Millimeter-wave (mmWave) positioning has emerged as a promising technology for next-generation intelligent systems. The advent of reconfigurable intelligent surfaces (RISs) has revolutionized high-precision mmWave localization by enabling dynamic manipulation of wireless propagation environments. This paper investigates a three-dimensional (3D) multi-input single-output (MISO) mmWave positioning system assisted by multiple RISs. We introduce a measurement framework incorporating sequential RIS activation and directional beamforming to fully exploit virtual line-of-sight (VLoS) paths. The theoretical performance limits are rigorously analyzed through derivation of the Fisher information and subsequent positioning error bound (PEB). To minimize the PEB, two distinct optimization approaches are proposed for continuous and discrete phase shift configurations of RISs. For continuous phase shifts, a Riemannian manifold-based optimization algorithm is proposed. For discrete phase shifts, a heuristic algorithm incorporating the grey wolf optimizer is proposed. Extensive numerical simulations demonstrate the effectiveness of the proposed algorithms in reducing the PEB and validate the improvement in positioning accuracy achieved by multiple RISs.

In the present paper, we observationally constrain f (G) gravity at the background level using Type Ia supernovae from the Pantheon Plus (PP) sample, cosmic chronometer (CC) data, and the recent Baryon Acoustic Oscillation (BAO) measurements released by DESI. For the analysis, we consider two combinations of datasets: (i) PP + CC, and (ii) PP + CC + DESI BAO. In both cases, we determine the best-fit parameters by numerically solving the modified Friedmann equations for two distinct f (G) models, namely the power-law and exponential forms. This is achieved through Markov Chain Monte Carlo (MCMC) simulations. To assess the statistical significance of the f (G) models, we employ both the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Our results show that both f (G) models are statistically favored over the standard ΛCDM model. Notably, the exponential model exhibits an additional future transition at redshift closer to -0.1, indicating a possible return to a decelerating phase. This distinctive behavior sets it apart from both the power-law model and the ΛCDM scenario, which predict continued acceleration into the future.

The ratio of two densities provides a direct characterization of their differences. We consider the two-sample comparison problem by estimating this ratio given i.i.d. observations from two distributions. To this end, we propose additive tree models for density ratio estimation along with efficient algorithms using a new loss function, the balancing loss. The loss allows tree-based models to be trained using several algorithms originally designed for supervised learning, such as forward-stagewise optimization and gradient boosting. Moreover, the balancing loss resembles an exponential family kernel, and it can serve as a pseudo-likelihood with conjugate priors. This property enables generalized Bayesian inference on the density ratio using backfitting samplers designed for Bayesian additive regression trees (BART). Our Bayesian strategy provides uncertainty quantification for the inferred density ratio, which is critical for applications involving high-dimensional and data-limited distributions with potentially substantial uncertainty. We further show connections of the balancing loss to the exponential loss in binary classification and to the variational form of f-divergence, particularly the squared Hellinger distance. Numerical experiments demonstrate that our method achieves both accuracy and computational efficiency, while uniquely providing uncertainty quantification. Finally, we demonstrate its application to assessing the quality of generative models for microbiome compositional data.

More than a billion people around the world experience intermittence in their water supply, posing challenges for urban households in Global South cities. An intermittent water supply (IWS) system prompts water users to adapt to service deficits which entails coping costs. Adaptation and its impacts can vary between households within the same city, leading to intra-urban inequality. Studies on household adaptation to IWS through survey data are limited to exploring income-based heterogeneity and do not account for the multidimensional and non-linear nature of the data. There is a need for a standardized methodology for understanding household responses to IWS that acknowledges the heterogeneity of households characterized by sets of multiple underlying factors and that is applicable across different settings. Here, we develop an analysis pipeline that applies hierarchical clustering analysis (HCA) in combination with the Welch-two-sample t-test on household survey data from Amman, Jordan. We identify three clusters of households distinguished by a set of characteristics including income, water social network, supply duration, relocation and water quality problems and identify their group-specific adaptive strategies such as contacting the utility or accessing an alternate water source. This study uncovers the unequal nature of IWS adaptation in Amman, giving insights into the link between household characteristics and adaptive behaviors, while proposing a standardized method to reveal relevant heterogeneity in households adapting to IWS.

