Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Risks of Collecting Multidimensional Data Under Local Differential Privacy

The private collection of multiple statistics from a population is a fundamental statistical problem. One possible approach to realize this is to rely on the local model of differential privacy (LDP). Numerous LDP protocols have been developed for the task of frequency estimation of single and multiple attributes. These studies mainly focused on improving the utility of the algorithms to ensure the server performs the estimations accurately. In this paper, we investigate privacy threats (re-identification and attribute inference attacks) against LDP protocols for multidimensional data following two state-of-the-art solutions for frequency estimation of multiple attributes. To broaden the scope of our study, we have also experimentally assessed five widely used LDP protocols, namely, generalized randomized response, optimal local hashing, subset selection, RAPPOR and optimal unary encoding. Finally, we also proposed a countermeasure that improves both utility and robustness against the identified threats. Our contributions can help practitioners aiming to collect users' statistics privately to decide which LDP mechanism best fits their needs.
StreamNet: A WAE for White Matter Streamline Analysis

We present StreamNet, an autoencoder architecture for the analysis of the highly heterogeneous geometry of large collections of white matter streamlines. This proposed framework takes advantage of geometry-preserving properties of the Wasserstein-1 metric in order to achieve direct encoding and reconstruction of entire bundles of streamlines. We show that the model not only accurately captures the distributive structures of streamlines in the population, but is also able to achieve superior reconstruction performance between real and synthetic streamlines. Experimental model performance is evaluated on white matter streamlines resulting from T1-weighted diffusion imaging of 40 healthy controls using recent state of the art bundle comparison metric that measures fiber-shape similarities.
Mortaring for linear elasticity using low order finite elements

We introduce a stabilized mortar method for linear elasticity and compare it to the standard mixed mortar method without stabilization. We present the stability criteria of the lowest order mixed approximation and investigate its use for tie contact problems. Our numerical results demonstrate the stability and the convergence of the methods. Moreover, the results show that the low order mixed method can be successfully extended to three dimensions.
Space Filling Curves for Coverage Path Planning with Online Obstacle Avoidance

The paper presents a strategy for robotic exploration problem using Space-Filling curves (SFC). The strategy plans a path that avoids unknown obstacles while ensuring complete coverage of the free space in region of interest. The region of interest is first tessellated, and the tiles/cells are connected using a SFC pattern. A robot follows the SFC to explore the entire area. However, obstacles can block the systematic movement of the robot. We overcome this problem by determining an alternate path online that avoids the blocked cells while ensuring all the accessible cells are visited at least once. The proposed strategy chooses next waypoint based on the graph connectivity of the cells and the obstacle encountered so far. It is online, exhaustive and works in situations demanding non-uniform coverage. The completeness of the strategy is proved and its desirable properties are discussed with examples.
A Crypto-Assisted Approach for Publishing Graph Statistics with Node Local Differential Privacy

Publishing graph statistics under node differential privacy has attracted much attention since it provides a stronger privacy guarantee than edge differential privacy. Existing works related to node differential privacy assume a trusted data curator who holds the whole graph. However, in many applications, a trusted curator is usually not available due to privacy and security issues. In this paper, for the first time, we investigate the problem of publishing graph statistics under Node Local Differential privacy (Node-LDP), which does not rely on a trusted server. We propose an algorithm to publish the degree distribution with Node-LDP by exploring how to select the graph projection parameter in the local setting and how to execute the graph projection locally. Specifically, we propose a crypto-assisted local projection method based on cryptographic primitives, achieving the higher accuracy than our baseline pureLDP local projection method. Furthermore, we improve our baseline graph projection method from node-level to edge-level that preserves more neighboring information, owning better utility. Finally, extensive experiments on real-world graphs show that crypto-assisted parameter selection owns better utility than pureLDP parameter selection, and edge-level local projection provides higher accuracy than node-level local projection, improving by up to 57.2% and 79.8%, respectively.
Joint Demosaicing and Fusion of Multiresolution Compressed Acquisitions: Image Formation and Reconstruction Methods

—Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by the same focal plane array. In this work, we propose to model a multiresolution compressed acquisition (MRCA) in a generic framework, which natively includes acquisitions by conventional systems such as those based on spectral/color ﬁlter arrays, compressed coded apertures, and multiresolution sensing. We propose a model-based image reconstruction al- gorithm performing a joint demosaicing and fusion (JoDeFu) of any acquisition modeled in the MRCA framework. The JoDeFu reconstruction algorithm solves an inverse problem with a proximal splitting technique and is able to reconstruct an uncompressed image datacube at the highest available spatial and spectral resolution. An implementation of the code is available at https://github.com/danaroth83/jodefu .
Mimose: An Input-Aware Checkpointing Planner for Efficient Training on GPU

