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The notion of asymptotic unpredictability was recently introduced in (Commun. Nonlinear Sci. Numer. Simul. 134, 108029, 2024) for semiflows. Likewise unpredictable trajectories, asymptotically unpredictable ones are also capable of producing sensitivity in a dynamics, which is an indispensable feature of chaos. In the present study, we newly propose piecewise continuous asymptotically unpredictable functions, and investigate the existence and uniqueness of such solutions in a quasilinear impulsive system of differential equations comprising a term which is periodic in the time argument. The class of functions and the impulsive system under discussion admit regular discontinuity moments. Some techniques for obtaining discontinuous asymptotically unpredictable functions are additionally provided. Even though piecewise continuous unpredictable functions are asymptotically unpredictable, it is demonstrated that the converse is not true. In other words, the set of discontinuous unpredictable functions is properly contained in the set of asymptotically unpredictable ones. Appropriate examples are given with regard to all theoretical results., Comment: 19 pages

We reframe scene flow as the problem of estimating a continuous space and time PDE that describes motion for an entire observation sequence, represented with a neural prior. Our resulting unsupervised method, EulerFlow, produces high quality scene flow on real-world data across multiple domains, including large-scale autonomous driving scenes and dynamic tabletop settings. Notably, EulerFlow produces high quality flow on small, fast moving objects like birds and tennis balls, and exhibits emergent 3D point tracking behavior by solving its estimated PDE over long time horizons. On the Argoverse 2 2024 Scene Flow Challenge, EulerFlow outperforms all prior art, beating the next best unsupervised method by over 2.5x and the next best supervised method by over 10%., Comment: Project page at https://vedder.io/eulerflow

Active learning (AL) is a widely used technique for optimizing data labeling in machine learning by iteratively selecting, labeling, and training on the most informative data. However, its integration with formal privacy-preserving methods, particularly differential privacy (DP), remains largely underexplored. While some works have explored differentially private AL for specialized scenarios like online learning, the fundamental challenge of combining AL with DP in standard learning settings has remained unaddressed, severely limiting AL's applicability in privacy-sensitive domains. This work addresses this gap by introducing differentially private active learning (DP-AL) for standard learning settings. We demonstrate that naively integrating DP-SGD training into AL presents substantial challenges in privacy budget allocation and data utilization. To overcome these challenges, we propose step amplification, which leverages individual sampling probabilities in batch creation to maximize data point participation in training steps, thus optimizing data utilization. Additionally, we investigate the effectiveness of various acquisition functions for data selection under privacy constraints, revealing that many commonly used functions become impractical. Our experiments on vision and natural language processing tasks show that DP-AL can improve performance for specific datasets and model architectures. However, our findings also highlight the limitations of AL in privacy-constrained environments, emphasizing the trade-offs between privacy, model accuracy, and data selection accuracy.

Deep learning models often function as black boxes, providing no straightforward reasoning for their predictions. This is particularly true for computer vision models, which process tensors of pixel values to generate outcomes in tasks such as image classification and object detection. To elucidate the reasoning of these models, class activation maps (CAMs) are used to highlight salient regions that influence a model's output. This research introduces KPCA-CAM, a technique designed to enhance the interpretability of Convolutional Neural Networks (CNNs) through improved class activation maps. KPCA-CAM leverages Principal Component Analysis (PCA) with the kernel trick to capture nonlinear relationships within CNN activations more effectively. By mapping data into higher-dimensional spaces with kernel functions and extracting principal components from this transformed hyperplane, KPCA-CAM provides more accurate representations of the underlying data manifold. This enables a deeper understanding of the features influencing CNN decisions. Empirical evaluations on the ILSVRC dataset across different CNN models demonstrate that KPCA-CAM produces more precise activation maps, providing clearer insights into the model's reasoning compared to existing CAM algorithms. This research advances CAM techniques, equipping researchers and practitioners with a powerful tool to gain deeper insights into CNN decision-making processes and overall behaviors., Comment: 5 pages, 4 figures, Published to IEEE MMSP 2024

We present the first example of an interacting Carroll supersymmetric field theory with both temporal and spatial derivatives, belonging to the Galileon class, where the non-linear field equation remains second-order in derivative. To achieve this, we introduce two novel tools. First, we demonstrate that the two-dimensional Galilei/Carroll duality can be extended to higher dimensions, and includes the supersymmetry, by expressing the generators in a spinor basis. We then show that Carroll superalgebras are naturally connected to Euclidean, rather than Poincar\'e, superalgebras. Using the real multiplet of the three-dimensional N $=1$ Euclidean supersymmetry, we construct the scalar multiplet for N $=2$ Carroll supersymmetry and develop a tensor calculus to realize the aforementioned model. These results offer new insights into the structure of genuine higher-dimensional Carroll field theories and Carroll supersymmetry. While these tools are utilized to build a specific model, we anticipate that they possess broader applications in Carrollian physics., Comment: 7 pages

