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Starting with the Dirac equation for an electron in a constant electromagnetic background on a noncommutative (NC) plane, we obtain a gauge invariant description of the system. Surprisingly, the dynamics of the system is dictated by the standard form of Lorentz force law, once the effective magnetic and electric fields $\left( B^{NC}, \, E^{NC} \right)$ correct up to leading order in the NC parameter are identified. The Hall effect is studied using the NC corrected fields in the non-relativistic (NR) limit. This shows that noncommutativity affects the cyclotron frequency, but leaves the Hall conductivity unaffected at least to first order in the NC parameter. Owing to the NC corrected magnetic field, the hyperfine splitting of Hydrogen atom spectrum also shows a first order correction which helps establish an upper bound on the spatial NC parameter., Comment: 9 pages, laTex, no figures

Two-dimensional (2D) Van der Waals compounds exhibit interesting electronic and magnetic properties due to complex intra-layer and inter-layer interactions, which are of immense importance in realizing exotic physics as well as advanced technology. Various experimental and theoretical studies led to significantly different ground state properties often contrasting each other. Here, we studied a novel 2D material, CrSiTe3 employing magnetic, specific heat and Raman measurements. Experimental results reveal evidence of incipient antiferromagnetism below 1 kOe concomitant to ferromagnetic order at 33 K. Antiferromagnetic and ferromagnetic interactions coexists at low field in the temperature regime, 15 - 33 K. Low field data reveal an additional magnetic order below 15 K, which melts on application of external magnetic field and remain dark in the heat capacity data. Raman spectra exhibit anomalies at the magnetic transitions; an evidence of strong spin-lattice coupling. Below 15 K, Eg modes exhibit hardening while Ag modes become significantly softer suggesting weakening of the inter-layer coupling at low temperatures which might be a reason for the unusual magnetic ground state and field induced melting of the magnetic order. These results reveal evidence of exceptional ground state properties linked to spin-lattice coupling and also suggest a pathway to study complex magnetism in such technologically important materials., Comment: 6 figures

Energy transfer in the dark sector of the universe gives rise to new phenomena of special interest in modern cosmology. When dark energy is modeled as a phantom scalar field, interactions become crucial to avoid Big Rip singularities. In this work, we revisit the phase-space analysis of the field equations by introducing a new set of dimensionless variables distinct from the traditional Hubble normalization approach. These new variables define a compactified phase space for the evolution of physical parameters. We demonstrate that these compactified variables offer fresh insights into the phase-space analysis in gravitational theories, particularly when the dark energy fluid is allowed to possess a negative kinetic energy density., Comment: 18 pages, 2 tables, 8 compound figures

As multicore hardware is becoming increasingly common in real-time systems, traditional scheduling techniques that assume a single worst-case execution time for a task are no longer adequate, since they ignore the impact of shared resources on execution time. When tasks execute concurrently on different cores, their execution times often vary substantially with their allocated budgets of shared resources, such as cache and memory bandwidth. Even under a specific resource allocation, the resource use pattern of a task also changes with time during a job execution. It is therefore important to consider the relationship between multicore resources and execution time in task modeling and scheduling algorithm design. In this paper, we propose a much more precise execution model for DAG-based real-time tasks that captures the time-varying resource use characteristics of a task under different budgets of shared resources. We present a generative resource profiling algorithm that efficiently predicts, from limited measurement data, the resource profile of a task at any time during its execution under a given resource budget. The generative profiles can then be used to construct the execution models for tasks, using which one can make informed resource allocation decisions. We further introduce a multicore resource allocation and deadline decomposition co-design technique for DAG-based tasks that leverages the generated execution models to jointly allocate resources and deadlines to subtasks, to maximize resource efficiency and schedulability. Our evaluation results show that our generative profiling algorithm achieves high accuracy while being efficient, and that our co-allocation technique substantially improves schedulability compared to a state-of-the-art deadline decomposition method.

The Anderson localization (AL) and the non-Hermitian skin effect (NHSE) are two distinct wavefunction-localization phenomena arising out of disorder and non-reciprocity, respectively. An integration of both in a single framework will provide a platform to study the interplay between the two. In this connection, we consider the one-dimensional Aubry-Andr\'e (AA) model, which has garnered significant attention among the disordered models due to its self-dual properties. Hence, we investigate a non-reciprocal AA model with complex phase modulation and implement it in suitably designed topolectrical circuits featuring an interface, segregating two non-equivalent circuit networks. In the circuit, the voltage profile localizes at the interface due to the NHSE, while the AL limits localization phenomena within the vicinity of the excitation node. This competing phenomenon leads to a controllable, node-dependent localization or even partial delocalization of the output voltage. Our results not only provide a practical platform for experimentally studying and controlling the wave localization phenomenon but also highlight the potential of circuit architectures in designing highly sensitive sensors and information transfer in communication devices., Comment: 11 pages, (5+2) figures

We perform a detailed Lie symmetry analysis for the hyperbolic system of partial differential equations that describe the one-dimensional Shallow Water magnetohydrodynamics equations within a rotating reference frame. We consider a relaxing condition $\mathbf{\mathbf{\nabla }}\left( h\mathbf{B} \right) \neq 0$ for the one-dimensional problem, which has been used to overcome unphysical behaviors. The hyperbolic system of partial differential equations depends on two parameters: the constant gravitational potential $g$ and the Coriolis term $f_{0}$, related to the constant rotation of the reference frame. For four different cases, namely $g=0,~f_{0}=0$; $g\neq 0\,,~f_{0}=0$; $g=0$, $f_{0}\neq 0$; and $g\neq 0$, $f_{0}\neq 0$ the admitted Lie symmetries for the hyperbolic system form different Lie algebras. Specifically the admitted Lie algebras are the $L^{10}=\left\{ A_{3,3}\rtimes A_{2,1}\right\} \otimes _{s}A_{5,34}^{a}$; $% L^{8}=A_{2,1}\rtimes A_{6,22}$; $L^{7}=A_{3,5}\rtimes\left\{ A_{2,1}\rtimes A_{2,1}\right\} $; and $L^{6}=A_{3,5}\rtimes A_{3,3}~$respectively, where we use the Morozov-Mubarakzyanov-Patera classification scheme. For the general case where $f_{0}g\neq 0$, we derive all the invariants for the Adjoint action of the Lie algebra $L^{6}$ and its subalgebras, and we calculate all the elements of the one-dimensional optimal system. These elements are then considered to define similarity transformations and construct analytic solutions for the hyperbolic system., Comment: 32 pages, 3 figures

