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Human pose estimation involves detecting and tracking the positions of various body parts using input data from sources such as images, videos, or motion and inertial sensors. This paper presents a novel approach to human pose estimation using machine learning algorithms to predict human posture and translate them into robot motion commands using ultra-wideband (UWB) nodes, as an alternative to motion sensors. The study utilizes five UWB sensors implemented on the human body to enable the classification of still poses and more robust posture recognition. This approach ensures effective posture recognition across a variety of subjects. These range measurements serve as input features for posture prediction models, which are implemented and compared for accuracy. For this purpose, machine learning algorithms including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and deep Multi-Layer Perceptron (MLP) neural network are employed and compared in predicting corresponding postures. We demonstrate the proposed approach for real-time control of different mobile/aerial robots with inference implemented in a ROS 2 node. Experimental results demonstrate the efficacy of the approach, showcasing successful prediction of human posture and corresponding robot movements with high accuracy.

Recent breakthroughs in deep learning and generative systems have significantly fostered the creation of synthetic media, as well as the local alteration of real content via the insertion of highly realistic synthetic manipulations. Local image manipulation, in particular, poses serious challenges to the integrity of digital content and societal trust. This problem is not only confined to multimedia data, but also extends to biological images included in scientific publications, like images depicting Western blots. In this work, we address the task of localizing synthetic manipulations in Western blot images. To discriminate between pristine and synthetic pixels of an analyzed image, we propose a synthetic detector that operates on small patches extracted from the image. We aggregate patch contributions to estimate a tampering heatmap, highlighting synthetic pixels out of pristine ones. Our methodology proves effective when tested over two manipulated Western blot image datasets, one altered automatically and the other manually by exploiting advanced AI-based image manipulation tools that are unknown at our training stage. We also explore the robustness of our method over an external dataset of other scientific images depicting different semantics, manipulated through unseen generation techniques.

This paper presents an online method for joint channel estimation and decoding in massive MIMO-OFDM systems using complex-valued neural networks (CVNNs). The study evaluates the performance of various CVNNs, such as the complex-valued feedforward neural network (CVFNN), split-complex feedforward neural network (SCFNN), complex radial basis function (C-RBF), fully-complex radial basis function (FC-RBF) and phase-transmittance radial basis function (PT-RBF), in realistic 5G communication scenarios. Results demonstrate improvements in mean squared error (MSE), convergence, and bit error rate (BER) accuracy. The C-RBF and PT-RBF architectures show the most promising outcomes, suggesting that RBF-based CVNNs provide a reliable and efficient solution for complex and noisy communication environments. These findings have potential implications for applying advanced neural network techniques in next-generation wireless systems., Comment: XLII Simp\'osio Brasileiro de Telecomunica\c{c}\~oes e Processamento de Sinais (SBrT 2024)

This paper provides an analytical framework to incorporate the deployment of behind-the-meter energy storage coupled with rooftop solar, and their associated revenue streams, in the context of equitable energy policy interventions. We propose an extension to the Justice40 optimization model by adding storage and incorporating more realistic solar compensation mechanisms, such as net-billing, which allows for temporal revenue differentiation and the economic viability of behind-the-meter energy storage devices. The extended model includes household-level PV plus storage co-deployment alongside existing interventions, such as weatherization, rooftop PV only, community solar, and community wind. From a modeling perspective, we propose a novel approximation method to represent storage operations and revenue streams without expanding the temporal dimension of model, thus maintaining its computational efficiency. The proposed model is validated using a case study in Wayne County, Michigan, involving 3,651 energy insecure households.

Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke applications that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio's flexibility in tackling pressing societal challenges. Curio is available at https://urbantk.org/curio., Comment: Accepted at IEEE VIS 2024. Source code available at https://urbantk.org/curio

We characterize the complexity functions of subshifts up to asymptotic equivalence. The complexity function of every aperiodic function is non-decreasing, submultiplicative and grows at least linearly. We prove that conversely, every function satisfying these conditions is asymptotically equivalent to the complexity function of a recurrent subshift, equivalently, a recurrent infinite word. Our construction is explicit, algorithmic in nature and is philosophically based on constructing certain 'Cantor sets of integers', whose 'gaps' correspond to blocks of zeros. We also prove that every non-decreasing submultiplicative function is asymptotically equivalent, up a linear error term, to the complexity function of a minimal subshift.

