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We derive the Fokker-Planck equation for an active particle with both radial and tangential force and perturbative force, and its approximate solution of joint probability density is obtained. In t>>u and u=0 regions, an active particle leads to a super-diffusive distribution for radial velocity, while the mean squared tangential velocity with both radial and tangential force and perturbative force behaviors the Gaussian diffusion. As a result, the joint probability density obtained may be similarly consistent with that for the self-propelled particle., Comment: 14 pages, 2 Tables

Consider a collection of data generators which could represent, e.g., humans equipped with a smart-phone or wearables. We want to train a personalized (or tailored) model for each data generator even if they provide only small local datasets. The available local datasets might fail to provide sufficient statistical power to train high-dimensional models (such as deep neural networks) effectively. One possible solution is to identify similar data generators and pool their local datasets to obtain a sufficiently large training set. This paper proposes a novel method for sequentially identifying similar (or relevant) data generators. Our method is similar in spirit to active sampling methods but does not require exchange of raw data. Indeed, our method evaluates the relevance of a data generator by evaluating the effect of a gradient step using its local dataset. This evaluation can be performed in a privacy-friendly fashion without sharing raw data. We extend this method to non-parametric models by a suitable generalization of the gradient step to update a hypothesis using the local dataset provided by a data generator.

Earth observation (EO) plays a crucial role in creating and sustaining a resilient and prosperous society that has far reaching consequences for all life and the planet itself. Remote sensing platforms like satellites, airborne platforms, and more recently dones and UAVs are used for EO. They collect large amounts of data and this needs to be downlinked to Earth for further processing and analysis. Bottleneck for such high throughput acquisition is the downlink bandwidth. Data-centric solutions to image compression is required to address this deluge. In this work, semantic compression is studied through a compressed learning framework that utilizes only fast and sparse matrix-vector multiplication to encode the data. Camera noise and a communication channel are the considered sources of distortion. The complete semantic communication pipeline then consists of a learned low-complexity compression matrix that acts on the noisy camera output to generate onboard a vector of observations that is downlinked through a communication channel, processed through an unrolled network and then fed to a deep learning model performing the necessary downstream tasks; image classification is studied. Distortions are compensated by unrolling layers of NA-ALISTA with a wavelet sparsity prior. Decoding is thus a plug-n-play approach designed according to the camera/environment information and downstream task. The deep learning model for the downstream task is jointly fine-tuned with the compression matrix and the unrolled network through the loss function in an end-to-end fashion. It is shown that addition of a recovery loss along with the task dependent losses improves the downstream performance in noisy settings at low compression ratios.

Contrastive Language and Image Pairing (CLIP), a transformative method in multimedia retrieval, typically trains two neural networks concurrently to generate joint embeddings for text and image pairs. However, when applied directly, these models often struggle to differentiate between visually distinct images that have similar captions, resulting in suboptimal performance for image-based similarity searches. This paper addresses the challenge of optimizing CLIP models for various image-based similarity search scenarios, while maintaining their effectiveness in text-based search tasks such as text-to-image retrieval and zero-shot classification. We propose and evaluate two novel methods aimed at refining the retrieval capabilities of CLIP without compromising the alignment between text and image embeddings. The first method involves a sequential fine-tuning process: initially optimizing the image encoder for more precise image retrieval and subsequently realigning the text encoder to these optimized image embeddings. The second approach integrates pseudo-captions during the retrieval-optimization phase to foster direct alignment within the embedding space. Through comprehensive experiments, we demonstrate that these methods enhance CLIP's performance on various benchmarks, including image retrieval, k-NN classification, and zero-shot text-based classification, while maintaining robustness in text-to-image retrieval. Our optimized models permit maintaining a single embedding per image, significantly simplifying the infrastructure needed for large-scale multi-modal similarity search systems.

