For a left artinian ring A, we study the lattice torsA of torsion pairs in the category of finitely generated A-modules by considering an isomorphic lattice formed by certain closed sets in a topological space associated to A, the Ziegler spectrum of the unbounded derived category of ModA. Torsion pairs in torsA turn out to be adjacent if and only if the associated closed sets are related by an operation which is induced by mutation of cosilting complexes. We describe this operation from several perspectives and present a number of applications in the case when A is a finite dimensional algebra.

Next-generation spintronic sensors aim to overcome the limitations of traditional tunneling-magnetoresistance (TMR) devices, such as complex manufacturing, high $1/f$ noise, and significant offsets. This work presents a comprehensive modeling and experimental validation of a magnetic field sensor based on Spin Hall Magnetoresistance (SMR) in a Wheatstone bridge configuration. Utilizing a multiphysics approach, we simulate the interplay between SMR, Anisotropic Magnetoresistance (AMR), and Spin-Orbit Torque (SOT) using a Stoner-Wohlfarth model complemented by a Fuchs-Sondheimer analysis of current distribution. To account for the presence of magnetic domains, we incorporate a modified Stoner-Wohlfarth framework that considers non-uniform magnetization and domain wall motion through a "truncated astroid" approach, allowing for a statistical distribution of single-domain particles. The model is validated against experimental measurements of Pt/$\text{Fe}_{60}\text{Co}_{20}\text{B}_{20}$ and Ta/$\text{Fe}_{60}\text{Co}_{20}\text{B}_{20}$ bilayers patterned into Hall bars and Wheatstone bridges. The model provides critical design guidelines for optimizing material properties, layer thickness, and device layout to minimize power consumption and maximize sensitivity in SMR-based sensing applications.

We prove new Sobolev type inequalities on compact Kähler manifolds with positive Ricci curvature. A proof of an already existing Sobolev inequality in the classical Bidaut-Véron and Véron approach is also discussed.

Distributed Bragg reflectors (DBRs) can be fabricated by electrochemically etching nitride epitaxial structures consisting of alternating layers of highly n-type doped and non-intentionally doped (NID) GaN. Threading dislocations (TDs) can be electrochemically etched into transport pipelines that can carry the etchant through the NID layers to access the doped material. Experimentally this has been shown to involve a mechanism where the etching pathway may follow one TD into a doped layer and then propagate sideways through the doped layer to continue via a different TD. Across multiple layers this process creates complex pore structures that have been described as 'cascades'. Here, we build a stochastic simulation for the DBR etching process that can reproduce some key features of the observed microstructures including the cascade morphology. By comparing the simulation output to samples etched at a range of voltages, we show that we can reproduce variations in experimental chronoamperometry data with applied bias by varying the probability of etching the doped layers within the simulation. The outputs of the resulting simulations replicate the experimentally observed cascade morphology. At higher voltages, experimental data reveal a lower proportion of cascade features, a trend that is also replicated by the simulations for relevant probability values. Outputs of the simulations also correlate well with experimental chronoamperometry data for samples where - unlike in a DBR - the thicknesses of the doped layers vary through the epitaxial multilayer, suggesting that the probabilistic simulation can be applied to a range of structures to help understand the dislocation-mediated electrochemical etching process.

Complex systems with nonlinear response mechanisms can be applied as reservoir computers for energy-efficient machine learning tasks. Historically explored at the macro- and meso-scale, physical reservoir computing has recently been extended to the atomic scale via chemical mixtures with strong and dynamic heterogeneity. Here we explore the possibility that configurational degeneracy within disordered materials might form the basis for solid-state atomic-scale reservoirs. Our proof-of-concept uses the disordered metal-organic framework DUT-8, which undergoes a series of disorder-disorder transitions on exposure to different guest species. We show that variations in X-ray diffuse scattering associated with these transitions function as suitable readouts for machine learning applications. A combination of nonlinearity and memory effects in the DUT-8 response allows the system to carry out both classification and time-series machine learning tasks with accuracies comparable to those of mesoscale physical reservoir computers. Our results suggest a new avenue for exploiting correlated disorder in solid phases whenever the nature of that disorder can be modulated through external perturbations-a phenomenon we term `responsive disorder'.