Multimodal key information extraction (KIE) models have been studied extensively on semi-structured documents. However, their investigation on unstructured documents is an emerging research topic. The paper presents an approach to adapt a multimodal transformer (i.e., ViBERTgrid previously explored on semi-structured documents) for unstructured financial documents, by incorporating a BiLSTM-CRF layer. The proposed ViBERTgrid BiLSTM-CRF model demonstrates a significant improvement in performance (up to 2 percentage points) on named entity recognition from unstructured documents in financial domain, while maintaining its KIE performance on semi-structured documents. As an additional contribution, we publicly released token-level annotations for the SROIE dataset in order to pave the way for its use in multimodal sequence labeling models., Comment: Accepted in MIDAS (The 8th Workshop on MIning DAta for financial applicationS) workshop of ECML PKDD 2023 conference

Feeding the growing human population sustainably amidst climate change is one of the most important challenges in the 21st century. Current practices often lead to the overuse of agronomic inputs, such as synthetic fertilizers and water, resulting in environmental contamination and diminishing returns on crop productivity. The complexity of agricultural systems, involving plant-environment interactions and human management, presents significant scientific and technical challenges for developing sustainable practices. Addressing these challenges necessitates transdisciplinary research, involving intense collaboration among fields such as plant science, engineering, computer science, and social sciences. Here, we present five case studies from two research centers demonstrating successful transdisciplinary approaches toward more sustainable water and fertilizer use. These case studies span multiple scales. Starting from whole-plant signaling, we explore how reporter plants can transform our understanding of plant communication and enable efficient application of water and fertilizers. We then show how new fertilizer technologies could increase the availability of phosphorus in the soil. To accelerate advancements in breeding new cultivars, we discuss robotic technologies for high-throughput plant screening in different environments at a population scale. At the ecosystem scale, we investigate phosphorus recovery from aquatic systems and methods to minimize phosphorus leaching. Finally, as agricultural outputs affect all people, we show how to integrate stakeholder perspectives and needs into the research. With these case studies, we hope to encourage the scientific community to adopt transdisciplinary research and promote cross-training among biologists, engineers, and social scientists to drive discovery and innovation in advancing sustainable agricultural systems.

Rate-Splitting Multiple Access (RSMA) is a promising strategy for ensuring robust transmission in multi-antenna wireless systems. In this paper, we investigate the performance of RSMA in a downlink Decode-and-Forward (DF) relay scenario under two phases with imperfect Channel State Information (CSI) at the transmitter and the relay. In particular, in the first phase, the Base Station (BS) initially transmits to both BS Users (BUs) and the relay. In the second phase, the relay decodes and forwards the received signals to Relay Users (RUs) outside the BS coverage area. Furthermore, we investigate a scenario where the relay broadcasts a common stream intended for the RUs in the second phase. Due to the broadcast nature of the transmission, this stream is inadvertently received by both the RUs and the BUs. Concurrently, the BS utilizes Spatial Division Multiple Access (SDMA) to transmit private streams to the BUs, resulting in BUs experiencing residual interference from the common stream transmitted from relay. Incorporating this residual common stream interference into our model results in a significant enhancement of the overall sum-rate achieved at the BUs. We derive a tractable lower bound on the ergodic sum-rates, enables us to develop closed-form solutions for power allocation that maximize the overall sum-rate in both phases. Extensive simulations validate that our proposed power allocation algorithm, in conjunction with a low-complexity precoder, significantly improves the sum-rate performance of DF relay RSMA networks compared to the SDMA-based benchmark designs under imperfect CSI at the transmitter and relay.

Spectral Graph Convolutional Networks (GCNs) have gained popularity in graph machine learning applications due, in part, to their flexibility in specification of network propagation rules. These propagation rules are often constructed as polynomial filters whose coefficients are learned using label information during training. In contrast to learned polynomial filters, explicit filter functions are useful in capturing relationships between network topology and distribution of labels across the network. A number of algorithms incorporating either approach have been proposed; however the relationship between filter functions and polynomial approximations is not fully resolved. This is largely due to the ill-conditioned nature of the linear systems that must be solved to derive polynomial approximations of filter functions. To address this challenge, we propose a novel Arnoldi orthonormalization-based algorithm, along with a unifying approach, called G-Arnoldi-GCN that can efficiently and effectively approximate a given filter function with a polynomial. We evaluate G-Arnoldi-GCN in the context of multi-class node classification across ten datasets with diverse topological characteristics. Our experiments show that G-Arnoldi-GCN consistently outperforms state-of-the-art methods when suitable filter functions are employed. Overall, G-Arnoldi-GCN opens important new directions in graph machine learning by enabling the explicit design and application of diverse filter functions. Code link: https://github.com/mustafaCoskunAgu/GArnoldi-GCN

In this paper, we construct the pair of finite bivariate biorthogonal M-Konhauser polynomials, reduced to the finite orthogonal polynomials $M_{n}^{(p,q)}(t)$, by choosing appropriate parameters in order to obtain a relation between the Jacobi Konhauser polynomials and this new finite bivariate biorthogonal polynomials $_{K}M_{n;\upsilon}^{(p,q)}(z,t)$ similar to the relation between the classical Jacobi polynomials $P_{n}^{(p,q)}(t)$ and the finite orthogonal polynomials $M_{n}^{(p,q)}(t)$. Several properties like generating function, operational/integral representation are derived and some applications like fractional calculus, Fourier transform and Laplace transform are studied thanks to that new transition relation and the definition of finite bivariate M-Konhauser polynomials.