The enhancement of Neel temperature ($T_N$) of low-$T_{N}$ antiferromagnets in antiferromagnetic bilayers AF1/AF2, where the $T_N$ of AF1 is larger than AF2 (for example enhancement of $T_{N}$ of CoO in CoO/NiO or FeO in FeO/CoO), is a subject of considerable interest. One essential question needs to be answered in these bilayers: is the interfacial coupling a short-range one or long-range that mediates the effect of the AF1 layers on the magnetic properties of AF2 layer? To understand the systematics of the magnetic coupling across the interface, we investigate the plane-resolved magnetotransport properties of antiferromagnetic bilayers using an electron-hole symmetric one-band Hubbard model at half-filling, employing a semi-classical Monte Carlo method. In our model Hamiltonian calculations, we set Coulomb repulsion $U_{1} = 8$ to mimic high-$T_{N}$ AF1 layer, whereas we use $U_{2}$ = $2\times U_{1}$ to mimic the low-$T_{N}$ AF2 layer. Our calculations show that the $T_{N}$ of the low-$T_{N}$ antiferromagnet enhances substantially when it's thickness is small, similar to experiments, giving rise to single magnetic transition temperature of the bilayer system. These findings are well supported by a single peak in temperature-dependent specific heat. However, for larger thicknesses, the $T_{N}$ of the low-$T_{N}$ antiferromagnet approaches towards its bulk value and constituent antiferromagnetic layers align antiferromagnetically at two separate temperatures and two maxima are observed in specific heat data. Our calculations also show that the delocalization of moments is more or less confined near the interface indicating the short-ranged nature of the proximity effect. Our obtained results are consistent with the experimental observations. A detailed discussion of the modifications that will occur if we use $U_{1} = 8$ and $U_{2}$ = $0.5\times U_{1}$ will also be addressed., Comment: 15 pages

We measure the branching fraction and $\it CP$-violating flavor-dependent rate asymmetry of $B^{0} \to \pi^{0} \pi^{0}$ decays reconstructed using the Belle II detector in an electron-positron collision sample containing $387 \times 10^{6}$ $B\overline{B}$ pairs. Using an optimized event selection, we find $126\pm 20$ signal decays in a fit to background-discriminating and flavor-sensitive distributions. The resulting branching fraction is $(1.25 \pm 0.23)\times 10^{-6}$ and the $\it CP$-violating asymmetry is $0.03 \pm 0.30$.

For a given ground cost, approximating the Monge optimal transport map that pushes forward a given probability measure onto another has become a staple in several modern machine learning algorithms. The fourth-order Ma-Trudinger-Wang (MTW) tensor associated with this ground cost function provides a notion of curvature in optimal transport. The non-negativity of this tensor plays a crucial role for establishing continuity for the Monge optimal transport map. It is, however, generally difficult to analytically verify this condition for any given ground cost. To expand the class of cost functions for which MTW non-negativity can be verified, we propose a provably correct computational approach which provides certificates of non-negativity for the MTW tensor using Sum-of-Squares (SOS) programming. We further show that our SOS technique can also be used to compute an inner approximation of the region where MTW non-negativity holds. We apply our proposed SOS programming method to several practical ground cost functions to approximate the regions of regularity of their corresponding optimal transport maps.

Quantifying the uncertainty in the factual parametric knowledge of Large Language Models (LLMs), especially in a black-box setting, poses a significant challenge. Existing methods, which gauge a model's uncertainty through evaluating self-consistency in responses to the original query, do not always capture true uncertainty. Models might respond consistently to the origin query with a wrong answer, yet respond correctly to varied questions from different perspectives about the same query, and vice versa. In this paper, we propose a novel method, DiverseAgentEntropy, for evaluating a model's uncertainty using multi-agent interaction under the assumption that if a model is certain, it should consistently recall the answer to the original query across a diverse collection of questions about the same original query. We further implement an abstention policy to withhold responses when uncertainty is high. Our method offers a more accurate prediction of the model's reliability and further detects hallucinations, outperforming other self-consistency-based methods. Additionally, it demonstrates that existing models often fail to consistently retrieve the correct answer to the same query under diverse varied questions even when knowing the correct answer.

We investigate the inseparability of states generated by superposition of a multipartite pure entangled state with a product state. In particular, we identify specific multipartite entangled states that will always produce inseparability after superposition with an arbitrary completely product state. Thus, these entangled states are unconditionally superposition-robust, and we refer to this phenomenon as ``unconditional inseparability of superposition" in multipartite quantum systems. In this way, we complete the picture of unconditional inseparability of superposition which was introduced earlier for bipartite systems. We also characterize the superposition of a multipartite pure entangled state with a pure bi-separable state. However, the present analysis allows us to obtain a more general result, viz. there exists at least one pure entangled state in any multipartite Hilbert space such that its superposition with an arbitrary completely product state always yields an entangled state. On the other hand, for any bipartite pure entangled state, if at least one of the subsystems is a qubit, then it is always possible to find a suitable product state such that the superposition of the entangled state and the product state produces a product state. In this way, we find a feature of multipartite quantum systems which is in sharp contrast with that of bipartite ones. We then show how unconditional inseparability of superposition can be useful in exhibiting an indistinguishability property within the local unambiguous state discrimination problem., Comment: 9 + epsilon pages, 1 Figure

The cosmological history and evolution are examined for gravitational models with interaction in the dark sector of the universe. In particular, we consider the dark energy to be described by a phantom scalar field and the dark matter $\rho _{m}$ as a pressureless ideal gas. We introduce the interacting function $Q=\beta \left( t\right) \rho_{m}$, where the function $% \beta \left( t\right) $ is considered to be proportional to $\dot{\phi}, \dot{\phi}^{2}H^{-1},~H$, or a constant parameter with dimensions of $\left[H_{0}\right] $. For the four interacting models, we study in details the phase space by calculating the stationary points. The latter are applied to reconstruct the cosmological evolution. Compactified variables are essential to understand the complete picture of the phase space and to conclude about the cosmological viability of these interacting models. The detailed analysis is performed for the exponential potential $V\left( \phi \right) =V_{0}e^{\lambda \phi }$. The effects of other scalar field potential functions on the cosmological dynamics are examined., Comment: 33 pages, 13 Figures

We perform a detailed investigation of interacting phantom cosmology, by applying the powerful method of dynamical system analysis. We consider two well-studied interaction forms, namely one global and one local one, while the novel ingredient of our work is the examination of new potentials for the phantom field. Our analysis shows the existence of saddle matter-dominated points, stable dark-energy dominated points, and scaling accelerating solutions, that can attract the Universe at late times. As we show, some of the stable accelerating scaling attractors, in which dark matter and dark energy can co-exist, alleviating the cosmic coincidence problem, are totally new, even for the previously studied interaction rates, and arise purely from the novel potential forms., Comment: 17 pages including bibliography; 10 tables, 4 figures; comments are welcome