We derive a Fokker-Planck equation for joint probability density for an active particle coupled two heat reservoirs with harmonic, viscous, random forces. The approximate solution for the joint distribution density of all-to-all and three others topologies is solved, which apply an exponential correlated Gaussian force in three-time regions of correlation time. Mean squared displacement, velocity behaviors in the form of super-diffusion, while the mean squared displacement, velocity has the Gaussian form, normal diffusion. Concomitantly, the Kurtosis, correlation coefficient, and moment from moment equation are approximately and numerically calculated., Comment: 13 pages, 3 Tables

We firstly study the Navier-Stokes equation for the motion of a passive particle with harmonic, viscous, perturbative forces, subject to an exponentially correlated Gaussian force. Secondly, from the Fokker-Planck equation in an incompressible conducting fluid of magnetic field, we approximately obtain the solution of the joint probability density by using double Fourier transforms in three-time domains. In addition, the kurtosis, the correlation coefficient, and the moment from moment equation are numerically calculated., Comment: 19 pages, 5 Tables

Managing high-frequency data in a limit order book (LOB) is a complex task that often exceeds the capabilities of conventional time-series forecasting models. Accurately predicting the entire multi-level LOB, beyond just the mid-price, is essential for understanding high-frequency market dynamics. However, this task is challenging due to the complex interdependencies among compound attributes within each dimension, such as order types, features, and levels. In this study, we explore advanced multidimensional sequence-to-sequence models to forecast the entire multi-level LOB, including order prices and volumes. Our main contribution is the development of a compound multivariate embedding method designed to capture the complex relationships between spatiotemporal features. Empirical results show that our method outperforms other multivariate forecasting methods, achieving the lowest forecasting error while preserving the ordinal structure of the LOB.

We present a proof-of-concept simulation-based inference on $\Omega_{\rm m}$ and $\sigma_{8}$ from the SDSS BOSS LOWZ NGC catalog using neural networks and domain generalization techniques without the need of summary statistics. Using rapid lightcone simulations, ${\rm L{\scriptsize -PICOLA}}$, mock galaxy catalogs are produced that fully incorporate the observational effects. The collection of galaxies is fed as input to a point cloud-based network, ${\texttt{Minkowski-PointNet}}$. We also add relatively more accurate ${\rm G{\scriptsize ADGET}}$ mocks to obtain robust and generalizable neural networks. By explicitly learning the representations which reduces the discrepancies between the two different datasets via the semantic alignment loss term, we show that the latent space configuration aligns into a single plane in which the two cosmological parameters form clear axes. Consequently, during inference, the SDSS BOSS LOWZ NGC catalog maps onto the plane, demonstrating effective generalization and improving prediction accuracy compared to non-generalized models. Results from the ensemble of 25 independently trained machines find $\Omega_{\rm m}=0.339 \pm 0.056$ and $\sigma_{8}=0.801 \pm 0.061$, inferred only from the distribution of galaxies in the lightcone slices without relying on any indirect summary statistics. A single machine that best adapts to the ${\rm G{\scriptsize ADGET}}$ mocks yields a tighter prediction of $\Omega_{\rm m}=0.282 \pm 0.014$ and $\sigma_{8}=0.786 \pm 0.036$. We emphasize that adaptation across multiple domains can enhance the robustness of the neural networks in observational data., Comment: 28 pages, 14 figures; Accepted for publication in ApJ

We propose a new mechanism of primordial black hole formation via an aborted phase transition during the early matter-dominated stage of reheating after inflation. In reheating, induced by the decay of a pressureless fluid dominating the Universe at the end of inflation, dubbed as reheaton, the temperature of the radiation bath typically increases, reaching a maximum temperature $T_{\rm max}$, and then decreases. We consider a first-order phase transition induced by the increase of the temperature that is aborted as $T_{\rm max}$ is higher than the critical temperature but not sufficiently high for the bubble nucleation rate to overcome the expansion of the Universe. Although bubbles never fully occupy the space, some may be nucleated and expand until the temperature once again decreases to the critical temperature. We argue that these bubbles shrink and disappear as the temperature drops further, leaving behind macroscopic spherical regions with positive density perturbations. These perturbed regions accrete the surrounding matter (reheatons) and eventually collapse into primordial black holes whose mass continues to grow until the onset of radiation domination. We estimate the abundance of these primordial black holes in terms of the bubble nucleation rate at $T_{\rm max}$, and demonstrate that the abundance can be significantly large from a phenomenological perspective., Comment: 7 pages + Supplementary Material, 4 figures

