arXiv subjects

Yupeng Li

Publications and source records attributed to Yupeng Li.

At least 55 records · Page 3Linked to original sources

arXiv2024

Discovery of a Magnetic Topological Semimetal Eu$_3$In$_2$As$_4$ with a Single Pair of Weyl Points

Magnetic Weyl semimetal (MWS) is a unique topological state with open surface Fermi arc states and other exotic transport phenomena. However, most reported MWSs show multiple pairs of Weyl points and complicated Fermi surfaces, which increases the difficulty of the investigation into the intrinsic chiral transport property. In this wor, we successfully synthesized a soft magnetic Weyl semimetal Eu$_3$In$_2$As$_4$ with a single pair of Weyl points under magnetic fields. The Shubnikov de Haas (SdH) oscillation with a single frequency, as well as a linear hall resistance with the same carrier density, is observed up to 50 Tesla, indicating a single pair of Weyl points around the Fermi level with a massless fermion ($m^* = 0.121 m_0$, $π$ Berry phase). Such a single pair of Weyl points is further confirmed by the density functional theory calculations. The magnetic ordering and band topology can be easily tuned by the external magnetic field. The field-induced MWS Eu$_3$In$_2$As$_4$ with a single pair of Weyl points is a good platform to detect chiral transport properties, including possible quantum anomalous Hall effect.

cond-mat.mes-hall↗

arXiv2023

Adversarial Bandits with Multi-User Delayed Feedback: Theory and Application

The multi-armed bandit (MAB) models have attracted significant research attention due to their applicability and effectiveness in various real-world scenarios such as resource allocation, online advertising, and dynamic pricing. As an important branch, the adversarial MAB problems with delayed feedback have been proposed and studied by many researchers recently where a conceptual adversary strategically selects the reward distributions associated with each arm to challenge the learning algorithm and the agent experiences a delay between taking an action and receiving the corresponding reward feedback. However, the existing models restrict the feedback to be generated from only one user, which makes models inapplicable to the prevailing scenarios of multiple users (e.g. ad recommendation for a group of users). In this paper, we consider that the delayed feedback results are from multiple users and are unrestricted on internal distribution. In contrast, the feedback delay is arbitrary and unknown to the player in advance. Also, for different users in a round, the delays in feedback have no assumption of latent correlation. Thus, we formulate an adversarial MAB problem with multi-user delayed feedback and design a modified EXP3 algorithm MUD-EXP3, which makes a decision at each round by considering the importance-weighted estimator of the received feedback from different users. On the premise of known terminal round index $T$, the number of users $M$, the number of arms $N$, and upper bound of delay $d_{max}$, we prove a regret of $\mathcal{O}(\sqrt{TM^2\ln{N}(N\mathrm{e}+4d_{max})})$. Furthermore, for the more common case of unknown $T$, an adaptive algorithm AMUD-EXP3 is proposed with a sublinear regret with respect to $T$. Finally, extensive experiments are conducted to indicate the correctness and effectiveness of our algorithms.

cs.LG↗

arXiv2023

A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions

The 5' UTR, a regulatory region at the beginning of an mRNA molecule, plays a crucial role in regulating the translation process and impacts the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduced a language model for 5' UTR, which we refer to as the UTR-LM. The UTR-LM is pre-trained on endogenous 5' UTRs from multiple species and is further augmented with supervised information including secondary structure and minimum free energy. We fine-tuned the UTR-LM in a variety of downstream tasks. The model outperformed the best-known benchmark by up to 42% for predicting the Mean Ribosome Loading, and by up to 60% for predicting the Translation Efficiency and the mRNA Expression Level. The model also applies to identifying unannotated Internal Ribosome Entry Sites within the untranslated region and improves the AUPR from 0.37 to 0.52 compared to the best baseline. Further, we designed a library of 211 novel 5' UTRs with high predicted values of translation efficiency and evaluated them via a wet-lab assay. Experiment results confirmed that our top designs achieved a 32.5% increase in protein production level relative to well-established 5' UTR optimized for therapeutics.

cs.LG↗

arXiv2023

The Influence of Dimensionality on the Charge Density Wave Transition and Its Application on Mid-infrared Photodetection

