Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Wireless Fingerprinting: Generalizing Across Space and Time

Can we distinguish between two wireless transmitters sending exactly the same message, using the same protocol? The opportunity for doing so arises due to subtle nonlinear variations across transmitters, even those made by the same manufacturer. Since these effects are difficult to model explicitly, we investigate learning device fingerprints using complex-valued deep neural networks (DNNs) that take as input the complex baseband signal at the receiver. Such fingerprints should be robust to ID spoofing, and to distribution shifts across days and locations due to clock drift and variations in the wireless channel. In this paper, we point out that, unless proactively discouraged from doing so, DNNs learn these strong confounding features rather than the subtle nonlinear characteristics that are the basis for stable signatures. Thus, a network trained on data collected during one day performs poorly on a different day, and networks allowed access to post-preamble information rely on easily-spoofed ID fields. We propose and evaluate strategies, based on augmentation and estimation, to promote generalization across realizations of these confounding factors, using data from WiFi and ADS-B protocols. We conclude that, while DNN training has the advantage of not requiring explicit signal models, significant modeling insights are required to focus the learning on the effects we wish to capture.
Adaptive Distributed Stochastic Gradient Descent for Minimizing Delay in the Presence of Stragglers

We consider the setting where a master wants to run a distributed stochastic gradient descent (SGD) algorithm on n workers each having a subset of the data. Distributed SGD may suffer from the effect of stragglers, i.e., slow or unresponsive workers who cause delays. One solution studied in the literature is to wait at each iteration for the responses of the fastest k < n workers before updating the model, where k is a fixed parameter. The choice of the value of k presents a trade-off between the runtime (i.e., convergence rate) of SGD and the error of the model. Towards optimizing the error-runtime trade-off, we investigate distributed SGD with adaptive k. We first design an adaptive policy for varying k that optimizes this trade-off based on an upper bound on the error as a function of the wallclock time which we derive. Then, we propose an algorithm for adaptive distributed SGD that is based on a statistical heuristic. We implement our algorithm and provide numerical simulations which confirm our intuition and theoretical analysis.
Hydrogenated amorphous silicon detectors for particle detection, beam flux monitoring and dosimetry in high-dose radiation environment

Hydrogenated amorphous silicon (a-Si:H) has remarkable radiation resistance properties and can be deposited on a lot of different substrates. A-Si:H based particle detectors have been built since mid 1980s as planar p-i-n or Schottky diode structures; the thickness of these detectors ranged from 1 to 50 μm. However MIP detection using planar structures has always been problematic due to the poor S/N ratio related to the high leakage current at high depletion voltage and the low charge collection efficiency. The usage of 3D detector architecture can be beneficial for the possibility to reduce inter-electrode distance and increase the thickness of the detector for larger charge generation compared to planar structures. Such a detector can be used for future hadron colliders for its radiation resistance and also for X-ray imaging. Furthermore the possibility of a-Si:H deposition on flexible materials (like kapton) can be exploited to build flexible and thin beam flux measurement detectors and x-ray dosimeters.
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy

Adversarial training augments the training set with perturbations to improve the robust error (over worst-case perturbations), but it often leads to an increase in the standard error (on unperturbed test inputs). Previous explanations for this tradeoff rely on the assumption that no predictor in the hypothesis class has low standard and robust error. In this work, we precisely characterize the effect of augmentation on the standard error in linear regression when the optimal linear predictor has zero standard and robust error. In particular, we show that the standard error could increase even when the augmented perturbations have noiseless observations from the optimal linear predictor. We then prove that the recently proposed robust self-training (RST) estimator improves robust error without sacrificing standard error for noiseless linear regression. Empirically, for neural networks, we find that RST with different adversarial training methods improves both standard and robust error for random and adversarial rotations and adversarial $\ell_\infty$ perturbations in CIFAR-10.
Irrational periods of Hilbert Eisenstein series via toroidal compactification

We show that the periods of the holomorphic Eisenstein series of level 1, parallel weight 2, on a Hilbert modular surface are not rational, even up to scaling. This is deduced from a study of the mixed Hodge structure on the cohomology of the Hilbert modular surface, where we find extension classes related to the units of the real quadratic field. We prove similar results for Hilbert modular varieties of all dimensions, and produce extensions of Galois representations in etale cohomology. The key point is to study the restriction of the canonical extension of the Hodge bundle to the boundary of a smooth toroidal compactification.
Counterfactual fairness: removing direct effects through regularization

Building machine learning models that are fair with respect to an unprivileged group is a topical problem. Modern fairness-aware algorithms often ignore causal effects and enforce fairness through modifications applicable to only a subset of machine learning models. In this work, we propose a new definition of fairness that incorporates causality through the Controlled Direct Effect (CDE). We develop regularizations to tackle classical fairness measures and present a causal regularization that satisfies our new fairness definition by removing the impact of unprivileged group variables on the model outcomes as measured by the CDE. These regularizations are applicable to any model trained using by iteratively minimizing a loss through differentiation. We demonstrate our approaches using both gradient boosting and logistic regression on: a synthetic dataset, the UCI Adult (Census) Dataset, and a real-world credit-risk dataset. Our results were found to mitigate unfairness from the predictions with small reductions in model performance.
Warped product pointwise bi-slant submanifolds in metallic Riemannian manifolds.

