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We propose a method to design a magnetic field detector using a noisy two-qubit system. The system evolves under a noisy Heisenberg interaction Hamiltonian, and we investigate its behavior by calculating both the $l_1$-norm of quantum coherence and the return probability in the presence and absence of an external magnetic field. We allow for decoherence modeled by quasi-static charge noise in the exchange coupling of the two-qubit system and find that, while the magnetic field does not significantly influence the decoherence process, it introduces a distinct oscillation in the return probability over time. Importantly, the oscillation frequency is directly proportional to the strength of the applied magnetic field, providing a clear signature that can be used for magnetic field detection. These results point towards the feasibility of realizing a practical quantum-based magnetic field detector, with the ability to operate under noisy conditions while maintaining sensitivity to the field strength.

What are the signatures of the onset of catastrophe? Here we present the rich system physics characterizing the stochasticity driven transition from homeostasis to breakdown in an experimentally motivated and analytically tractable minimal model. Recent high-precision experiments on individual bacterial cells, growing and dividing repeatedly in a variety of environments, have revealed a previously unknown intergenerational scaling law which not only uniquely determines the stochastic map governing homeostasis, but also, as we show here, offers quantitative insights into the transition from the "conspiracy principle" regime (homeostasis) to the "catastrophe principle" regime and then to system breakdown. These transitions occur as a single parameter is tuned; we predict the precise values of the parameter which mark the characteristic progression of the transition. Emergence of asymptotically scale invariant distributions with quantifiable power law tails and the reverse monotonicity of the conditional exceedance distribution further characterize the transition to catastrophe.

One of today's most significant and transformative technologies is the rapidly developing field of artificial intelligence (AI). Deined as a computer system that simulates human cognitive processes, AI is present in many aspects of our daily lives, from the self-driving cars on the road to the intelligence (AI) because some AI systems are so complex and opaque. With millions of parameters and layers, these system-deep neural networks in particular-make it difficult for humans to comprehend accountability, prejudice, and justice are raised by the opaqueness of its decision-making process. AI has a lot of potential, but it also comes with a lot of difficulties and moral dilemmas. In the context of explainable artificial intelligence (XAI), trust is crucial as it ensures that AI systems behave consistently, fairly, and ethically. In the present article, we explore XAI, reliable XAI, and several practical uses for reliable XAI. Once more, we go over the three main components-transparency, explainability, and trustworthiness of XAI-that we determined are pertinent in this situation. We present an overview of recent scientific studies that employ trustworthy XAI in various application fields. In the end, trustworthiness is crucial for establishing and maintaining trust between humans and AI systems, facilitating the integration of AI systems into various applications and domains for the benefit of society., Comment: 28 pages, 14 figures

Scent marks play a crucial role in both territorial and sexual communication in many species. We investigated how free-ranging dogs respond to scent marks from individuals of different identities in terms of sex and group, across varying strategic locations within their territory. Both male and female dogs showed heightened interest in scent marks compared to control, exhibiting stronger territorial responses,. with males being more territorial than females. Overmarking behaviour was predominantly observed in males, particularly in response to male scent marks and those from neighbouring groups. Behavioural cluster analysis revealed distinct responses to different scent marks, with neighbouring group male scents eliciting the most distinct reactions. Our findings highlight the multifaceted role of scent marks in free-ranging dog communication, mediating both territorial defence and intrasexual competition. The differential responses based on the identity and gender of the scent-marker emphasize the complexity of olfactory signalling in this species. This study contributes to understanding the social behaviour of dogs in their natural habitat, and opens up possibilities for future explorations in the role of olfactory cues in the social dynamics of the species.