Larger deep learning models usually lead to higher model quality with an ever-increasing GPU memory footprint. Although tensor checkpointing techniques have been proposed to enable training under a restricted GPU memory budget, the input tensor dynamics have been unexploited for optimizing performance while reducing GPU memory footprint. Specifically, due to the diverse datasets and subsequent data argumentation, the input tensor size per mini-batch is dynamic during the training process, leading to a changing GPU memory footprint. However, to leverage such input tensor dynamics in checkpointing, there are two challenges to be solved. First, the checkpointing plan needs to be determined during runtime due to the dynamics of input tensors. Second, the checkpointing plan needs to be applied on the fly without significantly deteriorating the performance. In this paper, we propose Mimose, an input-aware tensor checkpointing planner respecting the memory budget while enabling efficient model training on GPU. Mimose builds a lightweight but accurate prediction model of GPU memory usage online, without pre-analyzing the model. It generates a tensor checkpointing plan based on per-layer memory prediction and applies it to training progress on the fly. It also adopts a caching strategy to avoid having to regenerate the plan for repeated input size. Our experiments show that Mimose achieves superior training throughput compared to state-of-the-art memory planners under the same GPU memory budgets.
Hybrid mixed discontinuous Galerkin finite element method for incompressible miscible displacement problem

A new hybrid mixed discontinuous Galerkin finite element (HMDGFE) method is constructed for incompressible miscible displacement problem. In this method, the hybrid mixed finite element (HMFE) procedure is considered to solve pressure and velocity equations, and a new hybrid mixed discontinuous Galerkin procedure is constructed to solve the concentration equation with upwind technique. Compared with other traditional discontinuous Galerkin methods, the new method can reach global systems with less unknowns and sparser stencils. The consistency and conservation of the method are analyzed, the stability and optimal error estimates are also derived by the new technique.
MARTI-4: new model of human brain, considering neocortex and basal ganglia - learns to play Atari game by reinforcement learning on a single CPU

We present Deep Control – new ML architecture of cortico-striatal brain circuits, which use whole cortical column as a structural element, instead of a singe neuron. Based on this architecture, we present MARTI - new model of human brain, considering neocortex and basal ganglia. This model is designed to implement expedient behavior and is capable to learn and achieve goals in unknown environments. We introduce a novel surprise feeling mechanism, that significantly improves reinforcement learning process through inner rewards. We use OpenAI Gym environment to demonstrate MARTI learning on a single CPU just in several hours.
Adversarial Detection: Attacking Object Detection in Real Time

Intelligent robots rely on object detection models to perceive the environment. Following advances in deep learning security it has been revealed that object detection models are vulnerable to adversarial attacks. However, prior research primarily focuses on attacking static images or offline videos. Therefore, it is still unclear if such attacks could jeopardize real-world robotic applications in dynamic environments. This paper bridges this gap by presenting the first real-time online attack against object detection models. We devise three attacks that fabricate bounding boxes for nonexistent objects at desired locations. The attacks achieve a success rate of about 90% within about 20 iterations. The demo video is available at https://youtu.be/zJZ1aNlXsMU.
Core orientations and magnetic fields in isolated molecular clouds

Molecular clouds are sites of star formation. Magnetic fields are believed to play an important role in their dynamics and shaping morphology. We aim to study any possible correlation that might exist between the magnetic fields orientation inside the clouds and the magnetic fields at envelope scales and their connection with respect to the observed morphology of the selected clouds. We examine the magnetic field orientation towards the clouds L1512, L1523, L1333, L1521E, L1544, L1517, L1780, and L183, using optical and Planck polarization observations. We also found the correlation between the ambient magnetic field and core orientations derived using Astrodendrogram on the Herschel 250 $\mu$m data. We find that the magnetic fields derived from optical and Planck agree with each other. The derived magnetic fields are aligned along the observed emission of each cloud as seen in Herschel 250 $\mu$m data. We also find that the relative orientation between the cores and the magnetic fields is random. This lack of correlation may arise due to the fact that the core orientation could also be influenced by the different magnetization within individual clouds at higher densities or the feedback effects which may vary from cloud to cloud. The estimated magnetic field strength and the mass-to-flux ratio suggest that all the clouds are in a magnetically critical state except L1333, L1521E, and L183, where the cloud envelope could be strongly supported by the magnetic field lines.
An Adaptive Column Compression Family for Self-Driving Databases