We develop an analytically tractable single-step diffusion model based on a linear denoiser and present explicit formula for the Kullback-Leibler divergence between generated and sampling distribution, taken to be isotropic Gaussian, showing the effect of finite diffusion time and noise scale. Our study further reveals that the monotonic fall phase of Kullback-Leibler divergence begins when the training dataset size reaches the dimension of the data points. Along the way, we provide a mathematically precise definition of memorization to non-memorization transition when only finite number of data points are available. It is shown that the simplified model also features this transition during the monotonic fall phase of the aforementioned Kullback-Leibler divergence. For large-scale practical diffusion models, we explain why higher number of diffusion steps enhance production quality based on the theoretical arguments presented before. In addition, we show that higher diffusion steps does not necessarily help in reducing memorization. These two facts combined suggests existence of an optimal number of diffusion steps for finite number of training samples., Comment: 31 pages

We report measurements of the absolute branching fractions $\mathcal{B}(B_s^0 \to D_s^{\pm} X)$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X)$, and $\mathcal{B}(B_s^0 \to D^{\pm} X)$, where the latter is measured for the first time. The results are based on a 121.4\,fb$^{-1}$ data sample collected at the $\Upsilon(10860)$ resonance by the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. We reconstruct one $B_s^0$ meson in $e^+e^- \to \Upsilon(10860) \to B_s^{*} \bar{B}_s^{*}$ events and measure yields of $D_s^+$, $D^0$, and $D^+$ mesons in the rest of the event. We obtain $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (68.6 \pm 7.2 \pm 4.0)\%$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (21.5 \pm 6.1 \pm 1.8)\%$, and $\mathcal{B}(B_s^0 \to D^{\pm} X) = (12.6 \pm 4.6 \pm 1.3)\%$, where the first uncertainty is statistical and the second is systematic. Averaging with previous Belle measurements gives $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (63.4 \pm 4.5 \pm 2.2)\%$ and $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (23.9 \pm 4.1 \pm 1.8)\%$. For the $B_s^0$ production fraction at the $\Upsilon(10860)$, we find $f_s = (21.4^{+1.5}_{-1.7})\%$., Comment: 23 pages, 9 figures, submitted to JHEP

We explore the influence of interactions between dark matter (DM) and dark energy (DE) on the cosmic evolution of the Universe within a viscous dark energy (VDE) framework. Moving beyond traditional interacting dark energy (IDE) models, we propose a generalized IDE model adaptable to diverse IDE scenarios via IDE coupling parameters. In order to investigate deviations from $\Lambda$CDM across cosmic epochs by highlighting how viscous and the interactions between DM and DE impact cosmic density and expansion rates, we consider a model agnostic form of VDE. Eventually we perform a Bayesian analysis using the Union 2.1 Supernova Ia dataset and Markov Chain Monte Carlo (MCMC) sampling to obtain optimal values of model parameters. This comprehensive analysis provides insights about the interplay between viscous and IDE in shaping the Universe's expansion history., Comment: 13 pages, 7 figures

Connecting incompatibility in measurements with the violation of local realism is one of the fundamental avenues of research. For two qubits, any incompatible pair of projective measurements can violate Clauser-Horne-Shimony-Holt (CHSH) inequality for some states, and there is a monotonic relationship between the level of measurement incompatibility (projective) and the violation. However, in the case of two qutrits, we exhibit that the violation of the Collins-Gisin-Linden-Massar-Popescu (CGLMP) inequality responds non-monotonically with the amount of incompatibility; we term this more nonlocality with less incompatibility. Furthermore, unlike in the CHSH case, the maximally violating state in higher dimensions depends on the amount of measurement incompatibility. We illustrate that similar patterns can also be observed in an experimentally viable interferometric measuring technique. In such a measurement scenario, we provide an explicit example of incompatible (not jointly measurable) measurements that do not violate the CGLMP inequality for any shared quantum state. We extend our study of incompatibility in the prepare and measure scenario, focusing on quantum random access codes (QRACs). Surprisingly, we show that the monotonicity of average success probability with measurement incompatibility does not hold for higher dimensions, as opposed to two dimensions, even though the maximum probability of QRAC behaves monotonically with incompatibility., Comment: 16 pages, 6 figures

We present measurements of $B \to K{}^{*}(892)\gamma$ decays using $365\,{\rm fb}^{-1}$ of data collected from 2019 to 2022 by the Belle~II experiment at the SuperKEKB asymmetric-energy $e^+e^-$ collider. The data sample contains $(387 \pm 6) \times 10^6$ $B\overline{B}$ events. We measure branching fractions ($\mathcal{B}$) and $C\!P$ asymmetries ($\mathcal{A}_{C\!P}$) for both $B^{0}\to K{}^{*0}\gamma$ and $B^{+}\to K{}^{*+}\gamma$ decays. The difference in $C\!P$ asymmetries ($\Delta \mathcal{A}_{C\!P}$) and the isospin asymmetry ($\Delta_{0+}$) between these neutral and charged channels are also measured. We obtain the following branching fractions and $C\!P$ asymmetries: $\mathcal{B} (B^{0} \to K{}^{*0}\gamma) = (4.14 \pm 0.10 \pm 0.11 ) \times 10^{-5}$, $\mathcal{B} (B^{+} \to K{}^{*+}\gamma) = (4.02 \pm 0.13 \pm 0.13 )\times 10^{-5}$, $\mathcal{A}_{C\!P} (B^{0} \to K{}^{*0}\gamma) = (-3.3 \pm 2.3 \pm 0.4 )\%$, and $\mathcal{A}_{C\!P} (B^{+} \to K{}^{*+}\gamma) = (-0.7 \pm 2.9 \pm 0.6 )\%$. The measured difference in $C\!P$ asymmetries is $\Delta \mathcal{A}_{C\!P} = (+2.6 \pm 3.8 \pm 0.7 )\%$, and the measured isospin asymmetry is $\Delta_{0+} = (+5.0 \pm 2.0 \pm 1.5 )\%$. The first uncertainties listed are statistical and the second are systematic. These results are consistent with world-average values and theory predictions.