We measure the time-integrated CP asymmetry in $D^{0} \rightarrow K^{0}_{S}K^{0}_{S}$ decays reconstructed in $e^{+}e^{-} \rightarrow c\overline{c}$ events collected by the Belle and Belle II experiments. The corresponding data samples have integrated luminosities of 980 fb$^{-1}$ and 427 fb$^{-1}$, respectively. The D$^{0}$ decays are required to originate from the $D^{*+} \rightarrow D^{0}\pi^{+}$ decay, which determines the charm flavor at production time. A control sample of $D^{0} \rightarrow K^{+}K^{-}$ decays is used to correct for production and detection asymmetries.The result, ($-$1.4 $\pm$ 1.3(\text{stat.})$\pm$ 0.1(\text{syst.}))\%, is consistent with previous determinations and with CP symmetry., Comment: 10 pages, 3 figures. arXiv admin note: text overlap with arXiv:2410.22961

The possibility of topological phase transition with or without a magnetic flux trapped in the cells of a class of decorated lattices is explored in details.Using a tight binding Hamiltonian and a real space decimation scheme we analytically obtain the non-dispersive and dispersive energy bands, and exactly locate the eigenvalues at which energy gaps close.We find that despite the local breaking of the time reversal symmetry, as a magnetic field is turned on, the topological invariant exhibits quantization, and a clear appearance of edge localized modes is observed exactly at the gap-closing energy eigenvalues in the topologically non-trivial insulating phase.The bulk boundary correspondence is obeyed. The protection of the edge states by a chiral symmetry is confirmed for both the presence and absence of magnetic flux. Our studies have been extended to other kinds of decorated lattices where topological phase transition is possible only above a threshold value of the hopping integral describing these lattice. Our results are analytically exact.

In autonomous robot navigation, terrain cost assignment is typically performed using a semantics-based paradigm in which terrain is first labeled using a pre-trained semantic classifier and costs are then assigned according to a user-defined mapping between label and cost. While this approach is rapidly adaptable to changing user preferences, only preferences over the types of terrain that are already known by the semantic classifier can be expressed. In this paper, we hypothesize that a machine-learning-based alternative to the semantics-based paradigm above will allow for rapid cost assignment adaptation to preferences expressed over new terrains at deployment time without the need for additional training. To investigate this hypothesis, we introduce and study PACER, a novel approach to costmap generation that accepts as input a single birds-eye view (BEV) image of the surrounding area along with a user-specified preference context and generates a corresponding BEV costmap that aligns with the preference context. Using both real and synthetic data along with a combination of proposed training tasks, we find that PACER is able to adapt quickly to new user preferences while also exhibiting better generalization to novel terrains compared to both semantics-based and representation-learning approaches.

We perform a model-independent measurement of the $D^0$-$\overline{D}{}^0$ mixing parameters using samples of $e^+e^-$-collision data collected by the Belle and Belle II experiments that have integrated luminosities of $951\ \text{fb}^{-1}$ and $408\ \text{fb}^{-1}$, respectively. Approximately $2.05\times10^6$ neutral $D$ mesons are reconstructed in the $D^0\rightarrow K^0_{S}\pi^+\pi^-$ channel, with the neutral $D$ flavor tagged by the charge of the pion in the $D^{*+}\rightarrow D^0\pi^+$ decay. Assuming charge-parity symmetry, the mixing parameters are measured to be $ x = (4.0\pm1.7\pm0.4)\times 10^{-3} $ and $ y = (2.9\pm1.4\pm0.3)\times 10^{-3}$, where the first uncertainties are statistical and the second systematic. The results are consistent with previous determinations.

