arXiv subjects

Yishi Lin

Publications and source records attributed to Yishi Lin.

13 recordsLinked to original sources

arXiv2023

Direct observation of chiral edge current at zero magnetic field in odd-layer MnBi$_2$Te$_4$

The chiral edge current is the boundary manifestation of the Chern number of a quantum anomalous Hall (QAH) insulator. Its direct observation is assumed to require well-quantized Hall conductance, and is so far lacking. The recently discovered van der Waals antiferromagnet MnBi$_2$Te$_4$ is theorized as a QAH in odd-layers but has shown Hall resistivity below the quantization value at zero magnetic field. Here, we perform scanning superconducting quantum interference device (sSQUID) microscopy on these seemingly failed QAH insulators to image their current distribution. When gated to the charge neutral point, our device exhibits edge current, which flows unidirectionally on the odd-layer boundary both with vacuum and with the even-layer. The chirality of such edge current reverses with the magnetization of the bulk. Surprisingly, we find the edge channels coexist with finite bulk conduction even though the bulk chemical potential is in the band gap, suggesting their robustness under significant edge-bulk scattering. Our result establishes the existence of chiral edge currents in a topological antiferromagnet and offers an alternative for identifying QAH states.

cond-mat.mes-hall↗

arXiv2021

Superconductivity in CuIr2-xAlxTe4 telluride chalcogenides

The relationship between charge-density-wave (CDW) and superconductivity (SC), two vital physical phases in condensed matter physics, has always been the focus of scientists' research over the past decades. Motivated by this research hotspot, we systematically studied the physical properties of the layered telluride chalcogenide superconductors CuIr2-xAlxTe4. Through the resistance and magnetization measurements, we found that the CDW order was destroyed by a small amount of Al doping. Meanwhile, the superconducting transition temperature (Tc) kept changing with the change of doping amount and rose towards the maximum value of 2.75 K when x = 0.075. The value of normalized specific heat jump for the highest Tc sample CuIr1.925Al0.075Te4 was 1.53, which was larger than the BCS value of 1.43 and showed that bulk superconducting nature. In order to clearly show the relationship between SC and CDW states, we propose a phase diagram of Tc vs. doping content.

cond-mat.supr-con↗

arXiv2021

Stable Adversarial Learning under Distributional Shifts

Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data. Recently, there are robust learning methods aiming at this problem by minimizing the worst-case risk over an uncertainty set. However, they equally treat all covariates to form the decision sets regardless of the stability of their correlations with the target, resulting in the overwhelmingly large set and low confidence of the learner.In this paper, we propose Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set and conduct differentiated robustness optimization, where covariates are differentiated according to the stability of their correlations with the target. We theoretically show that our method is tractable for stochastic gradient-based optimization and provide the performance guarantees for our method. Empirical studies on both simulation and real datasets validate the effectiveness of our method in terms of uniformly good performance across unknown distributional shifts.

cs.LG↗

arXiv2021

Observation of robust edge superconductivity in Fe(Se,Te) under strong magnetic perturbation

The iron-chalcogenide high temperature superconductor Fe(Se,Te) (FST) has been reported to exhibit complex magnetic ordering and nontrivial band topology which may lead to novel superconducting phenomena. However, the recent studies have so far been largely concentrated on its band and spin structures while its mesoscopic electronic and magnetic response, crucial for future device applications, has not been explored experimentally. Here, we used scanning superconducting quantum interference device microscopy for its sensitivity to both local diamagnetic susceptibility and current distribution in order to image the superfluid density and supercurrent in FST. We found that in FST with 10% interstitial Fe, whose magnetic structure was heavily disrupted, bulk superconductivity was significantly suppressed whereas edge still preserved strong superconducting diamagnetism. The edge dominantly carried supercurrent despite of a very long magnetic penetration depth. The temperature dependence of the superfluid density and supercurrent distribution were distinctively different between the edge and the bulk. Our Heisenberg modeling showed that magnetic dopants stabilize anti-ferromagnetic spin correlation along the edge, which may contribute towards its robust superconductivity. Our observations hold implication for FST as potential platforms for topological quantum computation and superconducting spintronics.

cond-mat.supr-con↗

arXiv2020

Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision

A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users' experiences and cause irreversible damage. A typical alternative is offline causal inference, which analyzes logged data alone to make decisions. However, these decisions are not adaptive to the new incoming data, and so a wrong decision will continuously hurt users' experiences. To overcome the aforementioned limitations, we propose a framework to unify offline causal inference algorithms (e.g., weighting, matching) and online learning algorithms (e.g., UCB, LinUCB). We propose novel algorithms and derive bounds on the decision accuracy via the notion of "regret". We derive the first upper regret bound for forest-based online bandit algorithms. Experiments on two real datasets show that our algorithms outperform other algorithms that use only logged data or online feedbacks, or algorithms that do not use the data properly.