Directional optical sources can give rise to the directional excitation and propagation of light. The directionality of the conventional directional dipole (CDD) sources are attributed to the interference of the electric and/or magnetic dipoles, while the effect of the toroidal dipole on optical directionality remains unexplored.} Here, we numerically and analytically investigate the directional properties of the toroidal dipole. We show that the toroidal dipole can replace the electric dipole in the CDD sources to form the pseudo directional dipoles (PDDs), which can be applied to achieve analogous near-field directional coupling with a silicon waveguide. Moreover, the directionality of the PDDs can be flexibly controlled by changing the geometric parameters of the toroidal dipole, leading to tunable asymmetric coupling between the sources and the waveguide. These new types of directional sources provide more degrees of freedom for tailoring the optical directionality compared to the conventional sources. The results open new possibilities for directional light manipulation and can find applications in on-chip optical routing, waveguiding, and nanophotonic communications., Comment: 21 pages, 6 figures

We present the first measurement of the missing energy due to nuclear effects in monoenergetic, muon neutrino charged-current interactions on carbon, originating from $K^+ \rightarrow \mu^+ \nu_\mu$ decay-at-rest ($E_{\nu_\mu}=235.5$ MeV), performed with the JSNS$^2$ liquid scintillator based experiment. Towards characterizing the neutrino interaction, ostensibly $\nu_\mu n \rightarrow \mu^- p$ or $\nu_\mu$$^{12}\mathrm{C}$ $\rightarrow \mu^-$$^{12}\mathrm{N}$, and in analogy to similar electron scattering based measurements, we define the missing energy as the energy transferred to the nucleus ($\omega$) minus the kinetic energy of the outgoing proton(s), $E_{m} \equiv \omega-\sum T_p$, and relate this to visible energy in the detector, $E_{m}=E_{\nu_\mu}~(235.5~\mathrm{MeV})-m_\mu~(105.7~\mathrm{MeV}) - E_{vis}$. The missing energy, which is naively expected to be zero in the absence of nuclear effects (e.g. nucleon separation energy, Fermi momenta, and final-state interactions), is uniquely sensitive to many aspects of the interaction, and has previously been inaccessible with neutrinos. The shape-only, differential cross section measurement reported, based on a $(77\pm3)$% pure double-coincidence KDAR signal (621 total events), provides an important benchmark for models and event generators at 100s-of-MeV neutrino energies, characterized by the difficult-to-model transition region between neutrino-nucleus and neutrino-nucleon scattering, and relevant for applications in nuclear physics, neutrino oscillation measurements, and Type-II supernova studies.

In this work, we aim to analyze and optimize the EnCLAP framework, a state-of-the-art model in automated audio captioning. We investigate the impact of modifying the acoustic encoder components, explore pretraining with different dataset scales, and study the effectiveness of a reranking scheme. Through extensive experimentation and quantitative analysis of generated captions, we develop EnCLAP++, an enhanced version that significantly surpasses the original., Comment: Accepted to DCASE2024 Workshop

In this technical report, we describe our submission to DCASE2024 Challenge Task6 (Automated Audio Captioning) and Task8 (Language-based Audio Retrieval). We develop our approach building upon the EnCLAP audio captioning framework and optimizing it for Task6 of the challenge. Notably, we outline the changes in the underlying components and the incorporation of the reranking process. Additionally, we submit a supplementary retriever model, a byproduct of our modified framework, to Task8. Our proposed systems achieve FENSE score of 0.542 on Task6 and mAP@10 score of 0.386 on Task8, significantly outperforming the baseline models., Comment: DCASE2024 Challenge Technical Report. Ranked 2nd in Task 6 Automated Audio Captioning

Large language models (LLMs) have emerged due to their capability to generate high-quality content across diverse contexts. To reduce their explosively increasing demands for computing resources, a mixture of experts (MoE) has emerged. The MoE layer enables exploiting a huge number of parameters with less computation. Applying state-of-the-art continuous batching increases throughput; however, it leads to frequent DRAM access in the MoE and attention layers. We observe that conventional computing devices have limitations when processing the MoE and attention layers, which dominate the total execution time and exhibit low arithmetic intensity (Op/B). Processing MoE layers only with devices targeting low-Op/B such as processing-in-memory (PIM) architectures is challenging due to the fluctuating Op/B in the MoE layer caused by continuous batching. To address these challenges, we propose Duplex, which comprises xPU tailored for high-Op/B and Logic-PIM to effectively perform low-Op/B operation within a single device. Duplex selects the most suitable processor based on the Op/B of each layer within LLMs. As the Op/B of the MoE layer is at least 1 and that of the attention layer has a value of 4-8 for grouped query attention, prior PIM architectures are not efficient, which place processing units inside DRAM dies and only target extremely low-Op/B (under one) operations. Based on recent trends, Logic-PIM adds more through-silicon vias (TSVs) to enable high-bandwidth communication between the DRAM die and the logic die and place powerful processing units on the logic die, which is best suited for handling low-Op/B operations ranging from few to a few dozens. To maximally utilize the xPU and Logic-PIM, we propose expert and attention co-processing., Comment: 15 pages, 16 figures, accepted at MICRO 2024

