arXiv subjects

Tianyi Yang

Publications and source records attributed to Tianyi Yang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Prospects for measuring electroweak production of $Zγγ$ and 2 jets at the LHC

Vector boson scattering (VBS) serves as a powerful channel for probing the Standard Model, particularly the electroweak symmetry breaking mechanism. Currently, studies of VBS mainly focus on $2 \to 2$ scattering. In this study, we investigate the $2\to 3$ VBS process of $\text{p p} \to Zγγ+ 2~\text{jets}$ through Monte Carlo simulations, including signal generation and background analysis. The signal significance is evaluated across different phase-space regions. With an integrated luminosity of 500 fb$^{-1}$, the signal significance can reach about 4.5 $σ$. These results suggest that, given the ongoing release of the LHC Run 3 dataset, there will be promising opportunities to explore and potentially discover a series of $2\to 3$ VBS processes, starting with the $Zγγ$ channel.

hep-ph↗

arXiv2025

The kinetic Sunyaev Zeldovich effect as a benchmark for AGN feedback models in hydrodynamical simulations: insights from DESI + ACT

Baryonic feedback remains one of the largest uncertainties in cosmological hydrodynamical simulations, with different prescriptions producing divergent predictions for the fraction of gas expelled from halos, the radial extent of the gas expulsion and the impact on large scale matter clustering. We present the first systematic study of the kinetic Sunyaev-Zel'dovich (kSZ) effect across a wide range of simulations (FLAMINGO, ANTILLES, BAHAMAS, SIMBA, FABLE and their variants), and compare them directly to DESI Year 1 + ACT kSZ measurements. We ensure a like-for-like comparison with observations by developing a robust methodology that accounts for the halo mass selection using galaxy-galaxy lensing, cosmic variance, miscentering and satellites, establishing the kSZ effect as a new benchmark for the simulations. We find that fiducial feedback models are disfavoured by >3 sigma, while simulations with more powerful AGN feedback within the FLAMINGO and BAHAMAS suites, as well as SIMBA, reproduce the observed kSZ signal within <2 sigma. We use the ANTILLES simulation suite to demonstrate that the amplitude of the kSZ effect is a strong predictor of matter power spectrum suppression, competitive with baryon fraction metrics. These results establish the kSZ as a critical probe for evaluating feedback physics and for advancing the fidelity of cosmological simulations.

astro-ph.CO↗

arXiv2025

Connected Trading Cycles

Incentivizing the existing participants to invite new participants to join an auction, matching or cooperative game have been extensively studied recently. One common challenge to design such incentive in these games is that the invitees and inviters are competitors. To have such an incentive, we normally have to sacrifice some of the traditional properties. Especially, in a housing market (one kind of one-sided matching), we cannot maintain the traditional stability and optimality. The previous studies proposed some new matching mechanisms to have the invitation incentive (part of the incentive compatibility), but did not have any guarantee on stability and optimality. In this paper, we propose new notions of stability and optimality which are achievable with incentive compatibility. We weaken stability and optimality on a special structure (complete components) on networks. We first prove that the weakened notions are the best we can achieve with incentive compatibility. Then, we propose three mechanisms (Swap With Neighbors, Leave and Share, and Connected Trading Cycles) to satisfy the desirable properties. Connected Trading Cycles is the first mechanism to satisfy the best stability and optimality compatible with incentive compatibility.

cs.GT↗

arXiv2024

Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications

With the proliferation of LLM-integrated applications such as GPT-s, millions are deployed, offering valuable services through proprietary instruction prompts. These systems, however, are prone to prompt extraction attacks through meticulously designed queries. To help mitigate this problem, we introduce the Raccoon benchmark which comprehensively evaluates a model's susceptibility to prompt extraction attacks. Our novel evaluation method assesses models under both defenseless and defended scenarios, employing a dual approach to evaluate the effectiveness of existing defenses and the resilience of the models. The benchmark encompasses 14 categories of prompt extraction attacks, with additional compounded attacks that closely mimic the strategies of potential attackers, alongside a diverse collection of defense templates. This array is, to our knowledge, the most extensive compilation of prompt theft attacks and defense mechanisms to date. Our findings highlight universal susceptibility to prompt theft in the absence of defenses, with OpenAI models demonstrating notable resilience when protected. This paper aims to establish a more systematic benchmark for assessing LLM robustness against prompt extraction attacks, offering insights into their causes and potential countermeasures. Resources of Raccoon are publicly available at https://github.com/M0gician/RaccoonBench.

