Search

ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce the ASVspoof 5 database which is generated in crowdsourced fashion from data collected in diverse acoustic conditions (cf. studio-quality data for earlier ASVspoof databases) and from ~2,000 speakers (cf. ~100 earlier). The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models. Among them are attacks generated with a mix of legacy and contemporary text-to-speech synthesis and voice conversion models, in addition to adversarial attacks which are incorporated for the first time. ASVspoof 5 protocols comprise seven speaker-disjoint partitions. They include two distinct partitions for the training of different sets of attack models, two more for the development and evaluation of surrogate detection models, and then three additional partitions which comprise the ASVspoof 5 training, development and evaluation sets. An auxiliary set of data collected from an additional 30k speakers can also be used to train speaker encoders for the implementation of attack algorithms. Also described herein is an experimental validation of the new ASVspoof 5 database using a set of automatic speaker verification and spoof/deepfake baseline detectors. With the exception of protocols and tools for the generation of spoofed/deepfake speech, the resources described in this paper, already used by participants of the ASVspoof 5 challenge in 2024, are now all freely available to the community., Comment: Database link: https://zenodo.org/records/14498691, Database mirror link: https://huggingface.co/datasets/jungjee/asvspoof5, ASVspoof 5 Challenge Workshop Proceeding: https://www.isca-archive.org/asvspoof_2024/index.html

The galaxy cluster PSZ2 G181.06+48.47 ($z=0.234$) is a post-merging system that exhibits symmetric double radio relics separated by ~2.7 Mpc. We present the first weak-lensing analysis of PSZ2 G181.06+48.47 and propose possible merging scenarios using numerical simulations. Our analysis with Subaru Hyper Suprime-Cam imaging identifies a binary dark matter structure consisting of northern and southern components, separated by ~500 kpc. Assuming Navarro-Frenk-White (NFW) halos, the masses for the northern and southern subclusters are $M_{200c}^{N} = 0.88_{-0.30}^{+0.35} \times 10^{14} M_{\odot}$ and $M_{200c}^{S} = 2.71_{-0.48}^{+0.51} \times 10^{14} M_{\odot}$, respectively. By superposing the two NFW halos, we determine the total mass of the cluster to be $M_{200c} = 4.22_{-1.00}^{+1.10} \times 10^{14} M_{\odot}$ ($M_{500c} = 2.90_{-0.69}^{+0.75} \times 10^{14} M_{\odot}$). Our mass estimate suggests that the two relics are located around the cluster $R_{200c}$, where the density of the intracluster medium is very low. Our idealized simulations find that an off-axis collision of a 3:1 major merger can simultaneously reproduce the observed relic and dark matter halo separations. From these findings, we suggest that the system is observed ~0.9 Gyr after the first pericenter passage and is returning from the first apocenter., Comment: Submitted to ApJ on Jan 12, 2025. 21 pages, 14 figures

We report upgraded Giant Metrewave Radio Telescope and Karl J. Jansky Very Large Array radio observations of a low-mass merging galaxy cluster PSZ2 G181.06+48.47. This exceptional galaxy cluster hosts two megaparsec-scale diffuse sources, symmetrically located with respect to the cluster center and separated by about 2.6 Mpc in projection. We detect these low surface brightness sources in our new high-frequency observations (0.3-2 GHz) and classify them as radio relics associated with merger-driven shock fronts. The southwest relic exhibits an inverted morphology and shows evidence of spectral steepening in the post-shock region, potentially tracing a high Mach number shock ($\sim 4$) under the framework of diffusive shock acceleration. The northeast relic is found to be highly polarized with a 22% average polarization fraction at 1.5 GHz and aligned magnetic field vectors. Its spectral and polarization properties, along with the presence of a nearby tailed galaxy, support re-acceleration scenarios. The merger axis defined by the two relics is tilted by $\sim 45$ degree with respect to the plane of the sky, which implies an unprecedented physical separation of $\sim 3.5$ Mpc. We also detect a possible faint radio halo, suggesting weak turbulence in the central cluster region. We conclude that the faint double relics can be best explained by two outward moving shock waves in which particles are (re-)accelerated and that the cluster is in an evolved merger state. PSZ2 G181.06+48.47 presents a unique opportunity to investigate particle acceleration in low mass systems characterized by large relic separations., Comment: 24 pages, 11 figures, Accepted for publication in ApJ

