Twisted bilayer MoTe$_2$ (tMoTe$_2$) is an emergent platform for exploring exotic quantum phases driven by the interplay between nontrivial band topology and strong electron correlations. Direct experimental access to its momentum-resolved electronic structure is essential for uncovering the microscopic origins of the correlated topological phases therein. Here, we report angle-resolved photoemission spectroscopy (ARPES) measurements of tMoTe$_2$, revealing pronounced twist-angle-dependent band reconstruction shaped by orbital character, interlayer coupling, and moiré potential modulation. Density functional theory (DFT) captures the qualitative evolution, yet underestimates key energy scales across twist angles, highlighting the importance of electronic correlations. Notably, the hole effective mass at the K point exhibits a non-monotonic dependence on twist angle, peaking near 2°, consistent with band flattening at the magic angle predicted by continuum models. Via electrostatic gating and surface dosing, we further visualize the evolution of electronic structure versus doping, enabling direct observation of the conduction band minimum and confirm tMoTe$_2$ as a direct band gap semiconductor. These results establish a spectroscopic foundation for modeling and engineering emergent quantum phases in this moiré platform.

Modern information retrieval must reconcile short, ambiguous queries with increasingly diverse and dynamic corpora. Query expansion (QE) remains a core technique for mitigating vocabulary mismatch, but its design space has been reshaped by pre-trained and large language models (PLMs/LLMs). This survey reviews QE methods in the PLM/LLM era and provides a unified view of the emerging landscape. We first summarize how different model families enable new expansion behaviors, including stronger contextualization, more controllable generation, and instruction-following. We then organize recent techniques along four complementary design dimensions: where expansion is injected in the pipeline, how it is grounded and interacts with corpus evidence, how it is learned or aligned, and how structured knowledge such as knowledge graphs is incorporated. Beyond taxonomy, we synthesize application patterns and deployment considerations across representative retrieval settings, highlighting practical trade-offs among effectiveness, controllability, grounding quality, and operating cost. Finally, we outline open challenges and future directions toward more reliable, safe, efficient, and continually adaptive QE under real-world constraints.

Pair-instability should prevent the direct formation of black holes above about $50M_\odot$ creating a pair-instability mass gap. Yet gravitational-wave observations have detected black holes in this mass range. These systems can be explained with uncertainties in massive-star evolution, or hierarchical mergers in stellar clusters, which are expected to produce large spins with isotropic orientations. Here we present evidence for the pair-instability mass gap in the LIGO--Virgo--KAGRA fourth transient catalog, with a lower edge at $44.3^{+5.9}_{-3.5}\,M_\odot$. We also obtain a measurement of the ${}^{12}\mathrm{C}(α,γ){}^{16}\mathrm{O}$ reaction rate, yielding an $S$-factor of $268^{+195}_{-116}\,\mathrm{keV\,b}$, a parameter critical for modeling helium burning and stellar evolution. The data reveal two populations: a low-spin group with no black holes above the gap, and a high-spin, isotropic group that extends across the full mass range and occupies the gap, consistent with hierarchical mergers. These findings are consistent with pair-instability playing a role in shaping the black hole mass spectrum, point to a connection between gravitational wave astronomy and nuclear astrophysics, and highlight dense stellar clusters as key environments in the growth of black holes.

We show that an arithmetic path integral over the $\ell$-torsion of a Jacobian $J[\ell]$ is equal to the trace of the Frobenius action on a representation of the Heisenberg group $H(J[\ell])$, up to an explicitly determined sign. This is an arithmetic analogue of trace--path integral formulae which arise in quantum field theory, where path integrals over a space of sections of a fibration over a circle can be expressed as the trace of the monodromy action on a Hilbert space.

We study the local behavior of integral points on log pairs near a fixed rational point in the boundary by means of an integral approximation constant. In light of Siegel's theorem about integral points on curves and McKinnon's conjecture on rational approximation constants, we conjecture that integral points that are close to the fixed point in archimedean topology should lie on certain rational curves with at most two points at infinity on weakly log Fano varieties. We verify this conjecture for a number of examples.

