arXiv subjects

Xiao Hu

Publications and source records attributed to Xiao Hu.

At least 163 records · Page 9Linked to original sources

arXiv2023

NOCAP: Near-Optimal Correlation-Aware Partitioning Joins

Storage-based joins are still commonly used today because the memory budget does not always scale with the data size. One of the many join algorithms developed that has been widely deployed and proven to be efficient is the Hybrid Hash Join (HHJ), which is designed to exploit any available memory to maximize the data that is joined directly in memory. However, HHJ cannot fully exploit detailed knowledge of the join attribute correlation distribution. In this paper, we show that given a correlation skew in the join attributes, HHJ partitions data in a suboptimal way. To do that, we derive the optimal partitioning using a new cost-based analysis of partitioning-based joins that is tailored for primary key - foreign key (PK-FK) joins, one of the most common join types. This optimal partitioning strategy has a high memory cost, thus, we further derive an approximate algorithm that has tunable memory cost and leads to near-optimal results. Our algorithm, termed NOCAP (Near-Optimal Correlation-Aware Partitioning) join, outperforms the state-of-the-art for skewed correlations by up to $30\%$, and the textbook Grace Hash Join by up to $4\times$. Further, for a limited memory budget, NOCAP outperforms HHJ by up to $10\%$, even for uniform correlation. Overall, NOCAP dominates state-of-the-art algorithms and mimics the best algorithm for a memory budget varying from below $\sqrt{\|\text{relation}\|}$ to more than $\|\text{relation}\|$.

cs.DB↗

arXiv2023

General Space-Time Tradeoffs via Relational Queries

In this paper, we investigate space-time tradeoffs for answering Boolean conjunctive queries. The goal is to create a data structure in an initial preprocessing phase and use it for answering (multiple) queries. Previous work has developed data structures that trade off space usage for answering time and has proved conditional space lower bounds for queries of practical interest such as the path and triangle query. However, most of these results cater to only those queries, lack a comprehensive framework, and are not generalizable. The isolated treatment of these queries also fails to utilize the connections with extensive research on related problems within the database community. The key insight in this work is to exploit the formalism of relational algebra by casting the problems as answering join queries over a relational database. Using the notion of boolean {\em adorned queries} and {\em access patterns}, we propose a unified framework that captures several widely studied algorithmic problems. Our main contribution is three-fold. First, we present an algorithm that recovers existing space-time tradeoffs for several problems. The algorithm is based on an application of the {\em join size bound} to capture the space usage of our data structure. We combine our data structure with {\em query decomposition} techniques to further improve the tradeoffs and show that it is readily extensible to queries with negation. Second, we falsify two proposed conjectures in the existing literature related to the space-time lower bound for path queries and triangle detection for which we show unexpectedly better algorithms. This result opens a new avenue for improving several algorithmic results that have so far been assumed to be (conditionally) optimal. Finally, we prove new conditional space-time lower bounds for star and path queries.

cs.DS↗

arXiv2023

A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables

Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG signals collected from such devices are susceptible to corruption from noise and motion artifacts, which cause errors in heart rate estimation. Typical denoising approaches filter or reconstruct the signal in ways that eliminate much of the morphological information, even from the clean parts of the signal that would be useful to preserve. In this work, we develop an algorithm for denoising PPG signals that reconstructs the corrupted parts of the signal, while preserving the clean parts of the PPG signal. Our novel framework relies on self-supervised training, where we leverage a large database of clean PPG signals to train a denoising autoencoder. As we show, our reconstructed signals provide better estimates of heart rate from PPG signals than the leading heart rate estimation methods. Further experiments show significant improvement in Heart Rate Variability (HRV) estimation from PPG signals using our algorithm. We conclude that our algorithm denoises PPG signals in a way that can improve downstream analysis of many different health metrics from wearable devices.