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

The structural and electronic properties of the CaMnGe$_2$O$_6$ and SrMnGe$_2$O$_6$ clinopyroxene systems have been investigated by means of perturbed angular correlation (PAC) measurements, performed at ISOLDE, combined with $ab-initio$ electronic structure calculations within the density functional theory (DFT) framework. The partial density of states (PDOS) of the CaMnGe$_2$O$_6$ and SrMnGe$_2$O$_6$ stable compounds has been determined, and it has been observed that the requirement of including an on-site Hubbard-$U$ potential was necessary in order to describe the highly correlated Mn $3d$-states. By considering $U_{eff}$=4 eV, we obtained a band gap width of 1.82 eV and 1.70 eV, for the CaMnGe$_2$O$_6$ and SrMnGe$_2$O$_6$, respectively. Combining electric field gradient (EFG) first principles calculations, using a supercell scheme, with experimental PAC results, we were able to infer that the Cd probe can replace either the $A$ (Ca, Sr) or the Mn sites in the crystalline structures. We also showed that Cd substitution is expected to lead to a reduction in the width of the band gap in these systems, evidencing opportunities for potential band-gap engineering.

Understanding the quantum nature of the gravitational field is undoubtedly one of the greatest challenges in theoretical physics. Despite significant progress, a complete and consistent theory remains elusive. However, in the weak field approximation -- where curvature effects are small -- we can explore some expected properties of such a theory. Particularly relevant to this study is the quantum nature of gravitational waves, which are represented as small perturbations in flat spacetime. In this framework, a quantum description of these perturbations, as a quantum field, is feasible, leading to the emergence of the graviton. Here we consider a non-relativistic quantum system interacting with such a field. We employ the consistent histories approach to quantum mechanics, which allows us to frame classical questions in a quantum context, to define a fluctuation relation for this system. As a result, thermodynamic entropy must be produced in the system due to its unavoidable interaction with the quantum fluctuations of spacetime., Comment: Comments are welcome

Text embedding models have been popular for information retrieval applications such as semantic search and Question-Answering systems based on Retrieval-Augmented Generation (RAG). Those models are typically Transformer models that are fine-tuned with contrastive learning objectives. Many papers introduced new embedding model architectures and training approaches, however, one of the key ingredients, the process of mining negative passages, remains poorly explored or described. One of the challenging aspects of fine-tuning embedding models is the selection of high quality hard-negative passages for contrastive learning. In this paper we propose a family of positive-aware mining methods that leverage the positive relevance score for more effective false negatives removal. We also provide a comprehensive ablation study on hard-negative mining methods over their configurations, exploring different teacher and base models. We demonstrate the efficacy of our proposed methods by introducing the NV-Retriever-v1 model, which scores 60.9 on MTEB Retrieval (BEIR) benchmark and 0.65 points higher than previous methods. The model placed 1st when it was published to MTEB Retrieval on July 07, 2024.

Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.

We investigate the dynamics of fermion localization within the framework of $f(T, B)$ gravity featuring non-minimal couplings. Starting from the Dirac action for a spin-$1/2$ fermion in a five-dimensional spacetime governed by torsional $f(T, B)$ gravity, we derive the Dirac equation and we explore its solutions under various non-minimal coupling functions. We examine two realistic forms of the torsional non-minimal coupling and reveal distinct behaviors that impact the localization of both massless and massive fermionic modes on the brane. Additionally, we employ probabilistic measurements, including Shannon entropy theory, Fisher information theory, and relative probability, to analyze the localization of these fermionic modes. The observed effects offer potential insights into probing torsional modifications.