We introduce Boundless, a photo-realistic synthetic data generation system for enabling highly accurate object detection in dense urban streetscapes. Boundless can replace massive real-world data collection and manual ground-truth object annotation (labeling) with an automated and configurable process. Boundless is based on the Unreal Engine 5 (UE5) City Sample project with improvements enabling accurate collection of 3D bounding boxes across different lighting and scene variability conditions. We evaluate the performance of object detection models trained on the dataset generated by Boundless when used for inference on a real-world dataset acquired from medium-altitude cameras. We compare the performance of the Boundless-trained model against the CARLA-trained model and observe an improvement of 7.8 mAP. The results we achieved support the premise that synthetic data generation is a credible methodology for training/fine-tuning scalable object detection models for urban scenes.

In today's digital society, issues related to digital privacy have become increasingly important. Issues such as data breaches result in misuse of data, financial loss, and cyberbullying, which leads to less user trust in digital services. This research investigates the impact of digital privacy laws on user trust by comparing the regulations in the Netherlands, Ghana, and Malaysia. The study employs a comparative case study method, involving interviews with digital privacy law experts, IT educators, and consumers from each country. The main findings reveal that while the General Data Protection Regulation (GDPR) in the Netherlands is strict, its practical impact is limited by enforcement challenges. In Ghana, the Data Protection Act is underutilized due to low public awareness and insufficient enforcement, leading to reliance on personal protective measures. In Malaysia, trust in digital services is largely dependent on the security practices of individual platforms rather than the Personal Data Protection Act. The study highlights the importance of public awareness, effective enforcement, and cultural considerations in shaping the effectiveness of digital privacy laws. Based on these insights, a recommendation framework is proposed to enhance digital privacy practices, also aiming to provide valuable guidance for policymakers, businesses, and citizens in navigating the challenges of digitalization.

This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of deep reinforcement learning (RL) policies. The proposed approach augments deep RL agents with a PID controller layer, incorporating prior knowledge from control theory into the learning process. CIRL improves performance and robustness by combining the best of both worlds: the disturbance-rejection and setpoint-tracking capabilities of PID control and the nonlinear modeling capacity of deep RL. Simulation studies conducted on a continuously stirred tank reactor system demonstrate the improved performance of CIRL compared to both conventional model-free deep RL and static PID controllers. CIRL exhibits better setpoint-tracking ability, particularly when generalizing to trajectories outside the training distribution, suggesting enhanced generalization capabilities. Furthermore, the embedded prior control knowledge within the CIRL policy improves its robustness to unobserved system disturbances. The control-informed RL framework combines the strengths of classical control and reinforcement learning to develop sample-efficient and robust deep reinforcement learning algorithms, with potential applications in complex industrial systems.

Non-prehensile manipulation in high-dimensional systems is challenging for a variety of reasons. One of the main reasons is the computationally long planning times that come with a large state space. Trajectory optimisation algorithms have proved their utility in a wide variety of tasks, but, like most methods struggle scaling to the high dimensional systems ubiquitous to non-prehensile manipulation in clutter as well as deformable object manipulation. We reason that, during manipulation, different degrees of freedom will become more or less important to the task over time as the system evolves. We leverage this idea to reduce the number of degrees of freedom considered in a trajectory optimisation problem, to reduce planning times. This idea is particularly relevant in the context of model predictive control (MPC) where the cost landscape of the optimisation problem is constantly evolving. We provide simulation results under asynchronous MPC and show our methods are capable of achieving better overall performance due to the decreased policy lag whilst still being able to optimise trajectories effectively., Comment: 18 pages, 4 figures, accepted to WAFR 2024

The development of Digital Twins (DTs) is hindered by a lack of specialized, open-source solutions that can meet the demands of dynamic applications. This has caused state-of-the-art DT applications to be validated using offline data. However, this approach falls short of integrating real-time data, which is one of the most important characteristics of DTs. This can limit the validating effectiveness of DT applications in cases such as aeronautical ad-hoc networks (AANETs). Considering this, we develop a Real-Time Digital Twin Platform and implement core network selection in AANETs as a case study. In this, we implement microservice-based architecture and design a robust data pipeline. Additionally, we develop an interactive user interface using open-source tools. Using this, the platform supports real-time decision-making in the presence of data retrieval failures.

