Miracle-dz/UniFAR-FTUs huggingface dataset:
A Linear Reduction Method for Local Differential Privacy and Log-lift

This paper considers the problem of publishing data <tex>$X$</tex> while protecting the correlated sensitive information <tex>$S$</tex>. We propose a linear method to generate the sanitized data <tex>$Y$</tex> with the same alphabet <tex>$\mathcal{Y}=\mathcal{X}$</tex> that attains local differential privacy (LDP) and log-lift at the same time. It is revealed that both LDP and log-lift are inversely proportional to the statistical distance between conditional probability <tex>$P_{Y\vert S}(x\vert s)$</tex> and marginal probability <tex>$P_{Y}(x)$</tex>: the closer the two probabilities are, the more private <tex>$Y$</tex> is. Specifying <tex>$P_{Y\vert S}(x\vert s)$</tex> that linearly reduces this distance <tex>$\vert P_{Y\vert S}(x\vert s)-P_{Y}(x)\vert =(1-\alpha)\vert P_{X\vert S}(x\vert s)-P_{X}(x)\vert, \forall s, x$</tex> for some <tex>$\alpha\in(0,1]$</tex>, we study the problem of how to generate <tex>$\mathrm{Y}$</tex> from the original data <tex>$S$</tex> and <tex>$X$</tex>. The Markov randomization/sanitization scheme <tex>$P_{Y\vert X}(x\vert x^{\prime})=P_{Y\vert S,X}(x\vert s,x^{\prime})$</tex> is obtained by solving linear equations. The optimal non-Markov sanitization, the transition probability <tex>$P_{Y\vert S,X}(x\vert s,x^{\prime})$</tex> that depends on <tex>$S$</tex>,, can be determined by maximizing the data utility subject to linear equality constraints on data privacy. We compute the solution for two linear utility function: the expected distance and total variance distance. It is shown that the non-Markov randomization significantly improves data utility and the marginal probability <tex>$P_{X}(x)$</tex> remains the same after the linear sanitization method: <tex>$P_{Y}(x)=P_{X}(x),\forall x\in \mathcal{X}$</tex>.
Ridge approximation for thin nematic polymer networks

Nematic polymer networks (NPNs) are nematic elastomers within which the nematic director is enslaved to the elastic deformation. The elastic free energy of a NPN sheet of thickness $h$ has both stretching and bending components (the former scaling like h, the latter scaling like $h^3$). NPN sheets bear a director field $\mathbf{m}$ imprinted in them (usually, uniformly throughout their thickness); they can be activated by changing the nematic order (e.g. by illumination or heating). This paper illustrates an attempt to compute the bending energy of a NPN sheet and to show which role it can play in determining the activated shape. Our approach is approximate: the activated surface consists of flat sectors connected by ridges, where the unit normal jumps and the bending energy is concentrated. By increasing the number of ridges, we should get closer to the real situation, where the activated surface is smooth and the bending energy is distributed on it. The method is applied to a disk with imprinted a spiraling hedgehog. It is shown that upon activation the disk, like a tiny hand, is able to grab a rigid lamina.
Probabilistic Robustness Analysis for DNNs based on PAC Learning

This paper proposes a black box based approach for analysing deep neural networks (DNNs). We view a DNN as a function f from inputs to outputs, and consider the local robustness property for a given input. Based on scenario optimization technique in robust control design, we learn the score difference function f i − f ℓ with respect to the target label ℓ and attacking label i . We use a linear template over the input pixels, and learn the corresponding coefﬁcients of the score difference function, based on a reduction to a linear programming (LP) problems. To make it scalable, we propose optimizations including components based learning and focused learning. The learned function offers a probably approximately correct (PAC) guarantee for the robustness property. Since the score difference function is an approximation of the local behaviour of the DNN, it can be used to generate potential adversarial examples, and the original network can be used to check whether they are spurious or not. Finally, we focus on the input pixels with large absolute coefﬁcients, and use them to explain the attacking scenario. We have implemented our approach in a prototypical tool DeepPAC. Our experimental results show that our framework can handle very large neural networks like ResNet152 with 6 . 5 M neurons, and often generates adversarial examples which are very close to the decision boundary.
Disentangled Sequence Clustering for Human Intention Inference

