Query rewriting, the process of transforming queries into semantically equivalent yet more efficient variants, is crucial for database optimization. Existing solutions predominantly rely on either rule-based heuristics or Large Language Models (LLMs). However, traditional rule-based methods lack adaptability, while LLM-based approaches incur prohibitive inference costs and privacy risks. In contrast, Small Language Models (SLMs) present a compelling middle ground, potentially offering both flexibility and efficiency. However, the development of such compact models is severely bottlenecked by the scarcity of high-quality, domain-specific training data. To bridge this gap, we introduce LASER, a data-centric framework designed to empower small models for robust SQL optimization. First, to address the scarcity of existing benchmarks and the limited optimization headroom of generic synthetic queries, we construct SQL-MCTS, a large-scale corpus of complex slow queries. We employ an MCTS-based hybrid expansion strategy that combines rule-guided anti-patterns with LLM mutations to evolve structurally expressive seeds into execution-verified slow variants. Second, to enable the model to autonomously discover latency-aware rewriting patterns, we propose SQL-GRPO, a specialized alignment strategy adapted from Group Relative Policy Optimization. By integrating Anchored Group Advantage to refine advantage estimation and Complexity-Adaptive Dynamic Rollout to efficiently allocate exploration budgets, this approach effectively empowers compact models to master execution-based optimization logic. Implemented on Qwen3 models, LASER significantly outperforms rule-based systems and LLMs in execution efficiency, while exhibiting robust zero-shot transferability with minimal overhead.

In this article, we consider extended tame persistence commutative differential graded algebras (CDGAs) associated with relative Sullivan algebras. In particular, if the relative Sullivan algebra is a model for a map between spaces, then the persistence CDGA is isomorphic to the persistence object obtained by a Postnikov tower for the map with the polynomial de Rham functor in the homotopy category of extended tame persistence CDGAs. Moreover, the interleaving distance in the homotopy category (IHC) in the sense of Lanari and Scoccola enables us to introduce a pseudodistance on the homotopy set of maps via the persistence CDGA models for maps. In contrast to persistence cochain complexes, the IHC of persistence CDGAs does not coincide with the cohomology interleaving distance in general. Due to the reason, we also discuss formalities of a persistence CDGA with interleavings. Computational examples of the pseudodistances between maps are showcased.

In this study, we experimentally investigated the wall heat transfer and flow field configuration of incident-reflected shock wave-turbulent boundary layer interactions on a cooled wall in supersonic flow. Wind tunnel experiments were conducted at a Mach number of 2.0 and a total temperature of 289 K. To create a cooled-wall state, the wind tunnel wall was cooled to a cryogenic temperature using liquid nitrogen at 77.4 K. In addition to conventional measurements, such as the schlieren visualization method and pressure measurements, cryogenic temperature-sensitive paint was employed to clarify the relationship between the flow field configuration and wall heat flux on a cryogenically cooled wall. The wall surface temperature of the cryogenically cooled wall was 95 K, corresponding to a wall-to-recovery temperature ratio of 0.34. The oil flow image and wall surface temperature distribution indicated a quasi-two-dimensional flow at the center of the wind tunnel. The schlieren images and wall pressure distributions showed that the separation point under the cooled-wall condition shifted downstream compared with that under the uncooled-wall condition. Based on the temperature distribution obtained from the cooled-wall experiments, the wall heat flux at the separation point reduced due to the outward flow from the wall. The peak wall pressure ratio and wall heat flux ratio normalized by their upstream values exhibited trends consistent with previously reported data under the cooled-wall condition. These results suggest that the cryogenic temperature-sensitive paint is a powerful tool for investigating the effects of wall temperature on the shock wave-turbulent boundary layer interactions on cryogenically cooled walls.

