Search

The success of learning-based coding techniques and the development of learning-based image coding standards, such as JPEG-AI, point towards the adoption of such solutions in different fields, including the storage of biometric data, like fingerprints. However, the peculiar nature of learning-based compression artifacts poses several issues concerning their impact and effectiveness on extracting biometric features and landmarks, e.g., minutiae. This problem is utterly stressed by the fact that most models are trained on natural color images, whose characteristics are very different from usual biometric images, e.g, fingerprint or iris pictures. As a matter of fact, these issues are deemed to be accurately questioned and investigated, being such analysis still largely unexplored. This study represents the first investigation about the adaptability of learning-based image codecs in the storage of fingerprint images by measuring its impact on the extraction and characterization of minutiae. Experimental results show that at a fixed rate point, learned solutions considerably outperform previous fingerprint coding standards, like JPEG2000, both in terms of distortion and minutiae preservation. Indeed, experimental results prove that the peculiarities of learned compression artifacts do not prevent automatic fingerprint identification (since minutiae types and locations are not significantly altered), nor do compromise image quality for human visual inspection (as they gain in terms of BD rate and PSNR of 47.8% and +3.97dB respectively)., Comment: Accepted ad Wifs 2024

Entity Matching (EM) is crucial for identifying equivalent data entities across different sources, a task that becomes increasingly challenging with the growth and heterogeneity of data. Blocking techniques, which reduce the computational complexity of EM, play a vital role in making this process scalable. Despite advancements in blocking methods, the issue of fairness; where blocking may inadvertently favor certain demographic groups; has been largely overlooked. This study extends traditional blocking metrics to incorporate fairness, providing a framework for assessing bias in blocking techniques. Through experimental analysis, we evaluate the effectiveness and fairness of various blocking methods, offering insights into their potential biases. Our findings highlight the importance of considering fairness in EM, particularly in the blocking phase, to ensure equitable outcomes in data integration tasks.

In this paper, I consider a $f(R, G, T)$ modified gravity model where $R$ represents the Ricci scalar, $G$ denotes the Gauss-Bonnet invariant, and $T$ signifies the trace of the stress-energy tensor. This model is coupled with two distinct types of scalar fields. In the flat Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) universe, the necessary conditions for a successful bounce are achieved. Under these circumstances, it is demonstrated that the equation of state (EoS) parameter cannot cross the phantom divider when only the inflaton scalar field is considered. Appropriate conditions to preserve the conservation of energy law are obtained. The absence of radiation domination is confirmed by referencing one of the collections of the Planck 2018 report. Moreover, it has been demonstrated that this is a general model, encompassing other models such as Weyl conformal geometry and the inflation epoch. Numerical calculations and graphs are used to confirm the results., Comment: 32 pages, 13 figures

With security threats increasing in frequency and severity, it is critical that we consider the important role of threat hunters. These highly-trained security professionals learn to see, identify, and intercept security threats. Many recent works and existing tools in cybersecurity are focused on automating the threat hunting process, often overlooking the critical human element. Our study shifts this paradigm by emphasizing a human-centered approach to understanding the lived experiences of threat hunters. By observing threat hunters during hunting sessions and analyzing the rich insights they provide, we seek to advance the understanding of their cognitive processes and the tool support they need. Through an in-depth observational study of threat hunters, we introduce a model of how they build and refine their mental models during threat hunting sessions. We also present 23 themes that provide a foundation to better understand threat hunter needs and five actionable design propositions to enhance the tools that support them. Through these contributions, our work enriches the theoretical understanding of threat hunting and provides practical insights for designing more effective, human-centered cybersecurity tools., Comment: 17 Pages; 4 Figures; 4 Tables

Investigating the genetic architecture of complex diseases is challenging due to the highly polygenic and interactive landscape of genetic and environmental factors. Although genome-wide association studies (GWAS) have identified thousands of variants for multiple complex phenotypes, conventional statistical approaches can be limited by simplified assumptions such as linearity and lack of epistasis models. In this work, we trained artificial neural networks for predicting complex traits using both simulated and real genotype/phenotype datasets. We extracted feature importance scores via different post hoc interpretability methods to identify potentially associated loci (PAL) for the target phenotype. Simulations we performed with various parameters demonstrated that associated loci can be detected with good precision using strict selection criteria, but downstream analyses are required for fine-mapping the exact variants due to linkage disequilibrium, similarly to conventional GWAS. By applying our approach to the schizophrenia cohort in the Estonian Biobank, we were able to detect multiple PAL related to this highly polygenic and heritable disorder. We also performed enrichment analyses with PAL in genic regions, which predominantly identified terms associated with brain morphology. With further improvements in model optimization and confidence measures, artificial neural networks can enhance the identification of genomic loci associated with complex diseases, providing a more comprehensive approach for GWAS and serving as initial screening tools for subsequent functional studies. Keywords: Deep learning, interpretability, genome-wide association studies, complex diseases

