Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Neural Network Classification via Double Regularization

The presence of mislabeled observations in data is a notoriously challenging problem in statistics and machine learning, associated with poor generalization properties for both traditional classifiers and, perhaps even more so, flexible classifiers like neural networks. Here we propose a novel double regularization of the neural network training loss that combines a penalty on the complexity of the classification model and an optimal reweighting of training observations. The combined penalties result in improved generalization properties and strong robustness against overfitting in different settings of mislabeled training data and also against variation in initial parameter values when training. We provide a theoretical justification for our proposed method derived for a simple case of logistic regression. We demonstrate the double regularization model, here denoted by DRFit, for neural net classification of (i) MNIST and (ii) CIFAR-10, in both cases with simulated mislabeling. We also illustrate that DRFit identifies mislabeled data points with very good precision. This provides strong support for DRFit as a practical of-the-shelf classifier, since, without any sacrifice in performance, we get a classifier that simultaneously reduces overfitting against mislabeling and gives an accurate measure of the trustworthiness of the labels.
Analysis of the influence of diode reverse recovery on the operation and design of high-frequency rectifiers

Diode reverse recovery can cause anomalies in high-frequency rectifier operation, including self-sustaining quasiperiodic oscillations. The output voltage can increase considerably beyond that predicted by the usual analysis. This nonlinear effect is analyzed using a piecewise-linear diode model. The theoretical results agree with experimental measurements. Design guidelines are presented to avoid overvoltage and instability in practical converters.
Communication-Efficient Distributed SGD With Compressed Sensing

We consider large scale distributed optimization over a set of edge devices connected to a central server, where the limited communication bandwidth between the server and edge devices imposes a significant bottleneck for the optimization procedure. Inspired by recent advances in federated learning, we propose a distributed stochastic gradient descent (SGD) type algorithm that exploits the sparsity of the gradient, when possible, to reduce communication burden. At the heart of the algorithm is to use compressed sensing techniques for the compression of the local stochastic gradients at the device side; and at the server side, a sparse approximation of the global stochastic gradient is recovered from the noisy aggregated compressed local gradients. We conduct theoretical analysis on the convergence of our algorithm in the presence of noise perturbation incurred by the communication channels, and also conduct numerical experiments to corroborate its effectiveness.
Measure and Improve Robustness in NLP Models: A Survey

As NLP models achieved state-of-the-art performances over benchmarks and gained wide applications, it has been increasingly important to ensure the safe deployment of these models in the real world, e.g., making sure the models are robust against unseen or challenging scenarios. Despite robustness being an increasingly studied topic, it has been separately explored in applications like vision and NLP, with various definitions, evaluation and mitigation strategies in multiple lines of research. In this paper, we aim to provide a unifying survey of how to define, measure and improve robustness in NLP. We first connect multiple definitions of robustness, then unify various lines of work on identifying robustness failures and evaluating models’ robustness. Correspondingly, we present mitigation strategies that are data-driven, model-driven, and inductive-prior-based, with a more systematic view of how to effectively improve robustness in NLP models. Finally, we conclude by outlining open challenges and future directions to motivate further research in this area.
Data Placement for Multi-Tenant Data Federation on the Cloud

Due to privacy concerns of users and law enforcement in data security and privacy, it becomes more and more difficult to share data among organizations. Data federation brings new opportunities to the data-related cooperation among organizations by providing abstract data interfaces. With the development of cloud computing, organizations store data on the cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either execution time or monetary cost, and do not consider data partitioning for hard constraints. In this paper, we propose an approach to enable data processing on the cloud with the data from different organizations. The approach consists of a data federation platform named FedCube and a Lyapunov-based data placement algorithm. FedCube enables data processing on the cloud. We use the data placement algorithm to create a plan in order to partition and store data on the cloud so as to achieve multiple objectives while satisfying the constraints based on a multi-objective cost model. The cost model is composed of two objectives, i.e., reducing monetary cost and execution time. We present an experimental evaluation to show our proposed algorithm significantly reduces the total cost (up to 69.8%) compared with existing approaches.
Strong coupling in an Au plasmonic antenna–
SiO2
 layer system: A hybrid-mode analysis

A detailed analysis of the optical response of a system accommodating several coupled modes is needed for the complete understanding of the strong coupling effect. In this paper, we report on the analysis of scattering cross section spectra of Au antennas on a SiO$_{2}$ layer on a Si substrate in the IR region. A classical model of coupled oscillators is used for determining the resonant energies, damping rates and coupling strengths of four phonon polariton modes in the SiO$_{2}$ layer coupled to a localized surface plasmon mode in a Au antenna. The calculated Hopfield mixing coefficients then show the contribution of the individual uncoupled modes to the hybrid modes of the coupled system.
Alteration of serum leptin and LEP/LEPR promoter methylation in Prader-Willi syndrome

