Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and Formal Privacy Guarantees

Empirical economic research crucially relies on highly sensitive individual datasets. At the same time, increasing availability of public individual-level data makes it possible for adversaries to potentially de-identify anonymized records in sensitive research datasets. This increasing disclosure risk has incentivised large data curators, most notably the US Census bureau and several large companies including Apple, Facebook and Microsoft to look for algorithmic solutions to provide formal non-disclosure guarantees for their secure data. The most commonly accepted formal data security concept in the Computer Science community is differential privacy. It restricts the interaction of researchers with the data by allowing them to issue queries to the data. The differential privacy mechanism then replaces the actual outcome of the query with a randomised outcome. While differential privacy does provide formal data security guarantees, its impact on the identification of empirical economic models and on the performance of estimators in those models has not been sufficiently studied. Since privacy protection mechanisms are inherently finite-sample procedures, we define the notion of identifiability of the parameter of interest as a property of the limit of experiments. It is linked to the asymptotic behavior in measure of differentially private estimators. We demonstrate that particular instances of regression discontinuity design and average treatment effect may be problematic for inference with differential privacy because their estimators can only be ensured to converge weakly with their asymptotic limit remaining random and, thus, may not be estimated consistently. This result is clearly supported by our simulation evidence. Our analysis suggests that many other estimators that rely on nuisance parameters may have similar properties with the requirement of differential privacy.
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model

To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity. We propose such a loss function based on Watson's perceptual model, which computes a weighted distance in frequency space and accounts for luminance and contrast masking. We extend the model to color images, increase its robustness to translation by using the Fourier Transform, remove artifacts due to splitting the image into blocks, and make it differentiable. In experiments, VAEs trained with the new loss function generated realistic, high-quality image samples. Compared to using the Euclidean distance and the Structural Similarity Index, the images were less blurry; compared to deep neural network based losses, the new approach required less computational resources and generated images with less artifacts.
ALTERNATIVE PROTEIN SOURCES AS SUBSTITUTES FOR FISHMEAL IN THE DIET OF YOUNG TILAPIA OREOCHROMIS NILOTICUS (LINN.)

Fifteen test diets were theoretically formulated to contain 33.32% dietary protein, dry matter, using a mixture of alternative protein sources and fishmeal in various proportions. Test diets were fed to fingerlings (initial weight 3.2±0.2 g) in triplicate tanks for eight weeks at 5% body weight per day in three portions. At the end of each experimental phase, fish carcass from the feeding groups was homogenized. Freeze-dried samples of fish at the beginning and end of the experiments as well as samples of the test diets were analyzed for proximate composition. Fish fed diet 1 containing 43% fishmeal recorded the highest weight gain, highest specific growth rate (SGR, 3.46%/d) and lowest food conversion ratio (FCR, 1.11). When part of the fishmeal was substituted with 18% soybean meal and 5% blood meal (diet 8), SGR and FCR were 3.02%/d and 1.34, respectively, not significantly different from diet 1 at 0.05 probability. Results showed that proper combination of alternative protein sources can provide 42-45% of the protein required by Oreochromis niloticus (33.32% dietary protein, dry matter). In such a combination, soybean meal can replace up to 25% of the fishmeal as a protein source. Blood meal exceeding 6%, groundnut cake beyond 10%, soybean meal above 20%, and wheat bran beyond 10% retarded fish growth. High mortality was generally observed when these levels were exceeded, even in only one of the protein sources.
Graphs of curves and arcs quasi-isometric to big mapping class groups

Following the work of Rosendal and Mann and Rafi, we try to answer the following question: when is the mapping class group of an infinite-type surface quasi-isometric to a graph whose vertices are curves on that surface? With the assumption of tameness as defined by Mann and Rafi, we describe a necessary and sufficient condition, called translatability, for a geometrically nontrivial big mapping class group to admit such a quasi-isometry. In addition, we show that the mapping class group of the plane minus a Cantor set is quasi-isometric to the loop graph defined by Bavard, which we believe represents the first example of a mapping class group known to be non-elementary hyperbolic.
The word problem of the Brin-Higman-Thompson groups