Shaping thermoplastic sheets into three-dimensional products is challenging since overheating results in failed manufactured parts and wasted material. To this end, we propose an indirect data-driven predictive control approach using Model Predictive Control (MPC) capable of handling temperature constraints and heating-power saturation while delivering enhanced precision, overshoot control, and settling times compared to state-of-the-art methods. We employ a Non-linear Auto-Regressive with Exogenous inputs (NARX) model to define a linear control-oriented model at each operating point. Using a high-fidelity simulator, several simulation studies have been conducted to evaluate the proposed method's robustness and performance under parametric uncertainty, indicating overshoot and average steady-state error less than $2^\circ \mathrm{C}$ and $0.7^\circ \mathrm{C}$ ($7^\circ \mathrm{C}$ and $2^\circ \mathrm{C}$) for the nominal (worst-case) scenario. Finally, we applied the proposed method to a lab-scale thermoforming platform, resulting in a close response to the simulation analysis with overshoot and average steady-state error metrics less than $5.3^\circ \mathrm{C}$ and $1^\circ \mathrm{C}$, respectively.

Recent advances in reinforcement learning (RL) have predominantly leveraged neural network-based policies for decision-making, yet these models often lack interpretability, posing challenges for stakeholder comprehension and trust. Concept bottleneck models offer an interpretable alternative by integrating human-understandable concepts into neural networks. However, a significant limitation in prior work is the assumption that human annotations for these concepts are readily available during training, necessitating continuous real-time input from human annotators. To overcome this limitation, we introduce a novel training scheme that enables RL algorithms to efficiently learn a concept-based policy by only querying humans to label a small set of data, or in the extreme case, without any human labels. Our algorithm, LICORICE, involves three main contributions: interleaving concept learning and RL training, using a concept ensembles to actively select informative data points for labeling, and decorrelating the concept data with a simple strategy. We show how LICORICE reduces manual labeling efforts to to 500 or fewer concept labels in three environments. Finally, we present an initial study to explore how we can use powerful vision-language models to infer concepts from raw visual inputs without explicit labels at minimal cost to performance., Comment: 23 pages, 6 figures, 9 tables

High contrast imaging of extrasolar planets and circumstellar disks requires extreme wavefront stability. Such stability can be achieved with active wavefront control (WFC). The next generation of ground- and space-based telescopes will require a robust form of WFC in order to image planets at small inner working angles and extreme flux ratios with respect to the host star. WFC algorithms such as implicit Electric Field Conjugation (iEFC) reduce stellar leakage by minimizing the electric field within a given region of an image, creating a dark hole. iEFC utilizes an empirical approach to sense and remove speckles in the focal plane. While iEFC is empirically calibrated and can handle optical model errors, there are still model assumptions made during the calibration. The performance of iEFC will degrade if the system changes due to slow, optomechanical drifts. In this work, we assess the iEFC performance impacts of pupil misalignments on the deformable mirror and focal plane misalignments on the detector. We base our analysis on the MagAO-X instrument, an extreme AO system installed on the Magellan-Clay telescope, to develop iEFC misalignment tolerancing requirements for both ground- and space-based missions. We present end-to-end physical optics simulations of the MagAO-X instrument, demonstrating iEFC alignment tolerance., Comment: 8 pages, 6 figures, SPIE Astronomical Telescopes + Instrumentation 2024

Understanding the mechanisms behind decisions taken by large foundation models in sequential decision making tasks is critical to ensuring that such systems operate transparently and safely. In this work, we perform exploratory analysis on the Video PreTraining (VPT) Minecraft playing agent, one of the largest open-source vision-based agents. We aim to illuminate its reasoning mechanisms by applying various interpretability techniques. First, we analyze the attention mechanism while the agent solves its training task - crafting a diamond pickaxe. The agent pays attention to the last four frames and several key-frames further back in its six-second memory. This is a possible mechanism for maintaining coherence in a task that takes 3-10 minutes, despite the short memory span. Secondly, we perform various interventions, which help us uncover a worrying case of goal misgeneralization: VPT mistakenly identifies a villager wearing brown clothes as a tree trunk when the villager is positioned stationary under green tree leaves, and punches it to death., Comment: Mechanistic Interpretability Workshop at ICML 2024

