arXiv subjects

Cheng Xu

Publications and source records attributed to Cheng Xu.

At least 73 records · Page 4Linked to original sources

arXiv2024

Where do they come from? Identification of globular cluster escaped stars

Globular clusters (GCs), as old as our Galaxy, constantly lose their members to the field as they cross through the Milky Way (MW). These GC escaped stars (or escapees) are suggested to contribute significantly to the MW halo. If a star has left the host GC a long time ago, chemical finger prints, e.g., N enrichment, may reveal its origin. In this work, we aim to establish dynamical connections between N-rich field stars recently identified by LAMOST and the existing MW GCs. By constructing the full action distribution, and combining with metallicity, we found 29 potential GC progenitors for 15 N-rich field stars. Particularly, some of them may be related to MW accretion events. On the other hand, if a star recently left its host GC via tidal evaporation, it still maintain the kinematic properties of the cluster. Here we identify extra-tidal candidates based on their spatial locations, proper motions (PMs), and their position on color-magnitude-diagrams (CMDs). We successfully identified more than 1600 extra-tidal candidates in the vicinity of six Gaia-Enceladus (GE)-related GCs (i.e., NGC 1851, NGC 1904, NGC 6205, NGC 6341, NGC 6779, NGC 7089). The density map of the extra-tidal candidates is confirmed to be an efficient way to find extra-tidal structures. The possible two density peaks at opposite directions of the inner boundary is a good indicator for long stellar stream. Among 95 extra-tidal candidates with spectroscopic radial velocities and metallicity, 54 of them are confirmed to be GC escaped stars, as they share similar properties as host GCs. These extra-tidal candidates are ideal targets for follow-up spectroscopic observation, as it greatly improves the scientific outcome. Once statistically significant number of spectroscopic radial velocities and metallicities are available, the GC dynamical evolution (e.g., mass loss, rotation) can be carefully investigated.

astro-ph.GA↗

arXiv2024

COLE: A Column-based Learned Storage for Blockchain Systems

Blockchain systems suffer from high storage costs as every node needs to store and maintain the entire blockchain data. After investigating Ethereum's storage, we find that the storage cost mostly comes from the index, i.e., Merkle Patricia Trie (MPT). To support provenance queries, MPT persists the index nodes during the data update, which adds too much storage overhead. To reduce the storage size, an initial idea is to leverage the emerging learned index technique, which has been shown to have a smaller index size and more efficient query performance. However, directly applying it to the blockchain storage results in even higher overhead owing to the requirement of persisting index nodes and the learned index's large node size. To tackle this, we propose COLE, a novel column-based learned storage for blockchain systems. We follow the column-based database design to contiguously store each state's historical values, which are indexed by learned models to facilitate efficient data retrieval and provenance queries. We develop a series of write-optimized strategies to realize COLE in disk environments. Extensive experiments are conducted to validate the performance of the proposed COLE system. Compared with MPT, COLE reduces the storage size by up to 94% while improving the system throughput by $1.4\times$-$5.4\times$.

cs.DB↗

arXiv2024

Maximally Localized Wannier Orbitals, Interaction Models and Fractional Quantum Anomalous Hall Effect in Twisted Bilayer MoTe2

We investigate the moiré band structures and the strong correlation effects in twisted bilayer MoTe$_2$ for a wide range of twist angles, employing a combination of various techniques. Using large-scale first principles calculations, we pinpoint realistic continuum modeling parameters, subsequently deriving the maximally localized Wannier functions for the top three moiré bands. Simplifying our model with reasonable assumptions, we obtain a minimal two-band model, encompassing Coulomb repulsion, correlated hopping, and spin exchange. Our minimal interaction models pave the way for further exploration of the rich many-body physics in twisted MoTe$_2$. Furthermore, we explore the phase diagrams of the system through Hartree-Fock approximation and exact diagonalization. Our two-band exact diagonalization analysis underscores significant band-mixing effects in this system, which enlarge the optimal twist angle for fractional quantum anomalous Hall states.

