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Jun Xu

Publications and source records attributed to Jun Xu.

At least 343 records · Page 19Linked to original sources
arXiv2021

Characterization and Modeling of Native MOSFETs Down to 4.2 K

The extremely low threshold voltage (Vth) of native MOSFETs (Vth~0V@300K) is conducive to the design of cryogenic circuits. Previous research on cryogenic MOSFETs mainly focused on the standard threshold voltage (SVT) and low threshold voltage (LVT) MOSFETs. In this paper, we characterize native MOSFETs within the temperature range from 300K to 4.2K. The cryogenic Vth increases up to ~0.25V (W/L=10um/10um) and the improved subthreshold swing (SS)~14.30mV/dec@4.2K. The off-state current (Ioff) and the gate-induced drain leakage (GIDL) effect are ameliorated greatly. The step-up effect caused by the substrate charge and the transconductance peak effect caused by the energy quantization in different sub-bands are also discussed. Based on the EKV model, we modified the mobility calculation equations and proposed a compact model of large size native MOSFETs suitable for the range of 300K to 4.2K. The mobility-related parameters are extracted via a machine learning approach and the temperature dependences of the scattering mechanisms are analyzed. This work is beneficial to both the research on cryogenic MOSFETs modeling and the design of cryogenic CMOS circuits for quantum chips.

physics.app-ph↗
arXiv2021

MP-RW-LSH: An Efficient Multi-Probe LSH Solution to ANNS in $L_1$ Distance

Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-sensitive hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS-$L_2$, a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in $L_1$ distance. Another contribution of this work is to explain why a state-of-the-art ANNS-$L_1$ solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. We show that MP-RW-LSH uses 15 to 53 times fewer hash tables than CP-LSH for achieving similar query accuracies.

cs.DB↗
arXiv2021

On the ideal shortest vector problem over random rational primes

Any ideal in a number field can be factored into a product of prime ideals. In this paper we study the prime ideal shortest vector problem (SVP) in the ring $ \Z[x]/(x^{2^n} + 1) $, a popular choice in the design of ideal lattice based cryptosystems. We show that a majority of rational primes lie under prime ideals admitting a polynomial time algorithm for SVP. Although the shortest vector problem of ideal lattices underpins the security of Ring-LWE cryptosystem, this work does not break Ring-LWE, since the security reduction is from the worst case ideal SVP to the average case Ring-LWE, and it is one-way.

cs.CR↗
arXiv2021

Privacy-preserving Channel Estimation in Cell-free Hybrid Massive MIMO Systems

We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with $K$ users and $M$ access points (APs), each with $N_a$ antennas and $N_r< N_a$ radio frequency (RF) chains. When $K\ll M{N_a}$, efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all APs, which may enable the CPU to infer the private information of user locations. We therefore develop and analyze privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, two joint differentially private noisy matrix completion algorithms based respectively on Frank-Wolfe iteration and singular value decomposition are presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we show that the estimation error can be mitigated while maintaining the same privacy level by increasing the payload size with fixed pilot size; and the scaling laws of both the privacy-induced and privacy-independent error components in terms of payload size are characterized. Simulation results are provided to further demonstrate the tradeoff between privacy and channel estimation performance.

cs.IT↗
arXiv2020

Discovering Dialog Structure Graph for Open-Domain Dialog Generation

Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. In this paper, we conduct unsupervised discovery of dialog structure from chitchat corpora, and then leverage it to facilitate dialog generation in downstream systems. To this end, we present a Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover a unified human-readable dialog structure. The structure is a two-layer directed graph that contains session-level semantics in the upper-layer vertices, utterance-level semantics in the lower-layer vertices, and edges among these semantic vertices. In particular, we integrate GNN into DVAE to fine-tune utterance-level semantics for more effective recognition of session-level semantic vertex. Furthermore, to alleviate the difficulty of discovering a large number of utterance-level semantics, we design a coupling mechanism that binds each utterance-level semantic vertex with a distinct phrase to provide prior semantics. Experimental results on two benchmark corpora confirm that DVAE-GNN can discover meaningful dialog structure, and the use of dialog structure graph as background knowledge can facilitate a graph grounded conversational system to conduct coherent multi-turn dialog generation.

cs.AI↗
arXiv2020

Isospin effect on baryon and charge fluctuations from the pNJL model

We have studied the possible isospin corrections on the skewness and kurtosis of net-baryon and net-charge fluctuations in the isospin asymmetric matter formed in relativistic heavy-ion collisions at RHIC-BES energies, based on a 3-flavor polyakov-looped Nambu-Jona-Lasinio model. With typical scalar-isovector and vector-isovector couplings leading to the splitting of $u$ and $d$ quark chiral phase transition boundaries and critical points, we have observed dramatic isospin effects on the susceptibilities, especially those of net-charge fluctuations. Reliable experimental measurements at even lower collision energies are encouraged to confirm the observed isospin effects.

nucl-th↗
arXiv2020

Symmetry energy investigation with pion production from Sn+Sn systems

In the past two decades, pions created in the high density regions of heavy ion collisions have been predicted to be sensitive at high densities to the symmetry energy term in the nuclear equation of state, a property that is key to our understanding of neutron stars. In a new experiment designed to study the symmetry energy, the multiplicities of negatively and positively charged pions have been measured with high accuracy for central $^{132}$Sn+$^{124}$Sn, $^{112}$Sn+$^{124}$Sn, and $^{108}$Sn+$^{112}$Sn collisions at $E/A=270~\mathrm{MeV}$ with the S$π$RIT Time Projection Chamber. While the uncertainties of individual pion multiplicities are measured to 4\%, those of the charged pion multiplicity ratios are measured to 2\%. We compare these data to predictions from seven major transport models. The calculations reproduce qualitatively the dependence of the multiplicities and their ratios on the total neutron to proton number in the colliding systems. However, the predictions of the transport models from different codes differ too much to allow extraction of reliable constraints on the symmetry energy from the data. This finding may explain previous contradictory conclusions on symmetry energy constraints obtained from pion data in Au+Au system. These new results call for better understanding of the differences among transport codes, and new observables that are more sensitive to the density dependence of the symmetry energy.

nucl-ex↗
arXiv2020

Gradient-Induced Co-Saliency Detection

Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images. In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection (GICD) method. We first abstract a consensus representation for the grouped images in the embedding space; then, by comparing the single image with consensus representation, we utilize the feedback gradient information to induce more attention to the discriminative co-salient features. In addition, due to the lack of Co-SOD training data, we design a jigsaw training strategy, with which Co-SOD networks can be trained on general saliency datasets without extra pixel-level annotations. To evaluate the performance of Co-SOD methods on discovering the co-salient object among multiple foregrounds, we construct a challenging CoCA dataset, where each image contains at least one extraneous foreground along with the co-salient object. Experiments demonstrate that our GICD achieves state-of-the-art performance. Our codes and dataset are available at https://mmcheng.net/gicd/.

cs.CV↗
arXiv2020

Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains

One approach to matching texts from asymmetrical domains is projecting the input sequences into a common semantic space as feature vectors upon which the matching function can be readily defined and learned. In real-world matching practices, it is often observed that with the training goes on, the feature vectors projected from different domains tend to be indistinguishable. The phenomenon, however, is often overlooked in existing matching models. As a result, the feature vectors are constructed without any regularization, which inevitably increases the difficulty of learning the downstream matching functions. In this paper, we propose a novel match method tailored for text matching in asymmetrical domains, called WD-Match. In WD-Match, a Wasserstein distance-based regularizer is defined to regularize the features vectors projected from different domains. As a result, the method enforces the feature projection function to generate vectors such that those correspond to different domains cannot be easily discriminated. The training process of WD-Match amounts to a game that minimizes the matching loss regularized by the Wasserstein distance. WD-Match can be used to improve different text matching methods, by using the method as its underlying matching model. Four popular text matching methods have been exploited in the paper. Experimental results based on four publicly available benchmarks showed that WD-Match consistently outperformed the underlying methods and the baselines.

cs.CL↗
arXiv2020

Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields

Pronouns are often dropped in Chinese conversations and recovering the dropped pronouns is important for NLP applications such as Machine Translation. Existing approaches usually formulate this as a sequence labeling task of predicting whether there is a dropped pronoun before each token and its type. Each utterance is considered to be a sequence and labeled independently. Although these approaches have shown promise, labeling each utterance independently ignores the dependencies between pronouns in neighboring utterances. Modeling these dependencies is critical to improving the performance of dropped pronoun recovery. In this paper, we present a novel framework that combines the strength of Transformer network with General Conditional Random Fields (GCRF) to model the dependencies between pronouns in neighboring utterances. Results on three Chinese conversation datasets show that the Transformer-GCRF model outperforms the state-of-the-art dropped pronoun recovery models. Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.

cs.CL↗
arXiv2020

SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval

Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching. In this paper, we study the problem of transferring the deep knowledge of the pre-trained language model (PLM) to Term-based Sparse representations, aiming to improve the representation capacity of bag-of-words(BoW) method for semantic-level matching, while still keeping its advantages. Specifically, we propose a novel framework SparTerm to directly learn sparse text representations in the full vocabulary space. The proposed SparTerm comprises an importance predictor to predict the importance for each term in the vocabulary, and a gating controller to control the term activation. These two modules cooperatively ensure the sparsity and flexibility of the final text representation, which unifies the term-weighting and expansion in the same framework. Evaluated on MSMARCO dataset, SparTerm significantly outperforms traditional sparse methods and achieves state of the art ranking performance among all the PLM-based sparse models.

cs.IR↗
arXiv2020

Bayesian inference of nuclear symmetry energy from measured and imagined neutron skin thickness in $^{116,118,120,122,124,130,132}$Sn, $^{208}$Pb, and $^{48}$Ca

The neutron skin thickness $Δr_{np}$ in heavy nuclei has been known as one of the most sensitive terrestrial probes of the nuclear symmetry energy around $\frac{2}{3}$ of the saturation density $ρ_0$ of nuclear matter. Existing neutron skin data mostly from hadronic observables suffer from large uncertainties and their extraction from experiments are often strongly model dependent. While waiting eagerly for the promised model-independent and high-precision neutron skin data for $^{208}$Pb and $^{48}$Ca from the parity-violating electron scattering experiments (PREX-II and CREX at JLab as well as MREX at MESA), within the Bayesian statistical framework using the Skyrme-Hartree-Fock model we infer the posterior probability distribution functions (PDFs) of the slope parameter $L$ of the nuclear symmetry energy at $ρ_0$ from imagined $Δr_{np}(^{208}$Pb$)=0.15$, 0.20, and 0.30 fm as well as $Δr_{np}(^{48}$Ca$)=0.12$, 0.15, and 0.25 fm, with different $1σ$ error bars. The results are compared with the PDFs of $L$ inferred using the same approach from the available $Δr_{np}$ data for Sn isotopes from hadronic probes. They are also compared with results from a recent Bayesian analysis of the radius and tidal deformability data of canonical neutron stars from GW170817 and NICER. The neutron skin data for Sn isotopes gives $L=45.5^{+26.5}_{-21.6}$ MeV surrounding its mean value or $L=53.4^{+18.6}_{-29.5}$ MeV surrounding its maximum {\it a posteriori} value, respectively, with the latter smaller than but consistent with the $L=66_{-20}^{+12}$ MeV from the neutron star data within their 68\% confidence intervals. In order to provide additionally useful information on $L$ extracted from the $Δr_{np}$ of Sn isotopes, the experimental error bar of $Δr_{np}(^{208}$Pb) should be at least smaller than 0.06 fm aimed by some current experiments.

nucl-th↗
arXiv2020

Constraining isovector nuclear interactions with giant resonances within a Bayesian approach

We put a stringent constraint on the isovector nuclear interactions in the Skyrme-Hartree-Fock model from the centroid energy $E_{-1}$ of the isovector giant dipole resonance in $^{208}$Pb as well as its electric polarizability $α_D$. Using the Bayesian analysis method, $E_{-1}$ and $α_D$ are found to be mostly determined by the nuclear symmetry energy $E_{sym}$ at about $ρ^\star=0.05$ fm$^{-3}$ and the isovector nucleon effective mass $m_v^\star$ at the saturation density. At $90\%$ confidence level, we obtain $E_{sym}(ρ^\star) = 16.4 ^{+1.0}_{-0.9}$ MeV and $m_v^\star/m = 0.79 ^{+0.06}_{-0.06}$.

nucl-th↗
arXiv2020

QPS-r: A Cost-Effective Crossbar Scheduling Algorithm and Its Stability and Delay Analysis