Scoring models support decision-making in financial institutions. Their estimation and evaluation are based on the data of previously accepted applicants with known repayment behavior. This creates sampling bias: the available labeled data offers a partial picture of the distribution of candidate borrowers, which the model is supposed to score. The paper addresses the adverse effect of sampling bias on model training and evaluation. To improve scorecard training, we propose bias-aware self-learning - a reject inference framework that augments the biased training data by inferring labels for selected rejected applications. For scorecard evaluation, we propose a Bayesian framework that extends standard accuracy measures to the biased setting and provides a reliable estimate of future scorecard performance. Extensive experiments on synthetic and real-world data confirm the superiority of our propositions over various benchmarks in predictive performance and profitability. By sensitivity analysis, we also identify boundary conditions affecting their performance. Notably, we leverage real-world data from a randomized controlled trial to assess the novel methodologies on holdout data that represent the true borrower population. Our findings confirm that reject inference is a difficult problem with modest potential to improve scorecard performance. Addressing sampling bias during scorecard evaluation is a much more promising route to improve scoring practices. For example, our results suggest a profit improvement of about eight percent, when using Bayesian evaluation to decide on acceptance rates.

Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks have been extensively studied in unstructured data like images, applying them to tabular data, poses new challenges. These challenges arise from the inherent heterogeneity and complex feature interdependencies in tabular data, which differ from the image data. To account for this distinction, it is necessary to establish tailored imperceptibility criteria specific to tabular data. However, there is currently a lack of standardised metrics for assessing the imperceptibility of adversarial attacks on tabular data. To address this gap, we propose a set of key properties and corresponding metrics designed to comprehensively characterise imperceptible adversarial attacks on tabular data. These are: proximity to the original input, sparsity of altered features, deviation from the original data distribution, sensitivity in perturbing features with narrow distribution, immutability of certain features that should remain unchanged, feasibility of specific feature values that should not go beyond valid practical ranges, and feature interdependencies capturing complex relationships between data attributes. We evaluate the imperceptibility of five adversarial attacks, including both bounded attacks and unbounded attacks, on tabular data using the proposed imperceptibility metrics. The results reveal a trade-off between the imperceptibility and effectiveness of these attacks. The study also identifies limitations in current attack algorithms, offering insights that can guide future research in the area. The findings gained from this empirical analysis provide valuable direction for enhancing the design of adversarial attack algorithms, thereby advancing adversarial machine learning on tabular data., Comment: 33 pages

We address partition regularity problems for homogeneous quadratic equations. A consequence of our main results is that, under natural conditions on the coefficients $a,b,c$, for any finite coloring of the positive integers, there exists a solution to $ax^2+by^2=cz^2$ where $x$ and $y$ have the same color (and similar results for $x,z$ and $y,z$). For certain choices of $(a,b,c)$, our result is conditional on an Elliott-type conjecture. Our proofs build on and extend previous arguments of the authors dealing with the Pythagorean equation. We make use of new uniformity properties of aperiodic multiplicative functions and concentration estimates for multiplicative functions along arbitrary binary quadratic forms., Comment: 49 pages. Small changes made

X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating comprehensive clinical features and data integration. We present a novel technique to enhance the clinical context through augmentation techniques with clinical tabular data, thereby improving its applicability and reliability in AI medical diagnostics. To address this, we introduce a pioneering approach to clinical data augmentation that employs large language models (LLMs) to generate patient contextual synthetic data. This methodology is crucial for training more robust deep learning models in healthcare. It preserves the integrity of real patient data while enriching the dataset with contextually relevant synthetic features, significantly enhancing model performance. DALL-M uses a three-phase feature generation process: (i) clinical context storage, (ii) expert query generation, and (iii) context-aware feature augmentation. DALL-M generates new, clinically relevant features by synthesizing chest X-ray images and reports. Applied to 799 cases using nine features from the MIMIC-IV dataset, it created an augmented set of 91 features. This is the first work to generate contextual values for existing and new features based on patients' X-ray reports, gender, and age and to produce new contextual knowledge during data augmentation. Empirical validation with machine learning models, including Decision Trees, Random Forests, XGBoost, and TabNET, showed significant performance improvements. Incorporating augmented features increased the F1 score by 16.5% and Precision and Recall by approximately 25%. DALL-M addresses a critical gap in clinical data augmentation, offering a robust framework for generating contextually enriched datasets., Comment: we introduce a pioneering approach to clinical data augmentation that employs large language models (LLMs) to generate patient contextual synthetic data. It preserves the integrity of real patient data while enriching the dataset with contextually relevant synthetic features, significantly enhancing model performance