The exploitation of certification tools by end users represents a fundamental aspect of the development of quantum technologies as the hardware scales up beyond the regime of classical simulatability. Certifying quantum networks becomes even more crucial when the privacy of their users is exposed to malicious quantum nodes or servers as in the case of multi-client distributed blind quantum computing, where several clients delegate a joint private computation to remote quantum servers, such as federated quantum machine learning. In such protocols, security must be provided not only by keeping data hidden but also by verifying that the server is correctly performing the requested computation while minimizing the hardware assumptions on the employed devices. Notably, standard verification techniques fail in scenarios where the clients receive quantum states from untrusted sources such as, for example, in a recently demonstrated linear quantum network performing multi-client blind quantum computation. However, recent theoretical results provide techniques to verify blind quantum computations even in the case of untrusted state preparation. Equipped with such theoretical tools, in this work, we provide the first experimental implementation of a two-client verifiable blind quantum computing protocol in a distributed architecture. The obtained results represent novel perspectives for the verification of multi-tenant distributed quantum computation in large-scale networks., Comment: Minor changes in the description of the experimental setup

Developing a reliable vision system is a fundamental challenge for robotic technologies (e.g., indoor service robots and outdoor autonomous robots) which can ensure reliable navigation even in challenging environments such as adverse weather conditions (e.g., fog, rain), poor lighting conditions (e.g., over/under exposure), or sensor degradation (e.g., blurring, noise), and can guarantee high performance in safety-critical functions. Current solutions proposed to improve model robustness usually rely on generic data augmentation techniques or employ costly test-time adaptation methods. In addition, most approaches focus on addressing a single vision task (typically, image recognition) utilising synthetic data. In this paper, we introduce Per-corruption Adaptation of Normalization statistics (PAN) to enhance the model robustness of vision systems. Our approach entails three key components: (i) a corruption type identification module, (ii) dynamic adjustment of normalization layer statistics based on identified corruption type, and (iii) real-time update of these statistics according to input data. PAN can integrate seamlessly with any convolutional model for enhanced accuracy in several robot vision tasks. In our experiments, PAN obtains robust performance improvement on challenging real-world corrupted image datasets (e.g., OpenLoris, ExDark, ACDC), where most of the current solutions tend to fail. Moreover, PAN outperforms the baseline models by 20-30% on synthetic benchmarks in object recognition tasks.

Antibodies are widely used in life-sciences and medical therapy. Yet, broadly applicable methods are missing to determine, in the biological system of choice, antibody specificity and its quantitative contribution to e.g. immunofluorescence stainings. Thereby, antibody-based data often needs to be seen with caution. Here, we present a simple-to-use approach to characterize and quantify antibody binding properties directly in the system of choice. We determine an epitope accessibility distribution in the system of interest based on a computational analysis of antibody-dilution immunofluorescence stainings. This allows the selection of specific antibodies, the choice of a dilution to maximize signal-specificity, and an improvement of signal quantification. It further expands the scope of antibody-based imaging to detect changes of the subcellular nano-environment and allows for antibody multiplexing.

Magnetic Resonance Imaging (MRI) is a powerful technique employed for non-invasive in vivo visualization of internal structures. Sparsity is often deployed to accelerate the signal acquisition or overcome the presence of motion artifacts, improving the quality of image reconstruction. Image reconstruction algorithms use TV-regularized LASSO (Total Variation-regularized LASSO) to retrieve the missing information of undersampled signals, by cleaning the data of noise and while optimizing sparsity. A tuning parameter moderates the balance between these two aspects; its choice affecting the quality of the reconstructions. Currently, there is a lack of general deterministic techniques to choose these parameters, which are oftentimes manually selected and thus hinder the reliability of the reconstructions. Here, we present ALMA (Algorithm for Lagrange Multipliers Approximation), an iterative mathematics-inspired technique that computes tuning parameters for generalized LASSO problems during MRI reconstruction. We analyze quantitatively the performance of these parameters for imaging reconstructions via TV-LASSO in an MRI context on phantoms. Although our study concentrates on TV-LASSO, the techniques developed here hold significant promise for a wide array of applications. ALMA is not only adaptable to more generalized LASSO problems but is also robust to accommodate other forms of regularization beyond total variation. Moreover, it extends effectively to handle non-Cartesian sampling trajectories, broadening its utility in complex data reconstruction scenarios. More generally, ALMA provides a powerful tool for numerically solving constrained optimization problems across various disciplines, offering a versatile and impactful solution for advanced computational challenges.