cs.LG↗

arXiv2020

SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems

On social network platforms, a user's behavior is based on his/her personal interests, or influenced by his/her friends. In the literature, it is common to model either users' personal preference or their socially influenced preference. In this paper, we present a novel deep learning model SocialTrans for social recommendations to integrate these two types of preferences. SocialTrans is composed of three modules. The first module is based on a multi-layer Transformer to model users' personal preference. The second module is a multi-layer graph attention neural network (GAT), which is used to model the social influence strengths between friends in social networks. The last module merges users' personal preference and socially influenced preference to produce recommendations. Our model can efficiently fit large-scale data and we deployed SocialTrans to a major article recommendation system in China. Experiments on three data sets verify the effectiveness of our model and show that it outperforms state-of-the-art social recommendation methods.

cs.IR↗

arXiv2020

Charge density wave and superconductivity in the family of telluride chalcogenides Zn1-xCuxIr2-yN(N = Al, Ti, Rh)yTe4

The interplay between superconductivity and charge density wave (CDW)/metal-to-insulator transition (MIT) has long been interested and studied in condensed matter physics. Here we study systematically the charge density wave and superconductivity properties in the solid solutions Zn1-xCuxIr2-yN(N = Al, Ti, Rh)yTe4. Resistivity, magnetic susceptibility and specific heat measurements indicate that the CDW state was suppressed immediately while the superconducting critical temperature (Tc) differs from each system. In the Al- and Ti-substitution cases, Tc increase as y increases and reaches a maximum around 2.75 K and 2.84 K respectively at y = 0.075, followed by a decrease of Tc before the chemical phase boundary is reached at y = 0.2. Nevertheless, Tc decreases monotonously with Rh-doping content y increases and disappears above 0.3 with measuring temperature down to 2 K. Surprisingly, in the Zn1-xCuxIr2Te4 solid solution, Tc enhances as x increases and reaches a maximum value of 2.82 K for x = 0.5 but subsequently survives over the whole doping range of 0.00 - 0.9 despite Tc changes slightly with higher doping content, which differs from the observation of zinc doping suppressing the superconductivity quickly in the high Tc cuprate superconductors. The specific heat anomaly at the superconducting transitions for the representative optimal doping samples are all slightly higher than the BCS value of 1.43 and indicate bulk superconductivity in these compounds. Finally, the CDW transition temperature (TCDW) and superconducting transition temperature (Tc) vs. x/y content phase diagrams of Zn1-xCuxIr2-yN(N = Al, Ti, Rh)yTe4 have been established and compared, which offers good opportunity to study the competition between CDW and superconductivity in the telluride chalcogenides.

cond-mat.supr-con↗

arXiv2019

Superconductivity in the Ru-Doped CuIr2Te4 Telluride Chalcogenide

Here we report the effect of structural and superconductivity properties on Ru doped CuIr2Te4 telluride chalcogenide. XRD results suggest that the CuIr2-xRuxTe4 maintain the disordered trigonal structure with space group P3m1 (No. 164) for x less than 0.3. The lattice constants, a and c, both decrease with increasing Ru content. Temperature-dependent resistivity, magnetic susceptibility and specific-heat measurements are performed to characterize the superconducting properties systematically. Our results suggest that the optimal doping level for superconductivity in CuIr2-xRuxTe4 is x = 0.05, where Tc is 2.79 K with the Sommerfeld constant gamma of 11.52 mJ mol-1 K-2 and the specific-heat anomaly at the superconducting transition, is approximately 1.51, which is higher than the BCS value of 1.43, indicating CuIr1.95Ru0.05Te4 is a strongly electron-phonon coupled superconductor. The values of lower critical filed and upper critical field calculated from isothermal magnetization and magneto-transport measurements are 0.98 KOe and 2.47 KOe respectively, signifying that the compound is clearly a type-II superconductor. Finally, a dome-like shape superconducting Tcs vs. x content phase diagram is established, where the charge density wave disappears at x = 0.03 while superconducting transition temperature (Tc) rises until it reaches its peak at x = 0.05, then, with decreasing when x reaches 0.3. This feature of the competition between CDW and the superconductivity could be caused by tuning the Fermi surface and density of states with Ru chemical doping.

cond-mat.supr-con↗

arXiv2019

NbSeTe -A New Layered Transition Metal Dichalcogenide Superconductor

Transition metal dichalcogenides (TMDCs) usually exhibit layered polytypic structures due to the weak interlayer coupling. 2H-NbSe2 is one of the most widely studied in the pristine TMDC family due to its high superconducting transition temperature (Tc = 7.3K) and the occurrence of a charge-density wave (CDW) order below 33 K. The coexistence of CDW with superconductivity poses an intriguing open question about the relationship between Fermi surface nesting and Cooper pairing. Past studies of this issue have mostly been focused on doping 2H-NbSe2 by 3d transition metals without significantly changing its crystal structure. Here we replaced the Se by Te in 2H-NbSe2 in order to design a new 1T polytype layered TMDC NbSeTe, which adopts a trigonal structure with space group P-3m1. We successfully grew large size and high-quality single crystals of 1T-NbSeTe via the vapor transport method using I2 as the transport agent. Temperature-dependent resistivity and specific heat data revealed a bulk Tc at 1.3 K, which is the first observation of superconductivity in pure 1T-NbSeTe phase. This compound enlarged the family of superconducting TMDCs and provides an opportunity to study the interplay between CDW and superconductivity in the trigonal structure.