We investigate non-equilibrium transport of charge and heat through an interacting quantum dot coupled to a finite electron reservoir. Both the quantum dot and the finite reservoir are coupled to conventional electric contacts, i.e., infinite electron reservoirs, between which a bias voltage can be applied. We develop a phenomenological description of the system, combining a rate equation for transport through the quantum dot with standard linear response expressions for transport between the finite and infinite reservoirs. The finite reservoir is assumed to be in a quasi-equilibrium state with time-dependent chemical potential and temperature which we solve for self-consistently. We show that the finite reservoir can have a large impact on the stationary state transport properties, including a shift and broadening of the Coulomb diamond edges. We also demonstrate that there is a region around the conductance lines where a heat current flows out of the finite reservoir. Our results reveal the dependence of the temperature that can be reached by this thermoelectric cooling on the system parameters, in particular the coupling between the finite and infinite reservoirs and additional heat currents induced by electron-phonon couplings, and can thus serve as a guide to experiments on quantum dot-enabled thermoelectric cooling of finite electron reservoirs. Finally, we study the full dynamics of the system, with a particular focus on the timescales involved in the thermoelectric cooling., Comment: 6+2 pages, 4 figures

Time-lapse full-waveform inversion (FWI) has become a powerful tool for characterizing and monitoring subsurface changes in various geophysical applications. However, non-repeatability (NR) issues caused, for instance, by GPS inaccuracies, often make it difficult to obtain unbiased time-lapse models. In this work we explore the portability of combining a receiver-extension FWI approach and Bayesian analysis to mitigate time-lapse noises arising from NR issues. The receiver-extension scheme introduces an artificial degree of freedom in positioning receivers, intending to minimize kinematic mismatches between modeled and observed data. Bayesian analysis systematically explores several potential solutions to mitigate time-lapse changes not associated with reservoir responses, assigning probabilities to each scenario based on prior information and available evidence. We consider two different subsurface models to demonstrate the potential of proposed approaches. First, using the Marmousi model, we investigate two NR scenarios associated with background noise in seismic data. Second, using a challenging deep-water Brazilian pre-salt setting, we investigate several NR scenarios to simulate real-world challenges. Our results demonstrate that combining Bayesian analysis with the receiver-extension FWI strategy can mitigate adverse NR effects successfully, producing cleaner and more reliable time-lapse models than conventional approaches. The results also reveal that the proposed Bayesian weighted procedure is a valuable tool for determining time-lapse estimates through statistical analysis of pre-existing models, allowing its application in ongoing time-lapse (4D) projects., Comment: 27 pages, 16 figures. Submitted as a Journal Paper to IEEE Transactions on Geoscience and Remote Sensing

To address the need of coverage enhancement in the fifth generation (5G) of wireless cellular telecommunications, while taking into account possible bottlenecks related to deploying fiber based backhaul (e.g., required cost and time), the 3rd generation partnership project (3GPP) proposed in Release 18 the concept of network-controlled repeaters (NCRs). NCRs enhance previous radio frequency (RF) repeaters by exploring beamforming transmissions controlled by the network through side control information. In this context, this paper introduces the concept of NCR. Furthermore, we present a system level model that allows the performance evaluation of an NCR-assisted network. Finally, we evaluate the network deployment impact on the performance of NCR-assisted networks. As we show, with proper network planning, NCRs can boost the signal to interference-plus-noise ratio (SINR) of the user equipments (UEs) in a poor coverage of a macro base station. Furthermore, celledge UEs and uplink (UL) communications are the ones that benefit the most from the presence of NCRs., Comment: Paper accepted for publication in the conference proceedings of "19th International Symposium on Wireless Communication Systems" (ISWCS)

