Miracle-dz/UniFAR-FTUs huggingface dataset:
5D Interpolation and COV Migration: An Ideal Marriage

We show that the 5D minimum weighted norm interpolation algorithm can provide a very good approximation to an idealized acquisition experiment in which all traces share exactly the same offset and azimuth, or equivalently, the same inline and crossline offset vectors. Because this pure common offset vector, or “COV”, acquisition configuration gives rise to an ideal input data volume for common offset-and-azimuth Kirchhoff prestack migration, we assert that interpolation onto this COV output geometry grid produces a data volume which is optimal for several common downstream processing applications including minimization of sampling-induced migration artefacts on the stack, post-migration AVO, and post-migration AVAZ/VVAZ fracture detection. Moreover, for such applications we assert that there is no need to construct interpolated traces on a surface-consistent output grid comprising wellsampled source and receiver lines; in fact, such a procedure gives rise to a circuitous processing route in which these surface-consistent data must be subjected to a data binning operation whose sole purpose is to approximately simulate the aforementioned idealized acquisition configuration which could have been directly, and more accurately, estimated by specifying a COV target geometry within the 5D interpolation in the first place. Finally, we point out that while it is straightforward to construct a migration processing flow which accommodates this output COV target geometry , we acknowledge several practical reasons which might preclude ready adoption of this interpolation strategy and which might spur the user to instead interpolate data onto a surface-consistent grid.
Shallow turbulent wake flows: momentum and mass transfer due to large-scale coherent vortical structures

Shallow turbulent wake flows are generated by large obstacles - like islands or headlands - introduced to shallow turbulent open-channel flows. Large-scale quasi two-dimensional vortices may shed off from an obstacle, and advect downstream in a vertical shear flow with predominantly small-scale turbulence induced by bottom friction. Experimental, analytical, and numerical techniques are employed in the present study to characterize the mean flow and turbulence properties of shallow wakes; mechanisms of generation and decay of large-scale vortical structures are clarified, as is their influence on momentum and mass transport in shallow wakes; the global and local stability of shallow wakes is analyzed and evidenced from experimental data. Part I of this work covers non-intrusive optical measurement techniques especially adapted to investigate shallow shear flows. Flow velocities and mass concentrations are obtained (i) point-wise with high spatiotemporal resolution using a combinded LDV-LIF system, and (ii) field-wise using near-surface PIV and depth-averaged PCA systems with a coupling by phase-resolved averaging. Improved algorithms for the evaluation of mass concentrations are based on hydro-optical models of the underlying fluorescence and light attenuation processes. Part II addresses the time-mean description of shallow wake flows. The stochastic description of the turbulence fields displays a characteristic spectral distribution both of kinetic energy and of mass variance, which is partially consistent with the theory of unbounded 2D turbulence at large scales, and with the theory of homogeneous 3D turbulence at small scales. An integral wake model including the effect of bottom friction is derived analytically, and is validated by experimental data. The time-mean distributions of flow velocities and of mass concentration allow to identify wake near fields and far fields with specific asymptotic wake developments. Wake stability classes are associated with local stability regions suggested by linear stability analyses. Part III elaborates the structure and dynamics of quasi-periodic wake flows and the significance of large-scale eddies. This involves a structure identification scheme to educe individual vortices, and a phase-resolved averaging procedure to decompose the flow fields into large-scale coherent and small-scale turbulent parts. A Numerical Particle Tracking technique is employed to model the mass transport and to elucidate different diffusion and dispersion effects.
Learning ECG signal features without backpropagation

Representation learning has become a crucial area of research in machine learning, as it aims to discover efficient ways of representing raw data with useful features to increase the effectiveness, scope and applicability of downstream tasks such as classification and prediction. In this paper, we propose a novel method to generate representations for time series-type data. This method relies on ideas from theoretical physics to construct a compact representation in a data-driven way, and it can capture both the underlying structure of the data and task-specific information while still remaining intuitive, interpretable and verifiable. This novel methodology aims to identify linear laws that can effectively capture a shared characteristic among samples belonging to a specific class. By subsequently utilizing these laws to generate a classifier-agnostic representation in a forward manner, they become applicable in a generalized setting. We demonstrate the effectiveness of our approach on the task of ECG signal classification, achieving state-of-the-art performance.
On the creation of high spatial resolution imaging spectroscopy data from multi-temporal low spatial resolution imagery

