Nernst Effect as a Probe of Local Kondo Scattering in Heavy Fermions

A large, strongly temperature-dependent Nernst coefficient, $\nu$, is observed between $T$ = 2 K and 300 K for CeCu$_2$Si$_2$ and Ce$_{0.8}$La$_{0.2}$Cu$_2$Si$_2$. The enhanced $\nu(T)$ is determined by the asymmetry of the on-site Kondo (conduction electron$-4f$ electron) scattering rate. Taking into account the measured Hall mobility, $\mu_H$, the highly unusual thermopower, $S$, of these systems can be semiquantitatively described by $S(T)$ $=$ $-$$\nu(T)/\mu_H(T)$, which explicitly demonstrates that the thermopower originates from the local Kondo scattering process over a wide temperature range from far above to well below the coherence temperature ($\approx$ 20 K for CeCu$_2$Si$_2$). Our results suggest that the Nernst effect can act as a proper probe of local charge-carrier scattering. This promises an impact on exploring the unconventional enhancement of the thermopower in correlated materials suited for potential applications.Comment: 10 pages, 2 Figure

Resonant Charge Relaxation as a Likely Source of the Enhanced Thermopower in FeSi

The enhanced thermopower of the correlated semiconductor FeSi is found to be robust against the sign of the relevant charge carriers. At $T$\,$\approx$\,70 K, the position of both the high-temperature shoulder of the thermopower peak and the nonmagnetic-enhanced paramagnetic crossover, the Nernst coefficient $\nu$ assumes a large maximum and the Hall mobility $\mu _H$ diminishes to below 1 cm$^2$/Vs. These cause the dimension-less ratio $\nu$/$\mu_H$ $-$ a measure of the energy dispersion of the charge scattering time $\tau(\epsilon)$ $-$ to exceed that of classical metals and semiconductors by two orders of magnitude. Concomitantly, the resistivity exhibits a hump and the magnetoresistance changes its sign. Our observations hint at a resonant scattering of the charge carriers at the magnetic crossover, imposing strong constraints on the microscopic interpretation of the robust thermopower enhancement in FeSi.Comment: 5 pages, 3 figure

Enhanced electron correlations in FeSb2_2

FeSb$_2$ has been recently identified as a new model system for studying many-body renormalizations in a $d$-electron based narrow gap semiconducting system, strongly resembling FeSi. The electron-electron correlations in FeSb$_2$ manifest themselves in a wide variety of physical properties including electrical and thermal transport, optical conductivity, magnetic susceptibility, specific heat and so on. We review some of the properties that form a set of experimental evidences revealing the crucial role of correlation effects in FeSb$_2$. The metallic state derived from slight Te doping in FeSb$_2$, which has large quasiparticle mass, will also be introduced.Comment: 9 pages, 7 figures; submitted to Annalen der Physi

NSOTree: Neural Survival Oblique Tree

Survival analysis is a statistical method employed to scrutinize the duration until a specific event of interest transpires, known as time-to-event information characterized by censorship. Recently, deep learning-based methods have dominated this field due to their representational capacity and state-of-the-art performance. However, the black-box nature of the deep neural network hinders its interpretability, which is desired in real-world survival applications but has been largely neglected by previous works. In contrast, conventional tree-based methods are advantageous with respect to interpretability, while consistently grappling with an inability to approximate the global optima due to greedy expansion. In this paper, we leverage the strengths of both neural networks and tree-based methods, capitalizing on their ability to approximate intricate functions while maintaining interpretability. To this end, we propose a Neural Survival Oblique Tree (NSOTree) for survival analysis. Specifically, the NSOTree was derived from the ReLU network and can be easily incorporated into existing survival models in a plug-and-play fashion. Evaluations on both simulated and real survival datasets demonstrated the effectiveness of the proposed method in terms of performance and interpretability.Comment: 12 page

Simultaneously optimizing the interdependent thermoelectric parameters in Ce(Ni1−x_{1-x}Cux_x)2_2Al3_3

