Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled Markov chain Monte Carlo for high-dimensional regression with Half-t priors.

Continuous shrinkage priors are commonly used in Bayesian analysis of high-dimensional data, due to both their computational advantages and favorable statistical properties. We develop coupled Markov chain Monte Carlo (MCMC) algorithms for Bayesian shrinkage regression in high dimensions. Following Glynn & Rhee (2014), these couplings can then be used in parallel computation strategies and practical diagnostics of convergence. Focusing on a class of shrinkage priors which include the Horseshoe, we demonstrate the scalability of the proposed couplings with high-dimensional simulations and data from a genome-wide association study with 2000 rows and 100,000 covariates. The results highlight the impact of the shrinkage prior on the computational efficiency of the coupling procedure, and motivates priors where the local precisions are Half-t distributions with degree of freedom larger than one, which are statistically justifiable in terms of posterior concentration, and lead to practical computational costs.
Biblical names' relationships in the Gospel of Matthew, Mark, Luke, John and Acts of Apostles

In this paper we extrapolate the information about Bible's characters and places, and their interrelationships, by using text mining network-based approach. We study the narrative structure of the WEB version of 5 books: the Gospel of Matthew, Mark, Luke, John and Acts of the Apostles. The main focus is the protagonists' names interrelationships in an analytical way, namely using various network-based methods and descriptors. This corpus is processed for creating a network: we download the names of people and places from Wikipedia's list of biblical names, then we look for their co-occurrences in each verse and, at the end of this process, we get N co-occurred names. The strength of the link between two names is defined as the sum of the times that these occur together in all the verses, in this way we obtain 5 adjacency matrices (one per book) of N by N couples of names. After this pre-processing phase, for each of the 5 analysed books we calculate the main network centrality measures (classical degree, weighted degree, betweenness and closeness), the network vulnerability and we run the Community Detection algorithm to highlight the role of Messiah inside the overall networks and his groups (communities). We have found that the proposed approach is suitable for highlighting the structures of the names co-occurrences. The found frameworks' structures are useful for interpreting the characters' plots under a structural point of view.
Generating Out of Distribution Adversarial Attack Using Latent Space Poisoning

Traditional adversarial attacks rely upon the perturbations generated by gradients from the network which are generally safeguarded by gradient guided search to provide an adversarial counterpart to the network. In this letter, we propose a novel framework to generate adversarial examples where the actual image is not corrupted rather its latent space representation is utilized to tamper the inherent structure of the image while maintaining the perceptual quality intact and to act as legitimate data samples. As opposed to gradient-based attacks, the latent space poisoning exploits the inclination of classifiers to model the independent and identical distribution of the training dataset and tricks it by producing out of distribution samples. We train a disentangled variational autoencoder (<inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula>-VAE) to model the data in latent space and then we add noise perturbations using a class-conditioned distribution function to the latent space under the constraint that it is misclassified to the target label. Our empirical results on MNIST, SVHN, and CelebA dataset validate that the generated adversarial examples can easily fool robust <inline-formula><tex-math notation="LaTeX">$l_0$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$l_2$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$l_{\infty }$</tex-math></inline-formula> norm classifiers designed using provably robust defense mechanisms. The source code is made publicly available at <uri>https://github.com/Ujjwal-9/latent-space-poisoning</uri>
Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images

With the advanced imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to automatically detect and characterize the tumor regions in pathology images at large scale. From each identified tumor region, we extracted 30 well-defined descriptors that quantify its shape, geometry, and topology. We demonstrated how those descriptor features were associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial (n=143). Besides, a descriptor-based prognostic model was developed and validated in an independent patient cohort from The Cancer Genome Atlas Program program (n=318). This study proposes new insights into the relationship between tumor shape, geometrical, and topological features and patient prognosis. We provide software in the form of R code on GitHub: https://github.com/estfernandez/Slide_Image_Segmentation_and_Extraction.
Disc–jet coupling changes as a possible indicator for outbursts from GX 339−4 remaining within the X-ray hard state