In \cite{Ca2016} and \cite{Ca2018}, we introduced a notion of effective reducibility between set-theoretical $Π_{2}$-statements; in \cite{Ca2025}, this was extended to statements of arbitrary (potentially even infinite) quantifier complexity. We also considered a corresponding notion of Weihrauch reducibility, which allows only one call to the effectivizer of $ψ$ in a reduction of $ϕ$ to $ψ$. In Stammes \cite{StammesMaster}, a considerably refined analysis through interpolating between these two notions was proposed, where one asks how many calls to an effectivizer for $ψ$ are required for effectivizing $ϕ$. This allows us to make formally precise questions such as ``how many ordinals does one need to check for being cardinals in order to compute the cardinality of a given ordinal?'' and (partially) answer many of them. Many of these anwers turn out to be independent of ZFC.

We propose the complex singlet-extended 2-Higgs-Doublet Model (cS2HDM), a spin-0 Dark Matter (DM) model with a Higgs sector consisting of two Higgs doublets and a complex singlet, as a benchmark for LHC DM searches. The model predicts a pseudo-Nambu-Goldstone DM candidate whose interactions with nuclei are naturally suppressed, while allowing for all sources of CP-violation under the assumption of flavour alignment in the Yukawa sector, which enables CP-violating interactions of the Higgs bosons even in the alignment limit. This feature makes the model attractive for studies of electroweak baryogenesis while accommodating a Higgs-portal DM candidate with standard thermal freeze-out. We confront the model with a comprehensive set of theoretical and experimental constraints, including Higgs-boson signal strength measurements, searches for additional Higgs bosons, DM relic abundance and direct detection, as well as electroweak precision observables and the electron EDM, with emphasis on the impact of the new CP-violating sources. For DM direct detection, we perform a one-loop computation of DM-nucleon scattering including CP-violating effects. We provide a public software package to facilitate future phenomenological studies of the cS2HDM.

We develop a lifting theory for the exponential map of semi-Riemannian manifolds that overcomes the classical obstruction caused by its singularities. We show that every smooth path in the manifold admits, up to a nondecreasing reparametrization, a partial lift through the exponential map which is inextensible in its domain of definition. If the exponential map satisfies the path-continuation property-a natural topological condition-these lifts extend globally, yielding a general path-lifting theorem. This lifting approach yields new, alternative proofs of (generalizations of) a number of foundational results in semi-Riemannian geometry: the Hopf-Rinow theorem and Serre's classic theorem about multiplicity of connecting geodesics in the Riemannian case, as well as the Avez-Seifert theorem for globally hyperbolic spacetimes in Lorentzian geometry. More broadly, our results reveal the central role of the continuation property in obtaining geodesic connectivity across a wide range of semi-Riemannian geometries. This offers a unifying geometric principle that is complementary to the more traditional analytic, variational methods used in to investigate geodesic connectedness, and provides new insight into the structure of geodesics, both on geodesically complete and non-complete manifolds. We also briefly point out how the lifting theory developed here can etend to more general flow-inducing maps on the tangent bundle other than the geodesic flow, suggesting broader geometric applicability beyond the exponential map.

The extensive use of glyphosate in agriculture has raised environmental concerns due to its adverse effects on plants, animals, microorganisms, and humans. This study investigates the interactions between ionized glyphosate and single-walled carbon nanotubes (CNT) using computational simulations through semi-empirical tight-binding methods (GFN2-xTB) implemented in the xTB software. The analysis focused on different glyphosate ionization states corresponding to various pH levels: G1 (pH < 2), G2 (pH ~ 2-3), G3 (pH ~ 4-6), G4 (pH ~ 7-10), and G5 (pH > 10.6). Results revealed that glyphosate in G1, G3, G4, and G5 forms exhibited stronger interactions with CNT, demonstrating higher adsorption energies and greater electronic coupling. The neutral state (G2) showed lower affinity, indicating that molecular protonation significantly influences adsorption. Topological analysis and molecular dynamics confirmed the presence of covalent, non-covalent, and partially covalent interactions, while the CNT+G5 system demonstrated moderate interactions suitable for material recycling. These findings suggest that carbon nanotubes, with their extraordinary properties such as nanocapillarity, porosity, and extensive surface area, show promise for environmental monitoring and remediation of glyphosate contamination.