Recent research has shown that unsourced massive access (UMA) is naturally well-suited for over-the-air computation (AirComp), as it does not require knowledge of each individual signal, as demonstrated by the massive digital AirComp (MD-AirComp) scheme proposed in prior work. The MD-AirComp scheme has proven effective in federated edge learning and is highly compatible with current digital wireless networks. However, it depends on channel pre-equalization, which may amplify computation errors in the presence of channel estimation inaccuracies, thus limiting its practical use. In this paper, we propose a blind MD-AirComp+ scheme, which takes advantage of the channel hardening effect in massive multiple-input multiple-output (MIMO) systems. We provide an upper bound on the computation mean square error, analyze the trade-off between computation accuracy and communication overhead, and determine the optimal quantization level. Additionally, we introduce a deep unfolding algorithm to reduce the computational complexity of solving the underdetermined detection problem formulated as a least absolute shrinkage and selection operator optimization problem. Simulation results confirm the effectiveness of the proposed MD-AirComp+ framework, the optimal quantization selection strategy, and the low-complexity detection algorithm.

Solving large-scale nonlinear model predictive control (NMPC) problems at kilohertz (kHz) rates on standard processors remains a formidable challenge. This paper proposes a Koopman-BoxQP framework that i) learns a linear Koopman high-dimensional model, ii) eliminates the high-dimensional observables to construct a multi-step prediction model of the states and control inputs, iii) penalizes the multi-step prediction model into the objective, which results in a structured box-constrained quadratic program (BoxQP) whose decision variables include both the system states and control inputs, iv) develops a structure-exploited and warm-starting-supported variant of the feasible Mehrotra's interior-point algorithm for BoxQP. Numerical results demonstrate that Koopman-BoxQP can solve a large-scale NMPC problem with $1040$ variables and $2080$ inequalities at a kHz rate.

The fragility of quantum coherence fundamentally limits the scalability of quantum technologies, as unavoidable environmental interactions induce decoherence and rapidly degrade quantum properties. The Quantum Zeno Effect offers a powerful route to suppress quantum evolution and protect coherence through frequent measurements, irrespective of the underlying dynamics. However, existing implementations require prior knowledge of the quantum state, severely restricting their applicability. Here we introduce a state- and dynamics-independent protection protocol embedding the system in a larger Hilbert space, temporarily swapping the quantum information from its original degree of freedom to a decoherence-free ancillary one. We experimentally validate the protocol on a quantum optical platform, demonstrating robust preservation of coherence and purity for arbitrary polarization qubits under decoherence, thereby enabling the universal safeguarding of unknown quantum states.

Consider the budget-constrained random graph process introduced by Frieze, Krivelevich and Michaeli, where each time an edge is offered through the (standard) random graph process we must irrevocably decide whether to "purchase" this edge or not, with our goal being to construct a graph which satisfies some property within a given time $t$ and while purchasing at most $b$ edges. We consider the problem of constructing graphs containing certain fixed small subgraphs. We provide an optimal strategy for building a graph which contains a copy of $K_4$, showing that budget $b=ω(\max\{n^8/t^5,n^2/t\})$ suffices and that if $b=o(\max\{n^8/t^5,n^2/t\})$ then no strategy can a.a.s. produce a graph containing a copy of $K_4$. This resolves a problem raised by Iľkovič, León and Shu. More generally, we obtain analogously tight results for containing a wheel of any fixed size, or a graph consisting of a tree plus one additional universal vertex. We also tackle the problem of constructing graphs containing a copy of $K_5$, obtaining both lower and upper bounds on the optimal budget, though a gap remains in this case.

We consolidate coherence, athermality, and nonuniformity as sub-resources within an underlying quantum resource theory: instability. We formulate instability axiomatically as the transient information within a decaying physical system. Specifying a decay mechanism (e.g., dephasing, thermalization) recovers these familiar resources as specific manifestations of instability. We compute the one-shot distillation yield and dilution cost in various operational paradigms, and use them to pin down the extremal additive monotones. In the asymptotic regime, we show that all conversion rates are governed by a single additive monotone, and thereby we establish a universal second law for instability.

