Virtual reality (VR) conferencing has the potential to provide geographically dispersed users with an immersive environment, enabling rich social interactions and user experience using avatars. However, remote communication in VR inevitably introduces end-to-end (E2E) latency, which can significantly impact user experience. To clarify the impact of latency, we conducted subjective experiments to analyze how it influences interaction fluency from the perspective of quality perception and social presence from the perspective of social cognition, comparing VR conferencing with traditional video conferencing (VC). Specifically, interaction fluency emphasizes user perception of interaction pace and responsiveness and is assessed using Absolute Category Rating (ACR) method. In contrast, social presence focuses on the cognitive understanding of interaction, specifically whether individuals can comprehend the intentions, emotions, and behaviors expressed by others. It is primarily measured using the Networked Minds Social Presence Inventory (NMSPI). Building on this analysis, we further investigate the relationship between interaction fluency and social presence under different latency conditions to clarify the underlying perceptual and cognitive mechanisms. The findings from these subjective tests provide meaningful insights for optimizing the related systems, helping to improve interaction fluency and enhancing social presence in immersive virtual environments.

The habitability of terrestrial exoplanets orbiting M dwarfs is a key topic in the search for extraterrestrial life. The climates of these planets differ significantly from the Earth's due to their likely tidal locking, resulting in a hotter dayside and a colder nightside caused by uneven stellar irradiation. On tidally-locked planets around the outer edge of the habitable zone (HZ), although the definition of the classical HZ requires thick CO2 atmosphere, CO2 can condense onto the surface, leading to the reduction of greenhouse effect. However, the dayside permanent stellar irradiation could maintain a surface liquid water area. The onset of atmospheric collapse and the persistence of surface liquid water are governed by global heat redistribution which is influenced by factors such as atmospheric mass, stellar irradiation, and greenhouse effects. In this study, we used a three-dimensional global climate model to investigate the impact of atmospheric collapse on the presence of dayside surface liquid water. Our results indicate that surface liquid water could counter-intuitively persist despite atmospheric collapse. This is because the loss of atmospheric CO2 weakens not only the greenhouse effect but also daynight heat transport, leading to less redistribution of the energy of dayside insolation to the nightside. While atmospheric collapse is typically seen as an obstacle to maintaining a habitable climate, our findings suggest that it could play a positive role in sustaining surface liquid water on tidally-locked planets. Our work provides new light into the relationship between atmospheric collapse and planetary habitability.

This study investigates the effects of nail penetration speed on the safety outcomes of large-format automotive lithium-ion pouch cells. Through six controlled tests varying the speed of nail insertion, we observed that lower penetration speeds did not induce thermal runaway; instead, the cells exhibited self-discharge while the nail remained embedded. These findings suggest that penetration speed is a critical factor in the onset of thermal runaway, providing valuable insights for the development of safer battery systems and more effective safety testing protocols.

We construct the natural Frobenius structures on two families of rigid irregular $\check{G}$-connections on $\mathbb{G}_m$ (or $\mathbb{A}^1$) for a split simple group $\check{G}$: (i) the $θ$-connections arising from Vinberg's $θ$-groups introduced by Chen and Yun; (ii) the Airy connection of Jakob--Kamgarpour--Yi generalizing the classical Airy equations. These data form the $p$-adic companions of the $\ell$-adic local systems introduced by Yun and Jakob--Kamgarpour--Yi. Via the Frobenius structures, we study the local monodromy representations of these local systems at the unique wildly ramified point and verify the prediction of Reeder--Yu on epipelagic Langlands parameters in our setting. We calculate the global geometric monodromy group of a special Airy $\check{G}$-local system via its local monodromy. We show the cohomological rigidity and the physical rigidity of these local systems, as conjectured by Heinloth--Ngô--Yun.