We present quasi-simultaneous radio, (sub-)millimetre, and X-ray observations of the Galactic black hole X-ray binary GX 339−4, taken during its 2017–2018 outburst, where the source remained in the hard X-ray spectral state. During this outburst, GX 339−4 showed no atypical X-ray behaviour that may act as an indicator for an outburst remaining within the hard state. However, quasi-simultaneous radio and X-ray observations showed a flatter than expected coupling between the radio and X-ray luminosities (with a best-fitting relation of $L_{\rm radio} \propto L_{\rm X}^{0.39 \pm 0.06}$), when compared to successful outbursts from this system ($L_{\rm radio} \propto L_{\rm X}^{0.62 \pm 0.02}$). While our 2017–2018 outburst data only span a limited radio and X-ray luminosity range (∼1 order of magnitude in both, where more than 2 orders of magnitude in LX is desired), including data from other hard-only outbursts from GX 339−4 extends the luminosity range to ∼1.2 and ∼2.8 orders of magnitude, respectively, and also results in a flatter correlation (where $L_{\rm radio} \propto L_{\rm X}^{0.46 \pm 0.04}$). This result is suggestive that for GX 339−4 a flatter radio–X-ray correlation, implying a more inefficient coupling between the jet and accretion flow, could act as an indicator for a hard-only outburst. However, further monitoring of both successful and hard-only outbursts over larger luminosity ranges with strictly simultaneous radio and X-ray observations is required from different single sources to explore if this applies generally to the population of black hole X-ray binaries, or even GX 339−4 at higher hard-state luminosities.
Robust Domain Randomised Reinforcement Learning through Peer-to-Peer Distillation

In reinforcement learning, domain randomisation is an increasingly popular technique for learning more general policies that are robust to domain-shifts at deployment. However, naively aggregating information from randomised domains may lead to high variance in gradient estimation and unstable learning process. To address this issue, we present a peer-to-peer online distillation strategy for RL termed P2PDRL, where multiple workers are each assigned to a different environment, and exchange knowledge through mutual regularisation based on Kullback-Leibler divergence. Our experiments on continuous control tasks show that P2PDRL enables robust learning across a wider randomisation distribution than baselines, and more robust generalisation to new environments at testing.
Equivalence of variational principles to determine the speed of scalar reaction diffusion fronts.

The determination of the speed of travelling fronts of the scalar reaction diffusion equation has been the subject of much study. Using different approaches seemingly disconnected variational principles have been established. The purpose of this work is to show the connection between them. For monostable reaction terms, we prove that a principle established by Hadeler and Rothe in 1975 and a second one by Benguria and Depassier in 1996 are logically equivalent, that is, either can be derived from the other. Two variational principles, formulated for arbitrary reaction terms, are shown to be related by a suitable change of variables. Finally a variational principle proven for monostable reaction terms is shown to be a formulation of the two previous ones in yet another independent variable.
Galaxies within galaxies in the TIMER survey: stellar populations of inner bars are scaled replicas of main bars

Inner bars are frequent structures in the local Universe and thought to substantially influence the nuclear regions of disc galaxies. In this study we explore the structure and dynamics of inner bars by deriving maps and radial profiles of their mean stellar population content and comparing them to previous findings in the context of main bars. To this end, we exploit observations obtained with the integral-field spectrograph MUSE of three double-barred galaxies in the TIMER sample. The results indicate that inner bars can be clearly distinguished based on their stellar population properties alone. More precisely, inner bars show significantly elevated metallicities and depleted [α/Fe] abundances. Although they exhibit slightly younger stellar ages compared to the nuclear disc, the typical age differences are small, except at their outer ends. These ends of the inner bars are clearly younger compared to their inner parts, an effect known from main bars as orbital age separation. In particular, the youngest stars (i.e. those with the lowest radial velocity dispersion) seem to occupy the most elongated orbits along the (inner) bar major axis. We speculate that these distinct ends of bars could be connected to the morphological feature of ansae. Radial profiles of metallicity and [α/Fe] enhancements are flat along the inner bar major axis, but show significantly steeper slopes along the minor axis. This radial mixing in the inner bar is also known from main bars and indicates that inner bars significantly affect the radial distribution of stars. In summary, based on maps and radial profiles of the mean stellar population content and in line with previous TIMER results, inner bars appear to be scaled down versions of the main bars seen in galaxies. This suggests the picture of a ‘galaxy within a galaxy’, with inner bars in nuclear discs being dynamically equivalent to main bars in main galaxy discs.
GATSBI: An Online GTSP-Based Algorithm for Targeted Surface Bridge Inspection