Information Retrieval-based Bug Localization (IRBL) aims to identify buggy source files for a given bug report. Traditional and deep learning-based IRBL techniques often suffer from vocabulary mismatch and dependence on project-specific metadata. In contrast, recent Large Language Model (LLM)-based approaches struggle to provide appropriate context to the model: they either restrict analysis to a fixed set of candidate files, overwhelm the model with repository-wide information, or rely on explicit bug report cues to guide context collection. To address these issues, we propose GenLoc, a technique that combines semantic retrieval with LLM-driven code-exploration functions to iteratively analyze the code base and identify buggy files. We evaluate GenLoc on three complementary benchmarks, including large-scale and recent Java datasets as well as the Python based SWE-bench Lite dataset. Results demonstrate that GenLoc substantially outperforms traditional IRBL, deep learning-based approaches and recent LLM-based methods, while also localizing bugs that other techniques fail to detect.

We ask whether the optimal probe is entangled, and if so, what is its character and amount, for estimating the noise parameter of a large class of local quantum encoding processes that we refer to as vector encoding, examples of which include the local depolarizing and bit-flip channels. We first establish that vector encoding is invariably ``continuously commutative'' for optimal probes. We utilize this result to deal with the queries about entanglement in the optimal probe. We show that for estimating noise extent of the two-party arbitrary-dimensional local depolarizing channel, there is a descending staircase of optimal-probe entanglement for increasing depolarizing strength. For the multi-qubit case, the analysis again leads to a staircase, but which can now be monotonic or not, depending on the multiparty entanglement measure used. We also find that when sufficiently high depolarizing noise is to be estimated, fully product multiparty states are the only choice for being optimal probes. In many cases, for even moderately high depolarizing noise, fully product states are optimal. For two-qubit local bit-flip channels, the continuous commutativity of the channel and optimal probe implies that a product state suffices for obtaining the optimal precision.

A functional Bernstein - von Mises theorem is proved for posterior measures arising in a data assimilation problem with the two-dimensional Navier-Stokes equation where a Gaussian process prior is assigned to the initial condition of the system. The posterior measure, which provides the update in the space of all trajectories arising from a discrete sample of the (deterministic) dynamics, is shown to be approximated by a Gaussian random vector field arising from the solution to a linear parabolic PDE with Gaussian initial condition. The approximation holds in the strong sense of the supremum norm on the regression functions, showing that predicting future states of Navier-Stokes systems admits root(N)-consistent estimators even for commonly used nonparametric models. Consequences for coverage of credible bands and uncertainty quantification are discussed. A local asymptotic minimax theorem is derived that describes the lower bound for estimating the state of the nonlinear system, which is shown to be attained by the Bayesian data assimilation algorithm.

It is widely recognized that quantum computing has profound impacts on multiple fields, including but not limited to cryptography, machine learning, materials science, etc. To run quantum algorithms, it is essential to develop scalable quantum hardware with low noise levels and to design efficient fault-tolerant quantum computing (FTQC) schemes. Currently, various FTQC schemes have been developed for different hardware platforms. However, a comprehensive framework for the analysis and evaluation of these schemes is still lacking. In this work, we propose Quantum Logical Operations Per Second (QLOPS) as a metric for assessing the performance of FTQC schemes on quantum hardware platforms. This benchmarking framework will integrate essential relevant factors, e.g., the code rates of quantum error-correcting codes, the accuracy, throughput, and latency of the decoder. Through a resource analysis of factoring RSA-2048, we demonstrate that QLOPS reflects the practical requirements of quantum algorithm execution. This framework will enable the identification of bottlenecks in quantum hardware, providing potential directions for their development. Moreover, our results will help establish a comparative framework for evaluating FTQC designs. As this benchmarking approach considers practical applications, it may assist in estimating the hardware resources needed to implement quantum algorithms and offers preliminary insights into potential timelines.