We study Boundary time crystals (BTCs) in the presence of non-Markovian dynamics. In contrast to BTCs observed in earlier works in the Markovian regime, we show that non-Markovian dynamics can be highly beneficial for stabilizing BTCs over a wide range of parameter values, even in the presence of intermediate rates of dissipation. Notably, we also observe the emergence of higher-order limit cycles (HO-LCs) for some parameter regimes. We analyze the effect of non-Markovian dynamics on BTCs and HO-LCs using quantum Fisher information, time-averaged magnetization, a measure of non-Markovianity, and a dynamical phase diagram, all of which show complex behaviors with changing non-Markovianity parameters. Our studies can pave the way for stabilizing time crystals in dissipative systems, as well as lead to studies on varied dissipative dynamics on time translational symmetry breaking.

The existence of baryonium-bound or resonant states composed of a baryon and an antibaryon has long been postulated as a natural extension of conventional hadron spectroscopy. In the present work, we conduct a systematic investigation of the mass spectrum and internal configurations of light baryonium candidates exhibiting exotic quantum numbers that are inaccessible within the framework of the traditional quark model. Employing the method of QCD sum rules, we analyze nucleon-antinucleon and light hyperon-anti-hyperon systems with quantum numbers $J^{PC}=0^{--}$ and $0^{+-}$, which are quantum number combinations prohibited for conventional mesonic states. Our analysis reveals the potential existence of two $0^{--}$ $Λ$-$\barΛ$ baryonium states with masses $(2.90\pm0.09)$ GeV and $(3.36\pm0.09)$ GeV, respectively, as well as two $0^{+-}$ $Λ$-$\barΛ$ states with masses $(2.91\pm0.07)$ GeV and $(3.29\pm0.07)$ GeV, respectively. In addition, corresponding nucleon-antinucleon partner states are identified at $(2.69\pm0.07)$ GeV, $(3.07\pm0.08)$ GeV, $(2.86\pm0.07)$ GeV, and $(3.22\pm0.07)$ GeV, respectively. Furthermore, analogous $Ξ$-$\barΞ$ configurations are predicted with masses of $(3.10\pm0.09)$ GeV, $(3.54\pm0.07)$ GeV, $(3.08\pm0.08)$ GeV, and $(3.45\pm0.08)$ GeV, respectively. The possible decay modes of the light exotic baryonium states are analyzed, which are hopefully measurable in BESIII, BELLEII, and LHCb experiments.

Everlasting options, a relatively new class of perpetual financial derivatives, have emerged to tackle the challenges of rolling contracts and liquidity fragmentation in decentralized finance markets. This paper offers an in-depth analysis of markets for everlasting options, modeled using a dynamic proactive market maker. We examine the behavior of funding fees and transaction costs across varying liquidity conditions. Using simulations and modeling, we demonstrate that liquidity providers can aim to achieve a net positive PnL by employing effective hedging strategies, even in challenging environments characterized by low liquidity and high transaction costs. Additionally, we provide insights into the incentives that drive liquidity providers to support the growth of everlasting option markets and highlight the significant benefits these instruments offer to traders as a reliable and efficient financial tool.