Knowledge distillation provides an effective method for deploying complex machine learning models in resource-constrained environments. It typically involves training a smaller student model to emulate either the probabilistic outputs or the internal feature representations of a larger teacher model. By doing so, the student model often achieves substantially better performance on a downstream task compared to when it is trained independently. Nevertheless, the teacher's internal representations can also encode noise or additional information that may not be relevant to the downstream task. This observation motivates our primary question: What are the information-theoretic limits of knowledge transfer? To this end, we leverage a body of work in information theory called Partial Information Decomposition (PID) to quantify the distillable and distilled knowledge of a teacher's representation corresponding to a given student and a downstream task. Moreover, we demonstrate that this metric can be practically used in distillation to address challenges caused by the complexity gap between the teacher and the student representations., Comment: Accepted at NeurIPS 2024 Machine Learning and Compression Workshop

We theoretically explore the spin texture dynamics of a harmonically trapped spin-1 Bose-Einstein condensate with Rashba spin-orbit coupling and ferromagnetic spin-exchange interactions under a sinusoidally varying magnetic field along the $x$-direction. This interplay yields an intrinsic spin texture in the ground state, forming a linear chain of alternating skyrmions at the saddle points. Our study analyzes the spin-mixing dynamics for both a freely evolving and a controlled longitudinal magnetization. The spin-1 system exhibits the Einstein-de Hass effect for the first case, for which an exchange between the total orbital angular momentum and the spin angular momentum is observed, resulting in minimal oscillations about the initial position of the skyrmion chain. However, for the fixed magnetization dynamics, the skyrmion chain exhibits ample angular oscillations about the equilibrium position, with the temporary formation of new skyrmions and anti-skyrmions to facilitate the oscillatory motion. Keeping the magnetization constant, this contrast now stems from the exchange between the canonical and spin-dependent contribution to the orbital angular momentum. The variation in canonical angular momentum is linked to the angular oscillations, while the spin-dependent angular momentum accounts for the creation or annihilation of skyrmions. We confirm the presence of scissor mode excitations in the spin texture due to the angular skyrmion oscillations., Comment: 10 pages, 8 figures

Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for harmonizing structural brain MRI across multiple sites while preserving data privacy. PRISM employs a dual-branch autoencoder with contrastive learning and variational inference to disentangle anatomical features from style and site-specific variations, enabling unpaired image translation without traveling subjects or multiple MRI modalities. Our modular design allows harmonization to any target site and seamless integration of new sites without the need for retraining or fine-tuning. Using multi-site structural MRI data, we demonstrate PRISM's effectiveness in downstream tasks such as brain tissue segmentation and validate its harmonization performance through multiple experiments. Our framework addresses key challenges in medical AI/ML, including data privacy, distribution shifts, model generalizability and interpretability. Code is available at https://github.com/saranggalada/PRISM, Comment: This work has been submitted to ISBI 2025

The quark star is such an exotic star of our Universe. The limiting mass for quark stars essentially depends on rotational frequency apart from bag constant and other fundamental parameters. The analytical results obtained agree with the results of several relevant numerical estimates as well as observational evidence. Primordial black hole (PBH) is another hypothetical candidate of compact astrophysical objects. We explore the combined effect of PBH evaporation and the baryon-dark matter (DM) interaction in the 21cm scenario. We address both upper and lower bounds on several PBH and DM parameters using the observational excess (-500^{+200}_{-500} mK) of EDGES's experimental results. A similar investigation has been carried out in the framework of interacting dark energy (IDE) models. We consider three IDE models and eventually measure the allowed limits of IDE coupling parameters for individual cases. Keeping in view of the 21cm scenario, we also explore the multimessenger signals from possible rare decay of fundamental Heavy Dark Matters (HDM). HDM can undergo QCD cascade decay to produce leptons or $\gamma$ as end products. One of the multimessenger signals could be the source of ultra high energy neutrino ($\sim$PeV) signals at the IceCube detector whereas the other signal attributes to the cooling/heating of the baryons due to HDM decay and corresponding influences in the global 21cm signal. The consequence of late time decay of such HDMs may also have significant implications on the aspect of matter-antimatter asymmetry. The mass and lifetime of such Dark Matter particles have been obtained by performing a $\chi^2$ analysis with the PeV neutrino data of IceCube and finally, the amount of baryon asymmetry produced in the Universe is estimated if caused by Dark Matter decay., Comment: Ph.D. thesis (Awarded in March 2022)

Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require full re-training when a change is needed and inference-time ones typically require access to the reward model at each inference step. To address these limitations, we introduce inference-time model alignment method that learns encoded representations of preference dimensions, called \textit{Alignment Vectors} (AV). These representations are computed by subtraction of the base model from the aligned model as in model editing enabling dynamically adjusting the model behavior during inference through simple linear operations. Even though the preference dimensions can span various granularity levels, here we focus on three gradual response levels across three specialized domains: medical, legal, and financial, exemplifying its practical potential. This new alignment paradigm introduces adjustable preference knobs during inference, allowing users to tailor their LLM outputs while reducing the inference cost by half compared to the prompt engineering approach. Additionally, we find that AVs are transferable across different fine-tuning stages of the same model, demonstrating their flexibility. AVs also facilitate multidomain, diverse preference alignment, making the process 12x faster than the retraining approach.

Determination of molecular energetics and properties is one of the core challenges in the near-term quantum computing. To this end, hybrid quantum-classical algorithms are preferred for Noisy Intermediate Scale Quantum (NISQ) architectures. The Projective Quantum Eigensolver (PQE) is one such algorithms that optimizes the parameters of the chemistry-inspired unitary coupled cluster (UCC) ansatz using a conventional coupled cluster-like residual minimization. Such a strategy involves the projection of the Schrodinger equation on to linearly independent basis towards the parameter optimization, restricting the ansatz is solely defined in terms of the excitation operators. This warrants the inclusion of high-rank operators for strongly correlated systems, leading to increased utilization of quantum resources. In this manuscript, we develop a methodology for determining the generalized operators in terms of a closed form residual equations in the PQE framework that can be efficiently implemented in a quantum computer with manageable quantum resources. Such a strategy requires the removal of the underlying redundancy in high-rank excited determinants, generated due to the presence of the generalized operators in the ansatz, by projecting them on to an internally contracted lower dimensional manifold. With the application on several molecular systems, we have demonstrated our ansatz achieves similar accuracy to the (disentangled) UCC with singles, doubles and triples (SDT) ansatz, while utilizing an order of magnitude fewer quantum gates. Furthermore, when simulated under stochastic Gaussian noise or depolarizing hardware noise, our method shows significantly improved noise resilience compared to the other members of PQE family and the state-of-the-art variational quantum eigensolver., Comment: 10 pages, 4 figures

Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of text often contain conflicting information, and indiscriminately depending on this information may result in untruthful and inaccurate answers. To understand the gravity of this problem, we collect a human-annotated dataset, Question Answering with Conflicting Contexts (QACC), and find that as much as 25% of unambiguous, open domain questions can lead to conflicting contexts when retrieved using Google Search. We evaluate and benchmark three powerful Large Language Models (LLMs) with our dataset QACC and demonstrate their limitations in effectively addressing questions with conflicting information. To explore how humans reason through conflicting contexts, we request our annotators to provide explanations for their selections of correct answers. We demonstrate that by finetuning LLMs to explain their answers, we can introduce richer information into their training that guide them through the process of reasoning with conflicting contexts.