It is known that sparse recovery is possible if the number of measurements is in the order of the sparsity, but the corresponding decoders either lack polynomial decoding time or robustness to noise. Commonly, decoders that rely on a null space property are being used. These achieve polynomial time decoding and are robust to additive noise but pay the price by requiring more measurements. The non-negative least residual has been established as such a decoder for non-negative recovery. A new equivalent condition for uniform, robust recovery of non-negative sparse vectors with the non-negative least residual that is not based on null space properties is introduced. It is shown that the number of measurements for this equivalent condition only needs to be in the order of the sparsity. Further, it is explained why the robustness to additive noise is similar, but not equal, to the robustness of decoders based on null space properties., Comment: 20 pages

Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introduces Language Model-based Code Knowledge Tracing (CodeLKT), an innovative application of Language model-based Knowledge Tracing (LKT) to programming education. CodeLKT leverages pre-trained language models to process learning data, demonstrating superior performance over existing KT and Code KT models. We explore Domain Adaptive Pre-Training (DAPT) and Task Adaptive Pre-Training (TAPT), showing enhanced performance in the coding domain and investigating cross-domain transfer between mathematics and coding. Additionally, we present an theoretically-informed integrated system combining CodeLKT with large language models to generate personalized, in-depth feedback to support students' programming learning. This work advances the field of Code Knowledge Tracing by expanding the knowledge base with language model-based approach and offering practical implications for programming education through data-informed feedback., Comment: 9 pages, 2 figures

In a private database query scheme (PDQ), a server maintains a database, and users send queries to retrieve records of interest from the server while keeping their queries private. A crucial step in PDQ protocols based on homomorphic encryption is homomorphic compression, which compresses encrypted sparse vectors consisting of query results. In this work, we propose a new homomorphic compression scheme with PDQ as its main application. Unlike existing approaches, our scheme (i) can be efficiently implemented by fully exploiting homomorphic SIMD technique and (ii) enjoys both asymptotically optimal compression rate and asymptotically good decompression complexity. Experimental results show that our approach is 4.7x to 33.2x faster than the previous best results.

We propose a consistent framework with the $U(1)$ Peccei-Quinn (PQ) symmetry for obtaining the initial condition for axion kinetic misalignment from inflation. We introduce a PQ complex scalar field and an extra Higgs doublet, which are conformally coupled to gravity, and three right-handed neutrinos for the seesaw mechanism. In the DFSZ type scenarios for the axion, we obtain the PQ anomalies from the Standard Model quarks carrying nonzero PQ charges in some of two Higgs doublet models, solving the strong CP problem by the QCD potential for the axion. Assuming that the PQ symmetry is explicitly violated in the scalar potential by quantum gravity effects, we show that a sufficiently large initial axion velocity can be obtained at the end inflation while avoiding the axion quality problem. As inflation is driven by the radial distance from the origin in the space of scalar fields close to the pole of the kinetic terms in the Einstein frame, we obtain successful inflationary predictions and set the initial axion velocity at the end of inflation. Focusing on the pure PQ inflation with a small running quartic coupling for the PQ field, we discuss the post-inflationary dynamics for the inflaton and the axion. As a result, we show that a sufficiently high reheating temperature, can be obtained dominantly from the Higgs-portal couplings to the PQ field, while being consistent with axion kinetic misalignment, the stability for the Higgs fields during inflation and the non-restoration of the PQ symmetry after reheating., Comment: 31 pages, 4 figures

We explore the higher integrability of Green's functions associated with the second-order elliptic equation $a^{ij}D_{ij}u + b^i D_iu = f$ in a bounded domain $\Omega \subset \mathbb{R}^d$, and establish a version of Aleksandrov's maximum principle. In particular, we consider the drift term $b=(b^1, \ldots, b^d)$ in $L_d$ and the source term $f \in L_p$ for some $p < d$. This provides an alternative and analytic proof of a result by N. V. Krylov (2021) concerning $L_d$ drifts. The key step involves deriving a Gehring type inequality for Green's functions by using the Fabes-Stroock approach (1984).