The Space Coronagraph Optical Bench (SCoOB) is a high-contrast imaging testbed built to demonstrate starlight suppression techniques at visible wavelengths in a space-like vacuum environment. The testbed is designed to achieve ${<}10^{-8}$ contrast from $3-10\lambda/D$ in a one-sided dark hole using a liquid crystal vector vortex waveplate and a 952-actuator Kilo-C deformable mirror (DM) from Boston Micromachines (BMC). We have recently expanded the testbed to include a field stop for mitigation of stray/scattered light, a precision-fabricated pinhole in the source simulator, a Minus K passive vibration isolation table for jitter reduction, and a low-noise vacuum-compatible CMOS sensor. We report the latest contrast performance achieved using implicit electric field conjugation (iEFC) at a vacuum of ${\sim}10^{-6}$ Torr and over a range of bandpasses with central wavelengths from 500 to 650nm and bandwidths (BW) from $\ll 1\%$ to 15\%. Our jitter in vacuum is $<3\times10^{-3} \lambda/D$, and the best contrast performance to-date in a half-sided D-shaped dark hole is $2.2\times10^{-9}$ in a $\ll 1 \%$ BW, $4\times10^{-9}$ in a 2\% BW, and $2.5\times10^{-8}$ in a 15\% BW., Comment: 16 pages, 12 figures, SPIE Astronomical Telescopes and Instrumentation 2024

The correction of quasi-static wavefront errors within a coronagraphic optical system will be a key challenge to overcome in order to directly image exoplanets in reflected light. These quasi-static errors are caused by mid to high-order surface errors on the optical elements as a result of manufacturing processes. Using high-order wavefront sensing and control (HOWFSC) techniques that do not introduce non-common path aberrations, the quasi-static errors can be corrected within the desired region of interest designated as the dark hole. For the future Habitable Worlds Observatory (HWO), HOWFSC algorithms will be key to attaining the desired contrasts. To simulate the performance of HOWFSC with space rated processors, optical models for a 6 m class space-borne observatory and a coronagraph have been developed. Phenomena such as the Talbot effect and beamwalk are included in the simulations using combinations of ray-based modeling and end-to-end propagation techniques. After integrating the optical models with the embedded processors, simulations with realistic computation times can be performed to understand the computational hardware performance that will be needed to maintain the desired contrasts. Here, the details of the optical models are presented along with the HOWFSC methods utilized. Initial results of the HOWFSC methods are also included as a demonstration of how system drifts will degrade the contrast and require dark hole maintenance., Comment: 10 pages, 8 Figures, SPIE Astronomical Telescopes and Instrumentation 2024

Generative models are gaining significant attention as potential catalysts for a novel industrial revolution. Since automated sample generation can be useful to solve privacy and data scarcity issues that usually affect learned biometric models, such technologies became widely spread in this field. In this paper, we assess the vulnerabilities of generative machine learning models concerning identity protection by designing and testing an identity inference attack on fingerprint datasets created by means of a generative adversarial network. Experimental results show that the proposed solution proves to be effective under different configurations and easily extendable to other biometric measurements., Comment: Paper submitted at "Pattern Recognition Letters", 9 pages, 6 images

Reinforcement learning (RL) has recently shown promise in predicting Alzheimer's disease (AD) progression due to its unique ability to model domain knowledge. However, it is not clear which RL algorithms are well-suited for this task. Furthermore, these methods are not inherently explainable, limiting their applicability in real-world clinical scenarios. Our work addresses these two important questions. Using a causal, interpretable model of AD, we first compare the performance of four contemporary RL algorithms in predicting brain cognition over 10 years using only baseline (year 0) data. We then apply SHAP (SHapley Additive exPlanations) to explain the decisions made by each algorithm in the model. Our approach combines interpretability with explainability to provide insights into the key factors influencing AD progression, offering both global and individual, patient-level analysis. Our findings show that only one of the RL methods is able to satisfactorily model disease progression, but the post-hoc explanations indicate that all methods fail to properly capture the importance of amyloid accumulation, one of the pathological hallmarks of Alzheimer's disease. Our work aims to merge predictive accuracy with transparency, assisting clinicians and researchers in enhancing disease progression modeling for informed healthcare decisions. Code is available at https://github.com/rfali/xrlad., Comment: Previous versions accepted to NeurIPS 2023's XAIA and AAAI 2024's XAI4DRL workshops

