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An aspect of interest in surveillance of diseases is whether the survival time distribution changes over time. By following data in health registries over time, this can be monitored, either in real time or retrospectively. With relevant risk factors registered, these can be taken into account in the monitoring as well. A challenge in monitoring survival times based on registry data is that data on cause of death might either be missing or uncertain. To quantify the burden of disease in such cases, excess hazard methods can be used, where the total hazard is modelled as the population hazard plus the excess hazard due to the disease. We propose a CUSUM procedure for monitoring for changes in the survival time distribution in cases where use of excess hazard models is relevant. The procedure is based on a survival log-likelihood ratio and extends previously suggested methods for monitoring of time to event to the excess hazard setting. The procedure takes into account changes in the population risk over time, as well as changes in the excess hazard which is explained by observed covariates. Properties, challenges and an application to cancer registry data will be presented.

Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in terms that are understandable to humans. When the model is viewed as a black box, the objective is to identify a small set of features that jointly determine the black box response with minimal error. However, finding such model-agnostic explanations is computationally demanding, as the problem is intractable even for binary classifiers. In this paper, the task is framed as a Constraint Optimization Problem, where the constraint solver seeks an explanation of minimum error and bounded size for an input data instance and a set of samples generated by the black box. From a theoretical perspective, this constraint programming approach offers PAC-style guarantees for the output explanation. We evaluate the approach empirically on various datasets and show that it statistically outperforms the state-of-the-art heuristic Anchors method.

We study the long-time behaviour of solutions to some classes of fourth-order nonlinear PDEs with non-monotone nonlinearities, which include the Landau--Lifshitz--Baryakhtar (LLBar) equation (with all relevant fields and spin torques) and the convective Cahn--Hilliard/Allen--Cahn (CH-AC) equation with a proliferation term, in dimensions $d=1,2,3$. Firstly, we show the global well-posedness, as well as the existence of global and exponential attractors with finite fractal dimensions for these problems. In the case of the exchange-dominated LLBar equation and the CH-AC equation without convection, an estimate for the rate of convergence of the solution to the corresponding stationary state is given. Finally, we show the existence of a robust family of exponential attractors when $d\leq 2$. As a corollary, exponential attractor of the LLBar equation is shown to converge to that of the Landau--Lifshitz--Bloch equation in the limit of vanishing exchange damping, while exponential attractor of the convective CH-AC equation is shown to converge to that of the convective Allen--Cahn equation in the limit of vanishing diffusion coefficient.

This work aims at investigating the impact of DNA geometry, compaction and calculation chain on DNA break and chromosome aberration predictions for high charge and energy (HZE) ions, using the Monte Carlo codes Geant4-DNA, RITRACKS and RITCARD. To ensure consistency of ion transport of both codes, we first compared microdosimetry and nanodosimetry spectra for different ions of interest in hadrontherapy and space research. The Rudd model was used for the transport of ions in both models. Developments were made in Geant4 (v11.2) to include periodic boundary conditions (PBC) to account for electron equilibrium in small targets. Excellent agreements were found for both microdosimetric and nanodosimetric spectra for all ion types, with and without PBC. Some discrepancies remain for low-energy deposition events, likely due to differences in electron interaction models. The latest results obtained using the newly available Geant4 example ``dsbandrepair'' will be presented and compared to DNA break predictions obtained with RITCARD.

Random-walk Monte Carlo simulations are widely used to predict the optical properties of complex, disordered materials. In presence of large heterogeneities (e.g., spatially-extended nonscattering regions in a turbid environment), an explicit description of the micro and macrostructures and of the light propagation therein is generally required, in addition to a statistical average over a representative set of microstructures, thereby making simulations in so-called ``quenched'' disorder particularly time-consuming. We explore here the possibility to model light transport in finite-size strongly heterogeneous media without an explicit description of the underlying microstructure but from the knowledge of typical random-walk trajectories in infinite-size media, that take correlations between successive interaction events into account. Simulations may thus be performed for media of any macroscopic shape and size more efficiently. We illustrate this approach, coined ``quasiannealed'', with the case of a two-phase emulsion consisting of transparent spherical droplets dispersed in a turbid medium. Good agreement with predictions from simulations in quenched disorder on the reflectance of finite-thickness slab is found for a large set of microstructure properties and thicknesses with typical errors on the reflectance on the order of a percent., Comment: 14 pages, 7 figures, 1 table

Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family, which make strong assumptions about the underlying spreading process, often represented as a small set of compact differential equations. Data-driven methods such as deep neural networks make no such assumptions and can capture the generative process in more detail, but fail in long-term forecasting due to data limitations. We propose a new hybrid method called MP-PINN (Multi-Phase Physics-Informed Neural Network) to overcome the limitations of these two major approaches. MP-PINN instils the spreading mechanism into a neural network, enabling the mechanism to update in phases over time, reflecting the dynamics of the epidemics due to policy interventions. Experiments on COVID-19 waves demonstrate that MP-PINN achieves superior performance over pure data-driven or model-driven approaches for both short-term and long-term forecasting.

In magnetic systems, angular momentum is carried by the spin and orbital degrees of freedom. Non-local devices can be used to study angular momentum transport. They consist of parallel heavy-metal nanowires placed on top of magnetic insulators like yttrium iron garnet (YIG), facilitating the transmission of information by magnons, generated by the accumulation of spin at the interface, created via the Spin Hall Effect (SHE) and detected via the inverse SHE (iSHE). It has been demonstrated that these processes have comparable efficiencies when the role of the detector and injector is reversed, which points to reciprocity of the processes. However, we show that by adding Ru as a source of direct and inverse orbital Hall effect (OHE), the system no longer exhibits this reciprocity. Specifically, the generation of magnons via the combination of SHE and OHE and detection via the iSHE is found to be about 35% more efficient than the inverse process for our system.