cond-mat.str-el↗

arXiv2023

Chemo-dynamical Nature of the Anticenter Stream and Monoceros Ring

In the epoch of deep photometric surveys, a large number of substructures, e.g., over-densities, streams, were identified. With the help of astrometry and spectroscopy, the community revealed a complex picture of our Milky Way (MW) after investigating their origins. Off-plane substructures Anticenter Stream (ACS) and Monoceros Ring (MNC), once considered as dissolving dwarf galaxies, were later found to share similar kinematics and metallicity with the Galactic outer thin disk. In this work, we aim to chemically tag ACS and MNC with high-accuracy abundances from the APOGEE survey. By extrapolating chemical abundance trends in the outer thin disk region (10 < Rgc < 18 kpc, 0 < |Zgc| < 3kpc), we found that ACS and MNC stars show consistent chemical abundances as the extrapolating values for 12 elements, including C, N, O, Mg, Al, Si, K, Ca, Cr, Mn, Co and Ni. The similar chemical patterns indicate that ACS and MNC have similar star formation history as the MW outer thin disk, meanwhile, we also excluded their dwarf galaxy association, as they are distinctive in multiple chemical spaces. The ages of ACS and MNC stars are consistent with the time of the first Sgr dSph passage, indicating their possible connection.

astro-ph.GA↗

arXiv2023

Observation of integer and fractional quantum anomalous Hall effects in twisted bilayer MoTe2

The interplay between strong correlations and topology can lead to the emergence of intriguing quantum states of matter. One well-known example is the fractional quantum Hall effect, where exotic electron fluids with fractionally charged excitations form in partially filled Landau levels. The emergence of topological moiré flat bands provides exciting opportunities to realize the lattice analogs of both the integer and fractional quantum Hall states without the need for an external magnetic field. These states are known as the integer and fractional quantum anomalous Hall (IQAH and FQAH) states. Here, we present direct transport evidence of the existence of both IQAH and FQAH states in twisted bilayer MoTe2 (AA stacked). At zero magnetic field, we observe well-quantized Hall resistance of h/e2 around moiré filling factor ν = -1 (corresponding to one hole per moiré unit cell), and nearly-quantized Hall resistance of 3h/2e2 around ν = -2/3, respectively. Concomitantly, the longitudinal resistance exhibits distinct minima around ν = -1 and -2/3. The application of an electric field induces topological quantum phase transition from the IQAH state to a charge transfer insulator at ν = -1, and from the FQAH state to a generalized Wigner crystal state, further transitioning to a metallic state at ν = -2/3. Our study paves the way for the investigation of fractionally charged excitations and anyonic statistics at zero magnetic field based on semiconductor moiré materials.

cond-mat.mes-hall↗

arXiv2023

The role of tidal interactions in the formation of slowly rotating early-type stars in young star clusters

The split main sequences found in the colour-magnitude diagrams of star clusters younger than ~600 Myr are suggested to be caused by the dichotomy of stellar rotation rates of upper main-sequence stars. Tidal interactions have been suggested as a possible explanation of the dichotomy of the stellar rotation rates. This hypothesis proposes that the slow rotation rates of stars along the split main sequences are caused by tidal interactions in binaries. To test this scenario, we measured the variations in the radial velocities of slowly rotating stars along the split main sequence of the young Galactic cluster NGC 2422 (~90 Myr) using spectra obtained at multiple epochs with the Canada-France-Hawai'i Telescope. Our results show that most slowly rotating stars are not radial-velocity variables. Using the theory of dynamical tides, we find that the binary separations necessary to fully or partially synchronise our spectroscopic targets, on time-scales shorter than the cluster age, predict much larger radial velocity variations across multiple-epoch observations, or a much larger radial velocity dispersion at a single epoch, than the observed values. This indicates that tidal interactions are not the dominant mechanism to form slowly rotating stars along the split main sequences. As the observations of the rotation velocity distribution among B- and A-type stars in binaries of larger separations hint at a much stronger effect of braking with age, we discuss the consequences of relaxing the constraints of the dynamical tides theory.

astro-ph.SR↗

arXiv2023

MMBench: Benchmarking End-to-End Multi-modal DNNs and Understanding Their Hardware-Software Implications

The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of workloads called multi-modal DNNs. Multi-modal DNNs are capable of interpreting and reasoning about information from multiple modalities, making them more applicable to real-world AI scenarios. In recent research, multi-modal DNNs have outperformed the best uni-modal DNN in a wide range of distributed computing applications from traditional multimedia systems to emerging autonomous edge systems. However, despite their importance and superiority, very limited research attention has been devoted to understand the characteristics of multi-modal DNNs and their implications on current computing software/hardware platforms. Existing benchmarks either target uni-modal DNNs or only focus on the algorithm characteristics of multi-modal DNNs. There lacks representative benchmark suites that provide comprehensive system and architecture level analysis of multi-modal networks. To advance the understanding of these multi-modal DNN workloads and facilitate related research, we present MMBench, an open-source, end-to-end benchmark suite consisting of a set of real-world multi-modal DNN workloads with relevant performance metrics for evaluation. We then use MMBench to conduct an in-depth analysis on the characteristics of multi-modal DNNs. We demonstrate their unique characteristics of clear multi-stage execution, frequent synchronization and high heterogeneity, which distinguish them from conventional uni-modal DNNs. Finally, we conduct a case study and extend our benchmark to edge devices. We hope that our work can provide insights for future software/hardware design and optimization to underpin multi-modal DNNs on both cloud and edge computing platforms.

cs.PF↗

arXiv2022

Efficiently Hardening SGX Enclaves against Memory Access Pattern Attacks via Dynamic Program Partitioning

Intel SGX is known to be vulnerable to a class of practical attacks exploiting memory access pattern side-channels, notably page-fault attacks and cache timing attacks. A promising hardening scheme is to wrap applications in hardware transactions, enabled by Intel TSX, that return control to the software upon unexpected cache misses and interruptions so that the existing side-channel attacks exploiting these micro-architectural events can be detected and mitigated. However, existing hardening schemes scale only to small-data computation, with a typical working set smaller than one or few times (e.g., $8$ times) of a CPU data cache. This work tackles the data scalability and performance efficiency of security hardening schemes of Intel SGX enclaves against memory-access pattern side channels. The key insight is that the size of TSX transactions in the target computation is critical, both performance- and security-wise. Unlike the existing designs, this work dynamically partitions target computations to enlarge transactions while avoiding aborts, leading to lower performance overhead and improved side-channel security. We materialize the dynamic partitioning scheme and build a C++ library to monitor and model cache utilization at runtime. We further build a data analytical system using the library and implement various external oblivious algorithms. Performance evaluation shows that our work can effectively increase transaction size and reduce the execution time by up to two orders of magnitude compared with the state-of-the-art solutions.

cs.CR↗

arXiv2022

On Shapley Value in Data Assemblage Under Independent Utility

In many applications, an organization may want to acquire data from many data owners. Data marketplaces allow data owners to produce data assemblage needed by data buyers through coalition. To encourage coalitions to produce data, it is critical to allocate revenue to data owners in a fair manner according to their contributions. Although in literature Shapley fairness and alternatives have been well explored to facilitate revenue allocation in data assemblage, computing exact Shapley value for many data owners and large assembled data sets through coalition remains challenging due to the combinatoric nature of Shapley value. In this paper, we explore the decomposability of utility in data assemblage by formulating the independent utility assumption. We argue that independent utility enjoys many applications. Moreover, we identify interesting properties of independent utility and develop fast computation techniques for exact Shapley value under independent utility. Our experimental results on a series of benchmark data sets show that our new approach not only guarantees the exactness of Shapley value, but also achieves faster computation by orders of magnitudes.

cs.DB↗

arXiv2022

Toward Minimal Misalignment at Minimal Cost in One-Stage and Anchor-Free Object Detection

