Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject's subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes a conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoor threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine's behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern as the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability.

In this work, we propose a simulation-based estimation approach using generative neural networks to determine dependencies of precipitation maxima and their underlying uncertainty in time and space. Within the common framework of max-stable processes for extremes under temporal and spatial dependence, our methodology allows estimating the process parameters and their respective uncertainty, but also delivers an explicit nonparametric estimate of the spatial dependence through the pairwise extremal coefficient function. We illustrate the effectiveness and robustness of our approach in a thorough finite sample study where we obtain good performance in complex settings for which closed-form likelihood estimation becomes intractable. We use the technique for studying monthly rainfall maxima in Western Germany for the period 2021-2023, which is of particular interest since it contains an extreme precipitation and consecutive flooding event in July 2021 that had a massive deadly impact. Beyond the considered setting, the presented methodology and its main generative ideas also have great potential for other applications.

Large Language Models (LLMs) are being integrated into applications such as chatbots or email assistants. To prevent improper responses, safety mechanisms, such as Reinforcement Learning from Human Feedback (RLHF), are implemented in them. In this work, we bypass these safety measures for ChatGPT, Gemini, and Deepseek by making them impersonate complex personas with personality characteristics that are not aligned with a truthful assistant. First, we create elaborate biographies of these personas, which we then use in a new session with the same chatbots. Our conversations then follow a role-play style to elicit prohibited responses. Using personas, we show that prohibited responses are provided, making it possible to obtain unauthorized, illegal, or harmful information when querying ChatGPT, Gemini, and Deepseek. We show that these chatbots are vulnerable to this attack by getting dangerous information for 40 out of 40 illicit questions in GPT-4.1-mini, Gemini-1.5-flash, 39 out of 40 in GPT-4o-mini, 38 out of 40 in GPT-3.5-turbo, and 2 out of 2 cases in Gemini-2.5-flash and DeepSeek V3. The attack can be carried out manually or automatically using a support LLM, and has proven effective against models deployed between 2023 and 2025.

Currently, there is a trend for the wider public to rely on LLMs for financial or legal consultation, medical and mental support (Chatterji et al., 2025), often accepting the advice provided without necessarily seeking logical verification or empirical validation. While one might be fortunate enough to encounter a model with a particularly solid 'ground truth' or with auxiliary logic-symbolic reasoning capabilities, it remains a somewhat uncertain endeavour. Output is simply taken at face value, without further question. Yet, careless reliance on AI to answer our questions and to judge our output is a violation of Grice's Maxim of Quality as well as a violation of Lemoine's legal Maxim of Innocence. A low-sensitivity plagiarism scanner may produce a Type II error by failing to detect difference (the null hypothesis wrongly maintained). The fallacy of affirming the consequent occurs when the failure to detect difference is then interpreted as evidence of equivalence or demonstration of AI authorship. If the test is specified so that 'AI-generated' is effectively treated as the default H0, then a finding of 'no difference from AI' is taken as support for that null. Such a mis-specified test results in students being treated as guilty (AI/plagiarism) unless suspects can generate sufficient detectable difference from AI output, which yields false accusations under a fair null hypothesis (that the student wrote the work). To avoid LLMs becoming a sorcerer's apprentice, knowledge is required about which inference systems are or should become integrated for an LLM to become a trustworthy sparring partner. We end on a wider perspective where the formalisation of the observer effect shows that uncertainty, classification, and interpretation are already shaped by the human or artificial agency's belief system, affective state, and tolerance for ambiguity, rather than at the stage of LLM output.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

We investigate practical algorithms for inconsistency-tolerant query answering over prioritized knowledge bases, which consist of a logical theory, a set of facts, and a priority relation between conflicting facts. We consider three well-known semantics (AR, IAR and brave) based upon two notions of optimal repairs (Pareto and completion). Deciding whether a query answer holds under these semantics is (co)NP-complete in data complexity for a large class of logical theories, and SAT-based procedures have been devised for repair-based semantics when there is no priority relation, or the relation has a special structure. The present paper introduces the first SAT encodings for Pareto- and completion-optimal repairs w.r.t. general priority relations and proposes several ways of employing existing and new encodings to compute answers under (optimal) repair-based semantics, by exploiting different reasoning modes of SAT solvers. The comprehensive experimental evaluation of our implementation compares both (i) the impact of adopting semantics based on different kinds of repairs, and (ii) the relative performances of alternative procedures for the same semantics.