Personalized large language models (LLMs) rely on memory retrieval to incorporate user-specific histories, preferences, and contexts. Existing approaches either overload the LLM by feeding all the user's past memory into the prompt, which is costly and unscalable, or simplify retrieval into a one-shot similarity search, which captures only surface matches. Cognitive science, however, shows that human memory operates through a dual process: Familiarity, offering fast but coarse recognition, and Recollection, enabling deliberate, chain-like reconstruction for deeply recovering episodic content. Current systems lack both the ability to perform recollection retrieval and mechanisms to adaptively switch between the dual retrieval paths, leading to either insufficient recall or the inclusion of noise. To address this, we propose RF-Mem (Recollection-Familiarity Memory Retrieval), a familiarity uncertainty-guided dual-path memory retriever. RF-Mem measures the familiarity signal through the mean score and entropy. High familiarity leads to the direct top-K Familiarity retrieval path, while low familiarity activates the Recollection path. In the Recollection path, the system clusters candidate memories and applies alpha-mix with the query to iteratively expand evidence in embedding space, simulating deliberate contextual reconstruction. This design embeds human-like dual-process recognition into the retriever, avoiding full-context overhead and enabling scalable, adaptive personalization. Experiments across three benchmarks and corpus scales demonstrate that RF-Mem consistently outperforms both one-shot retrieval and full-context reasoning under fixed budget and latency constraints. Our code can be found in the Reproducibility Statement.

In this work we investigate the inverse problem of recovering one point source in the heat equation from sparse boundary measurement, i.e., the flux data at several points on the boundary. We prove the unique recovery of the location and piecewise constant in time amplitude when the domain is the unit ball in $\mathbb{R}^d$ ($d\geq2$), and the unique recovery of the location and compactly supported amplitude when the domain is simply connected, smooth and bounded in $\mathbb{R}^2$, under mild conditions on the observational points. The proof combines distinct analytical tools, including the representation of the flux data via Laplacian eigenfunctions on the unit ball, a detailed analysis of the properties of the heat and Poisson kernels, as well as methods drawn from complex analysis. Further we present several numerical experiments to illustrate the feasibility of the recovery from sparse boundary data.

Proton ordering in water ice is a paradigmatic order-disorder transition in a locally constrained system. The ice rules require exactly two hydrogens close to each oxygen, restricting the disorder to an exponentially large yet strongly correlated manifold of hydrogen-bond configurations. Within this constrained space, meV-scale energy differences drive the transition from disordered ice Ih to ordered ice XI, while distinct configurations are separated by eV-scale barriers. These barriers hinder equilibration in experiments, and efficient sampling of this space with the required energy accuracy has remained a long-standing challenge in simulation. We address this by combining a machine learning interatomic potential with loop updates that preserve the ice rules and continuous updates of atomic coordinates, enabling equilibrium sampling with ab initio accuracy and capturing configurational entropic effects. In systems of up to 360 water molecules, with over 10^6 samples retained per temperature point, the simulations reveal clear first-order transition signatures at 83 K: a negative Binder cumulant, a bimodal potential energy distribution, and a sharp step in the lattice aspect ratio. Nuclear quantum effects are estimated to lower the transition temperature by approximately 20 K, bringing the prediction closer to the experimental value of 72 K.

Large Vision-Language Models (LVLMs) undergo safety alignment to suppress harmful content. However, current defenses predominantly target explicit malicious patterns in the input representation, often overlooking the vulnerabilities inherent in compositional reasoning. In this paper, we identify a systemic flaw where LVLMs can be induced to synthesize harmful logic from benign premises. We formalize this attack paradigm as \textit{Reasoning-Oriented Programming}, drawing a structural analogy to Return-Oriented Programming in systems security. Just as ROP circumvents memory protections by chaining benign instruction sequences, our approach exploits the model's instruction-following capability to orchestrate a semantic collision of orthogonal benign inputs. We instantiate this paradigm via \tool{}, an automated framework that optimizes for \textit{semantic orthogonality} and \textit{spatial isolation}. By generating visual gadgets that are semantically decoupled from the harmful intent and arranging them to prevent premature feature fusion, \tool{} forces the malicious logic to emerge only during the late-stage reasoning process. This effectively bypasses perception-level alignment. We evaluate \tool{} on SafeBench and MM-SafetyBench across 7 state-of-the-art 0.LVLMs, including GPT-4o and Claude 3.7 Sonnet. Our results demonstrate that \tool{} consistently circumvents safety alignment, outperforming the strongest existing baseline by an average of 4.67\% on open-source models and 9.50\% on commercial models.

