Electric Hall effects generated by an in-plane magnetic field have recently gained attention owing to their intrinsic origin in topological electronic states and potential application in magnetic field sensing. In pratice, the measured transverse electric voltage typically combines contributions from multiple phenomena, such as anisotropy and Berry curvature effects, leading to interpretative ambiguities of the measurement signal. Here, we introduce a universal framework that disentangles these contributions via their distinct field-reversal symmetries and angular dependencies. Leveraging a 12-terminal Hall bar for independent control of the electric and in-plane magnetic field directions, we exemplify this method by analyzing the transverse electric voltage recorded on the the ferromagnetic Weyl semimetal Fe3Sn in an in-plane geometry. The standardized approach presented in this work will guide future studies of in-plane Hall responses in magnetic and topological materials.

In this paper, we establish a compactness theorem for gradient Ricci solitons with scalar curvature bounds and uniform lower bounds of harmonic coordinates. Our approach is to bootstrap regularity in harmonic coordinates by exploiting the soliton equation. As an application, we show that the regular part of any noncollapsed limit of gradient Ricci solitons with bounded Ricci curvature is smooth. Further, we show that a steady gradient Ricci soliton is asymptotically cylindrical under an $\mathcal{L}^{1}$-decay assumption on its Ricci curvature.

In this paper, we develop the notion of representability of co-dimension three cycles on a fourfold in terms of zero cycles modulo rational equivalence on surfaces.

This paper develops a highly general convex duality framework for the perturbed utility route choice (PURC) model. We show that the traveler's constrained, potentially non-smooth utility maximization problem admits a dual formulation: an unconstrained concave maximization problem with a differentiable objective. The unique optimal flow can be recovered link-by-link from any dual solution via the convex conjugates of link perturbation functions. These properties enable efficient gradient-based optimization for large-scale networks and fast computation for sensitivity analysis. Finally, the framework reveals a structural analogy between PURC and current flow in electrical circuits.

Let $F$ be a finite extension of $\mathbb{Q}_p$. The so-called supersingular representations are the basic building blocks in the theory of mod $p$ representations of ${\rm GL}_2(F)$. The space of pro-$p$-Iwahori invariants of a universal module played a crucial role in the construction of the supersingular representations of ${\rm GL}_2(\mathbb{Q}_p)$. In this paper, we give an explicit description of the pro-$p$-Iwahori invariants of the universal module $π_r$ for $r = 0, q - 1$ using the Iwahori-Hecke model. We also determine the action of the pro-$p$-Iwahori-Hecke algebra on these newly found invariants. As an application, we recover $π_r$ functorially from its space of $I(1)$-invariants and extend a theorem of Ollivier for any totally ramified extension of $\mathbb{Q}_p$ other than itself.

The first paper in this series introduced a \emph{short-to-long mixing} condition that captures mean-field GOE/GUE edge universality in the supercritical sparsity regime, for symmetric/Hermitian random matrices with independent entries and a Markov variance profile. This condition reduces the universality problem to the mixing properties of the underlying Markov chains. In this paper, we develop new \emph{short-to-long comparison} conditions that extend the analysis to the subcritical and critical sparsity regimes. Specifically, we prove that two inhomogeneous random matrices exhibit the same universal edge statistics whenever their variance-profile Markov chains are comparable, regardless of the fine details of the matrix entries. To illustrate the power of our Markov chain comparison theorem, we derive the spectral edge statistics for several prototypical models: random band matrices, the Wegner orbital model, and Hankel-profile random matrices. These comparisons uncover a rich landscape of both universal and non-universal phenomena -- shaped by geometric structure, spike patterns, and domains of stable attraction -- features that lie fundamentally beyond the reach of classical random matrix theory.