In this paper we investigate the existence, uniqueness and exponential stability of pseudo almost periodic (PAP-) mild solutions of the parabolic-elliptic (P-E) Keller-Segel system on a bounded domain $\Omega\in \mathbb{R}^n$ with smooth boundary. First, the well-posedness of the corresponding linear system is established by using the smoothing estimates of the Neumann heat semigroup on $\Omega$. Then, the existence of PAP-mild solution of linear system is done by proving a Massera-type principle. Next, we obtain the well-posedness of such solutions for semilinear system by using the results of linear system and fixed point arguments. The exponential stability is proven by using again the estimates of the Neumann heat semigroup. Finally, we discuss also such results for the case of the Keller-Segel system on the framework of real hyperbolic manifolds., Comment: 12 pages

We investigate the dependence of the yield of positive secondary ions created upon impact of primary He, B and Ne ions on geometry and electronic and nuclear energy deposition by the projectiles. We employ pulsed beams in the medium energy regime and a large position-sensitive, time-of-flight detection system to ensure accurate quantification. As a target, we employ a single crystalline Si(100) self-supporting 50 nm thick membrane thus featuring two identical surfaces enabling simultaneous measurements in backscattering and transmission geometry. Electronic sputtering is identified as the governing mechanism for the desorption of hydrogen and molecular species found on the surfaces. Nevertheless, larger energy deposition to the nuclear subsystem by heavier projectiles as well as due to the directionality of the collision cascade appears to act in synergy with the electronic energy deposition leading to an overall increase in secondary ion yields. A higher yield of ions sputtered from the matrix is observed in transmission geometry only for B and Ne ions, consistent with the observed role of nuclear stopping.

Agricultural landscapes are quite complex, especially in the Global South where fields are smaller, and agricultural practices are more varied. In this paper we report on our progress in digitizing the agricultural landscape (natural and man-made) in our study region of India. We use high resolution imagery and a UNet style segmentation model to generate the first of its kind national-scale multi-class panoptic segmentation output. Through this work we have been able to identify individual fields across 151.7M hectares, and delineating key features such as water resources and vegetation. We share how this output was validated by our team and externally by downstream users, including some sample use cases that can lead to targeted data driven decision making. We believe this dataset will contribute towards digitizing agriculture by generating the foundational baselayer., Comment: 34 pages, 7 tables, 15 figs

In this paper, we propose the development of an interactive platform between humans and a dual-arm robotic system based on the Robot Operating System (ROS) and a multimodal artificial intelligence model. Our proposed platform consists of two main components: a dual-arm robotic hardware system and software that includes image processing tasks and natural language processing using a 3D camera and embedded computing. First, we designed and developed a dual-arm robotic system with a positional accuracy of less than 2 cm, capable of operating independently, performing industrial and service tasks while simultaneously simulating and modeling the robot in the ROS environment. Second, artificial intelligence models for image processing are integrated to execute object picking and classification tasks with an accuracy of over 90%. Finally, we developed remote control software using voice commands through a natural language processing model. Experimental results demonstrate the accuracy of the multimodal artificial intelligence model and the flexibility of the dual-arm robotic system in interactive human environments., Comment: In The 25th National Conference on Electronics, Communications and Information Technology (REV-ECIT 2022), Hanoi, Vietnam. in Vietnamese language

Human understanding of language is robust to different word choices as far as they represent similar semantic concepts. To what extent does our human intuition transfer to language models, which represent all subwords as distinct embeddings? In this work, we take an initial step on measuring the role of shared semantics among subwords in the encoder-only multilingual language models (mLMs). To this end, we form "semantic tokens" by merging the semantically similar subwords and their embeddings, and evaluate the updated mLMs on 5 heterogeneous multilingual downstream tasks. Results show that the general shared semantics could get the models a long way in making the predictions on mLMs with different tokenizers and model sizes. Inspections on the grouped subwords show that they exhibit a wide range of semantic similarities, including synonyms and translations across many languages and scripts. Lastly, we found the zero-shot results with semantic tokens are on par or even better than the original models on certain classification tasks, suggesting that the shared subword-level semantics may serve as the anchors for cross-lingual transferring., Comment: 8 pages, 9 figures

Discovering new solid-state materials requires rapidly exploring the vast space of crystal structures and locating stable regions. Generating stable materials with desired properties and compositions is extremely difficult as we search for very small isolated pockets in the exponentially many possibilities, considering elements from the periodic table and their 3D arrangements in crystal lattices. Materials discovery necessitates both optimized solution structures and diversity in the generated material structures. Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements. We propose the Symmetry-aware Hierarchical Architecture for Flow-based Traversal (SHAFT), a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties. In particular, our model decomposes the exponentially large materials space into a hierarchy of subspaces consisting of symmetric space groups, lattice parameters, and atoms. We demonstrate that SHAFT significantly outperforms state-of-the-art iterative generative methods, such as Generative Flow Networks (GFlowNets) and Crystal Diffusion Variational AutoEncoders (CDVAE), in crystal structure generation tasks, achieving higher validity, diversity, and stability of generated structures optimized for target properties and requirements.

