arXiv subjects

Yu Huang

Publications and source records attributed to Yu Huang.

At least 199 records · Page 11Linked to original sources

arXiv2023

Signatures of Chiral Superconductivity in Chiral Molecule Intercalated Tantalum Disulfide

Chiral superconductors, a unique class of unconventional superconductors in which the complex superconducting order parameter winds clockwise or counter-clockwise in the momentum space, represent a topologically non-trivial system with direct implications for topological quantum computing. Intrinsic chiral superconductors are extremely rare, with only a few arguable examples including heavy fermion metals (UTe$_2$, UPt$_3$) and perovskite superconductor Sr$_2$RuO$_4$. Chiral molecules with neither mirror nor inversion symmetry have been widely investigated, in which the spin degeneracy may be lifted by the molecular chirality. Thus, a combination of superconductivity with chiral molecules may lead to a spin-polarized ground state for realizing chiral superconductivity. Herein we report the first investigation of unconventional superconductivity in chiral molecule intercalated tantalum disulfide (TaS$_2$) and reveal key signatures of chiral superconductivity. Little-Parks measurements demonstrate a robust and reproducible half-flux quantum phase shift in both left- and right-handed chiral molecule intercalated TaS$_2$, which is absent in pristine TaS$_2$ or achiral molecule intercalated TaS$_2$, highlighting the essential role of molecular chirality in inducing unconventional superconductivity. The robust half-flux quantum phase shift demonstrates unconventional superconductivity and constitutes strong evidence supporting a chiral superconducting ordering parameter. Critical current measurements at lower temperature reveal a peculiar asymmetric phase shift under opposite supercurrent, with a relative phase difference approaching the unity of π at below 0.5 K, further supporting topologically non-trivial superconductivity. Our study signifies the potential of hybrid superlattices with intriguing coupling between the crystalline atomic layers and the self-assembled molecular layers.

cond-mat.supr-con↗

arXiv2023

Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting

The semiconductor industry is one of the most technology-evolving and capital-intensive market sectors. Effective inspection and metrology are necessary to improve product yield, increase product quality and reduce costs. In recent years, many semiconductor manufacturing equipments are equipped with sensors to facilitate real-time monitoring of the production process. These production-state and equipment-state sensor data provide an opportunity to practice machine-learning technologies in various domains, such as anomaly/fault detection, maintenance scheduling, quality prediction, etc. In this work, we focus on the task of soft sensing regression, which uses sensor data to predict impending inspection measurements that used to be measured in wafer inspection and metrology systems. We proposed an LSTM-based regressor and designed two loss functions for model training. Although engineers may look at our prediction errors in a subjective manner, a new piece-wise evaluation metric was proposed for assessing model accuracy in a mathematical way. The experimental results demonstrated that the proposed model can achieve accurate and early prediction of various types of inspections in complicated manufacturing processes.

cs.LG↗

arXiv2022

Tunable Causal Consistency: Specification and Implementation

To achieve high availability and low latency, distributed data stores often geographically replicate data at multiple sites called replicas. However, this introduces the data consistency problem. Due to the fundamental tradeoffs among consistency, availability, and latency in the presence of network partition, no a one-size-fits-all consistency model exists. To meet the needs of different applications, many popular data stores provide tunable consistency, allowing clients to specify the consistency level per individual operation. In this paper, we propose tunable causal consistency (TCC). It allows clients to choose the desired session guarantee for each operation, from the well-known four session guarantees, i.e., read your writes, monotonic reads, monotonic writes, and writes follow reads. Specifically, we first propose a formal specification of TCC in an extended (vis,ar) framework originally proposed by Burckhardt et al. Then we design a TCC protocol and develop a prototype distributed key-value store called TCCSTORE. We evaluate TCCSTORE on Aliyun. The latency is less than 38ms for all workloads and the throughput is up to about 2800 operations per second. We also show that TCC achieves better performance than causal consistency and requires a negligible overhead when compared with eventual consistency.

cs.DC↗

arXiv2022

Low Complexity First: Duration-Centric ISI Mitigation in Molecular Communication via Diffusion

In this paper, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval. Its rationale is to exploit the discarded duration (i.e., the symbol duration outside this optimal interval) to relieve ISI in the target system. Following this idea, we formulate an objective function to quantify the impact of the discarded time on bit error rate (BER) performance. Besides, an optimally reusable interval within the discarded duration is derived in closed form, which applies to both the absorbing and passive receivers. Finally, numerical results validate our analysis and show that for the considered MCvD system, significant BER improvements can be achieved by using the derived reusable duration.