cond-mat.supr-con↗

arXiv2019

The Unusual Suppression of Superconducting Transition Temperature in Double-Doping 2H-NbSe$_2$

2H-NbSe2 is one of the most widely researched transition metal dichalcogenide (TMD) superconductors, which undergoes charge-density wave (CDW) transition at TCDW about 33 K and superconducting transition at Tc of 7.3 K. To explore the relation between its superconductivity and Fermi surface nesting, we combined S substitution with Cu intercalation in 2H-NbSe2 to make CuxNbSe2-ySy. Upon systematic substitution of S and intercalation of Cu ions into 2H-NbSe2, we found that when the Cu and S contents increases, the Tc decreases in CuxNbSe2-ySy. While at higher x and y values, Tc keeps a constant value near 2 K, which is not commonly observed for a layered TMD. For comparison, we found the simultaneous substitution of Nb by Cu and Se by S in CuxNb1-xSe2-ySy lowered the Tc substantially faster. We construct a superconducting phase diagrams for our double-doping compounds in contrast with the related single-ions doping systems.

cond-mat.supr-con↗

arXiv2019

Quantum oscillations and nontrivial topological state in a compensated semimetal TaP2

We report systematic magneto-transport measurements and ab initio calculations on single-crystalline TaP2, a new member of the transition-metal dipnictides. We observed unsaturated magnetoresistance (MR) reaching ~ 700% at a magnetic field (B) of 9 T at 2 K along with striking Shubnikov-de Hass (SdH) oscillations. Our analysis on the SdH oscillations reveals nonzero Berry phase, indicating nontrivial band topology. The analysis also uncovers three fundamental magnetic oscillation frequencies of 72 T, 237 T, and 356 T, consistent with the theoretical calculations which reveal one hole pocket and two electron pockets at the L point and one electron pocket at the Z point of the Brillouin zone. We also found negative longitudinal MR (n-MR) within a narrow window of the angles between B and the electric current (I). The n-MR could be fitted with the Adler-Bell-Jackiw chiral anomaly equation but the origin remains yet ambiguous. The ab inito calculations suggest TaP2 as a weak topological insulator with the Z2 indices of (0; 111), which exhibits topological surface states on the (001) surface.

cond-mat.str-el↗

arXiv2017

Boosting Information Spread: An Algorithmic Approach

The majority of influence maximization (IM) studies focus on targeting influential seeders to trigger substantial information spread in social networks. In this paper, we consider a new and complementary problem of how to further increase the influence spread of given seeders. Our study is motivated by the observation that direct incentives could "boost" users so that they are more likely to be influenced by friends. We study the $k$-boosting problem which aims to find $k$ users to boost so that the final "boosted" influence spread is maximized. The $k$-boosting problem is different from the IM problem because boosted users behave differently from seeders: boosted users are initially uninfluenced and we only increase their probability to be influenced. Our work also complements the IM studies because we focus on triggering larger influence spread on the basis of given seeders. Both the NP-hardness of the problem and the non-submodularity of the objective function pose challenges to the $k$-boosting problem. To tackle the problem on general graphs, we devise two efficient algorithms with the data-dependent approximation ratio. For the $k$-boosting problem on bidirected trees, we present an efficient greedy algorithm and a rounded dynamic programming that is a fully polynomial-time approximation scheme. We conduct extensive experiments using real social networks and synthetic bidirected trees. We show that boosting solutions returned by our algorithms achieves boosts of influence that are up to several times higher than those achieved by boosting solutions returned by intuitive baselines, which have no guarantee of solution quality. We also explore the "budget allocation" problem in our experiments. Compared with targeting seeders with all budget, larger influence spread is achieved when we allocation the budget to both seeders and boosted users.

cs.SI↗

arXiv2014

Algorithmic Design for Competitive Influence Maximization Problems

Given the popularity of the viral marketing campaign in online social networks, finding an effective method to identify a set of most influential nodes so to compete well with other viral marketing competitors is of upmost importance. We propose a "General Competitive Independent Cascade (GCIC)" model to describe the general influence propagation of two competing sources in the same network. We formulate the "Competitive Influence Maximization (CIM)" problem as follows: Under a prespecified influence propagation model and that the competitor's seed set is known, how to find a seed set of $k$ nodes so as to trigger the largest influence cascade? We propose a general algorithmic framework TCIM for the CIM problem under the GCIC model. TCIM returns a $(1-1/e-ε)$-approximate solution with probability at least $1-n^{-\ell}$, and has an efficient time complexity of $O(c(k+\ell)(m+n)\log n/ε^2)$, where $c$ depends on specific propagation model and may also depend on $k$ and underlying network $G$. To the best of our knowledge, this is the first general algorithmic framework that has both $(1-1/e-ε)$ performance guarantee and practical efficiency. We conduct extensive experiments on real-world datasets under three specific influence propagation models, and show the efficiency and accuracy of our framework. In particular, we achieve up to four orders of magnitude speedup as compared to the previous state-of-the-art algorithms with the approximate guarantee.

cs.SI↗