Modern in-memory databases are typically used for high-performance workloads, therefore they have to be optimized for small memory footprint and high query speed at the same time. Data compression has the potential to reduce memory requirements but often reduces query speed too. In this paper we propose a novel, adaptive compressor that offers a new trade-off point of these dimensions, achieving better compression than LZ4 while reaching query speeds close to the fastest existing segment encoders. We evaluate our compressor both with synthetic data in isolation and on the TPC-H and Join Order Benchmarks, integrated into a modern relational column store, Hyrise.
Understanding Longitudinal Behaviors of Toxic Accounts on Reddit

Toxic comments are the top form of hate and harassment experienced online. While many studies have investigated the types of toxic comments posted online, the effects that such content has on people, and the impact of potential defenses, no study has captured the long-term behaviors of the accounts that post toxic comments or how toxic comments are operationalized. In this paper, we present a longitudinal measurement study of 929K accounts that post toxic comments on Reddit over an 18 month period. Combined, these accounts posted over 14 million toxic comments that encompass insults, identity attacks, threats of violence, and sexual harassment. We explore the impact that these accounts have on Reddit, the targeting strategies that abusive accounts adopt, and the distinct patterns that distinguish classes of abusive accounts. Our analysis forms the foundation for new time-based and graph-based features that can improve automated detection of toxic behavior online and informs the nuanced interventions needed to address each class of abusive account.
Cubic-Regularized Newton for Spectral Constrained Matrix Optimization and its Application to Fairness

Matrix functions are utilized to rewrite smooth spectral constrained matrix optimization problems as smooth unconstrained problems over the set of symmetric matrices which are then solved via the cubic-regularized Newton method. A second-order chain rule identity for matrix functions is proven to compute the higher-order derivatives to implement cubic-regularized Newton, and a new convergence analysis is provided for cubic-regularized Newton for matrix vector spaces. We demonstrate the applicability of our approach by conducting numerical experiments on both synthetic and real datasets. In our experiments, we formulate a new model for estimating fair and robust covariance matrices in the spirit of the Tyler's M-estimator (TME) model and demonstrate its advantage.
Multiwavelength Modeling the SED of Luminous Supersoft X-Ray Sources in Large Magellanic Cloud and Small Magellanic Cloud

Classical supersoft X-ray sources (SSSs) are understood as close binary systems in which a massive white dwarf accretes from its companion at rates sustaining steady hydrogen burning on its surface generating bolometric luminosities of 1036–2 × 1038 erg s−1. Here, we perform for the first time the global supersoft X-rays to near-infrared (NIR) spectral energy distribution (SED) for the brightest SSSs in the Large Magellanic Cloud and Small Magellanic Cloud. We test a model in which the ultraviolet–NIR is dominated by emission from a compact (unresolved) circumstellar nebula represented by the ionized gas outflowing from the SSS. The SED models correspond to luminosities of SSSs of a few times 1038–1039 erg s−1, radiating at blackbody temperatures of ≈3 × 105 K, and indicate a nebular continuum, whose emission measure of ≳2 × 1060 cm−3 corresponds to a wind mass loss at rates ≳2 × 10−6 M ⊙ yr−1. Such extreme parameters suggest that the brightest SSSs could be unidentified optical novae in a post-nova SSS state sustained at a high long-lasting luminosity by resumed accretion, possibly at super-Eddington rates. New observations and theoretical multiwavelength modeling of the global SED of SSSs are needed to reliably determine their parameters, and thus understand their proper stage in stellar evolution.
Understanding skills for OSS communities on GitHub

The development of open source software (OSS) is a broad field which requires diverse skill sets. For example, maintainers help lead the project and promote its longevity, technical writers assist with documentation, bug reporters identify defects in software, and developers program the software. However, it is unknown which skills are used in OSS development as well as OSS contributors' general attitudes towards skills in OSS. In this paper, we address this gap by administering a survey to a diverse set of 455 OSS contributors. Guided by these responses as well as prior literature on software development expertise and social factors of OSS, we develop a model of skills in OSS that considers the many contexts OSS contributors work in. This model has 45 skills in the following 9 categories: technical skills, working styles, problem solving, contribution types, project-specific skills, interpersonal skills, external relations, management, and characteristics. Through a mix of qualitative and quantitative analyses, we find that OSS contributors are actively motivated to improve skills and perceive many benefits in sharing their skills with others. We then use this analysis to derive a set of design implications and best practices for those who incorporate skills into OSS tools and platforms, such as GitHub.
A Review of Sparse Expert Models in Deep Learning