cs.CR↗

arXiv2024

SPES: Towards Optimizing Performance-Resource Trade-Off for Serverless Functions

As an emerging cloud computing deployment paradigm, serverless computing is gaining traction due to its efficiency and ability to harness on-demand cloud resources. However, a significant hurdle remains in the form of the cold start problem, causing latency when launching new function instances from scratch. Existing solutions tend to use over-simplistic strategies for function pre-loading/unloading without full invocation pattern exploitation, rendering unsatisfactory optimization of the trade-off between cold start latency and resource waste. To bridge this gap, we propose SPES, the first differentiated scheduler for runtime cold start mitigation by optimizing serverless function provision. Our insight is that the common architecture of serverless systems prompts the concentration of certain invocation patterns, leading to predictable invocation behaviors. This allows us to categorize functions and pre-load/unload proper function instances with finer-grained strategies based on accurate invocation prediction. Experiments demonstrate the success of SPES in optimizing serverless function provision on both sides: reducing the 75th-percentile cold start rates by 49.77% and the wasted memory time by 56.43%, compared to the state-of-the-art. By mitigating the cold start issue, SPES is a promising advancement in facilitating cloud services deployed on serverless architectures.

cs.SE↗

arXiv2024

Large-scale Outdoor Cell-free mMIMO Channel Measurement in an Urban Scenario at 3.5 GHz

The design of cell-free massive MIMO (CF-mMIMO) systems requires accurate, measurement-based channel models. This paper provides the first results from the by far most extensive outdoor measurement campaign for CF-mMIMO channels in an urban environment. We measured impulse responses between over 20,000 potential access point (AP) locations and 80 user equipments (UEs) at 3.5 GHz with 350 MHz bandwidth (BW). Measurements use a "virtual array" approach at the AP and a hybrid switched/virtual approach at the UE. This paper describes the sounder design, measurement environment, data processing, and sample results, particularly the evolution of the power-delay profiles (PDPs) as a function of the AP locations, and its relation to the propagation environment.

eess.SP↗

arXiv2024

Searches for multi-Z boson productions and anomalous gauge boson couplings at a muon collider

Multi-boson productions can be exploited as novel probes either for standard model precision tests or new physics searches, and have become one of those popular topics in the ongoing LHC experiments, and in future collider studies, including those for electron-positron and muon-muon colliders. Here we focus on two examples, i.e., ZZZ direct productions through $μ^{+}μ^{-}$ annihilation at a 1 TeV muon collider, and ZZ productions through vector boson scattering at a 10 TeV muon collider, with an integrated luminosity of $10 \, \text{ab}^{-1}$. Various channels are considered, including, such as $ZZZ \rightarrow 4l2ν$ and $ZZZ \rightarrow 4l + 2 \text{ jets}$, etc. Expected significance on these multi-Z boson production processes are provided based on a detailed Monte Carlo study and signal background analysis. Sensitives on anomalous gauge boson couplings are also presented.

hep-ex↗

arXiv2024

ICE-SEARCH: A Language Model-Driven Feature Selection Approach

This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses the crossover and mutation capabilities inherent in LLMs within an evolutionary framework, significantly improving FS through the model's comprehensive world knowledge and its adaptability to a variety of roles. Our evaluation of this methodology spans three crucial MPA tasks: stroke, cardiovascular disease, and diabetes, where ICE-SEARCH outperforms traditional FS methods in pinpointing essential features for medical applications. ICE-SEARCH achieves State-of-the-Art (SOTA) performance in stroke prediction and diabetes prediction; the Decision-Randomized ICE-SEARCH ranks as SOTA in cardiovascular disease prediction. The study emphasizes the critical role of incorporating domain-specific insights, illustrating ICE-SEARCH's robustness, generalizability, and convergence. This opens avenues for further research into comprehensive and intricate FS landscapes, marking a significant stride in the application of artificial intelligence in medical predictive analytics.

cs.LG↗

arXiv2024

MicroRes: Versatile Resilience Profiling in Microservices via Degradation Dissemination Indexing

Microservice resilience, the ability of microservices to recover from failures and continue providing reliable and responsive services, is crucial for cloud vendors. However, the current practice relies on manually configured rules specific to a certain microservice system, resulting in labor-intensity and flexibility issues, given the large scale and high dynamics of microservices. A more labor-efficient and versatile solution is desired. Our insight is that resilient deployment can effectively prevent the dissemination of degradation from system performance metrics to user-aware metrics, and the latter directly affects service quality. In other words, failures in a non-resilient deployment can impact both types of metrics, leading to user dissatisfaction. With this in mind, we propose MicroRes, the first versatile resilience profiling framework for microservices via degradation dissemination indexing. MicroRes first injects failures into microservices and collects available monitoring metrics. Then, it ranks the metrics according to their contributions to the overall service degradation. It produces a resilience index by how much the degradation is disseminated from system performance metrics to user-aware metrics. Higher degradation dissemination indicates lower resilience. We evaluate MicroRes on two open-source and one industrial microservice system. The experiments show MicroRes' efficient and effective resilience profiling of microservices. We also showcase MicroRes' practical usage in production.

cs.SE↗

arXiv2023

The physics case for neutrino-neutrino collisions

Addressing the mass origin and properties of neutrinos is of strong interest to particle physics, baryogenesis and cosmology. Popular explanations involve physics beyond the standard model, for example, the dimension-5 Weinberg operator or heavy Majorana neutrinos arising from ``seesaw'' models. The current best direct limits on the electron neutrino mass, derived from nuclei beta decay or neutrinoless double beta decay processes, are at the sub-electronvolt level. Here we propose a novel neutrino neutrino collider where the neutrino beam is generated from TeV scale muon decays. Such collisions can happen between either neutrinos and anti-neutrinos, or neutrinos and neutrinos. We find that with a tiny integrated luminosity of about $10^{-5}$/fb we can already expect to observe direct neutrino anti-neutrino annihilation, $ν\barν\rightarrow {\rm Z}$, which also opens the door to explore neutrino related resonances $ν\barν\rightarrow {\rm X}$. The low luminosity requirement can accommodate a relatively large emittance muon beam. Such a device would also allow for probing heavy Majorana neutrino and effective Majorana neutrino mass through $νν\rightarrow {\rm H H}$ to a competitive level, for both electron and muon types.

hep-ph↗

arXiv2023

Muon Beam for Neutrino CP Violation: connecting energy and neutrino frontiers

We propose here a proposal to connect neutrino and energy frontiers, by exploiting collimated muon beams for neutrino oscillations, which generate symmetric neutrino and antineutrino sources: $μ^+\rightarrow e^+\,\barν_μ\, ν_{e}$ and $μ^-\rightarrow e^-\, ν_μ \,\barν_{e}$. Interfacing with long baseline neutrino detectors such as DUNE and T2K, this experiment can be applicable to measure tau neutrino properties, and also to probe neutrino CP phase, by measuring muon electron (anti-)neutrino mixing or tau (anti-)neutrino appearance, and differences between neutrino and antineutrino rates. There are several significant benefits leading to large neutrino flux and high sensitivity on CP phase, including 1) collimated and manipulable muon beams, which lead to a larger acceptance of neutrino sources in the far detector side; 2) symmetric $μ^+$ and $μ^-$ beams, and thus symmetric neutrino and antineutrino sources, which make this proposal ideally useful for measuring neutrino CP violation. More importantly, $\barν_{e,μ}\rightarrow\barν_τ$ and $ν_{e,μ}\rightarrow ν_τ$, and, $\barν_{e}\rightarrow\barν_μ$ and $ν_{e}\rightarrow ν_μ$ oscillation signals can be collected simultaneously, with no needs for separate specific runs for neutrinos or antineutrinos. Based on a simulation of neutrino oscillation experiment, we estimate $10^4$ tau (anti-) neutrinos can be collected within 5 years which makes this proposal suitable for a brighter tau neutrino factory. Moreover, more than 7 standard deviations of sensitivity can be reached for $\dcp = |π/2|$, within only five ears of data taking, by combining tau and muon (anti-) neutrino appearances. With the development of a more intensive muon beam targeting future muon collider, the neutrino potential of the current proposal will surely be further improved.