We show that the word problem of the Brin-Higman-Thompson group $n G_{k,1}$ is {\sf coNP}-complete for all $n \ge 2$ and all $k \ge 2$. For this we prove that $n G_{k,1}$ is finitely generated, and that $n G_{k,1}$ contains a subgroup of $2 G_{2,1}$ that can represent bijective circuits. We also show that for all $n \ge 1$ and $k \ge 2$: \ If $\,K = 1 + (k-1)\,N\,$ for some $N \ge 1$, then $n G_{K,1} \le n G_{k,1}$. In particular, $n G_{K,1} \le n G_{2,1}$ for all $K \ge 2$.
Beltrami fields exhibit knots and chaos almost surely

Abstract In this paper, we show that, with probability $1$ , a random Beltrami field exhibits chaotic regions that coexist with invariant tori of complicated topologies. The motivation to consider this question, which arises in the study of stationary Euler flows in dimension 3, is V.I. Arnold’s 1965 speculation that a typical Beltrami field exhibits the same complexity as the restriction to an energy hypersurface of a generic Hamiltonian system with two degrees of freedom. The proof hinges on the obtention of asymptotic bounds for the number of horseshoes, zeros and knotted invariant tori and periodic trajectories that a Gaussian random Beltrami field exhibits, which we obtain through a nontrivial extension of the Nazarov–Sodin theory for Gaussian random monochromatic waves and the application of different tools from the theory of dynamical systems, including Kolmogorov–Arnold–Moser (KAM) theory, Melnikov analysis and hyperbolicity. Our results hold both in the case of Beltrami fields on ${\mathbb {R}}^3$ and of high-frequency Beltrami fields on the 3-torus.
Machine learning-based clinical prediction modeling - A practical guide for clinicians

In the emerging era of big data, larger available clinical datasets and computational advances have sparked a massive interest in machine learning-based approaches. The number of manuscripts related to machine learning or artificial intelligence has exponentially increased over the past years. As analytical machine learning tools become readily available for clinicians to use, the understanding of key concepts and the awareness of analytical pitfalls are increasingly required for clinicians, investigators, reviewers and editors, who even as experts in their clinical field, sometimes find themselves insufficiently equipped to evaluate machine learning methodologies. In the first section, we provide explanations on the general principles of machine learning, as well as analytical steps required for successful machine learning-based predictive modelling - which is the focus of this series. In further sections, we review the importance of resampling, overfitting and model generalizability as well as feature reduction and selection (Part II), strategies for model evaluation, reporting and discussion of common caveats and other points of significance (Part III), as well as offer a practical guide to classification (Part IV) and regression modelling (Part V), with a complete coding pipeline. Methodological rigor and clarity as well as understanding of the underlying reasoning of the internal workings of a machine learning approach are required, otherwise predictive applications despite being strong analytical tools are not well accepted into the clinical routine. Going forward, machine learning and artificial intelligence shape and influence modern medicine across disciplines including the field of neurosurgery.
The Gaussian equivalence of generative models for learning with two-layer neural networks

Understanding the impact of data structure on learning in neural networks remains a key challenge for the theory of neural networks. Many theoretical works on neural networks do not explicitly model training data, or assume that inputs are drawn independently from some factorised probability distribution. Here, we go beyond the simple i.i. d. modelling paradigm by studying neural networks trained on data drawn from structured generative models. We make three contributions: First, we establish rigorous conditions under which a class of generative models shares key statistical properties with an appropriately chosen Gaussian feature model. Second, we use this Gaussian equivalence theorem (GET) to derive a closed set of equations that describe the dynamics of two-layer neural networks trained using one-pass stochastic gradient descent on data drawn from a large class of generators. We complement our theoretical results by experiments demonstrating how our theory applies to deep, pre-trained generative models.
On the Generalization Benefit of Noise in Stochastic Gradient Descent