Efficient qubit reset and leakage reduction are essential for scalable superconducting quantum computing, particularly in the context of quantum error correction. However, such operations often require additional on-chip components. Here, we propose and experimentally demonstrate a hardware-efficient approach to qubit reset and readout using a multi-mode Purcell filter in a superconducting quantum circuit. We exploit the inherent multi-mode structure of a coplanar waveguide resonator, using its fundamental and second-order modes for qubit reset and readout, respectively, thereby avoiding additional components. Implemented in a flip-chip architecture, our device achieves unconditional reset with residual excitation below 1\% in 220 ns, and a leakage reduction unit that selectively resets the second excited state within 62 ns with a residual $|f\rangle$ population of 6.1\%, accounting for the readout error. Despite the qubits being directly coupled to the filter in our configuration, the measured relaxation times are not degraded owing to intrinsic Purcell protection provided by an auxiliary mode. To our knowledge, this is the first experimental trial that exploits different-order modes of a microwave resonator for distinct qubit operations, representing a new direction toward scalable, hardware-efficient quantum processor design.

Following the discovery of graphene, interest in van der Waals (vdW) materials has surged; however, advancing physics beyond graphene requires quantum vdW materials platforms that host versatile, strongly interacting many-body states. Here, using scanning tunneling microscopy and spectroscopy at 300 mK, we uncover multiple competing electronic states in the van der Waals metal CeTe3: charge-ordered antiferromagnetic phases forming stripe and checkerboard orders. Remarkably, their competition is tuned by a modest in-plane magnetic field (~1.5 T), revealing strongly intertwined multiple frustrations involving antiferromagnetism, charge order, and Fermi-surface instabilities. Quasiparticle-interference imaging directly identifies the momentum-space origin of these competitions on the representative semimetals Fermi surface. While the observations can be understood at a basic level in terms of Kondo coupling between localized Ce 4f moments and itinerant Te 5p electrons, our results reveal a much richer phenomenology: an unusually broad electronic reconstruction extending to an energy scale of roughly 30 meV from EF, which realizes and deforms antiferromagnetic charge-ordered states and signals strongly correlated interactions beyond a weak-coupling description. Beyond establishing CeTe3 as a model platform, our results demonstrate that competing instabilities in antiferromagnetic two-dimensional metals/semimetals generate versatile electronic phases, opening a route to tunable nanoscale quantum states governed by the intertwined effects of correlation, symmetry, and topology.

Studying the matter distribution in the universe through the Lyman-$α$ forest allows us to constrain small-scale physics in the high-redshift regime. Spectroscopic quasar surveys are generating increasingly large datasets that require efficient algorithms to compute correlation functions. Moreover, cosmological analyses based on Lyman-$α$ forests can significantly benefit from incorporating higher-order statistics alongside traditional two-point correlations. In this work, we present Lya2pcf, a pipeline designed to compute three-dimensional two-point and three-point correlation functions using Lyman-$α$ forest data. The code implements standard algorithms widely used in current spectroscopic surveys for computing the two-point correlation function with its distortion matrix, covariance matrices; and it naturally extends the two-point estimator to three-point correlations. Thanks to GPU optimization, Lya2pcf achieves a substantial reduction in computational time for both the two-point correlation function and its distortion matrix when compared to the widely used PICCA code. We apply Lya2pcf to data from the Sloan Digital Sky Survey (SDSS) sixteenth data release (DR16) and a Dark Energy Spectroscopic Instrument Year-5 (DESI Y5) mock dataset, demonstrating overall performance gains over PICCA, especially on GPUs. We show the first measurement of the anisotropic three-point correlation function on a large spectroscopic sample for all possible triangles with scales up to 80 Mpc/h. The estimator's fast computation and the resulting signal-to-noise ratio -- above one for many triangle configurations -- demonstrate the viability of incorporating three-point statistics into future cosmological inference analyses, particularly with the larger datasets expected from Stage IV spectroscopic surveys.

The superfluid stiffness fundamentally constrains the transition temperature of superconductors, especially in the strongly coupled regime. However, accurately determining this inherently quantum many-body property in microscopic models remains a significant challenge. In this work, we show how the \textit{quantum many-body bootstrap} framework, specifically the reduced density matrix (RDM) bootstrap, can be leveraged to obtain rigorous lower bounds on the superfluid stiffness in frustration-free interacting models with superconducting ground state. We numerically apply the method to a special class of frustration free models, which are known as quantum geometric nesting models, for flat-band superconductivity, where we uncover a general relation between the stiffness and the pair mass. Going beyond the familiar Hubbard case within this class, we find how additional interactions, notably simple magnetic couplings, can enhance the superfluid stiffness. Furthermore, we find that the RDM bootstrap unexpectedly reveals that the trion-type correlations are essential for bounding the stiffness, offering new insights on the structure of these models. A straightforward generalization of the method can lead to bounds on susceptibilities complementary to variational approaches. Our findings underscore the immense potential of the quantum many-body bootstrap as a powerful tool to derive rigorous bounds on physical quantities beyond energy.