$ω$~Centauri, the most massive globular cluster in the Milky Way, exhibits a level of stellar population complexity that has long resisted a unified chemical characterisation. We exploit high-resolution near-infrared spectroscopy from the Milky Way Mapper survey (MWM DR19) to construct one of the largest homogeneously analysed samples of $ω$~Cen members to date. Applying Ward-linkage hierarchical clustering in a seven-dimensional chemical abundance space, without prior assumptions on population number or boundaries, we identify ten chemically distinct stellar populations. Their nucleosynthetic signatures trace four enrichment channels: iron-peak, $α$-element, CNO-cycle, and high-temperature proton-capture processes. The populations organise into two dominant groups separated by a large light-element spread at a modest iron baseline, consistent with AGB-driven self-enrichment. This dichotomy reflects distinct enrichment pathways: core-collapse supernovae establish the iron baseline, while AGB stars dominate light-element and $s$-process enrichment. A decoupled rise in $s$-process abundances relative to iron-peak elements, together with sub-dominant Type~Ia contributions across all metallicities, indicates evolution on timescales shorter than the characteristic Type~Ia delay time. One intermediate-metallicity population retains a primordial composition, providing evidence for spatially segregated enrichment within the progenitor. The most metal-rich component may trace star formation continuing after accretion into the Milky Way halo. All populations lie in the accreted regime of the $[\mathrm{Al/Fe}]$--$[\mathrm{Mg/Mn}]$ plane, supporting an ex-situ origin. These results reinforce the interpretation of $ω$~Cen as the remnant nucleus of an accreted dwarf galaxy and provide a framework for future chemo-dynamical studies.

The Karch-Randall braneworld concerns the physics of an AdS$_{d}$ brane embedded in an ambient gravitational AdS$_{d+1}$ spacetime. The gravitational theory induced on the AdS$_{d}$ brane has a very light but massive graviton. It has been established that the zero graviton mass limit of the $d$-dimensional graviton propagator is smooth at tree-level. Furthermore, this smoothness was conjectured to persist to the quantum level. This conjecture suggests that the massive graviton on the AdS$_{d}$ brane is due to spontaneous symmetry breaking, which is consistent with its holographic dual description. In this letter, we show that the zero mass limit of the partition function is a theory of a massless graviton and a decoupled massive vector. The zero mass limit is not the basic Randall-Sundrum II model, but a theory with these additional decoupled vector degrees of freedom coupled only to gravity. The proof relies on deriving the fully covariant description of the $d$-dimensional gravity theory which enables us to compute the one-loop partition function. At the end, we comment on the implications of this result to the physics of entanglement islands.

Relaxation of far-from-equilibrium quantum fluids, intimately related to the emergence of long-range order, is theoretically associated with the decay of a turbulent isotropic tangle of vortex lines. We observe and study such decaying quantum turbulence in a homogeneous 3D atomic Bose gas. Using matter-wave techniques to magnify the gas density distribution, and then imaging a thin slice of the magnified cloud, we observe imprints of randomly oriented vortex lines and measure the vortex line-length density $\mathcal{L}$. The observed decay of $\mathcal{L}$ agrees with the prediction for Vinen `ultraquantum' turbulence. Although our weakly interacting gases are highly compressible, their large-scale dynamics are consistent with the behavior of an incompressible hydrodynamic fluid, with the decay of $\mathcal{L}$ not depending on the strength of the interatomic interactions and being similar to that in the strongly interacting superfluid helium.