We introduce a novel state-space architecture for diffusion models, effectively harnessing spatial and frequency information to enhance the inductive bias towards local features in input images for image generation tasks. While state-space networks, including Mamba, a revolutionary advancement in recurrent neural networks, typically scan input sequences from left to right, they face difficulties in designing effective scanning strategies, especially in the processing of image data. Our method demonstrates that integrating wavelet transformation into Mamba enhances the local structure awareness of visual inputs and better captures long-range relations of frequencies by disentangling them into wavelet subbands, representing both low- and high-frequency components. These wavelet-based outputs are then processed and seamlessly fused with the original Mamba outputs through a cross-attention fusion layer, combining both spatial and frequency information to optimize the order awareness of state-space models which is essential for the details and overall quality of image generation. Besides, we introduce a globally-shared transformer to supercharge the performance of Mamba, harnessing its exceptional power to capture global relationships. Through extensive experiments on standard benchmarks, our method demonstrates superior results compared to DiT and DIFFUSSM, achieving faster training convergence and delivering high-quality outputs. The codes and pretrained models are released at https://github.com/VinAIResearch/DiMSUM.git., Comment: Accepted to NeurIPS 2024. Project page: https://hao-pt.github.io/dimsum/

This paper concerns the numerical approximation for the invariant distribution of Markovian switching L\'evy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we propose an approximation scheme that can be applied to stochastic differential equations with super-linear growth drift and diffusion coefficients.

Direct Z-scheme heterostructures with enhanced redox potential are increasingly regarded as promising materials for solar-driven water splitting. This potential arises from the synergistic interaction between the intrinsic dipoles in Janus materials and the interfacial electric fields across the layers. In this study, we explore the photocatalytic potential of 20 two-dimensional (2D) Janus transition metal dichalcogenide (TMDC) heterobilayers for efficient water splitting. Utilizing density functional theory (DFT) calculations, we first screen these materials based on key properties such as band gaps and the magnitude of intrinsic electric fields to identify promising candidates. We then evaluate additional critical factors, including carrier mobility and surface chemical reactions, to fully assess their performance. The intrinsic dipole moments in Janus materials generate built-in electric fields that enhance charge separation and reduce carrier recombination, thereby improving photocatalytic efficiency. Furthermore, we employ the Fr\"{o}hlich interaction model to quantify the mobility contributions from the longitudinal optical phonon mode, providing detailed insights into how carrier mobility, influenced by phonon scattering, affects photocatalytic performance. Our results reveal that several Janus-TMDC heterobilayers, including WSe$_2$-SWSe, WSe$_2$-TeWSe, and WS$_2$-SMoSe, exhibit strong absorption in the visible spectrum and achieve solar-to-hydrogen (STH) conversion efficiencies of up to 33.24%. These findings demonstrate the potential of Janus-based Z-scheme systems to overcome existing limitations in photocatalytic water splitting by optimizing the electronic and structural properties of 2D materials. This research highlights a viable pathway for advancing clean energy generation through enhanced photocatalytic processes., Comment: 30 pages, 6 figures, 4 tables

We elaborate further on the one-loop effective action of the IKKT model on 3 + 1 dimensional covariant quantum spacetime in the presence of fuzzy extra dimensions. In particular, we describe the one-loop effective action in terms of a remarkable $SO(1, 9)$ character, which allows to evaluate the pertinent traces over the internal modes explicitly. This also allows to estimate the higher-order contributions (in the internal flux $\mathcal{F}_{\mathtt{IJ}}$) to the one-loop effective action in a systematic way. We show that all higher-order contributions are generally suppressed and UV finite, which justifies the previous treatment of the induced gravitational action. We also obtain explicit expressions for the effective Newton constant, and determine the dynamics of the Kaluza-Klein scale $\Delta_{\mathcal{K}}$ of the fuzzy extra dimensions $\mathcal{K}$., Comment: 2 figures

Human motion analysis offers significant potential for healthcare monitoring and early detection of diseases. The advent of radar-based sensing systems has captured the spotlight for they are able to operate without physical contact and they can integrate with pre-existing Wi-Fi networks. They are also seen as less privacy-invasive compared to camera-based systems. However, recent research has shown high accuracy in recognizing subjects or gender from radar gait patterns, raising privacy concerns. This study addresses these issues by investigating privacy vulnerabilities in radar-based Human Activity Recognition (HAR) systems and proposing a novel method for privacy preservation using Differential Privacy (DP) driven by attributions derived with Integrated Decision Gradient (IDG) algorithm. We investigate Black-box Membership Inference Attack (MIA) Models in HAR settings across various levels of attacker-accessible information. We extensively evaluated the effectiveness of the proposed IDG-DP method by designing a CNN-based HAR model and rigorously assessing its resilience against MIAs. Experimental results demonstrate the potential of IDG-DP in mitigating privacy attacks while maintaining utility across all settings, particularly excelling against label-only and shadow model black-box MIA attacks. This work represents a crucial step towards balancing the need for effective radar-based HAR with robust privacy protection in healthcare environments., Comment: Accepted at WACV 2025. 12 pages, 7 figures

We propose a tamed-adaptive Milstein scheme for stochastic differential equations in which the first-order derivatives of the coefficients are locally H\"older continuous of order $\alpha$. We show that the scheme converges in the $L_2$-norm with a rate of $(1+\alpha)/2$ over both finite intervals $[0, T]$ and the infinite interval $(0, +\infty)$, under certain growth conditions on the coefficients.

Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First, because FL depends on a central server to aggregate local training models, this is a single point of failure. The server might function maliciously. Second, due to its distributed nature, FL might encounter backdoor attacks by participating clients. They can poison the local model before submitting to the server. Either type of attack, on the server or the client side, would severely degrade learning accuracy. We propose FedBlock, a novel blockchain-based FL framework that addresses both of these security risks. FedBlock is uniquely desirable in that it involves only smart contract programming, thus deployable atop any blockchain network. Our framework is substantiated with a comprehensive evaluation study using real-world datasets. Its robustness against backdoor attacks is competitive with the literature of FL backdoor defense. The latter, however, does not address the server risk as we do., Comment: This paper has been accepted as a full paper for the IEEE Special Session Federated Learning on Big Data 2024 (IEEE BigData 2024)

Recently, transformer-based techniques incorporating superpoints have become prevalent in 3D instance segmentation. However, they often encounter an over-segmentation problem, especially noticeable with large objects. Additionally, unreliable mask predictions stemming from superpoint mask prediction further compound this issue. To address these challenges, we propose a novel framework called MSTA3D. It leverages multi-scale feature representation and introduces a twin-attention mechanism to effectively capture them. Furthermore, MSTA3D integrates a box query with a box regularizer, offering a complementary spatial constraint alongside semantic queries. Experimental evaluations on ScanNetV2, ScanNet200 and S3DIS datasets demonstrate that our approach surpasses state-of-the-art 3D instance segmentation methods., Comment: 14 pages, 9 figures, 7 tables, conference

Singing is one of the most cherished forms of human entertainment. However, creating a beautiful song requires an accompaniment that complements the vocals and aligns well with the song instruments and genre. With advancements in deep learning, previous research has focused on generating suitable accompaniments but often lacks precise alignment with the desired instrumentation and genre. To address this, we propose a straightforward method that enables control over the accompaniment through text prompts, allowing the generation of music that complements the vocals and aligns with the song instrumental and genre requirements. Through extensive experiments, we successfully generate 10-second accompaniments using vocal input and text control.

6G wireless networks are expected to provide seamless and data-based connections that cover space-air-ground and underwater networks. As a core partition of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) have been envisioned to provide countless real-time intelligent applications. To realize this, promoting AI techniques into SAGIN is an inevitable trend. Due to the distributed and heterogeneous architecture of SAGIN, federated learning (FL) and then quantum FL are emerging AI model training techniques for enabling future privacy-enhanced and computation-efficient SAGINs. In this work, we explore the vision of using FL/QFL in SAGINs. We present a few representative applications enabled by the integration of FL and QFL in SAGINs. A case study of QFL over UAV networks is also given, showing the merit of quantum-enabled training approach over the conventional FL benchmark. Research challenges along with standardization for QFL adoption in future SAGINs are also highlighted., Comment: This work has been accepted by IEEE Conference on Standards for Communications and Networking

Mixture of Experts (MoEs) plays an important role in the development of more efficient and effective large language models (LLMs). Due to the enormous resource requirements, studying large scale MoE algorithms remain in-accessible to many researchers. This work develops \emph{LibMoE}, a comprehensive and modular framework to streamline the research, training, and evaluation of MoE algorithms. Built upon three core principles: (i) modular design, (ii) efficient training; (iii) comprehensive evaluation, LibMoE brings MoE in LLMs more accessible to a wide range of researchers by standardizing the training and evaluation pipelines. Using LibMoE, we extensively benchmarked five state-of-the-art MoE algorithms over three different LLMs and 11 datasets under the zero-shot setting. The results show that despite the unique characteristics, all MoE algorithms perform roughly similar when averaged across a wide range of tasks. With the modular design and extensive evaluation, we believe LibMoE will be invaluable for researchers to make meaningful progress towards the next generation of MoE and LLMs. Project page: \url{https://fsoft-aic.github.io/fsoft-LibMoE.github.io}., Comment: 15 pages, 9 figures

Determinantal point processes (DPPs) are random configurations of points with tunable negative dependence. Because sampling is tractable, DPPs are natural candidates for subsampling tasks, such as minibatch selection or coreset construction. A \emph{coreset} is a subset of a (large) training set, such that minimizing an empirical loss averaged over the coreset is a controlled replacement for the intractable minimization of the original empirical loss. Typically, the control takes the form of a guarantee that the average loss over the coreset approximates the total loss uniformly across the parameter space. Recent work has provided significant empirical support in favor of using DPPs to build randomized coresets, coupled with interesting theoretical results that are suggestive but leave some key questions unanswered. In particular, the central question of whether the cardinality of a DPP-based coreset is fundamentally smaller than one based on independent sampling remained open. In this paper, we answer this question in the affirmative, demonstrating that \emph{DPPs can provably outperform independently drawn coresets}. In this vein, we contribute a conceptual understanding of coreset loss as a \emph{linear statistic} of the (random) coreset. We leverage this structural observation to connect the coresets problem to a more general problem of concentration phenomena for linear statistics of DPPs, wherein we obtain \emph{effective concentration inequalities that extend well-beyond the state-of-the-art}, encompassing general non-projection, even non-symmetric kernels. The latter have been recently shown to be of interest in machine learning beyond coresets, but come with a limited theoretical toolbox, to the extension of which our result contributes. Finally, we are also able to address the coresets problem for vector-valued objective functions, a novelty in the coresets literature., Comment: Accepted at NeurIPS 2024 (Spotlight Paper). Authors are listed in alphabetical order

Gasper, the consensus protocol currently employed by Ethereum, typically requires 64 to 95 slots -- the units of time during which a new chain extending the previous one by one block is proposed and voted -- to finalize. This means that under ideal conditions -- where the network is synchronous, and all chain proposers, along with more than two-thirds of the validators, behave as dictated by the protocol -- proposers construct blocks on a non-finalized chain that extends at least 64 blocks. This exposes a significant portion of the blockchain to potential reorganizations during changes in network conditions, such as periods of asynchrony. Specifically, this finalization delay heightens the network's exposure to Maximum Extractable Value (MEV) exploits, which could undermine the network's integrity. Furthermore, the extended finalization period forces users to balance the trade-off between economic security and transaction speed. To address these issues and speed up finality, we introduce a partially synchronous finality gadget, which we combine with two dynamically available consensus protocols -- synchronous protocols that ensure safety and liveness even with fluctuating validator participation levels. This integration results in secure ebb-and-flow protocols [SP 2021], achieving finality within three slots after a proposal and realizing 3-slot finality.