The study of planetary habitability beyond Earth remains a central and challenging project in planetary science. Analysis of large volumes of planetary data from space missions such as CoRoT, Kepler, and JWST is directed ultimately at finding a planet similar to Earth, the Earth's twin, and answering the question of potential exo-habitability. The Earth Similarity Index (ESI) is a first step in this quest, ranging from 1 (Earth) to 0 (totally dissimilar to Earth). To identify planets that may be habitable to the extreme forms of life, we introduce the Mars Similarity Index (MSI). However, extreme forms of life have also been hypothesized under specific conditions in the upper atmosphere of Venus, motivating comparative habitability studies beyond Earth and Mars. The Venus Similarity Index (VSI), introduced here, is defined as the geometric mean of radius, density, escape velocity, and surface temperature, normalized in Venus units (VU). VSI values range from 0 (complete dissimilarity) to 1 (maximum similarity). The VSI provides a comparative framework for identifying Venus-like planetary environments within exoplanet populations. To explore habitability evolution, we further introduce the Ancient Venus Similarity Index (AVSI) and the Future Earth Similarity Index (FESI) to examine early Venusian conditions relative to ancient Earth and to assess potential future evolutionary pathways for Earth-like planets.

We present a framework that reformulates gravitational lensing as an optical phenomenon governed by an effective refractive index, enabling exploration of modified gravity theories using undergraduate-level mathematics and optics. After deriving the general deflection angle for arbitrary spherically symmetric fields, we establish the observational baseline using standard general relativity, including the lens equation and Einstein ring properties. Assuming the optical relation holds for modified effective potentials, we apply the formalism to deep-MOND, Yukawa-type, and power-law ($f(R)$) models, providing closed-form analytical expressions for the deflection angle and Einstein radius. Numerical ray-tracing simulations validate these analytical results. This framework serves as a conceptual bridge to contemporary research, offering students computational experience and critical awareness of gravitational lensing foundations.

This paper proposes an original method for estimating the velocity of a target by leveraging the multiband capabilities of modern Integrated Sensing And Communication (ISAC) systems. Traditional Doppler estimation relies on regular sampling rates, but ISAC systems often face irregular packet arrival times because they reuse opportunistic communication traffic. This non-deterministic timing increases the risk of Doppler ambiguity and aliasing, degrading velocity estimation accuracy. To resolve this, we advocate exploiting frequency diversity across multiple carrier frequencies to observe Doppler shifts without imposing restrictions on packet timing or requiring dedicated sensing overhead. A multiband velocity estimation problem is here formulated as a mixed-integer quadratic program by utilizing phase differences from all possible pairwise packet combinations. By integrating at least one unambiguous phase measurement, the system can reconstruct the true target velocity even under sporadic traffic conditions. Simulation results using realistic traffic traces demonstrate that this approach significantly outperforms multiband likelihood-based and single-band algorithms, with accuracy improving as frequency separation between bands and inter-packet time intervals increase. This framework provides a zero-overhead solution for robust velocity estimation in dynamic ISAC environments.

Improving predictions of the geomagnetic impact of coronal mass ejections (CMEs) requires understanding how solar source properties relate to in-situ measurements at Earth. However, major geomagnetic storms frequently arise from interacting CMEs, complicating the link back to their solar origins. We analyze a CME interaction event that caused a major geomagnetic storm in 2024 October 10-11 (D$_{st}$ $\sim$-333 nT). Multiviewpoint observations reveal that the storm was related to a sympathetic eruption involving a quiescent filament and an active-region CME. The coronagraph on board the Advanced Space-based Solar Observatory clearly shows that this sympathetic eruption resulted in two distinct CMEs. Due to the overlap of the CMEs in the coronagraph field of view (FOV), a spheroid shock model was used to fit the observed shock. Kinematic analysis indicates that the interacting CMEs had completed their impulsive acceleration phase before entering the coronagraph FOV, with a slow deceleration continuing beyond 100 R$_\odot$. In-situ measurements indicate that the enhanced southward magnetic fields, arising from compression during CME interactions, were the primary driver of the storm. Compared to photospheric fields, the in-situ magnetic fields suggest that the trailing CME maintained flux-rope-like signatures consistent with the source region. In contrast, the compressed leading CME displayed varying magnetic configurations between Wind and STEREO-A, featuring distorted flux-rope signatures and inconsistent inferred axis orientations. Our study bridges solar source dynamics to in-situ multipoint measurements, providing key insights for space weather prediction. Nevertheless, the direct linkage between source-region magnetic field configurations and these measurements remains tentative and requires further investigation.