Electroweak triplet Higgs sector extensions are well-motivated scenarios to address lepton flavour observations. These models can also be strongly constrained by combining precise, indirect low-energy measurements with direct searches for exotic, doubly charged Higgs bosons. Together, these searches set competitive constraints on the type-II seesaw mechanism. In this work, we consider extensions of the type-II seesaw, specifically through the lens of a modified collider phenomenology. Surveying motivated extensions, we map out changes in expected correlations, focusing on the modified production and decay phenomenology of exotic Higgs particles. This enables us to assess the robustness of the type-II seesaw collider constraints against extended new-physics contributions that modify standard sensitivity expectations and projections.

In this paper, we consider the following nonlinear disordered Stark model: $${\bf i}\partial_tu_n+δ(u_{n+1}+u_{n-1})+nu_n+v_nu_n+ε|u_n|^{2}u_n=0,\quad n\in\mathbb{Z}.$$ By employing the diagonalization of the associated linear operators and the KAM theory for nonlinear Hamiltonian systems, we establish that for parameters $δ$ and $\varepsilon$ in a reasonable range, and for most realization of random variables $v=\{v_n\}_{n \in \mathbb{Z}}$, there exist time quasi-periodic and spatially localized states that exhibit arbitrary power-law spatial decay.

We show that incoherent operations (IOs) can achieve the state transformations that are forbidden under dephasing-covariant incoherent operations (DIOs), thereby resolving the open problem posed by Chitambar and Gour [Phys. Rev. Lett. 117, 030401 (2016)]. We further demonstrate that no set of IO monotones suffices to characterize state convertibility under strictly incoherent operations (SIOs), and that monotones common to IOs and DIOs are insufficient to characterize convertibility under DIOs.

We initiate the systematic search for planets in the 2023 data of the Korea Microlensing Telescope Network (KMTNet), focusing on those planets found by the KMTNet AnomalyFinder with low preliminary estimates of the mass-ratio, $q<2\times 10^{-4}$. The 2023 season is the first for which the photometry of all events was re-reduced prior to the AnomalyFinder search, potentially increasing its sensitivity to planets. We find three strong low-$q$ planet candidates, KMT-2023-BLG-0164 ($q\sim 1.3\times 10^{-4}$), KMT-2023-BLG-1286 ($q\sim 1.9\times 10^{-4}$), and KMT-2023-BLG-1746 ($q\sim 8\times 10^{-5}$). KMT-2023-BLG-0164 is notable in that the source is projected on a very bright ($I=16.0$) foreground star, which is either the planet's host or (more likely) a companion to the host. We obtain a spectrum, finding that its mass and distance are $M\sim 1.0\,M_\odot$ and $D\sim 1.5$ kpc, the latter being the distance of the lens ($D_L$) regardless of whether the spectroscopic target is the host or its companion. We also analyze two other candidates, KMT-2023-BLG-0614 and KMT-2023-BLG-1593, which are unlikely to enter the statistical sample due to their ambiguous interpretations as possible non-planetary events.

We study passive scalar mixing by parallel shear flows in the presence of weak molecular diffusion. We recover the sharp uniform-in-diffusivity mixing rate for shear flows with finitely many critical points, recently proven in [1]. Our approach is based on the stochastic representation formula of the associated advection-diffusion equation and yields two short proofs. The first uses a stochastic integration-by-parts argument and gives optimal mixing under the weakest regularity assumption required in the zero-diffusion case, answering Question II in [1, Section 4]. The second adopts a dynamical systems perspective and provides a proof of shear-induced mixing that, to our knowledge, is new even in the zero-diffusivity setting.

We introduce a framework for simulating hybrid oscillator-qubit quantum processors on qubit-only systems through position encoding. By encoding continuous-variable position and momentum wave functions into qubit amplitudes, our method efficiently simulates all Gaussian and conditional Gaussian operations -- encompassing the phase-space instruction set (beam splitter, single-qubit rotation, conditional displacement) and extending to squeezing, conditional squeezing, conditional rotation, and conditional beam splitter -- using $O\!\left(\log^2\!\left(Γ+ \log(1/ε)\right)\right)$ qubit gates per hybrid gate, where $Γ$ is the Fock-level bound and $ε$ is the target precision. This polylogarithmic per-gate complexity represents an exponential improvement over Fock basis encoding approaches, which require exponential quantum or classical resources in the number of qubits per mode. We provide rigorous numerical characterization of quantum Fourier transform errors for Fock-bounded states, enabling precise resource estimation for practical implementations. This work establishes that hybrid oscillator-qubit algorithms can be implemented on qubit processors with polynomial overhead, providing new insights into the computational power trade-offs between discrete-variable and hybrid continuous-discrete-variable quantum computing.