Substitution of Cu for Ni in the Kondo lattice system CeNi$_2$Al$_3$ results in a simultaneous optimization of the three interdependent thermoelectric parameters: thermoelectric power, electrical and thermal conductivities, where the electronic change in conduction band induced by the extra electron of Cu is shown to be crucial. The obtained thermoelectric figure of merit $zT$ amounts to 0.125 at around 100 K, comparable to the best values known for Kondo compounds. The realization of ideal thermoelectric optimization in Ce(Ni$_{1-x}$Cu$_x$)$_2$Al$_3$ indicates that proper electronic tuning of Kondo compounds is a promising approach to efficient thermoelectric materials for cryogenic application.Comment: 4 pages, 4 figures. Accepted for publication in Physical Review

Highly Dispersive Electron Relaxation and Colossal Thermoelectricity in the Correlated Semiconductor FeSb2_2

We show that the colossal thermoelectric power, $S(T)$, observed in the correlated semiconductor FeSb$_2$ below 30\,K is accompanied by a huge Nernst coefficient $\nu(T)$ and magnetoresistance MR$(T)$. Markedly, the latter two quantities are enhanced in a strikingly similar manner. While in the same temperature range, $S(T)$ of the reference compound FeAs$_2$, which has a seven-times larger energy gap, amounts to nearly half of that of FeSb$_2$, its $\nu(T)$ and MR$(T)$ are intrinsically different to FeSb$_2$: they are smaller by two orders of magnitude and have no common features. With the charge transport of FeAs$_2$ successfully captured by the density functional theory, we emphasize a significantly dispersive electron-relaxation time $\tau(\epsilon_k)$ due to electron-electron correlations to be at the heart of the peculiar thermoelectricity and magnetoresistance of FeSb$_2$.Comment: 8 pages, 5 figure

Huge Thermoelectric Power Factor: FeSb2 versus FeAs2 and RuSb2

The thermoelectric power factor of the narrow-gap semiconductor FeSb2 is greatly enhanced in comparison to the isostructural homologues FeAs2 and RuSb2. Comparative studies of magnetic and thermodynamic properties provide evidence that the narrow and correlated bands as well as the associated enhanced thermoelectricity are only specific to FeSb2. Our results point to the potential of FeSb2 for practical thermoelectric application at cryogenic temperatures and stimulate the search for new correlated semiconductors along the same lines.Comment: 14 pages, 4 figures, published in Applied Physics Expres

A Situation-aware Enhancer for Personalized Recommendation

When users interact with Recommender Systems (RecSys), current situations, such as time, location, and environment, significantly influence their preferences. Situations serve as the background for interactions, where relationships between users and items evolve with situation changes. However, existing RecSys treat situations, users, and items on the same level. They can only model the relations between situations and users/items respectively, rather than the dynamic impact of situations on user-item associations (i.e., user preferences). In this paper, we provide a new perspective that takes situations as the preconditions for users' interactions. This perspective allows us to separate situations from user/item representations, and capture situations' influences over the user-item relationship, offering a more comprehensive understanding of situations. Based on it, we propose a novel Situation-Aware Recommender Enhancer (SARE), a pluggable module to integrate situations into various existing RecSys. Since users' perception of situations and situations' impact on preferences are both personalized, SARE includes a Personalized Situation Fusion (PSF) and a User-Conditioned Preference Encoder (UCPE) to model the perception and impact of situations, respectively. We conduct experiments of applying SARE on seven backbones in various settings on two real-world datasets. Experimental results indicate that SARE improves the recommendation performances significantly compared with backbones and SOTA situation-aware baselines.Comment: Accepted at the International Conference on Database Systems for Advanced Applications (DASFAA 2024

Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction

Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retraining models on recent data daily or weekly. However, the delay of conversion behavior usually leads to incorrect labeling, which is called delayed feedback problem. Existing work may fail to introduce the correct information about false negative samples due to data sparsity and dynamic data distribution. To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples. Firstly, we theoretically prove that the label-corrected loss is an unbiased estimate of the oracle loss using true labels. Then, as there are no ready training data for label correction, counterfactual labeling is used to construct artificial training data. Furthermore, since counterfactual labeling utilizes only partial training data, we design an embedding-based alternative training method to enhance performance. Comparative experiments on both public and private datasets and detailed analyses show that our proposed approach effectively alleviates the delayed feedback problem and consistently outperforms the previous state-of-the-art methods.Comment: accepted by KDD 202