This paper provides a sparse signal recovery algorithm, DU-PSISTA (Deep Unfolded-Periodic Sketched Iterative Shrinkage-Thresholding Algorithm), which aims to balance computational efficiency and accuracy for recovering high-dimensional sparse signals, and a convergence analysis under sufficient conditions. DU-PSISTA introduces a random matrix projection known as sketching to reduce the dimensionality of gradient computations and periodically alternates between the standard ISTA and the sketched variant. This hybrid structure enables flexible control over the trade-off between accuracy and computational complexity through a pre-configurable period parameter. The algorithm includes many parameters to be tuned such as step sizes and thresholding factors so that we incorporate deep unfolding that optimizes the parameters through data-driven training, enabling the algorithm to adaptively improve convergence speed and performance. We show that the proposed method achieves a linear-type contraction to a neighborhood of the true sparse signal with properly selected parameters. The analysis provides an interpretation for the effectiveness of the hybrid structure to improve recovery accuracy. Numerical experiments confirm that our method achieves comparable recovery performance to conventional deep unfolded ISTA while reducing computational complexity, especially when the period parameter and sketch size are properly selected. The results are also consistent with the theoretical insights.

In this paper, we study the Littlewood theory associated with the quantum super immanants and supersymmetric polynomials, including both the super case and the quantum generalization. In the setting of quantum super Schur-Weyl duality between the quantum superalgebra $U_q(\mathfrak{gl}_{m|n})$ and the Iwahori-Hecke algebra $\mathcal{H}_r$ of type A, we explicitly construct basis vectors of the $(U_q(\mathfrak{gl}_{m|n}), \mathcal{H}_r)$-bimodule on the tensor product space $(\mathbb{C}^{m|n})^{\otimes r}$. Using this construction, we interpret the quantum super immanants via weight spaces of covariant tensor representations of $U_q(\mathfrak{gl}_{m|n})$.

Optical skyrmions has recently unlocked topological quasiparticle textures of light, rising in prominence for next-generation ultra-robust information processing. However, to date, their study hasbeen mainly confined to coherent laser fields. Here we extend skyrmions to much general light sources of partially coherent, stochastic optical fields. We define stochastic optical skyrmions and uncover a hidden regime where spatial coherence acts as a primary determinant of topological stability. While environmental randomness typically degrades fully coherent states, we demonstrate that engineered partial coherence provides a self-healing mechanism that preserves topology under extreme turbulence. Moreover, we show that the coherence structure can be actively tailored to trigger on-demand topological phase transitions, such as skyrmion-to-skyrmionium conversion and skyrmion lattice splitting. These findings redefine the boundaries of topological photonics, paving the way for resilient and high-fidelity information platforms that remain operational in general, non-ideal, real-world environments.

The prediction of sensory attributes from ingredient-level formulations is an emerging challenge at the intersection of food science and artificial intelligence. We address the fundamental question of whether the taste of a food can be predicted from its ingredients by treating recipes as composite materials. We apply Hashin--Shtrikman (HS) and Reuss--Voigt (RV) bounds, techniques originally developed for elastic moduli, to predict five taste dimensions (sweetness, sourness, bitterness, umami, saltiness) on a curated dataset of 70 recipes decomposed into 209 ingredient-level taste references with trained-panel ground truth. The bounds provided an additive baseline but systematically under-predict perceived taste: 77\% of actual taste values exceeded the HS upper bound, with the exceedance rate ranging from 26\% (bitterness) to 97\% (saltiness). We traced this gap to specific processing chemistry (Maillard reactions, caramelization, evaporative concentration, protein hydrolysis, and nucleotide synergy) and introduced a hybrid model that augments the HS baseline with eight chemistry-proxy features encoding these mechanisms. Our results show that our interpretable hybrid model eliminates the systematic bias and reduces mean absolute error by 27--62\% for sweetness, sourness, umami, and saltiness while using only 10 interpretable features, achieving performance comparable to a black-box Lasso regression on 115 per-ingredient features. We further demonstrate constrained inverse design via Differential Evolution, recovering ingredient formulations that match target taste profiles subject to compositional bounds.

