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Autonomous robotic inspection, where a robot moves through its environment and inspects points of interest, has applications in industrial settings, structural health monitoring, and medicine. Planning the paths for a robot to safely and efficiently perform such an inspection is an extremely difficult algorithmic challenge. In this work we consider an abstraction of the inspection planning problem which we term Graph Inspection. We give two exact algorithms for this problem, using dynamic programming and integer linear programming. We analyze the performance of these methods, and present multiple approaches to achieve scalability. We demonstrate significant improvement both in path weight and inspection coverage over a state-of-the-art approach on two robotics tasks in simulation, a bridge inspection task by a UAV and a surgical inspection task using a medical robot.

In this paper, we study a more general version of multidimensional Bohr radii for the holomorphic functions defined on unit ball of $\ell^n_q\,\,(1\leq q\leq \infty)$ spaces with values in arbitrary complex Banach spaces. More precisely, we study the multidimensional Bohr radii for bounded linear operators between complex Banach spaces, primarily motivated by the work of A. Defant, M. Maestre, and U. Schwarting [Adv. Math. 231 (2012), pp. 2837--2857]. We obtain the exact asymptotic estimates of multidimensional Bohr radius for both finite and infinite dimensional Banach spaces. As an application, we find the lower bound of arithmetic Bohr radius., Comment: 19 pp; Some typos has been corrected

Test set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render benchmarks obsolete. To mitigate this, many recent benchmarks crowdsource new prompts and evaluations from human or LLM judges; however, these can introduce significant biases, and break down when scoring hard questions. In this work, we introduce a new benchmark for LLMs designed to be immune to both test set contamination and the pitfalls of LLM judging and human crowdsourcing. We release LiveBench, the first benchmark that (1) contains frequently-updated questions from recent information sources, (2) scores answers automatically according to objective ground-truth values, and (3) contains a wide variety of challenging tasks, spanning math, coding, reasoning, language, instruction following, and data analysis. To achieve this, LiveBench contains questions that are based on recently-released math competitions, arXiv papers, news articles, and datasets, and it contains harder, contamination-free versions of tasks from previous benchmarks such as Big-Bench Hard, AMPS, and IFEval. We evaluate many prominent closed-source models, as well as dozens of open-source models ranging from 0.5B to 110B in size. LiveBench is difficult, with top models achieving below 65% accuracy. We release all questions, code, and model answers. Questions will be added and updated on a monthly basis, and we will release new tasks and harder versions of tasks over time so that LiveBench can distinguish between the capabilities of LLMs as they improve in the future. We welcome community engagement and collaboration for expanding the benchmark tasks and models.

Let $S_n$ denote the set of permutations of $n$ labels. We consider a class of Gibbs probability models on $S_n$ that is a subfamily of the so-called Mallows model of random permutations. The Gibbs energy is given by a class of right invariant divergences on $S_n$ that includes common choices such as the Spearman foot rule and the Spearman rank correlation. Mukherjee in 2016 computed the limit of the (scaled) log partition function (i.e. normalizing factor) of such models as $n\rightarrow \infty$. Our objective is to compute the exact limit, as $n\rightarrow \infty$, without the log. We conjecture that this limit is given by the Fredholm determinant of an integral operator related to the so-called Schr\"odinger bridge probability distributions from optimal transport theory. We provide partial evidence for this conjecture, although the argument lacks a final error bound that is needed for it to become a complete proof., Comment: 11 pages

We present multi-frequency (5-345 GHz) and multi-resolution radio observations of 1ES 1927+654, widely considered one of the most unusual and extreme changing-look active galactic nuclei (CL-AGN). The source was first designated a CL-AGN after an optical outburst in late 2017 and has since displayed considerable changes in X-ray emission, including the destruction and rebuilding of the X-ray corona in 2019-2020. Radio observations prior to 2023 show a faint and compact radio source not unusual for radio-quiet AGN. Starting in February 2023, 1ES 1927+654 began exhibiting a radio flare with a steep exponential rise, reaching a peak 60 times previous flux levels, and has maintained this higher level of radio emission for nearly a year. The 5-23 GHz spectrum is broadly similar to gigahertz-peaked radio sources, which are understood to be young radio jets less than ~1000 years old. Recent high-resolution VLBA observations at 23.5 GHz now show resolved extensions on either side of the core, with a separation of 0.14 pc, consistent with a new and mildly relativistic bipolar outflow. A steady increase in the soft X-ray band (0.3-2 keV) concurrent with the radio may be consistent with jet-driven shocked gas, though further observations are needed to test alternate scenarios. This source joins a growing number of CL-AGN and tidal disruption events which show late-time radio activity, years after the initial outburst., Comment: Submitted to ApJ Letters 24 June 2023. 15 pages, 4 figures, 1 table

Assertions have been the de facto collateral for simulation-based and formal verification of hardware designs for over a decade. The quality of hardware verification, \ie, detection and diagnosis of corner-case design bugs, is critically dependent on the quality of the assertions. There has been a considerable amount of research leveraging a blend of data-driven statistical analysis and static analysis to generate high-quality assertions from hardware design source code and design execution trace data. Despite such concerted effort, all prior research struggles to scale to industrial-scale large designs, generates too many low-quality assertions, often fails to capture subtle and non-trivial design functionality, and does not produce any easy-to-comprehend explanations of the generated assertions to understand assertions' suitability to different downstream validation tasks. Recently, with the advent of Large-Language Models (LLMs), there has been a widespread effort to leverage prompt engineering to generate assertions. However, there is little effort to quantitatively establish the effectiveness and suitability of various LLMs for assertion generation. In this paper, we present AssertionBench, a novel benchmark to evaluate LLMs' effectiveness for assertion generation quantitatively. AssertioBench contains 100 curated Verilog hardware designs from OpenCores and formally verified assertions for each design generated from GoldMine and HARM. We use AssertionBench to compare state-of-the-art LLMs to assess their effectiveness in inferring functionally correct assertions for hardware designs. Our experiments demonstrate how LLMs perform relative to each other, the benefits of using more in-context exemplars in generating a higher fraction of functionally correct assertions, and the significant room for improvement for LLM-based assertion generators., Comment: 14 pages, 7 figures, NIPS 2024