eess.SP↗

arXiv2022

ViSearch: Weak Consistency Measurement for Replicated Data Types

Large-scale replicated data type stores often resort to eventual consistency to guarantee low latency and high availability. It is widely accepted that programming over eventually consistent data stores is challenging, since arbitrary divergence among replicas is allowed. Moreover, pragmatic protocols actually achieve consistency guarantees stronger than eventual consistency, which can be and need to be utilized to facilitate the reasoning of and programming over replicated data types. Toward the challenges above, we propose the ViSearch framework for precise measurement of eventual consistency semantics. ViSearch employs the visibility-arbitration specification methodology in concurrent programming, which extends the linearizability-based specification methodology with a dynamic visibility relation among operations, in addition to the standard dynamic happen-before and linearization relations. The consistency measurement using ViSearch is NP-hard in general. To enable practical and efficient consistency measurement in replicated data type stores, the ViSearch framework refactors the existing brute-force checking algorithm to a generic algorithm skeleton, which further enables efficient pruning of the search space and effective parallelization. We employ the ViSearch framework for consistency measurement in two replicated data type stores Riak and CRDT-Redis. The experimental evaluation shows the usefulness and cost-effectiveness of consistency measurement based on the ViSearch framework in realistic scenarios.

cs.DC↗

arXiv2022

Remove-Win: a Design Framework for Conflict-free Replicated Data Types

Distributed storage systems employ replication to improve performance and reliability. To provide low latency data access, replicas are often required to accept updates without coordination with each other, and the updates are then propagated asynchronously. This brings the critical challenge of conflict resolution among concurrent updates. Conflict-free Replicated Data Type (CRDT) is a principled approach to addressing this challenge. However, existing CRDT designs are tricky, and hard to be generalized to other data types. A design framework is in great need to guide the systematic design of new CRDTs. To address this challenge, we propose RWF -- the Remove-Win design Framework for CRDTs. RWF leverages the simple but powerful remove-win strategy to resolve conflicting updates, and provides generic design for a variety of data container types. Two exemplar implementations following RWF are given over the Redis data type store, which demonstrate the effectiveness of RWF. Performance measurements of our implementations further show the efficiency of CRDT designs following RWF.

cs.DC↗

arXiv2022

DeepTPI: Test Point Insertion with Deep Reinforcement Learning

Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this paper, we propose a novel TPI approach based on deep reinforcement learning (DRL), named DeepTPI. Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement. Specifically, we model circuits as directed graphs and design a graph-based value network to estimate the action values for inserting different test points. The policy of the DRL agent is defined as selecting the action with the maximum value. Moreover, we apply the general node embeddings from a pre-trained model to enhance node features, and propose a dedicated testability-aware attention mechanism for the value network. Experimental results on circuits with various scales show that DeepTPI significantly improves test coverage compared to the commercial DFT tool. The code of this work is available at https://github.com/cure-lab/DeepTPI.

cs.LG↗

arXiv2022

Provable Generalization of Overparameterized Meta-learning Trained with SGD

Despite the superior empirical success of deep meta-learning, theoretical understanding of overparameterized meta-learning is still limited. This paper studies the generalization of a widely used meta-learning approach, Model-Agnostic Meta-Learning (MAML), which aims to find a good initialization for fast adaptation to new tasks. Under a mixed linear regression model, we analyze the generalization properties of MAML trained with SGD in the overparameterized regime. We provide both upper and lower bounds for the excess risk of MAML, which captures how SGD dynamics affect these generalization bounds. With such sharp characterizations, we further explore how various learning parameters impact the generalization capability of overparameterized MAML, including explicitly identifying typical data and task distributions that can achieve diminishing generalization error with overparameterization, and characterizing the impact of adaptation learning rate on both excess risk and the early stopping time. Our theoretical findings are further validated by experiments.