Non-Hermitian systems have Riemann surface structures of complex eigenvalues that admit singularities known as exceptional points. Combining with geometric phases of eigenstates gives rise to unique properties of non-Hermitian systems, and their classifications have been studied recently. However, the physical realizations of classes of the classifications have been relatively limited because a small number of modes and exceptional points are involved. In this work, we show in microcavities that all five classes [J.-W. Ryu, et al., Commun. Phys. 7, 109 (2024)] of three modes can emerge with three exceptional points. In demonstrations, we identified various combinations of exceptional points within a two-dimensional parameter space of a single microcavity and defined five distinct encircling loops based on three selected exceptional points. According to the classification, these loops facilitate different mode exchanges and the acquisition of additional geometric phases during the adiabatic encircling of exceptional points. Our results provide a broad description of the geometric phases-associated topology induced by multiple exceptional points in realistic physical systems., Comment: 7 pages, 5 figures

Referring image segmentation (RIS) requires dense vision-language interactions between visual pixels and textual words to segment objects based on a given description. However, commonly adapted dual-encoders in RIS, e.g., Swin transformer and BERT (uni-modal encoders) or CLIP (a multi-modal dual-encoder), lack dense multi-modal interactions during pre-training, leading to a gap with a pixel-level RIS task. To bridge this gap, existing RIS methods often rely on multi-modal fusion modules that interact two encoders, but this approach leads to high computational costs. In this paper, we present a novel RIS method with a single-encoder, i.e., BEiT-3, maximizing the potential of shared self-attention across all framework components. This enables seamless interactions of two modalities from input to final prediction, producing granularly aligned multi-modal features. Furthermore, we propose lightweight yet effective decoder modules, a Shared FPN and a Shared Mask Decoder, which contribute to the high efficiency of our model. Our simple baseline with a single encoder achieves outstanding performances on the RIS benchmark datasets while maintaining computational efficiency, compared to the most recent SoTA methods based on dual-encoders., Comment: ArXiv pre-print

This study investigates the application of machine/deep learning to classify snoring sounds excited at different levels of the upper airway in patients with obstructive sleep apnea (OSA) using data from drug-induced sleep endoscopy (DISE). The snoring sounds of 39 subjects were analyzed and labeled according to the Velum, Oropharynx, Tongue Base, and Epiglottis (VOTE) classification system. The dataset, comprising 5,173 one-second segments, was used to train and test models, including Support Vector Machine (SVM), Bidirectional Long Short-Term Memory (BiLSTM), and ResNet-50. The ResNet-50, a convolutional neural network (CNN), showed the best overall performance in classifying snoring acoustics, particularly in identifying multi-level obstructions. The study emphasizes the potential of integrating snoring acoustics with deep learning to improve the diagnosis and treatment of OSA. However, challenges such as limited sample size, data imbalance, and differences between pharmacologically induced and natural snoring sounds were noted, suggesting further research to enhance model accuracy and generalizability.

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

With advanced synthetic techniques, a wide variety of well-defined graphene nano-ribbons (GNRs) can be produced with atomic precision. Hence, finding the relation between their structures and properties becomes important for the rational design of GNRs. In this work, we explore the complete chemical space of gulf-edged zigzag graphene nanoribbons (ZGNR-Gs), a subclass of zigzag GNRs in which the zigzag edges miss carbon atoms in a regular sequence. We demonstrate that the electronic properties of ZGNR-Gs depend on four structural parameters: ribbon width, gulf edge size, unit length, and gulf offset. Using tight-binding calculations and the Hubbard model, we find that all ZGNR-Gs are semiconductors with varying band gaps; there are no metals in this class of materials. Notably, when spin polarization is considered, most ZGNR-Gs exhibit antiferromagnetic behavior, with the spin moments and spin-induced band gap opening being stabilized by longer zigzag segments at the edges. Furthermore, we provide simple empirical rules that describe the Z2 topological invariant based on the aforementioned structural parameters. By analyzing the full chemical space of ZGNR-Gs, we offer insights into the design of GNRs with desired electronic, magnetic, and topological properties for nanoelectronic applications.