The Hyperspectral Infrared Imager (HyspIRI) mission aims to provide global imaging spectroscopy data to the benefit of especially ecosystem studies. The onboard spectrometer will collect radiance spectra from the visible to short wave infrared (VSWIR) regions (400-2500 nm). The mission calls for fine spectral resolution (10 nm band width) and as such will enable scientists to perform material characterization, species classification, and even sub-pixel mapping. However, the global coverage requirement results in a relatively low spatial resolution (GSD 30m), which restricts applications to objects of similar scales. We therefore have focused on the assessment of sub-pixel vegetation structure from spectroscopy data in past studies. In this study, we investigate the development or reconstruction of higher spatial resolution imaging spectroscopy data via fusion of multi-temporal data sets to address the drawbacks implicit in low spatial resolution imagery. The projected temporal resolution of the HyspIRI VSWIR instrument is 15 days, which implies that we have access to as many as six data sets for an area over the course of a growth season. Previous studies have shown that select vegetation structural parameters, e.g., leaf area index (LAI) and gross ecosystem production (GEP), are relatively constant in summer and winter for temperate forests; we therefore consider the data sets collected in summer to be from a similar, stable forest structure. The first step, prior to fusion, involves registration of the multi-temporal data. A data fusion algorithm then can be applied to the pre-processed data sets. The approach hinges on an algorithm that has been widely applied to fuse RGB images. Ideally, if we have four images of a scene which all meet the following requirements - i) they are captured with the same camera configurations; ii) the pixel size of each image is x; and iii) at least r2 images are aligned on a grid of x/r - then a high-resolution image, with a pixel size of x/r, can be reconstructed from the multi-temporal set. The algorithm was applied to data from NASA's classic Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-C; GSD 18m), collected between 2013-2015 (summer and fall) over our study area (NEON's Southwest Pacific Domain; Fresno, CA) to generate higher spatial resolution imagery (GSD 9m). The reconstructed data set was validated via comparison to NEON's imaging spectrometer (NIS) data (GSD 1m). The results showed that algorithm worked well with the AVIRIS-C data and could be applied to the HyspIRI data.
On invariant holonomies between centers

We prove that for $C^{1+\theta}$, $\theta$-bunched, dynamically coherent partially hyperbolic diffeomorphisms, the stable and unstable holonomies between center leaves are $C^1$ and the derivative depends continuously on the points and on the map. Also for $C^{1+\theta}$, $\theta$-bunched partially hyperbolic diffeomorphism, the derivative cocycle restricted to the center bundle has invariant continuous holonomies which depend continuously on the map. This generalizes previous results by Pugh-Shub-Wilkinson, Burns-Wilkinson, Brown, Obata, Avila-Santamaria-Viana, Marin.
Energy optimization for Full-Duplex Wireless-Powered IoT Networks using Rotary-Wing UAV with Multiple Antennas

In this paper, we propose a novel design for the rotary-wing unmanned aerial vehicle (UAV)-enabled full-duplex (FD) wireless-powered Internet of Things (IoT) networks. In this network, the UAV is equipped with an antenna array, and the $K$ IoT sensors, which are distributed randomly, use single-antenna to communicate. By sending the energy, the UAV as a hybrid access point, charges the sensors and collects information from them. Then, to manage the time and optimize the energy, the sensors are divided into N groups, so that the UAV equipped with multi-input multi-output (MIMO) technology can serve the sensors in a group, during the total time $T$. We provide a simple implementation of the wireless power transfer protocol in the sensors by using the time division multiple access (TDMA) scheme to receive information from the users. In other words, the sensors of each group receive energy from the UAV, when it hovers over the sensors of the previous group, and also when the UAV flies over the previous group to the current group. The sensors of each group send their information to the UAV, when the UAV is hovering over their group. Under these assumptions, we formulate two optimization problems: a sum throughput maximization problem, and a total time minimization problem. Numerical results show that our proposed optimal network provides better performance than the existing networks. In fact, our novel design can serve more sensors at the cost of using more antennas compared to that of the conventional networks.
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care