Transferring a physical system from an initial to a final state while minimizing energetic losses is an interdisciplinary control problem that bridges stochastic thermodynamics and optimal transport theory. Recent research typically considers problems in which the optimal solution is realized via conservative forces, but whether this situation applies depends on the problem's constraints. In systems with complex topologies like discrete networks, the optimal, dissipation-minimizing protocol involves applying nonconservative forces along cycles if the timescales of the transitions in the network are fixed. We show that although nonconservative driving is optimal in this setting, a conservative protocol exists whose dissipation is at most twice the optimal one. This finding is complemented with an example modeling transport across an energy barrier, which illustrates such improvements of order 1 explicitly. Qualitatively, conservative driving falls short of achieving optimality because direct transport across the barrier is avoided. We conclude with a discussion that the optimality of nonconservative driving might be a generic phenomenon: As fewer degrees of freedom can be optimized, additional degrees of freedom due to adding nonconservative forces become more significant.

In the context of range monitoring for particle therapy, this study presents the first experimental results obtained with the TIARA detector using carbon-ion beams at the CNAO clinical center in Pavia, Italy. TIARA is based on the Prompt Gamma Timing (PGT) technique, which measures the time of flight (TOF) between incident ions and prompt gamma rays (PGs) emitted during nuclear interactions in the target. While the TIARA prototype has previously been validated with protons, carbons present a more challenging scenario due to their higher linear energy transfer, nuclear fragmentation products, and the continuous beam time structure of synchrotron accelerators. Experiments were performed by irradiating PMMA targets of different thicknesses with 200 MeV/u carbon beams. A coincidence time resolution of 279$\pm$35 ps FWHM was achieved, outperforming results previously obtained with protons at the same facility. A range accuracy of 4.74$\pm$0.36 mm at a 2$σ$ confidence level was measured at clinical intensity, when considering 5600 detected PGs, corresponding to the grouping of four irradiation spots of 2.4$\cdot$10$^6$ ions each. Overall, the results demonstrate that PGT-based range monitoring remains viable for carbon-ion beams, although increased background from secondary protons indicates that detector configuration adaptations are required.

We present a quasipolynomial-time approximation scheme (QPTAS) for the Maximum Independent Set (\textsc{MWIS}) in graphs with a bounded number of pairwise vertex-disjoint and non-adjacent long induced cycles. More formally, for every fixed $s$ and $t$, we show a QPTAS for \textsc{MWIS} in graphs that exclude $sC_t$ as an induced minor. Combining this with known results, we obtain a QPTAS for the problem of finding a largest induced subgraph of bounded treewidth with given hereditary property definable in Counting Monadic Second Order Logic, in the same classes of graphs. This is a step towards a conjecture of Gartland and Lokshtanov which asserts that for any planar graph $H$, graphs that exclude $H$ as an induced minor admit a polynomial-time algorithm for the latter problem. This conjecture is notoriously open and even its weaker variants are confirmed only for very restricted graphs $H$.

We prove that hypergraphs defined by low-degree polynomial inequalities contain large homogeneous subsets. Formally, let $H$ be an $r$-uniform hypergraph on $N$ vertices that is semialgebraic of constant description complexity, and each defining polynomial has degree at most $D$. Then $H$ contains a clique or an independent set of size $n$, where $N\leq \mbox{tw}_{3D^3}(n)$.

Tailoring the thermal expansion of martensitic materials by crystallographic texture and anisotropic variation of lattice parameters is a promising route to a flexible design of thermally stable systems. NiTi alloys are prototype materials in this respect, with shape-memory and superelastic properties owing to their thermoelastic martensitic transformations. Here, we propose a method to realize finely tunable coefficients of thermal expansion (CTE) for the NiTi alloy based upon a special combination of mechanical and thermal training. We achieve a near-zero in-plane CTE that is smaller in value than that of the FeNi-based Invar alloy. Atomistic simulations and theoretical calculations guide the method design and clarify the underlying mechanisms of the relationship between the processing conditions, the microstructural evolution, and the thermal expansion behavior. The directions for further, finer adjustments of the CTE without constraints on the shape of the materials are indicated.