Identifying spatially contiguous clusters and repeated spatial patterns (RSP) characterized by similar underlying distributions that are spatially apart is a key challenge in modern spatial statistics. Existing constrained clustering methods enforce spatial contiguity but are limited in their ability to identify RSP. We propose a novel nonparametric framework that addresses this limitation by combining constrained clustering with a post-clustering reassigment step based on the maximum mean discrepancy (MMD) statistic. We employ a block permutation strategy within each cluster that preserves local attribute structure when approximating the null distribution of the MMD. We also show that the MMD$^2$ statistic is asymptotically consistent under second-order stationarity and spatial mixing conditions. This two-stage approach enables the detection of clusters that are both spatially distant and similar in distribution. Through simulation studies that vary spatial dependence, cluster sizes, shapes, and multivariate dimensionality, we demonstrate the robustness of our proposed framework in detecting RSP. We further illustrate its applicability through an analysis of spatial proteomics data from patients with triple-negative breast cancer. Overall, our framework presents a methodological advancement in spatial clustering, offering a flexible and robust solution for spatial datasets that exhibit repeated patterns.

Inspired by the recent works of M. Kontsevich--Y. Tschinkel and J. Nicaise--J. C. Ottem on specialization of birational types for smooth families (in the scheme category) and J. Koll{á}r's work on fiberwise bimeromorphism, we focus on characterizing the fiberwise bimeromorphism and utilizing the characterization to investigate the specialization of bimeromorphic types for non-smooth families in the complex analytic setting. We provide several criteria for a bimeromorphic map between two families over the same base to be fiberwise bimeromorphic. By combining these criteria with the relative Barlet cycle space theoretic argument motivated by D. Mumford--U. Persson, K. Timmerscheidt and T. de Fernex--D. Fusi, we establish the specialization of bimeromorphic types for locally Moishezon families with fibers having only canonical singularities and being of non-negative Kodaira dimension. These specialization results can easily lead to criteria for locally strongly bimeromorphic isotriviality. Throughout this paper, we unveil the connections among the four classical topics in bimeromorphic geometry: the deformation behavior of plurigenera (or even $1$-genus), fiberwise bimeromorphism, specialization of bimeromorphic types, and the bimeromorphic version of the deformation rigidity.

Nine statistically significant decay channels are observed in LHC data around a mass of 650 GeV. We interpret three of them as coming from a 20 GeV wide resonance observed in $e^+e^-$ , 2 photons and ZZ which could be a $J=2$ Kaluza Klein graviton resonance called T690 (T for tensor with J=2). This hypothesis is reinforced by noting that this signal is produced by VBF and it disappears in ZZ when treated as a scalar. Given that the six other excesses have poor mass resolution, one cannot exclude the presence of an additional wide scalar resonance called H650. Assuming a Randall Sundrum RS model, we conclude that LHC observes the predicted sequence T376, T690 and T1000. At variance with the RS model, T690 weakly couples to gluon pairs, suggesting a composite model interpretation. It does significantly couple to $e^+e^-$ which has implications for $e^+e^-$ colliders. Perturbative unitarity requirements predict $T^{++} \to W^+W^+$ and $T^+\to ZW$ resonances, again indicated by LHC data. Assuming BR($e^+e^-$ )$\sim$0.25%, deduced from ATLAS and CMS, this scenario offers excellent prospects for abundantly (Gigafactory) producing a sequence of narrow resonances at future $e^+e^-$ colliders. For heavy scalars, the situation is less clear. Following ATLAS and CMS, we expect that the top loop contribution to the gluon-gluon fusion mechanism ggF could produce a deficit rather than an excess in the mass distribution of top pairs, which prevents a standard estimate of the statistical significance for heavy resonances. It seems that the pseudo-scalar and scalar resonances A490 and H650, indicated by other channels, create observable deviations in the t t analyses presented by ATLAS and CMS. The scalar resonances seem to form the triple Higgs doublet structure predicted by Weinberg. The present note summarises these arguments and collects available indications in view of electing a future collider.