Prader-Willi syndrome (PWS) is a rare neurodevelopmental disorder based on a loss of paternally expressed but maternally imprinted genes in chromosome region 15q11-13. During child development, PWS usually results in insatiable appetite with subsequent obesity representing the major mortality factor. The neurobiological basis of PWS-typical hyperphagia has remained poorly understood. Many PWS-typical abnormalities are based on hypothalamic dysregulation, the region in which hunger and satiety are hormonally regulated, with the hormone leptin being a main long-term regulator of satiety. Previous studies in PWS have inconsistently shown leptin alterations solely in early childhood, without investigating the leptin system on an epigenetic level. The present study investigates serum leptin levels (S-leptin) and methylation of the leptin (LEP) and leptin receptor gene (LEPR) promoter in 24 individuals with PWS compared to 13 healthy controls matched for sex, age, and body mass index (BMI) and relates the results to the extent of hyperphagia in PWS. S-Leptin levels were obtained by Enzyme-linked Immunosorbent Assay. LEP/LEPR-promoter methylation was assessed by DNA-bisulfite-sequencing, hyperphagia by Hyperphagia Questionnaire for Clinical Trials (HQ-CT). PWS and control groups differed significantly in S-leptin levels with higher S-leptin in PWS. Methylation analysis showed significant differences in mean promoter methylation rate both for LEP and LEPR with a lower methylation rate in PWS. LEPR, but not LEP methylation correlated with S-leptin levels. S-leptin and both LEP and LEPR methylation did not correlate with HQ-CT scores in PWS. The present study is the first to show significantly elevated S-leptin levels in an adult PWS cohort combined with an altered, downregulated LEP and LEPR promoter methylation status compared to BMI-matched controls. Analogous to previous studies, no link to the behavioral dimension could be drawn. Overall, the results suggest an increased leptin dysregulation in PWS, whereby the findings partly mirror leptin resistance seen in non-syndromic obesity.
Consequences of regularization ambiguities in Loop Quantum Cosmology

Ambiguities of the so-called Thiemann regularization in Loop Quantum Cosmology lead to freedom in how to construct a particular quantization prescription. So far three distinct examples of such have been proposed in the literature. For two of them, detailed analysis has been already performed in the literature. In this thesis, the methodology developed for these is applied to study in detail the third one, which will be referred to as mLQC-II. In particular, the mathematical properties of the operator of the quantum version of full Hamiltonian constraint are examined. These properties indicate that the evolution of the system is uniquely determined. Furthermore, an investigation of dynamics is performed by finding the expectation value of the volume as a function of the scalar field and constants of motion. This result is next compared to the trajectories predicted with the so-called effective dynamics. Finally, the main properties of studied prescription are compared against those of other (already investigated) ones. Niejednoznaczności wynikające z tak zwanej regularyzacji Thiemana w Pętlowej Kosmologii Kwantowej prowadzą do swobody w procedurze kwantowania. Do tej pory wyróżniono trzy konkretne wybory, z których dwa zostały już poddane szczegółowej analizie. W niniejszej dysertacji użyjemy wypracowanej już metodologii w szczegółowej analizie trzeciego wyboru, do którego będziemy odnosić się jako mLQC-II. W szczególności analizujemy własności operatora kwantowego wiązu hamiltonowskiego, które wskazują na jednoznacznie zdeterminowaną ewolucję systemu. Ponadto badanie dynamiki przeprowadzono poprzez obliczenie wartości oczekiwanej objętości jako funkcji zależnej od pola skalarnego oraz stałe ruchu. Wynik ten jest następnie porównywany z tymi otrzymanymi już w literaturze dla innych recept regularyzacji.
Acute COVID-19 severity and 16-month mental morbidity trajectories in patient populations of six nations