The identification of cells and particles based on their transport properties in microfluidic devices is crucial for numerous applications in biology and medicine. Neutrally buoyant particles transported in microfluidic channels, migrate laterally towards stable locations. However, the effect of the particle and flow properties on these focusing positions remain largely unknown. We conduct large scale simulations with dissipative particle dynamics, demonstrating that freely moving particles exhibit significant differences in their focusing patterns from particles that are prevented from rotation. In circular pipes, we observe drastic changes in rotating versus non-rotating focusing positions. We demonstrate that rotation-induced lateral lift force is significant, unlike previously believed, and is linearly dependent on the rotation magnitude. A simple phenomenological explanation extending existing theories is presented, that agrees well with our numerical findings. For flows in square ducts, we report four face-centered stable positions for the rotating particles, in accordance with experimental studies. However, the non-rotating particles stay scattered on a concentric one-dimensional annulus, thus revealing qualitatively different behavior with respect to the free ones. Our findings suggest new designs for micro-particle and cell sorting in microfluidics devices.

Extreme natural phenomena are occurring more frequently everyday in the world, challenging, among others, the infrastructure of communication networks. For instance, the devastating earthquakes in Turkiye in early 2023 showcased that, although communications became an imminent priority, existing mobile communication systems fell short with the operational requirements of harsh disaster environments. In this article, we present a novel framework for robust, resilient, adaptive, and open source sixth generation (6G) radio access networks (Open6GRAN) that can provide uninterrupted communication services in the face of natural disasters and other disruptions. Advanced 6G technologies, such as reconfigurable intelligent surfaces (RISs), cell-free multiple-input-multiple-output, and joint communications and sensing with increasingly heterogeneous deployment, consisting of terrestrial and non-terrestrial nodes, are robustly integrated. We advocate that a key enabler to develop service and management orchestration with fast recovery capabilities will rely on an artificial-intelligence-based radio access network (RAN) controller. To support the emergency use case spanning a larger area, the integration of aerial and space segments with the terrestrial network promises a rapid and reliable response in the case of any disaster. A proof-of-concept that rapidly reconfigures an RIS for performance enhancement under an emergency scenario is presented and discussed., Comment: Submitted to IEEE VTM

Effective diabetes management relies heavily on the continuous monitoring of blood glucose levels, traditionally achieved through invasive and uncomfortable methods. While various non-invasive techniques have been explored, such as optical, microwave, and electrochemical approaches, none have effectively supplanted these invasive technologies due to issues related to complexity, accuracy, and cost. In this study, we present a transformative and straightforward method that utilizes voice analysis to predict blood glucose levels. Our research investigates the relationship between fluctuations in blood glucose and vocal characteristics, highlighting the influence of blood vessel dynamics during voice production. By applying advanced machine learning algorithms, we analyzed vocal signal variations and established a significant correlation with blood glucose levels. We developed a predictive model using artificial intelligence, based on voice recordings and corresponding glucose measurements from participants, utilizing logistic regression and Ridge regularization. Our findings indicate that voice analysis may serve as a viable non-invasive alternative for glucose monitoring. This innovative approach not only has the potential to streamline and reduce the costs associated with diabetes management but also aims to enhance the quality of life for individuals living with diabetes by providing a painless and user-friendly method for monitoring blood sugar levels., Comment: 5 figure and 5 tables. This manuscript is a pre-print to be submitted to a journal or/and a conference. arXiv admin note: substantial text overlap with arXiv:2402.13812

Segmenting multiple objects (e.g., organs) in medical images often requires an understanding of their topology, which simultaneously quantifies the shape of the objects and their positions relative to each other. This understanding is important for segmentation networks to generalize better with limited training data, which is common in medical image analysis. However, many popular networks were trained to optimize only pixel-wise performance, ignoring the topological correctness of the segmentation. In this paper, we introduce a new topology-aware loss function, which we call PI-Att, that explicitly forces the network to minimize the topological dissimilarity between the ground truth and prediction maps. We quantify the topology of each map by the persistence image representation, for the first time in the context of a segmentation network loss. Besides, we propose a new mechanism to adaptively calculate the persistence image at the end of each epoch based on the network's performance. This adaptive calculation enables the network to learn topology outline in the first epochs, and then topology details towards the end of training. The effectiveness of the proposed PI-Att loss is demonstrated on two different datasets for aorta and great vessel segmentation in computed tomography images.

In this paper, a finite set of biorthogonal polynomials in two variables is produced using Konhauser polynomials. Some properties containing operational and integral representation, Laplace transform, fractional calculus operators of this family are studied. Also, computing Fourier transform for the new set, a new family of biorthogonal functions are derived via Parseval's identity. On the other hand, this finite set is modified by adding two new parameters in order to have semigroup property and construct fractional calculus operators. Further, integral equation and integral operator are also derived for the modified version.