Phase change materials (PCMs) that exhibit volatile resistive switching are promising for emulating neuronal oscillators. Charge transfer insulators, such as ReNiO3 (where Re represents rare earth metals like Pr, Nd, Sm, Eu...), form a family of PCMs with tunable metal-insulator transition (MIT) temperatures across a broad range. Notably, MIT can be adjusted via chemical doping or strain engineering. EuNiO3, in particular, is an attractive choice for oscillator devices given its bulk transition temperature (TMI) of approximately 190{\deg}C, which is well above room temperature, reducing crosstalk issues while remaining low enough to support energy-efficient applications. We demonstrate a method to stabilize high-quality epitaxial EuNiO3 thin films through scalable reactive RF sputtering and post-annealing, optimizing oxygen partial pressures and annealing temperatures across two substrates. Growth at low or zero oxygen partial pressures resulted in amorphous samples, while higher pressures improved crystallinity but led to the stabilization of Ruddlesden-Popper (RP) phases [An+1BnO3n+1] and associated faults. Post-annealing enhanced crystallinity in all samples, transforming RP phases and faults toward the perovskite phase. We conducted operando XRD and transport measurements on our films, finding the transition temperatures of perovskite phase samples grown on LAO and LSAT to be approximately 300{\deg}C and 250{\deg}C, respectively. We attribute the increase in TMI for LAO samples to in-plane compressive strain (-0.76%), which reduces the Ni-O-Ni bond angles in-plane. Similarly, LSAT samples experience in-plane tensile strain (2.1%), which decreases out-of-plane Ni-O-Ni bond angles, increasing TMI compared to bulk. However, this is counteracted by oxygen vacancies due to lowered formation energies. Films that stabilized in RP phases did not exhibit any transition., Comment: 7 pages, 4 figures, 1 table

Large language model advancements have enabled the development of multi-agent frameworks to tackle complex, real-world problems such as to automate tasks that require interactions with diverse tools, reasoning, and human collaboration. We present MARCO, a Multi-Agent Real-time Chat Orchestration framework for automating tasks using LLMs. MARCO addresses key challenges in utilizing LLMs for complex, multi-step task execution. It incorporates robust guardrails to steer LLM behavior, validate outputs, and recover from errors that stem from inconsistent output formatting, function and parameter hallucination, and lack of domain knowledge. Through extensive experiments we demonstrate MARCO's superior performance with 94.48% and 92.74% accuracy on task execution for Digital Restaurant Service Platform conversations and Retail conversations datasets respectively along with 44.91% improved latency and 33.71% cost reduction. We also report effects of guardrails in performance gain along with comparisons of various LLM models, both open-source and proprietary. The modular and generic design of MARCO allows it to be adapted for automating tasks across domains and to execute complex usecases through multi-turn interactions., Comment: EMNLP 2024 Industry Track

We present a pioneering model of the interaction between the solar wind and the surrounding interstellar medium that includes the possibility of different pressures in the directions parallel and perpendicular to the magnetic field. The outer heliosheath region is characterized by a low rate of turbulent scattering that would permit a development of pressure anisotropy. The effect is best seen on the interstellar side of the heliopause, where a narrow region is developed with an excessive perpendicular pressure resembling a plasma depletion layer typical of planetary magnetspheres. The magnitude of this effect is relatively small owing to the fact that proton-proton collisions don't permit the anisotropy to approach a stability threshold. We also show that if turbulence in the interstellar medium is weak, a much broader anisotropic boundary layer can exist, with a dominant perpendicular pressure in the southern hemisphere and a dominant parallel pressure in the north., Comment: 13 pages, 3 figures

We explore the impact of introducing IIIA-group element Ga in place of IIB-group element Zn in binary intermetallic GdZn on its magnetic and magnetocaloric properties, as well as explicate the modified electronic band structure of the compound. The magnetic transition temperature of the compound decreases with the increase of Ga concentration in GdZn1-xGax (x = 0-0.1) while the crystal structure (CsCl-prototype) and lattice parameters remain unchanged. Our detailed analysis of magnetization and magnetocaloric data conclusively proves that long-ranged magnetic ordering exists in the sample, despite the magnetic interaction considerably weakening with the increase of Ga. The experimental data is rationalized using both theoretical machine learning model and first-principle density functional theory.The electronic band structure of GdZn is manifested with some unusual complex features which gradually diminish with Ga doping and conventional sinusoidal feature of Ruderman-Kittel-Kasuya- Yosida (RKKY)-type interactions also disappears. A mean-field theory model is developed and can successfully describe the overall magnetocaloric behavior of the GdZn1-xGax series of samples

The 2-adic valuation of an integer n which is the exponent of the highest power of 2 that divides n. In this paper, we give representations of certain restricted partition functions in terms of 2-adic valuation.