In this paper, we first prove that the following generalized conservation principle holds on complete Riemannian manifolds: for every \(0<s<1\) and \(t>0\), \[ T_t^{(s)}\mathbf 1+\int_0^t T_τ^{(s)}\mathcal R_s\,dτ=1 \qquad\text{on }M, \] where \(\mathcal R_s\) is the intrinsic killing term measuring the loss of mass of the subordinate semigroup, and the condition \(\mathcal R_s\equiv0\) is equivalent to the stochastic completeness of \(M\). We then provide several new nonlocal characterizations of stochastic completeness. In particular, we show that stochastic completeness is equivalent to genuinely nonlocal conditions, including the zero-mean identity \[ \int_M (-Δ)^sφ\,dV_g=0 \qquad\forall\,φ\in C_c^\infty(M), \] as well as the uniqueness of bounded distributional solutions to the associated fractional elliptic and parabolic equations. We also revisit the equivalent \(L^1\)-core characterization for the generator of the heat semigroup, which plays an important role in our approach. In addition, we prove \(L^p\)-contractivity and smoothing properties of the subordinate semigroup, establish both short-time and long-time asymptotic results for the fractional heat kernel, derive the short-time asymptotics of jump probabilities for the associated Markov process, and study the variational characterization and minimality properties of the fractional resolvent. Together, these results provide a unified analytic and probabilistic framework for the fractional Laplacian on complete Riemannian manifolds.

Large Language Models (LLMs) are increasingly deployed in automated software engineering for tasks such as API migration. While LLMs are able to identify migration patterns, they often make mistakes and fail to produce correct glue code to invoke the new API in place of the old one. We call this issue Scaffolding Hallucination, a failure mode where models generate incorrect calling contexts by inventing Phantom Symbols -- such as imaginary imports, constructors, and constants -- that do not exist in the API specification. In this paper, we show that standard metrics cannot be relied upon to detect these instances of hallucination. We propose Hallucination Inspector, a static analysis tool to detect Scaffolding Hallucination in LLM-generated code. Our approach includes a lightweight evaluation framework that verifies symbols extracted from the abstract syntax tree against a knowledge base derived directly from software documentation for the API. A preliminary evaluation on Android API migrations demonstrates that our approach successfully identifies hallucinations and significantly reduces false positives compared to standard metrics and probabilistic judges

An anyon-chain-like lattice model with symmetry described by the Ising fusion category is studied. Combining numerical and analytical studies, we uncover a rich phase diagram that contains three phases: a symmetric critical phase and two categorical symmetry breaking phases. The symmetric phase lies in the same universality class as the usual critical Ising model. The first symmetry-breaking phase, dubbed the \emph{categorical ferromagnetic} phase, has the Ising fusion category fully broken and exhibits a threefold ground-state degeneracy, as expected from the generalized Landau paradigm. The other symmetry-breaking phase is analogous to a conventional antiferromagnet: it breaks lattice translation and part of the Ising fusion category, and therefore is termed the \emph{categorical antiferromagnetic} phase. Unlike ordinary antiferromagnetic states associated with finite invertible symmetry breaking, this phase itself is critical, being described by a fourfold degenerate Ising conformal field theory. We argue more generally that antiferromagnetic states associated with broken non-invertible symmetries have a large low-energy manifold that grows exponentially in system size, due to the greater-than-one quantum dimension of domain walls. We also numerically study the transitions between the three phases. The transition between the symmetric and categorical ferromagnetic phase is described by the $c=7/10$ tricritical Ising CFT, while the transition between the symmetric and categorical antiferromagnetic phases is less understood. Our numerical data suggest that the latter transition is continuous and described by a conformal field theory with central charge $c=3/2$.

Multi-band topological states enable robust and versatile wave manipulation across a variety of physical platforms. However, the emergence of multi-band topological states has relied on higher-frequency modes with complex spatial profiles, which constrains the realization of robust topological states due to fragile symmetry and pseudospin hybridization in these modes. Here, we show a general design principle for scalable multi-band topological states by replicating a robust fundamental topological mode in the frequency domain. By introducing hierarchical resonators as an internal degree of freedom into a quantum spin Hall-based lattice, multiple topological states emerge discretely in correspondence with the hierarchical levels while preserving the spatial profile of the fundamental mode at the host lattice. Implementing this design principle in a versatile microelectromechanical platform, we experimentally demonstrate that the fundamental and replicated topological modes propagate simultaneously in a single waveguide while suppressing mutual cross-talk. Our results establish topology replication as a universal strategy for designing multi-band topological systems and open routes toward multi-channel topological wave devices.