This study presents an innovative dynamic weighting knowledge distillation (KD) framework tailored for efficient Earth observation (EO) image classification (IC) in resource-constrained settings. Utilizing EfficientViT and MobileViT as teacher models, this framework enables lightweight student models, particularly ResNet8 and ResNet16, to surpass 90% in accuracy, precision, and recall, adhering to the stringent confidence thresholds necessary for reliable classification tasks. Unlike conventional KD methods that rely on static weight distribution, our adaptive weighting mechanism responds to each teacher model's confidence, allowing student models to prioritize more credible sources of knowledge dynamically. Remarkably, ResNet8 delivers substantial efficiency gains, achieving a 97.5% reduction in parameters, a 96.7% decrease in FLOPs, an 86.2% cut in power consumption, and a 63.5% increase in inference speed over MobileViT. This significant optimization of complexity and resource demands establishes ResNet8 as an optimal candidate for EO tasks, combining robust performance with feasibility in deployment. The confidence-based, adaptable KD approach underscores the potential of dynamic distillation strategies to yield high-performing, resource-efficient models tailored for satellite-based EO applications. The reproducible code is accessible on our GitHub repository., Comment: Under revisions

We present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities. PARTNR tasks exhibit characteristics of everyday tasks, such as spatial, temporal, and heterogeneous agent capability constraints. We employ a semi-automated task generation pipeline using Large Language Models (LLMs), incorporating simulation in the loop for grounding and verification. PARTNR stands as the largest benchmark of its kind, comprising 100,000 natural language tasks, spanning 60 houses and 5,819 unique objects. We analyze state-of-the-art LLMs on PARTNR tasks, across the axes of planning, perception and skill execution. The analysis reveals significant limitations in SoTA models, such as poor coordination and failures in task tracking and recovery from errors. When LLMs are paired with real humans, they require 1.5x as many steps as two humans collaborating and 1.1x more steps than a single human, underscoring the potential for improvement in these models. We further show that fine-tuning smaller LLMs with planning data can achieve performance on par with models 9 times larger, while being 8.6x faster at inference. Overall, PARTNR highlights significant challenges facing collaborative embodied agents and aims to drive research in this direction., Comment: Alphabetical author order

Electrochemical biosensors ("E-AB" or "E-DNA" type sensors) that utilize square-wave voltammetry originated in academic labs with a few standard experimental configurations for the electrochemical cell and data analysis. We report here on adaptations of these approaches that are friendly to novice scientists such as those in undergraduate laboratories. These approaches utilize commercially available components, low volumes, work over extended periods and enable facile analysis using a custom excel sheet.

A recently developed new approach, called ``Empirical Wavelet Transform'', aims to build 1D adaptive wavelet frames accordingly to the analyzed signal. In this paper, we present several extensions of this approach to 2D signals (images). We revisit some well-known transforms (tensor wavelets, Littlewood-Paley wavelets, ridgelets and curvelets) and show that it is possible to build their empirical counterpart. We prove that such constructions lead to different adaptive frames which show some promising properties for image analysis and processing.

Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularly in knowledge-intensive domains like healthcare. This study introduces LEAF: Learning and Evaluation Augmented by Fact-Checking, a novel approach designed to enhance the factual reliability of LLMs, with a focus on medical question answering (QA). LEAF utilizes a dual strategy to enhance the factual accuracy of responses from models such as Llama 3 70B Instruct and Llama 3 8B Instruct. The first strategy, Fact-Check-Then-RAG, improves Retrieval-Augmented Generation (RAG) by incorporating fact-checking results to guide the retrieval process without updating model parameters. The second strategy, Learning from Fact-Checks via Self-Training, involves supervised fine-tuning (SFT) on fact-checked responses or applying Simple Preference Optimization (SimPO) with fact-checking as a ranking mechanism, both updating LLM parameters from supervision. These findings suggest that integrating fact-checked responses whether through RAG enhancement or self-training enhances the reliability and factual correctness of LLM outputs, offering a promising solution for applications where information accuracy is crucial., Comment: 22 pages, 9 figures

The recent laser excitation of the 229Th isomeric transition in a solid-state host opens the door for a portable solid-state nuclear optical clock. However, at present the vacuum-ultraviolet laser systems required for clock operation are not conducive to a fieldable form factor. Here, we propose a possible solution to this problem by using 229Th-doped nonlinear optical crystals, which would allow clock operation without a vacuum-ultraviolet laser system and without the need of maintaining the crystal under vacuum.

We consider a maximum entropy edge weight model for shortest path networks that allows for negative weights. Given a graph $G$ and possible weights $\mathcal{W}$ typically consisting of positive and negative values, the model selects edge weights $w \in \mathcal{W}^m$ uniformly at random from all weights that do not introduce a negative cycle. We propose an MCMC process and show that (i) it converges to the required distribution and (ii) that the mixing time on the cycle graph is polynomial. We then engineer an implementation of the process using a dynamic version of Johnson's algorithm in connection with a bidirectional Dijkstra search. We empirically study the performance characteristics of an implementation of the novel sampling algorithm as well as the output produced by the model.

Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and real-time disaster response. This paper presents a novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques. The proposed system leverages Discrete Task-Oriented Joint Source-Channel Coding (DT-JSCC) and Semantic Data Augmentation (SA) integrate cognitive semantic processing with inter-satellite links, enabling efficient analysis and transmission of multispectral imagery for improved object detection, pattern recognition, and real-time decision-making. Cognitive Semantic Augmentation (CSA) is introduced to enhance a system's capability to process and transmit semantic information, improving feature prioritization, consistency, and adaptation to changing communication and application needs. The end-to-end architecture is designed for next-generation satellite networks, such as those supporting 6G, demonstrating significant improvements in fewer communication rounds and better accuracy over federated learning., Comment: 8 Pages, 5 figures, Magazine. arXiv admin note: substantial text overlap with arXiv:2409.15246

Endovascular surgical tool reconstruction represents an important factor in advancing endovascular tool navigation, which is an important step in endovascular surgery. However, the lack of publicly available datasets significantly restricts the development and validation of novel machine learning approaches. Moreover, due to the need for specialized equipment such as biplanar scanners, most of the previous research employs monoplanar fluoroscopic technologies, hence only capturing the data from a single view and significantly limiting the reconstruction accuracy. To bridge this gap, we introduce Guide3D, a bi-planar X-ray dataset for 3D reconstruction. The dataset represents a collection of high resolution bi-planar, manually annotated fluoroscopic videos, captured in real-world settings. Validating our dataset within a simulated environment reflective of clinical settings confirms its applicability for real-world applications. Furthermore, we propose a new benchmark for guidewrite shape prediction, serving as a strong baseline for future work. Guide3D not only addresses an essential need by offering a platform for advancing segmentation and 3D reconstruction techniques but also aids the development of more accurate and efficient endovascular surgery interventions. Our project is available at https://airvlab.github.io/guide3d/., Comment: Accepted to ACCV 2024

Gradual semantics have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI. In this paper, we provide a novel methodology for obtaining gradual semantics for structured argumentation frameworks, where the building blocks of arguments and relations between them are known, unlike in QBAFs, where arguments are abstract entities. Differently from existing approaches, our methodology accommodates incomplete information about arguments' premises. We demonstrate the potential of our approach by introducing two different instantiations of the methodology, leveraging existing gradual semantics for QBAFs in these more complex frameworks. We also define a set of novel properties for gradual semantics in structured argumentation, discuss their suitability over a set of existing properties. Finally, we provide a comprehensive theoretical analysis assessing the instantiations, demonstrating the their advantages over existing gradual semantics for QBAFs and structured argumentation.

We investigate the convergence of the primal-dual algorithm for composite optimization problems when the objective functions are weakly convex. We introduce a modified duality gap function, which is a lower bound of the standard duality gap function. Under the sharpness condition of this new function, we identify the area around the set of saddle points where we obtain the convergence of the primal-dual algorithm. We give numerical examples and applications in image denoising and deblurring to demonstrate our results.

The cognitive faculty of visual reasoning necessitates the integration of multimodal perceptual processing and commonsense and external knowledge of the world. In recent years, a plethora of large vision-language models (LVLMs) have been proposed, demonstrating outstanding power and exceptional proficiency in commonsense reasoning across diverse domains and tasks. Nevertheless, training such LVLMs requires a lot of costly resources. Recent approaches, instead of training LVLMs from scratch on various large datasets, focus on exploring ways to take advantage of the capabilities of many different LVLMs, such as ensemble methods. In this work, we propose self-ensemble, a novel method that improves the generalization and visual reasoning of the model without updating any parameters, a training-free method. Our key insight is that we realized that LVLM itself can ensemble without the need for any other LVLMs, which helps to unlock their internal capabilities. Extensive experiments on various benchmarks demonstrate the effectiveness of our method in achieving state-of-the-art (SOTA) performance on SketchyVQA, Outside Knowledge VQA, and out-of-distribution VQA tasks.

While smart contracts are foundational elements of blockchain applications, their inherent susceptibility to security vulnerabilities poses a significant challenge. Existing training datasets employed for vulnerability detection tools may be limited, potentially compromising their efficacy. This paper presents a method for improving the quantity and quality of smart contract vulnerability datasets and evaluates current detection methods. The approach centers around semantic-preserving code transformation, a technique that modifies the source code structure without altering its semantic meaning. The transformed code snippets are inserted into all potential locations within benign smart contract code, creating new vulnerable contract versions. This method aims to generate a wider variety of vulnerable codes, including those that can bypass detection by current analysis tools. The paper experiments evaluate the method's effectiveness using tools like Slither, Mythril, and CrossFuzz, focusing on metrics like the number of generated vulnerable samples and the false negative rate in detecting these vulnerabilities. The improved results show that many newly created vulnerabilities can bypass tools and the false reporting rate goes up to 100% and increases dataset size minimum by 2.5X.

Thermoelectricity in telluride materials is often improved by replacing telluride with selenium in its crystal. Most work, however, focuses on bulk crystal and leaves the 2D thin films intact. In this paper, we optimize the fabrication of selenium-doped bismuth telluride (Bi$_2$Te$_{3-\rm{x}}$Se$_{\rm{x}}$) thin films using a 3-source thermal co-evaporation. Thermoelectric properties, including the Seebeck coefficient and electrical resistivity, are systematically characterized to evaluate the material's performance for thermoelectric applications near room temperature. The thin films were deposited under carefully controlled conditions, with the evaporation rates of bismuth, tellurium, and selenium precisely monitored to achieve the desired stoichiometry and crystalline phase. Finally, thermoelectricity in Bi$_2$Te$_{3-\rm{x}}$Se$_{\rm{x}}$ at the ultra-thin regime is investigated. We consistently obtain films with thickness near 30 nm with a Seebeck coefficient of 400 $\mu$V/K and a power factor of 1 mW/mK$^2$.