Sparse expert models are a thirty-year old concept re-emerging as a popular architecture in deep learning. This class of architecture encompasses Mixture-of-Experts, Switch Transformers, Routing Networks, BASE layers, and others, all with the unifying idea that each example is acted on by a subset of the parameters. By doing so, the degree of sparsity decouples the parameter count from the compute per example allowing for extremely large, but efficient models. The resulting models have demonstrated significant improvements across diverse domains such as natural language processing, computer vision, and speech recognition. We review the concept of sparse expert models, provide a basic description of the common algorithms, contextualize the advances in the deep learning era, and conclude by highlighting areas for future work.
Finite-Time Error Bounds for Greedy-GQ

Greedy-GQ with linear function approximation, originally proposed in \cite{maei2010toward}, is a value-based off-policy algorithm for optimal control in reinforcement learning, and it has a non-linear two timescale structure with a non-convex objective function. This paper develops its finite-time error bounds. We show that the Greedy-GQ algorithm converges as fast as $\mathcal{O}({1}/{\sqrt{T}})$ under the i.i. d. \ setting and $\mathcal{O}({\log T}/{\sqrt{T}})$ under the Markovian setting. We further design a variant of the vanilla Greedy-GQ algorithm using the nested-loop approach, and show that its sample complexity is $\mathcal{O}({\log(1/\epsilon)\epsilon^{-2}})$, which matches with the one of the vanilla Greedy-GQ. Our finite-time error bounds match with one of the stochastic gradient descent algorithms for general smooth non-convex optimization problems. Our finite-sample analysis provides theoretical guidance on choosing step-sizes for faster convergence in practice and suggests the trade-off between the convergence rate and the quality of the obtained policy. Our techniques in this paper provide a general approach for finite-sample analysis of non-convex two timescale value-based reinforcement learning algorithms.
Multiwavelength Study of Dark Globule DC 314.8–5.1: Point-source Identification and Diffuse Emission Characterization

We present an analysis of multiwavelength observations of the dark globule DC 314.8–5.1, using data from the Gaia optical, Two Micron All Star Survey near-infrared, and Wide-field Infrared Survey Explorer mid-infrared surveys, dedicated imaging with the Spitzer Space Telescope, and X-ray data obtained with the Swift X-Ray Telescope (XRT). The main goal was to identify possible pre-main-sequence stars (PMSs) and young stellar objects (YSOs) associated with the globule. For this, we studied the infrared colors of all point sources within the boundaries of the cloud. After removing sources with nonstellar spectra, we investigated the Gaia parallaxes for the YSO candidates and found that none are physically related to DC 314.8–5.1. In addition, we searched for X-ray emission from PMSs with Swift-XRT, and found no 0.5–10 keV emission down to a luminosity level ≲1031 erg s−1, typical of a PMS with mass ≥2 M ⊙. Our detailed inspection therefore supports a very young, “prestellar core” evolutionary stage for the cloud. Based on archival Planck and IRAS data, we moreover identify the presence of hot dust, with temperatures ≳100 K, in addition to the dominant dust component at 14 K, originating with the associated reflection nebula.
Pseudo-LiDAR for Visual Odometry

As one of the important tasks in the field of robotics and machine vision, visual odometry provides tremendous help for various applications such as navigation, location, and so on. Conventionally, the task of visual odometry mainly relies on the input of continuous images. However, it is very complicated for the odometry network to learn the epipolar geometry information provided by the images. Since the 6-degree-of-freedom (DoF) pose transformation occurs in 3-D space and learning poses from 3-D point clouds are more straightforward, this article introduces the concept of pseudo-LiDAR to the odometry task. The pseudo-LiDAR point cloud is formed by backprojecting the depth map generated from the image into 3-D space. Due to the limitation of calculation power, most current algorithms based on the point cloud need to sample 8192 points from the point cloud as input, but such an approach makes the rich point cloud information in the pseudo-LiDAR point cloud not fully utilized. To address this problem, a projection-aware algorithm is adopted, which achieves efficient point cloud learning and improves the accuracy of the network while preserving the 3-D structure information in the pseudo-LiDAR point cloud. Finally, an image-only 2-D–3-D fusion module is proposed to enhance the pseudo-LiDAR point features using information such as the texture and color of the images. Through multimodal fusion, the network achieves a deeper understanding of the environment. Experiments on the KITTI dataset prove the effectiveness of our method. The source code will be open-sourced at https://github.com/IRMVLab/Pseudo-LiDAR-for-Visual-Odometry.
Machine learning for dynamically predicting the onset of renal replacement therapy in chronic kidney disease patients using claims data

Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data. To evaluate the model, we studied approximately 3 million Medicare beneficiaries for which we made over 8 million predictions. We showed that the model can identify at risk patients with over 90% sensitivity and specificity. Although additional work is required before this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcomes.