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Recent advancements in Code Large Language Models (CodeLLMs) have predominantly focused on open-ended code generation tasks, often neglecting the critical aspect of code understanding and comprehension. To bridge this gap, we present CodeMMLU, a comprehensive multiple-choice question-answer benchmark designed to evaluate the depth of software and code understanding in LLMs. CodeMMLU includes over 10,000 questions sourced from diverse domains, encompassing tasks such as code analysis, defect detection, and software engineering principles across multiple programming languages. Unlike traditional benchmarks, CodeMMLU assesses models's ability to reason about code rather than merely generate it, providing deeper insights into their grasp of complex software concepts and systems. Our extensive evaluation reveals that even state-of-the-art models face significant challenges with CodeMMLU, highlighting deficiencies in comprehension beyond code generation. By underscoring the crucial relationship between code understanding and effective generation, CodeMMLU serves as a vital resource for advancing AI-assisted software development, ultimately aiming to create more reliable and capable coding assistants.

The research on audio clue-based target speaker extraction (TSE) has mostly focused on modeling the mixture and reference speech, achieving high performance in English due to the availability of large datasets. However, less attention has been given to the consistent properties of human speech across languages. To bridge this gap, we introduce WHYV (Wanna Hear Your Voice), which addresses the challenge of transferring TSE models from one language to another without fine-tuning. In this work, we proposed a gating mechanism that be able to modify specific frequencies based on the speaker's acoustic features. The model achieves an SI-SDR of 17.3544 on clean English speech and 13.2032 on clean speech mixed with Wham! noise, outperforming all other models in its ability to adapt to different languages., Comment: Submitted to ICASSP 2025

We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustness. BLT encodes bytes into dynamically sized patches, which serve as the primary units of computation. Patches are segmented based on the entropy of the next byte, allocating more compute and model capacity where increased data complexity demands it. We present the first FLOP controlled scaling study of byte-level models up to 8B parameters and 4T training bytes. Our results demonstrate the feasibility of scaling models trained on raw bytes without a fixed vocabulary. Both training and inference efficiency improve due to dynamically selecting long patches when data is predictable, along with qualitative improvements on reasoning and long tail generalization. Overall, for fixed inference costs, BLT shows significantly better scaling than tokenization-based models, by simultaneously growing both patch and model size.

As data-privacy requirements are becoming increasingly stringent and statistical models based on sensitive data are being deployed and used more routinely, protecting data-privacy becomes pivotal. Partial Least Squares (PLS) regression is the premier tool for building such models in analytical chemistry, yet it does not inherently provide privacy guarantees, leaving sensitive (training) data vulnerable to privacy attacks. To address this gap, we propose an $(\epsilon, \delta)$-differentially private PLS (edPLS) algorithm, which integrates well-studied and theoretically motivated Gaussian noise-adding mechanisms into the PLS algorithm to ensure the privacy of the data underlying the model. Our approach involves adding carefully calibrated Gaussian noise to the outputs of four key functions in the PLS algorithm: the weights, scores, $X$-loadings, and $Y$-loadings. The noise variance is determined based on the global sensitivity of each function, ensuring that the privacy loss is controlled according to the $(\epsilon, \delta)$-differential privacy framework. Specifically, we derive the sensitivity bounds for each function and use these bounds to calibrate the noise added to the model components. Experimental results demonstrate that edPLS effectively renders privacy attacks, aimed at recovering unique sources of variability in the training data, ineffective. Application of edPLS to the NIR corn benchmark dataset shows that the root mean squared error of prediction (RMSEP) remains competitive even at strong privacy levels (i.e., $\epsilon=1$), given proper pre-processing of the corresponding spectra. These findings highlight the practical utility of edPLS in creating privacy-preserving multivariate calibrations and for the analysis of their privacy-utility trade-offs., Comment: 14 pages, 5 figure

Due to the structural limitations of Graph Neural Networks (GNNs), in particular with respect to conventional neighborhoods, alternative aggregation strategies have recently been investigated. This paper investigates graph structure in message passing, aimed to incorporate topological characteristics. While the simplicity of neighborhoods remains alluring, we propose a novel perspective by introducing the concept of 'cover' as a generalization of neighborhoods. We design the Grothendieck Graph Neural Networks (GGNN) framework, offering an algebraic platform for creating and refining diverse covers for graphs. This framework translates covers into matrix forms, such as the adjacency matrix, expanding the scope of designing GNN models based on desired message-passing strategies. Leveraging algebraic tools, GGNN facilitates the creation of models that outperform traditional approaches. Based on the GGNN framework, we propose Sieve Neural Networks (SNN), a new GNN model that leverages the notion of sieves from category theory. SNN demonstrates outstanding performance in experiments, particularly on benchmarks designed to test the expressivity of GNNs, and exemplifies the versatility of GGNN in generating novel architectures.

The formation of iron oxide nanoparticles (NPs) presents challenges such as efficiency losses and fine dust emissions in practical iron combustion systems, highlighting the need for deeper understanding of the formation mechanisms and thermochemical conditions. This study combines experiments and multi-scale simulations to analyze NP clouds generated by single iron particles burning in high-temperature oxidizing environments. The ambient gas conditions were provided by a laminar flat flame burner, with post-flame oxygen mole fractions varied between 20, 30, and 40 vol% at a constant temperature of ~1800K. High-speed in-situ diagnostics were used to measure particle size, NP initiation, NP cloud evolution, and microparticle surface temperature history. The experimental setup utilized three 10kHz imaging systems: one for two-color pyrometry and two for diffusive-backlight illumination (DBI), targeting particle size and NP measurements. The findings showcase the powerful capabilities of multi-physics diagnostics in quantifying NP initiation time and temperature, which depend on particle size and ambient oxygen concentration. CFD simulations revealed enhanced convection velocity driven by increased Stefan flow, which transported NPs toward parent iron particles under high-oxygen conditions. This delayed the detection of NP clouds, leading to higher microparticle temperatures at NP initiation. Molecular dynamics (MD) simulations uncovered FeO2(g) as a key NP precursor, forming when Fe atoms dissociate from the liquid phase. The initial temperature significantly influenced the resulting nanocluster composition, with Fe(II) dominating at higher temperatures and Fe(III) at lower temperatures. This integrated approach enhances understanding of NP formation in iron combustion, offering insights into the conditions affecting nanoparticle characteristics.

Major depressive disorder is a prevalent and serious mental health condition that negatively impacts your emotions, thoughts, actions, and overall perception of the world. It is complicated to determine whether a person is depressed due to the symptoms of depression not apparent. However, their voice can be one of the factor from which we can acknowledge signs of depression. People who are depressed express discomfort, sadness and they may speak slowly, trembly, and lose emotion in their voices. In this study, we proposed the Dynamic Convolutional Block Attention Module (Dynamic-CBAM) to utilized with in an Attention-GRU Network to classify the emotions by analyzing the audio signal of humans. Based on the results, we can diagnose which patients are depressed or prone to depression then so that treatment and prevention can be started as soon as possible. The research delves into the intricate computational steps involved in implementing a Attention-GRU deep learning architecture. Through experimentation, the model has achieved an impressive recognition with Unweighted Accuracy (UA) rate of 0.87 and 0.86 Weighted Accuracy (WA) rate and F1 rate of 0.87 in the VNEMOS dataset. Training code is released in https://github.com/fiyud/Emotional-Vietnamese-Speech-Based-Depression-Diagnosis-Using-Dynamic-Attention-Mechanism, Comment: 9 Page, 5 Figures