The dynamic control of novel states of matter beyond thermodynamic equilibrium is a fundamental pursuit in condensed matter physics. Intense terahertz fields have enabled metal-insulator transitions, superconductivity, quantum paraelectric ferroelectricity, and room-temperature magnetization via circularly polarized terahertz electric fields. These effects hinge on the excitation of infrared-active soft phonon modes by terahertz fields. Expanding this concept, recent theory suggests that ferroelectric polarization may be manipulated through terahertz twisted light, transferring orbital angular momentum to create ferroelectric skyrmions. Our study experimentally demonstrates that such control is possible in quasi-2D ferroelectric CsBiNb2O7 using twisted UV light with orbital angular momentum (OAM). By resonantly exciting both the ferroelectric mode and the octahedral tilting mode, twisted UV light dynamically modulates the ferroelectric polarization. We employ in-situ X-ray Bragg coherent diffractive imaging, twisted optical Raman spectroscopy, and density functional theory to three-dimensionally resolve ionic displacement fields and polarization texture changes. Our observations reveal deterministic, reversible twisted light-induced strain and ionic displacements within the unit cell, causing substantial microscopic polarization changes. This interaction between twisted photons, phonon modes, and induced ionic displacements breaks symmetry and stabilizes a non-equilibrium ferroelectric phase with topological solitons. These findings offer a new path to control ferroelectricity and magnetism, opening avenues for novel optoelectronic devices such as ultrafast non-volatile memory switches by using light to coherently control ferroic states., Comment: 14 pages, 5 figures

Melody stuck in your head, also known as "earworm", is tough to get rid of, unless you listen to it again or sing it out loud. But what if you can not find the name of that song? It must be an intolerable feeling. Recognizing a song name base on humming sound is not an easy task for a human being and should be done by machines. However, there is no research paper published about hum tune recognition. Adapting from Hum2Song Zalo AI Challenge 2021 - a competition about querying the name of a song by user's giving humming tune, which is similar to Google's Hum to Search. This paper covers details about the pre-processed data from the original type (mp3) to usable form for training and inference. In training an embedding model for the feature extraction phase, we ran experiments with some states of the art, such as ResNet, VGG, AlexNet, MobileNetV2. And for the inference phase, we use the Faiss module to effectively search for a song that matched the sequence of humming sound. The result comes at nearly 94\% in MRR@10 metric on the public test set, along with the top 1 result on the public leaderboard.

Massive Multiple-Input Multiple-Output (massive MIMO) technology stands as a cornerstone in 5G and beyonds. Despite the remarkable advancements offered by massive MIMO technology, the extreme number of antennas introduces challenges during the channel estimation (CE) phase. In this paper, we propose a single-step Deep Neural Network (DNN) for CE, termed Iterative Sequential DNN (ISDNN), inspired by recent developments in data detection algorithms. ISDNN is a DNN based on the projected gradient descent algorithm for CE problems, with the iterative iterations transforming into a DNN using the deep unfolding method. Furthermore, we introduce the structured channel ISDNN (S-ISDNN), extending ISDNN to incorporate side information such as directions of signals and antenna array configurations for enhanced CE. Simulation results highlight that ISDNN significantly outperforms another DNN-based CE (DetNet), in terms of training time (13%), running time (4.6%), and accuracy (0.43 dB). Furthermore, the S-ISDNN demonstrates even faster than ISDNN in terms of training time, though its overall performance still requires further improvement.

Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. To address this problem, this paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals to transmit their data when the shared spectrum is occupied by mobile users. To obtain the optimal spectrum access policy, i.e., stay idle or access the shared spectrum and perform active transmissions or backscattering ambient RF signals for transmissions, to maximize the average throughput for D2D users, deep reinforcement learning (DRL) can be adopted. However, DRL-based solutions may require long training time due to the curse of dimensionality issue as well as complex deep neural network architectures. For that, we develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters compared to DRL thanks to the quantum superposition and quantum entanglement principles. Specifically, instead of using conventional deep neural networks, the proposed quantum RL algorithm uses a parametrized quantum circuit to approximate an optimal policy. Extensive simulations then demonstrate that the proposed solution not only can significantly improve the average throughput of D2D devices when the shared spectrum is busy but also can achieve much better performance in terms of convergence rate and learning complexity compared to existing DRL-based methods., Comment: 12 pages, 7 figures

In this paper we establish a conformal scattering theory for the defocusing semilinear wave equations on the Schwarzschild spacetime. We combine the energy and pointwise decay results of solutions obtained in \cite{Yang} and a Sobolev embedding on spacelike hypersurfaces to establish the energy estimates in two sides between the energy flux of solution through the Cauchy initial hypersurface $\Sigma_0=\left\{ t=0 \right\}$ and the one through the null conformal boundaries $\mathfrak{H}^+\cup \scri^+$ (resp. $\mathfrak{H}^-\cup \scri^-$). Combining these estimates and the well-posedness of Cauchy and Goursat problems for nonlinear wave equations we provide a bounded linear and locally Lipschitz scattering operator which maps the past scattering data to the future scattering data. Our results extend also the previous works \cite{BaSeZho1990,Jo2012,Jo2019} to an asymptotically flat spacetime such as Schwarzschild spacetime which is static and spherically symmetric., Comment: 17 pages