Markov Junior is a probabilistic programming language used for procedural content generation across various domains. However, its reliance on manually crafted and tuned probabilistic rule sets, also called grammars, presents a significant bottleneck, diverging from approaches that allow rule learning from examples. In this paper, we propose a novel solution to this challenge by introducing a genetic programming-based optimization framework for learning hierarchical rule sets automatically. Our proposed method ``Markov Senior'' focuses on extracting positional and distance relations from single input samples to construct probabilistic rules to be used by Markov Junior. Using a Kullback-Leibler divergence-based fitness measure, we search for grammars to generate content that is coherent with the given sample. To enhance scalability, we introduce a divide-and-conquer strategy that enables the efficient generation of large-scale content. We validate our approach through experiments in generating image-based content and Super Mario levels, demonstrating its flexibility and effectiveness. In this way, ``Markov Senior'' allows for the wider application of Markov Junior for tasks in which an example may be available, but the design of a generative rule set is infeasible., Comment: 8 pages, to be published in the Proceedings of the IEEE Conference on Games 2024, demo implementation can be found here: https://github.com/ADockhorn/MarkovSenior

Echoed conditional displacement (ECD) gates for bosonic systems have become the key element for real-time quantum error correction beyond the break-even point. These gates are characterized by a single complex parameter $\beta$, and can be constructed using Gaussian pulses and free evolutions with the help of an ancillary transmon qubit. We show that there is a lower bound for the gate time in the standard construction of an ECD gate. We present a method for optimizing the pulse shape of an ECD gate using a pulse-shaping technique subject to a set of experimental constraints. Our optimized pulse shapes remain symmetric, and can be applied to a range of target values of $\beta$ by tuning only the amplitude. We demonstrate that the total gate time of an ECD gate for a small value of $\beta$ can be reduced either by relaxing the no-overlap constraint on the primitives used in the standard construction or via our optimal-control method. We show a slight advantage of the optimal-control method by demonstrating a reduction in the preparation time of a $|+Z_\mathrm{GKP}>$ logical state by $\thicksim$$10\%$.

Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles (UAVs). Quadrotor UAVs are used in important missions such as search and rescue, counter terrorism, firefighting, surveillance and cargo transportation. While performing these tasks, quadrotors must operate in noisy environments. Therefore, a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance. While many researchers focus only on white Gaussian noise in their studies, all colored noises should be considered during quadrotor's operation. In this study, it is aimed to design a robust controller that is resistant to all colored noises. Firstly, a nonlinear model of the quadrotor was created with MATLAB. Then, a backstepping control design that is resistant to colored noises was realized. The designed backstepping controller was tested under Gaussian white noise, pink noise, brown noise, blue noise and purple noise. PID and Lyapunov-based controller designs were also carried out and their time responses (rise time, overshoot, settling time) were compared with those of backstepping controller. When the values obtained was examined, it was proven that the proposed backstepping controller had the least overshoot and shortest settling time under all noise types., Comment: 18 pages, 9 figures

We present a novel data-driven simulation environment for modeling traffic in metropolitan street intersections. Using real-world tracking data collected over an extended period of time, we train trajectory forecasting models to learn agent interactions and environmental constraints that are difficult to capture conventionally. Trajectories of new agents are first coarsely generated by sampling from the spatial and temporal generative distributions, then refined using state-of-the-art trajectory forecasting models. The simulation can run either autonomously, or under explicit human control conditioned on the generative distributions. We present the experiments for a variety of model configurations. Under an iterative prediction scheme, the way-point-supervised TrajNet++ model obtained 0.36 Final Displacement Error (FDE) in 20 FPS on an NVIDIA A100 GPU., Comment: CVPR 2024 Workshop POETS Oral

Local differential privacy (LDP) has recently emerged as a popular privacy standard. With the growing popularity of LDP, several recent works have applied LDP to spatial data, and grid-based decompositions have been a common building block in the collection and analysis of spatial data under DP and LDP. In this paper, we study three grid-based decomposition methods for spatial data under LDP: Uniform Grid (UG), PrivAG, and AAG. UG is a static approach that consists of equal-sized cells. To enable data-dependent decomposition, PrivAG was proposed by Yang et al. as the most recent adaptive grid method. To advance the state-of-the-art in adaptive grids, in this paper we propose the Advanced Adaptive Grid (AAG) method. For each grid cell, following the intuition that the cell's intra-cell density distribution will be affected by its neighbors, AAG performs uneven cell divisions depending on the neighboring cells' densities. We experimentally compare UG, PrivAG, and AAG using three real-world location datasets, varying privacy budgets, and query sizes. Results show that AAG provides higher utility than PrivAG, demonstrating the superiority of our proposed approach. Furthermore, UG's performance is heavily dependent on the choice of grid size. When the grid size is chosen optimally in UG, AAG still beats UG for small queries, but UG beats AAG for large (coarse-grained) queries.

The growing complexity of networks and the variety of future scenarios with diverse and often stringent performance requirements call for a higher level of automation. Intent-based management emerges as a solution to attain high level of automation, enabling human operators to solely communicate with the network through high-level intents. The intents consist of the targets in the form of expectations (i.e., latency expectation) from a service and based on the expectations the required network configurations should be done accordingly. It is almost inevitable that when a network action is taken to fulfill one intent, it can cause negative impacts on the performance of another intent, which results in a conflict. In this paper, we aim to address the conflict issue and autonomous management of intent-based networking, and propose an online learning method based on the hierarchical multi-armed bandits approach for an effective management. Thanks to this hierarchical structure, it performs an efficient exploration and exploitation of network configurations with respect to the dynamic network conditions. We show that our algorithm is an effective approach regarding resource allocation and satisfaction of intent expectations.

