arXiv subjects

Peng Jiang

Publications and source records attributed to Peng Jiang.

At least 217 records · Page 12Linked to original sources

arXiv2023

3D Model-free Visual Localization System from Essential Matrix under Local Planar Motion

Visual localization plays a critical role in the functionality of low-cost autonomous mobile robots. Current state-of-the-art approaches for achieving accurate visual localization are 3D scene-specific, requiring additional computational and storage resources to construct a 3D scene model when facing a new environment. An alternative approach of directly using a database of 2D images for visual localization offers more flexibility. However, such methods currently suffer from limited localization accuracy. In this paper, we propose an accurate and robust multiple checking-based 3D model-free visual localization system to address the aforementioned issues. To ensure high accuracy, our focus is on estimating the pose of a query image relative to the retrieved database images using 2D-2D feature matches. Theoretically, by incorporating the local planar motion constraint into both the estimation of the essential matrix and the triangulation stages, we reduce the minimum required feature matches for absolute pose estimation, thereby enhancing the robustness of outlier rejection. Additionally, we introduce a multiple-checking mechanism to ensure the correctness of the solution throughout the solving process. For validation, qualitative and quantitative experiments are performed on both simulation and two real-world datasets and the experimental results demonstrate a significant enhancement in both accuracy and robustness afforded by the proposed 3D model-free visual localization system.

cs.RO↗

arXiv2023

A Large Language Model Enhanced Conversational Recommender System

Conversational recommender systems (CRSs) aim to recommend high-quality items to users through a dialogue interface. It usually contains multiple sub-tasks, such as user preference elicitation, recommendation, explanation, and item information search. To develop effective CRSs, there are some challenges: 1) how to properly manage sub-tasks; 2) how to effectively solve different sub-tasks; and 3) how to correctly generate responses that interact with users. Recently, Large Language Models (LLMs) have exhibited an unprecedented ability to reason and generate, presenting a new opportunity to develop more powerful CRSs. In this work, we propose a new LLM-based CRS, referred to as LLMCRS, to address the above challenges. For sub-task management, we leverage the reasoning ability of LLM to effectively manage sub-task. For sub-task solving, we collaborate LLM with expert models of different sub-tasks to achieve the enhanced performance. For response generation, we utilize the generation ability of LLM as a language interface to better interact with users. Specifically, LLMCRS divides the workflow into four stages: sub-task detection, model matching, sub-task execution, and response generation. LLMCRS also designs schema-based instruction, demonstration-based instruction, dynamic sub-task and model matching, and summary-based generation to instruct LLM to generate desired results in the workflow. Finally, to adapt LLM to conversational recommendations, we also propose to fine-tune LLM with reinforcement learning from CRSs performance feedback, referred to as RLPF. Experimental results on benchmark datasets show that LLMCRS with RLPF outperforms the existing methods.

cs.IR↗

arXiv2023

Local neural operator for solving transient partial differential equations on varied domains

Artificial intelligence (AI) shows great potential to reduce the huge cost of solving partial differential equations (PDEs). However, it is not fully realized in practice as neural networks are defined and trained on fixed domains and boundaries. Herein, we propose local neural operator (LNO) for solving transient PDEs on varied domains. It comes together with a handy strategy including boundary treatments, enabling one pre-trained LNO to predict solutions on different domains. For demonstration, LNO learns Navier-Stokes equations from randomly generated data samples, and then the pre-trained LNO is used as an explicit numerical time-marching scheme to solve the flow of fluid on unseen domains, e.g., the flow in a lid-driven cavity and the flow across the cascade of airfoils. It is about 1000$\times$ faster than the conventional finite element method to calculate the flow across the cascade of airfoils. The solving process with pre-trained LNO achieves great efficiency, with significant potential to accelerate numerical calculations in practice.

cs.LG↗

arXiv2023

A radio pulsar phase from SGR J1935+2154 provides clues to the magnetar FRB mechanism

The megajansky radio burst, FRB 20200428, and other bright radio bursts detected from the Galactic source SGR J1935+2154 suggest that magnetars can make fast radio bursts (FRBs), but the emission site and mechanism of FRB-like bursts are still unidentified. Here we report the emergence of a radio pulsar phase of the magnetar five months after FRB 20200428. 795 pulses were detected in 16.5 hours over 13 days by the Five-hundred-meter Aperture Spherical Radio telescope, with luminosities about eight decades fainter than FRB 20200428. The pulses were emitted in a narrow phase window anti-aligned with the X-ray pulsation profile observed by the X-ray telescopes. The bursts, conversely, appear in random phases. This dichotomy suggests that radio pulses originate from a fixed region within the magnetosphere, but bursts occur in random locations and are possibly associated with explosive events in a dynamically evolving magnetosphere. This picture reconciles the lack of periodicity in cosmological repeating FRBs within the magnetar engine model.