Question-answering handwritten documents is a challenging task with numerous real-world applications. This paper proposes a novel recognition-based approach that improves upon the previous state-of-the-art on the HW-SQuAD and BenthamQA datasets. Our model incorporates transformer-based document retrieval and ensemble methods at the model level, achieving an Exact Match score of 82.02% and 69% in HW-SQuAD and BenthamQA datasets, respectively, surpassing the previous best recognition-based approach by 10.89% and 3%. We also enhance the document retrieval component, boosting the top-5 retrieval accuracy from 90% to 95.30%. Our results demonstrate the significance of our proposed approach in advancing question answering on handwritten documents. The code and trained models will be publicly available to facilitate future research in this critical area of natural language., Comment: 16 pages

The reactivated Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE-R) serendipitously caught the Type II supernova SN 2023ixf in Messier 101 on the rise, starting day 3.6 through day 10.9, and on the late-time decline from days 211 through 213 and days 370 through 372. We have considered these mid-infrared (mid-IR) data together with observations from the ultraviolet (UV) through the near-IR, when possible. At day 3.6 we approximated the optical emission with a hot, ~26,630 K blackbody, with a notable UV excess likely from strong SN shock interaction with circumstellar matter (CSM). In the IR, however, a clear excess is also obvious, and we fit it with a cooler, ~1,620 K blackbody with radius of ~2.6 x 10^{15} cm, consistent with dust in the progenitor's circumstellar shell likely heated by the UV emission from the CSM interaction. On day 10.8, the light detected was consistent with SN ejecta-dominated emission. At late times we also observed a clear NEOWISE-R excess, which could arise either from newly formed dust in the inner ejecta or in the contact discontinuity between the forward and reverse shocks, or from more distant pre-existing dust grains in the SN environment. Furthermore, the large 4.6 micron excess at late times can also be explained by the emergence of the carbon monoxide 1--0 vibrational band. SN 2023ixf is the best-observed SN IIP in the mid-IR during the first several days after explosion and one of the most luminous such SNe ever seen., Comment: 22 pages, 8 figures, submitted to AAS Journals

Mixed-anion compounds, which incorporate multiple types of anions into materials, displays tailored crystal structures and physical/chemical properties, garnering immense interests in various applications such as batteries, catalysis, photovoltaics, and thermoelectrics. However, detailed studies regarding correlations between crystal structure, chemical bonding, and thermal/vibrational properties are rare for these compounds, which limits the exploration of mixed-anion compounds for associated thermal applications. In this work, we investigate the lattice dynamics and thermal transport properties of the metal chalcohalides, CuBiSCl2. A high-purity polycrystalline CuBiSCl2 sample, successfully synthesized via modified solid-state synthetic method, exhibits a low lattice thermal conductivity of 0.9-0.6 W m-1 K-1 from 300 to 573 K. By combining various experimental techniques including 3D electron diffraction with theoretical calculations, we elucidate the origin of low lattice thermal conductivity in CuBiSCl2. The stereo-chemical activity of the 6s2 lone pair of Bi3+ favors an asymmetric environment with neighboring anions involving both short and long bond lengths. This particularity often implies weak bonding, low structure dimensionality, and strong anharmonicity, leading to low lattice thermal conductivity. In addition, the strong two-fold linear S-Cu-S coordination with weak Cu -- Cl interactions induces large anisotropic vibration of Cu or structural disorder, which enables strong phonon-phonon scattering and decreases lattice thermal conductivity. The investigations into lattice dynamics and thermal transport properties of CuBiSCl2 broadens the scope of the existing mixed-anion compounds suitable for the associated thermal applications, offering a new avenue for the search of low thermal conductivity materials in low-cost mixed-anion compounds.

The presence of linear paths in parameter space between two different network solutions in certain cases, i.e., linear mode connectivity (LMC), has garnered interest from both theoretical and practical fronts. There has been significant research that either practically designs algorithms catered for connecting networks by adjusting for the permutation symmetries as well as some others that more theoretically construct paths through which networks can be connected. Yet, the core reasons for the occurrence of LMC, when in fact it does occur, in the highly non-convex loss landscapes of neural networks are far from clear. In this work, we take a step towards understanding it by providing a model of how the loss landscape needs to behave topographically for LMC (or the lack thereof) to manifest. Concretely, we present a `mountainside and ridge' perspective that helps to neatly tie together different geometric features that can be spotted in the loss landscape along the training runs. We also complement this perspective by providing a theoretical analysis of the barrier height, for which we provide empirical support, and which additionally extends as a faithful predictor of layer-wise LMC. We close with a toy example that provides further intuition on how barriers arise in the first place, all in all, showcasing the larger aim of the work -- to provide a working model of the landscape and its topography for the occurrence of LMC., Comment: ICML 2024 HiLD workshop paper

This paper addresses the problem of determining the maximum number of edges in a unit distance graph (UDG) of $n$ vertices using computer search. An unsolved problem of Paul Erd\H{o}s asks the maximum number of edges $u(n)$ a UDG of $n$ vertices can have. Those UDGs that attain $u(n)$ are called "maximally dense." In this paper, we seek to demonstrate a computer algorithm to generate dense UDGs for vertex counts up to at least 100. Via beam search with an added visitation metric, our algorithm finds all known maximally dense UDGs up to isomorphism at the push of a button. In addition, for $15 < n$, where $u(n)$ is unknown, i) the algorithm finds all previously published densest UDGs up to isomorphism for $15 < n \le 30$, and ii) the rate of growth of $u(n)/n$ remains similar for $30 < n$. The code and database of over 60 million UDGs found by our algorithm will be open-sourced at time of publication.

From the LOFAR Two-metre Sky Survey second data release (LoTSS DR2) at 144 MHz, we identified a peculiar radio galaxy, J0011+3217. It has a large, one-sided diffuse secondary wing that stretches up to 0.85 Mpc (roughly 85\% of the size of the primary lobe). The linear size of the primary lobe of the galaxy is 0.99 Mpc. This peculiar source is a giant radio galaxy with a misaligned primary lobe. There is an optical galaxy 16 kpc (7 arcsec) from the host active galactic nucleus of J0011+3217. J0011+3217 has a radio luminosity of $1.65\times 10^{26}$ W Hz$^{-1}$ at 144 MHz with a spectral index of $-0.80$ between 144 and 607 MHz. J0011+3217 is located 1.2 Mpc from the centre of the Abell 7 cluster. The Abell 7 cluster has a redshift of 0.104 and a mass ($M_{500}$) of 3.71 $\times 10^{14}$ M$_\odot$. The cluster is associated with strong X-ray emission. We studied the X-ray emission around the cluster and from the region surrounding J0011+3217 using an XMM-Newton image of J0011+3217, and we analysed the velocity structure and spatial distribution of galaxies in the cluster, showing that J0011+3217 inhabits an offset group of galaxies that are moving with respect to Abell 7. The off-axis distortion, or bending, of the primary lobe of J0011+3217 in the outer edges has a strong effect on the relative motion of the surrounding medium; this in turn causes the bending of the jets in the opposite direction of the cluster (like wide-angle tailed sources). We suggest that the morphology of J0011+3217 is influenced by ram pressure created by the Abell 7 cluster, highlighting the complex interactions between the source and the surrounding cluster environment., Comment: Accepted for publication in Astronomy and Astrophysics