This paper studies approximate solutions of a linear fractional vector optimization problem without requiring boundedness of the constraint set. We establish necessary and sufficient conditions for approximating weakly efficient points of such a problem via some properties of the objective function and a technical lemma related to the intersection of the topological closure of the cone generated by a subset of the Euclidean space and the interior of the negative orthant. As a consequence, we obtain necessary conditions and sufficient conditions for approximate efficient solutions to the considered problem. Applications of these results to linear vector optimization are considered., Comment: 2 figures

In this paper, we give an explicit formula of the Igusa local zeta function of a Thom-Sebastiani type sum of two separated-variable Newton non-critical polynomials. Data for the description are available on their Newton polyhedra., Comment: 17 pages

We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the capabilities of a teacher model (specifically GPT-4), phi-4 substantially surpasses its teacher model on STEM-focused QA capabilities, giving evidence that our data-generation and post-training techniques go beyond distillation. Despite minimal changes to the phi-3 architecture, phi-4 achieves strong performance relative to its size -- especially on reasoning-focused benchmarks -- due to improved data, training curriculum, and innovations in the post-training scheme.

Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This paper bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the paper proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.

In this paper, we establish a Mather-Yau theorem for higher Nash blowup algebras, demonstrating that the isomorphism type of the local ring of any hypersurface singularity, defined over an arbitrary field, is fully determined by its higher Nash blowup algebras. The classical Mather-Yau theorem (1982) asserts that for isolated complex hypersurface singularities, the isomorphism type of the local ring is determined by the Tjurina algebra. In positive characteristic, this result was extended by considering the higher Tjurina algebras by Greuel and Pham (2017) under the assumptions of an algebraically closed ground field and isolated singularities. Our work begins by proving the stability of higher Nash blowup algebras under contact equivalence in a very general framework. Specifically, we show that the higher Nash blowup algebras of any system of elements in an analytic or geometric ring remain invariant under contact equivalence. For complex hypersurface singularities, this stability was conjectured by Hussain, Ma, Yau, and Zuo, and was recently verified by Le and Yasuda. Finally, the converse is established using a classical result of Samuel (1956)., Comment: 7 pages

The ability to reconstruct high-fidelity fluid flow fields from sparse sensor measurement is critical for many science and engineering applications, but remains a huge challenge. This challenge is caused by the large difference between the dimensions of the state space and the observational space, making the operator that provides the mapping from the state space to the observational space ill-conditioned and non-invertible. As a result, deriving the forward map from the observational space to the state space as the inverse of the measurement operator is nearly impossible. While traditional methods, including sparse optimization, data assimilation, or machine learning based regressive reconstruction, are available, they often struggle with generalization and computational efficiency, particularly when non-uniform or varying discretization of the domain are considered. In this work, we propose FLRONet, a novel operator learning framework designed to reconstruct full-state flow fields from sparse sensor measurements in space and time. FLRONet utilizes a branch-trunk architecture, where the branch network integrates sensor observations from multiple time instances, and the trunk network encodes the entire temporal domain. This design allows FLRONet to achieve highly accurate, discretization-independent reconstructions at any time within the observation window. Although the popular three-dimensional Fourier Neural Operator offers similar functionalities, our results show that FLRONet surpasses it in both accuracy and efficiency. FLRONet not only achieves superior performance in approximating the true operator but also exhibits significantly faster inference at high-fidelity discretizations.

This study demonstrates the application of quantum computing based quantum annealing to seismic traveltime inversion, a critical approach for inverting highly accurate velocity models. The seismic inversion problem is first converted into a Quadratic Unconstrained Binary Optimization problem, which the quantum annealer is specifically designed to solve. We then solve the problem via quantum annealing method. The inversion is applied on a synthetic velocity model, presenting a carbon storage scenario at depths of 1000-1300 meters. As an application example, we also show the capacity of quantum computing to handle complex, noisy data environments. This work highlights the emerging potential of quantum computing in geophysical applications, providing a foundation for future developments in high-precision seismic imaging.