As LLMs are increasingly deployed in global applications, the importance of cultural sensitivity becomes paramount, ensuring that users from diverse backgrounds feel respected and understood. Cultural harm can arise when these models fail to align with specific cultural norms, resulting in misrepresentations or violations of cultural values. This work addresses the challenges of ensuring cultural sensitivity in LLMs, especially in small-parameter models that often lack the extensive training data needed to capture global cultural nuances. We present two key contributions: (1) A cultural harm test dataset, created to assess model outputs across different cultural contexts through scenarios that expose potential cultural insensitivities, and (2) A culturally aligned preference dataset, aimed at restoring cultural sensitivity through fine-tuning based on feedback from diverse annotators. These datasets facilitate the evaluation and enhancement of LLMs, ensuring their ethical and safe deployment across different cultural landscapes. Our results show that integrating culturally aligned feedback leads to a marked improvement in model behavior, significantly reducing the likelihood of generating culturally insensitive or harmful content. Ultimately, this work paves the way for more inclusive and respectful AI systems, fostering a future where LLMs can safely and ethically navigate the complexities of diverse cultural landscapes., Comment: Accepted at NAACL 2025 (Main track). [Project Page](https://neuralsentinel.github.io/KaleidoCulture/)

We have investigated the magnetic properties of a non-symmorphic correlated material, TbAgSb2. It consists of a quasi-two-dimensional structure of Sb atoms which hosts topologically non-trivial fermions. We prepared high quality single crystals of the material. The magnetic and transport properties exhibit antiferromagnetic transition at 11.3 K. A strong magneto-crystalline anisotropy is observed with the magnetic moments preferentially aligned within the ab-plane at low temperatures. A broad peak in the magnetic susceptibility along H||c direction suggests influence of crystal electric field in magnetism, which is also manifested in the specific heat data. The temperature dependent electrical resistivity at high field shows interesting signature of field induced magnetic structure reorientation / superzone formation. These results highlight the significance of Sb square nets in a non-symmorphic correlated system exhibiting complex magnetism and electronic properties., Comment: 3 pages, 3 figures

With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a source of various insights, but with the increase in computational power and the need to understand deeper relationships between entities, the need to design new techniques has arisen. For this graph data analysis has become an extraordinary tool for understanding the data, which reveals more realistic and flexible modelling of complex relationships. Recently, Graph Neural Networks (GNNs) have shown great promise in various applications, such as social network analysis, recommendation systems, drug discovery, and more. However, many adversarial attacks can happen over the data, whether during training (poisoning attack) or during testing (evasion attack), which can adversely manipulate the desired outcome from the GNN model. Therefore, it is crucial to make the GNNs robust to such attacks. The existing robustness methods are computationally demanding and perform poorly when the intensity of attack increases. This paper presents a computationally efficient framework, namely, pLapGNN, based on weighted p-Laplacian for making GNNs robust. Empirical evaluation on real datasets establishes the efficacy and efficiency of the proposed method., Comment: 5 pages, 2 figures

A neural network-based framework is developed and experimentally demonstrated for the problem of estimating the shape of a soft continuum arm (SCA) from noisy measurements of the pose at a finite number of locations along the length of the arm. The neural network takes as input these measurements and produces as output a finite-dimensional approximation of the strain, which is further used to reconstruct the infinite-dimensional smooth posture. This problem is important for various soft robotic applications. It is challenging due to the flexible aspects that lead to the infinite-dimensional reconstruction problem for the continuous posture and strains. Because of this, past solutions to this problem are computationally intensive. The proposed fast smooth reconstruction method is shown to be five orders of magnitude faster while having comparable accuracy. The framework is evaluated on two testbeds: a simulated octopus muscular arm and a physical BR2 pneumatic soft manipulator., Comment: 6 pages + reference, 5 figures, submitted to ICRA 2025

The large-scale deployment of Solidity smart contracts on the Ethereum mainnet has increasingly attracted financially-motivated attackers in recent years. A few now-infamous attacks in Ethereum's history includes DAO attack in 2016 (50 million dollars lost), Parity Wallet hack in 2017 (146 million dollars locked), Beautychain's token BEC in 2018 (900 million dollars market value fell to 0), and NFT gaming blockchain breach in 2022 ($600 million in Ether stolen). This paper presents a comprehensive investigation of the use of large language models (LLMs) and their capabilities in detecting OWASP Top Ten vulnerabilities in Solidity. We introduce a novel, class-balanced, structured, and labeled dataset named VulSmart, which we use to benchmark and compare the performance of open-source LLMs such as CodeLlama, Llama2, CodeT5 and Falcon, alongside closed-source models like GPT-3.5 Turbo and GPT-4o Mini. Our proposed SmartVD framework is rigorously tested against these models through extensive automated and manual evaluations, utilizing BLEU and ROUGE metrics to assess the effectiveness of vulnerability detection in smart contracts. We also explore three distinct prompting strategies-zero-shot, few-shot, and chain-of-thought-to evaluate the multi-class classification and generative capabilities of the SmartVD framework. Our findings reveal that SmartVD outperforms its open-source counterparts and even exceeds the performance of closed-source base models like GPT-3.5 and GPT-4 Mini. After fine-tuning, the closed-source models, GPT-3.5 Turbo and GPT-4o Mini, achieved remarkable performance with 99% accuracy in detecting vulnerabilities, 94% in identifying their types, and 98% in determining severity. Notably, SmartVD performs best with the `chain-of-thought' prompting technique, whereas the fine-tuned closed-source models excel with the `zero-shot' prompting approach.

We revise the cosmological interaction between dark energy and dark matter. More precisely, we focus on models that support compartmentalization or co-existence in the dark sector of the universe. Within the framework of a homogeneous and isotropic, spatially flat Friedmann--Lema\^itre--Robertson--Walker geometry, we analyse the asymptotic behaviour of the physical parameters for two interacting models, where dark energy and dark matter have constant equations of state parameters, in the presence of dark radiation, when dark energy is described by a quintessence scalar field. For each model, we determine the asymptotic solutions and attempt to understand how the interaction affects the cosmological evolution and history., Comment: 40 pages, 17 figures; version to appear in Physics of the Dark Universe