We consider a cell-free massive multiple-input multiple-output (CFmMIMO) network operating in dynamic time division duplex (DTDD). The switching point between the uplink (UL) and downlink (DL) data transmission phases can be adapted dynamically to the instantaneous quality-of-service (QoS) requirements in order to improve energy efficiency (EE). To this end, we formulate a problem of optimizing the DTDD switching point jointly with the UL and DL power control coefficients, and the large-scale fading decoding (LSFD) weights for EE maximization. Then, we propose an iterative algorithm to solve the formulated challenging problem using successive convex approximation with an approximate stationary solution. Simulation results show that optimizing switching points remarkably improves EE compared with baseline schemes that adjust switching points heuristically., Comment: Presented at the Asilomar Conference on Signals, Systems, and Computers 2023

We study the asymptotic frequentist coverage of credible sets based on a novel Bayesian approach for a multiple linear regression model under variable selection. We initially ignore the issue of variable selection, which allows us to put a conjugate normal prior on the coefficient vector. The variable selection step is incorporated directly in the posterior through a sparsity-inducing map and uses the induced prior for making an inference instead of the natural conjugate posterior. The sparsity-inducing map minimizes the sum of the squared l2-distance weighted by the data matrix and a suitably scaled l1-penalty term. We obtain the limiting coverage of various credible regions and demonstrate that a modified credible interval for a component has the exact asymptotic frequentist coverage if the corresponding predictor is asymptotically uncorrelated with other predictors. Through extensive simulation, we provide a guideline for choosing the penalty parameter as a function of the credibility level appropriate for the corresponding coverage. We also show finite-sample numerical results that support the conclusions from the asymptotic theory. We also provide the credInt package that implements the method in R to obtain the credible intervals along with the posterior samples.

Fractal geometry presents us with a self-similarity in their pattern at various length scales that is prevalent in our natural world. We present theoretical model of a Sierpinski gasket (SPG) fractal geometry with a deterministic perturbation in the form of a hierarchical distribution of magnetic flux. Such flux configuration induces a deterministic disorder in the Aharonov-Bohm (AB) phases picked up by the electron wavefunction. Using the tight-binding formalism, we show that by tunning the strength of the hierarchy parameter of those AB phases, one can systematically engineer quantum states in a SPG fractal lattice. In addition to this, we have also observed that by controlling the strength of this hierarchy parameter in the magnetic flux, one can effectively regulate the persistent current in the SPG fractal structure. This characteristic is found to be true for various filling factors. Our results could be useful for designing nanoelectronic devices using molecular fractal structures fabricated by chemical synthesis technique., Comment: 5 page, 6 figures; Sierpinski gasket (SPG) fractal geometry; Comments are welcome

Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includes encyclopedic documents that harbor a vast amount of general knowledge (e.g., Wikipedia) but also potentially overlap with benchmark datasets used for evaluating LLMs. Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions. To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. RepLiQA is a collection of five splits of test sets, four of which have not been released to the internet or exposed to LLM APIs prior to this publication. Each sample in RepLiQA comprises (1) a reference document crafted by a human annotator and depicting an imaginary scenario (e.g., a news article) absent from the internet; (2) a question about the document's topic; (3) a ground-truth answer derived directly from the information in the document; and (4) the paragraph extracted from the reference document containing the answer. As such, accurate answers can only be generated if a model can find relevant content within the provided document. We run a large-scale benchmark comprising several state-of-the-art LLMs to uncover differences in performance across models of various types and sizes in a context-conditional language modeling setting. Released splits of RepLiQA can be found here: https://huggingface.co/datasets/ServiceNow/repliqa.

We introduce a novel discretization scheme for Wasserstein gradient flows that involves successively computing Schr\"{o}dinger bridges with the same marginals. This is different from both the forward/geodesic approximation and the backward/Jordan-Kinderlehrer-Otto (JKO) approximations. The proposed scheme has two advantages: one, it avoids the use of the score function, and, two, it is amenable to particle-based approximations using the Sinkhorn algorithm. Our proof hinges upon showing that relative entropy between the Schr\"{o}dinger bridge with the same marginals at temperature $\epsilon$ and the joint distribution of a stationary Langevin diffusion at times zero and $\epsilon$ is of the order $o(\epsilon^2)$ with an explicit dependence given by Fisher information. Owing to this inequality, we can show, using a triangular approximation argument, that the interpolated iterated application of the Schr\"{o}dinger bridge approximation converge to the Wasserstein gradient flow, for a class of gradient flows, including the heat flow. The results also provide a probabilistic and rigorous framework for the convergence of the self-attention mechanisms in transformer networks to the solutions of heat flows, first observed in the inspiring work SABP22 in machine learning research., Comment: 36 pages, 1 figure

Language Models have previously shown strong negotiation capabilities in closed domains where the negotiation strategy prediction scope is constrained to a specific setup. In this paper, we first show that these models are not generalizable beyond their original training domain despite their wide-scale pretraining. Following this, we propose an automated framework called GNOME, which processes existing human-annotated, closed-domain datasets using Large Language Models and produces synthetic open-domain dialogues for negotiation. GNOME improves the generalizability of negotiation systems while reducing the expensive and subjective task of manual data curation. Through our experimental setup, we create a benchmark comparing encoder and decoder models trained on existing datasets against datasets created through GNOME. Our results show that models trained on our dataset not only perform better than previous state of the art models on domain specific strategy prediction, but also generalize better to previously unseen domains.