The Core Accretion model is widely accepted as the primary mechanism for forming planets up to a few Jupiter masses. However, the formation of super-massive planets remains a subject of debate, as their formation via the Core Accretion model requires super-solar metallicities. Assuming stellar atmospheric abundances reflect the composition of protoplanetary disks, and that disk mass scales linearly with stellar mass, we calculated the total amount of metals in planet-building materials that could contribute to the formation of massive planets. In this work, we studied a sample of 172 Jupiter-mass planets and 93 planets with masses exceeding 4 Mjup. Our results consistently demonstrate that planets with masses above 4 Mjup form in disks with at least as much metal content as those hosting planets with masses between 1 and 4 Mjup, often with slightly higher metallicity, typically exceeding that of the proto-solar disk. We interpret this as strong evidence that the formation of very massive Jupiters is feasible through Core Accretion and encourage planet formation modelers to test our observational conclusions., Comment: Accepted for publication in ApSS

Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tools is hampered by the presence of poor-quality segmentation outliers, particularly in out-of-distribution samples, making their implementation in clinical practice difficult. Therefore, there is a need for quality control (QC) to screen the quality of the segmentation results. Although numerous automatic QC methods have been developed for segmentation quality screening, most were designed for cardiac MRI segmentation, which involves a single modality and a single tissue type. Furthermore, most prior works only provided subject-level predictions of segmentation quality and did not identify erroneous parts segmentation that may require refinement. To address these limitations, we proposed a novel multi-task deep learning architecture, termed QCResUNet, which produces subject-level segmentation-quality measures as well as voxel-level segmentation error maps for each available tissue class. To validate the effectiveness of the proposed method, we conducted experiments on assessing its performance on evaluating the quality of two distinct segmentation tasks. First, we aimed to assess the quality of brain tumor segmentation results. For this task, we performed experiments on one internal and two external datasets. Second, we aimed to evaluate the segmentation quality of cardiac Magnetic Resonance Imaging (MRI) data from the Automated Cardiac Diagnosis Challenge. The proposed method achieved high performance in predicting subject-level segmentation-quality metrics and accurately identifying segmentation errors on a voxel basis. This has the potential to be used to guide human-in-the-loop feedback to improve segmentations in clinical settings.

The realization of graphene has provided a bench-top laboratory for quantum electrodynamics. The low-energy excitations of graphene are two-dimensional massless Dirac fermions with opposite chiralities at the $\pm$K valleys of the graphene Brillouin zone. It has been speculated that the electron-electron interactions in graphene could spontaneously break the chiral symmetry to induce a finite mass for Dirac fermions, in analogue to dynamical mass generation in elementary particles. The phenomenon is also known as the relativistic Mott transition and has not been observed in pristine graphene because the interaction strength is insufficient. Here, we report the realization of strongly correlated artificial graphene and the observation of the relativistic Mott transition in twisted WSe2 tetralayers. Using magneto transport, we show that the first $\Gamma$-valley moir\'e valence band mimics the low-energy graphene band structure. At half-band filling, the system exhibits hallmarks of massless Dirac fermions, including an anomalous Landau fan originated from a $\pi$-Berry phase and a square-root density dependence of the cyclotron mass. We tune the interaction across the semimetal-insulator transition by reducing the twist angle below about 2.7 degrees. The emergent insulator is compatible with an antiferromagnetic Mott insulator. Our results open the possibility of studying strongly correlated Dirac fermions in a condensed matter system.