Common object detection models consist of classification and regression branches, due to different task drivers, these two branches have different sensibility to the features from the same scale level and the same spatial location. The point-based prediction method, which is based on the assumption that the high classification confidence point has the high regression quality, leads to the misalignment problem. Our analysis shows, the problem is further composed of scale misalignment and spatial misalignment specifically. We aim to resolve the phenomenon at minimal cost: a minor adjustment of the head network and a new label assignment method replacing the rigid one. Our experiments show that, compared to the baseline FCOS, a one-stage and anchor-free object detection model, our model consistently get around 3 AP improvement with different backbones, demonstrating both simplicity and efficiency of our method.

cs.CV↗

arXiv2022

Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads

Lowering costs by driving high utilization across deep learning workloads is a crucial lever for cloud providers. We present Singularity, Microsoft's globally distributed scheduling service for highly-efficient and reliable execution of deep learning training and inference workloads. At the heart of Singularity is a novel, workload-aware scheduler that can transparently preempt and elastically scale deep learning workloads to drive high utilization without impacting their correctness or performance, across a global fleet of AI accelerators (e.g., GPUs, FPGAs). All jobs in Singularity are preemptable, migratable, and dynamically resizable (elastic) by default: a live job can be dynamically and transparently (a) preempted and migrated to a different set of nodes, cluster, data center or a region and resumed exactly from the point where the execution was preempted, and (b) resized (i.e., elastically scaled-up/down) on a varying set of accelerators of a given type. Our mechanisms are transparent in that they do not require the user to make any changes to their code or require using any custom libraries that may limit flexibility. Additionally, our approach significantly improves the reliability of deep learning workloads. We show that the resulting efficiency and reliability gains with Singularity are achieved with negligible impact on the steady-state performance. Finally, our design approach is agnostic of DNN architectures and handles a variety of parallelism strategies (e.g., data/pipeline/model parallelism).

cs.DC↗

arXiv2022

Interface inter-atomic electron-transition induced photon emission in contact-electrification

Contact electrification (CE) (or triboelectrification) is the phenomena where charges are produced through physical contact between two materials. Here we report the atomic featured photon emission spectra during CE between two solid materials. Photon emission provides the evidence that electron transfer takes place at the interface from an atom in one material to another atom in the other material during CE. This process is the contact electrification induced interface photon emission spectroscopy (CEIIPES). It naturally paves a way to a spectroscopy corresponding to the CE at an interface, which might impact our understanding of the interaction between solids, liquids, and gases. The physics presented here could be expanded to Auger electron excitation, x-ray emission, and electron emission in CE for general cases, which remain to be explored. This could lead to a general field that may be termed as contact electrification induced interface spectroscopy (CEIIS).

cond-mat.mtrl-sci↗

arXiv2021

3rd Place Solution to Google Landmark Recognition Competition 2021

In this paper, we show our solution to the Google Landmark Recognition 2021 Competition. Firstly, embeddings of images are extracted via various architectures (i.e. CNN-, Transformer- and hybrid-based), which are optimized by ArcFace loss. Then we apply an efficient pipeline to re-rank predictions by adjusting the retrieval score with classification logits and non-landmark distractors. Finally, the ensembled model scores 0.489 on the private leaderboard, achieving the 3rd place in the 2021 edition of the Google Landmark Recognition Competition.

cs.CV↗

arXiv2021

Photon-counting CT with silicon detectors: feasibility for pediatric imaging

X-ray detectors made of crystalline silicon have several advantages including low dark currents, fast charge collection and high energy resolution. For high-energy x-rays, however, silicon suffers from its low atomic number, which might result in low detection efficiency, as well as low energy and spatial resolution due to Compton scattering. We have used a monte-carlo model to investigate the feasibility of a detector for pediatric CT with 30 to 40 mm of silicon using x-ray spectra ranging from 80 to 140 kVp. A detection efficiency of 0.74 was found at 80 kVp, provided the noise threshold could be set low. Scattered photons were efficiently blocked by a thin metal shielding between the detector units, and Compton scattering in the detector could be well separated from photo absorption at 80 kVp. Hence, the detector is feasible at low acceleration voltages, which is also suitable for pediatric imaging. We conclude that silicon detectors may be an alternative to other designs for this special case.