It has long been argued that minibatch stochastic gradient descent can generalize better than large batch gradient descent in deep neural networks. However recent papers have questioned this claim, arguing that this effect is simply a consequence of suboptimal hyperparameter tuning or insufficient compute budgets when the batch size is large. In this paper, we perform carefully designed experiments and rigorous hyperparameter sweeps on a range of popular models, which verify that small or moderately large batch sizes can substantially outperform very large batches on the test set. This occurs even when both models are trained for the same number of iterations and large batches achieve smaller training losses. Our results confirm that the noise in stochastic gradients can enhance generalization. We study how the optimal learning rate schedule changes as the epoch budget grows, and we provide a theoretical account of our observations based on the stochastic differential equation perspective of SGD dynamics.
Learning predictive representations in autonomous driving to improve deep reinforcement learning

Reinforcement learning using a novel predictive representation is applied to autonomous driving to accomplish the task of driving between lane markings where substantial benefits in performance and generalization are observed on unseen test roads in both simulation and on a real Jackal robot. The novel predictive representation is learned by general value functions (GVFs) to provide out-of-policy, or counter-factual, predictions of future lane centeredness and road angle that form a compact representation of the state of the agent improving learning in both online and offline reinforcement learning to learn to drive an autonomous vehicle with methods that generalizes well to roads not in the training data. Experiments in both simulation and the real-world demonstrate that predictive representations in reinforcement learning improve learning efficiency, smoothness of control and generalization to roads that the agent was never shown during training, including damaged lane markings. It was found that learning a predictive representation that consists of several predictions over different time scales, or discount factors, improves the performance and smoothness of the control substantially. The Jackal robot was trained in a two step process where the predictive representation is learned first followed by a batch reinforcement learning algorithm (BCQ) from data collected through both automated and human-guided exploration in the environment. We conclude that out-of-policy predictive representations with GVFs offer reinforcement learning many benefits in real-world problems.
Orthogonal Deep Models as Defense Against Black-Box Attacks

Deep learning has demonstrated state-of-the-art performance for a variety of challenging computer vision tasks. On one hand, this has enabled deep visual models to pave the way for a plethora of critical applications like disease prognostics and smart surveillance. On the other, deep learning has also been found vulnerable to adversarial attacks, which calls for new techniques to defend deep models against these attacks. Among the attack algorithms, the black-box schemes are of serious practical concern since they only need publicly available knowledge of the targeted model. We carefully analyze the inherent weakness of deep models in black-box settings where the attacker may develop the attack using a model similar to the targeted model. Based on our analysis, we introduce a novel gradient regularization scheme that encourages the internal representation of a deep model to be orthogonal to another, even if the architectures of the two models are similar. Our unique constraint allows a model to concomitantly endeavour for higher accuracy while maintaining near orthogonal alignment of gradients with respect to a reference model. Detailed empirical study verifies that controlled misalignment of gradients under our orthogonality objective significantly boosts a model’s robustness against transferable black-box adversarial attacks. In comparison to regular models, the orthogonal models are significantly more robust to a range of $l_{p}$ norm bounded perturbations. We verify the effectiveness of our technique on a variety of large-scale models.
Parsummable categories as a strictification of symmetric monoidal categories

We prove that the homotopy theory of parsummable categories (as defined by Schwede) with respect to the underlying equivalences of categories is equivalent to the usual homotopy theory of symmetric monoidal categories. In particular, this yields a model of symmetric monoidal categories in terms of categories equipped with a strictly commutative, associative, and unital (but only partially defined) operation.
How Jupiter’s unusual magnetospheric topology structures its aurora