The boundary $\partial X$ of a boundary continuous Gromov hyperbolic space $X$ carries a natural Moebius structure on the boundary. For a proper, geodesically complete, boundary continuous Gromov hyperbolic space $X$, the boundary $\partial X$ equipped with its cross-ratio is a particular kind of quasi-metric space, called a quasi-metric antipodal space. Given a quasi-metric antipodal space $Z$, one may consider the family of all hyperbolic fillings of $Z$. In \cite{biswas2024quasi} it was shown that this family has a unique upper bound $\mathcal{M}(Z)$ (with respect to a natural partial order on hyperbolic fillings of $Z$), which can be described explicitly in terms of the cross-ratio on $Z$. As shown in \cite{biswas2024quasi}, the spaces $\mathcal{M}(Z)$ constitute a natural class of spaces called maximal Gromov hyperbolic spaces. A natural problem is to describe explicitly the maximal Gromov hyperbolic spaces $X$ whose boundary $\partial X$ is finite. We show that for a maximal Gromov hyperbolic space $X$ with boundary $\partial X$ of cardinality $n$, the space $X$ is isometric to a finite polyhedral complex embedded in $(\mathbb{R}^n, ||\cdot||_{\infty})$ with cells of dimension at most $n/2$, given by attaching $n$ half-lines to vertices of a compact polyhedral complex. In particular the geometry at infinity of $X$ is trivial. The combinatorics of the polyhedral complex is determined by certain relations $R \subset \partial X \times \partial X$ on the boundary $\partial X$, called antipodal relations. In \cite{biswas2024quasi} it was shown that maximal Gromov hyperbolic spaces are injective metric spaces. We give a shorter, simpler proof of this fact in the case of spaces with finite boundary. We also consider the space of deformations of a maximal Gromov hyperbolic space with finite boundary, and define an associated Teichmuller space.

As powerful Large Language Models (LLMs) are now widely used for numerous practical applications, their safety is of critical importance. While alignment techniques have significantly improved overall safety, LLMs remain vulnerable to carefully crafted adversarial inputs. Consequently, adversarial attack methods are extensively used to study and understand these vulnerabilities. However, current attack methods face significant limitations. Those relying on optimizing discrete tokens suffer from limited efficiency, while continuous optimization techniques fail to generate valid tokens from the model's vocabulary, rendering them impractical for real-world applications. In this paper, we propose a novel technique for adversarial attacks that overcomes these limitations by leveraging regularized gradients with continuous optimization methods. Our approach is two orders of magnitude faster than the state-of-the-art greedy coordinate gradient-based method, significantly improving the attack success rate on aligned language models. Moreover, it generates valid tokens, addressing a fundamental limitation of existing continuous optimization methods. We demonstrate the effectiveness of our attack on five state-of-the-art LLMs using four datasets., Comment: 8 pages, 6 figures

Using Large Language Models (LLMs) to produce robot programs from natural language has allowed for robot systems that can complete a higher diversity of tasks. However, LLM-generated programs may be faulty, either due to ambiguity in instructions, misinterpretation of the desired task, or missing information about the world state. As these programs run, the state of the world changes and they gather new information. When a failure occurs, it is important that they recover from the current world state and avoid repeating steps that they they previously completed successfully. We propose RoboRepair, a system which traces the execution of a program up until error, and then runs an LLM-produced recovery program that minimizes repeated actions. To evaluate the efficacy of our system, we create a benchmark consisting of eleven tasks with various error conditions that require the generation of a recovery program. We compare the efficiency of the recovery program to a plan built with an oracle that has foreknowledge of future errors., Comment: Under review at ACL Rolling Review