In this review, we discuss the relevance and impact of studying Calabi-Yau threefolds in the context of global model building in string phenomenology. First, taking a phenomenologist-friendly approach, we review how the topologies of the various divisors and curves of the compactifying CY threefolds play a crucial role for generating the various ``suitable" classes of effective scalar potentials, within the framework of the popular moduli stabilization schemes such as KKLT and LVS. Subsequently, we discuss the impact of the specifics of the CY threefold geometries in the minimal LVS inflationary models such as fibre inflation, in particular, along the challenges such as the inflaton field-range bound. In this regard, we discuss a multi-field approach in which several fibre moduli assist to drive successful inflation having a sufficient number of efolds, without getting close to their individual Kähler cone boundaries.

This is a first study of the cosmology of classical fractional gravity, a nonlocal proposal endowed with self-adjoint fractional d'Alembertian operators which serves as the basis for an ultraviolet-complete theory of quantum gravity. We derive the classical covariant nonlocal equations of motion for an arbitrary fractional exponent $γ$ and reduce them to the Friedmann equations on a homogeneous and isotropic cosmological background. We find that de Sitter is an exact stable solution and that bouncing exact solutions are sustained by phantom ($w<-1$) or ghost ($ρ<0$) fluids, in the latter case with a new type of finite-future singularity in the barotropic index. Different representations of the form factor give exactly the same solutions, thus confirming that the formulation of fractional field theories relies on a universality class of form factors. We compare these preliminary results with what obtained in multi-fractional cosmological models mimicking the spacetime geometry of fractional quantum gravity.

Boosting superconductivity by metallic reservoirs is the essence of Kivelson's bilayer proposal. One layer provides pairing to the electrons, while the weakly coupled metal provides additional phase coherence to those pairs by mediating extended-range pair-pair coupling. Demonstrating significant and unambiguous performance gains with strong-coupling methods for such set-ups had been difficult. In the present work, we study these systems doped away from half-filling, corresponding to a partially spin-polarized 1D Anderson- or Kondo-lattice. We show that this breaks the coexistence of dominant superconducting and density-density correlations decisively in favour or the former. Consequently, we provide evidence that in this doped regime, superconducting near-long-range order is not precluded by a small charge-gap in the thermodynamic limit, as we have recently shown to be the case at half-filling [JE Ebot $et$ $al.$, arXiv:2602.11153 [cond-mat.supr-con]]. We study the complex manner in which the enhancement of superconductivity in the pairing layer depends on the parameters of the metal, and especially that both pairing-limited and stiffness-limited regimes may appear in these systems. In addition to superconducting bilayers, our results are relevant, via a particle-hole transformation, for heavy-fermion Kondo-lattice materials in magnetic fields, as we provide previously lacking insight on the competition between antiferromagnetic and easy-plane magnetism, as well as a route for comprehensive indirect tests of Kivelson's bilayer proposal well beyond previous capabilities.

We investigate the intrinsic anomalous thermal Hall effect in d-wave altermagnets, where a transverse heat current is generated by a longitudinal temperature gradient in the absence of a magnetic field, with the leading response proportional to $(\nabla T)^3$. In these systems, the intrinsic Berry curvature-driven linear and thermal quantum-metric-driven second-order anomalous thermal Hall currents vanish as a consequence of crystalline symmetry. We show that the first nonvanishing contribution arises at third order in the temperature gradient and is governed by a nonlinear thermal Berry-connection polarizability, a quantity introduced in this work. Our analysis reveals a distinctive angular dependence of the anomalous thermal Hall conductance as the applied thermal gradient is rotated with respect to the crystal axes. We also find characteristic temperature and chemical-potential dependences that can be tested experimentally. These results identify unique quantum geometry-induced thermal responses and establish altermagnets as a promising platform for exploring intrinsic (i.e., scattering-time-independent) geometric transport phenomena.