Volatile methyl siloxanes (VMSs), widely present in consumer and industrial products, have attracted increasing concerns due to their persistence, bioaccumulation behavior, and adverse health effects. Beyond their environmental implications, VMSs also pose operational challenges for sensing technologies because they readily decompose on sensing materials to form silicon-based compounds (e.g., silica and silane) that irreversibly impair sensing performance, a phenomenon commonly known as siloxane poisoning. Despite its prevalence, the mechanistic basis of this deactivation remains poorly understood. Herein, we present the first comprehensive theoretical study of siloxane-induced poisoning in catalytic gas sensors. Guided by our self-developed AI Agent, Digital Sensor Platform (DigSen), we first identify siloxane poisoning as a previously overlooked yet high-impact research direction. Using hexamethyldisiloxane (HMDS) as a model compound, we then conducted first-principles calculations to uncover decomposition pathways across noble metal surfaces. Strikingly, a descriptor-based microkinetic volcano model is developed to capture the trade-off between sensing activity and resistance to poisoning, enabling predictive identification of anti-poisoning candidates. These insights not only elucidate the origin of siloxane poisoning but also demonstrate how AI-driven discovery, mechanistic theory, and experiments can be integrated into a closed-loop framework for catalytic sensor design. More broadly, this AI-guided paradigm represents a generalizable strategy for materials digital discovery, offering a transferable methodology that extends well beyond siloxane systems to diverse classes of materials challenges.

We use the two-flavor Linear Sigma Model with quarks as an effective description of QCD to investigate the nature of the chiral phase transition at finite baryon chemical potential and zero temperature. We work at one-loop order to set up and solve the system of self-consistent coupled equations for the particle pole masses. The chemical potential-dependent value of the chiral order parameter is obtained by minimizing the one-loop effective potential. This treatment goes beyond the conventional ring-diagram approximation and provides a description valid for arbitrary values of the chemical potential. We find that the phase transition is of first order, and occurs when the quark chemical potential reaches the value of the vacuum quark mass for the chosen set of parameters. The first order nature of the transition is signaled by the discontinuous behavior of the chiral condensate, the masses and the couplings. The thermodynamics of the system is readily implemented and in particular, we find that the square of the speed of sound exhibits a discontinuity at the phase transition and then smoothly approaches the conformal limit from below.

The measurement of galaxy morphological parameters from astronomical images features in a wide range of modern analyses, including galaxy evolution and cosmological weak lensing studies. The precision and accuracy of morphological parameter estimation can be influenced by several key factors. The effective seeing of the image, summarized by the point spread function (PSF), limits how galaxy features or light profiles are resolved. The pixel scale of the detector also influences the resolution and the amount of statistical information available for a given object. The depth of the observations determines the signal-to-noise ratio of the image. Improving each of these factors is very costly, either in terms of detector upgrades, observatory design, or observing time. Here, we develop a conditional generative adversarial network, called Neo, trained to transform existing ground-based images into sharper, finer-scale images comparable to space-based image quality. We demonstrate that Neo improves the accuracy of measured morphological parameters by factors of $2$-$10$ when trained to translate Subaru Hyper Suprime-Camera (HSC) images to approximate Hubble Space Telescope (HST) data. Neo is designed for applicability to ongoing, large-scale surveys such as the Legacy Survey of Space and Time (LSST) conducted by Vera C. Rubin Observatory in combination with space telescopes such as HST, James Webb Space Telescope, and Nancy Grace Roman Space Telescope. These results suggest that Neo could be used to improve both cosmological and galaxy evolution analyses based on massive, ground-based survey datasets like LSST. The model code is open source and available at https://purl.archive.org/neo/code.