We investigate the Chern-Simons-like formulation of exotic general massive gravity models within the framework of third-way to three-dimensional gravity. We classify our construction into two main approaches: one using torsional cosmological Einstein and exotic massive gravity equations, and the other a torsion-free approach. The former approach, while mathematically appealing, precludes the construction of critical exotic models where the central charges vanish. In contrast, the latter approach has a wider parameter space and allows for the construction of critical models. An explicit example of an exotic general model is provided to illustrate both methods. Our methodology represents the first step towards establishing the most general Chern-Simons-like formulation of third-way to three-dimensional gravity, which would enable the study of identifying its bulk/boundary unitary sector., Comment: 21 pages, no figures

We introduce a novel system of matching and scoring players in tournaments, called Multi-Tier Tournaments, illustrated by chess and based on the following rules: 1. Players are divided into skill-based tiers, based on their Elo ratings. 2. Starting with one or more mini-tournaments of the least skilled players (Tier 1), the winner or winners -- after playing multiple opponents -- move to the next-higher tier. 3. The winners progress to a final tier of the best-performing players from lower tiers as well as players with the highest Elo ratings. 4. Performance in each tier is given by a player's Tournament Score (TS), which depends on his/her wins, losses, and draws (not on his/her Elo rating). Whereas a player's Elo rating determines in which mini-tournament he/she starts play, TS and its associated tie-breaking rules determine whether a player moves up to higher tiers and, in the final mini-tournament, wins the tournament. This combination of players' past Elo ratings and current TS's provides a fair and accurate measure of a player's standing among the players in the tournament. We apply a variation of Multi-Tier Tournaments to the top 20 active chess players in the world (as of February 2024). Using a dataset of 1209 head-to-head games, we illustrate the viability of giving lower-rated players the opportunity to progress and challenge higher-rated players. We also briefly discuss the application of Multi-Tier Tournaments to baseball, soccer, and other sports that emphasize physical rather than mental skills., Comment: 24 pages

Predicting hospital length of stay (LOS) reliably is an essential need for efficient resource allocation at hospitals. Traditional predictive modeling tools frequently have difficulty acquiring sufficient and diverse data because healthcare institutions have privacy rules in place. In our study, we modeled this problem as an empirical graph where nodes are the hospitals. This modeling approach facilitates collaborative model training by modeling decentralized data sources from different hospitals without extracting sensitive data outside of hospitals. A local model is trained on a node (hospital) by aiming the generalized total variation minimization (GTVMin). Moreover, we implemented and compared two different federated learning optimization algorithms named federated stochastic gradient descent (FedSGD) and federated averaging (FedAVG). Our results show that federated learning enables accurate prediction of hospital LOS while addressing privacy concerns without extracting data outside healthcare institutions.

Intrinsically elastic robots surpass their rigid counterparts in a range of different characteristics. By temporarily storing potential energy and subsequently converting it to kinetic energy, elastic robots are capable of highly dynamic motions even with limited motor power. However, the time-dependency of this energy storage and release mechanism remains one of the major challenges in controlling elastic robots. A possible remedy is the introduction of locking elements (i.e. clutches and brakes) in the drive train. This gives rise to a new class of robots, so-called clutched-elastic robots (CER), with which it is possible to precisely control the energy-transfer timing. A prevalent challenge in the realm of CERs is the automatic discovery of clutch sequences. Due to complexity, many methods still rely on pre-defined modes. In this paper, we introduce a novel contact-implicit scheme designed to optimize both control input and clutch sequence simultaneously. A penalty in the objective function ensures the prevention of unnecessary clutch transitions. We empirically demonstrate the effectiveness of our proposed method on a double pendulum equipped with two of our newly proposed clutch-based Bi-Stiffness Actuators (BSA)., Comment: Accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024). The first two authors contributed equally to this work. A cited paper, "Posa M, Cantu C, and Tedrake R (2014)" has a corrigendum (https://doi.org/10.1177/0278364914533878) referencing earlier work by Dr. Kerim Yunt

Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models require many steps to converge, leading to slow inference time. Recently, there has been a trend in diffusion models for employing sophisticated noise schedules that involve more frequent iterations of timesteps at lower noise levels, thereby improving image generation and convergence speed. However, application of these ideas for solving inverse problems with diffusion models remain challenging, as these noise schedules do not perform well when using empirical tuning for the forward model log-likelihood term weights. To tackle these challenges, we propose zero-shot approximate posterior sampling (ZAPS) that leverages connections to zero-shot physics-driven deep learning. ZAPS fixes the number of sampling steps, and uses zero-shot training with a physics-guided loss function to learn log-likelihood weights at each irregular timestep. We apply ZAPS to the recently proposed diffusion posterior sampling method as baseline, though ZAPS can also be used with other posterior sampling diffusion models. We further approximate the Hessian of the logarithm of the prior using a diagonalization approach with learnable diagonal entries for computational efficiency. These parameters are optimized over a fixed number of epochs with a given computational budget. Our results for various noisy inverse problems, including Gaussian and motion deblurring, inpainting, and super-resolution show that ZAPS reduces inference time, provides robustness to irregular noise schedules and improves reconstruction quality. Code is available at https://github.com/ualcalar17/ZAPS, Comment: European Conference on Computer Vision (ECCV), 2024

Growing diversity and complexity of on-chip photonic applications requires rapid design of components with state-of-the-art operation metrics. Here, we demonstrate a highly flexible and efficient method for designing several classes of compact and low-loss integrated optical devices. By leveraging a data-driven approach, we represent devices in the form of cascaded eigenmode scattering matrices, through a data-driven eigenmode expansion method. We perform electromagnetic computations using parallel data processing techniques, demonstrating simulation of individual device responses in tens of milliseconds with physical accuracies matching 3D-FDTD. We then couple these simulations with nonlinear optimization algorithms to design silicon-based waveguide tapers, power splitters, and waveguide crossings with state-of-the-art performance and near-lossless operation. These three sets of devices highlight the broad computational efficiency of the design methodology shown, and the applicability of the demonstrated data-driven eigenmode expansion approach to a wide set of photonic design problems.

Transformers have rapidly overtaken CNN-based architectures as the new standard in audio classification. Transformer-based models, such as the Audio Spectrogram Transformers (AST), also inherit the fixed-size input paradigm from CNNs. However, this leads to performance degradation for ASTs in the inference when input lengths vary from the training. This paper introduces an approach that enables the use of variable-length audio inputs with AST models during both training and inference. By employing sequence packing, our method ElasticAST, accommodates any audio length during training, thereby offering flexibility across all lengths and resolutions at the inference. This flexibility allows ElasticAST to maintain evaluation capabilities at various lengths or resolutions and achieve similar performance to standard ASTs trained at specific lengths or resolutions. Moreover, experiments demonstrate ElasticAST's better performance when trained and evaluated on native-length audio datasets., Comment: Interspeech 2024. Code is available at https://github.com/JiuFengSC/ElasticAST

Structural superlubricity is an intriguing physical phenomenon, whereby sliding at a structurally incommensurate, atomically flat interface yields vanishingly small friction forces. Despite its recent experimental validation, critical questions remain regarding the physical limitations of the concept. In particular, it is not known whether the ultra-low friction state would persist at high sliding speeds relevant for practical, small-scale mechanical systems. Here, we perform sliding experiments via atomic force microscopy on gold nanoislands on graphite at increasing speeds, extracting interfacial friction forces under ambient conditions. A heterodyne detection methodology enables the extraction of extremely weak friction signals buried deep in the noise, revealing that the structurally superlubric regime extends over 100,000 nm/s with minimal changes in friction force, spanning three orders of magnitude in sliding speed. Our results contribute significantly to the pursuit of functional, superlubric mechanical devices.

With the increasing incidence of neurodegenerative diseases such as Alzheimer's Disease (AD), there is a need for further research that enhances detection and monitoring of the diseases. We present MORPHADE (Morphological Autoencoders for Alzheimer's Disease Detection), a novel unsupervised learning approach which uses deformations to allow the analysis of 3D T1-weighted brain images. To the best of our knowledge, this is the first use of deformations with deep unsupervised learning to not only detect, but also localize and assess the severity of structural changes in the brain due to AD. We obtain markedly higher anomaly scores in clinically important areas of the brain in subjects with AD compared to healthy controls, showcasing that our method is able to effectively locate AD-related atrophy. We additionally observe a visual correlation between the severity of atrophy highlighted in our anomaly maps and medial temporal lobe atrophy scores evaluated by a clinical expert. Finally, our method achieves an AUROC of 0.80 in detecting AD, out-performing several supervised and unsupervised baselines. We believe our framework shows promise as a tool towards improved understanding, monitoring and detection of AD. To support further research and application, we have made our code publicly available at github.com/ci-ber/MORPHADE., Comment: 11 pages, 5 figures

The rising demand for energy and spectrum resources in next-generation Internet-of-things (IoT) systems accounts for innovative modes of information and power transfer. One potential solution is to harness the active transmission capability of devices to facilitate data transmission and wireless energy harvesting (WEH) for backscatter communication so as to form a symbiotic radio (SR) environment in a mutualistic manner. Additionally, incorporating reconfigurable intelligent surfaces (RISs) into the SR environment can provide an additional link and enhance the reliability of backscatter communication, thereby reinforcing the symbiotic relationships between active and passive devices. This paper proposes a novel SR system where a standalone RIS sustains its functions through WEH based on a low-power RIS structure and establishes mutualistic symbiosis by utilizing a signal conveyed by the primary transmitter (PTx) to assist ongoing transmissions and convey information to the primary receiver (PRx). The PTx employs time index modulation (TIM) to transmit information to the PRx and power to the RIS and energy harvester (EH). A log-likelihood ratio (LLR)-based detector is presented to address challenges in the TIM scheme. Finally, the performance of the proposed scheme is investigated in terms of harvested direct current (DC) power at the RIS and EH, as well as the bit error rate (BER) at the PRx.