The recent advances in natural language processing (NLP), have led to a new trend of applying large language models (LLMs) to real-world scenarios. While the latest LLMs are astonishingly fluent when interacting with humans, they suffer from the misinformation problem by unintentionally generating factually false statements. This can lead to harmful consequences, especially when produced within sensitive contexts, such as healthcare. Yet few previous works have focused on evaluating misinformation in the long-form (LF) generation of LLMs, especially for knowledge-intensive topics. Moreover, although LLMs have been shown to perform well in different languages, misinformation evaluation has been mostly conducted in English. To this end, we present a benchmark, CARE-MI, for evaluating LLM misinformation in: 1) a sensitive topic, specifically the maternity and infant care domain; and 2) a language other than English, namely Chinese. Most importantly, we provide an innovative paradigm for building LF generation evaluation benchmarks that can be transferred to other knowledge-intensive domains and low-resourced languages. Our proposed benchmark fills the gap between the extensive usage of LLMs and the lack of datasets for assessing the misinformation generated by these models. It contains 1,612 expert-checked questions, accompanied with human-selected references. Using our benchmark, we conduct extensive experiments and found that current Chinese LLMs are far from perfect in the topic of maternity and infant care. In an effort to minimize the reliance on human resources for performance evaluation, we offer off-the-shelf judgment models for automatically assessing the LF output of LLMs given benchmark questions. Moreover, we compare potential solutions for LF generation evaluation and provide insights for building better automated metrics.
Evaluating AI systems under uncertain ground truth: a case study in dermatology

For safety, AI systems in health undergo thorough evaluations before deployment, validating their predictions against a ground truth that is assumed certain. However, this is actually not the case and the ground truth may be uncertain. Unfortunately, this is largely ignored in standard evaluation of AI models but can have severe consequences such as overestimating the future performance. To avoid this, we measure the effects of ground truth uncertainty, which we assume decomposes into two main components: annotation uncertainty which stems from the lack of reliable annotations, and inherent uncertainty due to limited observational information. This ground truth uncertainty is ignored when estimating the ground truth by deterministically aggregating annotations, e.g., by majority voting or averaging. In contrast, we propose a framework where aggregation is done using a statistical model. Specifically, we frame aggregation of annotations as posterior inference of so-called plausibilities, representing distributions over classes in a classification setting, subject to a hyper-parameter encoding annotator reliability. Based on this model, we propose a metric for measuring annotation uncertainty and provide uncertainty-adjusted metrics for performance evaluation. We present a case study applying our framework to skin condition classification from images where annotations are provided in the form of differential diagnoses. The deterministic adjudication process called inverse rank normalization (IRN) from previous work ignores ground truth uncertainty in evaluation. Instead, we present two alternative statistical models: a probabilistic version of IRN and a Plackett-Luce-based model. We find that a large portion of the dataset exhibits significant ground truth uncertainty and standard IRN-based evaluation severely over-estimates performance without providing uncertainty estimates.
Efficient Estimation of Average Treatment Effect on the Treated under Endogenous Treatment Assignment

In this paper, we consider estimation of average treatment effect on the treated (ATT), an interpretable and relevant causal estimand to policy makers when treatment assignment is endogenous. By considering shadow variables that are unrelated to the treatment assignment but related to interested outcomes, we establish identification of the ATT. Then we focus on efficient estimation of the ATT by characterizing the geometric structure of the likelihood, deriving the semiparametric efficiency bound for ATT estimation and proposing an estimator that can achieve this bound. We rigorously establish the theoretical results of the proposed estimator. The finite sample performance of the proposed estimator is studied through comprehensive simulation studies as well as an application to our motivating study.
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs

Forward invariance is a long-studied property in control theory that is used to certify that a dynamical system stays within some pre-specified set of states for all time, and also admits robustness guarantees (e.g., the certificate holds under perturbations). We propose a general framework for training and provably certifying robust forward invariance in Neural ODEs. We apply this framework to provide certified safety in robust continuous control. To our knowledge, this is the first instance of training Neural ODE policies with such non-vacuous certified guarantees. In addition, we explore the generality of our framework by using it to certify adversarial robustness for image classification.
Cardiac computed tomography and its use in clinical practice: a review

Cardiac computed tomography (CT) is an imaging modality that can visualize the anatomy of the heart and its surroundings. The technological advancement in cardiac CT gave better accuracy with lower radiation than earlier models. Its entire procedure took a relatively short time and minimal contact. Cardiac CT is an important imaging modality in clinical practice during emergencies and non-emergency settings and in planning comprehensive management of cardiac patients. Cardiac CT is useful in emergency settings, acting as a triple rule-out imaging modality. Cardiac CT in non-emergency settings can determine coronary artery calcium (CAC) score and detect left atrial appendage (LAA) thrombus before cardioversion. Furthermore, cardiac CT is now more commonly used as a modality in planning comprehensive management of cardiac patients’ especially coronary CT angiography (CCTA) in planning for cardiac intervention. This imaging modality becomes more relevant to use in clinical practice during the pandemic due to less contact with the patient needed during the procedure, and physicians may get used to cardiac CT. This article aims to explain the advantages of CCTA in emergency and non-emergency settings and discuss its limitation so that we can make the best use of it. This writing is a literature review. The literature search using PubMed and Google Scholar.
Numerical Computation of Compressible Fluid Flows