A graphene-optimized silicon carbide PIN detector was fabricated and its radiation tolerance under X-ray irradiation of 160 keV was evaluated. Its electrical properties, charge collection performance and time resolution of beta-particles (90Sr) are reported. After 1 MGy irradiation, the detector maintains an ultralow leakage current of approximately 2.2e-10 A @ 300 V and the C-V characteristics are basically consistent with full depletion at 120V. The time resolution of the graphene-optimized silicon carbide detector is 58.0 ps. The time resolution is comparable to that of state-of-the-art 4H-SiC low-gain avalanche detectors (LGADs). The G/RE 4H-SiC PIN detector exhibits outstanding time resolution performance. Compared with the time resolution of the RE 4H-SiC PIN detector, the time resolution of the G/RE 4H-SiC PIN detector has decreased by 39.6%. This demonstrates the significance of the graphene electrode design. The graphene detector exhibits a charge collection efficiency (CCE) of 99.24% after X-ray irradiation, along with excellent stability. The graphene-optimized silicon carbide detector maintains good timing resolution: 58.0ps before and 64.0ps after X-ray irradiation. Experimental results indicate that the CCE and time resolution performance exhibit good stability before and after irradiation. These results demonstrate stable performance under extreme X-ray exposure, highlighting the detectors potential for radiation-hard applications in high-energy physics, space missions, and nuclear reactor monitoring.

We study dynamic multi-battle contests and examine how the contest structure shapes dynamic incentives and determines the extent of rent dissipation. A discouragement effect often arises -- such as in tug-of-war and best-of-$K$ contests -- preventing full rent dissipation even when the series can extend infinitely. We identify a structural property, exchangeability, that contributes to the effect. Leveraging this insight, we establish a necessary and sufficient condition for almost-full rent dissipation. As an application, we introduce the iterated incumbency contest, which illustrates how volatility in the surrounding environment sustains dynamic incentives and generates almost-full rent dissipation, and thus offers insights into various competitive phenomena.

A monomial curve $C$ is defined by a sequence of coprime integers $0 = a_0 < a_1 < \cdots < a_k =: d$. One gap of this sequence is $a_{i+1} - a_i - 1$. Gruson--Lazarsfeld--Peskine bound (1983) says that $reg (C) \le d - k +2$, which is equal to the sum of all gaps plus 2. Lvovsky (1996) showed that it is enough to take the sum of two largest gaps plus 2. In this paper, under some specific conditions, we give several new bounds which are better than Lvovsky's bound. Our method relies on the study of Apery sets and Frobenius numbers. From this we can give new criteria to check the (arithmetically) Cohen--Macaulay and Buchsbaum property of $C$. Algorithms are provided to check these properties as well as to compute $ reg(C)$ and other invariants. We also give an application to study the structure of sumsets.

This paper addresses the challenge of quantitatively reconstructing initial acoustic sources from time-dependent wave measurements. We introduce novel indicator functions defined through spacetime integrals of acoustic data and carefully designed auxiliary functions. These indicators are foundational for both proving the uniqueness of source reconstruction and developing a quantitative direct sampling scheme. Our comprehensive numerical experiments demonstrate the robustness, accuracy, and computational efficiency of these methods, highlighting their potential for practical acoustic imaging applications.

A time-domain analytic expression for chromatographic peak shapes is derived within a stochastic-diffusive framework that incorporates axial diffusion (molecular and multipath/Eddy), finite initial spatial variance, a retention mechanism characterized by a high rate of short-duration events, and an arbitrary number of independent slow retention mechanisms, each characterized by its own rate of infrequent, long-duration events. A highly efficient evaluation scheme is derived for this expression. In the single-slow-mechanism case, it is two to four orders of magnitude faster than the previously available analytic route. Analytical derivatives with respect to all model parameters are also obtained, and each can be evaluated at computational cost comparable to that of the peak-shape expression. Illustrative fits to three literature peaks yielded full-profile RMSE values lower than those of the exponentially modified Gaussian in all tested cases, with minima ranging from 0.03 to 0.14 percent of peak height, compared with 0.43 to 5.57 percent for the reference model. Relative to the one-slow-mechanism formulation, allowing more than one slow mechanism produced a data-dependent improvement that exceeded one order of magnitude for one of the peaks.