Entity Matching (EM) is a critical task in numerous fields, such as healthcare, finance, and public administration, as it identifies records that refer to the same entity within or across different databases. EM faces considerable challenges, particularly with false positives and negatives. These are typically addressed by generating matching scores and apply thresholds to balance false positives and negatives in various contexts. However, adjusting these thresholds can affect the fairness of the outcomes, a critical factor that remains largely overlooked in current fair EM research. The existing body of research on fair EM tends to concentrate on static thresholds, neglecting their critical impact on fairness. To address this, we introduce a new approach in EM using recent metrics for evaluating biases in score based binary classification, particularly through the lens of distributional parity. This approach enables the application of various bias metrics like equalized odds, equal opportunity, and demographic parity without depending on threshold settings. Our experiments with leading matching methods reveal potential biases, and by applying a calibration technique for EM scores using Wasserstein barycenters, we not only mitigate these biases but also preserve accuracy across real world datasets. This paper contributes to the field of fairness in data cleaning, especially within EM, which is a central task in data cleaning, by promoting a method for generating matching scores that reduce biases across different thresholds.

Forecasts and future beliefs play a critical role in the harvest labor hiring planning, especially when errors in them entails fixing previous made decisions, which can carry extra costs or losses. In this article, we study the effect that errors in the forecast/belief can have in the wine grape harvest planning process and the losses of the product. Errors are reflected in the prediction of yields and in the estimation of rain transition probabilities have on the value and losses of product. Also, using a multi-stage stochastic optimization model we can study the effect that second stage decisions have on the ability fix the planning decisions, reduce product losses and generate value. In a first step, we develop a multi-stage stochastic model which considers grape growth uncertainty given a belief in future events. The model decisions variables are: hiring, firing and maintaining harvest labor through periods, and also the harvested quantities in each period and block. Once the model defines the plan for the coming epoch, some decisions are implemented and a deviation in the forecast is revealed and the decision maker can adjust future decisions and beliefs. Results indicate that the effect of the errors in yield determination is not symmetrical; underestimations of the yields have a more significant negative effect on the objective function, while overestimation does not. Flexibility to revise hiring decisions does not make a significant difference if the yields are overestimated. The model significantly reduces losses of the better-quality grapes, since they correspond to a significant proportion of the income and account for the largest portion of income loss. Last, grapes that have an early improvement of their quality give the decision-maker an extra level of flexibility to adjust the harvesting plan.

Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we propose PATIENT-{\Psi}, a novel patient simulation framework for cognitive behavior therapy (CBT) training. To build PATIENT-{\Psi}, we construct diverse patient cognitive models based on CBT principles and use large language models (LLMs) programmed with these cognitive models to act as a simulated therapy patient. We propose an interactive training scheme, PATIENT-{\Psi}-TRAINER, for mental health trainees to practice a key skill in CBT -- formulating the cognitive model of the patient -- through role-playing a therapy session with PATIENT-{\Psi}. To evaluate PATIENT-{\Psi}, we conducted a comprehensive user study of 13 mental health trainees and 20 experts. The results demonstrate that practice using PATIENT-{\Psi}-TRAINER enhances the perceived skill acquisition and confidence of the trainees beyond existing forms of training such as textbooks, videos, and role-play with non-patients. Based on the experts' perceptions, PATIENT-{\Psi} is perceived to be closer to real patient interactions than GPT-4, and PATIENT-{\Psi}-TRAINER holds strong promise to improve trainee competencies. Our code and data are released at \url{https://github.com/ruiyiw/patient-psi}., Comment: EMNLP 2024 Main, 9 pages, 5 figures

