Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Privacy Attacks in Machine Learning

As machine learning becomes more widely used, the need to study its implications in security and privacy becomes more urgent. Although the body of work in privacy has been steadily growing over the past few years, research on the privacy aspects of machine learning has received less focus than the security aspects. Our contribution in this research is an analysis of more than 45 papers related to privacy attacks against machine learning that have been published during the past seven years. We propose an attack taxonomy, together with a threat model that allows the categorization of different attacks based on the adversarial knowledge, and the assets under attack. An initial exploration of the causes of privacy leaks is presented, as well as a detailed analysis of the different attacks. Finally, we present an overview of the most commonly proposed defenses and a discussion of the open problems and future directions identified during our analysis.
Partially Observable Concurrent Kleene Algebra

We introduce partially observable concurrent Kleene algebra (POCKA), an algebraic framework to reason about concurrent programs with control structures, such as conditionals and loops. POCKA enables reasoning about programs that can access variables and values, which we illustrate through concrete examples. We prove that POCKA is a sound and complete axiomatisation of a model of partial observations, and show the semantics passes an important check for sequential consistency.
Vicariance patterns in the Mediterranean Sea: east–west cleavage and low dispersal in the endemic seagrass Posidonia oceanica

Aim The seagrass, Posidonia oceanica is a clonal angiosperm endemic to the Mediterranean Sea. Previous studies have suggested that clonal growth is far greater than sexual recruitment and thus leads to low clonal diversity within meadows. However, recently developed microsatellite markers indicate that there are many different genotypes, and therefore many distinct clones present. The low resolution of markers used in the past limited our ability to estimate clonality and assess the individual level. New high‐resolution dinucleotide microsatellites now allow genetically distinct individuals to be identified, enabling more reliable estimation of population genetic parameters across the Mediterranean Basin. We investigated the biogeography and dispersal of P. oceanica at various spatial scales in order to assess the influence of different evolutionary factors shaping the distribution of genetic diversity in this species.
Australia’s rangelands: a future vision

The rangelands of Australia and their communities can be represented as a diverse and interrelated complex of social and ecological systems. For the development of a sustainable future for both social and ecological systems, thinking needs to be at a level that accommodates the interaction of all systems and not just part of them. Historical trends show that continued emphasis on increased production in agriculture does little to improve the real wealth of the industry in the long term. Changing community attitudes in relation to the value of the rangelands present both threats and opportunities for the inhabitants of the rangelands. It is argued that capitalising on the wider range of values represented by the landscape beyond only production of generic agricultural products and mining presents a more viable path to an ecologically sustainable and socially acceptable future for the Australian rangelands.
Almost minimizers for the thin obstacle problem with variable coefficients

We study almost minimizers for the thin obstacle problem with variable Holder continuous coefficients and zero thin obstacle and establish their $C^{1,\beta}$ regularity on the either side of the thin space. Under an additional assumption of quasisymmetry, we establish the optimal growth of almost minimizers as well as the regularity of the regular set and a structural theorem on the singular set. The proofs are based on the generalization of Weiss- and Almgren-type monotonicity formulas for almost minimizers established earlier in the case of constant coefficients.
1-Certain Growth and Metabolic Indices of Stress induced by Visible Light and UV Radiation in Broad Bean Seedlings

Certain growth and metabolic indices of stress induced by visible light and UV radiation in broad bean seedlings. Phyton. (Horn, Austria) 52 (2): 203-218. The effects of either visible light or UV-radiation on growth and metabolism of broad bean (Vicia faba) seedlings were investigated. Exposure of seedlings to low and high visible light and UV-radiation, either alone or in combination, induced variable significant decreases in the levels of growth parameters throughout the experimental period, as compared with values of control seedlings grown in darkness or ambient visible light. In addition, induced pronounced significant changes in the total amount and in the relative composition of pigment fraction contents, associated with significant variable decreases in photosystem II (PSII) activity were observed. In relation to controls, direct exposure of broad bean seedlings to visible light and UV-radiation, induced significant variable changes in the total amount and in the relative composition of the carbohydrate pool. Concurrently with carbohydrate changes, significant variable increases in the activities of both invertase and a-amylase of broad bean seedlings were maintained throughout the entire period of the experiment.
Potential of medic and lucerne pastures in the rûens area of the Southern Cape