Efficient real-time solvers for forward and inverse problems are essential in engineering and science applications. Machine learning surrogate models have emerged as promising alternatives to traditional methods, offering substantially reduced computational time. Nevertheless, these models typically demand extensive training datasets to achieve robust generalization across diverse scenarios. While physics-based approaches can partially mitigate this data dependency and ensure physics-interpretable solutions, addressing scarce data regimes remains a challenge. Both purely data-driven and physics-based machine learning approaches demonstrate severe overfitting issues when trained with insufficient data. We propose a novel Tikhonov autoencoder model-constrained framework, called TAE, capable of learning both forward and inverse surrogate models using a single arbitrary observation sample. We develop comprehensive theoretical foundations including forward and inverse inference error bounds for the proposed approach for linear cases. For comparative analysis, we derive equivalent formulations for pure data-driven and model-constrained approach counterparts. At the heart of our approach is a data randomization strategy, which functions as a generative mechanism for exploring the training data space, enabling effective training of both forward and inverse surrogate models from a single observation, while regularizing the learning process. We validate our approach through extensive numerical experiments on two challenging inverse problems: 2D heat conductivity inversion and initial condition reconstruction for time-dependent 2D Navier-Stokes equations. Results demonstrate that TAE achieves accuracy comparable to traditional Tikhonov solvers and numerical forward solvers for both inverse and forward problems, respectively, while delivering orders of magnitude computational speedups.

Let $(R, \frak m)$ be a Noetherian local ring. This paper deals with the annihilator of Artinian local cohomology modules $H^i_{\frak m}(M)$ in the relation with the structure of the base ring $R$, for non negative integers $i$ and finitely generated $R$-modules $M$. Firstly, the catenarity and the unmixedness of local rings are characterized via the compatibility of annihilator of top local cohomology modules under localization and completion, respectively. Secondly, some necessary and sufficient conditions for a local ring being a quotient of a Cohen-Macaulay local ring are given in term of the annihilator of all local cohomology modules under localization and completion., Comment: 10 pages

We propose and present evidence for a conjectural global-local phenomenon concerning the $p$-rationality of $p$-height-zero characters. Specifically, if $\chi$ is a height-zero character of a finite group $G$ and $D$ is a defect group of the $p$-block of $G$ containing $\chi$, then the $p$-rationality of $\chi$ can be captured inside the normalizer $N_G(D)$.

Following a brief introduction to research, research processes, research types, papers, reviews, and evaluations, this paper presents a structured framework for addressing inconsistencies in research methodology, technical writing, quality assessment, and publication standards across academic disciplines. Using a four-dimensional evaluation model that focuses on 1) technical content, 2) structural coherence, 3) writing precision, and 4) ethical integrity, this framework not only standardizes review and publication processes but also serves as a practical guide for authors in preparing high-quality manuscripts. Each of these four dimensions cannot be compromised for the sake of another. Following that, we discuss the components of a research paper adhering to the four-dimensional evaluation model in detail by providing guidelines and principles. By aligning manuscripts with journal standards, reducing review bias, and enhancing transparency, the framework contributes to more reliable and reproducible research results. Moreover, by strengthening cross-disciplinary credibility, improving publication consistency, and fostering public trust in academic literature, this initiative is expected to positively influence both research quality and scholarly publishing's reputation.

Robust quantum control can achieve noise-resilience of quantum systems and quantum technological devices. While the need for noise-resilience grows with the number of fluctuating quantities, and thus typically with the number of qubits, most numerically exact optimal control techniques are limited to systems of few interacting qubits. This paper exploits quantum control that avoids explicit reference to quantum states in exponentially large Hilbert space. Exemplary control protocols for spin chains are discussed in terms of noise-resilient preparation of highly entangled states., Comment: 12 pages, 9 figures

Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Temporal Graph Neural Networks (STGNNs) have established themselves as capable predictors, they tend to suffer from distribution shifts that are common with the urban flow data due to the dynamics and unpredictability of spatial-temporal events. Unfortunately, in spatial-temporal applications, the dynamic environments can hardly be quantified via a fixed number of parameters, whereas learning time- and location-specific environments can quickly become computationally prohibitive. In this paper, we propose a novel framework named Memory-enhanced Invariant Prompt learning (MIP) for urban flow prediction under constant distribution shifts. Specifically, MIP is equipped with a learnable memory bank that is trained to memorize the causal features within the spatial-temporal graph. By querying a trainable memory bank that stores the causal features, we adaptively extract invariant and variant prompts (i.e., patterns) for a given location at every time step. Then, instead of intervening the raw data based on simulated environments, we directly perform intervention on variant prompts across space and time. With the intervened variant prompts in place, we use invariant learning to minimize the variance of predictions, so as to ensure that the predictions are only made with invariant features. With extensive comparative experiments on two public urban flow datasets, we thoroughly demonstrate the robustness of MIP against OOD data.