Symmetry-resolved entanglement entropy provides a powerful framework for probing the internal structure of quantum many-body states by decomposing entanglement into contributions from distinct symmetry sectors. In this work, we apply matrix product state techniques to study the bosonic, non-Abelian Moore-Read quantum Hall state, enabling precise numerical evaluation of both the full counting statistics and symmetry-resolved entanglement entropies. Our results reveal an approximate equipartition of entanglement among symmetry sectors, consistent with theoretical expectations and subject to finite-size corrections. The results also show that these expectations for symmetry-resolved entanglement entropy remain valid in the case of a non-Abelian state where the topological sectors cannot be distinguished by the Abelian $\mathrm{U}(1)$ symmetry alone, and where neutral and charged modes possess distinct velocities. We additionally perform a detailed comparison of the entanglement spectrum with predictions from the Li-Haldane conjecture, finding remarkable agreement, and enabling a more precise understanding of the effects of the distinct neutral and charged velocities. This not only provides a stringent test of the conjecture but also highlights its explanatory power in understanding the origin and structure of finite-size effects across different symmetry sectors.

We develop a Bayesian tree ensemble model to estimate heterogeneous treatment effects in censored survival data with high-dimensional covariates. Instead of imposing sparsity through the tree structure, we place a horseshoe prior directly on the step heights to achieve adaptive global-local shrinkage. This strategy allows flexible regularisation and reduces noise. We develop a reversible jump Gibbs sampler to accommodate the non-conjugate horseshoe prior within the tree ensemble framework. We show through extensive simulations that the method accurately estimates treatment effects in high-dimensional covariate spaces, at various sparsity levels, and under non-linear treatment effect functions. We further illustrate the practical utility of the proposed approach by a re-analysis of pancreatic ductal adenocarcinoma (PDAC) survival data from The Cancer Genome Atlas.

We develop foundations for a relational approach to quantum field theory (RQFT) based on the operational quantum reference frames (QRFs) framework considered in a relativistic setting. Unlike other efforts in combining QFT with QRFs, we use the latter to provide novel mathematical and conceptual foundations for the former. We focus on scalar fields in Minkowski spacetime and discuss the emergence of relational local (bounded) observables and (pointwise) fields from the consideration of Poincaré-covariant (quantum) frame observables defined over the space of (classical) inertial reference frames. We recover a relational notion of Poincaré covariance, with transformations on the system directly linked to the state preparations of the QRF. We introduce and analyse various causality conditions, and construct an explicit example of a covariant scalar relational quantum field which is causal relative to operationally meaningful preparations of a relativistic QRF. The theory makes direct contact with established foundational approaches to QFT. We demonstrate that the vacuum expectation values derived within our framework reproduce many of the essential properties of Wightman functions and carry out a detailed comparison of the proposed formalism with Wightman QFT with the frame smearing functions describing the QRF's localisation uncertainty playing the role of the Wightmanian test functions. We also show how the properties of algebras generated by relational local observables suitably extend the core axioms of Algebraic QFT. This work is an early step in revisiting the mathematical foundations of QFT from a relational and operational perspective.

Using a geometric construction, we solve Plateau's Problem in the Heisenberg group $\mathbb{H}^{1}$ for intrinsic graphs defined on a convex domain $D$, under a smallness condition either on the boundary $\partial D$ or on the Lipschitz boundary datum $φ: \partial D \to \mathbb{R}$. The proof relies on a calibration argument. We then apply these techniques to establish a new regularity result for $H$-perimeter minimizers.

We obtain the Green's function for fermions in an external non-Abelian gauge field with an $SU(N)$ symmetry group. As an external field, we examine the solution to the Yang-Mills equation presented as a plane wave on the light cone.