Jupiter’s slow dayside magnetic merging and fast rotation produce an unusual magnetic topology that can explain its polar aurora. Jupiter’s bright persistent polar aurora and Earth’s dark polar region indicate that the planets’ magnetospheric topologies are very different. High-resolution global simulations show that the reconnection rate at the interface between the interplanetary and jovian magnetic fields is too slow to generate a magnetically open, Earth-like polar cap on the time scale of planetary rotation, resulting in only a small crescent-shaped region of magnetic flux interconnected with the interplanetary magnetic field. Most of the jovian polar cap is threaded by helical magnetic flux that closes within the planetary interior, extends into the outer magnetosphere, and piles up near its dawnside flank where fast differential plasma rotation pulls the field lines sunward. This unusual magnetic topology provides new insights into Jupiter’s distinctive auroral morphology.
Invariant measures for contact Hamiltonian systems: symplectic sandwiches with contact bread

We prove that, under some natural conditions, Hamiltonian systems on a contact manifold C can be split into a Reeb dynamics on an open subset of C and a Liouville dynamics on a submanifold of C of codimension 1. For the Reeb dynamics we find an invariant measure. Moreover, we show that, under certain completeness conditions, the existence of an invariant measure for the Liouville dynamics can be characterized using the notion of a symplectic sandwich with contact bread.
Spectral properties of soft quantum waveguides

We consider a soft quantum waveguide described by a two-dimensional Schrödinger operator with an attractive potential in the form of a channel of a fixed profile built along an infinite smooth curve which is not straight but it is asymptotically straight in a suitable sense. Using the Birman–Schwinger principle we show that the discrete spectrum of such an operator is nonempty if the potential well defining the channel profile is deep and narrow enough. Some related problems are also mentioned.
Stellar masses of giant clumps in CANDELS and simulated galaxies using machine learning

A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to detect clumps in galaxy images. We use this method to detect clumps in the rest-frame optical and UV images of a complete sample of $\sim1500$ star forming galaxies at $1<z<3$ in the CANDELS survey as well as in images from the VELA zoom-in cosmological simulations. We show that observational effects have a dramatic impact on the derived clump properties leading to an overestimation of the clump mass up to a factor of 10, which highlights the importance of fair comparisons between observations and simulations and the limitations of current HST data to study the resolved structure of distant galaxies. After correcting for these effects with a mixture density network, we estimate that the clump stellar mass function follows a power-law down to the completeness limit ($10^{7}$ solar masses) with the majority of the clumps being less massive than $10^9$ solar masses. This is in better agreement with recent gravitational lensing based measurements. The simulations explored in this work overall reproduce the shape of the observed clump stellar mass function and clumpy fractions when confronted under the same conditions, although they tend to lie in the lower limit of the confidence intervals of the observations. This agreement suggests that most of the observed clumps are formed in-situ.
Classification of torus bundles that bound rational homology circles

In this article, we completely classify torus bundles over the circle that bound 4-manifolds with the rational homology of the circle. Along the way, we classify certain integral surgeries along chain links that bound rational homology balls and explore a connection to 3-braid closures whose double branched covers bound rational homology 4-balls.
Clustering and halo abundances in early dark energy cosmological models

