Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Distributed Learning with Privacy-Preserving Data Synthesis

—In the medical ﬁeld, multi-center collaborations are often sought to yield more generalizable ﬁndings by leveraging the heterogeneity of patient and clinical data. However, recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. Federated learning (FL) aims at sidestepping this limitation by bringing AI-based solutions to data owners and only sharing local AI models, or parts thereof, that need then to be aggregated. However, most of the existing federated learning solutions are still at their infancy and show several shortcomings, from the lack of a reliable and effective aggregation scheme able to retain the knowledge learned locally to weak privacy preservation as real data may be reconstructed from model updates. Furthermore, the majority of these approaches, especially those dealing with medical data, relies on a centralized distributed learning strategy that poses robustness, scalability and trust issues. In this paper we present a decentralized distributed method that, exploiting concepts from experience replay and generative adversarial research, effectively integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy. The proposed approach is tested on two tasks — tuberculosis and melanoma classiﬁcation — using multiple datasets in order to simulate realistic non-i.i. d. data scenarios. Re-sults show that our approach achieves performance comparable to both standard (non-federated) learning and federated methods in their centralized (thus, more favourable) formulation.
Riemannian data-dependent randomized smoothing for neural networks certification

Certification of neural networks is an important and challenging problem that has been attracting the attention of the machine learning community since few years. In this paper, we focus on randomized smoothing (RS) which is considered as the state-of-the-art method to obtain certifiably robust neural networks. In particular, a new data-dependent RS technique called ANCER introduced recently can be used to certify ellipses with orthogonal axis near each input data of the neural network. In this work, we remark that ANCER is not invariant under rotation of input data and propose a new rotationally-invariant formulation of it which can certify ellipses without constraints on their axis. Our approach called Riemannian Data Dependant Randomized Smoothing (RDDRS) relies on information geometry techniques on the manifold of covariance matrices and can certify bigger regions than ANCER based on our experiments on the MNIST dataset.
Improvements to Pan-STARRS1 Astrometry. II. Corrections for Differential Chromatic Refraction

In a previous paper, we applied the Gaia DR2 catalog to improve the astrometric accuracy of about 1.7 billion objects in Pan-STARRS1 Data Release 2 (PS1 DR2). We report here on further improvements made by utilizing Gaia EDR3 and correcting for the effects of differential chromatic refraction (DCR) in decl. We extend the correction algorithm in Paper I by iteratively subtracting color- and decl. -dependent PS1/Gaia EDR3 decl. residuals. We determine the astrometric improvement for ∼440 million reference objects that are point-like and cross-match to Gaia EDR3. For this set of objects, Gaia EDR3 provides a ∼3% improvement in PS1 astrometry over Gaia DR2, and DCR corrections provide an additional ∼5% improvement. DCR corrections increase substantially for objects observed away from the zenith. DCR corrections lead to an astrometric improvement of ∼30% for blue objects (0 < g − i < 1) that are 50° away from the zenith. The amplitude of systematic astrometric errors from these effects is substantially reduced to less than 1 mas for objects with PS1 colors in the range 0 < g − i < 4.5, which makes this a useful astrometric reference catalog in fields where there are few Gaia stars. The improved astrometric data will be available through the Mikulski Archive for Space Telescopes PS1 catalog interfaces.
Depth-based clustering analysis of directional data

A new depth-based clustering procedure for directional data is proposed. Such method is fully non-parametric and has the advantages to be ﬂexible and applicable even in high dimensions when a suitable notion of depth is adopted. The introduced technique is evaluated through an extensive simulation study. In addition, a real data example in text mining is given to explain its effectiveness in comparison with other existing directional clustering algorithms.
Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Therefore imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers dynamics to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful action data obtained during autonomous operations. By fine-tuning the successful data, the robot learns the dynamics among itself, its environment, and objects. We experimented with the task of scooping and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded the task more than 75% for other objects.
Understanding Robust Learning through the Lens of Representation Similarities

Representation learning, i.e. the generation of representations useful for downstream applications, is a task of fundamental importance that underlies much of the success of deep neural networks (DNNs). Recently, robustness to adversarial examples has emerged as a desirable property for DNNs, spurring the development of robust training methods that account for adversarial examples. In this paper, we aim to understand how the properties of representations learned by robust training differ from those obtained from standard, non-robust training. This is critical to diagnosing numerous salient pitfalls in robust networks, such as, degradation of performance on benign inputs, poor generalization of robustness, and increase in over-fitting. We utilize a powerful set of tools known as representation similarity metrics, across three vision datasets, to obtain layer-wise comparisons between robust and non-robust DNNs with different training procedures, architectural parameters and adversarial constraints. Our experiments highlight hitherto unseen properties of robust representations that we posit underlie the behavioral differences of robust networks. We discover a lack of specialization in robust networks' representations along with a disappearance of `block structure'. We also find overfitting during robust training largely impacts deeper layers. These, along with other findings, suggest ways forward for the design and training of better robust networks.
Absolute frequencies of H13C14N hydrogen cyanide transitions in the 1.5-µm region with the saturated spectroscopy and a sub-kHz scanning laser.