The increasing penetration of flexible loads, such as electric vehicles and AI data-centers necessitates new methodologies for quantifying electrical load hosting capacity under operational constraints and flexible connection agreements. We propose a risk-aware hosting capacity framework that explicitly accounts for both flexibility, in the form of load curtailment, and system reliability. The proposed method incorporates a Conditional Value-at-Risk (CVaR) constraint to control the tail risk of excessive curtailment, ensuring that extreme interventions remain limited. Additionally, a weighted $\ell_1$ approach is introduced to limit the number of utility-controlled interventions, enabling control over the frequency of curtailment actions. A regularization parameter is used to tune the intervention count to a desired intervention budget. The resulting optimization formulation is convex and efficiently solvable, allowing scalable implementation. Numerical results demonstrate that the proposed method significantly increases hosting capacity while maintaining strict risk guarantees and limiting intervention frequency, providing a practical balance between flexibility and reliability in distribution systems.

The number of connected components can be remembered by the von Neumann algebra among Artin groups, the only possible exception being the case that corresponds to the free group factor problem. In the case of Coxeter groups, this result is obtained in the absence of relatively hyperbolicity. We also discuss a specific case of the analogous problem in measure equivalence where each factor group is a product of nonabelian free groups.

In the future interstellar exploration at near-relativistic speeds will be possible using beamed energy laser propulsion. With this, spacecraft as small as gm mass picospacecraft become candidates for the exploration of deep space, with a trade space of velocity and mission duration versus mass. Here, we examine the potential science return from interstellar expeditions with Coracle laser-sail picospacecraft swarms and show how even with fast flybys at near relativistic velocities, a picospacecraft swarm could deliver gigapixel resolution of the target exoplanets. Our mission target is the planet Proxima b in the habitable zone (HZ) of the red dwarf Proxima Centauri, the tertiary (and nearest) component of the nearest star system, α Centauri. We explore science returns from such an expedition, both en route to Proxima and at the Proxima system, and conclude that initial small spacecraft expeditions would provide a substantial science return, including the ability to detect surface biology or a technological civilization, should either or both be established on the target planet.

The Collatz iteration is governed by two distinct update rules, depending on the parity of the current iterate: n(i+1)=3n(i)+1 for odd n(i), and n(i+1)=n(i)/2 for even n(i). We show that these rules can be written equivalently as a single parity controlled transformation, n(i+1)=((2s(i)+1)(2k(i)+s(i))+s(i))/2, where n(i)=2k(i)+s(i) and s(i) is the parity (0 or 1) of n(i), yielding a uniform, step aligned dynamical system in which parity variables are tracked explicitly. This reformulation removes the asymmetry of the traditional presentation and exposes structural regularities that are obscured when odd and even updates are treated separately. Building on this unified rule, we introduce a base octave decomposition, representing every integer uniquely in the form n=B+8(A-1) with B = 1 to 8. The resulting dynamics separate into parity dependent base transitions and affine updates of the octave index, inducing a finite directed transition skeleton lifted across scale levels. Refining the parity description yields a finite 128 state symbolic system that encodes all admissible transitions, including carry effects arising from higher order parity inheritance. Within this framework, we identify growth permitting and decay forcing channels and show that the only persistence mechanism (base 7 transitions in even octaves) is necessarily bounded by the 2 adic valuation of the octave index. An exhaustive enumeration of admissible return paths between persistence episodes establishes a non positive drift in a logarithmic octave coordinate. Because of these finite state constraints, trajectories are eventually confined to a contractive subnetwork associated with the terminal 1,2 cycle. The approach emphasizes structural organization and return map methods, and provides a symbolic framework for analyzing parity driven integer recurrences.