hep-ph↗

arXiv2023

Feedback-driven anisotropy in the circumgalactic medium for quenching galaxies in the SIMBA simulations

We use the SIMBA galaxy formation simulation suite to explore anisotropies in the properties of circumgalactic gas that result from accretion and feedback processes. We particularly focus on the impact of bipolar active galactic nuclei (AGN) jet feedback as implemented in SIMBA, which quenches galaxies and has a dramatic effect on large-scale gas properties. We show that jet feedback at low redshifts is most common in the stellar mass range $(1-5)\times 10^{10}M_\odot$, so we focus on galaxies with active jets in this mass range. In comparison to runs without jet feedback, jets cause lower densities and higher temperatures along the galaxy minor axis (SIMBA jet direction) at radii >=$0.5r_{200c}-4r_{200c}$ and beyond. This effect is less apparent at higher or lower stellar masses, and is strongest within green valley galaxies. The metallicity also shows strong anisotropy out to large scales, driven by star formation feedback. We find substantially stronger anisotropy at <=$0.5r_{200c}$, but this also exists in runs with no explicit feedback, suggesting that it is due to anisotropic accretion. Finally, we explore anisotropy in the bulk radial motion of the gas, finding that both star formation and AGN wind feedback contribute to pushing the gas outwards along the minor axis at <=1 Mpc, but AGN jet feedback further causes bulk outflow along the minor axis out to several Mpc, which drives quenching via gas starvation. These results provide observational signatures for the operation of AGN feedback in galaxy quenching.

astro-ph.GA↗

arXiv2023

A Roadmap towards Intelligent Operations for Reliable Cloud Computing Systems

The increasing complexity and usage of cloud systems have made it challenging for service providers to ensure reliability. This paper highlights two main challenges, namely internal and external factors, that affect the reliability of cloud microservices. Afterward, we discuss the data-driven approach that can resolve these challenges from four key aspects: ticket management, log management, multimodal analysis, and the microservice resilience testing approach. The experiments conducted show that the proposed data-driven AIOps solution significantly enhances system reliability from multiple angles.

cs.SE↗

arXiv2023

Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services

As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems' health status, becomes more and more critical and challenging. In particular, the dependency between different metrics and their historical patterns plays a critical role in pursuing prompt and accurate anomaly detection. Existing approaches fall short of industrial needs for being unable to capture such information efficiently. To fill this significant gap, in this paper, we propose CMAnomaly, an anomaly detection framework on multivariate monitoring metrics based on collaborative machine. The proposed collaborative machine is a mechanism to capture the pairwise interactions along with feature and temporal dimensions with linear time complexity. Cost-effective models can then be employed to leverage both the dependency between monitoring metrics and their historical patterns for anomaly detection. The proposed framework is extensively evaluated with both public data and industrial data collected from a large-scale online service system of Huawei Cloud. The experimental results demonstrate that compared with state-of-the-art baseline models, CMAnomaly achieves an average F1 score of 0.9494, outperforming baselines by 6.77% to 10.68%, and runs 10X to 20X faster. Furthermore, we also share our experience of deploying CMAnomaly in Huawei Cloud.