We study the influence of the temperature-dependent interaction between dark matter (DM) and neutrinos on the measurement of cosmological parameters. We pay attention to the neutrino mass effects, so that the derivation of Boltzmann equations needs to specify the concrete form of interaction. We work in a model in which the DM-neutrino scatterings are induced by a dimension-six operator, and present the details for deriving the full Boltzmann hierarchy for DM and neutrinos, including a novel method to obtain the fluid approximation for modes entering the horizon. It is shown that our interaction can induce the dark acoustic oscillation in the DM-neutrino fluid, leaving distinct signatures on the CMB and matter power spectra. By using the latest CMB and BAO datasets from Planck, DESI and ACT, the constraint on today's DM-neutrino interaction parameter for the normal neutrino mass ordering reaches $u^0_{χ-ν} \lesssim {\cal O}(10^{-13})$, nearly nine orders stronger than that for temperature-independent case in the literature. This can be understood by noting that the scattering cross section increases nearly quadratically with cosmological temperature in the early universe, leading to enhanced effects. We have investigated alternative scenarios with different neutrino mass assumptions. In particular, models with degenerate neutrino masses give rise to weaker constraint of $u^0_{χ-ν} \lesssim {\cal O}(10^{-11})$, showing the importance to incorporate the realistic neutrino mass ordering in the fits. Finally, when employing the logarithmic flat prior for $u^0_{χ-ν}$, we have shown hints to a nonzero interaction at $95\%$ CL by combining Planck, DESI and ACT data.

With the rapid improvement of LLMs' coding capabilities, the bottleneck of LLM-based automated software development is shifting from generating correct code to eliciting users' requirements. Despite growing interest, the interview competence of LLMs in conversational requirements elicitation remains fully underexplored. Existing evaluations often depend on a few scenarios, real user interaction, and subjective human scoring, which hinders systematic and quantitative comparison. To address these challenges, we propose ReqElicitGym, an interactive and automatic evaluation environment for assessing interview competence in conversational requirements elicitation. Specifically, ReqElicitGym introduces a new evaluation dataset and designs both an interactive oracle user and a task evaluator. The dataset contains 101 website requirements elicitation scenarios spanning 10 application types. Both the oracle user and the task evaluator achieve high agreement with real users and expert judgment. Using our ReqElicitGym, any automated conversational requirements elicitation approach (e.g., LLM-based agents) can be evaluated in a reproducible and quantitative manner through interaction with the environment. Based on our ReqElicitGym, we conduct a systematic empirical study on seven representative LLMs, and the results show that current LLMs still exhibit limited interview competence in uncovering implicit requirements. Particularly, they elicit less than half of the users' implicit requirements, and their effective elicitation questions often emerge in later turns of the dialogue. Besides, we found LLMs can elicit interaction and content implicit requirements, but consistently struggle with style-related requirements. We believe ReqElicitGym will facilitate the evaluation and development of automated conversational requirements elicitation.

The work investigates the problem of whether a context-free language is a subset of a group language. A.~V. Anisimov has shown that the problem of determining the unambiguity of finite automata is a special case of this problem. Then the question of finding polynomial algorithm verifying the inclusion of context-free languages in group languages naturally arises. The article focuses on this open problem. For the purpose, the paper describes an unconventional method of description of context-free languages, namely a representation with the help of a finite digraph whose arcs are labelled with a specially defined monoid $\mathcal{U}$. Also, we define a semiring $\mathcal{S}_\mathcal{U}$ whose elements are the set $2^\mathcal{U}$ of all subsets of $\mathcal{U}$ and with operations - product and union of the elements of $2^\mathcal{U}$. The described algorithm executes no more than $O(n^3)$ operations in $\mathcal{S}_\mathcal{U}$.

Backend enrichment is now widely deployed in sensitive domains such as product recommendation pipelines, healthcare, and finance, where models are trained on confidential data and retrieve private features whose values influence inference behavior while remaining hidden from the API caller. This paper presents the first hardware-level backend retrieval data-stealing attack, showing that accelerator optimizations designed for performance can directly undermine data confidentiality and bypass state-of-the-art privacy defenses. Our attack, FEATUREBLEED, exploits zero-skipping in AI accelerators to infer private backend-retrieved features solely through end-to-end timing, without relying on power analysis, DVFS manipulation, or shared-cache side channels. We evaluate FEATUREBLEED on three datasets spanning medical and non-medical domains: Texas-100X (clinical records), OrganAMNIST (medical imaging), and Census-19 (socioeconomic data). We further evaluate FEATUREBLEED across three hardware backends (Intel AVX, Intel AMX, and NVIDIA A100) and three model architectures (DNNs, CNNs, and hybrid CNN-MLP pipelines), demonstrating that the leakage generalizes across CPU and GPU accelerators, data modalities, and application domains, with an adversarial advantage of up to 98.87 percentage points. Finally, we identify the root cause of the leakage as sparsity-driven zero-skipping in modern hardware. We quantify the privacy-performance-power trade-off: disabling zero-skipping increases Intel AMX per-operation energy by up to 25 percent and incurs 100 percent performance overhead. We propose a padding-based defense that masks timing leakage by equalizing responses to the worst-case execution time, achieving protection with only 7.24 percent average performance overhead and no additional power cost.