Relics are diffuse, highly-polarized radio sources that trace merger-driven shocks at the periphery of merging galaxy clusters. The LOFAR survey recently discovered a rare example of double relics in the low-mass cluster PSZ2 G181.06+48.47. Through a detailed exploration of new Chandra and XMM-Newton observations, we reveal that PSZ2 G181.06+48.47 has a lower mass ($M_{500,X}=2.32^{+0.29}_{-0.25}\times10^{14}$ M$_{\odot}$) than previously thought. Despite its cool global temperature of $kT_{500}=3.62^{+0.15}_{-0.07}$ keV, PSZ2 G181.06+48.47 is one of the most disturbed clusters in the Planck sample, with a complex morphological and thermodynamic structure. We discover a set of three discontinuities within <500 kpc of the cluster center, and, from a surface brightness analysis, place $5\sigma$ upper limits of $M_{NE}<1.43$ and $M_{SW}<1.57$ for any shock associated with the relic locations. We also revise established scaling relations for double radio-relics by adding 12 new systems not included in previous work. The PSZ2 G181.06+48.47 relics have the widest separation (scaled for $r_{500}$) of all known double-relic systems. The exceptional distance from the cluster center ($>r_{200}$), indicates the relics may be associated with shocks in the ``run-away" phase. We propose that this late-stage, post-apocenter merger is captured as the two subclusters with a mass ratio of 1.2-1.4 fall back into each other. The outer relic shocks were likely produced at the first core passage, while the inner discontinuities are associated with the second infall., Comment: Accepted to ApJ on Feb 13, 2025. 28 pages, 10 figures. Companion paper discussing the radio properties can be found at arXiv:2501.08390. Companion weak-lensing reconstruction paper can be found at arXiv:2501.09067

Self-supervised learning (SSL) methods based on the instance discrimination tasks with InfoNCE have achieved remarkable success. Despite their success, SSL models often struggle to generate effective representations for unseen-domain data. To address this issue, research on unsupervised domain generalization (UDG), which aims to develop SSL models that can generate domain-irrelevant features, has been conducted. Most UDG approaches utilize contrastive learning with InfoNCE to generate representations, and perform feature alignment based on strong assumptions to generalize domain-irrelevant common features from multi-source domains. However, existing methods that rely on instance discrimination tasks are not effective at extracting domain-irrelevant common features. This leads to the suppression of domain-irrelevant common features and the amplification of domain-relevant features, thereby hindering domain generalization. Furthermore, strong assumptions underlying feature alignment can lead to biased feature learning, reducing the diversity of common features. In this paper, we propose a novel approach, DomCLP, Domain-wise Contrastive Learning with Prototype Mixup. We explore how InfoNCE suppresses domain-irrelevant common features and amplifies domain-relevant features. Based on this analysis, we propose Domain-wise Contrastive Learning (DCon) to enhance domain-irrelevant common features. We also propose Prototype Mixup Learning (PMix) to generalize domain-irrelevant common features across multiple domains without relying on strong assumptions. The proposed method consistently outperforms state-of-the-art methods on the PACS and DomainNet datasets across various label fractions, showing significant improvements. Our code will be released. Our project page is available at https://github.com/jinsuby/DomCLP., Comment: Code page: https://github.com/jinsuby/DomCLP