Exciton-magnon transitions provide a fundamental optical fingerprint of coupled excitonic and magnetic excitations in antiferromagnets. However, controlling such coupled excitations by external fields remains a key challenge. Here we report the temperature and magnetic-field evolution of exciton-magnon coupling in the magnetoelectric antiferromagnet LiNiPO$_4$ using pulsed magnetic fields up to 50 T. The magnon sideband intensity exhibits sharp switching across field-induced magnetic phases, with strong suppression in plateau phases and enhancement in canted spin states. This behavior is attributed to the interplay between the thermal magnon population and the spin-dependent optical transition matrix element. These results demonstrate that magnetic-field control of spin degrees of freedom enables selective switching of exciton-magnon coupling in antiferromagnets.

We prove Calderon-Zygmund estimates for generalized double phase equations with Orlicz growth and variable matrix weights. The operator combines a non-uniformly elliptic double phase structure with a degenerate or singular matrix weight satisfying a small log-BMO condition. Under appropriate structural assumptions, we show that higher integrability of the weighted datum yields higher integrability of the weighted gradient of weak solutions. Our results extend the existing Calderon-Zygmund theory for double phase problems and weighted elliptic equations to a unified framework capturing the interaction between Orlicz growth and matrix-weighted structures, thereby building upon and unifying the results in [BBO20] and [BCR26].

In this paper, we address the problem of computing maximal state-control invariant sets using failing trajectories. We introduce the concept of state-control invariance, which extends control invariance from the state space to the joint state-control space. The maximal state-control invariant (MSCI) set simultaneously encodes the maximal control invariant set (MCI) and, for each state in the MCI, the set of control inputs that preserve invariance. We prove that the state projection of the MSCI is the MCI and the state-dependent sections of the MSCI are the admissible invariance-preserving inputs. Building on this framework, we develop a Failure-Aware Iterative Learning (FAIL) algorithm for deterministic linear time invariant systems with polytopic constraints. The algorithm iteratively updates a constraint set in the state-control space by learning predecessor halfspaces from one-step failing state-input pairs, without knowing the dynamics. For each failure, FAIL learns the violated halfspaces of the predecessor of the constraint set by a regression on failing trajectories. We prove that the learned constraint set converges monotonically to the MSCI. Numerical experiments on a double integrator system validate the proposed approach.

We present MemoryDiorama, a prototype system that introduces augmented memory cues, a concept that extends captured personal media with AI-generated contextual information to enhance autobiographical memory recall. MemoryDiorama transforms everyday photos into dynamic 3D dioramas in mixed reality by integrating LLM-based scene analysis with 3D object generation, animation, and spatial composition. The system extracts geographic information, object attributes, lighting conditions, and atmospheric elements from the photos. It then animates these elements with generative components such as object animations, human motion, geographical effects, and particle effects to provide richer cues for memory recall. We evaluated MemoryDiorama in a within-subject user study with 18 participants, comparing three conditions: Photo-Only, Static Diorama, and MemoryDiorama. Compared with both Photo-Only and Static Diorama, MemoryDiorama elicited more internal and in-cue details during recall. It also increased perceptual details and visual vividness ratings, suggesting richer recollective experience.

Short and long-short gamma-ray bursts (GRBs) are widely believed to be powered by neutron star mergers. In this work, we calculate local rate of such GRBs and find a relatively high value of $\sim 786-2468~{\rm Gpc^{-3}~yr^{-1}}$ when including the very narrow collimation event GRB 061201. Considering that its redshift is not very reliable, after excluding this event, the rate is $\sim 195-666~{\rm Gpc^{-3}~yr^{-1}}$. We also calculate the electromagnetically (EM) bright neutron star merger rate inferred from the LIGO/Virgo/KAGRA observations up to the end of the first epoch of the O4 run, and derive a rate of $\sim 66-347~{\rm Gpc^{-3}~yr^{-1}}$. This rate is somewhat lower than the value obtained from the GRBs, even after excluding GRB 061201. The non-detection of any viable EM bright merger in the O4b and O4c observing runs favors an even lower rate, which starts to challenge the neutron star merger origin of the short and long-short GRBs and may suggest additional contribution from the mergers of other compact object (like the neutron star-white dwarf) binaries, as speculated initially by King et al. (2007) in interpreting the long-short event GRB 060614.

Suprathermal fusion reactions, initiated by energetic particles slowing down and scattering in dense plasmas, can modify the burn dynamics at inertial confinement fusion (ICF) regimes. A 0D time-dependent Monte-Carlo code has been developed to assess the suprathermal energy gain from fast fusions in DT, deuterium, $^{11}$BH$_3$ and $^{11}$BHDT fuels. It incorporates modified Li-Petrasso stopping powers, thermal broadening of cross-sections, anisotropic nuclear elastic and neutron elastic scattering, and a physical model for the p$^{11}$B alpha-particle spectra. Results show that earlier predictions of suprathermal criticality in pure deuterium are overestimated by more than an order of magnitude; no realistic density-temperature regime supports a self-sustaining chain reaction. Only DT demonstrates a critical regime provided there is no neutron leakage. Fast protons in $^{11}$BH$_3$ have an optimum energy of 4 MeV for maximising suprathermal enhancement. In this case the additional energy from fast fusions is unlikely to exceed 40% of the initial proton beam energy. The possibility of an alpha-particle-driven "avalanche" mechanism is ruled out since the ionic stopping is dominated by collisions involving small energy transfer. Suprathermal multiplication processes are dominated by neutron-driven ion up-scattering and likely play a limited role in purely aneutronic fuels.

We report x-ray diffraction and emission spectroscopy of FeO under laser-driven shock compression between 31-199 GPa. FeO retains the B1 (rocksalt) structure along the Hugoniot to the melt boundary at 191 GPa. While the phase and volume are broadly consistent with results from static compression, we observe an anomalous 7-10% volume collapse around 60 GPa absent in static experiments. We identify this as an isostructural high-spin to low-spin metallic transition in FeO. The low-spin state is directly evidenced by x-ray emission spectroscopy at 180 GPa.

Artificial atoms based on color centers in silicon (SiCCs) have recently emerged as promising candidates for highly integrable and scalable key components in photonic quantum technology, including telecom single-photon sources and spin memory devices. A novel all-epitaxial fabrication technique for SiCCs, based on ultra-low-temperature (ULT) molecular beam epitaxy (MBE), addresses limitations of conventional fabrication via ion implantation, such as vertical ion straggle and collateral crystal lattice damage. This method solely relies on self-assembly of SiCCs during kinetically-limited growth of (carbon-doped) Si(:C) at ULTs <~350°C. The latter requires an extraordinary pristine growth environment to prevent unintended defect formation caused by the incorporation of impurities from the background vapor; however, so far, no study has specifically addressed how exactly the vacuum conditions during epitaxy influence SiCC formation, their optical properties, and the quality of the surrounding crystal matrix. Here, we investigate the impact of the growth pressure and the substrate temperature on the self-assembly and photoluminescence (PL) properties of important SiCCs, such as W, G, G', and T centers. Further, we use PL and Doppler broadening variable energy positron annihilation spectroscopy to emphasize the role of the growth pressure in suppressing the luminescence background, which is crucial for advancing quantum photonics applications.

The European Union is developing the European Quantum Communication Infrastructure (EuroQCI) as a pan-European network to provide secure communication capabilities across Member States, including governmental and critical-infrastructure domains. While the strategic objective is defined at EU level, the required scale and structure of national quantum key distribution (QKD) networks remain largely unspecified. This work addresses the question of how to plan and size national terrestrial QKD networks to support critical infrastructure and public authorities. We propose a reproducible planning methodology that estimates network size, total fiber length, and the number of required QKD components based on a small set of explicit assumptions. The approach is demonstrated for Austria, where a synthetic but structured network model is constructed and evaluated using Monte Carlo simulation. The model focuses on terrestrial QKD infrastructure and explicitly excludes space-based segments. It estimates endpoint counts, trusted repeater node requirements, and hop-length distributions under realistic operational constraints. The Austrian case is then used as a baseline to derive scaling rules for other EU Member States based on population and geographic extent. The results provide first-order planning estimates for national QKD backbone sizes across Europe. These estimates are not intended as deployment designs but as planning-level references that support early-stage cost assessment and infrastructure dimensioning under the EuroQCI framework.

Security Information and Event Management (SIEM) systems make it possible for detecting intrusion anomalies in real-time manner by their applied security rules. However, the heterogeneity of vendor-specific rules (e.g., Splunk SPL, Microsoft KQL, IBM AQL, Google YARA-L, and RSA ESA) makes cross-platform rule reuse extremely difficult, requiring deep domain knowledge for reliable conversion. As a result, an autonomous and accurate rule conversion framework can significantly lead to effort savings, preserving the value of existing rules. In this paper, we propose ARuleCon, an agentic SIEM-rule conversion approach. Using ARuleCon, the security professionals do not need to distill the source rules' logic, the documentation of the target rules and ARuleCon can purposely convert to the target vendors without more intervention. To achieve this, ARuleCon is equipped with conversion/schema mismatches, and Python-based consistency check that running both source and target rules in controlled test environments to mitigate subtle semantic drifts. We present a comprehensive evaluation of ARuleCon ranging from textual alignment and the execution success, showcasing ARuleCon can convert rules with high fidelity, outperforming the baseline LLM model by 15% averagely. Finally, we perform case studies and interview with our industry collaborators in Singtel Singapore, which showcases that ARuleCon can significantly save expert's time on understanding cross-SIEM's documentation and remapping logic.

Understanding the kinetics of drug-protein interactions is paramount for drug design, yet the field lacks large-scale, dynamic data to move beyond static structural analysis. Here, we present DD-03B, a massively scalable database providing dynamic, all-atom dissociation trajectories for a broad set of ligand-protein complexes. Utilising and extending a validated computational pipeline, we generated dissociation trajectories for 19,037 ligand-protein complexes sourced from PDBbind+v2020R1, resulting in a repository of approximately 0.3 billion simulation frames totalling 40 TB in size. For these systems-which possess experimental binding affinities (kd) but typically lack measured koff rates-we computed and assigned dissociation rate constants through trajectory reweighting. Our analysis reveals that protein-ligand complexes can be categorised into three mechanistic types (pathway-dominant, open-pocket, and entropy-pocket systems), each requiring distinct strategies for accurate kinetic characterisation. Together with our previously released DD-13M, DD-03B forms the core of the expandable Dissociation Dynamic Database (DDD) project, which will be continuously augmented with new trajectories. This large-scale, publicly available resource establishes a critical foundation for training and benchmarking next-generation generative AI models to predict and optimise drug-protein dissociation kinetics.