Multiferroic domain walls in functional oxides exhibit properties distinct from the bulk and are increasingly exploited as active elements in nanoelectronic and photonic devices. Deterministic control of domain populations has typically remained limited to local control, or removal with temperature. Here we demonstrate continuous, reversible manipulation of the ferroelastic domain structure in single-crystal LaAlO$_3$ using in-situ uniaxial strain. Combining atomic force microscopy, X-ray diffraction, and Raman spectroscopy with first-principles calculations we map the complete microscopic evolution of the twin domain population through the strain-driven transition from the rhombohedral $R\bar{3}c$ ground state toward the predicted orthorhombic $Fmmm$ phase. Applied strains below $0.5\%$ produce pronounced surface flattening and large-scale domain reorganisation, establishing uniaxial strain as a technically accessible control parameter for ferroelastic domain engineering. These results open a route to active, real-time programming of domain architectures in LaAlO$_3$-based heterostructures, with implications for strain-tunable superconducting interfaces, nanoscale phonon-polariton optics, and ultrafast lattice control.

We classify fillable contact structures on all negative-definite star-shaped plumbings. Along the way, we show that such Seifert fibred spaces admit a unique negative maximal twisting number, and compute it explicitly using the Alexander filtration in lattice cohomology. In particular, we show that the negative-twisting tight structures on these manifolds are induced by the Stein structures on the minimal resolution of the underlying complex surface singularity. As an application, we provide a necessary condition for a negative-definite Seifert fibred space to admit a separating contact-type embedding in a strong symplectic filling of a generalised $L$-space.

We prove superpolynomial length lower bounds for the semantic tree-like Frege refutation system with bounded line size. Concretely, for any function $n^{2-\varepsilon} \leq s(n) \leq 2^{n^{1-\varepsilon}}$ we exhibit an explicit family $\mathcal{A}$ of $n$-variate CNF formulas $A$, each of size $|A| \le s(n)^{1+\varepsilon}$, such that if $A$ is chosen uniformly from $\mathcal{A}$, then asymptotically almost surely any tree-like Frege refutation of $A$ in line-size $s(n)$ is of length super-polynomial in $|A|$. Our lower bounds apply also to tree-like degree-$d$ threshold systems, for $d \approx \log\bigl(s(n)\bigr)$, that is, for $d$ up to $n^{1-\varepsilon}$. More generally, our lower bounds apply to the semantic version of these systems and to any semantic tree-like proof system where the number of distinct lines is bounded by $\exp\bigl(s(n)\bigr)$.

A new class of Semantic Numeration Systems, namely, positive rational Semantic Numeration Systems is introduced. For cardinal semantic operators, differences in the formation of carry (common carry) and remainders are defined. The properties of positive rational Semantic Numeration Systems as dynamical systems are formulated and illustrated through analytical and numerical examples. A first attempt at defining partial integer Semantic Numeration Systems is proposed.

Identity verification is a critical gateway to accessing government services and public benefits, yet contemporary systems are typically designed around visual interaction, leaving blind and low vision (BLV) individuals disproportionately burdened. In this work, we examine how BLV users navigate identity verification in government services and how current designs shape their access, security, and autonomy. Through a mixed methods study combining analysis of 219 Reddit posts and semi-structured interviews with 16 BLV participants, we uncover systemic accessibility breakdowns across both digital and in person verification processes. Our findings show that inaccessible verification workflows do not merely inconvenience users, they restructure how security is achieved in practice. We also identify how repeated verification demands, inaccessible physical infrastructure, and policy changes exacerbate exclusion from essential services. At the same time, participants articulate complex perspectives on AI, viewing it as both a critical accessibility aid and a growing vector for identity fraud.

Finite Larmor radius magnetohydrodynamics (FLR-MHD) provides a hybrid model of plasma that explains how turbulent energy cascade extends to sufficiently small parallel length scales, potentially leading to perpendicular heating of the ions in the solar corona and the solar wind. In this work, we derive exact laws for the cascades of energy and generalized helicity in fully developed FLR-MHD turbulence. In large and small scale limits, we obtain the exact laws for reduced MHD and electron reduced MHD turbulence respectively. Unlike ordinary or reduced MHD turbulence, a global stationary state is shown to be absent in the case of a strong imbalance between the Elsasser variables. This is due to the so-called helicity barrier, which leads to two separate stationary energy cascades with different cascade rates. Our derived exact laws enable us to calculate these two cascade rates and therefore their difference, which effectively provides the heating rate of the ions. In addition, we also derive alternative Banerjee-Galtier forms for the exact laws and hence obtain the relaxed states of FLR-MHD turbulence using the framework of recently proposed principle of vanishing nonlinear transfer. The relaxed states show alignment between the velocity and magnetic field fluctuations. However, due to strong anisotropy, no Beltrami alignment is possible for velocity and magnetic fields. Similarly to the exact laws, the relaxed states of reduced and electron reduced MHD emerge in the large and small scale limits, respectively.