An onboard prediction of dynamic parameters (e.g. Aerodynamic drag, rolling resistance) enables accurate path planning for EVs. This paper presents EV-PINN, a Physics-Informed Neural Network approach in predicting instantaneous battery power and cumulative energy consumption during cruising while generalizing to the nonlinear dynamics of an EV. Our method learns real-world parameters such as motor efficiency, regenerative braking efficiency, vehicle mass, coefficient of aerodynamic drag, and coefficient of rolling resistance using automatic differentiation based on dynamics and ensures consistency with ground truth vehicle data. EV-PINN was validated using 15 and 35 minutes of in-situ battery log data from the Tesla Model 3 Long Range and Tesla Model S, respectively. With only vehicle speed and time as inputs, our model achieves high accuracy and generalization to dynamics, with validation losses of 0.002195 and 0.002292, respectively. This demonstrates EV-PINN's effectiveness in estimating parameters and predicting battery usage under actual driving conditions without the need for additional sensors., Comment: This work has been submitted to the 2025 IEEE International Conference on Robotics and Automation (ICRA) for possible publication

The relationship between galaxies and their supermassive black holes (SMBHs) is an area of active research. One way to investigate this is to compare parsec-scale jets formed by SMBHs with the projected shape of their kiloparsec-scale host galaxies. We analyse Very Long Baseline Interferometry (VLBI) images of Active Galactic Nuclei (AGN) and optical images of their host galaxies. We compare the inner-jet position angle in VLBI-detected radio sources with the optical shapes of galaxies as measured by several large optical surveys. In total 6273 galaxy-AGN pairs were found. We carefully account for the systematics of the cross-matched sources and find that Dark Energy Spectroscopic Instrument Legacy Imaging Surveys data (DESI LS) is significantly less affected by them. Using DESI LS, with which 5853 galaxy-AGN pairs were cross-matched, we find a weak but significant alignment signal (with a p-value $\leqslant$ 0.01) between the parsec-scale AGN jet and the kpc projected minor axis of the optical host galaxy in sources with measured spectroscopic redshifts. Our results show that the observed source properties are connected over 3 orders of magnitude in scale. This points towards an intimate connection between the SMBH, their host galaxies and their subsequent evolution., Comment: 37 pages, 15 figures, published in Nature Astronomy: https://www.nature.com/articles/s41550-024-02407-4

We investigate the formation history of intrahalo light (IHL) using the high-resolution (~1 kpc), large-scale (~Gpc) cosmological hydrodynamical simulation, Horizon Run 5 (HR5). IHL particles are identified by carefully considering both their binding energies and positions with respect to the tidal radii of individual galaxies. By analyzing more than 1,200 galaxy groups and clusters with $\geq 10^{13} M_{\odot}$ and tracing their individual IHL particles back in time, we classify the origin of each IHL particle at each epoch based on the status of the originating galaxy into three categories: brightest halo galaxy (BHG) formation/merger, satellite galaxy stripping, and pre-processing. Our study reveals that the IHL production through BHG formation/merger is the predominant production channel, contributing over 60\% of the total IHL mass across all redshifts. The second most significant IHL production channel is pre-processing, providing more than 20\% in the final HR5 snapshot. Stripping is negligible at $z>4$ but becomes gradually more important as halos mature at $z<4$. Finally, we verify that IHL production through the disruption of dwarf galaxies and in-situ formation is negligible, contributing less than ~3\% and ~0.5\% to the total IHL production, respectively., Comment: Submitted to ApJ, 14 pages, 11 figures

This paper presents a bimanual haptic display based on collaborative robot arms. We address the limitations of existing robot arm-based haptic displays by optimizing the setup configuration and implementing inertia/friction compensation techniques. The optimized setup configuration maximizes workspace coverage, dexterity, and haptic feedback capability while ensuring collision safety. Inertia/friction compensation significantly improve transparency and reduce user fatigue, leading to a more seamless and transparent interaction. The effectiveness of our system is demonstrated in various applications, including bimanual bilateral teleoperation in both real and simulated environments. This research contributes to the advancement of haptic technology by presenting a practical and effective solution for creating high-performance bimanual haptic displays using collaborative robot arms., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