Two-dimensional charge density wave (CDW) materials received much attention for high responsivity and broadband photodetection in recent years, due to their collective electron transport and narrow bandgap. However, the high dark current density problem hinders their real application. Here we report a sharp CDW transition in quasi-1D (TaSe4)2I, and apply it for broadband photodetection. Especially at mid-infrared region, the device shows both high photo responsivity of 1.18e3 A/W and large light on-off ratio of 80, which is superior than 2D CDW TaS2 and most reported low-dimensional materials. The fact for such high performance lies on two aspects. One is the much lower dark current density resulted from the pseudo gap associated with 1D Luttinger liquid state, which is supported by finite size scaling of nonlinear I-V at variable temperatures and occurrence of 1D structural phase transition consolidated by In-situ Raman spectroscopy. The other is the high photocurrent associated with the Frohlich superconductivity state, manifested by an ultrasensitive switching, which can be only accessible in 1D CDW materials, in agreement with our density functional theory calculation. Our work thus reveals the pivotal role of dimensionality in CDW phase transition, and paves a way for implementing highly sensitive broadband photodetector.

cond-mat.mtrl-sci↗

arXiv2023

An Online Resource Scheduling for Maximizing Quality-of-Experience in Meta Computing

Meta Computing is a new computing paradigm, which aims to solve the problem of computing islands in current edge computing paradigms and integrate all the resources on a network by incorporating cloud, edge, and particularly terminal-end devices. It throws light on solving the problem of lacking computing power. However, at this stage, due to technical limitations, it is impossible to integrate the resources of the whole network. Thus, we create a new meta computing architecture composed of multiple meta computers, each of which integrates the resources in a small-scale network. To make meta computing widely applied in society, the service quality and user experience of meta computing cannot be ignored. Consider a meta computing system providing services for users by scheduling meta computers, how to choose from multiple meta computers to achieve maximum Quality-of-Experience (QoE) with limited budgets especially when the true expected QoE of each meta computer is not known as a priori? The existing studies, however, usually ignore the costs and budgets and barely consider the ubiquitous law of diminishing marginal utility. In this paper, we formulate a resource scheduling problem from the perspective of the multi-armed bandit (MAB). To determine a scheduling strategy that can maximize the total QoE utility under a limited budget, we propose an upper confidence bound (UCB) based algorithm and model the utility of service by using a concave function of total QoE to characterize the marginal utility in the real world. We theoretically upper bound the regret of our proposed algorithm with sublinear growth to the budget. Finally, extensive experiments are conducted, and the results indicate the correctness and effectiveness of our algorithm.

cs.DC↗

arXiv2023

A Survey of Machine Learning-Based Ride-Hailing Planning

Ride-hailing is a sustainable transportation paradigm where riders access door-to-door traveling services through a mobile phone application, which has attracted a colossal amount of usage. There are two major planning tasks in a ride-hailing system: (1) matching, i.e., assigning available vehicles to pick up the riders, and (2) repositioning, i.e., proactively relocating vehicles to certain locations to balance the supply and demand of ride-hailing services. Recently, many studies of ride-hailing planning that leverage machine learning techniques have emerged. In this article, we present a comprehensive overview on latest developments of machine learning-based ride-hailing planning. To offer a clear and structured review, we introduce a taxonomy into which we carefully fit the different categories of related works according to the types of their planning tasks and solution schemes, which include collective matching, distributed matching, collective repositioning, distributed repositioning, and joint matching and repositioning. We further shed light on many real-world datasets and simulators that are indispensable for empirical studies on machine learning-based ride-hailing planning strategies. At last, we propose several promising research directions for this rapidly growing research and practical field.

cs.LG↗

arXiv2022

Intensity modulated proton arc therapy via geometry-based energy selection for ependymoma