High-order wavefront sensing and control (HOWFSC) is key to create a dark hole region within the coronagraphic image plane where high contrasts are achieved. The Roman Coronagraph is expected to perform its HOWFSC with a ground-in-the-loop scheme due to the computational complexity of the Electric Field Conjugation (EFC) algorithm. This scheme provides the flexibility to alter the HOWFSC algorithm for given science objectives. The baseline HOWFSC scheme involves running EFC while observing a bright star such as {\zeta} Puppis to create the initial dark hole followed by a slew to the science target. The new implicit EFC (iEFC) algorithm removes the optical diffraction model from the controller, making the final contrast independent of model accuracy. While previously demonstrated with a single DM, iEFC is extended to two deformable mirror systems in order to create annular dark holes. The algorithm is then applied to the Wide-Field-of-View Shaped Pupil Coronagraph (SPC-WFOV) mode designed for the Roman Space Telescope using end-to-end physical optics models. Initial monochromatic simulations demonstrate the efficacy of iEFC as well as the optimal choice of modes for the SPC-WFOV instrument. Further simulations with a 3.6% wavefront control bandpass and a broader 10% bandpass then demonstrate that iEFC can be used in broadband scenarios to achieve contrasts below 1E-8 with Roman. Finally, an EMCCD model is implemented to estimate calibration times and predict the controller's performance. Here, 1E-8 contrasts are achieved with a calibration time of about 6.8 hours assuming the reference star is {\zeta} Puppis. The results here indicate that iEFC can be a valid HOWFSC method that can mitigate the risk of model errors associated with space-borne coronagraphs, but to maximize iEFC performance, lengthy calibration times will be required to mitigate the noise accumulated during calibration., Comment: 20 pages, 15 figures

In 2015, Kawarabayashi and Kreutzer proved the directed grid theorem confirming a conjecture by Reed, Johnson, Robertson, Seymour, and Thomas from the mid-nineties. The theorem states the existence of a function $f$ such that every digraph of directed tree-width $f(k)$ contains a cylindrical grid of order $k$ as a butterfly minor, but the given function grows non-elementarily with the size of the grid minor. In this paper we present an alternative proof of the directed grid theorem which is conceptually much simpler, more modular in its composition and also improves the upper bound for the function $f$ to a power tower of height 22. Our proof is inspired by the breakthrough result of Chekuri and Chuzhoy, who proved a polynomial bound for the excluded grid theorem for undirected graphs. We translate a key concept of their proof to directed graphs by introducing \emph{cycles of well-linked sets (CWS)}, and show that any digraph of high directed tree-width contains a large CWS, which in turn contains a large cylindrical grid, improving the result due to Kawarabayashi and Kreutzer from an non-elementary to an elementary function. An immediate application of our result is an improvement of the bound for Younger's conjecture proved by Reed, Robertson, Seymour and Thomas (1996) from a non-elementary to an elementary function. The same improvement applies to other types of Erd\H{o}s-P\'osa style problems on directed graphs. To the best of our knowledge this is the first significant improvement on the bound for Younger's conjecture since it was proved in 1996. We believe that the theoretical tools we developed may find applications beyond the directed grid theorem, in a similar way as the path-of-sets-system framework due to Chekuri and Chuzhoy (2016) did (see for example Hatzel, Komosa, Pilipczuk and Sorge (2022); Chekuri and Chuzhoy (2015); Chuzhoy and Nimavat (2019)).

Paraxial diffraction modeling based on the Fourier transform has seen widespread implementation for simulating the response of a diffraction-limited optical system. For systems where the paraxial assumption is not sufficient, a class of algorithms has been developed that employs hybrid propagation physics to compute the propagation of an elementary beamlet along geometric ray paths. These "beamlet decomposition" algorithms include the well-known Gaussian Beamlet Decomposition (GBD) algorithm, of which several variants have been created. To increase the computational efficiency of the GBD algorithm, we derive an alternative expression of the technique that utilizes the analytical propagation of beamlets to tilted planes. We then use this accelerated algorithm to conduct a parameter-space search to find the optimal combination of free parameters in GBD to construct the analytical Airy function. The experiment is conducted on a consumer-grade CPU, and a high-performance GPU, where the new algorithm is 34 times faster than the previously published algorithm on CPUs, and 67,513 times faster on GPUs., Comment: 19 pages, 10 figures, Accepted to Optics Express on April 18 2024

The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single bitstream, is a major requirement that has yet to be conveniently addressed, notably in most learning-based PC coding solutions. This paper proposes a quality scalability scheme, named Scalable Quality Hyperprior (SQH), adaptable to learning-based static point cloud geometry codecs, which uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode a high-quality version of a PC learning-based representation, based on an available lower quality base layer. SQH is integrated in the future JPEG PC coding standard, allowing to create a layered bitstream that can be used to progressively decode the PC geometry with increasing quality and fidelity. Experimental results show that SQH offers the quality scalability feature with very limited or no compression performance penalty at all when compared with the corresponding non-scalable solution, thus preserving the significant compression gains over other state-of-the-art PC codecs., Comment: Accepted at ICIP 2024