In \cite{NZ25}, the authors resolved the rational function analogue of the finiteness results for greatest common divisors of iterates of polynomials established in \cite{HT17}. These results may be viewed as dynamical generalizations of a classical problem concerning upper bounds for the greatest common divisors (GCDs) of two integer sequences studied by Bugeaud, Corvaja, and Zannier. The most delicate case arises when the maps involved are automorphisms, where the methods of \cite{NZ25} and \cite{HT17} rely heavily on Diophantine approximation and asymptotic analysis. In the present paper, we develop an alternative approach to the automorphism case. This method is more powerful, allowing us to give complete answers to the further questions posed in \cite{HT17}. In particular, we strengthen the main theorem of \cite{HT17} and provide an alternative proof of the main theorem of \cite{NZ25} in the automorphism setting. Moreover, we relate this dynamical GCD problem to a special case of a higher-dimensional generalization of the Dynamical Mordell--Lang Conjecture proposed by Junyi Xie. We establish this generalized conjecture when the dynamics arise from algebraic group actions. In addition, we resolve the corresponding special case associated with dynamical GCD questions when the maps involved are polynomials.

We investigate the operation of a qubit as a quantum thermal device within the repeated interaction framework, allowing for strong system-bath coupling and finite interaction times. We analyze two minimal models: an alternating-coupling setup, in which the qubit sequentially interacts with hot and cold baths, and a simultaneous-coupling setup, where both baths interact with the qubit during each collision. For the alternating model, we obtain an exact analytical solution for the limit-cycle state, valid for arbitrary coupling strengths and collision durations. Using this solution, we rigorously prove a no-go theorem for quantum refrigeration. We further demonstrate that, although work can be generated locally at individual system-bath contacts, the total work over a cycle is always nonpositive, precluding engine operation. In the absence of work, the model describes pure heat conduction, for which we derive a closed-form expression for the heat current and show that it exhibits a nonmonotonic turnover behavior. The simultaneous-coupling model is analyzed perturbatively. In the short-collision-time limit, it reproduces the same steady-state behavior as the alternating model, reinforcing the generality of the constraints identified. Our results establish fundamental limitations on qubit-based quantum thermal machines operating under Markovian repeated interactions and highlight the need for enriched models to realize functional quantum thermal devices.

Photonic processors have emerged as an attractive platform for fast and energy-efficient matrix-vector multiplication. However, they are susceptible to error due to their analog nature. Here, we present an error-correction technique that implements a correction offset to the optical en-/decoders of photonic processors. Our proposed method is general-purpose, does not require introducing any additional components to the photonic network, and can address errors stemming from unbalanced losses, 50/50 beamsplitter deviations, digital-to-analog conversion inaccuracies, and any unknown sources. In particular, we show that our method is highly effective in mitigating unbalanced-loss errors, a problem that has not previously been addressed by any error-correction technique. Using this approach, we achieve over 90% error reduction in large triangular meshes, overcoming a key obstacle to highly accurate photonic processors for information processing.

The bialgebraic abstract GSOS framework by Turi and Plotkin provides an elegant categorical approach to modelling the operational and denotational semantics of programming and process languages. In abstract GSOS, bisimilarity is always a congruence, and it coincides with denotational equivalence. This saves the language designer from intricate, ad-hoc reasoning to establish these properties. The bialgebraic perspective on operational semantics in the style of abstract GSOS has recently been extended to higher-order languages, preserving compositionality of bisimilarity. However, a categorical understanding of bialgebraic denotational semantics according to Turi and Plotkin's original vision has so far been missing in the higher-order setting. In the present paper, we develop a theory of adequate denotational semantics in higher-order abstract GSOS. The denotational models are parametric in an appropriately chosen semantic domain in the form of a locally final coalgebra for a behaviour bifunctor, whose construction is fully decoupled from the syntax of the language. Our approach captures existing accounts of denotational semantics such as semantic domains built via general step-indexing, previously introduced on a per-language basis, and is shown to be applicable to a wide range of different higher-order languages, e.g. simply typed and untyped languages, or languages with computational effects such as probabilistic or non-deterministic branching.