We developed a novel method of creating intensity modulated proton arc therapy (IMPAT) plans that uses computing resources efficiently and may offer a dosimetric benefit for patients with ependymoma or similar tumor geometries. Our IMPAT planning method consists of a geometry-based energy selection step with major scanning spot contributions as inputs computed using ray-tracing and single-Gaussian approximation of lateral spot profiles. Based on the geometric relation of scanning spots and dose voxels, our energy selection module selects a minimum set of energy layers at each gantry angle such that each target voxel is covered by sufficient scanning spots as specified by the planner, with dose contributions above the specified threshold. Finally, IMPAT plans are generated by robustly optimizing scanning spots of the selected energy layers using a commercial proton treatment planning system. The IMPAT plan quality was assessed for four ependymoma patients. Reference three-field IMPT plans were created with similar planning objective functions and compared with the IMPAT plans. In all plans, the prescribed dose covered 95% of the clinical target volume (CTV) while maintaining similar maximum doses for the brainstem. While IMPAT and IMPT achieved comparable plan robustness, the IMPAT plans achieved better homogeneity and conformity than the IMPT plans. The IMPAT plans also exhibited higher relative biological effectiveness (RBE) enhancement than did the corresponding reference IMPT plans for the CTV in all four patients and brainstem in three of them. The proposed method demonstrated potential as an efficient technique for IMPAT planning and may offer a dosimetric benefit for patients with ependymoma or tumors in close proximity to critical organs. IMPAT plans created using this method had elevated RBE enhancement associated with increased linear energy transfer.

physics.med-ph↗

arXiv2022

Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads

Target-specific stance detection on social media, which aims at classifying a textual data instance such as a post or a comment into a stance class of a target issue, has become an emerging opinion mining paradigm of importance. An example application would be to overcome vaccine hesitancy in combating the coronavirus pandemic. However, existing stance detection strategies rely merely on the individual instances which cannot always capture the expressed stance of a given target. In response, we address a new task called conversational stance detection which is to infer the stance towards a given target (e.g., COVID-19 vaccination) when given a data instance and its corresponding conversation thread. To tackle the task, we first propose a benchmarking conversational stance detection (CSD) dataset with annotations of stances and the structures of conversation threads among the instances based on six major social media platforms in Hong Kong. To infer the desired stances from both data instances and conversation threads, we propose a model called Branch-BERT that incorporates contextual information in conversation threads. Extensive experiments on our CSD dataset show that our proposed model outperforms all the baseline models that do not make use of contextual information. Specifically, it improves the F1 score by 10.3% compared with the state-of-the-art method in the SemEval-2016 Task 6 competition. This shows the potential of incorporating rich contextual information on detecting target-specific stances on social media platforms and implies a more practical way to construct future stance detection tasks.

cs.CL↗

arXiv2021

Coexistence of superconductivity and antiferromagentic order in Er$_{2}$O$_{2}$Bi with anti-ThCr$_{2}$Si$_{2}$ structure

We investigated the coexistence of superconductivity and antiferromagnetic order in the compound Er$_{2}$O$_{2}$Bi with anti-ThCr$_{2}$Si$_{2}$-type structure through resistivity, magnetization, specific heat measurements and first-principle calculations. The superconducting transition temperature $T_{\rm c}$ of 1.23 K and antiferromagnetic transition temperature $T_{\rm N}$ of 3 K are observed in the sample with the best nominal composition. The superconducting upper critical field $H_{\rm c2}$(0) and electron-phonon coupling constant $λ$$_{e-ph}$ in Er$_{2}$O$_{2}$Bi are similar to those in the previously reported non-magnetic superconductor Y$_{2}$O$_{2}$Bi with the same structure, indicating that the superconductivity in Er$_{2}$O$_{2}$Bi may have the same origin as in Y$_{2}$O$_{2}$Bi. The first-principle calculations of Er$_{2}$O$_{2}$Bi show that the Fermi surface is mainly composed of the Bi 6$p$ orbitals both in the paramagnetic and antiferromagnetic state, implying minor effect of the 4$f$ electrons on the Fermi surface. Besides, upon increasing the oxygen incorporation in Er$_{2}$O$_{x}$Bi, $T_{\rm c}$ increases from 1 to 1.23 K and $T_{\rm N}$ decreases slightly from 3 to 2.96 K, revealing that superconductivity and antiferromagnetic order may compete with each other. The Hall effect measurements indicate that hole-type carrier density indeed increases with increasing oxygen content, which may account for the variations of $T_{\rm c}$ and $T_{\rm N}$ with different oxygen content.

cond-mat.supr-con↗

arXiv2020

Enhanced anisotropic superconductivity in the topological nodal-line semimetal InxTaS2