Least absolute shrinkage and selection operator or Lasso is one of the widely used regularization methods in regression. Statisticians usually implement Lasso in practice by choosing the penalty parameter in a data-dependent way, the most popular being the $K-$fold cross-validation (or $K-$fold CV). However, inferential properties, such as the variable selection consistency and $n^{1/2}-$consistency, of the $K-$fold CV based Lasso estimator and validity of the Bootstrap approximation are still unknown. In this paper, we consider the heteroscedastic linear regression model and show only under some moment type conditions that the Lasso estimator with $K$-fold CV based penalty is $n^{1/2}-$consistent, but not variable selection consistent. Additionally, we establish the validity of Bootstrap in approximating the distribution of the $K-$fold CV based Lasso estimator. Therefore, our results theoretically justify the use of $K-$fold CV based Lasso estimator to perform statistical inference in linear regression. We validate our Bootstrap method for the $K-$fold CV based Lasso estimator in finite samples based on simulations. We also implement our Bootstrap based inference on a real data set.

This paper proposes a novel non-anticipatory long-short-term coordinated dispatch framework for isolated microgrid with hybrid short-long-duration energy storages (LDES). We introduce a convex hull approximation model for nonconvex LDES electrochemical dynamics, facilitating computational tractability and accuracy. To address temporal coupling in SoC dynamics and long-term contracts, we generate hindsight-optimal state-of-charge (SoC) trajectories of LDES and netloads for offline training. In the online stage, we employ kernel regression to dynamically update the SoC reference and propose an adaptive online convex optimization (OCO) algorithm with SoC reference tracking and expert tracking to mitigate myopia and enable adaptive step-size optimization. We rigorously prove that both long-term and short-term policies achieve sublinear regret bounds over time, which improves with more regression scenarios, stronger tracking penalties, and finer convex approximations. Simulation results show that the proposed method outperforms state-of-the-art methods, reducing costs by 73.4%, eliminating load loss via reference tracking, and achieving an additional 2.4% cost saving via the OCO algorithm. These benefits scale up with longer LDES durations, and the method demonstrates resilience to poor forecasts and unexpected system faults.

A key challenge for targeted antipoverty programs in developing countries is that policymakers must rely on estimated rather than observed income, which leads to substantial targeting errors. The policy problem is not only to predict income, but to decide how noisy income estimates should be translated into feasible transfers. I formulate this as a statistical decision problem in which a policymaker chooses transfers to minimize a poverty-targeting loss subject to a fixed budget and a no-taxation constraint. I show that the standard plug-in rule, which treats estimated incomes as true, is inadmissible. I develop a nonparametric empirical Bayes targeting rule that assigns transfers using posterior distributions of true poverty gaps. Although the budget and no-taxation constraints make the targeting rule nonsmooth, Bayes regret is governed by the accuracy of the posterior functionals that determine the oracle allocation. In simulations using household survey data from nine African countries, the empirical Bayes rule reaches substantially more poor households and systematically improves poverty reduction relative to plug-in OLS and machine-learning benchmarks.

We decompose the intrinsic second-order nonlinear Hall effect (NLHE) of a generic multiband system into its quantum-geometric contributions within a fully quantum-mechanical, projector-based formalism. By expanding the nonlinear conductivity in powers of the quasiparticle lifetime $τ$, we recover the established Berry curvature dipole at order $τ$ and clarify discrepancies in previous literature concerning the (interband) quantum metric dipole (or Berry curvature polarizability) contribution at order $τ^0\textrm{.}$ Crucially, our method reveals an additional contribution at order $τ^0$, determined by the {\it intraband} quantum metric dipole (intraQMD), arising from additional virtual interband transitions captured within the fully quantum-mechanical treatment. The intraQMD contribution is generically nonzero in systems with broken time-reversal symmetry and can be distinguished from other geometric contributions by symmetry. Analytical results for low-energy models of topological band crossings, which are hotspots of quantum geometry, demonstrate how band topology influences each contribution. In particular, the intraQMD contribution is especially large in gapped Dirac cones in antiferromagnets. Through a comprehensive symmetry classification of all magnetic space groups, we identify several candidate materials that are expected to exhibit large intrinsic NLHE, including the topological antiferromagnets Yb$_3$Pt$_4$, CuMnAs, and CoNb$_3$S$_6$, as well as the nodal-plane material MnNb$_3$S$_6$.

