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Catherine Xu

Publications and source records attributed to Catherine Xu.

7 recordsLinked to original sources

Electrically controlled Heat Assisted Magnetic Recording in Intercalated 2D Magnets

The ever-increasing demand for fast, reliable, and energy-efficient information storage continues to push magnetic memory technologies toward their fundamental limits. Conventional scaling strategies, which rely on reducing bit size, inevitably run into the "magnetic recording trilemma," where signal-to-noise ratio, thermal stability, and writability cannot all be optimized simultaneously. Heat-assisted magnetic recording (HAMR) has emerged as the leading solution, enabling high-density storage by transiently heating the medium during the write cycle. However, the reliance on laser optics and plasmonic transducers restricts HAMR primarily to hard-disk drives, limiting its integration with on-chip or embedded architectures. Here, we demonstrate an electronic variant of HAMR in which Joule heating from low-current density current pulses facilitates data writing, while the anomalous Hall effect provides electronic readout. Employing intercalated 2D magnet Ni$_{1/4}$TaSe$_2$, we show direct evidence that current pulses heat the material above its Curie temperature, during which a small magnetic field of ~2mT (100 times smaller than the coercive field) enables efficient data writing. The all-electronic approach combined with the 2D magnetic medium creates timely opportunities to revisit the energy-assisted magnetization recording, enabling new recording schemes that combine fundamental novelty with technological impact.

cond-mat.mes-hall

Artificial Superintelligence May be Useless: Equilibria in the Economy of Multiple AI Agents

With recent development of artificial intelligence, it is more common to adopt AI agents in economic activities. This paper explores the economic actions of agents, including human agents and AI agents, in an economic game of trading products/services, and the equilibria in this economy involving multiple agents. We derive a range of equilibrium results and their corresponding conditions using a Markov chain stationary distribution based model. One distinct feature of our model is that we consider the long-term utility generated by economic activities instead of their short-term benefits. For the model consisting of two agents, we fully characterize all the possible economic equilibria and conditions. Interestingly, we show that unless each agent can at least double (not merely increase) its marginal utility by purchasing the other agent's products/services, purchasing the other agent's products/services will not happen in any economic equilibrium. We further extend our results to three and more agents, where we characterize more economic equilibria. We find that in some equilibria, the ``more powerful'' AI agents contribute zero utility to ``less capable'' agents.

econ.TH

Spin-Valley Locking in 2H-TaS2 and Its Co-Intercalated Counterpart: Roles of Surface Domains and Co Intercalation

Tuning and probing spin-valley coupling is key to understanding correlated ground states in 2$\it{H}$-TaS$_2$. Its magnetically intercalated analogue, Co$_{1/3}$TaS$_2$, introduces additional degrees of freedom, including modified interlayer coupling and magnetism, to modulate spin-valley physics. Surface-sensitive probes like ARPES are essential for accessing surface spin texture, yet previous studies on 2$\it{H}$-TMDs have reported conflicting results regarding spin-polarized bands, leaving open whether these discrepancies are intrinsic or extrinsic. Here we performed spatially resolved spin-ARPES measurements on 2$\it{H}$-TaS$_2$ and Co$_{1/3}$TaS$_2$. Our results reveal robust spin-valley locking on both compounds. Importantly, Co intercalation enhances interlayer hybridization and introduces magnetism while preserving the TaS$_2$-derived spin texture. We further observe a spatial reversal of the out-of-plane spin polarization, which we attribute to different surface domains. This effect complicates quantifying spin textures and may underlie prior inconsistent observations. Our findings provide microscopic insight into how interlayer interactions and surface domains together govern spin-valley phenomena in layered TMDs.

cond-mat.mtrl-sci

Discovery of Van Hove Singularities: Electronic Fingerprints of 3Q Magnetic Order in a van der Waals Quantum Magnet

Magnetically intercalated transition metal dichalcogenides are emerging as a rich platform for exploring exotic quantum states in van der Waals magnets. Among them, CoxTaS2 has attracted intense interest following the recent discovery of a distinctive 3Q magnetic ground state and a pronounced topological Hall effect below a critical doping of x=1/3, both intimately tied to cobalt concentration. To date, direct signatures of this enigmatic 3Q magnetic order in the electronic structure remain elusive. Here we report a comprehensive doping dependent angle resolved photoemission spectroscopy study that unveils these long-sought fingerprints. Our data reveal an unexpected "inverse Mexican hat" dispersion along the K-M-K direction, accompanied by two van Hove singularities. These features are consistent with theoretical predictions for a 3Q magnetic order near three-quarters band filling on a cobalt triangular lattice. These results provide evidence of 3Q magnetic order in the electronic structure, establishing TMD van der Waals magnets as tunable materials to explore the interplay between magnetism and topology.

cond-mat.mtrl-sci

Anisotropic Multi-Q Order in CoxTaS2

The cobalt-intercalated transition metal dichalcogenide Co$_x$TaS$_2$ hosts a rich landscape of magnetic phases that depend sensitively on $x$. While the stoichiometric compound with $x=1/3$ exhibits a single magnetic transition, samples with $x\leq 0.325$ display two transitions with an anomalous Hall effect (AHE) emerging in the lower temperature phase. Here, we resolve the spin structure in each phase by employing a suite of magneto-optical probes that include the discovery of anomalous magneto-birefringence -- a spontaneous time-reversal sensitive rotation of the principal optic axes. A symmetry-based analysis identifies the AHE-active phase as an anisotropic (2+1)\textbf{Q} state, in which magnetic modulation at one wavevector (\textbf{Q}) differs in symmetry from that at the remaining two. The (2+1)\textbf{Q} state naturally exhibits scalar spin chirality as a mechanism for the AHE and expands the classification of multi-Q magnetic phases.

cond-mat.str-el

Feature compression is the root cause of adversarial fragility in neural network classifiers

In this paper, we uniquely study the adversarial robustness of deep neural networks (NN) for classification tasks against that of optimal classifiers. We look at the smallest magnitude of possible additive perturbations that can change a classifier's output. We provide a matrix-theoretic explanation of the adversarial fragility of deep neural networks for classification. In particular, our theoretical results show that a neural network's adversarial robustness can degrade as the input dimension $d$ increases. Analytically, we show that neural networks' adversarial robustness can be only $1/\sqrt{d}$ of the best possible adversarial robustness of optimal classifiers. Our theories match remarkably well with numerical experiments of practically trained NN, including NN for ImageNet images. The matrix-theoretic explanation is consistent with an earlier information-theoretic feature-compression-based explanation for the adversarial fragility of neural networks.

cs.LG

To AI or not to AI, to Buy Local or not to Buy Local: A Mathematical Theory of Real Price

In the past several decades, the world's economy has become increasingly globalized. On the other hand, there are also ideas advocating the practice of ``buy local'', by which people buy locally produced goods and services rather than those produced farther away. In this paper, we establish a mathematical theory of real price that determines the optimal global versus local spending of an agent which achieves the agent's optimal tradeoff between spending and obtained utility. Our theory of real price depends on the asymptotic analysis of a Markov chain transition probability matrix related to the network of producers and consumers. We show that the real price of a product or service can be determined from the involved Markov chain matrix, and can be dramatically different from the product's label price. In particular, we show that the label prices of products and services are often not ``real'' or directly ``useful'': given two products offering the same myopic utility, the one with lower label price may not necessarily offer better asymptotic utility. This theory shows that the globality or locality of the products and services does have different impacts on the spending-utility tradeoff of a customer. The established mathematical theory of real price can be used to determine whether to adopt or not to adopt certain artificial intelligence (AI) technologies from an economic perspective.

econ.TH