Silica polymorphs and zeolites are fundamental to a wide range of industrial applications owing to their diverse structural characteristics, thermodynamic and mechanical stability under varying conditions and due to their geological importance. Computational modelling has played a crucial role in understanding the relationship between the structure and functionality of silicas and silicates including zeolites. In this study, we apply the MACE-MP-0 machine learnt interatomic potentials (ML-IP) to model the framework energies of siliceous zeolites and examine the phase transitions of silica and ZSM-5 polymorphs under high-pressure conditions. The results reproduce the known metastability of siliceous zeolites relative to {\alpha}-quartz, with energy differences between microporous and dense phases calculated by MACE-MP-0 medium ML-IP and density functional theory (DFT) methods closely aligning with experimental calorimetric data. The high-pressure simulations reveal distinct compression behaviour in the quartz, coesite, and stishovite polymorphs of silica, with coesite and stishovite showing increased stability at elevated pressures in line with experimental data. The calculated phase transition pressures from quartz to coesite (~3.5 GPa) and coesite to stishovite (~9 GPa) are close to experimental findings, demonstrating the reliability of MACE ML-IP in modelling the structural and energetic properties of silica polymorphs., Comment: 21 pages, 7 figures

We consider ($-\alpha$)-homogeneous solutions (stationary self-similar solutions of degree $-\alpha$) to the two-dimensional inviscid Boussinesq equations in a half-plane. We show their non-existence and existence with both regular and singular profile functions., Comment: 52 pages

Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass reconstruction can be mitigated using a deep learning approach based on a convolutional neural network (CNN). In this paper, we present our improvements and report on the detailed performance of our CNN algorithm applied to next-generation wide-field observations. Assuming the field of view ($3\deg.5 \times 3\deg.5$) and depth (27 mag at $5\sigma$) of the Vera C. Rubin Observatory, we generated training datasets of mock shear catalogs with a source density of 33 arcmin$^{-2}$ from cosmological simulation ray-tracing data. We find that the current CNN method provides high-fidelity reconstructions consistent with the true convergence field, restoring both small and large-scale structures. In addition, the cluster detection utilizing our CNN reconstruction achieves $\sim75$% completeness down to $\sim 10^{14}M_{\odot}$. We anticipate that this CNN-based mass reconstruction will be a powerful tool in the Rubin era, enabling fast and robust wide-field mass reconstructions on a routine basis., Comment: 11 pages, 8 figures. Published in ApJ

In realistic radar tracking, target measurement uncertainty (TMU) in terms of both detection probability and measurement error covariance is significantly affected by the target-to-radar (T2R) geometry. However, existing posterior Cramer-Rao Lower Bounds (PCRLBs) rarely investigate the fundamental impact of T2R geometry on target measurement uncertainty and eventually on mean square error (MSE) of state estimate, inevitably resulting in over-conservative lower bound. To address this issue, this paper firstly derives the generalized model of target measurement error covariance for bistatic radar with moving receiver and transmitter illuminating any type of signal, along with its approximated solution to specify the impact of T2R geometry on error covariance. Based upon formulated TMU model, an improved PCRLB (IPCRLB) fully accounting for both measurement origin uncertainty and geometry-dependent TMU is then re-derived, both detection probability and measurement error covariance are treated as state-dependent parameters when differentiating log-likelihood with respect to target state. Compared to existing PCRLBs that partially or completely ignore the dependence of target measurement uncertainty on T2R geometry, proposed IPCRLB provides a much accurate (less-conservative) lower bound for radar tracking in clutter with geometry-dependent TMU. The new bound is then applied to radar trajectory control to effectively optimize T2R geometry and exhibits least uncertainty of acquired target measurement and more accurate state estimate for bistatic radar tracking in clutter, compared to state-of-the-art trajectory control methods., Comment: 15 pages,12 figures

Las Americas Middle School is a newcomer program within Houston Independent School District (HISD) that serves students in Grades 4 through 8 who have been in the U.S. for less than one year. As a non-zoned school of choice, Las Americas has no attendance boundary, meaning that students and families must opt to enroll in the school. This brief analyzes how the trajectories of English acquisition vary among Las Americas newcomer students across different subgroups (economic disadvantage, initial English oral proficiency level, grade level upon arrival in HISD, classification as a student with interrupted formal education (SIFE) and refugee/asylee status). It also estimates the causal effects of attending Las Americas on students' English language acquisition.