The lead marketing ecosystem enables collection, sale, and use of personal data submitted via web forms to deliver personalized quotes in high-value verticals such as insurance. Despite its scale and sensitivity of the collected data, this ecosystem remains largely unexplored by the research community. We present the first empirical study of privacy and spam risks in lead marketing, developing an end-to-end measurement framework to trace data flows from data collection to consumer contact. Our setup instruments over 100 health-related lead-generation websites and monitors 200 controlled phone numbers and email addresses to understand downstream marketing practices. We observe sharing of highly personal and sensitive health information to more than 70 distinct third parties on these lead generation websites. By purchasing our own and other organic leads from three major lead platforms, we uncover deceptive brokerage practices, where consumer data is sold to unvetted buyers and often augmented or fabricated with attributes such as health status and weight. We received a total of over 8,000 telemarketing phone calls, 600 text messages, and 200 emails, where calls often began within seconds of form submission. Many campaigns relied on VoIP-based neighbor spoofing and high-frequency dialing, at times rendering phones unusable. Our experiments with phone and email opt-outs suggest phone-based opt-outs to help the most, although all were ineffective at completely stopping marketing communications. Analysis of 7,432 Better Business Bureau (BBB) complaints and reviews corroborates these findings from the consumer perspective. Overall, our results reveal a highly interconnected and non-compliant lead marketing ecosystem that aggressively monetizes sensitive consumer data.

Context: Large Language Models (LLMs) are increasingly used in modern software development, aiding in code generation, code completion, and refactoring through AI-powered assistants. While they accelerate development workflows, they often produce extraneous output, referred to as "babbling", which incurs additional cognitive, economic, and energy costs. Objective: This work investigates the problem of babbling in LLM-based code generation and proposes a practical, model-agnostic approach to reduce unnecessary output without compromising solution accuracy. Method: We introduce Babbling Suppression (BS), a method that integrates test execution into the LLM generation process by evaluating intermediate outputs and terminating generation once a solution passes all tests. This prevents excessive token generation while having no impact on model accuracy. An empirical study was conducted across two Python and two Java benchmarks, targeting four 3-4B parameter models and six 6-7B parameter models. Results: Our findings show that babbling occurs across all tested models, with higher frequency in Java than in Python. Applying BS significantly reduces energy consumption by up to 65% for Python and 62% for Java in models prone to babbling. Across 40 model-benchmark pairs, 29 showed reduced mean energy consumption, with reductions exceeding 20% in 22 cases. Generated token count decreased in 35 pairs, while the GPU energy-per-token overhead of BS remained below 10% for 26 pairs, decreased for 2, and reached a maximum of 24%, yielding net energy savings in most cases. Implications: BS can make AI-assisted programming more efficient and sustainable by reducing energy consumption and minimizing cognitive effort by developers. Its model-agnostic design allows easy integration, suggesting broad applicability.

While collisionless plasmas are ubiquitously present near astrophysical compact objects, the impact that their composition has on the high-energy emission is presently unknown. We present the first investigation of particle-acceleration mechanisms in kinetic, special-relativistic turbulence, modeling electrons, positrons, and protons with realistic mass ratios. Under global charge neutrality, we introduce a positron fraction and cover regimes ranging from an electron-proton plasma over to pair-dominated plasmas. Using a novel generalized Ohm's law for multispecies relativistic plasmas, we analyze particle acceleration due to electric fields in reconnection events that self-consistently emerge from turbulence. We demonstrate, for the first time, that energization occurs at reconnection current sheets driven by the divergence of the relativistic pressure tensor, which locally aligns with the particle velocity and leads to an efficient energy transfer. The imbalance between electrons and positrons systematically favors electron acceleration, highlighting the necessity of realistic multispecies modeling to capture the nonthermal contributions in accretion flows and relativistic jets from black holes.

Nonlinear phononics provides a route to control crystal structures through light-induced phonon excitation. In this study, we apply nonlinear phononics to an iron-based superconductor, LaFeAsO, with the aim of tuning its crystal structure toward the ideal one to enhance superconductivity. We simulate light-induced phonon dynamics on the anharmonic lattice potential determined by first-principles calculations. We find that the anion height $h$, a key structural parameter in iron-based superconductors, approaches its ideal value when an appropriate infrared-active phonon mode is selectively excited. This result suggests the possibility of controlling crystal structures and enhancing superconductivity in iron-based superconductors based on the concept of nonlinear phononics.