Education for sustainable development has evolved to include more constructive approaches and a better understanding of what is needed to align education with the cultural, societal, and pedagogical changes required to avoid the risks posed by an unsustainable society. This evolution aims to lead us toward viable, equitable, and sustainable futures. However, computing education, including software engineering, is not fully aligned with the current understanding of what is needed for transformational learning in light of our current challenges. This is partly because computing is primarily seen as a technical field, focused on industry needs. Until recently, sustainability was not a high priority for most businesses, including the digital sector, nor was it a prominent focus for higher education institutions and society. Given these challenges, we aim to propose a research roadmap to integrate sustainability principles and essential skills into the crowded computing curriculum, nurturing future software engineering professionals with a sustainability mindset. We conducted two extensive studies: a systematic review of academic literature on sustainability in computing education and a survey of industry professionals on their interest in sustainability and desired skills for graduates. Using insights from these studies, we identified key topics for teaching sustainability, including core sustainability principles, values and ethics, systems thinking, impact measurement, soft skills, business value, legal standards, and advocacy. Based on these findings, we will develop recommendations for future computing education programs that emphasise sustainability. The paper is accepted at the 2030 Software Engineering workshop, which is co-located with the FSE'24 conference.

We establish a theory for multivariate extreme value analysis of dynamical systems. Namely, we provide conditions adapted to the dynamical setting which enable the study of dependence between extreme values of the components of $\R^d$-valued observables evaluated along the orbits of the systems. We study this cross-sectional dependence, which results from the combination of a spatial and a temporal dependence structures. We give several illustrative applications, where concrete systems and dependence sources are introduced and analysed.

Context. The scale height of the spatial distribution of open clusters (OCs) in the Milky Way exhibits a well known increase with age which is usually interpreted as evidence for dynamical heating of the disc or of the disc having been thicker in the past. Aims. We address the increase of the scale height with age of the OC population from a different angle. We propose that the apparent thickening of the disc can be largely explained as a consequence of a stronger disruption of OCs near the Galactic plane by disc phenomena, namely encounters with giant molecular clouds (GMCs). Methods. We present a computational model that forms OCs with different initial masses and follows their orbits while subjecting them to different disruption mechanisms. To setup the model and infer its parameters, we use and analyse a Gaia-based OC catalogue (Dias et al. 2021). We investigate both the spatial and age distributions of the OC population and discuss the completeness of the sample. The simulation results are then compared to the observations. Results. Consistent with previous studies, the observations reveal that the SH of the spatial distribution of OCs increases with age. We find that it is very likely that the OC sample is incomplete even for the solar neighbourhood. The model simulations successfully reproduce the SH increase with age and the total number of OCs that survive with age up to 1 Gyr. For older OCs, the predicted SH from the model starts deviating from the observations, although remaining within the uncertainties of the observations. This can be related with effects of incompleteness and/or simplifications in the model. Conclusions. A selective disruption of OCs near the galactic plane through GMC encounters is able to explain the SH evolution of the OC population., Comment: This paper has been submitted to Astronomy and Astrophysics (A&A) on 15/04/2024

Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases. This is particularly crucial for drug names, where patients often use brand names like Advil or Tylenol instead of their generic equivalents. To study this, we create a new robustness dataset, RABBITS, to evaluate performance differences on medical benchmarks after swapping brand and generic drug names using physician expert annotations. We assess both open-source and API-based LLMs on MedQA and MedMCQA, revealing a consistent performance drop ranging from 1-10\%. Furthermore, we identify a potential source of this fragility as the contamination of test data in widely used pre-training datasets. All code is accessible at https://github.com/BittermanLab/RABBITS, and a HuggingFace leaderboard is available at https://huggingface.co/spaces/AIM-Harvard/rabbits-leaderboard., Comment: submitted for review, total 15 pages

Telepresence VR systems allow for face-to-face communication, promoting the feeling of presence and understanding of nonverbal cues. However, when discussing virtual 3D objects, limitations to presence and communication cause deictic gestures to lose meaning due to disparities in orientation. Current approaches use shared perspective, and avatar overlap to restore these references, which cause occlusions and discomfort that worsen when multiple users participate. We introduce a new approach to shared perspective in multi-user collaboration where the avatars are not co-located. Each person sees the others' avatars at their positions around the workspace while having a first-person view of the workspace. Whenever a user manipulates an object, others will see his/her arms stretching to reach that object in their perspective. SPARC combines a shared orientation and supports nonverbal communication, minimizing occlusions. We conducted a user study (n=18) to understand how the novel approach impacts task performance and workspace awareness. We found evidence that SPARC is more efficient and less mentally demanding than life-like settings., Comment: 14 pages 8 figures

Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems. However, they require vast amounts of ground truth depth data. Recent research has tackled this limitation using self-supervised learning techniques, but it requires multiple RGB-D sensors. Moreover, most existing approaches focus on denoising single isolated depth maps or specific subjects of interest, highlighting a need for methods to effectively denoise depth maps in real-time dynamic environments. This paper extends state-of-the-art approaches for depth-denoising commodity depth devices, proposing SelfReDepth, a self-supervised deep learning technique for depth restoration, via denoising and hole-filling by inpainting full-depth maps captured with RGB-D sensors. The algorithm targets depth data in video streams, utilizing multiple sequential depth frames coupled with color data to achieve high-quality depth videos with temporal coherence. Finally, SelfReDepth is designed to be compatible with various RGB-D sensors and usable in real-time scenarios as a pre-processing step before applying other depth-dependent algorithms. Our results demonstrate our approach's real-time performance on real-world datasets. They show that it outperforms state-of-the-art denoising and restoration performance at over 30fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications., Comment: 13pp, 5 figures, 1 table

RGB-D sensors face multiple challenges operating under open-field environments because of their sensitivity to external perturbations such as radiation or rain. Multiple works are approaching the challenge of perceiving the 3D position of objects using monocular cameras. However, most of these works focus mainly on deep learning-based solutions, which are complex, data-driven, and difficult to predict. So, we aim to approach the problem of predicting the 3D objects' position using a Gaussian viewpoint estimator named best viewpoint estimator (BVE) powered by an extended Kalman filter (EKF). The algorithm proved efficient on the tasks and reached a maximum average Euclidean error of about 32 mm. The experiments were deployed and evaluated in MATLAB using artificial Gaussian noise. Future work aims to implement the system in a robotic system.

This paper proposes Bayesian Adaptive Trials (BAT) as both an efficient method to conduct trials and a unifying framework for evaluation social policy interventions, addressing limitations inherent in traditional methods such as Randomized Controlled Trials (RCT). Recognizing the crucial need for evidence-based approaches in public policy, the proposal aims to lower barriers to the adoption of evidence-based methods and align evaluation processes more closely with the dynamic nature of policy cycles. BATs, grounded in decision theory, offer a dynamic, ``learning as we go'' approach, enabling the integration of diverse information types and facilitating a continuous, iterative process of policy evaluation. BATs' adaptive nature is particularly advantageous in policy settings, allowing for more timely and context-sensitive decisions. Moreover, BATs' ability to value potential future information sources positions it as an optimal strategy for sequential data acquisition during policy implementation. While acknowledging the assumptions and models intrinsic to BATs, such as prior distributions and likelihood functions, the paper argues that these are advantageous for decision-makers in social policy, effectively merging the best features of various methodologies.

Let $L$ and $M$ denote the classical Lagrange and Markov spectra, respectively. It is known that $L\subset M$ and that $M\setminus L\neq\varnothing$. Inspired by three questions asked by the third author in previous work investigating the fractal geometric properties of the Lagrange and Markov spectra, we investigate the function $d_{loc}(t)$ that gives the local Hausdorff dimension at a point $t$ of $L'$. Specifically, we construct several intervals (having non-trivial intersection with $L'$) on which $d_{loc}$ is non-decreasing. We also prove that the respective intersections of $M'$ and $M''$ with these intervals coincide. Furthermore, we completely characterize the local dimension of both spectra when restricted to those intervals. Finally, we demonstrate the largest known elements of the difference set $M\setminus L$ and describe two new maximal gaps of $M$ nearby., Comment: 51 pages, 3 figures