It is shown that for a class of state integral models on shaped pseudo 3-manifolds, including the edge formulation of Teichmüller TQFT, the Boltzmann weight assigned to a tetrahedron solves the tetrahedron equation. The dihedral angles of the tetrahedron play the role of spectral parameters.

We present a multi-wavelength investigation of radio sources in the globular cluster M22 (NGC6656) using VLA, HST, and Chandra observations. Among the eight identified counterparts, we highlight VLA22 as the most promising stellar-mass black hole (BH) candidate. Its radio and X-ray luminosities follow the established $L_{R}-L_{X}$ correlation for quiescent black hole low-mass X-ray binaries (BH-LMXBs), while its moderately steep radio spectrum and X-ray spectral hardening further support this classification. Analysis of two potential optical counterparts-a bright main-sequence star and a faint subgiant/red giant-suggests a binary system with a relatively long orbital period. The discovery of VLA22 consistent with recent retention models that stellar-mass BH can be retained within globular clusters over Hubble timescales. Additionally, VLA19 exhibits a characteristically inverted radio spectrum ($α= 0.79 \pm 0.39, S_ν\propto ν^α$) indicative of a compact jet, while VLA40 also aligns with the BH $L_{R}-L_{X}$ track, though both require further observations to definitively confirm their nature.

Low-energy quadrupole collective states in even-even tellurium (Te) isotopes are studied using the interacting boson model with configuration mixing. The corresponding Hamiltonian is determined by means of the microscopic nuclear structure calculations within the self-consistent mean-field method employing a given energy density functional and pairing interaction. Calculated low-energy levels for nonyrast states show a parabolic behavior characteristic of the shape-coexisting structure. The intruder prolate-shape configuration is shown to mix strongly with the normal oblate-shape configuration, and play an important role in determining the low-lying structure in the Te isotopes near the middle of the neutron major shell closures.

Controlling crystallographic orientation in quasi-van der Waals (vdW) epitaxy remains a fundamental challenge, especially for material systems located near the boundary between weakly and strongly coupled growth regimes. In such marginal systems, epitaxial selection is governed by a delicate thermodynamic competition between surface-energy penalties and interfacial interaction gains, giving rise to two archetypal limits: vdW-dominated free-epitaxy and strong interfacial coupling dominated locked-epitaxy. However, dynamically driving transitions between these regimes has remained elusive. Here, we demonstrate that external light irradiation can deterministically induce such a transition. Using the thermodynamically frustrated Fe4N/mica interface as a model system, we show that photo-excited carriers act as a chemical potentiator, significantly enhancing the interfacial chemical affinity. Within a quantitative thermodynamic description, this optical modulation increases the locking criterion (I_lock)-defined as the ratio of interfacial energy gain to surface-energy cost-beyond its critical threshold. As a result, the system switches from vdW-dominated free-epitaxy with (001) orientation to chemically locked-epitaxy with (111) orientation. Our findings establish light as a non-invasive and switchable control knob to dynamically reconfigure the interfacial energy landscape in quasi-vdW epitaxy, enabling programmable access to distinct epitaxial states beyond intrinsic material limitations.

Transitioning a strategy from backtest to live trading is a common failure point for quantitative systems due to parameter overfitting, selection bias, and sensitivity to regime changes. This paper presents the AlgoXpert Alpha Research Framework, a standardized protocol that evaluates strategies across three stages: In Sample (IS), which focuses on stable parameter regions instead of single optima; Walk Forward Analysis (WFA) using rolling windows and purge gaps to reduce information leakage, supported by majority pass and catastrophic veto rules; and Out of Sample (OOS) testing under strict parameter lock with no further tuning. The framework applies a defense in depth structure that includes structural safeguards such as cliff veto, execution controls such as spread and leverage guards, and equity protection mechanisms such as circuit breakers and a kill switch. A case study on USDJPY M5 intraday data demonstrates how to detect overfitting through performance decay and drawdown behavior across chronological stages. A post validation comparison of four alpha variants (v1 to v4) shows rank reversal when the objective changes from maximizing Sharpe to minimizing maximum drawdown, highlighting the trade off between risk adjusted performance and tail risk control.