astro-ph.HE↗

arXiv2023

Auditing Frameworks Need Resource Isolation: A Systematic Study on the Super Producer Threat to System Auditing and Its Mitigation

System auditing is a crucial technique for detecting APT attacks. However, attackers may try to compromise the system auditing frameworks to conceal their malicious activities. In this paper, we present a comprehensive and systematic study of the super producer threat in auditing frameworks, which enables attackers to either corrupt the auditing framework or paralyze the entire system. We analyze that the main cause of the super producer threat is the lack of data isolation in the centralized architecture of existing solutions. To address this threat, we propose a novel auditing framework, NODROP, which isolates provenance data generated by different processes with a threadlet-based architecture design. Our evaluation demonstrates that NODROP can ensure the integrity of the auditing frameworks while achieving an average 6.58% higher application overhead compared to vanilla Linux and 6.30% lower application overhead compared to a state-of-the-art commercial auditing framework, Sysdig across eight different hardware configurations.

cs.CR↗

arXiv2023

Sub-second periodic radio oscillations in a microquasar

Powerful relativistic jets are one of the ubiquitous features of accreting black holes in all scales. GRS 1915+105 is a well-known fast-spinning black-hole X-ray binary with a relativistic jet, termed as a ``microquasar'', as indicated by its superluminal motion of radio emission. It exhibits persistent x-ray activity over the last 30 years, with quasi-periodic oscillations of $\sim 1-10$ Hz and 34 and 67 Hz in the x-ray band. These oscillations likely originate in the inner accretion disk, but other origins have been considered. Radio observations found variable light curves with quasi-periodic flares or oscillations with periods of $\sim 20-50$ minutes. Here we report two instances of $\sim$5 Hz transient periodic oscillation features from the source detected in the 1.05-1.45 GHz radio band that occurred in January 2021 and June 2022, respectively. Circular polarization was also observed during the oscillation phase.

astro-ph.HE↗

arXiv2023

Measuring Item Global Residual Value for Fair Recommendation

In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various recommendation systems are designed not only to satisfy users' personalized information needs but also to allocate items' exposure. However, recent recommendation studies mainly focus on modeling user preferences to present satisfying results and maximize user interactions, while paying little attention to developing item-side fair exposure mechanisms for rational information delivery. This may lead to serious resource allocation problems on the item side, such as the Snowball Effect. Furthermore, unfair exposure mechanisms may hurt recommendation performance. In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items. We first conduct empirical analyses of feed scenarios to explore exposure problems between items with distinct uploaded times. This points out that unfair exposure caused by the time factor may be the major cause of the Snowball Effect. Then, we propose to explicitly model item-level customized timeliness distribution, Global Residual Value (GRV), for fair resource allocation. This GRV module is introduced into recommendations with the designed Timeliness-aware Fair Recommendation Framework (TaFR). Extensive experiments on two datasets demonstrate that TaFR achieves consistent improvements with various backbone recommendation models. By modeling item-side customized Global Residual Value, we achieve a fairer distribution of resources and, at the same time, improve recommendation performance.

cs.IR↗

arXiv2023

Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools

Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry: (1) overlooking client-side overhead; (2) imbalanced alarm triage cost and interpretation cost; and (3) excessive server-side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry.

cs.CR↗

arXiv2023

An enhanced finite difference time domain method for two dimensional Maxwell's equations

An efficient finite-difference time-domain (FDTD) algorithm is built to solve the transverse electric 2D Maxwell's equations with inhomogeneous dielectric media where the electric fields are discontinuous across the dielectric interface. The new algorithm is derived based upon the integral version of the Maxwell's equations as well as the relationship between the electric fields across the interface. It is an improvement over the contour-path effective-permittivity algorithm by including some extra terms in the formulas. The scheme is validated in solving the scattering of a dielectric cylinder with exact solution from Mie theory and is then compared with the above contour-path method, the usual staircase and the volume-average method. The numerical results demonstrate that the new algorithm has achieved significant improvement in accuracy over the other methods. Furthermore, the algorithm has a simple structure and can be merged into any existing FDTD software package very easily.

physics.comp-ph↗

arXiv2023

Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation

Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice in decision-making processes like interactive recommendation. Offline RL faces the value overestimation problem. To address it, existing methods employ conservatism, e.g., by constraining the learned policy to be close to behavior policies or punishing the rarely visited state-action pairs. However, when applying such offline RL to recommendation, it will cause a severe Matthew effect, i.e., the rich get richer and the poor get poorer, by promoting popular items or categories while suppressing the less popular ones. It is a notorious issue that needs to be addressed in practical recommender systems. In this paper, we aim to alleviate the Matthew effect in offline RL-based recommendation. Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction. It inspires us to add a penalty term to relax the pessimism on states with high entropy of the logging policy and indirectly penalizes actions leading to less diverse states. This leads to the main technical contribution of the work: Debiased model-based Offline RL (DORL) method. Experiments show that DORL not only captures user interests well but also alleviates the Matthew effect. The implementation is available via https://github.com/chongminggao/DORL-codes.