In an input-queued switch, a crossbar schedule, or a matching between the input ports and the output ports needs to be computed in each switching cycle, or time slot. Designing switching algorithms with very low computational complexity, that lead to high throughput and small delay is a challenging problem. There appears to be a fundamental tradeoff between the computational complexity of the switching algorithm and the resultants throughput and delay. Parallel maximal matching algorithms (adapted for switching) appear to have stricken a sweet spot in this tradeoff, and prior work has shown the following performance guarantees. Using maximal matchings in every time slot results in at least 50% switch throughput and order-optimal (i.e., independent of the switch size N) average delay bounds for various traffic arrival processes. On the other hand, their computational complexity can be as low as $O(log^2N)$ per port/processor, which is much lower than those of the algorithms such as maximum weighted matching which ensures better throughput performance. In this work, we propose QPS-r, a parallel iterative switching algorithm that has the lowest possible computational complexity: O(1) per port. Using Lyapunov stability analysis, we show that the throughput and delay performance is identical to that of maximal matching algorithm. Although QPS-r builds upon an existing technique called Queue-Proportional Sampling (QPS), in this paper, we provide analytical guarantees on its throughput and delay under i.i.d. traffic as well as a Markovian traffic model which can model many realistic traffic patterns. We also demonstrate that QPS-3 (running 3 iterations) has comparable empirical throughput and delay performances as iSLIP (running $log_2 N$ iterations), a refined and optimized representative maximal matching algorithm adapted for switching.

cs.PF↗
arXiv2020

Space- and Computationally-Efficient Set Reconciliation via Parity Bitmap Sketch (PBS)

Set reconciliation is a fundamental algorithmic problem that arises in many networking, system, and database applications. In this problem, two large sets A and B of objects (bitcoins, files, records, etc.) are stored respectively at two different network-connected hosts, which we name Alice and Bob respectively. Alice and Bob communicate with each other to learn $AΔB$, the difference between A and B, and as a result the reconciled set $A\bigcup B$. Current set reconciliation schemes are based on either Invertible Bloom Filters (IBF) or Error-Correction Codes (ECC). The former has a low computational complexity of O(d), where d is the cardinality of $AΔB$, but has a high communication overhead that is several times larger than the theoretical minimum. The latter has a low communication overhead close to the theoretical minimum, but has a much higher computational complexity of $O(d^2)$. In this work, we propose Parity Bitmap Sketch (PBS), an ECC- based set reconciliation scheme that gets the better of both worlds: PBS has both a low computational complexity of O(d) just like IBF-based solutions and a low communication overhead of roughly twice the theoretical minimum. A separate contribution of this work is a novel rigorous analytical framework that can be used for the precise calculation of various performance metrics and for the near-optimal parameter tuning of PBS.

cs.DB↗
arXiv2020

Learning to Learn Kernels with Variational Random Features

In this work, we introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable. We formulate the optimization of MetaVRF as a variational inference problem by deriving an evidence lower bound under the meta-learning framework. To incorporate shared knowledge from related tasks, we propose a context inference of the posterior, which is established by an LSTM architecture. The LSTM-based inference network can effectively integrate the context information of previous tasks with task-specific information, generating informative and adaptive features. The learned MetaVRF can produce kernels of high representational power with a relatively low spectral sampling rate and also enables fast adaptation to new tasks. Experimental results on a variety of few-shot regression and classification tasks demonstrate that MetaVRF delivers much better, or at least competitive, performance compared to existing meta-learning alternatives.

cs.LG↗
arXiv2020

SoK: All You Ever Wanted to Know About x86/x64 Binary Disassembly But Were Afraid to Ask

Disassembly of binary code is hard, but necessary for improving the security of binary software. Over the past few decades, research in binary disassembly has produced many tools and frameworks, which have been made available to researchers and security professionals. These tools employ a variety of strategies that grant them different characteristics. The lack of systematization, however, impedes new research in the area and makes selecting the right tool hard, as we do not understand the strengths and weaknesses of existing tools. In this paper, we systematize binary disassembly through the study of nine popular, open-source tools. We couple the manual examination of their code bases with the most comprehensive experimental evaluation (thus far) using 3,788 binaries. Our study yields a comprehensive description and organization of strategies for disassembly, classifying them as either algorithm or else heuristic. Meanwhile, we measure and report the impact of individual algorithms on the results of each tool. We find that while principled algorithms are used by all tools, they still heavily rely on heuristics to increase code coverage. Depending on the heuristics used, different coverage-vs-correctness trade-offs come in play, leading to tools with different strengths and weaknesses. We envision that these findings will help users pick the right tool and assist researchers in improving binary disassembly.

cs.CR↗