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.

The persistent issue of human bias in recruitment processes poses a formidable challenge to achieving equitable hiring practices, particularly when influenced by demographic characteristics such as gender and race of both interviewers and candidates. Asynchronous Video Interviews (AVIs), powered by Artificial Intelligence (AI), have emerged as innovative tools aimed at streamlining the application screening process while potentially mitigating the impact of such biases. These AI-driven platforms present an opportunity to customize the demographic features of virtual interviewers to align with diverse applicant preferences, promising a more objective and fair evaluation. Despite their growing adoption, the implications of virtual interviewer identities on candidate experiences within AVIs remain underexplored. We aim to address this research and empirical gap in this paper. To this end, we carried out a comprehensive between-subjects study involving 218 participants across six distinct experimental conditions, manipulating the gender and skin color of an AI virtual interviewer agent. Our empirical analysis revealed that while the demographic attributes of the agents did not significantly influence the overall experience of interviewees, variations in the interviewees' demographics significantly altered their perception of the AVI process. Further, we uncovered that the mediating roles of Social Presence and Perception of the virtual interviewer critically affect interviewees' perceptions of fairness (+), privacy (-), and impression management (+).

This paper introduces a generic filter-based state estimation framework that supports two state-decoupling strategies based on cross-covariance factorization. These strategies reduce the computational complexity and inherently support true modularity -- a perquisite for handling and processing meshed range measurements among a time-varying set of devices. In order to utilize these measurements in the estimation framework, positions of newly detected stationary devices (anchors) and the pairwise biases between the ranging devices are required. In this work an autonomous calibration procedure for new anchors is presented, that utilizes range measurements from multiple tags as well as already known anchors. To improve the robustness, an outlier rejection method is introduced. After the calibration is performed, the sensor fusion framework obtains initial beliefs of the anchor positions and dictionaries of pairwise biases, in order to fuse range measurements obtained from new anchors tightly-coupled. The effectiveness of the filter and calibration framework has been validated through evaluations on a recorded dataset and real-world experiments.

A technique has recently been developed for tracking short-term spectral variations in Galactic cosmic rays (GCRs) using data from a single neutron monitor (NM), by collecting histograms of the time delay between successive neutron counts and extracting the leader fraction $L$ as a proxy of the spectral index. Here we analyze $L$ from four Antarctic NMs during 2015 March to 2023 September. We have calibrated $L$ from the South Pole NM with respect to a daily spectral index determined from published data of GCR proton fluxes during 2015--2019 from the Alpha Magnetic Spectrometer (AMS-02) aboard the International Space Station. Our results demonstrate a robust correlation between the leader fraction and the spectral index fit over the rigidity range 2.97--16.6 GV for AMS-02 data, with uncertainty 0.018 in the daily spectral index as inferred from $L$. In addition to the 11-year solar activity cycle, a wavelet analysis confirms a 27-day periodicity in the GCR flux and spectral index corresponding to solar rotation, especially near sunspot minimum, while the flux occasionally exhibited a strong harmonic at 13.5 days, and that the magnetic field component along a nominal Parker spiral (i.e., the magnetic sector structure) is a strong determinant of such spectral and flux variations, with the solar wind speed exerting an additional, nearly rigidity-independent influence on flux variations. Our investigation affirms the capability of ground-based NM stations to accurately and continuously monitor cosmic ray spectral variations in the long-term future., Comment: 17 pages, 10 figures

In this dissertation, we investigate the issue of robust localization in swarms of heterogeneous mobile agents with multiple and time-varying sensing modalities. Our focus is the development of filter-based and decoupled estimators under the assumption that agents possess communication and processing capabilities. Based on the findings from Distributed Collaborative State Estimation and modular sensor fusion, we propose a novel Kalman filter decoupling paradigm, which is termed Isolated Kalman Filtering (IKF). This paradigm is formally discussed and the treatment of delayed measurement is studied. The impact of approximation made was investigated on different observation graphs and the filter credibility was evaluated on a linear system in a Monte Carlo simulation. Finally, we propose a multi-agent modular sensor fusion approach based on the IKF paradigm, in order to cooperatively estimate the global state of a multi-agent system in a distributed way and fuse information provided by different on-board sensors in a computationally efficient way. As a consequence, this approach can be performed distributed among agents, while (i) communication between agents is only required at the moment of inter-agent joint observations, (ii) one agent acts as interim master to process state corrections isolated, (iii) agents can be added and removed from the swarm, (iv) each agent's full state can vary during mission (each local sensor suite can be truly modular), and (v) delayed and multi-rate sensor updates are supported. Extensive evaluation on realistic simulated and real-world data sets show that the proposed Isolated Kalman Filtering (IKF) paradigm, is applicable for both, truly modular single agent estimation and distributed collaborative multi-agent estimation problems., Comment: PhD thesis

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.