The presence of unhealthy nodes in cloud infrastructure signals the potential failure of machines, which can significantly impact the availability and reliability of cloud services, resulting in negative customer experiences. Effectively addressing unhealthy node mitigation is therefore vital for sustaining cloud system performance. This paper introduces Deoxys, a causal inference engine tailored to recommending mitigation actions for unhealthy node in cloud systems to minimize virtual machine downtime and interruptions during unhealthy events. It employs double machine learning combined with causal forest to produce precise and reliable mitigation recommendations based solely on limited observational data collected from the historical unhealthy events. To enhance the causal inference model, Deoxys further incorporates a policy fallback mechanism based on model uncertainty and action overriding mechanisms to (i) improve the reliability of the system, and (ii) strike a good tradeoff between downtime reduction and resource utilization, thereby enhancing the overall system performance. After deploying Deoxys in a large-scale cloud infrastructure at Microsoft, our observations demonstrate that Deoxys significantly reduces average VM downtime by 53% compared to a legacy policy, while leading to 49.5% lower VM interruption rate. This substantial improvement enhances the reliability and stability of cloud platforms, resulting in a seamless customer experience.

This paper presents a scaling study on the planning phase of a multi-energy system (MES), which is becoming increasingly prominent in the energy sector. The research aims to investigate the interactions and challenges associated with integrating heat and electrical systems and scaling their components. In this context, interaction between these two domains are investigated and the size of the distributed energy resources in the MES is scaled to examine the impact of sizing on the integrating networks and their controlling system. To achieve this, the paper uses sensitivity analysis and a meta-modeling technique, both incorporated in a toolbox for scaling analysis. These methodologies are validated through simulations, and the results obtained from the simulations can contribute to the advancement of MESs and their implementation in laboratory and field testing., Comment: 6 pages, 9 figures, conference proceedings Asia Meeting on Environment and Electrical Engineering (EEE-AM) 2023

High performance, reliability and safety are crucial properties of any Software-Defined-Networking (SDN) system. Although the use of Deep Reinforcement Learning (DRL) algorithms has been widely studied to improve performance, their practical applications are still limited as they fail to ensure safe operations in exploration and decision-making. To fill this gap, we explore the design of a Control Barrier Function (CBF) on top of Deep Reinforcement Learning (DRL) algorithms for load-balancing. We show that our DRL-CBF approach is capable of meeting safety requirements during training and testing while achieving near-optimal performance in testing. We provide results using two simulators: a flow-based simulator, which is used for proof-of-concept and benchmarking, and a packet-based simulator that implements real protocols and scheduling. Thanks to the flow-based simulator, we compared the performance against the optimal policy, solving a Non Linear Programming (NLP) problem with the SCIP solver. Furthermore, we showed that pre-trained models in the flow-based simulator, which is faster, can be transferred to the packet simulator, which is slower but more accurate, with some fine-tuning. Overall, the results suggest that near-optimal Quality-of-Service (QoS) performance in terms of end-to-end delay can be achieved while safety requirements related to link capacity constraints are guaranteed. In the packet-based simulator, we also show that our DRL-CBF algorithms outperform non-RL baseline algorithms. When the models are fine-tuned over a few episodes, we achieved smoother QoS and safety in training, and similar performance in testing compared to the case where models have been trained from scratch., Comment: Accepted to Computer Communications 2024. arXiv admin note: text overlap with arXiv:2401.05525

Vision science imposes rigorous requirements for the design and execution of psychophysical studies and experiments. These requirements ensure precise control over variables, accurate measurement of perceptual responses, and reproducibility of results, which are essential for investigating visual perception and its underlying mechanisms. Since different experiments have different requirements, not all aspects of a display system are critical for a given setting. Therefore, some display systems may be suitable for certain types of experiments but unsuitable for others. An additional challenge is that the performance of consumer systems is often highly dependent on specific monitor settings and firmware behavior. Here, we evaluate the performance of four display systems: a consumer LCD gaming monitor, a consumer OLED gaming monitor, a consumer OLED TV, and a VPixx PROPixx projector system. To allow the reader to assess the suitability of these systems for different experiments, we present a range of different metrics: luminance behavior, luminance uniformity across display surface, estimated gamma values and linearity, channel additivity, channel dependency, color gamut, pixel response time, and pixel waveform. In addition, we exhaustively report the monitor firmware settings used. Our analyses show that current consumer-level OLED display systems are promising, and adequate to fulfill the requirements of some critical vision science experiments, allowing laboratories to run their experiments even without investing in high-quality professional display systems. For example, the tested Asus OLED gaming monitor shows excellent response time, a sharp square waveform even at 240 Hz, a color gamut that covers 94% of DCI-P3 color space, and the best luminance uniformity among all four tested systems, making it a favorable option on price-to-performance ratio., Comment: 29 pages, 13 figures, 4 tables

In context of highly complex energy system experiments, sensitivity analysis is gaining more and more importance to investigate the effects changing parameterization has on the outcome. Thus, it is crucial how to design an experiment to efficiently use the available resources. This paper describes the functionality of a toolbox designed to support the users in design of experiment for (co-)simulation and hardware tests. It provides a structure for object-oriented description of the parameterization and variations and performs sample generation based on this to provide a complete parameterization for the recommended experiment runs. After execution of the runs, it can also be used for analysis of the results to calculate and visualize the effects. The paper also presents two application cases using the toolbox which show how it can be implemented in sensitivity analysis studies with the co-simulation framework mosaik and a hybrid energy storage experiment., Comment: 7 pages, 6 figures, 2 tables, conference proceedings of OSMSES 2024