Webshell attacks are becoming more common, requiring robust detection mechanisms to protect web applications. The dissertation clearly states two research directions: scanning web application source code and analyzing HTTP traffic to detect webshells. First, the dissertation proposes ASAF, an advanced DL-Powered Source-Code Scanning Framework that uses signature-based methods and deep learning algorithms to detect known and unknown webshells. We designed the framework to enable programming language-specific detection models. The dissertation used PHP for interpreted language and ASP.NET for compiled language to build a complete ASAF-based model for experimentation and comparison with other research results to prove its efficacy. Second, the dissertation introduces a deep neural network that detects webshells using real-time HTTP traffic analysis of web applications. The study proposes an algorithm to improve the deep learning model's loss function to address data imbalance. We tested and compared the model to other studies on the CSE-CIC-IDS2018 dataset to prove its efficacy. We integrated the model with NetIDPS to improve webshell identification. Automatically blacklist attack source IPs and block URIs querying webshells on the web server to prevent these attacks., Comment: PhD dissertation

In 1966, Erd\H{o}s, Goodman, and P\'{o}sa showed that if $G$ is an $n$-vertex graph, then at most $\lfloor n^2/4 \rfloor$ cliques of $G$ are needed to cover the edges of $G$, and the bound is best possible as witnessed by the balanced complete bipartite graph. This was generalized independently by Gy\H{o}ri--Kostochka, Kahn, and Chung, who showed that every $n$-vertex graph admits an edge-decomposition into cliques of total `cost' at most $2 \lfloor n^2/4 \rfloor$, where an $i$-vertex clique has cost $i$. Erd\H{o}s suggested the following strengthening: every $n$-vertex graph admits an edge-decomposition into cliques of total cost at most $\lfloor n^2/4 \rfloor$, where now an $i$-vertex clique has cost $i-1$. We prove fractional relaxations and asymptotically optimal versions of both this conjecture and a conjecture of Dau, Milenkovic, and Puleo on covering the $t$-vertex cliques of a graph instead of the edges. Our proofs introduce a general framework for these problems using Zykov symmetrization, the Frankl-R\"odl nibble method, and the Szemer\'edi Regularity Lemma.

Using toric modifications and some compatibility we compute the local $p$-adic zeta function of a plane curve singularity. Thanks to the compatibility, we can work over the analytic change of variables formula for $p$-adic integrals, hence avoid adapting to the algebraic setting and Denef's formula., Comment: 23 pages