In bio-social models, cooperative behaviour has evolved as an adaptive strategy, playing multi-functional roles. One of such roles in populations is to increase the success of survival and reproduction of individuals and their families or social groups. Moreover, collective decision-making in cooperative behaviour is an aspect that is used to study the dynamic behaviour of individuals within a social group. In this paper, we have focused on population dynamics by considering a predator-prey model as our main exemplification, where the generalist predator has adopted a cooperative hunting strategy while consuming their prey. In particular, we have analyzed the dynamic nature of the system when a nonlocal term is introduced in the cooperation. First, the Turing instability condition has been studied for the local model around the coexisting steady-state, followed by the Turing and non-Turing patterns in the presence of the nonlocal interaction term. This work is also concerned with the existence of travelling wave solutions for predator-prey interaction with the nonlocal cooperative hunting strategy. Such solutions are reported for local as well as for nonlocal models. We have characterized the invading speed of the predator with the help of the minimal wave speed of travelling wave solutions connecting the predator-free state to the co-existence state. The travelling waves are found to be non-monotonic in this system. The formation of wave trains has been demonstrated for an extended range of nonlocal interactions. Finally, the importance of psychological effects in shaping the dynamics of nonlocal collective behaviour is demonstrated with several representative examples., Comment: 39 pages, 34 figures

Controlling light propagation through complex media plays a significant role in a wide range of applications ranging from astronomical observations to microscopy. Although, several advances have been made based on adaptive optics, optical phase conjugation and wavefront shaping, but many of these involve challenges. Recently, controlling light propagation in complex media by simply structuring light has shown promising capabilities. We present experimental and numerical investigations of abruptly autofocusing of circular Airy derivative beams (CADBs) in complex media. We find that up to a relatively high turbulence strength, CADB possesses relatively good abrupt autofocusing, however, efficiency and autofocusing position vary with the strength of turbulence. Further, the spatial distortions in CADB caused by turbulence are quantified by an overlap integral, which shows that CADB possesses reasonably good resilience against the turbulence. The diffraction efficiency of CADB changes by a factor of ~ 1.7 with increasing strength of turbulence from zero to high, indicating good confinement of intensity at autofocusing. The focused beam spot size grows gradually with increasing the strength of turbulence, specifically, it grows by a factor of ~ 2 for a strong turbulence, indicating reasonably good focusing abilities. The results of CADB are compared with a Gaussian beam, and find that CADB possesses superior focusing abilities in turbulent media. We have carried out a detailed analysis of these observations based on Zernike polynomials, which reveals that different kinds of aberrations present in turbulent media leads to distortions in the spatial structure as well as other properties of CADBs. Our results can be used for various applications, such as in biomedical treatment, seismology, optical tweezers and material processing., Comment: 20 pages, 23 figures

The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model's output. Many scientific models are based on differential equations, where differentiable programming plays a crucial role in calculating model sensitivities, inverting model parameters, and training hybrid models that combine differential equations with data-driven approaches. Furthermore, recognizing the strong synergies between inverse methods and machine learning offers the opportunity to establish a coherent framework applicable to both fields. Differentiating functions based on the numerical solution of differential equations is non-trivial. Numerous methods based on a wide variety of paradigms have been proposed in the literature, each with pros and cons specific to the type of problem investigated. Here, we provide a comprehensive review of existing techniques to compute derivatives of numerical solutions of differential equations. We first discuss the importance of gradients of solutions of differential equations in a variety of scientific domains. Second, we lay out the mathematical foundations of the various approaches and compare them with each other. Third, we cover the computational considerations and explore the solutions available in modern scientific software. Last but not least, we provide best-practices and recommendations for practitioners. We hope that this work accelerates the fusion of scientific models and data, and fosters a modern approach to scientific modelling.

In this work, we demonstrate that the modulation of carrier density can alter the superconducting transition temperature by up to $204$ mK in epitaxial Indium Nitride on Gallium Nitride, accounting for the $10$% of the transition temperature in ungated conditions. Our samples are likely free from strong localization effects and significant granularity, as indicated by $( k_{f l} \gg 1 )$, suggesting that the primary determinant of the transition temperature in InN is carrier density, rather than disorder scattering. The observed behavior is consistent with BCS s-wave superconductivity, corroborated by the superconducting parameters we measured. Furthermore, we observed a $60$% bipolar suppression of the supercurrent in our experiments., Comment: 6 pages, 5 figures, supplementary material attached. Comments are welcome

For a sequence of Boolean functions $f_n : \{-1, 1\}^{V_n} \longrightarrow \{-1, 1\}$, with random input given by some probability measure $\mathbb{P}_n$, we say that there is sparse reconstruction for $f_n$ if there is a sequence of subsets $U_n \subseteq V_n$ of coordinates satisfying $|U_n| = o(|V_n|)$ such that knowing the spins in $U_n$ gives us a non-vanishing amount of information about the value of $f_n$. In the first part of this work, we showed that if the $\mathbb{P}_n$s are product measures, then no sparse reconstruction is possible for any sequence of transitive functions. In this sequel, we consider spin systems that are relatives of IID measures in one way or another, with our main focus being on the Ising model on finite transitive graphs or exhaustions of lattices. We prove that no sparse reconstruction is possible for the entire high temperature regime on Euclidean boxes and the Curie-Weiss model, while sparse reconstruction for the majority function of the spins is possible in the critical and low temperature regimes. We give quantitative bounds for two-dimensional boxes and the Curie-Weiss model, sharp in the latter case. The proofs employ several different methods, including factor of IID and FK random cluster representations, strong spatial mixing, a generalization of discrete Fourier analysis to Divide-and-Color models, and entropy inequalities., Comment: 77 pages

Stochastic resetting has emerged as a useful strategy to reduce the completion time for a broad class of first passage processes. In the canonical setup, one intermittently resets a given system to its initial configuration only to start afresh and continue evolving in time until the target goal is met. This is, however, an instantaneous process and thus less feasible for any practical purposes. A crucial generalization in this regard is to consider a finite-time return process which has significant ramifications to the first passage properties. Intriguingly, it has recently been shown that for diffusive search processes, returning in finite but stochastic time can gain significant speed-up over the instantaneous resetting process. Unlike diffusion which has a diverging mean completion time, in this paper, we ask whether this phenomena can also be observed for a first passage process with finite mean completion time. To this end, we explore the set-up of a classical drift-diffusive search process in one dimension with stochastic resetting and further assume that the return phase is modulated by a potential $U(x)=\lambda |x|$ with $\lambda>0$. For this process, we compute the mean first passage time exactly and underpin its characteristics with respect to the resetting rate and potential strength. We find a unified phase space that allows us to explore and identify the system parameter regions where stochastic return supersedes over both the underlying process and the process under instantaneous resetting. Furthermore and quite interestingly, we find that for a range of parameters the mean completion time under stochastic return protocol can be reduced further than the \textit{optimally restarted} instantaneous processes. We thus believe that resetting with stochastic returns can serve as a better optimization strategy owing to its dominance over classical first passage under resetting., Comment: Preliminary version