physics.ins-det↗

arXiv2020

Cost-Effective Data Feeds to Blockchains via Workload-Adaptive Data Replication

Feeding external data to a blockchain, a.k.a. data feed, is an essential task to enable blockchain interoperability and support emerging cross-domain applications, notably stablecoins. Given the data-intensive feeds in real life (e.g., high-frequency price updates) and the high cost in using blockchain, namely Gas, it is imperative to reduce the Gas cost of data feeds. Motivated by the constant-changing workloads in finance and other applications, this work focuses on designing a dynamic, workload-aware approach for cost effectiveness in Gas. This design space is understudied in the existing blockchain research which has so far focused on static data placement. This work presents GRuB, a cost-effective data feed that dynamically replicates data between the blockchain and an off-chain cloud storage. GRuB's data replication is workload-adaptive by monitoring the current workload and making online decisions w.r.t. data replication. A series of online algorithms are proposed that achieve the bounded worst-case cost in blockchain's Gas. GRuB runs the decision-making components on the untrusted cloud off-chain for lower Gas costs, and employs a security protocol to authenticate the data transferred between the blockchain and cloud. The overall GRuB system can autonomously achieve low Gas costs with changing workloads. We built a GRuB prototype functional with Ethereum and Google LevelDB, and supported real applications in stablecoins. Under real workloads collected from the Ethereum contract-call history and mixed workloads of YCSB, we systematically evaluate GRuB's cost which shows a saving of Gas by 10% ~ 74%, with comparison to the baselines of static data-placement.

cs.CR↗

arXiv2019

Double Supervised Network with Attention Mechanism for Scene Text Recognition

In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition. It incorporates one text attention module during feature extraction which enforces the model to focus on text regions and the whole framework is supervised by two branches. One supervision branch comes from context-level modelling and another comes from one extra supervision enhancement branch which aims at tackling inexplicit semantic information at character level. These two supervisions can benefit each other and yield better performance. The proposed approach can recognize text in arbitrary length and does not need any predefined lexicon. Our method outperforms the current state-of-the-art methods on three text recognition benchmarks: IIIT5K, ICDAR2013 and SVT reaching accuracy 88.6%, 92.3% and 84.1% respectively which suggests the effectiveness of the proposed method.

cs.CV↗

arXiv2019

Detection and Classification of Breast Cancer Metastates Based on U-Net

This paper presents U-net based breast cancer metastases detection and classification in lymph nodes, as well as patient-level classification based on metastases detection. The whole pipeline can be divided into five steps: preprocessing and data argumentation, patch-based segmentation, post processing, slide-level classification, and patient-level classification. In order to reduce overfitting and speedup convergence, we applied batch normalization and dropout into U-Net. The final Kappa score reaches 0.902 on training data.

eess.IV↗

arXiv2019

Infer Implicit Contexts in Real-time Online-to-Offline Recommendation

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from traditional recommendation where individual preference is mostly static, O2O recommendation should be dynamic to capture variation of users' purposes across time and location. However, precisely inferring users' real-time contexts information, especially those implicit ones, is extremely difficult, and it is a central challenge for O2O recommendation. In this paper, we propose a new approach, called Mixture Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit contexts and consequently, to improve the quality of real-time O2O recommendation. In MACDAE, we first leverage the interaction among users, items, and explicit contexts to infer users' implicit contexts, then combine the learned implicit-context representation into an end-to-end model to make the recommendation. MACDAE works quite well in the real system. We conducted both offline and online evaluations of the proposed approach. Experiments on several real-world datasets (Yelp, Dianping, and Koubei) show our approach could achieve significant improvements over state-of-the-arts. Furthermore, online A/B test suggests a 2.9% increase for click-through rate and 5.6% improvement for conversion rate in real-world traffic. Our model has been deployed in the product of "Guess You Like" recommendation in Koubei.

cs.IR↗