The human auditory system has the ability to selectively focus on key speech elements in an audio stream while giving secondary attention to less relevant areas such as noise or distortion within the background, dynamically adjusting its attention over time. Inspired by the recent success of attention models, this study introduces a dual-path attention module in the bottleneck layer of a concurrent speech enhancement network. Our study proposes an attention-based dual-path RNN (DAT-RNN), which, when combined with the modified complex-valued frequency transformation network (CFTNet), forms the DAT-CFTNet. This attention mechanism allows for precise differentiation between speech and noise in time-frequency (T-F) regions of spectrograms, optimizing both local and global context information processing in the CFTNet. Our experiments suggest that the DAT-CFTNet leads to consistently improved performance over the existing models, including CFTNet and DCCRN, in terms of speech intelligibility and quality. Moreover, the proposed model exhibits superior performance in enhancing speech intelligibility for cochlear implant (CI) recipients, who are known to have severely limited T-F hearing restoration (e.g., >10%) in CI listener studies in noisy settings show the proposed solution is capable of suppressing non-stationary noise, avoiding the musical artifacts often seen in traditional speech enhancement methods. The implementation of the proposed model will be publicly available.

Protein phosphorylation provides a dynamic readout of cellular signaling yet remains difficult to detect at low abundance and stoichiometry. Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) using particle-in-pore plasmonic nanopores offers label-free molecular detection with submolecular sensitivity. However, reliable identification of subtle post-translational modifications (PTMs) is hindered by the stochastic nature of SM-SERS signals, partial excitation of peptide residues within the plasmonic hotspot, and background interference. Here, we introduce a physics-informed deep learning framework to decode complex SM-SERS dynamics and identify single-peptide PTMs. The model integrates multiple-instance learning with a temporal encoder combining temporal convolutional networks and bidirectional gated recurrent units to capture both local spectral variability and long-range blinking dynamics. To address diffusion-driven spectral heterogeneity, long spectral trajectories are segmented using Pearson-correlation, enabling weakly supervised training under label ambiguity. This framework robustly distinguishes single peptide phosphorylation despite strong background interference and stochastic signal fluctuations. By coupling nanoplasmonic confinement with spatiotemporal deep learning, our approach enables high-fidelity detection of single-molecule phosphorylation events and advances ultrasensitive phosphoproteomic analysis.

Ascent sequences form a central class of combinatorial objects, as they are in bijection with several important families such as (2+2)-free posets, Stoimenow matchings, and other Fishburn objects, and are enumerated by the Fishburn numbers. We study pattern avoidance in ascent sequences for the five patterns of length 4: $0101$, $0102$, $0112$, $0120$, and $0121$. These patterns arise naturally from recent work on pattern avoidance in related families of Fishburn objects, including Stoimenow matchings and (2+2)-free posets. We enumerate ascent sequences avoiding any subset of these patterns, with the exception of the sets $\{0120\}$, $\{0121\}$, and $\{0120,0121\}$, for which the enumeration remains open. Our results reveal that the corresponding avoidance classes fall into $16$ Wilf equivalence classes and exhibit a wide range of enumerative behaviour, including connections to classical sequences such as the Catalan and Fibonacci numbers, as well as polynomial formulas and rational generating functions; several of the sequences we obtain appear to be new. Our methods combine structural decompositions with generating-tree techniques and, in several cases, rely on reductions to shorter patterns via restricted growth functions. This work contributes to the broader study of pattern avoidance across Fishburn families and highlights further connections between ascent sequences and other combinatorial structures.