We study the problem of visual surface inspection of a bridge for defects using an Unmanned Aerial Vehicle (UAV). We do not assume that the geometric model of the bridge is known beforehand. Our planner, termed GATSBI, plans a path in a receding horizon fashion to inspect all points on the surface of the bridge. The input to GATSBI consists of a 3D occupancy map created online with LiDAR scans. Occupied voxels corresponding to the bridge in this map are semantically segmented and used to create a bridge-only occupancy map. Inspecting a bridge voxel requires the UAV to take images from a desired viewing angle and distance. We then create a Generalized Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints for inspecting the bridge voxels and use an off-the-shelf GTSP solver to find the optimal path for the given instance. As the algorithm sees more parts of the environment over time, it replans the path to inspect novel parts of the bridge while avoiding obstacles. We evaluate the performance of our algorithm through high-fidelity simulations conducted in AirSim and real-world experiments. We compare the performance of GATSBI with a classical exploration algorithm. Our evaluation reveals that targeting the inspection to only the segmented bridge voxels and planning carefully using a GTSP solver leads to a more efficient and thorough inspection than the baseline algorithm.
Setting the Record Straighter on Shadow Banning

Shadow banning consists for an online social net-work in limiting the visibility of some of its users, without them being aware of it. Twitter declares that it does not use such a practice, sometimes arguing about the occurrence of "bugs" to justify restrictions on some users. This paper is the first to address the plausibility of shadow banning on a major online platform, by adopting both a statistical and a graph topological approach.We first conduct an extensive data collection and analysis campaign, gathering occurrences of visibility limitations on user profiles (we crawl more than 2.5 millions of them). In such a black-box observation setup, we highlight the salient user profile features that may explain a banning practice (using machine learning predictors). We then pose two hypotheses for the phenomenon: i) limitations are bugs, as claimed by Twitter, and ii) shadow banning propagates as an epidemic on user-interaction ego-graphs. We show that hypothesis i) is statistically unlikely with regards to the data we collected. We then show some interesting correlation with hypothesis ii), suggesting that the interaction topology is a good indicator of the presence of groups of shadow banned users on the service.
A Real-Time Whole Page Personalization Framework for E-Commerce

E-commerce platforms consistently aim to provide personalized recommendations to drive user engagement, enhance overall user experience, and improve business metrics. Most e-commerce platforms contain multiple carousels on their homepage, each attempting to capture different facets of the shopping experience. Given varied user preferences, optimizing the placement of these carousels is critical for improved user satisfaction. Furthermore, items within a carousel may change dynamically based on sequential user actions, thus necessitating online ranking of carousels. In this work, we present a scalable end-to-end production system to optimally rank item-carousels in real-time on the Walmart online grocery homepage. The proposed system utilizes a novel model that captures the user’s affinity for different carousels and their likelihood to interact with previously unseen items. Our system is flexible in design and is easily extendable to settings where page components need to be ranked. We provide the system architecture consisting of a model development phase and an online inference framework. To ensure low-latency, various optimizations across these stages are implemented. We conducted extensive online evaluations to benchmark against the prior experience. In production, our system resulted in an improvement in item discovery, an increase in online engagement, and a significant lift on add-to-carts (ATCs) per visitor on the homepage.
Spectral Clustering via Adaptive Layer Aggregation for Multi-Layer Networks

Abstract One of the fundamental problems in network analysis is detecting community structure in multi-layer networks, of which each layer represents one type of edge information among the nodes. We propose integrative spectral clustering approaches based on effective convex layer aggregations. Our aggregation methods are strongly motivated by a delicate asymptotic analysis of the spectral embedding of weighted adjacency matrices and the downstream k-means clustering, in a challenging regime where community detection consistency is impossible. In fact, the methods are shown to estimate the optimal convex aggregation, which minimizes the misclustering error under some specialized multi-layer network models. Our analysis further suggests that clustering using Gaussian mixture models is generally superior to the commonly used k-means in spectral clustering. Extensive numerical studies demonstrate that our adaptive aggregation techniques, together with Gaussian mixture model clustering, make the new spectral clustering remarkably competitive compared to several popularly used methods. Supplementary materials for this article are available online.
Performance Analysis of Age of Information in Ultra-Dense Internet of Things (IoT) Systems with Noisy Channels