Coexistence of topological bands and charge density wave (CDW) in topological materials has attracted immense attentions because of their fantastic properties, such as axionic-CDW, three-dimensional quantum Hall effect, etc. In this work, a nodal-line semimetal InxTaS2 characterized by CDW and superconductivity is successfully synthesized, whose structure and topological bands (two separated Wely rings) are similar to In0.58TaSe2. A 2 x 2 commensurate CDW is observed at low temperature in InxTaS2, identified by transport properties and STM measurements. Moreover, superconductivity emerges below 0.69 K, and the anisotropy ratio of upper critical field [Gamma = H||ab c2(0)=H||c c2(0)] is significantly enhanced compared to 2H-TaS2, which shares the same essential layer unit. According to the Lawrence-Doniach model, the enhanced Gamma may be explained by the reduced effective mass in kx-ky plane, where Weyl rings locate. Therefore, this type of layered topological systems may offer a platform to investigate highly anisotropic superconductivity and to understand the extremely large upper critical field in the bulk or in the two-dimensional limit.

cond-mat.supr-con↗

arXiv2020

Anisotropic gapping of topological Weyl rings in the charge-density-wave superconductor InxTaSe2

Topological materials and topological phases have recently become a hot topic in condensed matter physics. In this work, we report a topological nodal-line semimetal InxTaSe2, in the presence of both charge density wave (CDW) and superconductivity. In the x = 0.58 samples, the 2 * /3 commensurate CDW (CCDW) and the 2 * 2 CCDW are observed below 116 K and 77 K, respectively. Consistent with theoretical calculations, the spin-orbital coupling gives rise to two two-fold-degenerate nodal rings (Weyl rings) connected by drumhead surface states, confirmed by angle-resolved photoemission spectroscopy. Our results suggest that the 2 * 2 CCDW ordering gaps out one Weyl ring in accordance with the CDW band folding, while the other Weyl ring remains gapless with intact surface states. In addition, superconductivity emerges at 0.91 K, with the upper critical field deviating from the s-wave behavior at low temperature, implying possibly unconventional superconductivity. Therefore, InxTaSe2 represents an interesting material system to study the interplay between CDW, nontrivial band topology and superconductivity.

cond-mat.supr-con↗

arXiv2020

Mobile big data analysis with machine learning

This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently adopted methods of data analysis are reviewed. Three typical applications of MBD analysis, namely wireless channel modeling, human online and offline behavior analysis, and speech recognition in the internet of vehicles, are introduced respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

cs.LG↗

arXiv2019

Exploring Topological Superconductivity in Topological Materials

The exploration of topological superconductivity and Majorana zero modes has become a rapidly developing field. Many types of proposals to realize topological superconductors have been presented, and significant advances have been recently made. In this review, we conduct a survey on the experimental progress in possible topological superconductors and induced superconductivity in topological insulators or semimetals as well as artificial structures. The approaches to inducing superconductivity in topological materials mainly include high pressure application, the hard-tip point contact method, chemical doping or intercalation, the use of artificial topological superconductors, and electric field gating. The evidence supporting topological superconductivity and signatures of Majorana zero modes are also discussed and summarized.

cond-mat.supr-con↗

arXiv2018

Pressure-induced superconductivity in topological semimetal NbAs2

Topological superconductivity with Majorana bound states, which are critical to implement nonabelian quantum computation, may be realized in three-dimensional semimetals with nontrivial topological feature, when superconducting transition occurs in the bulk. Here, we report pressure-induced superconductivity in a transition-metal dipnictide NbAs2. The emergence of superconductivity is not accompanied by any structural phase transition up to the maximum experimental pressure of 29.8 GPa, as supported by pressure-dependent synchrotron X-ray diffraction and Raman spectroscopy. Intriguingly, the Raman study reveals rapid phonon mode hardening and broadening above 10 GPa, in coincident with the superconducting transition. Using first principle calculations, we determine Fermi surface change induced by pressure, which steadily increases the density of states without breaking the electron-hole compensation. Noticeably, the main hole pocket of NbAs2 encloses one time-reversal-invariant momenta of the monoclinic lattice, suggesting NbAs2 as a candidate of topological superconductors.