This brief evaluates the causal effect of attending Las Americas Middle School on newcomer students' early high school outcomes. Using administrative data from the Houston Independent School District (HISD) spanning the 2007-2008 through 2018-2019 school years, the study examined the academic performance, course-taking patterns, and school engagement of newcomer students who did and did not attend Las Americas. Attending Las Americas increased newcomer students' English end-of-course (EOC) exam scores and decreased students' likelihood of receiving disciplinary actions. Newcomer students who attended Las Americas fared similarly to their newcomer peers at other middle schools on all other outcomes. The brief concludes with a discussion of the limitations of the analysis, as well as potential implications for policy and practice.

We consider an autonomous navigation robot that can accept human commands through natural language to provide services in an indoor environment. These natural language commands may include time, position, object, and action components. However, we observe that the positional components within such commands usually refer to objects in the environment that may contain different levels of positional ambiguity. For example, the command "Go to the chair!" may be ambiguous when there are multiple chairs of the same type in a room. In order to disambiguate these commands, we employ a large language model and a large vision-language model to conduct multiple turns of conversations with the user. We propose a two-level approach that utilizes a vision-language model to map the meanings in natural language to a unique object ID in images and then performs another mapping from the unique object ID to a 3D depth map, thereby allowing the robot to navigate from its current position to the target position. To the best of our knowledge, this is the first work linking foundation models to the positional ambiguity issue.

For the 2D incompressible Euler equations, we establish global-in-time ($t \in \mathbb{R}$) stability of vortex quadrupoles satisfying odd symmetry with respect to both axes. Specifically, if the vorticity restricted to a quadrant is signed, sufficiently concentrated and close to its radial rearrangement up to a translation in $L^1$, we prove that it remains so for all times. The main difficulty is that the kinetic energy maximization problem in a quadrant -- the typical approach for establishing vortex stability -- lacks a solution, as the kinetic energy continues to increase when the vorticity escapes to infinity. We overcome this by taking dynamical information into account: finite-time desingularization result is combined with monotonicity of the first moment and a careful analysis of the interaction energies between vortices. The latter is achieved by new pointwise estimates on the Biot--Savart kernel and quantitative stability results for general interaction kernels. Moreover, with a similar strategy we obtain stability of a pair of opposite-signed Lamb dipoles moving away from each other., Comment: 33 pages, 6 figures

For axisymmetric flows without swirl, we prove the upper bound of $t^{4/3}$ for the growth of the vorticity maximum, which was conjectured by Childress [Phys. D, 2008] and supported by numerical computations from Childress--Gilbert--Valiant [J. Fluid Mech. 2016]. The key is to estimate the velocity maximum by the kinetic energy together with conserved quantities involving the vorticity., Comment: 21 pages, 2 figures

Neural codecs have become crucial to recent speech and audio generation research. In addition to signal compression capabilities, discrete codecs have also been found to enhance downstream training efficiency and compatibility with autoregressive language models. However, as extensive downstream applications are investigated, challenges have arisen in ensuring fair comparisons across diverse applications. To address these issues, we present a new open-source platform ESPnet-Codec, which is built on ESPnet and focuses on neural codec training and evaluation. ESPnet-Codec offers various recipes in audio, music, and speech for training and evaluation using several widely adopted codec models. Together with ESPnet-Codec, we present VERSA, a standalone evaluation toolkit, which provides a comprehensive evaluation of codec performance over 20 audio evaluation metrics. Notably, we demonstrate that ESPnet-Codec can be integrated into six ESPnet tasks, supporting diverse applications., Comment: Accepted by SLT