A detailed study of the particle identification by the Focusing Aerogel Ring Imaging CHerenkov subsystem at the future charm superfactory detector is presented. The dedicated signal ring reconstruction algorithm is implemented in the detector simulation, the algorithm performance is tested with single particles generated within the Aurora framework. Two Boosted Decision Trees-based classifiers for the particle identification have been developed for various assumptions about photosensor noise levels. The approach is validated with the analysis of the D0->Kmunu decays, for which the systematic uncertainty and background contribution related to the pion/muon separation performance can be minimised due to high efficiency of the particle identification algorithm.

Multipass cells enable long optical path lengths in compact volumes and are central to quantum technologies such as atomic magnetometers and optical quantum memories. In optical magnetometry, multipass geometries enhance sensitivity by increasing optical depth, reducing photon shot noise, and enabling quantum non-demolition detection. However, in conventional cylindrical multipass cells, Lissajous beam trajectories lead to repeated revisiting and incomplete mirror coverage, limiting effective volume utilization. Here we present a recirculating multipass alkali cell that overcomes these limitations by increasing the active-to-cell volume ratio and minimizing beam spot overlap. We develop an analytical ABCD-matrix model to predict beam trajectories, spot sizes, and astigmatism, validated by Zemax simulations. We further introduce a general analytical framework for spin correlation noise that incorporates astigmatism and spatial intensity distributions. By deriving the spin-noise time-correlation function and spectrum, we show how beam intensity profiles influence spin diffusion noise. Our results demonstrate improved beam coverage, reduced spot overlap, and enhanced spin correlation, particularly for concave mirrors with long focal lengths, while showing that avoiding tightly-focused regions significantly suppresses spin diffusion noise. These findings establish recirculating multipass cells as a practical, high-performance platform for precision atomic sensing and other multipass-cavity-based quantum devices.

This article studies the fundamental problem of using i.i.d. coin tosses from an entropy source to efficiently generate random variables $X_i \sim P_i$ $(i \ge 1)$, where $(P_1, P_2, \dots)$ is a random sequence of rational discrete probability distributions subject to an \textit{arbitrary} stochastic process. Our method achieves an amortized expected entropy cost within $\varepsilon > 0$ bits of the information-theoretically optimal Shannon lower bound using $O(\log(1/\varepsilon))$ space. This result holds both pointwise in terms of the Shannon information content conditioned on $X_i$ and $P_i$, and in expectation to obtain a rate of $\mathbb{E}[H(P_1) + \dots + H(P_n)]/n + \varepsilon$ bits per sample as $n \to \infty$ (where $H$ is the Shannon entropy). The combination of space, time, and entropy properties of our method improves upon the Knuth and Yao (1976) entropy-optimal algorithm and Han and Hoshi (1997) interval algorithm for online sampling, which require unbounded space. It also uses exponentially less space than the more specialized methods of Kozen and Soloviev (2022) and Shao and Wang (2025) that generate i.i.d. samples from a fixed distribution. Our online sampling algorithm rests on a powerful algorithmic technique called \textit{randomness recycling}, which reuses a fraction of the random information consumed by a probabilistic algorithm to reduce its amortized entropy cost. On the practical side, we develop randomness recycling techniques to accelerate a variety of prominent sampling algorithms. We show that randomness recycling enables state-of-the-art runtime performance on the Fisher-Yates shuffle when using a cryptographically secure pseudorandom number generator, and that it reduces the entropy cost of discrete Gaussian sampling. Accompanying the manuscript is a performant software library in the C programming language.