We adopt a two-dimensional tensor-network (TN) ansatz to simulate variational quantum algorithms on two-dimensional qubit architectures, demonstrating its capability to accurately simulate deep circuits through the Quantum Approximate Optimization Algorithm (QAOA) applied to Ising spin-glass problems on heavy-hexagonal and square lattices. For heavy-hexagonal problems with up to three-body interactions, parameters trained on small instances and transferred to systems an order of magnitude larger improve the sampled energy distribution only up to intermediate depths, indicating a fundamental limit of parameter concentration as a transfer strategy. By extending the training itself with TN simulations on larger system sizes, we avoid local minima and obtain lower-energy samples. Analyses of entanglement growth and importance sampling show that the simulation remains classically feasible with moderate bond dimension. We find that parameter concentration also persists on square lattices, albeit at substantially higher computational cost to perform reliable sampling. Overall, our TN framework not only provides an efficient and controlled framework for benchmarking variational quantum algorithms on two-dimensional lattices, but also serves as an effective surrogate model for training variational algorithms.

ReRAM-based in-memory computing (IMC) architectures are promising candidates for energy-efficient matrix-vector multiplication. While scaling the size of ReRAM arrays allows for the amortization of power-hungry peripheral circuits like DACs and ADCs, it simultaneously introduces more parasitic along the signal path. Because of these challenges, current design methodologies often lack practical guidelines to balance these effects at early design stage, forcing designers to rely on time-consuming, iterative transistor-level simulations. In this work, we propose a comprehensive framework for design space exploration that enables the selection of optimal array size, ADC resolution, and system frequency without requiring exhaustive simulations. The framework utilizes a specialized testbench to extract parameters from a limited set of representative transistor-level simulations. These parameters are then used to accurately predict the performance of arbitrary architectures. We demonstrate the effectiveness of this framework through two realistic design cases aimed at maximizing energy efficiency (TOPs/s/W). The results show that the framework successfully identifies optimal architectural configurations under strict power and error constraints, providing an efficient path for high-performance IMC design.

This paper presents a low-power cache architecture based on the series interconnection of conventional 6-transistor static random-access memory (6T SRAM) cells. The proposed approach aims to reduce leakage power in SRAM-based cache memories without increasing the transistor count of the memory cell itself. In the proposed architecture, adjacent cells within a column are reconfigured in a serial topology, thereby exploiting the stacking effect to suppress leakage current, particularly during hold operation. This architectural modification requires corresponding changes to the addressing and sensing structure of the cache, including adjustments to the column organization and readout path. To evaluate the proposed method, transient simulations were carried out using Keysight ADS. The simulation results show that the proposed architecture reduces leakage power compared with the conventional SRAM interconnection scheme while preserving the use of standard 6T SRAM cells.

We report thermodynamic evidence for field-induced mode-selective quantum criticality in the layered rare-earth magnet BaNd2ZnS5 (BNZS). Below the Neel temperature TN = 2.9 K, spin-orbit-entangled Nd3+ moments form two symmetry-inequivalent low-energy spin-excitation modes arising from Kramers doublet physics under a magnetic field, with distinct gaps Delta_L and Delta_H. For magnetic fields applied along the [110] direction, the lower-energy gap Delta_L softens continuously and collapses at a critical field Hc ~ 2 T, while the higher-energy gap Delta_H remains gapped, leaving the system in an intermediate partially critical phase. Despite the partial nature of the criticality, thermodynamic measurements reveal a continuous quantum phase transition. The ac susceptibility shows universal scaling behavior, with chi_ac(T, H) collapsing onto a single scaling function and following chi_ac ~ T^-0.2 at criticality. A finite residual Sommerfeld coefficient gamma_0 further indicates the emergence of gapless excitations confined to a single symmetry sector near the quantum critical point. In contrast to conventional quantum criticality based on global softening of low-energy excitations, BNZS exhibits a selective breakdown of Kramers-doublet excitations due to its strong anisotropic interactions. Our results establish BNZS as a spin-orbit-coupled rare-earth magnet where quantum criticality is not global but mode-selective, with anisotropic interactions enabling independent criticality in distinct excitation sectors.