We study the $L^q$-regularity of the density of barycenters of $N$ probability measures on $\mathbb{R}^d$ with respect to the $p$-Wasserstein metric ($1<p<\infty$). According to a previous result by the first author and collaborators, if one marginal is absolutely continuous, so is the $W_p$-barycenter. The next natural question is whether the $L^q$- regularity on the marginals is also preserved for any $q > 1$, as in the classical case ($p=2$) of Agueh--Carlier, or for $W_p$-geodesics ($N=2$). Here we prove that this is the case if one marginal belongs to $L^q$ and the supports of all the marginals satisfy suitable geometric assumptions. However, we show that, as soon as $N>2$, it is possible to find examples of $W_p$-barycenters which are not $q$-integrable, even if one marginal is compactly supported and bounded, thus highlighting the role played by the geometry of the supports. Furthermore, we provide a general estimate of the $L^q$-norm, including a detailed study of the sources of singularities, and a characterization of the $W_p$-barycenters à la Agueh--Carlier in terms of the associated Kantorovich potentials. Finally, we explicitly compute the $W_p$-barycenters of measures obtained as push-forward of special affine transformations. In this case, regularity holds without any additional requirement on the supports.

We present general-relativistic radiation magnetohydrodynamics simulations of binary neutron star mergers performed with GR-Athena++. Neutrino transport is treated using a moment-based, energy-integrated scheme (M1), augmented by neutrino number density evolution (N0). Our implementation is validated through an extensive suite of standard tests and demonstrated to perform robustly under adaptive mesh refinement. As a first application, we simulate the gravitational collapse of a uniformly rotating, magnetized neutron star, demonstrating stable radiation evolution through apparent-horizon formation using a novel excision technique based on the tapering of state vector evolution inside the horizon. To further test robustness in highly dynamic environments, we apply our code to two demanding binary neutron star merger scenarios. We investigate a long-lived remnant with the DD2 equation of state, evolved with full general-relativistic magnetohydrodynamics and M1 neutrino transport. Following this, a gravitational collapse scenario with the SFHo equation of state is explored. We showcase long-term stable evolution on neutrino cooling time-scales, demonstrating robust handling of excision and stable evolution of the post-collapse accretion phase in three-dimensional mergers with magnetic fields and neutrino radiation.

We present rigidity results for overdetermined problems associated to the rotationally invariant Poisson equation $-Δ_{g_\mathcal{M}} u = f(r)$ in a model manifold $\mathcal{M} = [0,S) \times_h \mathbb S^{N-1}$ with warping function $h$. The variable $r$ ranges in the interval $[0,S)$, whose endpoint $S$ is positive and possibly infinite. The first part of the paper deals with the problem \[ \begin{array}{ll} -Δ_{g_\mathcal{M}} {u}=f(r) &\mbox{in $Ω$}, u=φ(r) &\mbox{on $\partial Ω$}, \frac{\partial u}{\partial ν} = κ(r) &\mbox{on $\partial Ω$}, \end{array} \] where $Ω\subset \mathcal{M}$ is a bounded domain containing the point $O \in \mathcal{M}$ corresponding to $r = 0$, $ν$ is the exterior unit normal vector on $\partial Ω$, and $f$, $φ$, $κ$ are three prescribed functions. In the second part of the paper, we consider a similar overdetermined problem for the exterior Bernoulli problem in a domain $Ω\setminus \overline B_{R_0}(O)$, where $B_{R_0}(O)$ denotes the geodesic ball centered at $O$ with radius $R_0$, within the class of functions that vanish on $\partial B_{R_0}(O)$. In both cases, we give conditions on $f$, $φ$ and $κ$ implying that the solution $u$ is radial and $Ω$ is a geodesic ball centered at $O$. Our results apply in particular to the three space forms $\mathbb{R}^N$, $\mathbb{H}^N$ and $\mathbb{S}^N$.