We carry out bond-strength based analysis for the migration barrier ($E_{\rm B}$) of oxygen vacancies in rutile-type 3$d$ transition-metal dioxides by combining density-functional theory (DFT) and the bond-valence model. The covalent and ionic contributions to chemical bonding are explicitly decomposed and quantified by the sum of the integrated crystal orbital Hamilton population ($S_c$) and the Madelung energy ($S_i$), respectively. Both $S_c$ and $S_i$ exhibit strong correlations with the $E_{\rm B}$ from DFT ($E_{\rm B}^{\rm DFT}$), and their average $\bar{S}$ provides a reasonable estimate of $E_{\rm B}^{\rm DFT}$ across the oxide series. Inspired by the bond-valence model, two parameters are extracted by fitting to a large dataset of 3$d$ transition-metal dioxides. Our results show that using these parameters, $E_{\rm B}$ of oxygen vacancies can be efficiently estimated.

Diversity among computer scientists and technologists is necessary for the sustainable development of society through technological innovation. At UiT The Arctic University of Norway, only 13% of computer science students are women. Many find the learning curve in introductory computer science courses to be very steep, and thus, they drop out. Female students tend to be overrepresented in this group. The goal of this project was to improve the gender balance among computer science students at UiT by focusing on female first-year students and ensuring that they do not drop out of the study programs in the first year of study. The project established a seminar series for strengthening the basic programming-technical skills that many first-year students lack, and exposing them to different aspects and career paths within the computer science subject beyond the focus area of the study program. Results show positive developments, particularly related to the students' perceived introduction to basic technical topics. A comparison between 2024 and 2025 shows improvements in several of the areas addressed in the technical workshops, including use of file systems, terminals, debugging and the code development process. However, effects on dropout and study experience require more long-term measures.

We study current-driven domain-wall (DW) dynamics in antiferromagnets (AFMs) with Dzyaloshinskii-Moriya interaction (DMI). We obtain an exact analytical solution for spiral DW dynamics, applicable to both head-to-head DWs under bulk DMI and up-down DWs under interfacial DMI when the magnetic easy axis is aligned with the DMI vector. For the latter case experimentally relevant to synthetic AFMs with in-plane anisotropy, the solution predicts a constant DW velocity driven by nonadiabatic spin-transfer torque together with a steady rotation of the DW tilt angle induced by damping-like spin-orbit torque. Remarkably, the DW width shows unconventional current dependence, either pure elongation or contraction followed by elongation depending on damping and torque parameters, in sharp contrast to the Lorentz-type contraction known for antiferromagnetic (AF) DWs without DMI. These results provide an exact description of current-driven AF-DW dynamics and suggest experimentally accessible signatures of DMI-modified DW dynamics in synthetic AFMs.

In video conferencing, human faces serve as the primary visual focal points, playing multifaceted roles that enhance visual communication and emotional connection. However, we argue that a human face is also a side channel, which can unwittingly leak on-screen information through online video feeds. To demonstrate this, we conduct feasibility studies, which reveal that, illuminated by both ambient light and light emitted from displays, the human face can reflect optical variations of different on-screen content. The paper then proposes FaceTell, a novel side-channel attack system that eavesdrops on fine-grained application activities from pervasive yet subtle facial reflections during video conferencing. We implement FaceTell in a real-world testbed with three different brands of laptops and four mainstream video conferencing platforms. FaceTell is then evaluated with 24 human subjects across 13 unique indoor environments. With more than 12 hours of video data, FaceTell achieves a high accuracy of 99.32% for eavesdropping on 28 popular applications and is resilient to many practical impact factors. Finally, potential countermeasures are proposed to mitigate this new attack.

In this paper we develop a very special substitution method for solving a general linear programming problem (LPP). Of course the substitution is a kind of elimination of variable but this method must not be confused with the so-called Fourier-Motzkin elimination. The susbtitution developed in this paper only differs by the set of criteria that a variable must verify to be substitued. Most of the criteria are associated with the cost function of the LPP. We prove that the research of the criteria is strongly polynomial. Thus, the special substitution inehrits of the strong polynomiality which characterizes the classical substitution for linear systems. Moreover, as for the classical substitution the backward substitution for finding a vertex associated with the optimum is still valid and does not require to inverse a matrix.