The wide span and high density of lines in its rovibrational spectrum render hydrogen cyanide a useful spectroscopic media for referencing absolute frequencies of lasers in optical communication and dimensional metrology. We determined, for the first time to the best of our knowledge, the molecular transitions' center frequencies of the H13C14N isotope in the range from 1526 nm to 1566 nm with 1.3 × 10-10 fractional uncertainty. We investigated the molecular transitions with a highly coherent and widely tunable scanning laser that was precisely referenced to a hydrogen maser through an optical frequency comb. We demonstrated an approach to stabilize the operational conditions needed to maintain the constantly low pressure of the hydrogen cyanide to carry out the saturated spectroscopy with the third-harmonic synchronous demodulation. We demonstrated approximately a forty-fold improvement in the line centers' resolution compared to the previous result.
On approximating the rank of graph divisors

Baker and Norine initiated the study of graph divisors as a graph-theoretic analogue of the Riemann-Roch theory for Riemann surfaces. One of the key concepts of graph divisor theory is the {\it rank} of a divisor on a graph. The importance of the rank is well illustrated by Baker's {\it Specialization lemma}, stating that the dimension of a linear system can only go up under specialization from curves to graphs, leading to a fruitful interaction between divisors on graphs and curves. Due to its decisive role, determining the rank is a central problem in graph divisor theory. Kiss and T\'othm\'eresz reformulated the problem using chip-firing games, and showed that computing the rank of a divisor on a graph is NP-hard via reduction from the Minimum Feedback Arc Set problem. In this paper, we strengthen their result by establishing a connection between chip-firing games and the Minimum Target Set Selection problem. As a corollary, we show that the rank is difficult to approximate to within a factor of $O(2^{\log^{1-\varepsilon}n})$ for any $\varepsilon>0$ unless $P=NP$. Furthermore, assuming the Planted Dense Subgraph Conjecture, the rank is difficult to approximate to within a factor of $O(n^{1/4-\varepsilon})$ for any $\varepsilon>0$.
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs

Given an input image, and nothing else, our method returns the bounding boxes of objects in the image and phrases that describe the objects. This is achieved within an open world paradigm, in which the objects in the input image may not have been encountered during the training of the localization mechanism. Moreover, training takes place in a weakly supervised setting, where no bounding boxes are provided. To achieve this, our method combines two pre-trained networks: the CLIP image-to-text matching score and the BLIP image captioning tool. Training takes place on COCO images and their captions and is based on CLIP. Then, during inference, BLIP is used to generate a hypothesis regarding various regions of the current image. Our work generalizes weakly supervised segmentation and phrase grounding and is shown empirically to outperform the state of the art in both domains. It also shows very convincing results in the novel task of weakly-supervised open-world purely visual phrase-grounding presented in our work. For example, on the datasets used for benchmarking phrase-grounding, our method results in a very modest degradation in comparison to methods that employ human captions as an additional input. Our code is available at https://github.com/talshaharabany/what-is-where-by-looking and a live demo can be found at https://replicate.com/talshaharabany/what-is-where-by-looking.
Data Fusion for Radio Frequency SLAM with Robust Sampling

Precise indoor localization remains a challenging problem for a variety of essential applications. A promising approach to address this problem is to exchange radio signals between mobile agents and static physical anchors (PAs) that bounce off flat surfaces in the indoor environment. Radio frequency simultaneous localization and mapping (RF -SLAM) methods can be used to jointly estimates the time-varying location of agents as well as the static locations of the flat surfaces. Recent work on RF -SLAM methods has shown that each surface can be efficiently represented by a single master virtual anchor (MVA). The measurement model related to this MVA-based RF -SLAM method is highly nonlinear. Thus, Bayesian estimation relies on sampling-based techniques. The original MVA-based RF -SLAM method employs conventional “bootstrap” sampling. In challenging scenarios it was observed that the original method might converge to incorrect MVA positions corresponding to local maxima. In this paper, we introduce MVA-based RF-SLAM with an improved sampling technique that succeeds in the aforementioned challenging scenarios. Our simulation results demonstrate significant performance advantages.
Structure and stability of the Indian power transmission network