On-device deployments of large language models (LLMs) are rapidly proliferating across mobile and edge platforms. LLM inference comprises a compute-intensive prefill phase and a memory bandwidth-intensive decode phase, and the decode phase has been widely recognized as well-suited to processing-in-memory (PIM) in both academia and industry. However, practical PIM-enabled systems face two obstacles between these phases, a memory attribute inconsistency in which prefill favors placing weights in a cacheable region for reuse whereas decode requires weights in a non-cacheable region to reliably trigger PIM, and a weight layout inconsistency between host-friendly and PIM-aware layouts. To address these problems, we introduce \textit{PIM-SHERPA}, a software-only method for efficient on-device LLM inference by resolving PIM memory attribute and layout inconsistencies. PIM-SHERPA provides two approaches, DRAM double buffering (DDB), which keeps a single PIM-aware weights in the non-cacheable region while prefetching the swizzled weights of the next layer into small cacheable buffers, and online weight rearrangement with swizzled memory copy (OWR), which performs the on-demand swizzled memory copy immediately before GEMM. Compared to a baseline PIM emulation system, PIM-SHERPA achieves approximately 47.8 - 49.7\% memory capacity savings while maintaining comparable performance to the theoretical maximum on the Llama 3.2 model. To the best of our knowledge, this is the first work to identify the memory attribute inconsistency and propose effective solutions on product-level PIM-enabled systems.

Emotions play a central role in human communication, shaping trust, engagement, and social interaction. As artificial intelligence systems powered by large language models become increasingly integrated into everyday life, enabling them to reliably understand and generate human emotions remains an important challenge. While emotional expression is inherently multimodal, this thesis focuses on emotions conveyed through spoken language and investigates how acoustic and semantic information can be jointly modeled to advance both emotion understanding and emotion synthesis from speech. The first part of the thesis studies emotion-aware representation learning through pre-training. We propose strategies that incorporate acoustic and semantic supervision to learn representations that better capture affective cues in speech. A speech-driven supervised pre-training framework is also introduced to enable large-scale emotion-aware text modeling without requiring manually annotated text corpora. The second part addresses emotion recognition in conversational settings. Hierarchical architectures combining cross-modal attention and mixture-of-experts fusion are developed to integrate acoustic and semantic information across conversational turns. Finally, the thesis introduces a textless and non-parallel speech-to-speech framework for emotion style transfer that enables controllable emotional transformations while preserving speaker identity and linguistic content. The results demonstrate improved emotion transfer and show that style-transferred speech can be used for data augmentation to improve emotion recognition.

In this paper, the asymptotic behavior of the entrance probability of discounted aggregate claims of a certain family of rare sets is studied, considering the finite and infinite time horizons. This multivariate risk model, driven by a common counting process, has a constant interest rate and allows the interdependence of claim vectors. For the finite time horizon, the multivariate subexponential distribution of the common claim vector and the weak dependence structure of regression dependence are used. For the infinite time horizon, the claim vector is taken from a smaller distribution class, and the weak dependence structure is more general. Both results are derived under some additional assumptions on the moments of the counting process, which is fulfilled by all inhomogeneous renewal processes and many quasi-renewal processes, respectively. Moreover, the results are specialized to the multivariate regularly varying case, where more explicit results on the asymptotic behavior of the entrance probability of the discounted aggregate claims are derived. At the end of the paper, the results obtained are used to study the finite and infinite time horizon ruin problems of a risk model with eventual Brownian perturbations.