The knowledge of scintillation quenching of $\alpha$-particles plays a paramount role in understanding $\alpha$-induced backgrounds and improving the sensitivity of liquid argon-based direct detection of dark matter experiments. We performed a relative measurement of scintillation quenching in the MeV energy region using radioactive isotopes ($^{222}$Rn, $^{218}$Po and $^{214}$Po isotopes) present in trace amounts in the DEAP-3600 detector and quantified the uncertainty of extrapolating the quenching factor to the low-energy region., Comment: Submitted to Eur. Phys. J. C

Self-testing is a promising approach to certifying quantum states or measurements. Originally, it relied solely on the outcome statistics of the measurements involved in a device-independent (DI) setup. Extra physical assumptions about the system make the setup semi-DI. In the latter approach, we consider a prepare-and-measure scenario in which the dimension of the mediating particle is assumed to be two. In a setup involving four (three) preparations and three (two) projective measurements in addition to the target, we exemplify how to self-test any four- (three-) outcome extremal positive operator-valued measure using a linear witness. One of our constructions also achieves self-testing of any number of states with the help of as many projective measurements as the dimensionality of the space spanned by the corresponding Bloch vectors. These constructions are conjectured to be minimal in terms of the number of preparations and measurements required. In addition, we implement one of our prepare-and-measure constructions on IBM and IonQ quantum processors and certify the existence of a complex qubit Hilbert space based on the data obtained from these experiments., Comment: 15 pages, 1 figure, 1 table

When using large language models (LLMs) in high-stakes applications, we need to know when we can trust their predictions. Some works argue that prompting high-performance LLMs is sufficient to produce calibrated uncertainties, while others introduce sampling methods that can be prohibitively expensive. In this work, we first argue that prompting on its own is insufficient to achieve good calibration and then show that fine-tuning on a small dataset of correct and incorrect answers can create an uncertainty estimate with good generalization and small computational overhead. We show that a thousand graded examples are sufficient to outperform baseline methods and that training through the features of a model is necessary for good performance and tractable for large open-source models when using LoRA. We also investigate the mechanisms that enable reliable LLM uncertainty estimation, finding that many models can be used as general-purpose uncertainty estimators, applicable not just to their own uncertainties but also the uncertainty of other models. Lastly, we show that uncertainty estimates inform human use of LLMs in human-AI collaborative settings through a user study., Comment: Code available at: https://github.com/activatedgeek/calibration-tuning

We uncover a novel physical mechanism that enables a switch between attractive and repulsive Casimir forces when a Teflon surface interacts with a new form of quantum material (i.e., gapped metal) surface across different liquid media. We demonstrate the discovery of a zero-frequency Casimir effect, which, for the first time, reveals the potential for quantum switching within nanometer distances-a scale previously thought to be unattainable. Hence, our results introduce a new method to induce phase (stoichiometry)-controlled attraction-repulsion transitions and achieving quantum levitation in a liquid medium by tuning the liquid environment. This study thus not only advances our understanding of quantum forces at the nanoscale via their correlation to dielectric properties of involved materials but also opens up exciting possibilities for their manipulation in novel ways, forming the basis towards innovative advancements in nanoscale technology.

The ground state and excited state resonance dipole-dipole interaction energy between two elongated conducting molecules are explored. We review the current status for ground state interactions. This interaction is found to be of a much longer range than in the case when the molecules are pointlike and nonconducting. These are well known results found earlier by Davies, Ninham, and Richmond, and later, using a different formalism, by Rubio and co-workers. We show how the theory can be extended to excited state interactions. A characteristic property following from our calculation is that the interaction energy dependence with separation ($R$) goes like $f(R)/R^2$ both for resonance and for the van der Waals case in the long range limit. In some limits $f(R)$ has a logarithmic dependency and in others it takes constant values. We predict an unusual slow decay rate for the energy transfer between conducting molecules.

Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary-classification machine learning (ML) models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of auto-detected FRs. Using the properties of southward magnetic field the second model determines the geoeffectiveness of FRs. Our method identifies 88.6\% and 80\% large-scale ICMEs (duration $\ge 1$ day) observed at 1 au by Wind and Sun Earth Connection Coronal and Heliospheric Investigation (STEREO) mission, respectively. While testing with a continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during 2008 - 2014 period (recall= 0.875) but many false positives (precision= 0.56) as we do not take into account any additional solar wind properties than the magnetic properties. We found an accuracy of 0.88 when estimating the geoeffectiveness of the auto-detected FRs using our method. Thus, in space weather now-casting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and geoeffectiveness., Comment: 19 pages, 6 figures

Noisy unsharp measurements incorporated in quantum information protocols may hinder performance, reducing the quantum advantage. However, we show that, unlike projective measurements which completely destroy quantum correlations between nodes in quantum networks, sequential applications of noisy measurements can mitigate the adverse impact of noise in the measurement device on quantum information processing tasks. We demonstrate this in the case of concentrating entanglement on chosen nodes in quantum networks via noisy measurements performed by assisting qubits. In the case of networks with a cluster of three or higher number of qubits, we exhibit that sequentially performing optimal unsharp measurements on the assisting qubits yields localizable entanglement between two nodes akin to that obtained by optimal projective measurements on the same assisting qubits. Furthermore, we find that the proposed approach using consecutive noisy measurements can potentially be used to prepare desired states that are resource for specific quantum schemes. We also argue that assisting qubits have greater control over the qubits on which entanglement is concentrated via unsharp measurements, in contrast to sharp measurement-based protocols, which may have implications for secure quantum communication., Comment: 14 pages, 9 figures

With recent advances in video prediction, controllable video generation has been attracting more attention. Generating high fidelity videos according to simple and flexible conditioning is of particular interest. To this end, we propose a controllable video generation model using pixel level renderings of 2D or 3D bounding boxes as conditioning. In addition, we also create a bounding box predictor that, given the initial and ending frames' bounding boxes, can predict up to 15 bounding boxes per frame for all the frames in a 25-frame clip. We perform experiments across 3 well-known AV video datasets: KITTI, Virtual-KITTI 2 and BDD100k.