Abstract The potential of dryland lucerne and medic pastures was compared over a range of stocking rates, i.e. three, four, five, six, seven and eight merino ewes per hectare, in the Ruens area of the Southern Cape. It was found that the animal production potential of lucerne pastures was higher than that of medic pastures. This was attributed to the higher amount of available green pasture material during summer and the higher average legume content of the green material throughout the year on the lucerne pastures. Curvilinear relationships were apparent in most cases between production per animal and stocking rate on both pasture types.
Ordinal Regression with Fenton-Wilkinson Order Statistics: A Case Study of an Orienteering Race

In sports, individuals and teams are typically interested in final rankings. Final results, such as times or distances, dictate these rankings, also known as places. Places can be further associated with ordered random variables, commonly referred to as order statistics. In this work, we introduce a simple, yet accurate order statistical ordinal regression function that predicts relay race places with changeover-times. We call this function the Fenton-Wilkinson Order Statistics model. This model is built on the following educated assumption: individual leg-times follow log-normal distributions. Moreover, our key idea is to utilize Fenton-Wilkinson approximations of changeover-times alongside an estimator for the total number of teams as in the notorious German tank problem. This original place regression function is sigmoidal and thus correctly predicts the existence of a small number of elite teams that significantly outperform the rest of the teams. Our model also describes how place increases linearly with changeover-time at the inflection point of the log-normal distribution function. With real-world data from Jukola 2019, a massive orienteering relay race, the model is shown to be highly accurate even when the size of the training set is only 5% of the whole data set. Numerical results also show that our model exhibits smaller place prediction root-mean-square-errors than linear regression, mord regression and Gaussian process regression. Keywords—Fenton-Wilkinson approximation, German tank problem, log-normal distribution, order statistics, ordinal regression, orienteering, sports analytics, sports modeling.
The Atacama Cosmology Telescope: DR4 maps and cosmological parameters

We present new arcminute-resolution maps of the Cosmic Microwave Background temperature and polarization anisotropy from the Atacama Cosmology Telescope, using data taken from 2013–2016 at 98 and 150 GHz. The maps cover more than 17,000 deg2, the deepest 600 deg2 with noise levels below 10μK-arcmin. We use the power spectrum derived from almost 6,000 deg2 of these maps to constrain cosmology. The ACT data enable a measurement of the angular scale of features in both the divergence-like polarization and the temperature anisotropy, tracing both the velocity and density at last-scattering. From these one can derive the distance to the last-scattering surface and thus infer the local expansion rate, H0. By combining ACT data with large-scale information from WMAP we measure H0=67.6± 1.1 km/s/Mpc, at 68% confidence, in excellent agreement with the independently-measured Planck satellite estimate (from ACT alone we find H0=67.9± 1.5 km/s/Mpc). The ΛCDM model provides a good fit to the ACT data, and we find no evidence for deviations: both the spatial curvature, and the departure from the standard lensing signal in the spectrum, are zero to within 1σ; the number of relativistic species, the primordial Helium fraction, and the running of the spectral index are consistent with ΛCDM predictions to within 1.5–2. 2σ. We compare ACT, WMAP, and Planck at the parameter level and find good consistency; we investigate how the constraints on the correlated spectral index and baryon density parameters readjust when adding CMB large-scale information that ACT does not measure. The DR4 products presented here will be publicly released on the NASA Legacy Archive for Microwave Background Data Analysis.
Complexity of high dimensional Gaussian random fields with isotropic increments

We study the energy landscape of a model of a single particle on a random potential, that is, we investigate the topology of sublevel sets of smooth random fields on $\mathbb R^{N}$ of the form $X_N(x) +\frac\mu2 \|x\|^2,$ where $X_{N}$ is a Gaussian processes with isotropic increments. We derive asymptotic formulas for the mean number of critical points of index $k$ at different sublevel sets as the dimension $N$ goes to infinity.
Cross-Lingual Speaker Verification with Domain-Balanced Hard Prototype Mining and Language-Dependent Score Normalization