No trade theorems examine conditions under which agents cannot agree to disagree on the value of a security which pays according to some state of nature, thus preventing any mutual agreement to trade. A large literature has examined conditions which imply no trade, such as relaxing the common prior and common knowledge assumptions, as well as allowing for agents who are boundedly rational or ambiguity averse. We contribute to this literature by examining conditions on the private information of agents that reveals, or verifies, the true value of the security. We argue that these conditions can offer insights in three different settings: insider trading, the connection of low liquidity in markets with no trade, and trading using public blockchains and oracles.

Over the past decade, SPHERE scattered light observations of protoplanetary discs have revealed previously unseen features with unprecedented resolution. One such feature are radial streaks of reduced brightness that are commonly interpreted as shadows. A possible cause for these shadows is an embedded companion within the disc. In this work, we use 3D radiative transfer simulations with RADMC-3D to investigate the shadowing effects of embedded companions across a range of orbital distances (5-30 au) and companion masses (0.5-30 Jupiter masses). We model 0.1 $μ$m dust grains, which are well-coupled to the gas, to produce synthetic scattered light images of the disc. Companions with masses equal to or greater than 14 Jupiter masses consistently cast detectable shadows throughout the disc. We hence derive an empirical solution to describe the width and depth of the shadow as functions of companion mass and location. This scaling suggests that shadow features observed in scattered light images could serve as reliable indicators of companion mass and position, providing an indirect method for identifying and characterising otherwise challenging-to-detect objects within these discs. Additionally, our analysis reveals that companion shadows influence the disc thermal structure, with notable cooling effects that could impact disc chemistry and the dynamics of planet formation.

In this paper, we investigate pre-Calabi-Yau algebras and homotopy double Poisson algebras arising from homotopy Rota-Baxter structures. We introduce the notion of cyclic homotopy Rota-Baxter algebras, a class of homotopy Rota-Baxter algebras endowed with additional cyclic symmetry, and present a construction of such structures via a process called cyclic completion. We further introduce the concept of interactive pairs, consisting of two differential graded algebras-designated as the acting algebra and the base algebra-interacting through compatible module structures. We prove that if the acting algebra carries a suitable cyclic homotopy Rota-Baxter structure, then the base algebra inherits a natural pre-Calabi-Yau structure. Using the correspondence established by Fernandez and Herscovich between pre-Calabi-Yau algebras and homotopy double Poisson algebras, we describe the resulting homotopy Poisson structure on the base algebra in terms of homotopy Rota-Baxter algebra structure. In particular, we show that a module over an ultracyclic (resp. cyclic) homotopy Rota-Baxter algebra admits a (resp. cyclic) homotopy double Lie algebra structure.

Recent experiments reported evidence of superconductivity and re-entrant integer quantum anomalous Hall (RIQAH) insulator upon doping the $ν_e = 2/3$ fractional quantum anomalous Hall states (FQAH) in twisted MoTe${}_2$, separated by narrow resistive regions. Anyons of a FQAH generally have a finite effective mass, and when described by anyon-flux composite fermions (CF), experience statistical magnetic fields with a commensurate filling. Here, we show that most of the experimental observations can be explained by invoking the effects of disorder on the Landau-Hofstadter bands of CFs. In particular, by making minimal assumptions about the anyon energetics and dispersion, we show that doping anyons drives plateau transitions of CFs into integer quantum Hall states, which physically corresponds to either to a superconductor or to a RIQAH phase. We develop a dictionary that allows us to infer the response in these phases and the critical regions from the knowledge of the response functions of the plateau transitions. In particular, this allows us to relate the superfluid stiffness of the superconductor to the polarizability of CFs. As a first step towards a quantitative understanding, we borrow results from the celebrated integer quantum Hall plateau transitions to make quantitative prediction for the critical behavior of the superfluid stiffness, longitudinal and Hall conductivity, and response to out-of-plane magnetic field, all of which agree reasonably well with the experimental observations. Our results provide strong support for anyon superconductivity being the mechanism for the observed superconductor in the vicinity of the $ν_e = 2/3$ FQAH insulator.