The physical behavior of compressible fluid flow is quite different from incompressible fluid flow. Compressible fluid flows encounter discontinuities such as shocks; and their solution is complicated by the hyperbolic nature of the governing equations. In the present study the MacCormack scheme with Jameson‟s and TVD artificial viscosity has been implemented to solve the 2D Euler equation. One dimensional problems such as flows in a shock tube, Quasi 1D nozzle problems with transition from subsonic to supersonic, subsonic to subsonic flow, with and without shocks, have also been solved in the preliminary portion of the study. Test cases of external flow over a NACA 0012 airfoil for different inlet Mach numbers have been attempted and validated. An unsuccessful attempt was being made to implement variants of MacCormack scheme in explicit and semi implicit form.
M AD : Memory Allocation meets Software Diversity

—Vulnerabilities emanating from DRAM errors pose a vexing problem that remains, as of yet, unsolved and elusive but cannot be ignored. Prior defenses focused on speciﬁc details of early RowHammer attacks and fail to generalize with the generalizations of recent RowHammer attacks. Even worse, it is presently not clear that techniques from prior defenses will be able to cope with these generalizations or if an entirely new approach is required. Although still work-in-progress, we have identiﬁed a new approach that combines memory allocation with principles underlying software diversity and shows promising early results. At ﬁrst glance, software diversity seems to be an unlikely contender, since it faces seemingly insurmountable obstacles, primarily the lack of sufﬁcient entropy in memory subsystems. Our system— called M AD , short for memory allocation diversity—leverages two novel, complementary spatial diversiﬁcation techniques to overcome this entropy obstacle. Entropy aside, M AD offers ease-of-implementation, negligible performance impact, and is both hardware and software agnostic. From a security perspective, M AD ’s goal is to deter Row-Hammer attacks by delaying them to the maximum extent possible. Such a delay opens the door for a variety of additional responses, e.g., proactive rebooting, or complementary in-depth analysis of ongoing attacks that would be too slow for an always-on defense.
An optimization framework for analyzing nonlinear stability due to sparse finite-amplitude perturbations

Recent works have established the utility of sparsity-promoting norms for extracting spatially-localized instability mechanisms in fluid flows, with possible implications for flow control. However, these prior works have focused on linear dynamics of infinitesimal perturbations about a given baseflow. In this paper, we propose an optimization framework for computing sparse finite-amplitude perturbations that maximize transient growth in nonlinear systems. A variational approach is used to derive the first-order necessary conditions for optimality, which form the basis of our iterative direct-adjoint looping numerical solution algorithm. When applied to a reduced-order model of a sinusoidal shear flow at $Re=20$, our framework identifies that energy injection into a single vortical mode yields comparable energy amplification as the non-sparse optimal solution with energy distributed across all modes. Energy injection into three additional modes results in an identical transient growth as the non-sparse case. Subsequent analysis of the dynamic response of the flow establishes that these sparse optimal perturbations trigger many of the same nonlinear modal interactions that give rise to transient growth when all modes are perturbed in an optimal manner. It is also observed that as perturbation amplitude is increased, the maximum transient growth is achieved at an earlier time. Our results highlight the power of the proposed optimization framework for revealing dominant nonlinear modal interactions and sparse perturbation mechanisms for transient growth and instability in fluid flows. We anticipate the approach will be a useful tool in guiding the design of flow control strategies in the future.
A new methodology to structure a nutrition educational course: the results of a short-term educational-intervention pilot study

Abstract The incidence of malnutrition in developing countries, such as Kenya, is still high. The most recent international reports outline an increase in the prevalence of overnutrition in Sub-Saharan Africa, where undernutrition and hidden hunger conditions still represent an emergency. Among the factors influencing malnutrition conditions connected to dietary intake and eating habits, evidence shows the importance of food knowledge. Traditionally, in developing countries, women represent the pillar of the food system and impact their children's nutrition: food-based, women-centred strategies are recommended to address nutrient gaps, educate and empower women, and counteract malnutrition. The short communication presents the results of the ACTION project: an educational-intervention pilot study targeting childbearing-age women living in rural areas of Southern Kenya. To evaluate the feasibility of the educational course built, the authors administered to the women a food knowledge questionnaire, before and after the intervention. Study results describe an increase in women's food knowledge after attending the food education course. This encourages to increase education courses which can represent a starting point in improving food habits with a positive effect on the health condition in developing counties too.