In this paper, a dense Internet of Things (IoT) monitoring system is studied in which a large number of devices contend for transmitting timely status packets to their corresponding receivers over wireless noisy channels, using a carrier sense multiple access (CSMA) scheme. When each device completes one transmission, due to possible transmission failure, two cases with and without transmission feedback to each device must be considered. Particularly, for the case with no feedback, the device uses policy (I): It will go to an idle state and release the channel regardless of the outcome of the transmission. For the case with perfect feedback, if the transmission succeeds, the device will go to an idle state, otherwise it uses either policy (W), i.e., it will go to a waiting state and re-contend for channel access; or it uses policy (S), i.e., it will stay at a service state and occupy this channel to attempt another transmission. For those three policies, the closed-form expressions of the average age of information (AoI) of each device are characterized under schemes with and without preemption in service. It is shown that, for each policy, the scheme with preemption in service always achieves a smaller average AoI, compared with the scheme without preemption. Then, a mean-field approximation approach with guaranteed accuracy is developed to analyze the asymptotic performance for the considered system with an infinite number of devices and the effects of the system parameters on the average AoI are characterized. Simulation results show that the proposed mean-field approximation is accurate even for a small number of devices. The results also show that policy (S) achieves the smallest average AoI compared with policies (I) and (W), and the average AoI does not always decrease with the arrival rate for all three policies.
Operator as a Service: Stateful Serverless Complex Event Processing

Complex Event Processing (CEP) is a powerful paradigm for scalable data management that is employed in many real-world scenarios such as detecting credit card fraud in banks. The so-called complex events are expressed using a specification language that is typically implemented and executed on a specific runtime system. While the tight coupling of these two components has been regarded as the key for supporting CEP at high performance, such dependencies pose several inherent challenges as follows. (1) Application development atop a CEP system requires extensive knowledge of how the runtime system operates, which is typically highly complex in nature. (2) The specification language dependence requires the need of domain experts and further restricts and steepens the learning curve for application developers.In this paper, we propose CEPless, a scalable data management system that decouples the specification from the runtime system by building on the principles of serverless computing. CEPless provides "operator as a service" and offers flexibility by enabling the development of CEP application in any specification language while abstracting away the complexity of the CEP runtime system. As part of CEPless, we designed and evaluated novel mechanisms for in-memory processing and batching that enable the stateful processing of CEP operators even under high rates of ingested events. Our evaluation demonstrates that CEPless can be easily integrated into existing CEP systems like Apache Flink while attaining similar throughput under high scale of events (up to 100K events per second) and dynamic operator update in ~238 ms.
E3D: Event-Based 3D Shape Reconstruction

3D shape reconstruction is a primary component of augmented/virtual reality. Despite being highly advanced, existing solutions based on RGB, RGB-D and Lidar sensors are power and data intensive, which introduces challenges for deployment in edge devices. We approach 3D reconstruction with an event camera, a sensor with significantly lower power, latency and data expense while enabling high dynamic range. While previous event-based 3D reconstruction methods are primarily based on stereo vision, we cast the problem as multi-view shape from silhouette using a monocular event camera. The output from a moving event camera is a sparse point set of space-time gradients, largely sketching scene/object edges and contours. We first introduce an event-to-silhouette (E2S) neural network module to transform a stack of event frames to the corresponding silhouettes, with additional neural branches for camera pose regression. Second, we introduce E3D, which employs a 3D differentiable renderer (PyTorch3D) to enforce cross-view 3D mesh consistency and fine-tune the E2S and pose network. Lastly, we introduce a 3D-to-events simulation pipeline and apply it to publicly available object datasets and generate synthetic event/silhouette training pairs for supervised learning.
Z2
 lattice gauge theories and Kitaev's toric code: A scheme for analog quantum simulation

Kitaev's toric code is an exactly solvable model with $\mathbb{Z}_2$-topological order, which has potential applications in quantum computation and error correction. However, a direct experimental realization remains an open challenge. Here, we propose a building block for $\mathbb{Z}_2$ lattice gauge theories coupled to dynamical matter and demonstrate how it allows for an implementation of the toric-code ground state and its topological excitations. This is achieved by introducing separate matter excitations on individual plaquettes, whose motion induce the required plaquette terms. The proposed building block is realized in the second-order coupling regime and is well suited for implementations with superconducting qubits. Furthermore, we propose a pathway to prepare topologically non-trivial initial states during which a large gap on the order of the underlying coupling strength is present. This is verified by both analytical arguments and numerical studies. Moreover, we outline experimental signatures of the ground-state wavefunction and introduce a minimal braiding protocol. Detecting a $\pi$-phase shift between Ramsey fringes in this protocol reveals the anyonic excitations of the toric-code Hamiltonian in a system with only three triangular plaquettes. Our work paves the way for realizing non-Abelian anyons in analog quantum simulators.