In this research, we introduce a unified end-to-end Automated Defect Classification-Detection-Segmentation (ADCDS) framework for classifying, detecting, and segmenting multiple instances of semiconductor defects for advanced nodes. This framework consists of two modules: (a) a defect detection module, followed by (b) a defect segmentation module. The defect detection module employs Deformable DETR to aid in the classification and detection of nano-scale defects, while the segmentation module utilizes BoxSnake. BoxSnake facilitates box-supervised instance segmentation of nano-scale defects, supported by the former module. This simplifies the process by eliminating the laborious requirement for ground-truth pixel-wise mask annotation by human experts, which is typically associated with training conventional segmentation models. We have evaluated the performance of our ADCDS framework using two distinct process datasets from real wafers, as ADI and AEI, specifically focusing on Line-space patterns. We have demonstrated the applicability and significance of our proposed methodology, particularly in the nano-scale segmentation and generation of binary defect masks, using the challenging ADI SEM dataset where ground-truth pixelwise segmentation annotations were unavailable. Furthermore, we have presented a comparative analysis of our proposed framework against previous approaches to demonstrate its effectiveness. Our proposed framework achieved an overall mAP@IoU0.5 of 72.19 for detection and 78.86 for segmentation on the ADI dataset. Similarly, for the AEI dataset, these metrics were 90.38 for detection and 95.48 for segmentation. Thus, our proposed framework effectively fulfils the requirements of advanced defect analysis while addressing significant constraints., Comment: Accepted in ECCV 2024 2nd workshop on Vision-based InduStrial InspectiON (VISION)

Invariants are essential for ensuring the security and correctness of Solidity smart contracts, particularly in the context of blockchain's immutability and decentralized execution. This paper introduces InvSol, a novel framework for pre-deployment invariant generation tailored specifically for Solidity smart contracts. Unlike existing solutions, namely InvCon, InvCon+, and Trace2Inv, that rely on post-deployment transaction histories on Ethereum mainnet, InvSol identifies invariants before deployment and offers comprehensive coverage of Solidity language constructs, including loops. Additionally, InvSol incorporates custom templates to effectively prevent critical issues such as reentrancy, out-of-gas errors, and exceptions during invariant generation. We rigorously evaluate InvSol using a benchmark set of smart contracts and compare its performance with state-of-the-art solutions. Our findings reveal that InvSol significantly outperforms these tools, demonstrating its effectiveness in handling new contracts with limited transaction histories. Notably, InvSol achieves a 15% improvement in identifying common vulnerabilities compared to InvCon+ and is able to address certain crucial vulnerabilities using specific invariant templates, better than Trace2Inv.

The formation of quantized vortices in a superfluid above a certain critical trap rotation frequency serves as a hallmark signature of superfluidity. Based on the beyond mean field framework, crucial for the formation of exotic supersolid and droplet states, we investigate dynamic protocols for vortex nucleation in the superfluid and supersolid states of a dipolar Bose-Einstein condensate (BEC), at a significantly lower trap rotation frequency. We find that the critical rotation frequency of the trap varies with the dipole-dipole interaction strength and the polarization direction of the external magnetic field. Leveraging these characteristics of dipolar BECs, we demonstrate three dynamic protocols for vortex nucleation even when rotating below the critical rotation frequency viz.: (i) varying the $s$-wave scattering length, (ii) changing the polarizing angle, and (iii) successive modulation of both the scattering length and polarizing angle. These dynamic vortex seeding protocols could serve as important benchmarks for future experimental studies., Comment: 13 pages, 11 figures

Recent quantum algorithms pertaining to electronic structure theory primarily focus on threshold-based dynamic construction of ansatz by selectively including important many-body operators. These methods can be made systematically more accurate by tuning the threshold to include more number of operators into the ansatz. However, such improvements come at the cost of rapid proliferation of the circuit depth, especially for highly correlated molecular systems. In this work, we address this issue by the development of a novel theoretical framework that relies on the segregation of an ansatz into a dynamically selected core principal component, which is, by construction adiabatically decoupled from the remaining operators. This enables us to perform computations involving the principal component using extremely shallow-depth circuits whereas, the effect of the remaining auxiliary component is folded into the energy function via a cost-efficient non-iterative correction, ensuring the requisite accuracy. We propose a formalism that analytically predicts the auxiliary parameters from the principal ones, followed by a suite of non-iterative auxiliary subspace correction techniques with different levels of sophistication. The auxiliary subspace corrections incur no additional quantum resources, yet complement an inadequately expressive core of the ansatz to recover significant amount of electronic correlations. We have numerically validated the resource efficiency and accuracy of our formalism with a number of strongly correlated molecular systems., Comment: 14 pages, 5 figures

Stochastic process models for spatiotemporal data underlying random fields find substantial utility in a range of scientific disciplines. Subsequent to predictive inference on the values of the random field (or spatial surface indexed continuously over time) at arbitrary space-time coordinates, scientific interest often turns to gleaning information regarding zones of rapid spatial-temporal change. We develop Bayesian modeling and inference for directional rates of change along a given surface. These surfaces, which demarcate regions of rapid change, are referred to as ``wombling'' surface boundaries. Existing methods for studying such changes have often been associated with curves and are not easily extendable to surfaces resulting from curves evolving over time. Our current contribution devises a fully model-based inferential framework for analyzing differential behavior in spatiotemporal responses by formalizing the notion of a ``wombling'' surface boundary using conventional multi-linear vector analytic frameworks and geometry followed by posterior predictive computations using triangulated surface approximations. We illustrate our methodology with comprehensive simulation experiments followed by multiple applications in environmental and climate science; pollutant analysis in environmental health; and brain imaging., Comment: 198 pages

We present a measurement of $|V_{ub}|$ from a simultaneous study of the charmless semileptonic decays $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$, where $\ell = e, \mu$. This measurement uses a data sample of 387 million $B\overline{B}$ meson pairs recorded by the Belle~II detector at the SuperKEKB electron-positron collider between 2019 and 2022. The two decays are reconstructed without identifying the partner $B$ mesons. We simultaneously measure the differential branching fractions of $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$ decays as functions of $q^2$ (momentum transfer squared). From these, we obtain total branching fractions $B(B^0\to\pi^- \ell^+ \nu_{\ell}) = (1.516 \pm 0.042 (\mathrm{stat}) \pm 0.059 (\mathrm{syst})) \times 10^{-4}$ and $B(B^+\to\rho^0 \ell^+\nu_{\ell}) = (1.625 \pm 0.079 (\mathrm{stat}) \pm 0.180 (\mathrm{syst})) \times 10^{-4}$. By fitting the measured $B^0\to\pi^- \ell^+ \nu_{\ell}$ partial branching fractions as functions of $q^2$, together with constraints on the non-perturbative hadronic contribution from lattice QCD calculations, we obtain $|V_{ub}|$ = $(3.93 \pm 0.09 \pm 0.13 \pm 0.19) \times 10^{-3}$. Here, the first uncertainty is statistical, the second is systematic, and the third is theoretical.