cs.IR↗

arXiv2023

Searching for the nano-Hertz stochastic gravitational wave background with the Chinese Pulsar Timing Array Data Release I

Observing and timing a group of millisecond pulsars (MSPs) with high rotational stability enables the direct detection of gravitational waves (GWs). The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of widely spaced pulsar-pairs. The Chinese Pulsar Timing Array (CPTA) is a collaboration aiming at the direct GW detection with observations carried out using Chinese radio telescopes. This short article serves as a `table of contents' for a forthcoming series of papers related to the CPTA Data Release 1 (CPTA DR1) which uses observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here, after summarizing the time span and accuracy of CPTA DR1, we report the key results of our statistical inference finding a correlated signal with amplitude $\log A_{\rm c}= -14.4 \,^{+1.0}_{-2.8}$ for spectral index in the range of $α\in [-1.8, 1.5]$ assuming a GW background (GWB) induced quadrupolar correlation. The search for the Hellings-Downs (HD) correlation curve is also presented, where some evidence for the HD correlation has been found that a 4.6-$σ$ statistical significance is achieved using the discrete frequency method around the frequency of 14 nHz. We expect that the future International Pulsar Timing Array data analysis and the next CPTA data release will be more sensitive to the nHz GWB, which could verify the current results.

astro-ph.HE↗

arXiv2023

Performance of FAST with an Ultra-Wide Bandwidth Receiver at 500-3300 MHz

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has been running for several years. A new Ultra-Wide Bandwidth (UWB) receiver, simultaneously covering 500-3300 MHz, has been mounted in the FAST feed cabin and passed a series of observational tests. The whole UWB band is separated into four independent bands. Each band has 1048576 channels in total, resulted in a spectral resolution of 1 kHz. At 500-3300 MHz, the antenna gain is around 14.3-7.7 K/Jy, the aperture efficiency is around 0.56-0.30, the system temperature is around 88-130 K, and the HPBW is around 7.6-1.6 arcmin. The measured standard deviation of pointing accuracy is better than ~7.9 arcsec, when zenith angle (ZA) is within 26.4deg. The sensitivity and stability of the UWB receiver are confirmed to satisfy expectation by spectral observations, e.g., HI and OH. The FAST UWB receiver already has a good performance for taking sensitive observations in various scientific goals.

astro-ph.IM↗

arXiv2023

PIMMiner: A High-performance PIM Architecture-aware Graph Mining Framework

Graph mining applications, such as subgraph pattern matching and mining, are widely used in real-world domains such as bioinformatics, social network analysis, and computer vision. Such applications are considered a new class of data-intensive applications that generate massive irregular computation workloads and memory accesses, which degrade the performance significantly. Leveraging emerging hardware, such as process-in-memory (PIM) technology, could potentially accelerate such applications. In this paper, we propose PIMMiner, a high-performance PIM architecture graph mining framework. We first identify that current PIM architecture cannot be fully utilized by graph mining applications. Next, we propose a set of optimizations and interfaces that enhance the locality, and internal bandwidth utilization and reduce remote bank accesses and load imbalance through cohesive algorithm and architecture co-designs. We compare PIMMiner with several state-of-the-art graph mining frameworks and show that PIMMiner is able to outperform all of them significantly.

cs.AR↗

arXiv2023

ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor

Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dwell time. Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation. However, due to expensive online interactions, it is very difficult for RL algorithms to perform state-action value estimation, exploration and feature extraction when optimizing long-term engagement. In this paper, we propose ResAct which seeks a policy that is close to, but better than, the online-serving policy. In this way, we can collect sufficient data near the learned policy so that state-action values can be properly estimated, and there is no need to perform online exploration. ResAct optimizes the policy by first reconstructing the online behaviors and then improving it via a Residual Actor. To extract long-term information, ResAct utilizes two information-theoretical regularizers to confirm the expressiveness and conciseness of features. We conduct experiments on a benchmark dataset and a large-scale industrial dataset which consists of tens of millions of recommendation requests. Experimental results show that our method significantly outperforms the state-of-the-art baselines in various long-term engagement optimization tasks.