cs.LG↗

arXiv2022

Verifying Transactional Consistency of MongoDB

MongoDB is a popular general-purpose, document-oriented, distributed NoSQL database. It supports transactions in three different deployments: single-document transactions utilizing the WiredTiger storage engine in a standalone node, multi-document transactions in a replica set which consists of a primary node and several secondary nodes, and distributed transactions in a sharded cluster which is a group of multiple replica sets, among which data is sharded. A natural and fundamental question about MongoDB transactions is: What transactional consistency guarantee do MongoDB Transactions in each deployment provide? However, it lacks both concise pseudocode of MongoDB transactions in each deployment and formal specification of the consistency guarantees which MongoDB claimed to provide. In this work, we formally specify and verify the transactional consistency protocols of MongoDB. Specifically, we provide a concise pseudocode for the transactional consistency protocols in each MongoDB deployment, namely WIREDTIGER, REPLICASET, and SHARDEDCLUSTER, based on the official documents and source code. We then prove that WIREDTIGER, REPLICASET, and SHARDEDCLUSTER satisfy different variants of snapshot isolation, namely Strong-SI, Realtime-SI, and Session-SI, respectively. We also propose and evaluate efficient white-box checking algorithms for MongoDB transaction protocols against their consistency guarantees, effectively circumventing the NP-hard obstacle in theory.

cs.DC↗

arXiv2022

A Deep-Learning Usability Expansion Model of Ocean Observations

Today's ocean numerical prediction skills depend on the availability of in-situ and remote ocean observations at the time of the predictions only. Because observations are scarce and discontinuous in time and space, numerical models are often unable to accurately model and predict real ocean dynamics, leading to a lack of fulfillment of a range of services that require reliable predictions at various temporal and spatial scales. The process of constraining free numerical models with observations is known as data assimilation. The primary objective is to minimize the misfit of model states with the observations while respecting the rules of physics. The caveat of this approach is that measurements are used only once, at the time of the prediction. The information contained in the history of the measurements and its role in the determinism of the prediction is, therefore, not accounted for. Consequently, historical measurement cannot be used in real-time forecasting systems. The research presented in this paper provides a novel approach rooted in artificial intelligence to expand the usability of observations made before the time of the prediction. Our approach is based on the re-purpose of an existing deep learning model, called U-Net, designed specifically for image segmentation analysis in the biomedical field. U-Net is used here to create a Transform Model that retains the temporal and spatial evolution of the differences between model and observations to produce a correction in the form of regression weights that evolves spatially and temporally with the model both forward and backward in time, beyond the observation period. Using virtual observations, we show that the usability of the observation can be extended up to a one year prior or post observations.

eess.SP↗

arXiv2022

Invariance entropy for uncertain control systems

We introduce a notion of invariance entropy for uncertain control systems, which is, roughly speaking, the exponential growth rate of "branches" of "trees" that are formed by controls and are necessary to achieve invariance of controlled invariant subsets of the state space. This entropy extends the invariance entropy for deterministic control systems introduced by Colonius and Kawan (2009). We show that invariance feedback entropy, proposed by Tomar, Rungger, and Zamani (2020), is bounded from below by our invariance entropy. We generalize the formula for the calculation of entropy of invariant partitions obtained by Tomar, Kawan, and Zamani (2020) to quasi-invariant-partitions. Moreover, we also derive lower and upper bounds for entropy of a quasi-invariant-partition by spectral radii of its adjacency matrix and weighted adjacency matrix. With some reasonable assumptions, we obtain explicit formulas for computing invariance entropy for uncertain control systems and invariance feedback entropy for finite controlled invariant sets.

math.DS↗

arXiv2022

MET: Model Checking-Driven Explorative Testing of CRDT Designs and Implementations

Internet-scale distributed systems often replicate data at multiple geographic locations to provide low latency and high availability. The Conflict-free Replicated Data Type (CRDT) is a framework that provides a principled approach to maintaining eventual consistency among data replicas. CRDTs have been notoriously difficult to design and implement correctly. Subtle deep bugs lie in the complex and tedious handling of all possible cases of conflicting data updates. We argue that the CRDT design should be formally specified and model-checked to uncover deep bugs. The implementation further needs to be systematically tested. On the one hand, the testing needs to inherit the exhaustive nature of the model checking and ensures the coverage of testing. On the other hand, the testing is expected to find coding errors which cannot be detected by design level verification. Towards the challenges above, we propose the Model Checking-driven Explorative Testing (MET) framework. At the design level, MET uses TLA+ to specify and model check CRDT designs. At the implementation level, MET conducts model checking-driven explorative testing, in the sense that the test cases are automatically generated from the model checking traces. The system execution is controlled to proceed deterministically, following the model checking trace. The explorative testing systematically controls and permutes all nondeterministic message reorderings. We apply MET in our practical development of CRDTs. The bugs in both designs and implementations of CRDTs are found. As for bugs which can be found by traditional testing techniques, MET greatly reduces the cost of fixing the bugs. Moreover, MET can find subtle deep bugs which cannot be found by existing techniques at a reasonable cost. We further discuss how MET provides us with sufficient confidence in the correctness of our CRDT designs and implementations.