We study the problem of edge partitioning, where the goal is to partition the edge set of a graph into several parts. The replication factor of a vertex $v$ is the number of parts that contain edges incident to $v$. The goal is to minimize the average replication factor of the vertices while keeping the sizes of the parts nearly equal. We study the regime where the number of parts is significantly smaller than the size of the graph. To this end, we introduce a new class of edge partitioning algorithms. These algorithms guarantee asymptotically worst-case-optimal upper bounds on the replication factor for any constant number of parts $k$, and when $k$ grows slowly with the number of vertices. In particular, we show that the optimal replication factor for growing $k$ is $\sqrt{k}(1+o(1))$. The algorithms are computationally efficient, including in the LOCAL and CONGEST models, and can be implemented as stateless streaming algorithms in graph processing frameworks. Some of the worst-case graphs are complete graphs and jumbled graphs, also known as pseudo-random graphs. Our method generalizes a family of algorithms based on symmetric intersecting families of sets. Informally, we replace the symmetry condition by a weaker balance condition that is still sufficient for the algorithms. This relaxation makes it possible to construct such families with asymptotically optimal rank $\sqrt{k}(1+o(1))$.

We present the performance of an efficient radon trap using silver-zeolite Ag-ETS-10, measured with a spherical proportional counter filled with an argon/methane mixture. Our study compares the radon reduction capabilities of silver-zeolite and the widely used activated charcoal, both at room temperature. We demonstrate that silver-zeolite significantly outperforms activated charcoal by three orders of magnitude in radon capture. Given that radon is a major background contaminant in rare event searches, our findings highlight silver-zeolite as a highly promising adsorbent, offering compelling operational advantages for both current and future dark matter and neutrino physics experiments. Furthermore, this not only offers great promise for developing future radon reduction systems in underground laboratories, but also paves the way for innovative, multidisciplinary advancements with far-reaching implications in science, engineering and environmental health.

In particle physics and cosmology, distinguishing subtle new physics signals from established backgrounds is a fundamental and persistent challenge for phenomenologists. This paper discuss a simple and robust statistical framework to evaluate the compatibility of highly motivated (HM) theoretical models with the residuals of experimental results, focusing on scenarios where the data appear consistent with background predictions. A likelihood ratio test is developed that compares null and alternative hypotheses, emphasizing cases where new physics introduces small deviations from the background. The practicality of the framework is highlighted, and in addition to its limitations, strategies to simplify complex background modeling are discussed.

We show that a general nonlinearity $a(x,u)$ is uniquely determined, possibly up to a gauge, in a neighborhood of a fixed solution from boundary measurements of the corresponding semilinear equation. The main theorems are low regularity counterparts of the results in our recent paper (Johansson, Nurminen, Salo; ArXiv preprint 2312.12196).

Several studies have evaluated automatic techniques for classifying software issue reports to assist practitioners in effectively assigning relevant resources based on the type of issue. Currently, no comprehensive overview of this area has been published. A comprehensive overview will help identify future research directions and provide an extensive collection of potentially relevant existing solutions. This study aims to provide a comprehensive overview of the use of automatic techniques to classify issue reports. We conducted a systematic mapping study and identified 46 studies on the topic. The study results indicate that the existing literature applies various techniques for classifying issue reports, including traditional machine learning and deep learning-based techniques and more advanced large language models. Furthermore, we observe that these studies (a) lack the involvement of practitioners, (b) do not consider other potentially relevant adoption factors beyond prediction accuracy, such as the explainability, scalability, and generalizability of the techniques, and (c) mainly rely on archival data from open-source repositories only. Therefore, future research should focus on real industrial evaluations, consider other potentially relevant adoption factors, and actively involve practitioners.

We study the perturbed Sobolev spaces ${H^{s,p}_α(\mathbb{R}^d)}$, associated with singular perturbation $Δ_α$ of Laplace operator in Euclidean space of dimensions 2 and 3. We extend the $L^2$ theory of perturbed Sobolev space to the $L^p$ case, finding an analogue description in terms of standard Sobolev spaces. This enables us to extend the Strichartz estimates to the energy space and to treat the {local well-posedness} of the {Nonlinear Schrödinger equation} associated with this singular perturbation, with the contraction method.