cs.IR↗

arXiv2023

A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels

Here we present a structural similarity index measure (SSIM) guided conditional Generative Adversarial Network (cGAN) that generatively performs image-to-image (i2i) synthesis to generate photo-accurate protein channels in multiplexed spatial proteomics images. This approach can be utilized to accurately generate missing spatial proteomics channels that were not included during experimental data collection either at the bench or the clinic. Experimental spatial proteomic data from the Human BioMolecular Atlas Program (HuBMAP) was used to generate spatial representations of missing proteins through a U-Net based image synthesis pipeline. HuBMAP channels were hierarchically clustered by the (SSIM) as a heuristic to obtain the minimal set needed to recapitulate the underlying biology represented by the spatial landscape of proteins. We subsequently prove that our SSIM based architecture allows for scaling of generative image synthesis to slides with up to 100 channels, which is better than current state of the art algorithms which are limited to data with 11 channels. We validate these claims by generating a new experimental spatial proteomics data set from human lung adenocarcinoma tissue sections and show that a model trained on HuBMAP can accurately synthesize channels from our new data set. The ability to recapitulate experimental data from sparsely stained multiplexed histological slides containing spatial proteomic will have tremendous impact on medical diagnostics and drug development, and also raises important questions on the medical ethics of utilizing data produced by generative image synthesis in the clinical setting. The algorithm that we present in this paper will allow researchers and clinicians to save time and costs in proteomics based histological staining while also increasing the amount of data that they can generate through their experiments.

eess.IV↗

arXiv2023

Generative Flow Network for Listwise Recommendation

Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches the user's demand or interest. While most existing methods learn a pointwise scoring model that predicts the ranking score of each individual item, recent research shows that the listwise approach can further improve the recommendation quality by modeling the intra-list correlations of items that are exposed together. This has motivated the recent list reranking and generative recommendation approaches that optimize the overall utility of the entire list. However, it is challenging to explore the combinatorial space of list actions and existing methods that use cross-entropy loss may suffer from low diversity issues. In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality. The proposed solution, GFN4Rec, is a generative method that takes the insight of the flow network to ensure the alignment between list generation probability and its reward. The key advantages of our solution are the log scale reward matching loss that intrinsically improves the generation diversity and the autoregressive item selection model that captures the item mutual influences while capturing future reward of the list. As validation of our method's effectiveness and its superior diversity during active exploration, we conduct experiments on simulated online environments as well as an offline evaluation framework for two real-world datasets.

cs.IR↗

arXiv2023

FAST reveals new evidence for M94 as a merger

We report the first high-sensitivity HI observation toward the spiral galaxy M94 with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). From these observations, we discovered that M94 has a very extended HI disk, twice larger than that observed by THINGS, which is accompanied by an HI filament and seven HVCs (high velocity clouds) at different distances. The projected distances of these clouds and filament are less than 50 kpc from the galactic center. We measured a total integrated flux (including all clouds/filament) of 127.3 ($\pm$1) Jy km s$^{-1}$, corresponding to a H I mass of (6.51$\pm$0.06)$\times$10$^{8}$M$_{\odot}$, which is 63.0% more than that observed by THINGS. By comparing numerical simulations with the HI maps and the optical morphology of M94, we suggest that M94 is likely a remnant of a major merger of two galaxies, and the HVCs and HI filament could be the tidal features originated from the first collision of the merger happened about 5 Gyr ago. Furthermore, we found a seemingly isolated HI cloud at a projection distance of 109 kpc without any optical counterpart detected. We discussed the possibilities of the origin of this cloud, such as dark dwarf galaxy and RELHIC (REionization-Limited HI Cloud). Our results demonstrate that high-sensitivity and wide-field HI imaging is important in revealing the diffuse cold gas structures and tidal debris which is crucial to understanding the dynamical evolution of galaxies.

astro-ph.GA↗

arXiv2023

Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation

An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch time prediction framework should satisfy: first, despite its continuous value, watch time is also an ordinal variable and the relative ordering between its values reflects the differences in user preferences. Therefore the ordinal relations should be reflected in watch time predictions. Second, the conditional dependence between the video-watching behaviors should be captured in the model. For instance, one has to watch half of the video before he/she finishes watching the whole video. Third, modeling watch time with a point estimation ignores the fact that models might give results with high uncertainty and this could cause bad cases in recommender systems. Therefore the framework should be aware of prediction uncertainty. Forth, the real-life recommender systems suffer from severe bias amplifications thus an estimation without bias amplification is expected. Therefore we propose TPM for watch time prediction. Specifically, the ordinal ranks of watch time are introduced into TPM and the problem is decomposed into a series of conditional dependent classification tasks which are organized into a tree structure. The expectation of watch time can be generated by traversing the tree and the variance of watch time predictions is explicitly introduced into the objective function as a measurement for uncertainty. Moreover, we illustrate that backdoor adjustment can be seamlessly incorporated into TPM, which alleviates bias amplifications. Extensive offline evaluations have been conducted in public datasets and TPM have been deployed in a real-world video app Kuaishou with over 300 million DAUs. The results indicate that TPM outperforms state-of-the-art approaches and indeed improves video consumption significantly.

cs.IR↗