Large Language Models (LLMs) have shown remarkable capabilities in various domains, yet their economic impact has been limited by challenges in tool use and function calling. This paper introduces ThorV2, a novel architecture that significantly enhances LLMs' function calling abilities. We develop a comprehensive benchmark focused on HubSpot CRM operations to evaluate ThorV2 against leading models from OpenAI and Anthropic. Our results demonstrate that ThorV2 outperforms existing models in accuracy, reliability, latency, and cost efficiency for both single and multi-API calling tasks. We also show that ThorV2 is far more reliable and scales better to multistep tasks compared to traditional models. Our work offers the tantalizing possibility of more accurate function-calling compared to today's best-performing models using significantly smaller LLMs. These advancements have significant implications for the development of more capable AI assistants and the broader application of LLMs in real-world scenarios., Comment: 15 pages for main paper, 21 pages in total including references and appendix, 10 figures

General-purpose navigation in challenging environments remains a significant problem in robotics, with current state-of-the-art approaches facing myriad limitations. Classical approaches struggle with cluttered settings and require extensive tuning, while learning-based methods face difficulties generalizing to out-of-distribution environments. This paper introduces X-Mobility, an end-to-end generalizable navigation model that overcomes existing challenges by leveraging three key ideas. First, X-Mobility employs an auto-regressive world modeling architecture with a latent state space to capture world dynamics. Second, a diverse set of multi-head decoders enables the model to learn a rich state representation that correlates strongly with effective navigation skills. Third, by decoupling world modeling from action policy, our architecture can train effectively on a variety of data sources, both with and without expert policies: off-policy data allows the model to learn world dynamics, while on-policy data with supervisory control enables optimal action policy learning. Through extensive experiments, we demonstrate that X-Mobility not only generalizes effectively but also surpasses current state-of-the-art navigation approaches. Additionally, X-Mobility also achieves zero-shot Sim2Real transferability and shows strong potential for cross-embodiment generalization.

Optical metasurfaces provide novel solutions to label-free biochemical sensing by localizing light resonantly beyond the diffraction limit, thereby selectively enhancing light-matter interactions for improved analytical performance. However, high-Q resonances in metasurfaces are usually achieved in the reflection mode, which impedes metasurface integration into compact imaging systems. Here, we demonstrate a novel metasurface platform for advanced biochemical sensing based on the physics of the bound states in the continuum (BIC) and electromagnetically induced transparency (EIT) modes, which arise when two interfering resonances from a periodic pattern of tilted elliptic holes overlap both spectrally and spatially, creating a narrow transparency window in the mid-infrared spectrum. We experimentally measure these resonant peaks observed in transmission mode (Q~734 at ~8.8 um) in free-standing silicon membranes and confirm their tunability through geometric scaling. We also demonstrate the strong coupling of the BIC-EIT modes with a thinly coated PMMA film on the metasurface, characterized by a large Rabi splitting (32 cm-1) and biosensing of protein monolayers in transmission mode. Our new photonic platform can facilitate the integration of metasurface biochemical sensors into compact and monolithic optical systems while being compatible with scalable manufacturing, thereby clearing the way for on-site biochemical sensing in everyday applications.

We provide a compact expression for the generating function of correlators involving an arbitrary number of bosonic open string DDF states. The explicit correlators for $M$ DDF states can then be obtained by differentiating this generating function with respect to the DDF polarization tensors. The generating function depends on single and double complex integrals centred around the punctures corresponding to the insertion points of the DDF vertices on the real axis in the upper half-plane. We have explicitly evaluated these integrals for an arbitrary number of DDF states. To check our results, we computed some massive scalars and spin-2 amplitudes for $M=3$ and $M=4$, verifying that these amplitudes have the form expected from Lorentz invariance. Moreover, for the first excited levels, we also show that the DDF amplitudes with generic polarizations can be reassembled into Lorentz-covariant amplitudes, with emergent covariant polarizations that match the expressions obtained by comparing the string states in both the DDF and covariant formalisms., Comment: Latex, 83 pages

In this article we prove the Pohozaev identity for the semilinear Dirichlet problem of the form $-\Delta u + a(-\Delta)^s u = f(u)$ in $\Omega$, and $u=0$ in $\Omega^c$, where $a$ is a non-negative constant and $\Omega$ is a bounded $C^2$ domain. We also establish similar identity for systems of equations. As applications of this identity, we deduce a unique continuation property of eigenfunctions and also the nonexistence of nontrivial solutions in star-shaped domains under suitable condition on $f$.