We propose a denoising method for multimodal graph signals by an alternating minimization scheme that sequentially solves signal restoration and graph learning problems. Many complex-structured data, i.e., those on sensor networks, can capture multiple modalities at each measurement point, referred to as modalities. They are also assumed to have an underlying structure or correlations in modality as well as space. Such multimodal data are regarded as graph signals on a twofold graph and they are often corrupted by noise. Furthermore, their spatial/modality relationships are not always given a priori: We need to estimate twofold graphs during a denoising algorithm. In this paper, we consider a signal denoising method on twofold graphs, where graphs are learned simultaneously. Specifically, the graph learning subproblems are solved using the primal-dual splitting (PDS) algorithm, while the signal update has a closed-form solution. Parameters in this iterative algorithm are learned from training data by unrolling the iteration with deep algorithm unrolling. Experimental results on synthetic and real-world data demonstrate that the proposed method outperforms existing model- and deep learning-based graph signal denoising methods.

We propose a method that combines the closed testing framework with the concept of safe anytime-valid inference (SAVI) to compute lower confidence bounds for the true discovery proportion in a multiple testing setting. The proposed procedure provides confidence bounds that are valid at every observation time point and that are simultaneous for all possible subsets of hypotheses. While the hypotheses are assumed to be fixed over time, the subsets of interest may vary. Anytime-valid simultaneous confidence bounds allow us to sequentially update the bounds over time and allow for optional stopping. This is a desirable property in practical applications such as neuroscience, where data acquisition is costly and time-consuming. We also present a computational shortcut which makes the application of the proposed procedure feasible when the number of hypotheses under consideration is large. We illustrate the performance of the proposed method in a simulation study and give some practical guidelines on the implementation of the proposed procedure.

We consider the natural action of the quaternionic projective linear group $\mathrm{PSL}(n+1,\mathbb{H})$ on the quaternionic projective space $\mathbb{P}^n_{\mathbb{H}}$. We compute the Kulkarni limit sets for the cyclic subgroups of $\mathrm{PSL}(n+1,\mathbb{H})$.

LCM lattices were introduced by Gasharov, Peeva, and Welker as a way to study minimal free resolutions of monomial ideals. All LCM lattices are atomic and all atomic lattices arise as the LCM lattice of some monomial ideal. We systematically study other lattice properties of LCM lattices. For lattices associated to the edge ideal of a graph, we completely characterize the many standard lattice properties in terms of the associated graphs: Boolean, modular, upper semimodular, lower semimodular, supersolvable, coatomic, and complemented; edge ideals with graded LCM lattices were previously characterized by Nevo and Peeva as those associated to gap-free graphs. For arbitrary monomial ideals, we prove the Cohen-Macaulayness of minimal monomial ideals associated to modular lattices. We also prove separate necessary and sufficient lattice conditions for when the projective dimension of a monomial ideal matches the height of its LCM lattice. Finally, we show that LCM lattices of Gorenstein edge ideals are coatomic and raise questions about the lattice properties of arbitrary Gorenstein monomial ideals.

The Quantum Approximate Optimization Algorithm (QAOA) achieves monotonically improving performance with circuit depth $p$, yet the study of the high-depth regime has been obstructed by the exponential in $p$ cost of existing exact evaluation techniques. In this Letter, we prove that, in the infinite-size limit, the depth-$p$ QAOA state for the Sherrington-Kirkpatrick (SK) model converges to the state of a spin coupled to $p$ bosonic modes. We simulate the spin-boson system using matrix product states and provide numerical evidence that QAOA obtains a $(1-ε)$ approximation to the optimal energy of the SK model with circuit depth $O(n/ε^{1.13})$ in the average case. The modest computational cost of our approach allows us to optimize QAOA parameters and observe that QAOA achieves $\varepsilon\lesssim 2.2\%$ at $p=160$ in the infinite-size limit, extending far beyond $p\leq 20$ accessible to prior exact methods. Our mapping provides a many-body route to study and optimize high-depth QAOA in regimes previously inaccessible to exact evaluation.