Negative sampling strategies are widely used in implicit collaborative filtering to address issues like data sparsity and class imbalance. However, these methods often introduce false negatives, hindering the model's ability to accurately learn users' latent preferences. To mitigate this problem, existing methods adjust the negative sampling distribution based on statistical features from model training or the hardness of negative samples. Nevertheless, these methods face two key limitations: (1) over-reliance on the model's current representation capabilities; (2) failure to leverage the potential of false negatives as latent positive samples to guide model learning of user preferences more accurately. To address the above issues, we propose a Topology-aware Positive Sample Set Construction and Feature Optimization method (TPSC-FO). First, we design a simple topological community-aware false negative identification (FNI) method and observe that topological community structures in interaction networks can effectively identify false negatives. Motivated by this, we develop a topology-aware positive sample set construction module. This module employs a differential community detection strategy to capture topological community structures in implicit feedback, coupled with personalized noise filtration to reliably identify false negatives and convert them into positive samples. Additionally, we introduce a neighborhood-guided feature optimization module that refines positive sample features by incorporating neighborhood features in the embedding space, effectively mitigating noise in the positive samples. Extensive experiments on five real-world datasets and two synthetic datasets validate the effectiveness of TPSC-FO.

The environmental sustainability of Information Technology (IT) has emerged as a critical concern, driven by the need to reduce both energy consumption and greenhouse gas (GHG) emissions. In the context of cloud-native applications deployed across the cloud-edge continuum, this challenge translates into identifying energy-efficient deployment strategies that consider not only the computational demands of application components but also the environmental impact of the nodes on which they are executed. Generating deployment plans that account for these dynamic factors is non-trivial, due to fluctuations in application behaviour and variations in the carbon intensity of infrastructure nodes. In this paper, we present an approach for the automatic generation of deployment plans guided by green constraints. These constraints are derived from a continuous analysis of energy consumption patterns, inter-component communication, and the environmental characteristics of the underlying infrastructure. This paper introduces a methodology and architecture for the generation of a set of green-aware constraints that inform the scheduler to produce environmentally friendly deployment plans. We demonstrate how these constraints can be automatically learned and updated over time using monitoring data, enabling adaptive, energy-aware orchestration. The proposed approach is validated through realistic deployment scenarios of a cloud-native application, showcasing its effectiveness in reducing energy usage and associated emissions.

Despite major advances in oncology, many chemotherapeutic agents still cause severe side effects that reduce quality of life, motivating new approaches for early detection and targeted elimination of cancer cells. Luminescent transition metal complexes are promising biomolecular probes, since intercalation between DNA base pairs significantly changes their luminescence. However, reliable computational protocols to predict optical properties of transition metal intercalators are limited, making accurate absorption spectra calculations essential for screening candidates. Here, we benchmark methods for computing UV-Vis spectra of a Pt(II) pincer complex. The complex is studied both in isolation and intercalated in a small DNA model, representing probes designed to target DNA-associated molecular abnormalities. We find that the largest source of uncertainty stems from the exchange-correlation functional and recommend range-separated hybrids for robust spectral predictions. The Tamm-Dancoff approximation (TDA) and the resolution of identity (RI) approximations provide significant speedups for TD-DFT with only a modest loss of accuracy. Since geometry optimization is often the dominant cost, PBEh-3c emerges as an efficient alternative to conventional DFT, introducing errors comparable to those from TDA. Tight-binding methods (GFN-xTB) offer further acceleration, but yield larger deviations in structures and UV-Vis spectra; thus, unless extensive optimization is required, PBEh-3c provides the best balance between accuracy and efficiency.

Two-dimensional topological insulators are central to our understanding of the connection between topological symmetries in a material and its band electronics. Within this class of materials, a breadth of complex quantum behaviors, such as persistent spin-polarized current states in the presence of a broken time reversal symmetry, and temperature-independent topological protection of quantum states, are thought to exist. However, current studies using photoemission and spectroscopic analyses or transport experiments fail to provide insight into the interplay between the physical 2D manifold and the band topology itself, since they do not provide spatial resolution of the phenomena to be understood. In this work, we develop a methodology for applying magnetic force microscopy to such systems to address this issue. Using well-characterized 2D crystallites of bismuth telluride ($Bi_2$$Te_3$), we image the magnetic signal directly associated with topological edge states. The observed phase contrast is remarkably robust at a temperature of 25°C and occurs across crystallite sizes and shapes. A detailed analysis of the magnetic imaging suggests that the current observed is composed of two parts: the first is a persistent current ($I_{Persistent}$) as predicted by theory, and the second is due to Faraday induction, $I_{Faraday}$. Damping dynamics of the cantilever during imaging further suggest that this Faraday EMF is established by spin accumulation along the 1D edge channel of the crystal, which then converts to a charge current in the presence of time reversal symmetry breaking, creating a novel form of rectification in the channel. This unexpected result can prompt new ideas for topology-based circuit elements with extremely low losses and power consumption.