Masked Image Modeling (MIM) is a technique in self-supervised learning that focuses on acquiring detailed visual representations from unlabeled images by estimating the missing pixels in randomly masked sections. It has proven to be a powerful tool for the preliminary training of Vision Transformers (ViTs), yielding impressive results across various tasks. Nevertheless, most MIM methods heavily depend on the random masking strategy to formulate the pretext task. This strategy necessitates numerous trials to ascertain the optimal dropping ratio, which can be resource-intensive, requiring the model to be pre-trained for anywhere between 800 to 1600 epochs. Furthermore, this approach may not be suitable for all datasets. In this work, we propose a new masking strategy that effectively helps the model capture global and local features. Based on this masking strategy, SymMIM, our proposed training pipeline for MIM is introduced. SymMIM achieves a new SOTA accuracy of 85.9\% on ImageNet using ViT-Large and surpasses previous SOTA across downstream tasks such as image classification, semantic segmentation, object detection, instance segmentation tasks, and so on., Comment: Accepted at ICPR 2024

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

In this work, we discuss evaluating video foundation models in a fair and robust manner. Unlike language or image foundation models, many video foundation models are evaluated with differing parameters (such as sampling rate, number of frames, pretraining steps, etc.), making fair and robust comparisons challenging. Therefore, we present a carefully designed evaluation framework for measuring two core capabilities of video comprehension: appearance and motion understanding. Our findings reveal that existing video foundation models, whether text-supervised like UMT or InternVideo2, or self-supervised like V-JEPA, exhibit limitations in at least one of these capabilities. As an alternative, we introduce TWLV-I, a new video foundation model that constructs robust visual representations for both motion- and appearance-based videos. Based on the average top-1 accuracy of linear probing on five action recognition benchmarks, pretrained only on publicly accessible datasets, our model shows a 4.6%p improvement compared to V-JEPA (ViT-L) and a 7.7%p improvement compared to UMT (ViT-L). Even when compared to much larger models, our model demonstrates a 7.2%p improvement compared to DFN (ViT-H), a 2.7%p improvement compared to V-JEPA (ViT-H) and a 2.8%p improvement compared to InternVideo2 (ViT-g). We provide embedding vectors obtained by TWLV-I from videos of several commonly used video benchmarks, along with evaluation source code that can directly utilize these embeddings. The code is available at https://github.com/twelvelabs-io/video-embeddings-evaluation-framework., Comment: 17 pages; Twelve Labs Technical Report

The field of Natural Language Processing (NLP) has seen significant advancements with the development of Large Language Models (LLMs). However, much of this research remains focused on English, often overlooking low-resource languages like Korean. This oversight presents challenges due to the unique non-alphabetic token structure of Korean and the substantial memory and computational demands required for LLM training, which frequently lead to memory constraints and out-of-memory errors. To address these issues, we present RedWhale, a model specifically tailored for Korean language processing. RedWhale is developed using an efficient continual pretraining approach that includes a comprehensive Korean corpus preprocessing pipeline, a specialized tokenizer, an optimized model initialization technique, and a multistage pretraining strategy. These innovations collectively reduce training time and computational costs while maintaining high levels of accuracy and comprehension. By leveraging cross-lingual transfer learning, which exploits shared linguistic similarities across languages, RedWhale builds on English models to enhance Korean language processing. Experimental results demonstrate that RedWhale outperforms other leading models on Korean NLP benchmarks, including the Korean Balanced Evaluation of Significant Tasks (KoBEST), showing superior understanding and generation of Korean text. Furthermore, RedWhale showed no signs of convergence even after pretraining on 9.7 billion tokens, indicating the potential for further improvements with additional training. This work represents a significant advancement in bridging the linguistic divide, particularly in enhancing NLP capabilities for the Korean language.