Cold Dark Matter with cosmological constant (ΛCDM) cosmological models with early dark energy (EDE) have been proposed to resolve tensions between the Hubble constant $H_0=100\, h$ km ṡ−1Ṁpc−1 measured locally, giving h ≈ 0.73, and H0 deduced from Planck cosmic microwave background (CMB) and other early-Universe measurements plus ΛCDM, giving h ≈ 0.67. EDE models do this by adding a scalar field that temporarily adds dark energy equal to about 10 per cent of the cosmological energy density at the end of the radiation-dominated era at redshift z ∼ 3500. Here, we compare linear and non-linear predictions of a Planck-normalized ΛCDM model including EDE giving h = 0.728 with those of standard Planck-normalized ΛCDM with h = 0.678. We find that non-linear evolution reduces the differences between power spectra of fluctuations at low redshifts. As a result, at z = 0 the halo mass functions on galactic scales are nearly the same, with differences only 1–2 per cent. However, the differences dramatically increase at high redshifts. The EDE model predicts 50 per cent more massive clusters at z = 1 and twice more galaxy-mass haloes at z = 4. Even greater increases in abundances of galaxy-mass haloes at higher redshifts may make it easier to reionize the universe with EDE. Predicted galaxy abundances and clustering will soon be tested by the James Webb Space Telescope (JWST) observations. Positions of baryonic acoustic oscillations (BAOs) and correlation functions differ by about 2 per cent between the models – an effect that is not washed out by non-linearities. Both standard ΛCDM and the EDE model studied here agree well with presently available acoustic-scale observations, but the Dark Energy Spectroscopic Instrument and Euclid measurements will provide stringent new tests.
Atmospheric parameters of Cepheids from flux ratios with ATHOS

Context. The effective temperature is a key parameter governing the properties of a star. For stellar chemistry, it has the strongest impact on the accuracy of the abundances derived. Since Cepheids are pulsating stars, determining their effective temperature is more complicated than in the case of nonvariable stars. Aims. We want to provide a new temperature scale for classical Cepheids, with a high precision and full control of the systematics. Methods. Using a data-driven machine learning technique employing observed spectra, and in taking great care to accurately phase single-epoch observations, we tied flux ratios to (label) temperatures derived using the infrared surface brightness method. Results. We identified 143 flux ratios, which allow us to determine the effective temperature with a precision of a few Kelvin and an accuracy better than 150 K, which is in line with the most accurate temperature measures available to date. The method does not require a normalization of the input spectra and provides homogeneous temperatures for low- and high-resolution spectra, even at the lowest signal-to-noise ratios. Due to the lack of a dataset with a sufficient sample size for Small Magellanic Cloud Cepheids, the temperature scale does not extend to Cepheids with [Fe/H] < −0.6 dex. However, it nevertheless provides an exquisite, homogeneous means of characterizing Galactic and Large Magellanic Cloud Cepheids. Conclusions. The temperature scale will be extremely useful in the context of spectroscopic surveys for Milky Way archaeology with the WEAVE and 4MOST spectrographs. It paves the way for highly accurate and precise metallicity estimates, which will allow us to assess the possible metallicity dependence of Cepheids’ period-luminosity relations and, in turn, to improve our measurement of the Hubble constant H0.
Recovery of Binary Sparse Signals from Structured Biased Measurements

In this paper we study the reconstruction of binary sparse signals from partial random circulant measurements. We show that the reconstruction via the least-squares algorithm is as good as the reconstruction via the usually used program basis pursuit. We further show that we need as many measurements to recover an $s$-sparse signal $x_0\in\mathbb{R}^N$ as we need to recover a dense signal, more-precisely an $N-s$-sparse signal $x_0\in\mathbb{R}^N$. We further establish stability with respect to noisy measurements.
Effect of Xanthan Gum and CMC on the Structure and Texture of Corn Flour Pellets Expanded by Microwave Heating

ABSTRACT Third-generation snacks obtained by microwave expansion of glassy cereal pellets are often hard and nonuniform and need to be improved to become acceptable to the consumer. Gums such as carboxymethyl cellulose (CMC) and xanthan gum (XG) have the ability to improve the volume, structure, and texture of expanded cereal products, due to their effects on moisture retention and rheological properties. This study investigates the effect of 1% addition of CMC and XG on the structural and mechanical properties of samples obtained by microwave expansion of glassy corn pellets. Unexpanded, glassy pellets were obtained by extrusion and subsequent equilibration at aw = 0.53 at room temperature. The equilibrated pellets were expanded by microwave heating. The addition of gums significantly improved the shape, structural and textural uniformity of the microwave-expanded samples. These effects were attributed to the contribution of gums to the rheology and moisture sorption capacity of the matrix. It is our hyp.