BACKGROUND The aim of this multinational study was to assess the development of adverse mental health symptoms among individuals diagnosed with COVID-19 in the general population by acute infection severity up to 16 months after diagnosis. METHODS Participants consisted of 247 249 individuals from seven cohorts across six countries (Denmark, Estonia, Iceland, Norway, Scotland, and Sweden) recruited from April 2020 through August 2021. We used multivariable Poisson regression to contrast symptom-prevalence of depression, anxiety, COVID-19 related distress, and poor sleep quality among individuals with and without a diagnosis of COVID-19 at entry to respective cohorts by time (0-16 months) from diagnosis. We also applied generalised estimating equations (GEE) analysis to test differences in repeated measures of mental health symptoms before and after COVID-19 diagnosis among individuals ever diagnosed with COVID-19 over time. FINDINGS A total of 9979 individuals (4%) were diagnosed with COVID-19 during the study period and presented overall with a higher symptom burden of depression (prevalence ratio [PR] 1.18, 95% confidence interval [95% CI] 1.03-1.36) and poorer sleep quality (1.13, 1.03-1.24) but not with higher levels of symptoms of anxiety or COVID-19 related distress compared with individuals without a COVID-19 diagnosis. While the prevalence of depression and COVID-19 related distress attenuated with time, the trajectories varied significantly by COVID-19 acute infection severity. Individuals diagnosed with COVID-19 but never bedridden due to their illness were consistently at lower risks of depression and anxiety (PR 0.83, 95% CI 0.75-0.91 and 0.77, 0.63-0.94, respectively), while patients bedridden for more than 7 days were persistently at higher risks of symptoms of depression and anxiety (PR 1.61, 95% CI 1.27-2.05 and 1.43, 1.26-1.63, respectively) throughout the 16-month study period. CONCLUSION Acute infection severity is a key determinant of long-term mental morbidity among COVID-19 patients.
The Universal Specific Merger Rate of Dark Matter Halos

We employ a set of high resolution N-body simulations to study the merger rate of dark matter halos. We define a specific merger rate by normalizing the average number of mergers per halo with the logarithmic mass growth change of the hosts at the time of accretion. Based on the simulation results, we find that this specific merger rate, dNmerge(ξ∣M,z)/dξ/dlogM(z) , has a universal form, which is only a function of the mass ratio of merging halo pairs, ξ, and does not depend on the host halo mass, M, or redshift, z, over a wide range of masses (1012 ≲ M ≲ 1014 M ⊙ h −1) and merger ratios (ξ ≥ 1e − 2). We further test with simulations of different Ω m and σ 8, and get the same specific merger rate. The universality of the specific merger rate shows that halos in the universe are built up self-similarly, with a universal composition in the mass contributions and an absolute merger rate that grows in proportion to the halo mass growth. As a result, the absolute merger rate relates with redshift and cosmology only through the halo mass variable, whose evolution can be readily obtained from the universal mass accretion history (MAH) model of Zhao et al. Last, we show that this universal specific merger rate immediately predicts an universal unevolved subhalo mass function that is independent on the redshift, MAH or the final halo mass, and vice versa.
Invisibility enables super-visibility in electromagnetic imaging

Abstract. This paper is concerned with the inverse electromagnetic scattering problem for anisotropic media. We use the interior resonant modes to develop an inverse scattering scheme for imaging the scatterer. The whole procedure consists of three phases. First, we determine the interior Maxwell transmission eigenvalues of the scatterer from a family of far-field data by the mechanism of the linear sampling method. Next, we determine the corresponding transmission eigenfunctions by solving a constrained optimization problem. Finally, based on both global and local geometric properties of the transmission eigenfunctions, we design an imaging functional which can be used to determine the shape of the medium scatterer. We provide rigorous theoretical basis for our method. Numerical experiments verify the effectiveness, better accuracy and super-resolution results of the proposed scheme.
Inverse Optimal Safety Filters

Control barrier function quadratic programs (QP) safety filters are pointwise minimizers of the control effort at a given state, i.e., myopically optimal at each time. But are they optimal over the entire infinite time horizon? What does it mean for a controlled system to be “optimally safe” as opposed to, conventionally “optimally stable? ” When disturbances, deterministic and stochastic, have unknown upper bounds, how should safety be defined to allow a graceful degradation under disturbances? Can safety filters be designed to guarantee such weaker safety properties as well as the optimality of safety over the infinite time horizon? We pose and answer these questions for general systems affine in control and disturbances and illustrate the answers using several examples. In the process, using the existing QP safety filters, as well as more general safety-ensuring feedbacks, we generate entire families of safety filters that are optimal over the infinite horizon although they are conservative (favoring safety over “liveness”) relative to the standard QP.
Greedy-Step Off-Policy Reinforcement Learning

Most of the policy evaluation algorithms are based on the theories of Bellman Expectation and Optimality Equation, which derive two popular approaches - Policy Iteration (PI) and Value Iteration (VI). However, multi-step bootstrapping is often at cross-purposes with and off-policy learning in PI-based methods due to the large variance of multi-step off-policy correction. In contrast, VI-based methods are naturally off-policy but subject to one-step learning.In this paper, we deduce a novel multi-step Bellman Optimality Equation by utilizing a latent structure of multi-step bootstrapping with the optimal value function. Via this new equation, we derive a new multi-step value iteration method that converges to the optimal value function with exponential contraction rate $\mathcal{O}(\gamma^n)$ but only linear computational complexity. Moreover, it can naturally derive a suite of multi-step off-policy algorithms that can safely utilize data collected by arbitrary policies without correction.Experiments reveal that the proposed methods are reliable, easy to implement and achieve state-of-the-art performance on a series of standard benchmark datasets.
Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography

Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network. Methods: In this retrospective study, we evaluated 1,216 patients from two separate institutions who underwent CT for the presence of saccular IA>=2.5 mm. A two-step model was implemented: a 3D region proposal network for initial aneurysm detection and 3D DenseNetsfor false-positive reduction and further determination of suspicious IA. Free-response receiver operative characteristics (FROC) curve and lesion-/patient-level performance at established false positive per volume (FPPV) were also performed. Fisher's exact test was used to compare with a similar available model. Results: CADIA's sensitivities at 0.25 and 1 FPPV were 63.9% and 77.5%, respectively. Our model's performance varied with size and location, and the best performance was achieved in IA between 5-10 mm and in those at anterior communicating artery, with sensitivities at 1 FPPV of 95.8% and 94%, respectively. Our model showed statistically higher patient-level accuracy, sensitivity, and specificity when compared to the available model at 0.25 FPPV and the best F-1 score (P<=0.001). At 1 FPPV threshold, our model showed better accuracy and specificity (P<=0.001) and equivalent sensitivity. Conclusions: CADIA outperformed a comparable network in the detection task of IA. The addition of a false-positive reduction module is a feasible step to improve the IA detection models.
W-band beam-steerable MEMS-based reflectarray

This paper presents recent advances on reconfigurable reflectarrays at the University of Perugia. In particular, the activities carried out in the framework of the FP7 project ARASCOM (“MEMS and Liquid Crystal based” Agile Reflectarray Antennas for Security and COMmunication). As for ARASCOM outcomes, the purpose of the project is the design of a very large reconfigurable reflectarray controlled with micro-electro-mechanical systems (MEMS) for mm-wave imaging system at 76.5 GHz. A system with sufficient resolution to detect concealed weapons impose challenging requirements on the antenna, which shall be made of hundreds of thousands elements. The problem has been addressed by exploiting some innovative solutions and architectures that will be described in this document. In particular, the dimensioning of the reflectarray, the proposed 1-bit geometry of elementary cell, and the innovative biasing control architecture are reported together with the MEMS design and fabrication and the experimental results of a demonstrating board that validated the adopted procedure.
Conditional super-network weights

Modern Neural Architecture Search methods have repeatedly broken state-of-the-art results for several disciplines. The super-network, a central component of many such methods, enables quick estimates of accuracy or loss statistics for any architecture in the search space. They incorporate the network weights of all candidate architectures and can thus approximate specific ones by applying the respective operations. However, this design ignores potential dependencies between consecutive operations. We extend super-networks with conditional weights that depend on combinations of choices and analyze their effect. Experiments in NAS-Bench 201 and NAS-Bench-Macro-based search spaces show improvements in the architecture selection and that the resource overhead is nearly negligible for sequential network designs.
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator

We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation. Such an extension enables PeVFA to preserve values of multiple policies at the same time and brings an appealing characteristic, i.e., \emph{value generalization among policies}. We formally analyze the value generalization under Generalized Policy Iteration (GPI). From theoretical and empirical lens, we show that generalized value estimates offered by PeVFA may have lower initial approximation error to true values of successive policies, which is expected to improve consecutive value approximation during GPI. Based on above clues, we introduce a new form of GPI with PeVFA which leverages the value generalization along policy improvement path. Moreover, we propose a representation learning framework for RL policy, providing several approaches to learn effective policy embeddings from policy network parameters or state-action pairs. In our experiments, we evaluate the efficacy of value generalization offered by PeVFA and policy representation learning in several OpenAI Gym continuous control tasks. For a representative instance of algorithm implementation, Proximal Policy Optimization (PPO) re-implemented under the paradigm of GPI with PeVFA achieves about 40\% performance improvement on its vanilla counterpart in most environments.
Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery.

Methane (CH4) emission estimates from top-down studies over oil and gas basins have revealed systematic underestimation of CH4 emissions in current national inventories. Sparse but extremely large amounts of CH4 from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall oil and gas contribution. However, attribution to specific facilities remains a major challenge unless high-spatial-resolution images provide sufficient granularity within the oil and gas basin. In this paper, we monitor known oil and gas infrastructures across the globe using recurrent Sentinel-2 imagery to detect and quantify more than 1200 CH4 emissions. In combination with emission estimates from airborne and Sentinel-5P measurements, we demonstrate the robustness of the fit to a power law from 0.1 tCH4/h to 600 tCH4/h. We conclude here that the prevalence of ultraemitters (>25tCH4/h) detected globally by Sentinel-5P directly relates to emission occurrences below its detection threshold in the range >2tCH4/h, which correspond to large emitters covered by Sentinel-2. We also verified that this relation is also valid at a more local scale for two specific countries, namely, Algeria and Turkmenistan, and the Permian basin in the United States.