Since its invention by Arthur Ashkin and colleagues at Bell Labs in the 1970s, optical micromanipulation, also known as optical tweezers or laser tweezers, has evolved remarkably to become one of the most convenient and versatile tools for studying soft materials, including biological systems. Arthur Ashkin received the Nobel Prize in Physics in 2018 for enabling these extraordinary scientific advancements. Essentially, a focused laser beam is used to apply and measure minuscule forces from a few piconewtons to femtonewtons by utilizing light-matter interaction at mesoscopic length scales. Combined with advanced microscopy and position-sensing techniques, optical micromanipulations enable us to investigate diverse aspects of functional soft materials. These include studying mechanical responses through force-elongation measurements, examining the structural properties of complex fluids employing microrheology, analyzing chemical compositions using spectroscopy, and sorting cells through single-cell analysis. Furthermore, it is utilized in various soft-matter-based devices, such as laser scissors and optical motors in microfluidic channels. This chapter presents an overview of optical micromanipulation techniques by describing fundamental theories and explaining the design considerations of conventional single-trap and dual-trap setups as well as recent improvisations. We further discuss their capabilities and applications in probing exotic soft-matter systems and in developing widely utilized devices and technologies based on functional soft materials., Comment: 73 pages, 13 figures

In this article, we investigate a coupled phantom dark-energy cosmological model in which the coupling term between a phantom scalar field with an exponential potential and a pressureless dark-matter fluid is motivated by the warm inflationary paradigm. Using methods of qualitative analysis of dynamical systems, complemented by numerical solutions of the evolution equations, we study the late-time behavior of our model. We show that contrary to the uncoupled scenario, the coupled phantom model admits accelerated scaling solutions. However, they do not correspond to a final state of the universe's evolution and, therefore, cannot be used to solve the cosmological coincidence problem. Furthermore, we show that, for certain coupling parameter values, the total equation-of-state parameter's asymptotic behavior is significantly changed when compared to the uncoupled scenario, allowing for solutions less phantom even for steeper potentials of the phantom scalar field., Comment: 13 pages including references, 3 tables and 4 captioned figures; version accepted by Phys. Rev. D

Schr\"{o}dinger bridge--a stochastic dynamical generalization of optimal mass transport--exhibits a learning-control duality. Viewed as a stochastic control problem, the Schr\"{o}dinger bridge finds an optimal control policy that steers a given joint state statistics to another while minimizing the total control effort subject to controlled diffusion and deadline constraints. Viewed as a stochastic learning problem, the Schr\"{o}dinger bridge finds the most-likely distribution-valued trajectory connecting endpoint distributional observations, i.e., solves the two point boundary-constrained maximum likelihood problem over the manifold of probability distributions. Recent works have shown that solving the Schr\"{o}dinger bridge problem with state cost requires finding the Markov kernel associated with a reaction-diffusion PDE where the state cost appears as a state-dependent reaction rate. We explain how ideas from Weyl calculus in quantum mechanics, specifically the Weyl operator and the Weyl symbol, can help determine such Markov kernels. We illustrate these ideas by explicitly finding the Markov kernel for the case of quadratic state cost via Weyl calculus, recovering our earlier results but avoiding tedious computation with Hermite polynomials.

Deep learning-based semiconductor defect inspection has gained traction in recent years, offering a powerful and versatile approach that provides high accuracy, adaptability, and efficiency in detecting and classifying nano-scale defects. However, semiconductor manufacturing processes are continually evolving, leading to the emergence of new types of defects over time. This presents a significant challenge for conventional supervised defect detectors, as they may suffer from catastrophic forgetting when trained on new defect datasets, potentially compromising performance on previously learned tasks. An alternative approach involves the constant storage of previously trained datasets alongside pre-trained model versions, which can be utilized for (re-)training from scratch or fine-tuning whenever encountering a new defect dataset. However, adhering to such a storage template is impractical in terms of size, particularly when considering High-Volume Manufacturing (HVM). Additionally, semiconductor defect datasets, especially those encompassing stochastic defects, are often limited and expensive to obtain, thus lacking sufficient representation of the entire universal set of defectivity. This work introduces a task-agnostic, meta-learning approach aimed at addressing this challenge, which enables the incremental addition of new defect classes and scales to create a more robust and generalized model for semiconductor defect inspection. We have benchmarked our approach using real resist-wafer SEM (Scanning Electron Microscopy) datasets for two process steps, ADI and AEI, demonstrating its superior performance compared to conventional supervised training methods., Comment: Accepted for presentation at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2024 Industry Track

Precision in identifying nanometer-scale device-killer defects is crucial in both semiconductor research and development as well as in production processes. The effectiveness of existing ML-based approaches in this context is largely limited by the scarcity of data, as the production of real semiconductor wafer data for training these models involves high financial and time costs. Moreover, the existing simulation methods fall short of replicating images with identical noise characteristics, surface roughness and stochastic variations at advanced nodes. We propose a method for generating synthetic semiconductor SEM images using a diffusion model within a limited data regime. In contrast to images generated through conventional simulation methods, SEM images generated through our proposed DL method closely resemble real SEM images, replicating their noise characteristics and surface roughness adaptively. Our main contributions, which are validated on three different real semiconductor datasets, are: i) proposing a patch-based generative framework utilizing DDPM to create SEM images with intended defect classes, addressing challenges related to class-imbalance and data insufficiency, ii) demonstrating generated synthetic images closely resemble real SEM images acquired from the tool, preserving all imaging conditions and metrology characteristics without any metadata supervision, iii) demonstrating a defect detector trained on generated defect dataset, either independently or combined with a limited real dataset, can achieve similar or improved performance on real wafer SEM images during validation/testing compared to exclusive training on a real defect dataset, iv) demonstrating the ability of the proposed approach to transfer defect types, critical dimensions, and imaging conditions from one specified CD/Pitch and metrology specifications to another, thereby highlighting its versatility., Comment: 8 pages, 11 figures, to be presented at 2024 International Symposium ELMAR, and published by IEEE in the conference proceedings

We report measurements of time-dependent $CP$ asymmetries in $B^0 \to K^0_S \pi^0 \gamma$ decays based on a data sample of $(388\pm6)\times10^6$ $B\bar{B}$ events collected at the $\Upsilon(4S)$ resonance with the Belle II detector. The Belle II experiment operates at the SuperKEKB asymmetric-energy $e^+e^-$ collider. We measure decay-time distributions to determine $CP$-violating parameters $S$ and $C$. We determine these parameters for two ranges of $K^0_S \pi^0$ invariant mass: $m(K^0_S \pi^0)\in (0.8, 1.0)$ $GeV/c^2$, which is dominated by $B^0 \to K^{*0} (\to K^0_S \pi^0) \gamma$ decays, and a complementary region $m(K^0_S \pi^0)\in (0.6, 0.8)\cup(1.0, 1.8)$ $GeV/c^2$. Our results have improved precision as compared to previous measurements and are consistent with theory predictions., Comment: 10 pages, 4 figures