Mixed and multi methods research is often used in software engineering, but researchers outside of the social or human sciences often lack experience when using these designs. This paper provides guidelines and advice on how to design mixed and multi method research, and to encourage the intentional, rigourous, and innovative use of mixed methods in software engineering. It also presents key characteristics of core mixed method research designs. Through a number of fictitious but recognizable software engineering research scenarios and personas of prototypical researchers, we showcase how to choose suitable designs and consider the inevitable tradeoffs any design choice leads to. We furnish the paper with recommended best practices and several antipatterns that illustrate what to avoid in mixed and multi method research., Comment: 21 pages, 6 figures

Learned image compression codecs have recently achieved impressive compression performances surpassing the most efficient image coding architectures. However, most approaches are trained to minimize rate and distortion which often leads to unsatisfactory visual results at low bitrates since perceptual metrics are not taken into account. In this paper, we show that conditional diffusion models can lead to promising results in the generative compression task when used as a decoder, and that, given a compressed representation, they allow creating new tradeoff points between distortion and perception at the decoder side based on the sampling method.

Ensuring Conditional Independence (CI) constraints is pivotal for the development of fair and trustworthy machine learning models. In this paper, we introduce \sys, a framework that harnesses optimal transport theory for data repair under CI constraints. Optimal transport theory provides a rigorous framework for measuring the discrepancy between probability distributions, thereby ensuring control over data utility. We formulate the data repair problem concerning CIs as a Quadratically Constrained Linear Program (QCLP) and propose an alternating method for its solution. However, this approach faces scalability issues due to the computational cost associated with computing optimal transport distances, such as the Wasserstein distance. To overcome these scalability challenges, we reframe our problem as a regularized optimization problem, enabling us to develop an iterative algorithm inspired by Sinkhorn's matrix scaling algorithm, which efficiently addresses high-dimensional and large-scale data. Through extensive experiments, we demonstrate the efficacy and efficiency of our proposed methods, showcasing their practical utility in real-world data cleaning and preprocessing tasks. Furthermore, we provide comparisons with traditional approaches, highlighting the superiority of our techniques in terms of preserving data utility while ensuring adherence to the desired CI constraints.

Relaxing the sequential specification of a shared object is a way to obtain an implementation with better performance compared to implementing the original specification. We apply this approach to the Counter object, under the assumption that the number of times the Counter is incremented in any execution is at most a known bound $m$. We consider the $k$-multiplicative-accurate Counter object, where each read operation returns an approximate value that is within a multiplicative factor $k$ of the accurate value. More specifically, a read is allowed to return an approximate value $x$ of the number $v$ of increments previously applied to the counter such that $v/k \le x \le vk$. We present three algorithms to implement this object in a wait-free linearizable manner in the shared memory model using read-write registers. All the algorithms have read operations whose worst-case step complexity improves exponentially on that for an exact $m$-bounded counter (which in turn improves exponentially on that for an exact unbounded counter). Two of the algorithms have read step complexity that is asymptotically optimal. The algorithms differ in their requirements on $k$, step complexity of the increment operation, and space complexity., Comment: 26 pages, to be published in SIROCCO 2024 proceedings

LoRaWAN is a wireless technology that enables high-density deployments of IoT devices. Designed for Low Power Wide Area Networks (LPWAN), LoRaWAN employs large cells to service a potentially extremely high number of devices. The technology enforces a centralized architecture, directing all data generated by the devices to a single network server for data processing. End-to-end encryption is used to guarantee the confidentiality and security of data. In this demo, we present \edgelora, a system architecture designed to incorporate edge processing in LoRaWAN without compromising security and confidentiality of data. \edgelora maintains backward compatibility and addresses scalability issues arising from handling large amounts of data sourced from a diverse range of devices. The demo provides evidence on the advantages in terms of reduced latency, lower network bandwidth requirements, higher scalability, and improved security and privacy resulting from the application of the Edge processing paradigm to LoRaWAN., Comment: This paper is a pre-print version of the conference paper that has been accepted to ACM MobiCom 2023: Proceedings of the 29th Annual International Conference on Mobile Computing and Networking