We exploit the geometry of magnetic nanowires, which define 1D contacts to an encapsulated graphene channel, to introduce an out-of-plane component in the polarisation of spin carriers. By design, the magnetic nanowires traverse the angled sides of the 2D material heterostructure. Consequently, the easy axis of the nanowires is inclined, and so the local magnetisation is oblique at the injection point. As a result, when performing non-local spin valve measurements we simultaneously observe both switching and spin precession phenomena, implying the spin population possesses both in-plane and out-of-plane polarisation components. By comparing the relative magnitudes of these components, we quantify the angle of the total spin polarisation vector. The extracted angle is consistent with the angle of the nanowire at the graphene interface, evidencing that the effect is a consequence of the device geometry. This simple method of spin-based vector magnetometry provides an alternative technique to define the spin polarisation in 2D spintronic devices.

A cornerstone of geometric reconstruction, rotation averaging seeks the set of absolute rotations that optimally explains a set of measured relative orientations between them. In addition to being an integral part of bundle adjustment and structure-from-motion, the problem of synchronizing rotations also finds applications in visual simultaneous localization and mapping, where it is used as an initialization for iterative solvers, and camera network calibration. Nevertheless, this optimization problem is both non-convex and high-dimensional. In this paper, we address it from a maximum likelihood estimation standpoint and make a twofold contribution. Firstly, we set forth a novel primal-dual method, motivated by the widely accepted spectral initialization. Further, we characterize stationary points of rotation averaging in cycle graphs topologies and contextualize this result within spectral graph theory. We benchmark the proposed method in multiple settings and certify our solution via duality theory, achieving a significant gain in precision and performance., Comment: arXiv admin note: text overlap with arXiv:2109.08046

We develop a workflow based on full-waveform inversion (FWI) to estimate P-wave velocities in a deepwater Brazilian pre-salt field using the recently introduced circular shot ocean bottom node (OBN) acquisition geometry. Such a geometry comprises a source vessel sailing in large radius concentric circular trajectories and seismic signals are recorded by OBN arrays. The circular shot OBN survey provides mostly refracted waves separately from reflected waves, so the FWI process is mainly driven by diving waves. We introduce a new FWI workflow to analyze non-preprocessed OBN refraction data, which includes automated steps such as data selection solving an Eikonal equation, estimation of a source signature that accounts for ghost and bubble effects, and gradient preconditioning using a non-stationary filter and seismic illumination. We consider two objective functions based on the $L^1$ and $L^2$ norms. The FWI results demonstrated that using our proposed workflow with the $L^1$ norm objective function and the circular OBN survey can lead to an improvement in pre-salt velocity models. Furthermore, using these improved models we construct reverse-time migration (RTM) images of the conventional OBN dataset, showing significant improvements in the salt stratification, the base of salt, and the lateral resolution of the pre-salt area. The Brazilian pre-salt case study demonstrated that the circular shot OBN acquisition maximizes the illumination of deep reservoirs through the ultra-long offset and full-azimuth coverage that prioritizes the recording of diving waves., Comment: 25 pages, 18 figures. Submitted as a Journal Paper to IEEE Transactions on Geoscience and Remote Sensing

In slowly driven classical systems, work is a stochastic quantity and its probability distribution is known to satisfy the work fluctuation-dissipation relation, which states that the mean and variance of the dissipated work are linearly related. Recently, it was shown that generation of quantum coherence in the instantaneous energy eigenbasis leads to a correction to this linear relation in the slow-driving regime. Here, we go even further by investigating nonclassical features of work fluctuations in setups with more than one system. To do this, we first generalize slow control protocols to encompass multipartite systems, allowing for the generation of quantum correlations during the driving process. Then, focussing on two-qubit systems, we show that entanglement generation leads to a positive contribution to the dissipated work, which is distinct from the quantum correction due to local coherence generation known from previous work. Our results show that entanglement generated during slow control protocols, e.g. as an unavoidable consequence of qubit crosstalk, comes at the cost of increased dissipation.