In the bottomonium sector, the hindered magnetic dipole (M1) transitions between P-wave states $h_b(2P) \rightarrow \chi_{bJ}(1P) \gamma$, $J=0, \, 1, \, 2$, are expected to be severely suppressed according to the Relativized Quark Model, due to the spin flip of the $b$ quark. Nevertheless, a recent model following the coupled-channel approach predicts the corresponding branching fractions to be enhanced by orders of magnitude. In this Letter, we report the first search for such transitions. We find no significant signals and set upper limits at 90% CL on the corresponding branching fractions: $\mathcal{B}[h_b(2P)\to\gamma\chi_{b0}(1P)] < 2.7 \times 10^{-1}$, $\mathcal{B}[h_b(2P)\to\gamma\chi_{b1}(1P)] < 5.4 \times 10^{-3}$ and $\mathcal{B}[h_b(2P)\to\gamma\chi_{b2}(1P)] < 1.3 \times 10^{-2}$. These values help to constrain the parameters of the coupled-channel models. The results are obtained using a $121.4 \, fb^{-1}$ data sample taken around $\sqrt{s}= 10.860 \, GeV$ with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider.

We present a unifying approach to studying the replica symmetric solution in general diluted spin glass models on random $p$-uniform hypergraphs with sparsity parameter $\alpha$. Our result shows that there exist two key regimes in which the model exhibits replica symmetry and the free energy can be explicitly represented as the evaluation of an energy functional at the unique fixed point of a recursive distributional equation. One is called the high temperature regime, where the temperature and the sparsity parameter are essentially inversely proportional to each other; the other is the subcritical regime defined as $\alpha p (p-1)\leq 1$. In particular, the fact that the second regime is independent of the temperature parameter further allows us to deduce an analogous representation of the ground state energy in the subcritical regime. Along the way, we revisit several well-known formulas and also derive new ones for the free and ground state energies in the constraint satisfaction problem, Potts model, XY model, and continuous hardcore model.

The integration of machine learning into smart grid systems represents a transformative step in enhancing the efficiency, reliability, and sustainability of modern energy networks. By adding advanced data analytics, these systems can better manage the complexities of renewable energy integration, demand response, and predictive maintenance. Machine learning algorithms analyze vast amounts of data from smart meters, sensors, and other grid components to optimize energy distribution, forecast demand, and detect irregularities that could indicate potential failures. This enables more precise load balancing, reduces operational costs, and enhances the resilience of the grid against disturbances. Furthermore, the use of predictive models helps in anticipating equipment failures, thereby improving the reliability of the energy supply. As smart grids continue to evolve, the role of machine learning in managing decentralized energy sources and enabling real-time decision-making will become increasingly critical. However, the deployment of these technologies also raises challenges related to data privacy, security, and the need for robust infrastructure. Addressing these issues in this research authors will focus on realizing the full potential of smart grids, ensuring they meet the growing energy demands while maintaining a focus on sustainability and efficiency using Machine Learning techniques. Furthermore, this research will help determine the smart grid's essentiality with the aid of Machine Learning. Multiple ML algorithms have been integrated along with their pros and cons. The future scope of these algorithms are also integrated., Comment: 16 pages, 1 figure

Let $X$ be a smooth projective variety defined over a number field $k$ that has Brauer-Manin obstruction to the existence of rational points. We study the orders of the Brauer classes which capture the obstruction. We prove that one may require elements of arbitrarily large order to capture the obstruction. In other words, the exponent of the group which captures the obstruction has no upper bound., Comment: 8 pages. Comments are welcome