Equipping robots with the ability to infer human intent is a vital precondition for effective collaboration. Most computational approaches towards this objective derive a probability distribution of “intent” conditioned on the robot's perceived state. However, these approaches typically assume task-specific labels of human intent are known a priori. To overcome this constraint, we propose the Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a clustering framework capable of learning such a distribution of intent in an unsupervised manner. The proposed framework leverages recent advances in unsupervised learning to disentangle latent representations of sequence data, separating time-varying local features from time-invariant global attributes. As a novel extension, the DiSCVAE also infers a discrete variable to form a latent mixture model and thus enable clustering over these global sequence concepts, e.g. high-level intentions. We evaluate the DiSCVAE on a real-world human-robot interaction dataset collected using a robotic wheelchair. Our findings reveal that the inferred discrete variable coincides with human intent, holding promise for collaborative settings, such as shared control.
Multi-Structure Deep Segmentation With Shape Priors And Latent Adversarial Regularization

Automatic segmentation of the musculoskeletal system in pediatric magnetic resonance (MR) images is a challenging but crucial task for morphological evaluation in clinical practice. We propose a deep learning-based regularized segmentation method for multi-structure bone delineation in MR images, designed to overcome the inherent scarcity and heterogeneity of pediatric data. Based on a newly devised shape code discriminator, our adversarial regularization scheme enforces the deep network to follow a learnt shape representation of the anatomy. The novel shape priors based adversarial regularization (SPAR) exploits latent shape codes arising from ground truth and predicted masks to guide the segmentation network towards more consistent and plausible predictions. Our contribution is compared to state-of-the-art regularization methods on two pediatric musculoskeletal imaging datasets from ankle and shoulder joints.
Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey

—Defensive deception is a promising approach for cyber defense. Via defensive deception, the defender can anticipate attacker actions; it can mislead or lure attacker, or hide real resources. Although defensive deception is increasingly popular in the research community, there has not been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey paper focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artiﬁcial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.
Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection

Lexical semantic change detection is a new and innovative research field. The optimal fine-tuning of models including pre- and post-processing is largely unclear. We optimize existing models by (i) pre-training on large corpora and refining on diachronic target corpora tackling the notorious small data problem, and (ii) applying post-processing transformations that have been shown to improve performance on synchronic tasks. Our results provide a guide for the application and optimization of lexical semantic change detection models across various learning scenarios.
On the robustness of spin polarization for magnetic vortex accelerated proton bunches in density down-ramps

We investigate the effect of density down-ramps on the acceleration of ions via magnetic vortex acceleration in a near-critical density gas target by means of particle-in-cell simulations. The spin-polarization of the accelerated protons is robust for a variety of ramp lengths at around 80%. Significant increase of the ramp length is accompanied by collimation of low-polarization protons into the final beam and large transverse spread of the highly polarized protons with respect to the direction of laser propagation.
The Shifting Sands of Motivation: Revisiting What Drives Contributors in Open Source

Open Source Software (OSS) has changed drastically over the last decade, with OSS projects now producing a large ecosystem of popular products, involving industry participation, and providing professional career opportunities. But our field's understanding of what motivates people to contribute to OSS is still fundamentally grounded in studies from the early 2000s. With the changed landscape of OSS, it is very likely that motivations to join OSS have also evolved. Through a survey of 242 OSS contributors, we investigate shifts in motivation from three perspectives: (1) the impact of the new OSS landscape, (2) the impact of individuals' personal growth as they become part of OSS communities, and (3) the impact of differences in individuals' demographics. Our results show that some motivations related to social aspects and reputation increased in frequency and that some intrinsic and internalized motivations, such as learning and intellectual stimulation, are still highly relevant. We also found that contributing to OSS often transforms extrinsic motivations to intrinsic, and that while experienced contributors often shift toward altruism, novices often shift toward career, fun, kinship, and learning. OSS projects can leverage our results to revisit current strategies to attract and retain contributors, and researchers and tool builders can better support the design of new studies and tools to engage and support OSS development.
A comparative study of the electrical properties of TiO2 films grown by high-pressure reactive sputtering and atomic layer deposition