Near-term quantum computers are accessed through repeated circuit executions, which produce finite measurement records rather than exact deterministic outputs. In quantum reservoir computing, these records are converted to feature vectors for a classical readout. The standard expectation-value approach averages all shots from one labeled time step into a single feature vector. This reduces finite-shot noise, but it also gives the readout only one training example from many circuit executions. We introduce split-ensemble training: the same shots are split into groups, and each group average is used as a separate, partially denoised feature vector for the same target. The quantum circuit, task, and measurement budget remain unchanged. Across simulated forecasting benchmarks and real hardware experiments, this simple reorganization improves prediction when full averaging leaves the readout with too few training examples, with the strongest gains observed on hardware. Our results establish shot-record organization as a simple, broadly applicable algorithmic lever for improving near-term quantum learning without additional quantum hardware cost.

Long-context large language models (LLMs)-for example, Gemini-3.1-Pro and Qwen-3.5-are widely used to empower many real-world applications, such as retrieval-augmented generation, autonomous agents, and AI assistants. However, security remains a major concern for their widespread deployment, with threats such as prompt injection and knowledge corruption. To quantify the security risks faced by LLMs under these threats, the research community has developed heuristic-based and optimization-based red-teaming methods. Optimization-based methods generally produce stronger attacks than heuristic attacks and thus provide a more rigorous assessment of LLM security risks. However, they are often resource-intensive, requiring significant computation and GPU memory, especially for long context scenarios. The resource-intensive nature poses a major obstacle for the community (especially academic researchers) to systematically evaluate the security risks of long-context LLMs and assess the effectiveness of defense strategies at scale. In this work, we propose FlashRT, the first framework to improve the efficiency (in terms of both computation and memory) for optimization-based prompt injection and knowledge corruption attacks under long-context LLMs. Through extensive evaluations, we find that FlashRT consistently delivers a 2x-7x speedup (e.g., reducing runtime from one hour to less than ten minutes) and a 2x-4x reduction in GPU memory consumption (e.g., reducing from 264.1 GB to 65.7 GB GPU memory for a 32K token context) compared to state-of-the-art baseline nanoGCG. FlashRT can be broadly applied to black-box optimization methods, such as TAP and AutoDAN. We hope FlashRT can serve as a red-teaming tool to enable systematic evaluation of long-context LLM security. The code is available at: https://github.com/Wang-Yanting/FlashRT

In realistic nanoscale transport set-ups, electron-phonon coupling leads to the exchange of heat between phonon baths and electronic reservoirs with finite heat capacities. Such exchange affects the finite reservoir's temperature. However, this sensitivity of the finite reservoir temperature to the exchange of heat with the finite reservoir has remained unexplored for thermometry. Here, we fill this gap by combining current metrology techniques with a thermodynamic framework encompassing finite reservoirs. We focus on an experimentally realizable set-up with a quantum dot coupled to a finite reservoir and consider two distinct current-based strategies in the long time limit, namely monitoring quanta exchanged between the quantum dot and finite reservoir and the measurement of the total current flowing from the quantum dot into an infinite reservoir. A third strategy involves measurements of the quantum dot occupation. For a large but finite reservoir, we show that the Fisher information for all three strategies captures the finite reservoir's contribution to sensitivity through common factors. We also demonstrate that monitoring quanta exchanged between the system and finite reservoir in the long time limit achieves optimal precision. Finally, we provide an optimization analysis that explores how maximal precision can be achieved within each of the current-based strategies by tuning the gate voltage.