In A&A 412, 35 (2003) Blanchard, Douspis, Rowan-Robinson, and Sarkar (BDRS) slightly modified the primordial fluctuation spectrum and produced an excellent fit to WMAP's CMB power spectrum for an Einstein-de Sitter (EdS) universe, bypassing dark energy. Curiously, they obtained a Hubble value of $H_0\approx46$, in sharp conflict with the canonical range $H_0\sim67-73$. However, we will demonstrate that the reduced value of $H_0\approx46$ achieved by BDRS is fully compatible with the use of variable speed of light in analyzing the late-time cosmic acceleration observed in Type Ia supernovae (SNeIa). In arXiv:2412.04257 [gr-qc] we uncovered a hidden aspect in a generic class of scale-invariant actions: the dynamics of the dilaton can induce a variation in the speed of light as $c\propto\chi^{1/2}$, causing $c$ to vary alongside $\chi$ across spacetime. For an EdS universe with varying $c$, besides the effects of cosmic expansion, light waves emitted from distant SNeIa are further subject to a refraction effect, which alters the Lemaitre redshift relation to $1+z=a^{-3/2}$. Based on this new formula, we achieve a fit to the SNeIa Pantheon Catalog exceeding the quality of the $\Lambda$CDM model. Crucially, our approach does not require dark energy and produces $H_0=47.2$ in strong alignment with the BDRS finding of $H_0\approx46$. Hence, BDRS's analysis of the (early-time) CMB power spectrum and our variable-$c$ analysis of the (late-time) Hubble diagram of SNeIa fully agree on two counts: (i) the dark energy hypothesis is avoided, and (ii) $H_0$ is reduced to $\sim47$, which also yields an age $t_0=2/(3H_0)=13.8$ Gy for an EdS universe, without requiring dark energy. Most importantly, we will demonstrate that the late-time acceleration can be attributed to the declining speed of light in an expanding EdS universe, rather than to a dark energy component., Comment: 22 pages, 10 figures

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges between 0.001 and 0.02 of the electron charge e. With an exposure of 60 live days and a 5.5 tonne fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

In this paper, we study the concepts of normal functions and $\varphi$-normal functions in the framework of planar harmonic mappings. We establish the harmonic mapping counterpart of the well-known Zalcman-Pang lemma and as a consequence, we prove that a harmonic mapping whose spherical derivative is bounded away from zero is normal. Furthermore, we introduce the concept of the extended spherical derivative for harmonic mappings and obtain several sufficient conditions for a harmonic mapping to be $\varphi$-normal., Comment: 16 pages

We show that the existing generalized separation statements including the conventional extremal principle and its extensions differ {in the ways norms on product spaces are defined}. We prove a general separation statement with arbitrary product norms covering the existing results of this kind. The proof is divided into a series of claims and exposes the key steps and arguments used when proving generalized separation statements. As an application, we prove dual necessary (sufficient) conditions for an abstract product norm extension of the approximate stationarity (transversality) property., Comment: 21 pages

This paper introduces a novel one-hop sub-query result cache for processing graph read transactions, gR-Txs, in a graph database system. The one-hop navigation is from a vertex using either its in-coming or out-going edges with selection predicates that filter edges and vertices. Its cache entry identifies a unique one-hop sub-query (key) and its result set consisting of immutable vertex ids (value). When processing a gR-Tx, the query processor identifies its sequence of individual one-hop sub-queries and looks up their results in the cache. A cache hit fetches less data from the storage manager and eliminates the requirement to process the one-hop sub-query. A cache miss populates the cache asynchronously and in a transactional manner, maintaining the separation of read and write paths of our transactional storage manager. A graph read and write transaction, gRW-Tx, identifies the impacted cache entries and either deletes or updates them. Our implementation of the cache is inside the graph query processing engine and transparent to a user application. We evaluate the cache using our eCommerce production workload and with rules that re-write graph queries to maximize the performance enhancements observed with the cache. Obtained results show the cache enhances 95th and 99th percentile of query response times by at least 2x and 1.63x, respectively. When combined with query re-writing, the enhancements are at least 2.33x and 4.48x, respectively. An interesting result is the significant performance enhancement observed by the indirect beneficiaries of the cache, gRW-Txs and gR-Txs that do not reference one-hop sub-queries. The cache frees system resources to expedite their processing significantly.

We discuss how to understand the asymptotic resurgence number of a pair of graded families of ideals from combinatorial data of their associated convex bodies. When the families consist of monomial ideals, the convex bodies being considered are the Newton-Okounkov bodies of the families. When ideals in the second family are classical invariant ideals, for instance, determinantal ideals or ideals of Pfaffians, these convex bodies are constructed from the associated Rees packages., Comment: 16 pages; comments welcome