The determination of unseen companion masses ($M_1$) is essential for identifying compact objects in binary systems, yet obtaining reliable orbital inclinations remains one of the most difficult challenges. In this study, we focus on ten targets selected from a sample of 89 compact object candidates characterized by large mass functions, rapid rotation, and high-quality Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) spectra. We measure their projected rotational velocities ($v \sin i$) from the LAMOST medium-resolution spectra and, combined with stellar radii, derive orbital inclinations and the corresponding companion masses. Our results show that five sources exhibit mass ratios $M_1 / M_2 > 2/3$, with no detectable spectral signatures of the unseen companions, providing strong evidence for their compact nature. Two particularly notable cases, J0341 and J0359, host companions with inferred masses of $1.39^{+0.09}_{-0.10}$ $M_\odot$ and $1.34^{+0.08}_{-0.09}$ $M_\odot$, respectively. These masses suggest that the invisible objects are either neutron stars or massive white dwarfs with masses close to the Chandrasekhar limit. If they are white dwarfs, these two targets are highly likely to be Type Ia supernova progenitors. This study highlights the potential of $v \sin i$ measurements as a systematic approach to unveiling compact objects in binaries.

Giant low surface brightness galaxies (gLSBs) are galaxies with extremely extended, faint, optical disks over 50 kpc in radius and have high total masses which can reach 10^12 solar masses. The existence of such galaxies is problematic for current models of galaxy formation, since the major mergers responsible for the large total mass would likely have destroyed the extended optical disk. Examining the gas content of these galaxies is an important step in determining their formation mechanism, whether it be through slow gas accretion or the large disk (re)forming after a major merger. We present neutral atomic hydrogen (HI) observations of 19 gLSBs identified with the Hyper Suprime-Cam Subaru Strategic Program survey. Although most have high HI masses, they are generally lower than expected based on their large optical sizes, and we do identify some gLSBs with unusually low gas content. The HI spectra of these galaxies show evidence for a rotational disk, though these disks are more asymmetric than other galaxies with comparable mass. Four galaxies with similar surface brightness profiles to the gLSBs have also been selected from the Numerical Investigation of a Hundred Astrophysical Objects (NIHAO) simulation for comparison. There is evidence for significant galaxy mergers in the past for three of these NIHAO galaxies and these three galaxies show similar asymmetry in their HI spectra. Together, these results could indicate the large optical disk of gLSBs are the result of a recent merger.

We review some recent rigorous results on the semiclassical behavior ($ε\downarrow0$) of the scattering data of a non-self-adjoint Dirac operator with potential $A\exp\{iS/ε\}$ where both $A$ and $S$ are differentiable functions tending to constants as $x \to \pm \infty$. We have either employed the so-called exact WKB method, or the older WKB theory of Olver. Our analysis is motivated by the need to understand the semiclassical behaviour of the focusing cubic NLS equation with initial data $A\exp\{iS/ε\}$, in view of the well-known fact discovered by Zakharov and Shabat that the spectral analysis of the Dirac operator enables us to obtain the solution of the NLS equation via inverse scattering theory.

This paper studies the existence and singularity formation of supersonic expanding waves for the radially symmetric non-isentropic compressible Euler equations of polytropic gases. We introduce a suitable pair of gradient variables to characterize the rarefaction and compression properties of the solutions. Based on their Riccati equations, we construct several useful invariant domains to establish a series of priori estimates of solutions under some assumptions on the initial data. We show that the solution is smooth in the characteristic triangle or quadrangle domain if both of these two gradient variables are non-negative at the initial time. On the other hand, when one of these two variables is very negative at some initial point, the solution forms a singularity in finite time.

The advancement in sensitivity and field of view of next-generation wide-field survey telescopes requires astrometric measurements with high precision, even in the presence of significant geometric distortions. To address this challenge, we develop a Weighted Polynomial Distortion Correction in 2-Phase (WPDC-2P) method. This approach enhances stellar cross-matching, incorporates distance-based weighting into the traditional polynomial fitting, and employs a look-up table to absorb the remaining distortion residuals. Validated on simulated data from the Main Survey Camera of the \emph{Chinese Space Station Survey Telescope} (CSST), incorporating geometric distortions up to approximately $200$ pixels, the method achieves astrometric standard deviation ranging from 0.013 to 0.107 pixels (0.03 pixels for the $g$-1 detector) across all 18 detectors. Under extreme crowding conditions (e.g., globular cluster NGC 2298), the astrometric precision for the $g$-1 detector reaches 0.05-pixel level within the central region ($r_d < 4000$), despite a centroiding precision of $\sim$0.04 pixels. When applied to the Beijing-Arizona Sky Survey data, for which the standard pipeline delivers an astrometric uncertainty of $\sim$20 mas, our method reduces the positional scatter to $ σ_{Δα}=5.494$ mas (0.01 pixels) and $ σ_{Δδ}=9.981$ mas (0.02 pixels) using only a weighted 3rd-order polynomial correction. The method has been integrated into the CSST data processing pipeline and is prepared for further refinement using on-orbit calibration data.