Global QCD analyses provide the primary framework for extracting hadron structure from experimental data, yet the mechanisms by which data constrain non-perturbative functions remain difficult to interpret due to the high dimensionality and complexity of these fits. Here we develop a framework based on linear response and influence functions, which are gradient-based sensitivity measures that directly quantify how experimental information propagates to fitted quantities and observables. These quantities cleanly expose how data locally determines the central values and uncertainties of quantum correlation functions, as well as the correlations between them, providing a transparent and general framework for diagnosing information flow in inverse problems in QCD.

We introduce a refined immersed boundary (IB) methodology that is better-than-first-order accurate in practice, while preserving key properties of "continuous-forcing" IB approaches that retain a singular source term in the governing equations. Our method leverages a smoothed indicator (Heaviside) function, following ideas from multiphase flow and immersed layers formulations, to recast the IB solution as a composite of distinct interior and exterior fields. We demonstrate that, when cast through this composite-solution lens, prior continuous-forcing IB methods can be seen as neglecting terms in the governing and constraint equations that restrict the solution to first-order accuracy. We incorporate these terms to systematically improve accuracy without the need for heuristic corrections. In canonical Poisson problems, we empirically demonstrate second-order convergence, and in incompressible Navier-Stokes simulations the method achieves slightly sub-second-order performance. While our present study focuses on these cases, the framework suggests a path towards second-order accuracy or higher, with further extensions. This perspective reframes accuracy limitations typically attributed to IB schemes. Although continuous-forcing IB methods are often reported to be only first-order accurate, we show that neither smoothing nor interface interpolation inherently restricts attainable order. Moreover, we naturally incorporate this higher-order formulation into a projection-based solution process. The resulting algorithm simultaneously mitigates the spurious surface stresses produced by ill-conditioned linear systems and reduces sensitivity to geometric resolution, addressing both conditioning and accuracy concerns within a unified approach.

We study the melting of a domain wall in the quantum simple exclusion process with all-to-all hoppings (a.k.a. the charged SYK$_2$ model). We show that the real-time dynamics of physical quantities of interest can be obtained exploiting spectral results in random matrix theory. We first show that the eigenvalues of the correlation matrix corresponding to the initially charged subsystem evolve according to a Jacobi process, which is defined in terms of a closed system of stochastic differential equations. In turn, this observation allows us to obtain the real-time dynamics of all the eigenvalue moments. We present two physical applications. First, we study the dynamics of the averaged von Neumann entanglement entropy, arriving at a fully explicit expression in the thermodynamic limit. Second, we compute analytically the full-counting statistics of the charge. Our formula allows us to perform a thorough comparison with the full-counting statistics of the classical simple exclusion process. Notably, we show that, in the thermodynamic limit, the quantum and classical full-counting statistics coincide, with no finite-time corrections.

We study how long the SIRS process persists or how quickly it reaches extinction across various network topologies. Our results provide a three-part characterization of this process: In finite sparse graphs, we prove the existence of a regime where the process survives for an exponentially long time. In heavy-tailed networks with power-law-like exponents, we show that for all range of parameters, the survival time is exponential. Finally, for infinite trees, we find sufficient conditions for strong survival, showing the root is re-infected infinitely often even for light-tailed distributions like the Poisson distribution.

Filenames are a concise means of conveying information about source code to fellow developers. One such convention is util. Commonly understood to stand for "utility", filenames with the letters util are often an indication that the file contains code that may be broadly useful or reusable. Some projects use this convention heavily, for example, the Apache Tomcat server contains 925 files with util in the path name, which is 17.9% of all source code files in the tree. While the intent of the name may be to prevent duplicate code and reduce workload, what actually happens to util code over time? Do projects move away from util code as they mature? Are util files being used by fellow colleagues, or maintained and used by their author? The goal of our work is to help developers avoid creating unsafe and unused util files when developing their projects. We conducted a longitudinal mining study of the Git repositories of seven open source projects that have a long development history (Linux kernel, Django, FFmpeg, httpd, Struts, systemd, Tomcat). We analyzed how util usage, complexity, developer collaboration, and security are potentially correlated within these projects. Our longitudinal analysis was measured at 30-day intervals throughout the entire history of each project, resulting in 1773 snapshots over 147 project-years of development. We conducted rename tracking at every 30-day snapshot to examine util files over their entire lifetime in a codebase. For example, we found that a util file can be as much as 2.75 times more likely to be involved in a vulnerability than non-util files. While every project can adopt their own naming conventions, the ubiquity and longevity of util files shows a broader developer intent that is useful for understanding the socio-technical nature of software development.