In this paper, we study the properties of warped product pointwise bi-slant submanifolds in locally metallic Riemannian manifolds and construct some examples in Euclidean spaces. Also, we provide conditions for the two distributions of a proper warped product pointwise semi-slant (and respectively, hemi-slant) submanifold in a locally metallic Riemannian manifold to be integrable.
Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach

Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts to identify the SATDs manually. The current automated solutions do not have satisfactory precision and recall in identifying SATDs to fully automate the process. To solve the above problems, we propose a two-step framework called Jitterbug for identifying SATDs. Jitterbug first identifies the “easy to find” SATDs automatically with close to 100 percent precision using a novel pattern recognition technique. Subsequently, machine learning techniques are applied to assist human experts in manually identifying the remaining “hard to find” SATDs with reduced human effort. Our simulation studies on ten software projects show that Jitterbug can identify SATDs more efficiently (with less human effort) than the prior state-of-the-art methods.
On Pruning Adversarially Robust Neural Networks

In safety-critical but computationally resource-constrained applications, deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural network size (often millions of parameters). While the research community has extensively explored the use of robust training and network pruning \emph{independently} to address one of these challenges, we show that integrating existing pruning techniques with multiple types of robust training techniques, including verifiably robust training, leads to poor robust accuracy even though such techniques can preserve high regular accuracy. We further demonstrate that making pruning techniques aware of the robust learning objective can lead to a large improvement in performance. We realize this insight by formulating the pruning objective as an empirical risk minimization problem which is then solved using SGD. We demonstrate the success of the proposed pruning technique across CIFAR-10, SVHN, and ImageNet dataset with four different robust training techniques: iterative adversarial training, randomized smoothing, MixTrain, and CROWN-IBP. Specifically, at 99\% connection pruning ratio, we achieve gains up to 3.2, 10.0, and 17.8 percentage points in robust accuracy under state-of-the-art adversarial attacks for ImageNet, CIFAR-10, and SVHN dataset, respectively. Our code and compressed networks are publicly available at https://github.com/inspire-group/compactness-robustness
PoisSolver: a Tool for Modelling Silicon Dangling Bond Clocking Networks

Advancements in the fabrication of silicon dangling bonds (SiDBs) reveal a potential platform for clocked field coupled nanocomputing structures. This work introduces PoisSolver, a finite element simulator for investigating clocked SiDB systems in the SiQAD design tool. Three clocking schemes borrowed from prior work on quantum-dot cellular automata are examined as potential building blocks for a general clocking framework for SiDB circuits. These clocking schemes are implemented in SiQAD, and power estimates are performed with geometrically agnostic methods to characterise each clocking scheme. Clocking schemes using a 14 nm technology node are found to dissipate 10–100 µW cm-2at 1 GHz and 1-10W cm-2at 1 THz.
Solving the curvature and Hubble parameter inconsistencies through structure formation-induced curvature

Recently it has been noted by Di Valentino, Melchiorri and Silk (2019) that the enhanced lensing signal relative to that expected in the spatially flat ΛCDM model poses a possible crisis for the Friedmann–Lemaître–Robertson–Walker (FLRW) class of models usually used to interpret cosmological data. The ‘crisis’ amounts to inconsistencies between cosmological datasets arising when the FLRW curvature parameter Ω k0 is determined from the data rather than constrained to be zero a priori. Moreover, the already substantial discrepancy between the Hubble parameter as determined by Planck and local observations increases to the level of 5σ. While such inconsistencies might arise from systematic effects of astrophysical origin affecting the Planck cosmic microwave background (CMB) power spectra at small angular scales, it is an option that the inconsistencies are due to the failure of the FLRW assumption. In this paper we recall how the FLRW curvature ansatz is expected to be violated for generic relativistic spacetimes. We explain how the FLRW conservation equation for volume-averaged spatial curvature is modified through structure formation, and we illustrate in a simple framework how the curvature tension in a FLRW spacetime can be resolved—and is even expected to occur—from the point of view of general relativity. Requiring early-time convergence towards a Friedmannian model with a spatial curvature parameter Ω k0 equal to that preferred from the Planck power spectra resolves the Hubble tension within our dark energy-free model.
Is there Na I in the atmosphere of HD 209458b?