We present measurements of $B^{+}\rightarrow\rho^{+}\gamma$ and $B^{0}\rightarrow\rho^{0}\gamma$ decays using a combined data sample of $772 \times 10^6$ $B\overline{B}$ pairs collected by the Belle experiment and $387\times 10^6$ $B\overline{B}$ pairs collected by the Belle II experiment in $e^{+}e^{-}$ collisions at the $\Upsilon (4S)$ resonance. After an optimized selection, a simultaneous fit to the Belle and Belle II data sets yields $114\pm 12$ $B^{+}\rightarrow\rho^{+}\gamma$ and $99\pm 12$ $B^{0}\rightarrow\rho^{0}\gamma$ decays. The measured branching fractions are $(13.1^{+2.0 +1.3}_{-1.9 -1.2})\times 10^{-7}$ and $(7.5\pm 1.3^{+1.0}_{-0.8})\times 10^{-7}$ for $B^{+}\rightarrow\rho^{+}\gamma$ and $B^{0}\rightarrow\rho^{0}\gamma$ decays, respectively, where the first uncertainty is statistical and the second is systematic. We also measure the isospin asymmetry $A_{\rm I}(B\rightarrow\rho\gamma)=(10.9^{+11.2 +7.8}_{-11.7 -7.3})\%$ and the direct CP asymmetry $A_{CP}(B^{+}\rightarrow\rho^{+}\gamma)=(-8.2\pm 15.2^{+1.6}_{-1.2})\%$., Comment: 12 pages, 4 figures

We investigate the layer resolved magnetotransport properties of the antiferromagnetic/paramagnetic superlattices based on one band half-filled Hubbard model in three dimensions. In our set up the correlated layers (with on-site repulsion strength $U \ne$ 0) are intercalated between the uncorrelated (U = 0) layers. Our calculations based on the semi-classical Monte-Carlo technique show that the magnetic moments are induced in the uncorrelated layers at low temperatures due to kinetic hopping of the carriers across the interface. The average induced magnetic moment in the uncorrelated layer varies nonmonotonically with the $U$ values of the correlated layer. Interestingly, the induced magnetic moments are antiferromagnetically arranged in uncorrelated layers and mediates the antiferromagnetic ordering between correlated layers. As a result the whole SL system turns out to be antiferromagnetic insulating at low temperatures. For $U \sim$ bandwidth the local moments in the correlated planes increases as a function of the distance from the interface. Expectedly our in-plane resistivity calculations show that the metal insulator transition temperature of the central plane is larger than the edge planes in the correlated layers. On the other hand, although the induced moments in uncorrelated planes decreases considerably as move from edge planes to center planes the metal insulator transition temperature remains more or less same for all planes. The induced moments in uncorrelated layers gradually dissipates with increasing the thickness of uncorrelated layer and as a result the long range antiferromagnetic ordering vanishes in the superlattices similar to the experiments., Comment: 12 Figs

We present a search for the baryon number $B$ and lepton number $L$ violating decays $\tau^- \rightarrow \Lambda \pi^-$ and $\tau^- \rightarrow \bar{\Lambda} \pi^-$ produced from the $e^+e^-\to \tau^+\tau^-$ process, using a 364 fb$^{-1}$ data sample collected by the Belle~II experiment at the SuperKEKB collider. No evidence of signal is found in either decay mode, which have $|\Delta(B-L)|$ equal to $2$ and $0$, respectively. Upper limits at 90\% credibility level on the branching fractions of $\tau^- \rightarrow \Lambda\pi^-$ and $\tau^- \rightarrow \bar{\Lambda}\pi^-$ are determined to be $4.7 \times 10^{-8}$ and $4.3 \times 10^{-8}$, respectively., Comment: 8 pages, 4 figures

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning, but faces challenges with heterogeneous data distributions across clients. This paper presents FedSat, a novel FL approach specifically designed to simultaneously handle three forms of data heterogeneity, namely label skewness, missing classes, and quantity skewness, by proposing a prediction-sensitive loss function and a prioritized-class based weighted aggregation scheme. While the prediction-sensitive loss function enhances model performance on minority classes, the prioritized-class based weighted aggregation scheme ensures client contributions are weighted based on both statistical significance and performance on critical classes. Extensive experiments across diverse data-heterogeneity settings demonstrate that FedSat significantly outperforms state-of-the-art baselines, with an average improvement of 1.8% over the second-best method and 19.87% over the weakest-performing baseline. The approach also demonstrates faster convergence compared to existing methods. These results highlight FedSat's effectiveness in addressing the challenges of heterogeneous federated learning and its potential for real-world applications.

A series of data samples was collected with the Belle~II detector at the SuperKEKB collider from March 2019 to June 2022. We determine the integrated luminosities of these data samples using three distinct methodologies involving Bhabha ($e^+e^- \to e^+e^-(n\gamma)$), digamma ($e^+e^- \to \gamma\gamma(n\gamma)$), and dimuon ($e^+e^- \to \mu^+ \mu^- (n\gamma)$) events. The total integrated luminosity obtained with Bhabha, digamma, and dimuon events is ({426.88} $\pm$ 0.03 $\pm$ {2.61})~fb$^{-1}$, ({429.28} $\pm$ 0.03 $\pm$ {2.62})~fb$^{-1}$, and ({423.99} $\pm$ 0.04 $\pm$ {3.83})~fb$^{-1}$, where the first uncertainties are statistical and the second are systematic. The resulting total integrated luminosity obtained from the combination of the three methods is ({427.87 $\pm$ 2.01})~fb$^{-1}$., Comment: 12 pages, 3 figures; accepted for publication in Chinese Physics C

Spurious patterns refer to a mathematical association between two or more variables in a dataset that are not causally related. However, this notion of spuriousness, which is usually introduced due to sampling biases in the dataset, has classically lacked a formal definition. To address this gap, this work presents the first information-theoretic formalization of spuriousness in a dataset (given a split of spurious and core features) using a mathematical framework called Partial Information Decomposition (PID). Specifically, we disentangle the joint information content that the spurious and core features share about another target variable (e.g., the prediction label) into distinct components, namely unique, redundant, and synergistic information. We propose the use of unique information, with roots in Blackwell Sufficiency, as a novel metric to formally quantify dataset spuriousness and derive its desirable properties. We empirically demonstrate how higher unique information in the spurious features in a dataset could lead a model into choosing the spurious features over the core features for inference, often having low worst-group-accuracy. We also propose a novel autoencoder-based estimator for computing unique information that is able to handle high-dimensional image data. Finally, we also show how this unique information in the spurious feature is reduced across several dataset-based spurious-pattern-mitigation techniques such as data reweighting and varying levels of background mixing, demonstrating a novel tradeoff between unique information (spuriousness) and worst-group-accuracy., Comment: Accepted at ICML 2024 Workshop on Data-centric Machine Learning Research (DMLR): Datasets for Foundation Models