We present the study on the Indian power transmission network using the framework of a complex network and quantify its structural properties. For this, we build the network structure underlying the Indian power grid, using two of its most prevalent power lines. We construct an equivalent model of an exponential network and study its structural changes with changes in two parameters related to redundancy and dead-ends. Then we analyze its stability against cascading failures by varying these two parameters using the link failure model. This helps to gain insight into the relation of network topology to its stability, and indicates how the optimum choice of these parameters can result in a power grid structure with minimum failed links. We apply the same model to study the robustness of the Indian power grid against such failures. In this case, we find that when a link connected to a generator fails, it results in a cascade that spreads in the grid until it is split into two separate stable clusters of generators and consumers, with over one-third of its nodes nonfunctional.
Simultaneous recovery of attenuation and source density in SPECT

We show that under a certain non-cancellation condition the attenuated Radon transform uniquely determines piecewise constant attenuation $a$ and piecewise $C^2$ source density $f$ with jumps over real analytic boundaries possibly having corners. We also look at numerical examples in which the non-cancellation condition fails and show that unique reconstruction of multi-bang $a$ and $f$ is still appears to be possible although not yet explained by theoretical results.
Beyond IID: data-driven decision-making in heterogeneous environments

How should one leverage historical data when past observations are not perfectly indicative of the future, e.g., due to the presence of unobserved confounders which one cannot"correct"for? Motivated by this question, we study a data-driven decision-making framework in which historical samples are generated from unknown and different distributions assumed to lie in a heterogeneity ball with known radius and centered around the (also) unknown future (out-of-sample) distribution on which the performance of a decision will be evaluated. This work aims at analyzing the performance of central data-driven policies but also near-optimal ones in these heterogeneous environments. We first establish, for a general class of policies, a new connection between data-driven decision-making and distributionally robust optimization with a regret objective. We then leverage this connection to quantify the performance that is achievable by Sample Average Approximation (SAA) as a function of the radius of the heterogeneity ball: for any integral probability metric, we derive bounds depending on the approximation parameter, a notion which quantifies how the interplay between the heterogeneity and the problem structure impacts the performance of SAA. When SAA is not rate-optimal, we design and analyze problem-dependent policies achieving rate-optimality. We compare achievable guarantees for three widely-studied problems -- newsvendor, pricing, and ski rental -- under heterogeneous environments. Our work shows that the type of achievable performance varies considerably across different combinations of problem classes and notions of heterogeneity.
Symmetric generalized Heckman models

The sample selection bias problem arises when a variable of interest is correlated with a latent variable, and involves situations in which the response variable had part of its observations censored. Heckman (1976) proposed a sample selection model based on the bivariate normal distribution that ﬁts both the variable of interest and the latent variable. Recently, this assumption of normality has been relaxed by more ﬂexible models such as the Student- t distribution (Marchenko and Genton, 2012; Lachos et al., 2021). The aim of this work is to propose generalized Heckman sample selection models based on symmetric distributions (Fang et al., 1990). This is a new class of sample selection models, in which variables are added to the dispersion and correlation parameters. A Monte Carlo simulation study is performed to assess the behavior of the parameter estimation method. Two real data sets are analyzed to illustrate the proposed approach.
Linear response for pseudo-Hermitian Hamiltonian systems: Application to 
PT
-symmetric qubits

Motivated by the recent advances in modelling the pseudo-Hermitian Hamiltonian (pHH) systems using superconducting qubits we analyze their quantum dynamics subject to a small time-dependent perturbation. In particular, We develop the linear response theory formulation suitable for application to various pHH systems and compare it to the ones available in the literature. We derive analytical expressions for the generalized temporal quantum-mechanical correlation function C ( t ) and the time-dependent dynamic susceptibility χ ( t ) ∝ Im C ( t ). We apply our results to two PT symmetric non-Hermitian quantum systems: a single qubit and two unbiased/biased qubits coupled by the exchange interaction. For both systems we obtain the eigenvalues and eigenfunctions of the Hamiltonian, identify PT -symmetry unbroken and broken quantum phases and quantum phase transitions between them. The temporal oscillations of the dynamic susceptibility of the qubits polarization ( z -projection of the total spin), χ ( t ), relate to ac induced transitions between diﬀerent eigenstates and we analyze the dependencies of the oscillations frequency and the amplitude on the gain/loss parameter γ and the interaction strength g . Studying the time dependence of χ ( t ) we observe diﬀerent types of oscillations, i.e. undamped, heavily damped and ampliﬁed ones, related to the transitions between eigenstates with broken (unbroken) PT -symmetry. These predictions can be veriﬁed in the microwave transmission experiments allowing controlled simulation of the pHH systems.
Neutrinoless double beta decay and the muonium-to-antimuonium transition in models with a doubly charged scalar