In this paper we describe the top-scoring IDLab submission for the text-independent task of the Short-duration Speaker Verification (SdSV) Challenge 2020. The main difficulty of the challenge exists in the large degree of varying phonetic overlap between the potentially cross-lingual trials, along with the limited availability of in-domain DeepMine Farsi training data. We introduce domain-balanced hard prototype mining to fine-tune the state-of-the-art ECAPA-TDNN x-vector based speaker embedding extractor. The sample mining technique efficiently exploits speaker distances between the speaker prototypes of the popular AAM-softmax loss function to construct challenging training batches that are balanced on the domain-level. To enhance the scoring of cross-lingual trials, we propose a language-dependent s-norm score normalization. The imposter cohort only contains data from the Farsi target-domain which simulates the enrollment data always being Farsi. In case a Gaussian-Backend language model detects the test speaker embedding to contain English, a cross-language compensation offset determined on the AAM-softmax speaker prototypes is subtracted from the maximum expected imposter mean score. A fusion of five systems with minor topological tweaks resulted in a final MinDCF and EER of 0.065 and 1.45% respectively on the SdSVC evaluation set.
Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks

One of the important parameters for the assessment of glaucoma is optic nerve head (ONH) evaluation, which usually involves depth estimation and subsequent optic disc and cup boundary extraction. Depth is usually obtained explicitly from imaging modalities like optical coherence tomography (OCT) and is very challenging to estimate depth from a single RGB image. To this end, we propose a novel method using adversarial network to predict depth map from a single image. The proposed depth estimation technique is trained and evaluated using individual retinal images from INSPIRE-stereo dataset. We obtain a very high average correlation coefficient of 0.92 upon five fold cross validation outperforming the state of the art. We then use the depth estimation process as a proxy task for joint optic disc and cup segmentation.
Energy Metabolism in a Rapidly Developing Marine Fish Egg, the Red Drum (Sciaenops ocellata)

Rates of utilization of different lipid classes, glycogen, protein, and adenine nucleotides in the eggs of red drum (Sciaenops ocellata) were measured concurrently throughout embryonic development. Red drum eggs are small, pelagic, and very rapidly developing, going from fertilization to hatching in as little as 19 h at natural spawning temperatures. Wax esters comprised about 50% of the neutral lipid reserve along with triglyceride. Lipid was quantitatively the most important energy reserve supplying virtually all of the catabolic demand. Total lipid content decreased 30% from 23.6 to 16.6 mg/g during development. Glycogen decreased 53% from 0.279 to 0.103 mg/g. Protein did not contribute to catabolism. Total adenylates, primarily ATP, decreased from 564 to 271 nmol/g. Adenylate energy charge decreased from 0.87 to 0.58. Glycogen and lipid were used concurrently rather than sequentially during development. The rapid rate of development could be accounted for by simple thermal effects on metabolism rather.
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits

The principle of optimism in the face of uncertainty is one of the most widely used and successful ideas in multi-armed bandits and reinforcement learning. However, existing optimistic algorithms (primarily UCB and its variants) are often unable to deal with large context spaces. Essentially all existing well performing algorithms for general contextual bandit problems rely on weighted action allocation schemes; and theoretical guarantees for optimism-based algorithms are only known for restricted formulations. In this paper we study general contextual bandits under the realizability condition, and propose a simple generic principle to design optimistic algorithms, dubbed "Upper Counterfactual Confidence Bounds" (UCCB). We show that these algorithms are provably optimal and efficient in the presence of large context spaces. Key components of UCCB include: 1) a systematic analysis of confidence bounds in policy space rather than in action space; and 2) the potential function perspective that is used to express the power of optimism in the contextual setting. We further show how the UCCB principle can be extended to infinite action spaces, by constructing confidence bounds via the newly introduced notion of "counterfactual action divergence. "
Inferring network properties from time series via transfer entropy and mutual information: validation of bivariate versus multivariate approaches

Functional and effective networks inferred from time series are at the core of network neuroscience. Since it is common practice to compare network properties between patients and controls, it is crucial for inferred network models to reflect key underlying structural properties. However, even a few spurious links severely distort the shortest-path length and derived measures including the small-world coefficient, providing misleading insights into the macroscopic topology of the network. This poses a challenge for functional connectomes based on correlation, whose transitivity necessarily induce numerous false positives. We study how false positives bias the properties of the networks inferred by bivariate and multivariate algorithms. Mutual information, bivariate transfer entropy, and multivariate transfer entropy are respectively used for inferring functional, directed-functional, and effective networks. The validation is performed on synthetic ground truth networks with 100--200 nodes and various topologies (regular lattice, small-world, random, scale-free, modular), as well as on a real macaque connectome. The time series of node activity are simulated via autoregressive dynamics. We find that multivariate transfer entropy captures key properties of all network structures for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series; however, as available data increases, they are unable to control false positives (lower specificity). This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models.
Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

