦ψ⁢(Ω){\mathcal{K}_{\psi}(\Omega)} is the class of admissible functions v∈u0+W01,p⁢(Ω){v\in u_{0}+W^{1,p}_{0}(\Omega)} such that v≥ψ{v\geq\psi} a.e. in Ω, where u0∈W1,p⁢(Ω){u_{0}\in W^{1,p}(\Omega)} is a fixed boundary datum. Here we show that a Sobolev or Besov–Lipschitz regularity assumption on the gradient of the obstacle ψ transfers to the gradient of the solution, provided the partial map x↦Dξ⁢f⁢(x,ξ){x\mapsto D_{\xi}f(x,\xi)} belongs to a suitable Sobolev or Besov space. The novelty here is that we deal with sub-quadratic growth conditions with respect to the gradient variable, i.e. f⁢(x,ξ)≈a⁢(x)⁢|ξ|p{f(x,\xi)\approx a(x)\lvert\xi\rvert^{p}} with 1<p<2{1<p<2}, and where the map a belongs to a Sobolev or Besov–Lipschitz space.
Operation-Aware Soft Channel Pruning using Differentiable Masks

We propose a simple but effective data-driven channel pruning algorithm, which compresses deep neural networks in a differentiable way by exploiting the characteristics of operations. The proposed approach makes a joint consideration of batch normalization (BN) and rectified linear unit (ReLU) for channel pruning; it estimates how likely the two successive operations deactivate each feature map and prunes the channels with high probabilities. To this end, we learn differentiable masks for individual channels and make soft decisions throughout the optimization procedure, which facilitates to explore larger search space and train more stable networks. The proposed framework enables us to identify compressed models via a joint learning of model parameters and channel pruning without an extra procedure of fine-tuning. We perform extensive experiments and achieve outstanding performance in terms of the accuracy of output networks given the same amount of resources when compared with the state-of-the-art methods.
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery

In this paper, we study two important problems in the automated design of neural networks -- Hyper-parameter Optimization (HPO), and Neural Architecture Search (NAS) -- through the lens of sparse recovery methods. In the first part of this paper, we establish a novel connection between HPO and structured sparse recovery. In particular, we show that a special encoding of the hyperparameter space enables a natural group-sparse recovery formulation, which when coupled with HyperBand (a multi-armed bandit strategy), leads to improvement over existing hyperparameter optimization methods. Experimental results on image datasets such as CIFAR-10 confirm the benefits of our approach. In the second part of this paper, we establish a connection between NAS and structured sparse recovery. Building upon ``one-shot'' approaches in NAS, we propose a novel algorithm that we call CoNAS by merging ideas from one-shot approaches with a techniques for learning low-degree sparse Boolean polynomials. We provide theoretical analysis on the number of validation error measurements. Finally, we validate our approach on several datasets and discover novel architectures hitherto unreported, achieving competitive (or better) results in both performance and search time compared to the existing NAS approaches.
Survival of Grand and Otago Skinks Following Predator Control

Abstract Both grand (Oligosoma grande) and Otago skinks (Oligosoma otagense) are threatened with extinction. Predation by introduced mammalian predators, in particular feral cats, is thought to be the major cause of decline. Survival of 5 grand and 2 Otago skink populations was monitored for 3 years before and 3 years during intensive predator control, targeting feral cats but also culling ferrets. In addition, 1 Otago and 2 grand skink populations served as controls. For both species, capture–recapture modeling of skink survival failed to find evidence that survival was significantly influenced by the removal of predators, but retrospective power tests showed uncertainty in this result was high. It is possible the residual predator populations were sufficient to depress skink survival during the treatment interval. Seasonal modulation of apparent survival was evident for both species. Grand skink survival was higher over the winter months and summer survival was characterized by episodic lows, the cause of which remains a mystery. By contrast, Otago skink survival was higher in summer than in winter and did not exhibit the severe lows recorded for grand skinks. My study exemplifies the difficulty in conducting an experiment aimed at guiding management, in a complex predator- species-rich mainland environment.