Oxide–semiconductor interface quality of high-pressure reactive sputtered (HPRS) TiO2 films annealed in O2 at temperatures ranging from 600 to 900 °C, and atomic layer deposited (ALD) TiO2 films grown at 225 or 275 °C from TiCl4 or Ti(OC2H5)4, and annealed at 750 °C in O2, has been studied on silicon substrates. Our attention has been focused on the interfacial state and disordered-induced gap state densities. From our results, HPRS films annealed at 900 °C in oxygen atmosphere exhibit the best characteristics, with Dit density being the lowest value measured in this work (5–6 × 1011 cm−2 eV−1), and undetectable conductance transients within our experimental limits. This result can be due to two contributions: the increase of the SiO2 film thickness and the crystallinity, since in the films annealed at 900 °C rutile is the dominant crystalline phase, as revealed by transmission electron microscopy and infrared spectroscopy. In the case of annealing in the range of 600–800 °C, anatase and rutile phases coexist. Disorder-induced gap state (DIGS) density is greater for 700 °C annealed HPRS films than for 750 °C annealed ALD TiO2 films, whereas 800 °C annealing offers DIGS density values similar to ALD cases. For ALD films, the studies clearly reveal the dependence of trap densities on the chemical route used.
Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning

Contrastive learning (CL) has recently emerged as an effective approach to learning representation in a range of downstream tasks. Central to this approach is the selection of positive (similar) and negative (dissimilar) sets to provide the model the opportunity to ‘contrast’ between data and class representation in the latent space. In this paper, we investigate CL for improving model robustness using adversarial samples. We ﬁrst designed and performed a comprehensive study to understand how adversarial vulnerability behaves in the latent space. Based on these empirical evidences, we propose an effective and efﬁcient supervised contrastive learning to achieve model robustness against adversarial attacks. Moreover, we propose a new sample selection strategy that optimizes the positive/negative sets by removing redundancy and improving correlation with the anchor. Experiments conducted on benchmark datasets show that our Adversarial Supervised Contrastive Learning (ASCL) approach outperforms the state-of-the-art defenses by 2 . 6% in terms of the robust accuracy, whilst our ASCL with the proposed selection strategy can further gain 1 . 4% improvement with only 42 . 8% positives and 6 . 3% negatives compared with ASCL without a selection strategy. 1
Scaffolded Gait Learning of a Quadruped Robot with Bayesian Optimization

During learning trials, systems are exposed to different failure conditions which may break robotic parts before a safe behavior is discovered. Humans contour this problem by grounding their learning to a safer structure/control first and gradually increasing its difficulty. This paper presents the impact of a similar supports in the learning of a stable gait on a quadruped robot. Based on the psychological theory of instructional scaffolding, we provide different support settings to our robot, evaluated with strain gauges, and use Bayesian Optimization to conduct a parametric search towards a stable Raibert controller. We perform several experiments to measure the relation between constant supports and gradually reduced supports during gait learning, and our results show that a gradually reduced support is capable of creating a more stable gait than a support at a fixed height. Although gaps between simulation and reality can lead robots to catastrophic failures, our proposed method combines speed and safety when learning a new behavior.
H2 Formation on Grain Surfaces

The most abundant interstellar molecule, H2, is generally thought to form by recombination of H atoms on grain surfaces. On surfaces, hydrogen atoms can be physisorbed and chemisorbed and their mobility can be governed by quantum mechanical tunneling or thermal hopping. We have developed a model for molecular hydrogen formation on surfaces. This model solves the time-dependent kinetic rate equation for atomic and molecular hydrogen and their isotopes, taking the presence of physisorbed and chemisorbed sites, as well as quantum mechanical diffusion and thermal hopping, into account. The results show that the time evolution of this system is mainly governed by the binding energies and barriers against migration of the adsorbed species. We have compared the results of our model with experiments on the formation of HD on silicate and carbonaceous surfaces under irradiation by atomic H and D beams at low and at high temperatures. This comparison shows that including both isotopes, both physisorbed and chemisorbed wells, and both quantum mechanical tunneling and thermal hopping is essential for a correct interpretation of the experiments. This comparison allows us to derive the characteristics of these surfaces. For the two surfaces we consider, we determine the binding energy of H atoms and H2 molecules, as well as the barrier against diffusion for the H atoms to move from one site to another. We conclude that molecular hydrogen formation is efficient until quite high (~500 K) temperatures. At low temperatures, recombination between mobile physisorbed atoms and trapped chemisorbed atoms dominates. At higher temperatures, chemisorbed atoms become mobile, and this then drives molecular hydrogen formation. We have extended our model to astrophysically relevant conditions. The results show that molecular hydrogen formation proceeds with near unity efficiency at low temperatures (T ≤ 20 K). While the efficiency drops, molecular hydrogen formation in the ISM can be very efficient even at high temperatures, depending on the physical characteristics of the surface.
Hybrid tapered silicon nanowire/PEDOT:PSS solar cells