eess.SP↗

arXiv2023

Generative Job Recommendations with Large Language Model

The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recommendations deploy either collaborative filtering or person-job matching strategies. However, these models tend to operate as "black-box" systems and lack the capacity to offer explainable guidance to job seekers. Moreover, conventional matching-based recommendation methods are limited to retrieving and ranking existing jobs in the database, restricting their potential as comprehensive career AI advisors. To this end, here we present GIRL (GeneratIve job Recommendation based on Large language models), a novel approach inspired by recent advancements in the field of Large Language Models (LLMs). We initially employ a Supervised Fine-Tuning (SFT) strategy to instruct the LLM-based generator in crafting suitable Job Descriptions (JDs) based on the Curriculum Vitae (CV) of a job seeker. Moreover, we propose to train a model which can evaluate the matching degree between CVs and JDs as a reward model, and we use Proximal Policy Optimization (PPO)-based Reinforcement Learning (RL) method to further fine-tine the generator. This aligns the generator with recruiter feedback, tailoring the output to better meet employer preferences. In particular, GIRL serves as a job seeker-centric generative model, providing job suggestions without the need of a candidate set. This capability also enhances the performance of existing job recommendation models by supplementing job seeking features with generated content. With extensive experiments on a large-scale real-world dataset, we demonstrate the substantial effectiveness of our approach. We believe that GIRL introduces a paradigm-shifting approach to job recommendation systems, fostering a more personalized and comprehensive job-seeking experience.

cs.IR↗

arXiv2023

Differentially Private Data Release over Multiple Tables

We study synthetic data release for answering multiple linear queries over a set of database tables in a differentially private way. Two special cases have been considered in the literature: how to release a synthetic dataset for answering multiple linear queries over a single table, and how to release the answer for a single counting (join size) query over a set of database tables. Compared to the single-table case, the join operator makes query answering challenging, since the sensitivity (i.e., by how much an individual data record can affect the answer) could be heavily amplified by complex join relationships. We present an algorithm for the general problem, and prove a lower bound illustrating that our general algorithm achieves parameterized optimality (up to logarithmic factors) on some simple queries (e.g., two-table join queries) in the most commonly-used privacy parameter regimes. For the case of hierarchical joins, we present a data partition procedure that exploits the concept of {\em uniformized sensitivities} to further improve the utility.

cs.DB↗

arXiv2023

On Differentially Private Sampling from Gaussian and Product Distributions

Given a dataset of $n$ i.i.d. samples from an unknown distribution $P$, we consider the problem of generating a sample from a distribution that is close to $P$ in total variation distance, under the constraint of differential privacy (DP). We study the problem when $P$ is a multi-dimensional Gaussian distribution, under different assumptions on the information available to the DP mechanism: known covariance, unknown bounded covariance, and unknown unbounded covariance. We present new DP sampling algorithms, and show that they achieve near-optimal sample complexity in the first two settings. Moreover, when $P$ is a product distribution on the binary hypercube, we obtain a pure-DP algorithm whereas only an approximate-DP algorithm (with slightly worse sample complexity) was previously known.

cs.DS↗

arXiv2023

Resource-efficient quantum key distribution with integrated silicon photonics

Integrated photonics provides a promising platform for quantum key distribution (QKD) system in terms of miniaturization, robustness and scalability. Tremendous QKD works based on integrated photonics have been reported. Nonetheless, most current chip-based QKD implementations require additional off-chip hardware to demodulate quantum states or perform auxiliary tasks such as time synchronization and polarization basis tracking. Here, we report a demonstration of resource-efficient chip-based BB84 QKD with a silicon-based encoder and decoder. In our scheme, the time synchronization and polarization compensation are implemented relying on the preparation and measurement of the quantum states generated by on-chip devices, thus no need additional hardware. The experimental tests show that our scheme is highly stable with a low intrinsic QBER of $0.50\pm 0.02\%$ in a 6-h continuous run. Furthermore, over a commercial fiber channel up to 150 km, the system enables realizing secure key distribution at a rate of 866 bps. Our demonstration paves the way for low-cost, wafer-scale manufactured QKD system.

quant-ph↗

arXiv2023

Gap Opening in Protoplanetary Disks: Gas Dynamics from Global Non-ideal MHD Simulations with Consistent Thermochemistry

Recent high angular resolution ALMA observations have revealed numerous gaps in protoplanetary disks. A popular interpretation has been that planets open them. Most previous investigations of planet gap-opening have concentrated on viscous disks. Here, we carry out 2D (axisymmetric) global simulations of gap opening by a planet in a wind-launching non-ideal MHD disk with consistent thermochemistry. We find a strong concentration of poloidal magnetic flux in the planet-opened gap, where the gas dynamics are magnetically dominated. The magnetic field also drives a fast (nearly sonic) meridional gas circulation in the denser disk regions near the inner and outer edges of the gap, which may be observable through high-resolution molecular line observations. The gap is more ionized than its denser surrounding regions, with a better magnetic field-matter coupling. In particular, it has a much higher abundance of molecular ion HCO$^+$, consistent with ALMA observations of the well-studied AS 209 protoplanetary disk that has prominent gaps and fast meridional motions reaching the local sound speed. Finally, we provide fitting formulae for the ambipolar and Ohmic diffusivities as a function of the disk local density, which can be used for future 3D simulations of planet gap-opening in non-ideal MHD disks where thermochemistry is too computationally expensive to evolve self-consistently with the magneto-hydrodynamics.

astro-ph.EP↗

arXiv2023

PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning

Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance. However, existing methods, effective as they are, suffer from suboptimal performance, limited adaptability, and unsatisfactory computational efficiency. We propose a novel framework, PROTO, which overcomes the aforementioned limitations by augmenting the standard RL objective with an iteratively evolving regularization term. Performing a trust-region-style update, PROTO yields stable initial finetuning and optimal final performance by gradually evolving the regularization term to relax the constraint strength. By adjusting only a few lines of code, PROTO can bridge any offline policy pretraining and standard off-policy RL finetuning to form a powerful offline-to-online RL pathway, birthing great adaptability to diverse methods. Simple yet elegant, PROTO imposes minimal additional computation and enables highly efficient online finetuning. Extensive experiments demonstrate that PROTO achieves superior performance over SOTA baselines, offering an adaptable and efficient offline-to-online RL framework.

cs.LG↗

arXiv2023

Silicon photonics-integrated time-domain balanced homodyne detector in continuous-variable quantum key distribution

We designed and experimentally demonstrated a silicon photonics-integrated time-domain balanced homodyne detector (TBHD), whose optical part has dimensions of 1.5 mm * 0.4 mm. To automatically and accurately balance the detector, new variable optical attenuators were used, and a common mode rejection ratio of 86.9 dB could be achieved. In the quantum tomography experiment, the density matrix and Wigner function of a coherent state were reconstructed with 99.97 % fidelity. The feasibility of this TBHD in a continuous-variable quantum key distribution (CVQKD) system was also demonstrated. This facilitates the integration of the optical circuits of the CVQKD system based on the GG02 protocol on the silicon photonics chip using TBHD.

quant-ph↗

arXiv2023

Computing the Difference of Conjunctive Queries Efficiently

We investigate how to efficiently compute the difference result of two (or multiple) conjunctive queries, which is the last operator in relational algebra to be unraveled. The standard approach in practical database systems is to materialize the results for every input query as a separate set, and then compute the difference of two (or multiple) sets. This approach is bottlenecked by the complexity of evaluating every input query individually, which could be very expensive, particularly when there are only a few results in the difference. In this paper, we introduce a new approach by exploiting the structural property of input queries and rewriting the original query by pushing the difference operator down as much as possible. We show that for a large class of difference queries, this approach can lead to a linear-time algorithm, in terms of the input size and (final) output size, i.e., the number of query results that survive from the difference operator. We complete this result by showing the hardness of computing the remaining difference queries in linear time. Although a linear-time algorithm is hard to achieve in general, we also provide some heuristics that can provably improve the standard approach. At last, we compare our approach with standard SQL engines over graph and benchmark datasets. The experiment results demonstrate order-of-magnitude speedups achieved by our approach over the vanilla SQL.

cs.DB↗

arXiv2023

ONE PIECE: One Patchwork In Effectively Combined Extraction for grasp

For grasp network algorithms, generating grasp datasets for a large number of 3D objects is a crucial task. However, generating grasp datasets for hundreds of objects can be very slow and consume a lot of storage resources, which hinders algorithm iteration and promotion. For point cloud grasp network algorithms, the network input is essentially the internal point cloud of the grasp area that intersects with the object in the gripper coordinate system. Due to the existence of a large number of completely consistent gripper area point clouds based on the gripper coordinate system in the grasp dataset generated for hundreds of objects, it is possible to remove the consistent gripper area point clouds from many objects and assemble them into a single object to generate the grasp dataset, thus replacing the enormous workload of generating grasp datasets for hundreds of objects. We propose a new approach to map the repetitive features of a large number of objects onto a finite set.To this end, we propose a method for extracting the gripper area point cloud that intersects with the object from the simulator and design a gripper feature filter to remove the shape-repeated gripper space area point clouds, and then assemble them into a single object. The experimental results show that the time required to generate the new object grasp dataset is greatly reduced compared to generating the grasp dataset for hundreds of objects, and it performs well in real machine grasping experiments. We will release the data and tools after the paper is accepted.

cs.RO↗

arXiv2023

Coupling of magnetism and Dirac fermions in YbMnSb2

We report inelastic neutron scattering measurements of magnetic excitations in YbMnSb2, a low-carrier-density Dirac semimetal in which the antiferromagnetic Mn layers are interleaved with Sb layers that host Dirac fermions. We observe a considerable broadening of spin waves, which is consistent with substantial spin fermion coupling. The spin wave damping, $γ$, in YbMnSb2 is roughly twice larger compared to that in a sister material, YbMnBi2, where an indication of a small damping consistent with theoretical analysis of the spin-fermion coupling was reported. The inter-plane interaction between the Mn layers in YbMnSb2 is also much stronger, suggesting that the interaction mechanism is rooted in the same spin-fermion coupling. Our results establish the systematics of spin-fermion interactions in layered magnetic Dirac materials.

cond-mat.str-el↗

arXiv2023

Spin canting and lattice symmetry in La$_2$CuO$_4$

While the dominant magnetic interaction in La$_2$CuO$_4$ is superexchange between nearest-neighbor Cu moments, the pinning of the spin direction depends on weak anisotropic effects associated with spin-orbit coupling. The symmetry of the octahedral tilt pattern allows an out-of-plane canting of the Cu spins, which is compensated by an opposite canting in nearest-neighbor layers. A strong magnetic field applied perpendicular to the planes can alter the spin canting pattern to induce a weak ferromagnetic phase. In light of recent evidence that the lattice symmetry is lower than originally assumed, we take a new look at the nature of the field-induced spin-rotation transition. Comparing low-temperature neutron diffraction intensities for several magnetic Bragg peaks measured in fields of 0 and 14 T, we find that a better fit is provided by a model in which spins rotate within both neighboring planes but by different amounts, resulting in a noncollinear configuration. This model allows a more consistent relationship between lattice symmetry and spin orientation at all Cu sites.

cond-mat.str-el↗

arXiv2023

Deep Joint Source-Channel Coding with Iterative Source Error Correction

In this paper, we propose an iterative source error correction (ISEC) decoding scheme for deep-learning-based joint source-channel coding (Deep JSCC). Given a noisy codeword received through the channel, we use a Deep JSCC encoder and decoder pair to update the codeword iteratively to find a (modified) maximum a-posteriori (MAP) solution. For efficient MAP decoding, we utilize a neural network-based denoiser to approximate the gradient of the log-prior density of the codeword space. Albeit the non-convexity of the optimization problem, our proposed scheme improves various distortion and perceptual quality metrics from the conventional one-shot (non-iterative) Deep JSCC decoding baseline. Furthermore, the proposed scheme produces more reliable source reconstruction results compared to the baseline when the channel noise characteristics do not match the ones used during training.