In a network consisting of n nodes, our goal is to identify the most central k nodes with respect to the proposed definitions of centrality. Depending on the specific application, there exist several metrics for quantifying k-centrality, and the subset of the best k nodes naturally varies based on the chosen metric. In this paper, we propose two metrics and establish connections to a well-studied metric from the literature (specifically for stochastic matrices). We prove these three notions match for path graphs. We then list a few more control-theoretic notions and compare these various notions for a general randomly generated graph. Our first metric involves maximizing the shift in the smallest eigenvalue of the Laplacian matrix. This shift can be interpreted as an improvement in the time constant when the RC circuit experiences leakage at certain k capacitors. The second metric focuses on minimizing the Perron root of a principal sub-matrix of a stochastic matrix, an idea proposed and interpreted in the literature as manufacturing consent. The third one explores minimizing the Perron root of a perturbed (now super-stochastic) matrix, which can be seen as minimizing the impact of added stubbornness. It is important to emphasize that we consider applications (for example, facility location) when the notions of central ports are such that the set of the best k ports does not necessarily contain the set of the best k-1 ports. We apply our k-port selection metric to various network structures. Notably, we prove the equivalence of three definitions for a path graph and extend the concept of central port linkage beyond Fiedler vectors to other eigenvectors associated with path graphs.

The Chow class of the closure of the torus orbit of a point in a Grassmannian only depends on the matroid associated to the point. The Chow class can be extended to a matroid invariant of arbitrary matroids. We call the coefficients appearing in the expansion of the Chow class in the Schubert basis the Schubert coefficients of the matroid. These Schubert coefficients are conjectured by Berget and Fink to be non-negative. We compute the Schubert coefficients for all sparse paving matroids, and confirm their non-negativity., Comment: 10 pager, 3 figures

Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO$_2$ emissions. The importance of predicting carbon fluxes has led to the emerging field of data-driven carbon flux modelling (DDCFM), which uses statistical techniques to predict carbon fluxes from biophysical data. However, the field lacks a standardized dataset to promote comparisons between models. To address this gap, we present CarbonSense, the first machine learning-ready dataset for DDCFM. CarbonSense integrates measured carbon fluxes, meteorological predictors, and satellite imagery from 385 locations across the globe, offering comprehensive coverage and facilitating robust model training. Additionally, we provide a baseline model using a current state-of-the-art DDCFM approach and a novel transformer based model. Our experiments illustrate the potential gains that multimodal deep learning techniques can bring to this domain. By providing these resources, we aim to lower the barrier to entry for other deep learning researchers to develop new models and drive new advances in carbon flux modelling., Comment: 9 content pages, 11 reference pages, 9 appendix pages

We investigate the phenomenon of transition to synchronization in Sakaguchi-Kuramoto model in the presence of higher-order interactions and global order parameter adaptation. The investigation is done by performing extensive numerical simulations and low dimensional modeling of the system. Numerical simulations of the full system show both continuous (second order) as well as discontinuous transitions. The discontinuous transitions can either be associated with explosive (first order) or with tiered synchronization states depending on the choice of parameters. To develop an in depth understanding of the transition scenario in the parameter space we derive a reduced order model (ROM) using the Ott-Antonsen ansatz, the results of which closely matches with that of the numerical simulations of the full system. The simplicity and analytical accessibility of the ROM helps to conveniently unfold the transition scenario in the system having complex dependence on the parameters. Simultaneous analysis of the full system and the ROM clearly identifies the regions of the parameter space exhibiting different types of transitions. It is observed that the second order continuous transition is connected with a supercritical pitchfork bifurcation (PB) of the ROM. On the other hand, the discontinuous teired transition is associated with multiple saddle-node (SN) bifurcations along with a supercritical PB and the first order explosive transition involves a subcritical PB alongside a SN bifurcation., Comment: 11 pages, 11 figures

Networks incorporating higher-order interactions are increasingly recognized for their ability to introduce novel dynamics into various processes, including synchronization. Previous studies on synchronization within multilayer networks have often been limited to specific models, such as the Kuramoto model, or have focused solely on higher-order interactions within individual layers. Here, we present a comprehensive framework for investigating synchronization, particularly global synchronization, in multilayer networks with higher-order interactions. Our framework considers interactions beyond pairwise connections, both within and across layers. We demonstrate the existence of a stable global synchronous state, with a condition resembling the master stability function, contingent on the choice of coupling functions. Our theoretical findings are supported by simulations using Hindmarsh-Rose neuronal and R\"{o}ssler oscillators. These simulations illustrate how synchronization is facilitated by higher-order interactions, both within and across layers, highlighting the advantages over scenarios involving interactions within single layers.

There is a growing number of peculiar events that cannot be assigned to any of the main supernova (SN) classes. SN 1987A and a handful of similar objects, thought to be explosive outcomes of blue supergiant stars, belong to them: while their spectra closely resemble those of H-rich (IIP) SNe, their light-curve (LC) evolution is very different. Here we present the detailed photometric and spectroscopic analysis of SN 2021aatd, a peculiar Type II explosion: while its early-time evolution resembles that of the slowly evolving, double-peaked SN 2020faa (however, at a lower luminosity scale), after $\sim$40 days, its LC shape becomes similar to that of SN 1987A-like explosions. Beyond comparing LCs, color curves, and spectra of SN 2021aatd to that of SNe 2020faa, 1987A, and of other objects, we compare the observed spectra with our own SYN++ models and with the outputs of published radiative transfer models. We also modeled the pseudo-bolometric LCs of SNe 2021aatd and 1987A assuming a two-component (core+shell) ejecta, and involving the rotational energy of a newborn magnetar in addition to radioactive decay. We find that both the photometric and spectroscopic evolution of SN 2021aatd can be well described with the explosion of a $\sim$15 $M_\odot$ blue supergiant star. Nevertheless, SN 2021aatd shows higher temperatures and weaker Na ID and Ba II 6142 A lines than SN 1987A, which is reminiscent of rather to IIP-like atmospheres. With the applied two-component ejecta model (counting with both decay and magnetar energy), we can successfully describe the bolometric LC of SN 2021aatd, including the first $\sim$40-day long phase showing an excess compared to 87A-like SNe but being strikingly similar to that of the long-lived SN 2020faa. Nevertheless, finding a unified model that also explains the LCs of more luminous events (like SN 2020faa) is still a matter of concern., Comment: 24 pages, 18 figures, 10 tables; accepted for publication in A&A