A tapered silicon nanowire array (TSiNWs)/poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) (PEDOT:PSS) hybrid solar cell was obtained based on alkali treatment processing. TSiNWs are engineered by combining electroless etching and an alkali solution in which the morphology of the tapered nanowire can be controlled by changing the immersing time. The results show that the alkali treatment could taper the silicon nanowires so that the polymer could conformally infiltrate into SiNWs via spin-coating. The experimental results demonstrate that the length and morphology of the SiNWs are key factors for improving the cell performance. Compared to the SiNWs/polymer hybird device, the TSiNWs/polymer hybrid solar cell can achieve a high power conversion efficiency of 6.87% and short-circuit current density of 26.7 mA cm−2. Therefore, tapered silicon nanowires are promising structures for realizing economically viable hybrid solar cells based on a very simple, scalable, and low-cost solution route.
Zero-rate Reliability Function for Mismatched Decoding

We derive an upper bound on the reliability function of mismatched decoding for zero-rate codes. The bound is based on a result by Komlós that shows the existence of a subcode with certain symmetry properties. The bound is shown to coincide with the expurgated exponent at rate zero for a broad family of channel and decoding metric pairs. A full version of this paper is accessible at: https://arxiv.org/pdf/2101.10238. pdf
Accumulating Risk Capital Through Investing in Cooperation

Recent work on promoting cooperation in multi-agent learning has resulted in many methods which successfully promote cooperation at the cost of becoming more vulnerable to exploitation by malicious actors. We show that this is an unavoidable trade-off and propose an objective which balances these concerns, promoting both safety and long-term cooperation. Moreover, the trade-off between safety and cooperation is not severe, and you can receive exponentially large returns through cooperation from a small amount of risk. We study both an exact solution method and propose a method for training policies that targets this objective, Accumulating Risk Capital Through Investing in Cooperation (ARCTIC), and evaluate them in iterated Prisoner's Dilemma and Stag Hunt.
Spatio-temporal Data Augmentation for Visual Surveillance

Visual surveillance aims to detect a foreground object using a continuous image acquired from a fixed camera. Recent deep learning methods based on supervised learning show superior performance compared to classical background subtraction algorithms. However, there is still room for improvement in the static foreground, dynamic background, hard shadow, illumination changes, camouflage, etc. In addition, most of the deep learning-based methods operate well in environments similar to training. If the testing environments are different from training ones, their performance degrades. As a result, additional training in those operating environments is required to ensure good performance. Our previous work, which uses spatio-temporal input data consisting of several past images, background images, and the current image, showed promising results in different environments from training. However, it uses a simple U-NET structure. This paper proposes a data augmentation technique suitable for visual surveillance for additional performance improvement using the same network used in our previous work. In deep learning, most data augmentation techniques deal with spatial-level data augmentation techniques used in image classification and object detection. We propose two data augmentation methods of adjusting background model images and past images. The proposed algorithm improves performance in complex areas such as static foreground and ghost objects compared to previous studies. Through quantitative and qualitative evaluation using SBI, LASIESTA, and our dataset, we show superior performance compared to deep learning-based algorithms and background subtraction algorithms. In addition, it has a 30.2% and 27.9% reduction of false detection rate in the LASIESTA and SBI dataset, respectively, compared to our previous study.
Modeling Stellar Abundance Patterns Resulting from the Addition of Earth-like Planetary Material

We model the observed precision differential abundance patterns of three G-type dwarfs, assuming a mass of planetary or disk material was added to or subtracted from the atmosphere of the star. The two-parameter model is fit by linear regression. The stellar abundance patterns are corrected for Galactic chemical evolution (GCE). The differential abundances can be highly correlated with the elemental condensation temperature. We show how it is possible to match not only the slope, but the quantitative abundance differences, assuming a composition for the added material equal to that of the bulk earth. We also model the wide pair HIP 34407 and HIP 34426, where GCE corrections are assumed unnecessary. An appendix discusses issues of volatility and condensation temperature.
Conditional Generative Models for Counterfactual Explanations

Counterfactual instances offer human-interpretable insight into the local behaviour of machine learning models. We propose a general framework to generate sparse, in-distribution counterfactual model explanations which match a desired target prediction with a conditional generative model, allowing batches of counterfactual instances to be generated with a single forward pass. The method is flexible with respect to the type of generative model used as well as the task of the underlying predictive model. This allows straightforward application of the framework to different modalities such as images, time series or tabular data as well as generative model paradigms such as GANs or autoencoders and predictive tasks like classification or regression. We illustrate the effectiveness of our method on image (CelebA), time series (ECG) and mixed-type tabular (Adult Census) data.