HD 209458b was the first transiting planet discovered, and the first for which an atmosphere, in particular Na I, was detected. With time, it has become one of the most frequently studied planets, with a large diversity of atmospheric studies using low- and high-resolution spectroscopy. Here, we present transit spectroscopy observations of HD 209458b using the HARPS-N and CARMENES spectrographs. We fit the Rossiter-McLaughlin effect by combining radial velocity data from both instruments (nine transits in total), measuring a projected spin-orbit angle of − 1.6 ± 0.3 deg. We also present the analysis of high-resolution transmission spectroscopy around the Na I region at 590 nm, using a total of five transit observations. In contrast to previous studies where atmospheric Na I absorption is detected, we find that for all of the nights, whether individually or combined, the transmission spectra can be explained by the combination of the centre-to-limb variation and the Rossiter-McLaughlin effect. This is also observed in the time-evolution maps and transmission light curves, but at lower signal-to-noise ratio. Other strong lines such as Hα, Ca II IRT, the Mg I triplet region, and K I D1 are analysed, and are also consistent with the modelled effects, without considering any contribution from the exoplanet atmosphere. Thus, the transmission spectrum reveals no detectable Na I absorption in HD 209458b. We discuss how previous pioneering studies of this benchmark object may have overlooked these effects. While for some star–planet systems these effects are small, for other planetary atmospheres the results reported in the literature may require revision.
Teaching the old dog new tricks: supervised learning with constraints

Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness. Existing approaches typically apply constrained optimization techniques to ML training, enforce constraint satisfaction by adjusting the model design, or use constraints to correct the output. Here, we investigate a different, complementary, strategy based on "teaching" constraint satisfaction to a supervised ML method via the direct use of a state-of-the-art constraint solver: this enables taking advantage of decades of research on constrained optimization with limited effort. In practice, we use a decomposition scheme alternating master steps (in charge of enforcing the constraints) and learner steps (where any supervised ML model and training algorithm can be employed). The process leads to approximate constraint satisfaction in general, and convergence properties are difficult to establish; despite this fact, we found empirically that even a naive setup of our approach performs well on ML tasks with fairness constraints, and on classical datasets with synthetic constraints.
Robust Estimation, Prediction and Control with Linear Dynamics and Generic Costs

We develop a framework for the adaptive model predictive control of a linear system with unknown parameters and arbitrary bounded costs, in a critical setting where failures are costly and should be prevented at all time. Our approach builds on two ideas: first, we incorporate prior knowledge of the dynamics in the form of a known structure that shapes uncertainty, which can be tightened as we collect interaction data by building high-confidence regions through least-square regression. Second, in order to handle this uncertainty we formulate a robust control objective. Leveraging tools from the interval prediction literature, we convert the confidence regions on parameters into confidence sets on trajectories induced by the controls. These controls are then optimised resorting to tree-based planning methods. We eventually relax our modeling assumptions with a multi-model extension based on a data-driven robust model selection mechanism. The full procedure is designed to produce reasonable and safe behaviours at deployment while recovering an asymptotic optimality. We illustrate it on a practical case of autonomous driving at a crossroads intersection among vehicles with uncertain behaviours.
A Tight Composition Theorem for the Randomized Query Complexity of Partial Functions: Extended Abstract

We prove two new results about the randomized query complexity of composed functions. First, we show that the randomized composition conjecture is false: there are families of partial Boolean functions <tex>$f$</tex> and <tex>$g$</tex> such that <tex>$\mathrm{R}(f\circ g)\ll \mathrm{R}(f)\mathrm{R}(g)$</tex>. In fact, we show that the left hand side can be polynomially smaller than the right hand side (though in our construction, both sides are polylogarithmic in the input size of <tex>$f$</tex>). Second, we show that for all <tex>$f$</tex> and <tex>$g,\ \mathrm{R}(f\circ g) = \Omega(\text{noisyR}(f)\ \mathrm{R}(g))$</tex>, where <tex>$\text{noisyR}(f)$</tex> is a measure describing the cost of computing <tex>$f$</tex> on noisy oracle inputs. We show that this composition theorem is the strongest possible of its type: for any measure <tex>$M(\cdot)$</tex> satisfying <tex>$\mathrm{R}(f\circ g)=\Omega(M(f)\mathrm{R}(g))$</tex> for all <tex>$f$</tex> and <tex>$g$</tex>, it must hold that <tex>$\text{noisyR}(f)=\Omega(M(f))$</tex> for all <tex>$f$</tex>. We also give a clean characterization of the measure <tex>$\text{noisyR}(f)$</tex>: it satisfies <tex>$\text{noisyR}(f)=\Theta(\mathrm{R}(f\circ\text{GapMaj}_{n})/\mathrm{R}(\text{GapMaj}_{n}))$</tex>, where <tex>$n$</tex> is the input size of <tex>$f$</tex> and <tex>$\text{GapMaj}_{n}$</tex> is the <tex>$\sqrt{n}$</tex>-gap majority function on <tex>$n$</tex> bits.
Gaussian hierarchical latent Dirichlet allocation: Bringing polysemy back

Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as “bank. ” In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors which significantly improves the capture of polysemy compared with GLDA and CGTM. Our model learns the underlying topic distribution and hierarchical structure among topics simultaneously, which can be further used to understand the correlation among topics. Moreover, the added flexibility of our model does not necessarily increase the time complexity compared with GLDA and CGTM, which makes our model a good competitor to GLDA.
See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving

Visual perception is the most critical input for driving decisions. In this study, our aim is to understand relationship between saliency and driving decisions. We present a novel attention-based saliency map prediction model for making braking decisions This approach constructs a holistic model to the driving task and can be extended for other driving decisions like steering and acceleration. The proposed model is a deep neural network model that feeds extracted features from input image to a recurrent neural network with an attention mechanism. Then predicted saliency map is used to make braking decision. We trained and evaluated using driving attention dataset BDD-A, and saliency dataset CAT2000.
Adapting Planck's route to investigate the thermodynamics of the spin-half pyrochlore Heisenberg antiferromagnet

The spin-half pyrochlore Heisenberg antiferromagnet (PHAF) is one of the most challenging problems in the field of highly frustrated quantum magnetism. Stimulated by the seminal paper of M. ~Planck [M. ~Planck, Verhandl. Dtsch. phys. Ges. {\bf 2}, 202-204 (1900)] we calculate thermodynamic properties of this model by interpolating between the low- and high-temperature behavior. For that we follow ideas developed in detail by B. ~Bernu and G. ~Misguich and use for the interpolation the entropy exploiting sum rules [the ``entropy method'' (EM)]. We complement the EM results for the specific heat, the entropy, and the susceptibility by corresponding results obtained by the finite-temperature Lanczos method (FTLM) for a finite lattice of $N=32$ sites as well as by the high-temperature expansion (HTE) data. We find that due to pronounced finite-size effects the FTLM data for $N=32$ are not representative for the infinite system below $T \approx 0.7$. A similar restriction to $T \gtrsim 0.7$ holds for the HTE designed for the infinite PHAF. By contrast, the EM provides reliable data for the whole temperature region for the infinite PHAF. We find evidence for a gapless spectrum leading to a power-law behavior of the specific heat at low $T$ and for a single maximum in $c(T)$ at $T\approx 0.25$. For the susceptibility $\chi(T)$ we find indications of a monotonous increase of $\chi$ upon decreasing of $T$ reaching $\chi_0 \approx 0.1$ at $T=0$. Moreover, the EM allows to estimate the ground-state energy to $e_0\approx -0.52$.
The rise of active galactic nuclei in the galaxy evolution and assembly semi-analytic model

We present a new implementation of the GAlaxy Evolution and Assembly (GAEA) semi-analytic model, that features an improved modelling of the process of cold gas accretion onto supermassive black hole (SMBHs), derived from both analytic arguments and high-resolution simulations. We consider different scenarios for the loss of angular momentum required for the available cold gas to be accreted onto the central SMBHs, and we compare different combinations of triggering mechanisms, including galaxy mergers and disc instabilities in star forming discs. We compare our predictions with the luminosity function (LF) observed for Active Galactic Nuclei (AGN) and we confirm that a non-instantaneous accretion timescale (either in the form of a low-angular momentum reservoir or as an assumed light curve evolution) is needed in order to reproduce the measured evolution of the AGN-LF and the so-called AGN-downsizing trend. Moreover, we also study the impact of AGN feedback, in the form of AGN-driven outflows, on the SF properties of model galaxies, using prescriptions derived both from empirical studies or from numerical experiments. We show that AGN-driven outflows are effective in suppressing the residual star formation rate in massive galaxies ($> 10^{11} M_\odot$) without changing their overall assembly history. These winds also affect the SFR of lower mass galaxies, resulting in a too large fraction of passive galaxies at $< 10^{10} M_\odot$. Finally, we study the Eddington ratio distribution as a function of SMBH mass, showing that only objects more massive than $10^8 M_\odot$ are already in a self-regulated state as inferred from observations.
Some notes on concentration for $\alpha$-subexponential random variables.

We prove extensions of classical concentration inequalities for random variables which have $\alpha$-subexponential tail decay for any $\alpha \in (0,2]$. This includes Hanson--Wright type and convex concentration inequalities. We also provide some applications of these results. This includes uniform Hanson--Wright inequalities and concentration results for simple random tensors in the spirit of previous work by Klochkov--Zhivotovskiy and Vershynin.