The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. This paper demonstrates SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to sports archives. We present a novel interactive user interface (UI) based on the Chainlit framework which wraps around the core functionality, and enable users to interact with the SoccerRAG framework in a chatbot-like visual manner., Comment: accepted to CBMI 2024 as a demonstration; https://github.com/simula/soccer-rag. arXiv admin note: text overlap with arXiv:2406.01273

The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. This paper introduces SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to sports archives. Our evaluations indicate that SoccerRAG effectively handles complex queries, offering significant improvements over traditional retrieval systems in terms of accuracy and user engagement. The results underscore the potential of using RAG and LLMs in sports analytics, paving the way for future advancements in the accessibility and real-time processing of sports data., Comment: accepted to CBMI 2024 as a regular paper; https://github.com/simula/soccer-rag

Optically addressable spin-based quantum sensors enable nanoscale measurements of temperature, magnetic field, pH, and other physical properties of a system. Advancing the sensors beyond proof-of-principle demonstrations in living cells and multicellular organisms towards reliable, damage-free quantum sensing poses three distinct technical challenges. First, spin-based quantum sensing requires optical accessibility and microwave delivery. Second, any microelectronics must be biocompatible and designed for imaging living specimens. Third, efficient microwave delivery and temperature control are essential to reduce unwanted heating and to maintain an optimal biological environment. Here, we present the Quantum Biosensing Chip (Q-BiC), which facilitates microfluidic-compatible microwave delivery and includes on-chip temperature control. We demonstrate the use of Q-BiC in conjunction with nanodiamonds containing nitrogen vacancy centers to perform optically detected magnetic resonance in living systems. We quantify the biocompatibility of microwave excitation required for optically detected magnetic resonance both in vitro in HeLa cells and in vivo in the nematode Caenorhabditis elegans for temperature measurements and determine the microwave-exposure range allowed before detrimental effects are observed. In addition, we show that nanoscale quantum thermometry can be performed in immobilised but non-anaesthetised adult nematodes with minimal stress. These results enable the use of spin-based quantum sensors without damaging the biological system under study, facilitating the investigation of the local thermodynamic and viscoelastic properties of intracellular processes.

CHEOPS is a space telescope specifically designed to monitor transiting exoplanets orbiting bright stars. In September 2023, CHEOPS completed its nominal mission and remains in excellent operational conditions. The mission has been extended until the end of 2026. Scientific and instrumental data have been collected throughout in-orbit commissioning and nominal operations, enabling a comprehensive analysis of the mission's performance. In this article, we present the results of this analysis with a twofold goal. First, we aim to inform the scientific community about the present status of the mission and what can be expected as the instrument ages. Secondly, we intend for this publication to serve as a legacy document for future missions, providing insights and lessons learned from the successful operation of CHEOPS. To evaluate the instrument performance in flight, we developed a comprehensive monitoring and characterisation programme. It consists of dedicated observations that allow us to characterise the instrument's response. In addition to the standard collection of nominal science and housekeeping data, these observations provide input for detecting, modelling, and correcting instrument systematics, discovering and addressing anomalies, and comparing the instrument's actual performance with expectations. The precision of the CHEOPS measurements has enabled the mission objectives to be met and exceeded. Careful modelling of the instrumental systematics allows the data quality to be significantly improved during the light curve analysis phase, resulting in more precise scientific measurements. CHEOPS is compliant with the driving scientific requirements of the mission. Although visible, the ageing of the instrument has not affected the mission's performance., Comment: Accepted for publication in Astronomy and Astrophysics

The combined effects of imposed vertical mean magnetic field (B0) and rotation on heat transfer phenomenon driven by the Rayleigh-Taylor instability are investigated using DNS. In the hydrodynamic (HD) case (B0 = 0), as the rotation rate f increases from 4 to 8, the Coriolis force suppresses the growth of mixing layer height (h) and u3', leading to a reduction in heat transport. The imposed B0 forms vertically elongated thermal plumes that exhibit larger u3' and efficiently transport heat between hot and cold fluid. Therefore, we observe an enhancement in heat transfer in the initial regime of unbroken elongated plumes in f=0 MHD cases compared to the corresponding HD case. In the mixing regime, the flow is collimated along the vertical magnetic field lines due to imposed B0, resulting in a decrease in u3' and an increase in growth of h compared to f=0 HD case. This increase in h enhances heat transfer in the mixing regime of f=0 MHD over the corresponding HD case. When rotation is added along with imposed B0, the growth and breakdown of vertically elongated plumes are inhibited by the Coriolis force, reducing h and u3'. Consequently, heat transfer is also reduced in rotating MHD cases compared to corresponding f=0 MHD cases. The heat transfer in rotating MHD cases remains higher than in corresponding rotating HD cases. This also suggests that B0 mitigates the instability-suppressing effect of the Coriolis force. The t.k.e. budget reveals the conversion of t.k.e., generated by the buoyancy flux, into t.m.e..

In star formation regions, the complex organic molecules (COMs) that contain peptide bonds ($-$NH$-$C(=O)$-$) play a major role in the metabolic process because $-$NH$-$C(=O)$-$ is connected to amino acids (R-CHNH$_{2}$$-$COOH). Over the past few decades, many COMs containing peptide-like bonds have been detected in hot molecular cores (HMCs), hot corinos, and cold molecular clouds, however, their prebiotic chemistry is poorly understood. We present the first detection of the rotational emission lines of formamide (NH$_{2}$CHO) and isocyanic acid (HNCO), which contain peptide-like bonds toward the chemically rich HMC G358.93$-$0.03 MM1, using high-resolution and high-sensitivity Atacama Large Millimeter/submillimeter Array bands 6 and 7. We estimate that the column densities of NH$_{2}$CHO and HNCO toward G358.93$-$0.03 MM1 are (2.80$\pm$0.29)$\times$10$^{15}$ cm$^{-2}$ and (1.80$\pm$0.42)$\times$10$^{16}$ cm$^{-2}$ with excitation temperatures of 165 $\pm$ 21 K and 170 $\pm$ 32 K, respectively. The fractional abundances of NH$_{2}$CHO and HNCO toward G358.93$-$0.03 MM1 are (9.03$\pm$1.44)$\times$10$^{-10}$ and (5.80$\pm$2.09)$\times$10$^{-9}$. We compare the estimated abundances of NH$_{2}$CHO and HNCO with the existing three-phase warm-up chemical model abundance values and notice that the observed and modelled abundances are very close. We conclude that NH$_{2}$CHO is produced by the reaction of NH$_{2}$ and H$_{2}$CO in the gas phase toward G358.93$-$0.03 MM1. Likewise, HNCO is produced on the surface of grains by the reaction of NH and CO toward G358.93$-$0.03 MM1. We also find that NH$_{2}$CHO and HNCO are chemically linked toward G358.93$-$0.03 MM1., Comment: Published in Research in Astronomy and Astrophysics