cs.LG↗

arXiv2023

Mind the Gap: Offline Policy Optimization for Imperfect Rewards

Reward function is essential in reinforcement learning (RL), serving as the guiding signal to incentivize agents to solve given tasks, however, is also notoriously difficult to design. In many cases, only imperfect rewards are available, which inflicts substantial performance loss for RL agents. In this study, we propose a unified offline policy optimization approach, \textit{RGM (Reward Gap Minimization)}, which can smartly handle diverse types of imperfect rewards. RGM is formulated as a bi-level optimization problem: the upper layer optimizes a reward correction term that performs visitation distribution matching w.r.t. some expert data; the lower layer solves a pessimistic RL problem with the corrected rewards. By exploiting the duality of the lower layer, we derive a tractable algorithm that enables sampled-based learning without any online interactions. Comprehensive experiments demonstrate that RGM achieves superior performance to existing methods under diverse settings of imperfect rewards. Further, RGM can effectively correct wrong or inconsistent rewards against expert preference and retrieve useful information from biased rewards.

cs.LG↗

arXiv2022

Silicon-based decoder for polarization-encoding quantum key distribution

Silicon-based polarization-encoding quantum key distribution (QKD) has been widely studied, owing to its low cost and robustness. However, prior studies have utilized off-chip devices to demodulate the quantum states or perform polarization compensation, given the difficulty of fabricating polarized independent components on the chip. In this paper, we propose a fully chip-based decoder for polarization-encoding QKD. The chip realizes a polarization state analyzer and compensates for the BB84 protocol without requiring additional hardware. It is based on a polarization-to-path conversion method that uses a polarization splitter-rotator. The chip was fabricated using a standard silicon photonics foundry; it has a compact design and is suitable for mass production. In the experimental stability test, an average quantum bit error rate of $0.56\%$ was achieved through continuous operation for 10 h without any polarization feedback. Furthermore, using the developed feedback algorithm, the chip enabled the automatic compensation of the fiber polarization drift, which was emulated by a random fiber polarization scrambler. In the case of the QKD demonstration, we obtained a finite-key secret rate of 240 bps over a fiber spool of 100 km. This study represents an important step toward the integrated, practical, and large-scale deployment of QKD systems.

quant-ph↗

arXiv2022

Inside-Out Planet Formation. VII. Astrochemical Models of Protoplanetary Disks and Implications for Planetary Compositions

Inside-Out Planet Formation (IOPF) proposes that the abundant systems of close-in Super-Earths and Mini-Neptunes form in situ at the pressure maximum associated with the Dead Zone Inner Boundary (DZIB). We present a model of physical and chemical evolution of protoplanetary disk midplanes that follows gas advection, radial drift of pebbles and gas-grain chemistry to predict abundances from 300~au down to the DZIB near 0.2 au. We consider typical disk properties relevant for IOPF, i.e., accretion rates 1E-9 < dM/dt / (Msun/yr) < 1E-8 and viscosity parameter alpha = 1E-4, and evolve for fiducial duration of t = 1E5 years. For outer, cool disk regions, we find that C and up to 90% of O nuclei start locked in CO and O2 ice, which keeps abundances of CO2 and H2O one order of magnitude lower. Radial drift of icy pebbles is influential, with gas-phase abundances of volatiles enhanced up to two orders of magnitude at ice-lines, while the outer disk becomes depleted of dust. Disks with decreasing accretion rates gradually cool, which draws in icelines closer to the star. At <~1 au, advective models yield water-rich gas with C/O ratios <~ 0.1, which may be inherited by atmospheres of planets forming here via IOPF. For planetary interiors built by pebble accretion, IOPF predicts volatile-poor compositions. However, advectively-enhanced volatile mass fractions of ~10% can occur at the water ice line.

astro-ph.EP↗