The lepton number and flavor violations are important possible ingredients of the lepton physics. The neutrinoless double beta decay and the transition of the muonium into antimuonuim are related to those violations. The former can give us an essential part of fundamental physics, and there are plenty of experimental attempts to observe the process. The latter has also been one of the attractive phenomena, and the experimental bound will be updated in planned experiments at new high-intensity muon beamlines. In models with a doubly charged scalar, not only can those two processes be induced, but also the active neutrino masses can be induced radiatively. The flavor violating decays of the charged leptons constrain the flavor parameters of the models. We study how the muonium-to-antimuonium transition rate can be as large as the current experimental bound, and we insist that the updated bound of the transition rate will be useful to distinguish the models to generate the neutrinoless double beta decay via the doubly charged scalar. We also study a possible extension of the model to the left-right model which can induce the neutrinoless double beta decay.
Visual Guidance for User Placement in Avatar-Mediated Telepresence between Dissimilar Spaces.

Rapid advances in technology gradually realize immersive mixed-reality (MR) telepresence between distant spaces. This paper presents a novel visual guidance system for avatar-mediated telepresence, directing users to optimal placements that facilitate the clear transfer of gaze and pointing contexts through remote avatars in dissimilar spaces, where the spatial relationship between the remote avatar and the interaction targets may differ from that of the local user. Representing the spatial relationship between the user/avatar and interaction targets with angle-based interaction features, we assign recommendation scores of sampled local placements as their maximum feature similarity with remote placements. These scores are visualized as color-coded 2D sectors to inform the users of better placements for interaction with selected targets. In addition, virtual objects of the remote space are overlapped with the local space for the user to better understand the recommendations. We examine whether the proposed score measure agrees with the actual user perception of the partner's interaction context and find a score threshold for recommendation through user experiments in virtual reality (VR). A subsequent user study in VR investigates the effectiveness and perceptual overload of different combinations of visualizations. Finally, we conduct a user study in an MR telepresence scenario to evaluate the effectiveness of our method in real-world applications.
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL

Reinforcement learning (RL) in long horizon and sparse reward tasks is notoriously difficult and requires a lot of training steps. A standard solution to speed up the process is to leverage additional reward signals, shaping it to better guide the learning process. In the context of language-conditioned RL, the abstraction and generalisation properties of the language input provide opportunities for more efficient ways of shaping the reward. In this paper, we leverage this idea and propose an automated reward shaping method where the agent extracts auxiliary objectives from the general language goal. These auxiliary objectives use a question generation (QG) and question answering (QA) system: they consist of questions leading the agent to try to reconstruct partial information about the global goal using its own trajectory. When it succeeds, it receives an intrinsic reward proportional to its confidence in its answer. This incentivizes the agent to generate trajectories which unambiguously explain various aspects of the general language goal. Our experimental study shows that this approach, which does not require engineer intervention to design the auxiliary objectives, improves sample efficiency by effectively directing exploration.
Viscous vortex-pair-cylinder interactions near inviscid translating equilibria: a numerical study

Numerical simulations using the Lattice Boltzmann Method are presented of the following two-dimensional incompressible ﬂow problem. Starting from conﬁgurations corresponding to translating inviscid equilibria, namely, the translating F¨oppl equilibria (counter-rotating point vortex pair, either fore or aft of a translating circular cylinder) and the translating Hill equilibria (counter-rotating point vortex pair, either fore or aft of an elliptic cylinder), viscosity is turned on for t > 0 and the subsequent viscous interaction is simulated. The interaction is in a dynamically coupled setting where the neutrally buoyant cylinder is free to move along the symmetry axis under the action of the instantaneous ﬂuid stresses on its surface. It is observed that for starting conﬁgurations in which the vortex pair trails the cylinder, the viscous evolution stays close to the inviscid equilibrium. However, for starting conﬁgurations in which the vortex pair leads the cylinder, there is signiﬁcant deviation from the inviscid equilibrium. In such cases, the vortices either accelerate and leave the cylinder behind or, more interestingly, leave their leading positions and are attracted towards the trailing positions. In other words, the cylinder in such cases threads through the leading vortices, overtakes them and the vortices are observed to trail the cylinder again. The symmetry of the inviscid dynamics about the perpendicular axis is thus broken.