This paper introduces SpoofCeleb, a dataset designed for Speech Deepfake Detection (SDD) and Spoofing-robust Automatic Speaker Verification (SASV), utilizing source data from real-world conditions and spoofing attacks generated by Text-To-Speech (TTS) systems also trained on the same real-world data. Robust recognition systems require speech data recorded in varied acoustic environments with different levels of noise to be trained. However, existing datasets typically include clean, high-quality recordings (bona fide data) due to the requirements for TTS training; studio-quality or well-recorded read speech is typically necessary to train TTS models. Existing SDD datasets also have limited usefulness for training SASV models due to insufficient speaker diversity. We present SpoofCeleb, which leverages a fully automated pipeline that processes the VoxCeleb1 dataset, transforming it into a suitable form for TTS training. We subsequently train 23 contemporary TTS systems. The resulting SpoofCeleb dataset comprises over 2.5 million utterances from 1,251 unique speakers, collected under natural, real-world conditions. The dataset includes carefully partitioned training, validation, and evaluation sets with well-controlled experimental protocols. We provide baseline results for both SDD and SASV tasks. All data, protocols, and baselines are publicly available at https://jungjee.github.io/spoofceleb., Comment: 9 pages, 2 figures, 8 tables

Iterative self-training, or iterative pseudo-labeling (IPL) -- using an improved model from the current iteration to provide pseudo-labels for the next iteration -- has proven to be a powerful approach to enhance the quality of speaker representations. Recent applications of IPL in unsupervised speaker recognition start with representations extracted from very elaborate self-supervised methods (e.g., DINO). However, training such strong self-supervised models is not straightforward (they require hyper-parameter tuning and may not generalize to out-of-domain data) and, moreover, may not be needed at all. To this end, we show that the simple, well-studied, and established i-vector generative model is enough to bootstrap the IPL process for the unsupervised learning of speaker representations. We also systematically study the impact of other components on the IPL process, which includes the initial model, the encoder, augmentations, the number of clusters, and the clustering algorithm. Remarkably, we find that even with a simple and significantly weaker initial model like i-vector, IPL can still achieve speaker verification performance that rivals state-of-the-art methods., Comment: ICASSP 2025

Text-to-speech (TTS) systems are traditionally trained using modest databases of studio-quality, prompted or read speech collected in benign acoustic environments such as anechoic rooms. The recent literature nonetheless shows efforts to train TTS systems using data collected in the wild. While this approach allows for the use of massive quantities of natural speech, until now, there are no common datasets. We introduce the TTS In the Wild (TITW) dataset, the result of a fully automated pipeline, in this case, applied to the VoxCeleb1 dataset commonly used for speaker recognition. We further propose two training sets. TITW-Hard is derived from the transcription, segmentation, and selection of VoxCeleb1 source data. TITW-Easy is derived from the additional application of enhancement and additional data selection based on DNSMOS. We show that a number of recent TTS models can be trained successfully using TITW-Easy, but that it remains extremely challenging to produce similar results using TITW-Hard. Both the dataset and protocols are publicly available and support the benchmarking of TTS systems trained using TITW data., Comment: 5 pages, submitted to ICASSP 2025 as a conference paper

This paper explores the concept of external magnetic control for vine robots to enable their high curvature steering and navigation for use in endoluminal applications. Vine robots, inspired by natural growth and locomotion strategies, present unique shape adaptation capabilities that allow passive deformation around obstacles. However, without additional steering mechanisms, they lack the ability to actively select the desired direction of growth. The principles of magnetically steered growing robots are discussed, and experimental results showcase the effectiveness of the proposed magnetic actuation approach. We present a 25 mm diameter vine robot with integrated magnetic tip capsule, including 6 Degrees of Freedom (DOF) localization and camera and demonstrate a minimum bending radius of 3.85 cm with an internal pressure of 30 kPa. Furthermore, we evaluate the robot's ability to form tight curvature through complex navigation tasks, with magnetic actuation allowing for extended free-space navigation without buckling. The suspension of the magnetic tip was also validated using the 6 DOF localization system to ensure that the shear-free nature of vine robots was preserved. Additionally, by exploiting the magnetic wrench at the tip, we showcase preliminary results of vine retraction. The findings contribute to the development of controllable vine robots for endoluminal applications, providing high tip force and shear-free navigation., Comment: 13 pages, 10 figures