Previously in \cite{Tao:2025fch}, we constructed the $\ell$-loop planar integrands using loop components and loop kernels by some recursion rules. In this paper, we propose a new formalism to express the loop kernel recursion. We define ``graded inverse variables" to make the loop kernel recursion more elegant. And the graph factor, including the symmetry factor, can be figured out from each monomial of some variables. This new formalism makes the previous $\ell$-loop integrand recursion clearer.

The impact of new physics on the interactions of neutrinos with other particles can be parametrized by a set of effective four-fermion operators called non-standard neutrino interactions (NSIs). This NSI framework is useful for studying the complementarity between different types of neutrino experiments. In this work, we further compare the reach of neutrino experiments with high-energy collider experiments. Since high-energy colliders often probe the mass scale associated with the four-fermion operators, the effective field theory approach becomes invalid and explicit models must be utilized. We study a variety of representative simplified models including new U(1) gauge bosons, scalar leptoquarks, and heavy neutral leptons. For each of these, we examine the model parameter space constrained by NSI bounds from current and future neutrino experiments, and by data from the Large Hadron Collider and planned electron-positron and muon colliders. We find that in the models we study, with the possible exceptions of muon-philic leptoquarks and heavy neutral leptons mixing with electron or muon neutrinos, collider searches are more constraining than neutrino measurements. Additionally, we briefly comment on other model building possibilities for obtaining models where neutrino experiments are most constraining.

The InterPlanetary File System (IPFS) is a decentralized peer-to-peer (P2P) storage built on Kademlia, a Distributed Hash Table (DHT) structure commonly used in P2P systems and known for its proved scalability. However, DHTs susceptible to Sybil attacks, where a single entity controls multiple malicious nodes. Recent studies have shown that IPFS is affected by a passive content eclipse attack, leveraging Sybils, in which adversarial nodes hide received indexed information from other peers, making the content appear unavailable. Fortunately, the latest mitigation strategy coupling an attack detection based on statistical tests and a wider publication strategy upon detection was able to circumvent it. In this work, we present a new active attack in which malicious nodes return semantically correct but intentionally false data. The attack leverages strategic Sybil placement to evade detection and exploits an early termination in the actual Kubo, the main IPFS implementation. It achieves to fully eclipse content on recent Kubo versions. When evaluated against the most recent known mitigation, it successfully denies access to the target content in approximately 80% of lookup attempts. To address this vulnerability, we propose a new mitigation called SR-DHT-Store, which enables efficient, Sybil-resistant content publication without relying on attack detection. Instead, it uses systematic and precise use of region-based queries based on a dynamically computed XOR distance to the target ID. SR-DHT-Store can be combined with other defense mechanisms, fully mitigating passive and active Sybil attacks at a lower overhead while supporting an incremental deployment.

This paper addresses the prediction of positive rank for elliptic curves without the need to find a point of infinite order or compute L-functions. While the most common method relies on parity conjectures, a recent technique introduced by Dokchitser, Wiersema, and Evans predicts positive rank based on the value of a certain product of Tamagawa numbers, raising questions about its relationship to parity. We show that their method is a subset of the parity conjectures approach: whenever their method predicts positive rank, so does the use of parity conjectures. To establish this, we extend previous work on Brauer relations and regulator constants to a broader setting involving combinations of permutation modules known as K-relations. A central ingredient in our argument is demonstrating a compatibility between Tamagawa numbers and local root numbers.

Searches for neutrino isocurvature usually constrain a specific linear combination of isocurvature perturbations. In this work, we discuss realistic cosmological scenarios giving rise to neutrino isocurvature. We show that in general both, neutrino and matter isocurvature perturbations are generated, whose ratio we parameterize by a newly introduced mixing angle. We obtain the first limits on this new mixing angle from PLANCK data, and discuss novel insights into the early Universe that could be provided by future measurements.

In this paper, we present a recursive method for $\ell$-loop planar integrands in colored quantum field theories. We start with the classical equation of motion and then pick out the comb component, which will help us to define the loop kernels. Then we construct the $\ell$-loop integrands based on some recursion rules for the $\ell$-loop kernels. Finally, we reach a recursion formula for the $\ell$-loop planar integrands. Our method can be easily generalized to general quantum field theories, even non-Lagrangian theories, to obtain the planar part of the whole $\ell$-loop integrands.