Additive manufacturing, especially laser powder bed fusion (L-PBF), is widely used for fabricating metal parts with intricate geometries. However, parts produced via L-PBF suffer from varied surface roughness which affects the dynamic or fatigue properties. Accurate prediction of fatigue properties as a function of surface roughness is a critical requirement for qualifying L-PBF parts. In this work, an analytical methodology is put forth to predict the fatigue life of L-PBF components having heterogeneous surface roughness. Thirty-six Hastelloy X specimens are printed using L-PBF followed by industry-standard heat treatment procedures. Half of these specimens are built with as-printed gauge sections and the other half is printed as cylinders from which fatigue specimens are extracted via machining. Specimens are printed in a vertical orientation and an orientation 30 degree from the vertical axis. The surface roughness of the specimens is measured using computed tomography and parameters such as the maximum valley depth are used to build an extreme value distribution. Fatigue testing is conducted at an isothermal condition of 500-degree F. It is observed that the rough specimens fail much earlier compared to the machined specimens due to the deep valleys present on the surfaces of the former ones. The valleys act as notches leading to high strain localization. Following this observation, a functional relationship is formulated analytically that considers surface valleys as notches and correlates the strain localization around those notches with fatigue life, using the Coffin-Manson-Basquin and Ramberg-Osgood equation. In conclusion, the proposed analytical model successfully predicts the fatigue life of L-PBF specimens at an elevated temperature undergoing different strain loadings.

Script identification plays a vital role in applications that involve handwriting and document analysis within a multi-script and multi-lingual environment. Moreover, it exhibits a profound connection with human cognition. This paper provides a new database for benchmarking script identification algorithms, which contains both printed and handwritten documents collected from a wide variety of scripts, such as Arabic, Bengali (Bangla), Gujarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu, and Thai. The dataset consists of 1,135 documents scanned from local newspaper and handwritten letters as well as notes from different native writers. Further, these documents are segmented into lines and words, comprising a total of 13,979 and 86,655 lines and words, respectively, in the dataset. Easy-to-go benchmarks are proposed with handcrafted and deep learning methods. The benchmark includes results at the document, line, and word levels with printed and handwritten documents. Results of script identification independent of the document/line/word level and independent of the printed/handwritten letters are also given. The new multi-lingual database is expected to create new script identifiers, present various challenges, including identifying handwritten and printed samples and serve as a foundation for future research in script identification based on the reported results of the three benchmarks.

Utilizing inverse Mellin transform of the symmetric square $L$-function attached to Ramanujan tau function, Hafner and Stopple proved a conjecture of Zagier, which states that the constant term of the automorphic function $y^{12}|\Delta(z)|^2$ i.e., the Lambert series $y^{12}\sum_{n=1}^\infty \tau(n)^2 e^{-4 \pi n y}$ can be expressed in terms of the non-trivial zeros of the Riemann zeta function. This study examines certain Lambert series associated to Siegel cusp forms of degree $n$ twisted by a character $\chi$ and observes a similar phenomenon., Comment: 15 pages, comments are welcome! arXiv admin note: text overlap with arXiv:2305.07412

Deep learning has become a de facto method of choice for speech enhancement tasks with significant improvements in speech quality. However, real-time processing with reduced size and computations for low-power edge devices drastically degrades speech quality. Recently, transformer-based architectures have greatly reduced the memory requirements and provided ways to improve the model performance through local and global contexts. However, the transformer operations remain computationally heavy. In this work, we introduce WaveUNet squeeze-excitation Res2 (WSR)-based metric generative adversarial network (WSR-MGAN) architecture that can be efficiently implemented on low-power edge devices for noise suppression tasks while maintaining speech quality. We utilize multi-scale features using Res2Net blocks that can be related to spectral content used in speech-processing tasks. In the generator, we integrate squeeze-excitation blocks (SEB) with multi-scale features for maintaining local and global contexts along with gated recurrent units (GRUs). The proposed approach is optimized through a combined loss function calculated over raw waveform, multi-resolution magnitude spectrogram, and objective metrics using a metric discriminator. Experimental results in terms of various objective metrics on VoiceBank+DEMAND and DNS-2020 challenge datasets demonstrate that the proposed speech enhancement (SE) approach outperforms the baselines and achieves state-of-the-art (SOTA) performance in the time domain.

We show that thermal effective field theory controls the long-distance expansion of the partition function of a $d$-dimensional QFT, with an insertion of any finite-order spatial isometry. Consequently, the thermal partition function on a sphere displays a fractal-like structure as a function of angular twist, reminiscent of the behavior of a modular form near the real line. As an example application, we find that for CFTs, the effective free energy of even-spin minus odd-spin operators at high temperature is smaller than the usual free energy by a factor of $1/2^d$. Near certain rational angles, the partition function receives subleading contributions from "Kaluza-Klein vortex defects" in the thermal EFT, which we classify. We illustrate our results with examples in free and holographic theories, and also discuss nonperturbative corrections from worldline instantons., Comment: 45 pages + appendices, 7 figures; v2: references added

We present the Stagome lattice, a variant of the Kagome lattice, where one can make any of the bands completely flat by tuning an externally controllable magnetic flux. This systematically allows the energy of the flat band (FB) to coincide with the Fermi level. We have analytically calculated the compact localized states (CLS) associated to each of these flat bands appearing at different values of the magnetic flux. We also show that, this model features nontrivial topological properties with distinct integer values of the Chern numbers as a function of the magnetic flux. We argue that this mechanism for making any of the bands exactly flat could be of interest to address the FB superconductivity in such a system. Furthermore, we believe that the phenomenon of photonic flat band localization could be studied in the Stagome lattice structure, designed for instance using femtosecond laser induced single-mode waveguide arrays., Comment: 8 pages, 7 figures; A new 2D lattice model; Comments are welcome