The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of challenges and workshops that ran annually from 2019 to 2023. The challenges primarily evaluated the tasks of speaker recognition and diarisation under various settings including: closed and open training data; as well as supervised, self-supervised, and semi-supervised training for domain adaptation. The challenges also provided publicly available training and evaluation datasets for each task and setting, with new test sets released each year. In this paper, we provide a review of these challenges that covers: what they explored; the methods developed by the challenge participants and how these evolved; and also the current state of the field for speaker verification and diarisation. We chart the progress in performance over the five installments of the challenge on a common evaluation dataset and provide a detailed analysis of how each year's special focus affected participants' performance. This paper is aimed both at researchers who want an overview of the speaker recognition and diarisation field, and also at challenge organisers who want to benefit from the successes and avoid the mistakes of the VoxSRC challenges. We end with a discussion of the current strengths of the field and open challenges. Project page : https://mm.kaist.ac.kr/datasets/voxceleb/voxsrc/workshop.html, Comment: TASLP 2024

Escaping the middle-income trap requires a country to develop indigenous technological capabilities for high value-added innovation. This study examines the role of second-tier patent systems, known as utility models (UMs), in promoting such capability acquisition in less developed countries. UMs are designed to incentivize incremental and adaptive innovation through lower novelty standards than patents, but their long-term impact on the capability acquisition process remains underexplored. Using South Korea as a case study and drawing on the characteristics of technological regimes in catching-up economies, we present three key findings: First, the country's post-catch-up frontier technologies (U.S. patents) are more impactful (highly cited) when they build on Korean domestic UMs. This suggests that UM-based imitative and adaptive learning laid the foundation for the country's globally competitive capabilities. Second, the impact of UM-based learning diminishes as the country's economy develops. Third, frontier technologies rooted in UMs contribute more to the country's own specialization than to follow-on innovations by foreign actors, compared to technologies without UM linkages. We discuss how technological regimes and industrial policies in catching-up economies interact with the UM system to bridge the catching-up (imitation- and adaptation-based) and post-catching-up (specialization- and creativity-based) phases.

This study investigates the controversial role of Intellectual Property Rights (IPRs) in climate technology transfer and innovation in developing countries. Using a systematic literature review and expert interviews, we assess the role of IPRs on three sources of climate technology: (1) international technology transfer, (2) adaptive innovation, and (3) indigenous innovation. Our contributions are threefold. First, patents have limited impact in any of these channels, suggesting that current debates over IPRs may be directed towards the wrong targets. Second, trademarks and utility models provide incentives for climate innovation in the countries studied. Third, drawing from the results, we develop a framework to guide policy on how IPRs can work better in the broader context of climate and trade policies, outlining distinct mechanisms to support mitigation and adaptation. Our results indicate that market mechanisms, especially trade and demand-pull policies, should be prioritised for mitigation solutions. Adaptation differs, relying more on indigenous innovation due to local needs and low demand. Institutional mechanisms, such as finance and co-development, should be prioritised to build innovation capacities for adaptation.

ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previous challenges, the ASVspoof 5 database is built from crowdsourced data collected from a vastly greater number of speakers in diverse acoustic conditions. Attacks, also crowdsourced, are generated and tested using surrogate detection models, while adversarial attacks are incorporated for the first time. New metrics support the evaluation of spoofing-robust automatic speaker verification (SASV) as well as stand-alone detection solutions, i.e., countermeasures without ASV. We describe the two challenge tracks, the new database, the evaluation metrics, baselines, and the evaluation platform, and present a summary of the results. Attacks significantly compromise the baseline systems, while submissions bring substantial improvements., Comment: 8 pages, ASVspoof 5 Workshop (Interspeech2024 Satellite)