The introduction of the Transformer neural network, along with techniques like self-supervised pre-training and transfer learning, has paved the way for advanced models like BERT. Despite BERT's impressive performance, opportunities for further enhancement exist. To our knowledge, most efforts are focusing on improving BERT's performance in English and in general domains, with no study specifically addressing the legal Turkish domain. Our study is primarily dedicated to enhancing the BERT model within the legal Turkish domain through modifications in the pre-training phase. In this work, we introduce our innovative modified pre-training approach by combining diverse masking strategies. In the fine-tuning task, we focus on two essential downstream tasks in the legal domain: name entity recognition and multi-label text classification. To evaluate our modified pre-training approach, we fine-tuned all customized models alongside the original BERT models to compare their performance. Our modified approach demonstrated significant improvements in both NER and multi-label text classification tasks compared to the original BERT model. Finally, to showcase the impact of our proposed models, we trained our best models with different corpus sizes and compared them with BERTurk models. The experimental results demonstrate that our innovative approach, despite being pre-trained on a smaller corpus, competes with BERTurk.

Recent advances in archaeogenomics have granted access to previously unavailable biological information with the potential to further our understanding of past social dynamics at a range of scales. However, to properly integrate these data within archaeological narratives, new methodological and theoretical tools are required. Effort must be put into finding new methods for weaving together different datasets where material culture and archaeogenomic data are both constitutive elements. This is true on a small scale, when we study relationships at the individual level, and at a larger scale when we deal with social and population dynamics. Specifically, in the study of kinship systems it is essential to contextualize and make sense of biological relatedness through social relations, which, in archaeology, is achieved by using material culture as a proxy. In this paper we propose a Network Science framework to integrate archaeogenomic data and material culture at an intrasite scale to study biological relatedness and social organization at the Neolithic site of \c{C}atalh\"oy\"uk. Methodologically, we propose the use of network variance to investigate the concentration of biological relatedness and material culture within networks of houses. This approach allowed us to observe how material culture similarity between buildings gives valuable information on potential biological relationships between individuals and how biogenetic ties concentrate at specific localities on site.

Recently, huge uniaxial negative thermal expansion (NTE) along a $c$-axis has been observed in transition-metal ($Tr$) zirconides $Tr$Zr$_{2}$ with a tetragonal CuAl$_{2}$-type structure. In a recent study on FeZr$_{2}$ [M. Xu et al., Nat. Commun. 14, 4439 (2023)], the importance of optical phonons to the emergence of the $c$-axis NTE in FeZr$_{2}$ has been proposed. In this study, the physical properties of $Tr$Zr$_{2}$ ($Tr$ = Fe and Co$_{1-x}$Ni$_{x}$) have been studied by specific heat, sound velocity measurements, and theoretical phonon calculations to discuss the importance of optical phonons to the emergence of the $c$-axis NTE in CoZr$_{2}$ and FeZr$_{2}$. From analyses of lattice specific heat, we found that Ni substitution results in a systematic decrease in oscillator strength for the Einstein modes with 8.74 meV (CoZr$_{2}$). From phonon calculations, the low-energy optical phonon branches at the $\Gamma$ point were observed for CoZr$_{2}$ and FeZr$_{2}$ with $c$-axis NTE, but not in NiZr$_{2}$ with positive thermal expansion. The enhancement of phonon density of states near the above-mentioned optical phonon energy in CoZr$_{2}$ and FeZr$_{2}$ is consistent with the specific heat analyses. We propose the importance of the low-energy optical phonons to the emergence of the $c$-axis NTE in TrZr$_{2}$., Comment: 14 pages, 9 figures, and 3 tables

Affine subgroups having the same Coxeter number with the affine Coxeter groups W(An), W(Dn), and W(En) are constructed by graph folding technique. The affine groups W(Cn) and W(Bn) are obtained from the Coxeter groups W(A2n-1) and W(D2n-1) respectively. The affine groups W(E6), W(D6) and W(E8) lead to the affine groups W(F4), W(H3), and W(H4) respectively by graph folding. The latter two are the non-crystallographic groups where W(H3) plays a special role in the quasicrystallographic structures with icosahedral symmetry. A general construction of the affine dihedral subgroups is introduced, some of which, describe the existing planar quasicrystallography. In the construction of the root systems, sets of orthonormal vectors are used but a special non-orthogonal set of vectors in the formulation of the root system of W(An) is also introduced which has practical applications in the construction of the lattices An and An* and their Delone and Voronoi cells., Comment: 9 pages, 8 figures