cs.SE↗

arXiv2023

Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion

Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly detection focus solely on real-time detection, meaning that anomaly alerts are issued as soon as anomalies occur. However, anomalies can propagate and escalate into failures, making faster-than-real-time anomaly detection highly desirable for expediting downstream analysis and intervention. This paper proposes Maat, the first work to address anomaly anticipation of performance metrics in cloud services. Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting of metric forecasting and anomaly detection on forecasts. The metric forecasting stage employs a conditional denoising diffusion model to enable multi-step forecasting in an auto-regressive manner. The detection stage extracts anomaly-indicating features based on domain knowledge and applies isolation forest with incremental learning to detect upcoming anomalies. Thus, our method can uncover anomalies that better conform to human expertise. Evaluation on three publicly available datasets demonstrates that Maat can anticipate anomalies faster than real-time comparatively or more effectively compared with state-of-the-art real-time anomaly detectors. We also present cases highlighting Maat's success in forecasting abnormal metrics and discovering anomalies.

cs.SE↗

arXiv2023

Searching for Majorana Neutrinos at a Same-Sign Muon Collider

Majorana properties of neutrinos have long been a focus in the pursuit of possible new physics beyond the standard model, which has motivated lots of dedicated theoretical and experimental studies. A future same-sign muon collider is an ideal platform to search for Majorana neutrinos through the Lepton Number Violation process. Specifically, this t-channel kind of process is less kinematically suppressed and has a good advantage in probing Majorana neutrinos at high mass regions up to 10 TeV. In this paper, we perform a detailed fast Monte Carlo simulation study through examining three different final states: 1) pure-leptonic state with electrons or muons, 2) semi-leptonic state, and 3) pure-hadronic state in the resolved or merged categories. Furthermore, we perform a full simulation study on the pure-leptonic final state to validate our fast simulation results.

hep-ph↗

arXiv2023

Low Energy Neutrino and Mass Dark Matter Detection Using Freely Falling Atoms

We propose a new method to detect low-energy neutrinos and low-mass dark matter at or below the MeV scale, through their coherent scatterings from freely falling heavy atoms and the resulting kinematic shifts. We start with a simple calculation for illustration: for $10^7$ heavy atoms of a mass number around 100 with a small recoil energy of 1 meV, the corresponding velocities can reach $0.01, {\rm m/s}$ and produce significant kinematic shifts that can be detected. We then show that the proposed device should be able to probe vast low-energy regions of neutrinos from meV to MeV and can surpass previous limits on sub-MeV dark matter by several orders of magnitude. Such a proposal can be useful to (1) detect sub-MeV-scale dark matter: with $10^2$ atom guns shooting downwards, for example, CsI or lead clusters consisting of $10^{7}$ atoms with a frequency around $10^3$ Hz, it can already be sensitive to scattering cross-sections at the level of $10^{-33 (-34)}\rm{cm}^{2}$ for 1 (0.1) MeV dark matter and surpass current limits. Technological challenges include high-quality atom cluster production and injections. (2) Measure coherent neutrino-nuclei scatterings at the 0.1-1 MeV region for the first time: with $10^4$ atom guns shooting downwards CsI clusters consisting of $10^{11}$ atoms and a frequency of $10^{6}$ Hz. One can expect 10 events from MeV solar neutrinos to be observed per year. Furthermore, (3) this method can be extended to probe very low-energy neutrinos down to the eV-KeV region and may be able to detect the cosmic neutrino background, although it remains challenging.

hep-ph↗

arXiv2023

Heterogeneous Anomaly Detection for Software Systems via Attentive Multi-modal Learning

Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems. Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joint information among multi-source data. Consequently, many false predictions occur. To better understand the manifestations of system anomalies, we conduct a comprehensive empirical study based on a large amount of heterogeneous data, i.e., logs and metrics. Our study demonstrates that system anomalies could manifest distinctly in different data types. Thus, integrating heterogeneous data can help recover the complete picture of a system's health status. In this context, we propose HADES, the first work to effectively identify system anomalies based on heterogeneous data. Our approach employs a hierarchical architecture to learn a global representation of the system status by fusing log semantics and metric patterns. It captures discriminative features and meaningful interactions from multi-modal data via a novel cross-modal attention module, enabling accurate system anomaly detection. We evaluate HADES extensively on large-scale simulated and industrial datasets. The experimental results present the superiority of HADES in detecting system anomalies on heterogeneous data. We release the code and the annotated dataset for reproducibility and future research.

cs.SE↗