Exciton-phonon interactions in transition metal dichalcogenides (TMD) are strong and lead to phenomena such as coherent phonon generation. When stacked and twisted, their properties can be tuned by the twisting angle. In experiments with 1.1$^\circ$ twisted 2L WSe$_2$, a change of 0.1 Å in the interlayer distance was observed when light was shone on this material, and here we explain the microscopic mechanism behind this. Theoretical works to study such systems are limited because the Moiré unit cell is too large. To overcome this, we combined classical force field relaxations with our implementation of ab initio GW/Bethe-Salpeter excited state forces (ESF). From the relaxations we found that the low-angle twisting induced an in-plane strain field, the AB regions are large enough to be simulated as periodic AB stacked 2L WSe2, and the interlayer force constant becomes softer in relation to the perfect AB stacking. From the ab initio ESF we obtained that the in-plane strain increases the out of plane ESFs. Those two effects combined, the weakening of the interlayer force constant and strain dependence of the ESF, make light-induced changes in the interlayer distance of twisted 2L WSe2 stronger than in the perfectly stacked case, in agreement with experimental observations. Therefore, our results show that the exciton-phonon interactions can be tuned in twisted 2L TMDs and can be observed experimentally, which makes those materials excellent platforms to study light-induced changes in materials.

Ad-hoc queries over frequently updated data in a flat schema are common in real-time data analysis applications and often require very low latency. Online aggregation can achieve so by providing approximate aggregation answers with confidence bound guarantees. It relies on the ability to draw samples online in a linear time to sample size rather than database size, which can be supported by index-assisted Sampling-based Approximate Query Processing (S-AQP) systems. However, the query latencies of approximate queries in these systems can still suffer from excessive sampling cost required to achieve a desired confidence bound, due to increased sample size for data with high variance in value distribution and selectivity. Classic stratified sampling methods with Neyman allocation can minimize sample size in theory, but several challenges prevent it from being applicable in index-assisted S-AQP systems, including requiring apriori statistics, high optimization cost, and inaccurate sampling cost model based on sample size. Towards that, we design index-assisted stratified sampling for online aggregation, which features a two-phase sampling framework. Samples drawn from first phase are used for both online aggregation and optimizing future sampling cost, while the second phase continues the online aggregation using the optimized strata. We prove optimal stratification and sample size allocation strategies for index-based sampling cost model, and design several greedy and dynamic programming based optimization methods to balance optimization cost and effectiveness in cost reduction. We evaluate our methods on several real-world and synthetic datasets and queries, and the results show ours consistently achieve good speedup and, in extreme cases, up to 3x speedup and 98708x speedup, when compared to index-assisted uniform sampling and classic scan-based stratified sampling respectively.

We study geodesic motion of a test particle in Schwarzschild spacetime. Bound and scattering geodesics are commonly described using Darwin variables, which provide a convenient parametrization of the radial motion. However, this description breaks down at the separatrix and does not extend straightforwardly to plunging trajectories. We construct an analytic continuation of Darwin variables that yields a real parametrization of bound, scattering, and plunging Schwarzschild geodesics, thereby providing a unified kinematical description of all types of test-mass motion. As a proof of concept, we then apply these variables to a simple non-geodesic evolution in which the energy and angular momentum are driven by a constant external force. This toy model is not intended to represent a physical radiation-reaction model, but rather to illustrate how the extended variables can be used to follow an orbit through a transition to plunge using a single orbital phase variable across the separatrix.