In this work, we investigate the behavior of non-relativistic quantum particles immersed in a cosmic string space-time background. Our study involves the examination of these particles as they interact with a range of influences, including potential, magnetic, and quantum flux fields. We employ analytical methods to solve the associated wave equation, leading to the derivation of eigenvalue solutions for this quantum system. Subsequently, we leverage these eigenvalue solutions to scrutinize several potential models. For each model, we present and engage in a thorough discussion of the corresponding eigenvalue solutions. In an extension of our investigation, we explore the thermodynamic and magnetic properties of the quantum system when it is exposed to non-zero temperature conditions, denoted by $T \neq 0$. Our analysis encompasses the calculation of essential parameters such as the partition function for the system and other pertinent functions. Following these calculations, we meticulously examine and interpret the outcomes, shedding light on the system's behavior and characteristics in the presence of temperature variations. Furthermore, we calculate entropic information to investigate the location of particles in the system., Comment: 20 pages, 6 figures, 3 tables, accepted in IJGMMP (https://doi.org/10.1142/S0219887824502128)

Learning representations that capture rich semantic relationships and accommodate propositional calculus poses a significant challenge. Existing approaches are either contrastive, lacking theoretical guarantees, or fall short in effectively representing the partial orders inherent to rich visual-semantic hierarchies. In this paper, we propose a novel approach for learning visual representations that not only conform to a specified semantic structure but also facilitate probabilistic propositional reasoning. Our approach is based on a new nuclear norm-based loss. We show that its minimum encodes the spectral geometry of the semantics in a subspace lattice, where logical propositions can be represented by projection operators.

This study delves deeply into braneworld scenarios within modified gravity models, investigating their impact on particle localization and the structure of branes. Through a comprehensive blend of numerical analyses and theoretical inquiries, we unravel a nuanced correlation between deviations from standard General Relativity (GR) and the emergence of split branes. By employing probabilistic measurements, we pinpoint stable configurations that align with brane division intervals, thus challenging prevailing assumptions regarding the gravitational framework of our universe. Furthermore, our investigation extends to the localization of fermions within the brane, exposing intricate dynamics shaped by scalar field characteristics and modifications to gravitational models. By harnessing quantum information measurements, notably Shannon entropy, we discern heightened probabilities of fermion localization within the brane as gravitational models diverge from standard paradigms. This underscores the limitations of General Relativity in comprehensively describing the complexities inherent in our universe. Lastly, our exploration of massive fermions unveils their potential to breach the confines of the brane, hinting at promising avenues for future experimental endeavors aimed at probing the nature of extra dimensions and gravitational interactions. This suggests exciting prospects for advancing our understanding of fundamental physics beyond conventional boundaries.

The Skyrmion Hall Effect (SkHE) observed in ferromagnetic (FM) and ferrimagnetic (FI) skyrmions traveling due to a spin-polarized current can be a problematic issue when it comes to technological applications. By investigating the properties of FI skyrmions in racetracks through computational simulations, we have described the nature of their movement based on the relative values of the exchange, Dzyaloshinskii-Moriya, and anisotropy coupling constants. Beyond that, using a design strategy, a magnetic channel-like nano-device is proposed in which a spin-polarized current protocol is created to successfully control the channel on which the skyrmion will travel without the adverse SkHE. Additionally, a simple adjustment in the current strength can modify the skyrmion position sideways between different parallel channels in the nanostripe., Comment: 10 pages, 9 figures

Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data. In this study, we introduce Cross-Care, the first benchmark framework dedicated to assessing biases and real world knowledge in LLMs, specifically focusing on the representation of disease prevalence across diverse demographic groups. We systematically evaluate how demographic biases embedded in pre-training corpora like $ThePile$ influence the outputs of LLMs. We expose and quantify discrepancies by juxtaposing these biases against actual disease prevalences in various U.S. demographic groups. Our results highlight substantial misalignment between LLM representation of disease prevalence and real disease prevalence rates across demographic subgroups, indicating a pronounced risk of bias propagation and a lack of real-world grounding for medical applications of LLMs. Furthermore, we observe that various alignment methods minimally resolve inconsistencies in the models' representation of disease prevalence across different languages. For further exploration and analysis, we make all data and a data visualization tool available at: www.crosscare.net., Comment: Submitted for review, data visualization tool available at: www.crosscare.net

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.