We present a novel dynamic light scattering setup to probe, with time and space resolution, the microscopic dynamics of soft matter systems confined within millimeter-sized spherical drops. By using an ad-hoc optical layout, we tackle the challenges raised by refraction effects due to the unconventional shape of the samples. We first validate the setup by investigating the dynamics of a suspension of Brownian particles. The dynamics measured at different positions in the drop, and hence different scattering angles, are found to be in excellent agreement with those obtained for the same sample in a conventional light scattering setup. We then demonstrate the setup capabilities by investigating a bead made of a polymer hydrogel undergoing swelling. The gel microscopic dynamics exhibit a space dependence that strongly varies with time elapsed since the beginning of swelling. Initially, the dynamics in the periphery of the bead are much faster than in the core, indicative of non-uniform swelling. As the swelling proceeds, the dynamics slow down and become more spatially homogeneous. By comparing the experimental results to numerical and analytical calculations for the dynamics of a homogeneous, purely elastic sphere undergoing swelling, we establish that the mean square displacement of the gel strands deviates from the affine motion inferred from the macroscopic deformation, evolving from fast diffusive-like dynamics at the onset of swelling to slower, yet supradiffusive, rearrangements at later stages.

Predictive process monitoring is an evolving research field that studies how to train and use predictive models for operational decision-making. One of the problems studied in this field is that of predicting the sequence of upcoming activities in a case up to its completion, a.k.a. the case suffix. The prediction of case suffixes provides input to estimate short-term workloads and execution times under different resource schedules. Existing methods to address this problem often generate suffixes wherein some activities are repeated many times, whereas this pattern is not observed in the data. Closer examination shows that this shortcoming stems from the approach used to sample the successive activity instances to generate a case suffix. Accordingly, the paper introduces a sampling approach aimed at reducing repetitions of activities in the predicted case suffixes. The approach, namely Daemon action, strikes a balance between exploration and exploitation when generating the successive activity instances. We enhance a deep learning approach for case suffix predictions using this sampling approach, and experimentally show that the enhanced approach outperforms the unenhanced ones with respect to control-flow accuracy measures.

The MineRL BASALT competition has served to catalyze advances in learning from human feedback through four hard-to-specify tasks in Minecraft, such as create and photograph a waterfall. Given the completion of two years of BASALT competitions, we offer to the community a formalized benchmark through the BASALT Evaluation and Demonstrations Dataset (BEDD), which serves as a resource for algorithm development and performance assessment. BEDD consists of a collection of 26 million image-action pairs from nearly 14,000 videos of human players completing the BASALT tasks in Minecraft. It also includes over 3,000 dense pairwise human evaluations of human and algorithmic agents. These comparisons serve as a fixed, preliminary leaderboard for evaluating newly-developed algorithms. To enable this comparison, we present a streamlined codebase for benchmarking new algorithms against the leaderboard. In addition to presenting these datasets, we conduct a detailed analysis of the data from both datasets to guide algorithm development and evaluation. The released code and data are available at https://github.com/minerllabs/basalt-benchmark ., Comment: NeurIPS 2023 Datasets and Benchmarks Oral. Dataset links are available on Github: https://github.com/minerllabs/basalt-benchmark

The Nancy Grace Roman Space Telescope Coronagraph Instrument will enable the polarimetric imaging of debris disks and inner dust belts in the optical and near-infrared wavelengths, in addition to the high-contrast polarimetric imaging and spectroscopy of exoplanets. The Coronagraph uses two Wollaston prisms to produce four orthogonally polarized images and is expected to measure the polarization fraction with measurement errors < 3% per spatial resolution element. To simulate the polarization observations through the Hybrid Lyot Coronagraph (HLC) and Shaped Pupil Coronagraph (SPC), we model disk scattering, the coronagraphic point-response function, detector noise, speckles, jitter, and instrumental polarization and calculate the Stokes parameters. To illustrate the potential for discovery and a better understanding of known systems with both the HLC and SPC modes, we model the debris disks around Epsilon Eridani and HR 4796A, respectively. For Epsilon Eridani, using astrosilicates with 0.37+/-0.01 as the peak input polarization fraction in one resolution element, we recover the peak disk polarization fraction of 0.33+/-0.01. Similarly, for HR 4796A, for a peak input polarization fraction of 0.92+/-0.01, we obtain the peak output polarization fraction as 0.80+/-0.03. The Coronagraph design meets the required precision, and forward modeling is needed to accurately estimate the polarization fraction., Comment: 21 pages, 13 figures, Accepted in Publications of the Astronomical Society of the Pacific