Person detection and tracking (PDT) has seen significant advancements with 2D camera-based systems in the autonomous vehicle field, leading to widespread adoption of these algorithms. However, growing privacy concerns have recently emerged as a major issue, prompting a shift towards LiDAR-based PDT as a viable alternative. Within this domain, "Tracking-by-Detection" (TBD) has become a prominent methodology. Despite its effectiveness, LiDAR-based PDT has not yet achieved the same level of performance as camera-based PDT. This paper examines key components of the LiDAR-based PDT framework, including detection post-processing, data association, motion modeling, and lifecycle management. Building upon these insights, we introduce SpbTrack, a robust person tracker designed for diverse environments. Our method achieves superior performance on noisy datasets and state-of-the-art results on KITTI Dataset benchmarks and custom office indoor dataset among LiDAR-based trackers., Comment: 17 pages, 5 figures

Accurate survival prediction in radiotherapy (RT) is critical for optimizing treatment decisions. This study developed and validated the RT-Surv framework, which integrates general-domain, open-source large language models (LLMs) to structure unstructured electronic health records alongside structured clinical data. Using data from 34,276 patients and an external cohort of 852, the framework successfully transformed unstructured clinical information into structured formats. Incorporating LLM-structured clinical features improved the concordance index from 0.779 to 0.842 during external validation, demonstrating a significant performance enhancement. Key LLM-structured features, such as disease extent, general condition, and RT purpose, showed high predictive importance and aligned closely with statistically significant predictors identified through conventional statistical analyses, thereby improving model interpretability. Furthermore, the framework enhanced risk stratification, enabling more distinct differentiation among low-, intermediate-, and high-risk groups (p < 0.001) using LLM-structured clinical features. These findings highlight the potential of LLMs to convert unstructured data into actionable insights, improving predictive modeling and patient outcomes in clinics., Comment: 23 pages, 2 tables, 4 figures

Multi-suction-cup grippers are frequently employed to perform pick-and-place robotic tasks, especially in industrial settings where grasping a wide range of light to heavy objects in limited amounts of time is a common requirement. However, most existing works focus on using one or two suction cups to grasp only irregularly shaped but light objects. There is a lack of research on robust manipulation of heavy objects using larger arrays of suction cups, which introduces challenges in modeling and predicting grasp failure. This paper presents a general approach to modeling grasp strength in multi-suction-cup grippers, introducing new constraints usable for trajectory planning and optimization to achieve fast and reliable pick-and-place maneuvers. The primary modeling challenge is the accurate prediction of the distribution of loads at each suction cup while grasping objects. To solve for this load distribution, we find minimum spring potential energy configurations through a simple quadratic program. This results in a computationally efficient analytical solution that can be integrated to formulate grasp failure constraints in time-optimal trajectory planning. Finally, we present experimental results to validate the efficiency and accuracy of the proposed model.

We present a multiwavelength analysis of 29 merging galaxy clusters that exhibit radio relics. For each merging system, we perform a weak-lensing analysis on Subaru optical imaging. We generate high-resolution mass maps of the dark matter distributions, which are critical for discerning the merging constituents. Combining the weak-lensing detections with X-ray emission, radio emission, and galaxy redshifts, we discuss the formation of radio relics from the past collision. For each subcluster, we obtain mass estimates by fitting a multi-component NFW model with and without a concentration-mass relation. Comparing the two mass estimate techniques, we find that the concentration-mass relation underestimates (overestimates) the mass relative to fitting both parameters for high- (low-) mass subclusters. We compare the mass estimates of each subcluster to their velocity dispersion measurements and find that they preferentially lie below the expected velocity dispersion scaling relation, especially at the low-mass end (~$10^{14}\ M_\odot$). We show that the majority of the clusters that exhibit radio relics are in major mergers with a mass ratio below 1:4. We investigate the position of the mass peak relative to the galaxy luminosity peak, number density peak, and BCG locations and find that the BCG tends to better trace the mass peak position. Finally, we update a golden sample of 8 galaxy clusters that have the simplest geometries and can provide the cleanest picture of the past merger, which we recommend for further investigation to constrain the nature of dark matter and the acceleration process that leads to radio relics., Comment: 55 pages, 36 figures, submitted to ApJS