In the nervous system, information is conveyed by sequence of action potentials, called spikes-trains. As MacKay and McCulloch suggested, spike-trains can be represented as bits sequences coming from Information Sources (IS). Previously, we studied relations between spikes’ Information Transmission Rates (ITR) and their correlations, and frequencies. Now, I concentrate on the problem of how spikes fluctuations affect ITR. The IS are typically modeled as stationary stochastic processes, which I consider here as two-state Markov processes. As a spike-trains’ fluctuation measure, I assume the standard deviation σ, which measures the average fluctuation of spikes around the average spike frequency. I found that the character of ITR and signal fluctuations relation strongly depends on the parameter s being a sum of transitions probabilities from a no spike state to spike state. The estimate of the Information Transmission Rate was found by expressions depending on the values of signal fluctuations and parameter s. It turned out that for smaller s<1, the quotient ITRσ has a maximum and can tend to zero depending on transition probabilities, while for 1<s, the ITRσ is separated from 0. Additionally, it was also shown that ITR quotient by variance behaves in a completely different way. Similar behavior was observed when classical Shannon entropy terms in the Markov entropy formula are replaced by their approximation with polynomials. My results suggest that in a noisier environment (1<s), to get appropriate reliability and efficiency of transmission, IS with higher tendency of transition from the no spike to spike state should be applied. Such selection of appropriate parameters plays an important role in designing learning mechanisms to obtain networks with higher performance.
On quantitative aspects of model interpretability

Despite the growing body of work in interpretable machine learning, it remains unclear how to evaluate different explainability methods without resorting to qualitative assessment and user-studies. While interpretability is an inherently subjective matter, previous works in cognitive science and epistemology have shown that good explanations do possess aspects that can be objectively judged apart from fidelity), such assimplicity and broadness. In this paper we propose a set of metrics to programmatically evaluate interpretability methods along these dimensions. In particular, we argue that the performance of methods along these dimensions can be orthogonally imputed to two conceptual parts, namely the feature extractor and the actual explainability method. We experimentally validate our metrics on different benchmark tasks and show how they can be used to guide a practitioner in the selection of the most appropriate method for the task at hand.
Adaptive Quantile Trend Filtering

We study quantile trend filtering, a recently proposed method for one-dimensional nonparametric quantile regression. We show that the penalized version of quantile trend filtering attains minimax rates, off by a logarithmic factor, for estimating the vector of quantiles when its $k$th discrete derivative belongs to the class of bounded variation signals. Our results also show that the constrained version of trend filtering attains minimax rates in the same class of signals. Furthermore, we show that if the true vector of quantiles is piecewise polynomial, then the constrained estimator attains optimal rates up to a logarithmic factor. All of our results hold based on a robust metric and under minimal assumptions of the data generation mechanism. We also illustrate how our technical arguments can be used for analysing other shape constrained problems with quantile loss. Finally, we provide extensive experiments that show that quantile trend filtering can perform well, based on mean squared error criteria, under Gaussian, Cauchy, and t-distributed errors.
One-dimensional phase retrieval: regularization, box relaxation and uniqueness

Recovering a signal from its Fourier magnitude is referred to as phase retrieval, which occurs in different fields of engineering and applied physics. This paper gives a new characterization of the phase retrieval problem. Particularly useful is the analysis revealing that the common gradient-based regularization does not restrict the set of solutions to a smaller set. Specifically focusing on binary signals, we show that a box relaxation is equivalent to the binary constraint for Fourier-types of phase retrieval. We further prove that binary signals can be recovered uniquely up to trivial ambiguities under certain conditions. Finally, we use the characterization theorem to develop an efficient denoising algorithm.
Machine learning on the electron–boson mechanism in superconductors

To unravel pairing mechanism of a superconductor from limited, indirect experimental data is always a difficult task. It is common but sometimes dubious to explain by a theoretical model with some tuning parameters. In this work, we propose that the machine learning might infer pairing mechanism from observables like superconducting gap functions. For superconductivity within the Migdal–Eliashberg theory, we perform supervised learning between superconducting gap functions and electron–boson spectral functions. For simple spectral functions, the neural network can easily capture the correspondence and predict perfectly. For complex spectral functions, an autoencoder is utilized to reduce the complexity of the spectral functions to be compatible to that of the gap functions. After this complexity-reduction process, relevant information of the spectral function is extracted and good performance restores. Our proposed method can extract relevant information from data and can be applied to general function-to-function mappings with asymmetric complexities either in physics or other fields.