cond-mat.supr-con↗

arXiv2018

Quantum transport in a compensated semimetal W2As3 with nontrivial Z2 indices

We report a topological semimetal W2As3 with a space group C2/m. Based on the first-principles calculations, band crossings are partially gapped when spin-orbit coupling is included. The Z2 indices at the electron filling are [1;111], characterizing a strong topological insulator and topological surface states. From the magnetotransport measurements, nearly quadratic field dependence of magnetoresistance (MR) (B || [200]) at 3 K indicates an electron-hole compensated compound whose longitudinal MR reaches 115 at 3 K and 15 T. In addition, multiband features are detected from the high-magnetic-field Shubnikov-de Haas (SdH) oscillation, Hall resistivity, and band calculations. A nontrivial pi Berry's phase is obtained, suggesting the topological feature of this material. A two- band model can fit well the conductivity and Hall coefficient. Our experiments manifest that the transport properties of W2As3 are in good agreement with the theoretical calculations.

cond-mat.mtrl-sci↗

arXiv2018

Superconductivity in a misfit layered compound (SnSe)$_{1.16}$(NbSe$_2$)

The large size single crystals of (SnSe)$_{1.16}$(NbSe$_2$) misfit layered compound were grown and superconductivity with $T_c$ of 3.4 K was first discovered in this system. Powder X-ray diffraction (XRD) and high resolution transmission electron microscopy (HRTEM) clearly display the misfit feature between SnSe and NbSe$_2$ subsystems. The Sommerfeld coefficient $γ$ inferred from specific-heat measurements is 16.73 mJ mol$^{-1}$ K$^{-2}$, slightly larger than the usual misfit compounds. The normalized specific heat jump $Δ$$C_e$/$γ$$T_{\rm c}$ is about 0.98, and the electron-phonon coupling constant $λ$$_{e-ph}$ is estimated to be 0.80. The estimated value of the in-plane upper critical magnetic field, $H_{c2}^{ab}$(0), is about 7.82 T, exceeding the Pauli paramagnetic limit slightly. Both the specific-heat and $H_{c2}$ data suggest that (SnSe)$_{1.16}$(NbSe$_2$) is a multi-band superconductor.

cond-mat.supr-con↗

arXiv2018

Resilient Disaster Recovery Logistics of Distribution Systems: Co-Optimize Service Restoration with Repair Crew and Mobile Power Source Dispatch

Repair crews (RCs) and mobile power sources (MPSs) are critical resources for distribution system (DS) outage management after a natural disaster. However, their logistics is not well investigated. We propose a resilient scheme for disaster recovery logistics to co-optimize DS restoration with dispatch of RCs and MPSs. A novel co-optimization model is formulated to route RCs and MPSs in the transportation network, schedule them in the DS, and reconfigure the DS for microgrid formation coordinately, etc. The model incorporates different timescales of DS restoration and RC/MPS dispatch, the coupling of transportation and power networks, etc. To ensure radiality of the DS with variable physical structure and MPS allocation, we also model topology constraints based on the concept of spanning forest. The model is convexified equivalently and linearized into a mixed-integer linear programming. To reduce its computation time, preprocessing methods are proposed to pre-assign a minimal set of repair tasks to depots and reduce the number of candidate nodes for MPS connection. Resilient recovery strategies thus are generated to enhance service restoration, especially by dynamic formation of microgrids that are powered by MPSs and topologized by repair actions of RCs and network reconfiguration of the DS. Case studies demonstrate the proposed methodology.

math.OC↗

arXiv2018

Pressure induced superconductivity bordering a charge-density-wave state in NbTe4 with strong spinorbit coupling

Transition-metal chalcogenides host various phases of matter, such as charge-density wave (CDW), superconductors, and topological insulators or semimetals. Superconductivity and its competition with CDW in low-dimensional compounds have attracted much interest and stimulated considerable research. Here we report pressure induced superconductivity in a strong spin-orbit (SO) coupled quasi-one-dimensional (1D) transition-metal chalcogenide NbTe$_4$, which is a CDW material under ambient pressure. With increasing pressure, the CDW transition temperature is gradually suppressed, and superconducting transition, which is fingerprinted by a steep resistivity drop, emerges at pressures above 12.4 GPa. Under pressure $p$ = 69 GPa, zero resistance is detected with a transition temperature $T_c$ = 2.2 K and an upper critical field $H_{c2}$= 2 T. We also find large magnetoresistance (MR) up to 102\% at low temperatures, which is a distinct feature differentiating NbTe$_4$ from other conventional CDW materials.

cond-mat.supr-con↗