cs.DC↗

arXiv2022

DeepGate: Learning Neural Representations of Logic Gates

Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic. Most solutions apply well-developed DL models to solve specific EDA problems. While demonstrating promising results, they require careful model tuning for every problem. The fundamental question on "How to obtain a general and effective neural representation of circuits?" has not been answered yet. In this work, we take the first step towards solving this problem. We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate. Specifically, we propose transforming circuits into unified and-inverter graph format for learning and using signal probabilities as the supervision task in DeepGate. We then introduce a novel graph neural network that uses strong inductive biases in practical circuits as learning priors for signal probability prediction. Our experimental results show the efficacy and generalization capability of DeepGate.

cs.LG↗

arXiv2022

Detection Interval for Diffusion Molecular Communication: How Long is Enough?

Molecular communication has a key role to play in future medical applications, including detecting, analyzing, and addressing infectious disease outbreaks. Overcoming inter-symbol interference (ISI) is one of the key challenges in the design of molecular communication systems. In this paper, we propose to optimize the detection interval to minimize the impact of ISI while ensuring the accurate detection of the transmitted information symbol, which is suitable for the absorbing and passive receivers. For tractability, based on the signal-to-interference difference (SID) and signal-to-interference-and-noise amplitude ratio (SINAR), we propose a modified-SINAR (mSINAR) to measure the bit error rate (BER) performance for the molecular communication system with a variable detection interval. Besides, we derive the optimal detection interval in closed form. Using simulation results, we show that the BER performance of our proposed mSINAR scheme is superior to the competing schemes, and achieves similar performance to optimal intervals found by the exhaustive search.

cs.IT↗

arXiv2022

Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans

Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospective study, 38229 examinations (composed of 64063 individual breast scans from 14475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years +/- 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. Results: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P <= .001 for both; n = 250). Conclusion: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI.

cs.LG↗

arXiv2022

LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction

Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation for circuits, which preserves netlist data during the whole learning process, and enables the congestion information propagated geometrically and topologically. Based on the formulation, we further developed a heterogeneous graph neural network architecture LHNN, jointing the routing demand regression to support the congestion spot classification. LHNN constantly achieves more than 35% improvements compared with U-nets and Pix2Pix on the F1 score. We expect our work shall highlight essential procedures using machine learning for congestion prediction.

cs.LG↗

arXiv2022

Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

Despite the remarkable success of deep multi-modal learning in practice, it has not been well-explained in theory. Recently, it has been observed that the best uni-modal network outperforms the jointly trained multi-modal network, which is counter-intuitive since multiple signals generally bring more information. This work provides a theoretical explanation for the emergence of such performance gap in neural networks for the prevalent joint training framework. Based on a simplified data distribution that captures the realistic property of multi-modal data, we prove that for the multi-modal late-fusion network with (smoothed) ReLU activation trained jointly by gradient descent, different modalities will compete with each other. The encoder networks will learn only a subset of modalities. We refer to this phenomenon as modality competition. The losing modalities, which fail to be discovered, are the origins where the sub-optimality of joint training comes from. Experimentally, we illustrate that modality competition matches the intrinsic behavior of late-fusion joint training.

cs.LG↗

arXiv2022

A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification

The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years. In this paper, we propose a multi-characteristic learning (MCL) model with clusters to jointly learn discrepant pedestrian micro-Doppler signatures and fuse the knowledge learned from each cluster into final decisions. Time-Doppler spectrogram (TDS) and signal statistical features extracted from FMCW radar, as two categories of micro-Doppler signatures, are used in MCL to learn the micro-motion information inside pedestrians' free walking patterns. The experimental results show that our model achieves a higher accuracy rate and is more stable for pedestrian identification than other studies, which make our model more practical.

eess.SP↗