Unmanned aerial vehicle (UAV) swarms are increasingly explored for their potentials in various applications such as surveillance, disaster response, and military. However, UAV swarms face significant challenges of implementing effective and rapid decisions under dynamic and uncertain environments. The traditional decision-making frameworks, mainly relying on centralized control and rigid architectures, are limited by their adaptability and scalability especially in complex environments. To overcome these challenges, in this paper, we propose a hierarchical Observe-Orient-Decide-Act (H-OODA) loop based framework for the UAV swarm operation in uncertain environments, which is implemented by embedding the classical OODA loop across the cloud-edge-terminal layers, and leveraging the network function virtualization (NFV) technology to provide flexible and scalable decision-making functions. In addition, based on the proposed H-OODA framework, we joint autonomous decision-making and cooperative control to enhance the adaptability and efficiency of UAV swarms. Furthermore, we present some typical case studies to verify the improvement and efficiency of the proposed framework. Finally, the potential challenges and possible directions are analyzed to provide insights for the future H-OODA enabled UAV swarms.

Private Information Retrieval (PIR) allows a client to privately access a database without revealing which element is accessed. Initial PIR protocols based on Ring Learning with Errors (RLWE) demonstrated the practicality of PIR, but achieve limited throughput. Alternatively, high-throughput protocols leverage an offline phase that requires substantial client-side storage (e.g., hints in SimplePIR) or involve prohibitive communication costs during the offline phase (e.g., Piano). These limitations conflict with the practical constraints of resource-limited clients and are further exacerbated by dynamic databases, where updates necessitate costly regeneration and retransmission of hints. To address these challenges, we propose ZipPIR, a high-throughput PIR protocol that compresses LWE ciphertexts into significantly smaller Paillier ciphertexts. ZipPIR leverages the offline phase to obtain this size reduction without incurring the associated computational cost in the online phase. Moreover, under computational assumptions, ZipPIR features an almost silent offline phase, requiring no communication beyond an initial public key, enabling the server to independently generate and update hints during idle times without client interaction. ZipPIR achieves over 2 GB/s of throughput - comparable to state-of-the-art protocols such as SimplePIR - without the need for a large client-stored hint. For PIR over a 1 GB database, ZipPIR has up to 10x higher throughput than existing protocols with no client-side storage, while requiring less than 200 KB of server-side storage per client, significantly enhancing scalability for practical deployments. While prior PIR protocols using Paillier are very inefficient, ZipPIR is the first PIR protocol using Paillier that achieves throughput that is competitive with state-of-the-art PIR protocols.

3HWC J0630+186 is one of the very-high-energy gamma-ray sources in the third High-Altitude Water Cherenkov (HAWC) catalog, its origin and source are, however, not clearly identified. The only possible associated source is PSR J0630+19 depart from the center of 3HWC J0630+186. A few TeV halos of pulsars are currently believed the most dominant TeV-PeV gamma-ray sources, and PSR J0630+19 was firstly discovered by Arecibo survey with normal pulsar period, but its age and spin-down luminosity are not available. It is then difficult to determine if 3HWC J0630+186 and PSR J0630+19 are associated or not. With the awarded telescope time in five-hundred-meter aperture spherical radio telescope (FAST) observing cycle, we have obtained the follow-up timing observations of PSR J0630+19 with observed duration more than one year. From our pulsar data analysis, we determined a more precise position and derived parameters via pulsar timing. The parameters indicate that it is an old pulsar with energy loss too low to power the very-high-energy emissions from 3HWC J0630+186.