Abacus Level 4 Model Test Papers

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In this blogpost we introduce another batch of supporting papers released yesterday on April 11, just one week after releasing our cosmological results using BAO from galaxies and quasars and the Lyman-alpha forest.

These papers describe the methods by which we ensure that all results properly take into account systematic effects, including: incomplete galaxy sampling, human biases, and imaging systematics. For a general overview of how DESI selects its targets, see this blog post, and for more information about survey validation see this blog post.

Most papers attributed to this category are yet to come out. But one of them, the paper presenting the DESI Blinding strategy, was already released yesterday, given its synergy with the BAO papers released a week ago, and with the Full Shape papers also released yesterday. It represents a major stepping stone validating the DESI 2024 Blinding strategy for the BAO and RSD (Full Shape) analysis.

This figure shows for one particular case the fitted isotropic and anisotropic BAO dilation parameters scaled to the expectation obtained from 8 different blindingalues of (w0, wa) and either positive (fnl=20) or negative (fnl=-20) primordial non-Gaussianity. Deviations from 1 are observed only for very extreme pairs of blinding values.

Both these approaches have advantages and disadvantages. Template fits are designed to extract only the most robust information and allows for a modular interpretation. For example, they allow us to decouple the information on expansion history, growth history, and shape in an effective way. On the other hand, the extra compression step within the template fit method can erase some of the cosmological information within 2-point statistics. Direct fits allow us to squeeze all of the cosmological information out of the data. At the same time, their results are, by nature, model-dependent, and they do not provide the same means of performing diagnostic tests such as template fits.

Short: This paper models the redshift-space galaxy power spectrum into the quasi-linear regime with several different EFT models, compares the different models to each other, and tests each using the AbacusSummit simulations.

Long: This paper demonstrates the level of consistency between the different effective field theory models used for fitting galaxy power spectra in redshift space. We show, by fitting to Abacus cubic mocks, that velocileptors (Lagrangian and Eulerian PT versions), PyBird, and FOLPSv give consistent constraints in LCDM and ShapeFit parameters with differences in means of

Short: This paper includes validation testing of various features of the analysis using the Velocileptors pipeline in combination with AbacusSummit mocks. Studies the dependence of the results on parameter compression, scale cuts, joint fitting, beyond-Lambda CDM modeling, inclusion of external data, and more.

Long: We present systematics tests and comparisons of three different modeling methods (Full-modeling, ShapeFit, and standard template) within velocileptors for modeling the galaxy power spectrum in redshift space using a Lagrangian effective field theory framework. We fit Abacus N-body simulations created to mimic the LRG, ELG, and QSO tracers that DESI targets, and show that ShapeFit and Full-modeling have consistent constraints and similar constraining power on LCDM models. We demonstrate the behavior of the three modeling methods for a variety of fitting settings with/without including BAO information in order to describe optimal fitting settings for velocileptors for DESI Y1 analyses and beyond.

Long: In this paper, we compare the constraints of cosmological parameters from Shapefit and Full-Modelling with PyBird. We do this with the DESI cubic box mocks for Luminous Red galaxies (LRG), Emission Line Galaxies (ELG), and Quasi Steller Object (QSO) for the LCDM, wCDM, and oCDM models. We found for all three cosmological models tested, the constraints from Shapefit and Full-Shape are consistent with each other. Furthermore, the constraints from both methodologies agree with the underlying cosmology.

Long: This work conducts a thorough comparison of various methodologies for modeling the full shape of the two-point statistics in configuration space. We investigate the performance of both direct fits (Full-Modeling) and the parameter compression approaches (ShapeFit and Standard) with CLPT-EFT. Our pipeline recovers unbiased cosmological parameter values for a 1-year DESI volume. We also present the comparisons of the configuration space version of different EFT models.

Papers are available for download below to registered attendees now and to everyone beginning [INSERT DATE]. Paper abstracts are available to everyone now. Copyright to the individual works is retained by the author[s].

Zhenghang Ren, Mingxuan Fan, Zilong Wang, Junxue Zhang, and Chaoliang Zeng, iSING Lab@The Hong Kong University of Science and Technology; Zhicong Huang and Cheng Hong, Ant Group; Kai Chen, iSING Lab@The Hong Kong University of Science and Technology and University of Science and Technology of China

Secure Collaborative Machine Learning (SCML) suffers from high communication cost caused by secure computation protocols. While modern datacenters offer high-bandwidth and low-latency networks with Remote Direct Memory Access (RDMA) capability, existing SCML implementation remains to use TCP sockets, leading to inefficiency. We present CORA1 to implement SCML over RDMA. By using a protocol-aware design, CORA identifies the protocol used by the SCML program and sends messages directly to the remote party's protocol buffer, improving the efficiency of message exchange. CORA exploits the chance that the SCML task is determined before execution and the pattern is largely input-irrelevant, so that CORA can plan message destinations on remote hosts at compile time. CORA can be readily deployed with existing SCML frameworks such as Piranha with its socket-like interface. We evaluate CORA in SCML training tasks, and our results show that CORA can reduce communication cost by up to 11x and achieve 1.2x - 4.2x end-to-end speedup over TCP in SCML training.

We introduce ABACuS, a new low-cost hardware-counterbased RowHammer mitigation technique that performance-, energy-, and area-efficiently scales with worsening RowHammer vulnerability. We observe that both benign workloads and RowHammer attacks tend to access DRAM rows with the same row address in multiple DRAM banks at around the same time. Based on this observation, ABACuS's key idea is to use a single shared row activation counter to track activations to the rows with the same row address in all DRAM banks. Unlike state-of-the-art RowHammer mitigation mechanisms that implement a separate row activation counter for each DRAM bank, ABACuS implements fewer counters (e.g., only one) to track an equal number of aggressor rows.

Our comprehensive evaluations show that ABACuS securely prevents RowHammer bitflips at low performance/energy overhead and low area cost. We compare ABACuS to four state-of-the-art mitigation mechanisms. At a nearfuture RowHammer threshold of 1000, ABACuS incurs only 0.58% (0.77%) performance and 1.66% (2.12%) DRAM energy overheads, averaged across 62 single-core (8-core) workloads, requiring only 9.47 KiB of storage per DRAM rank. At the RowHammer threshold of 1000, the best prior lowarea-cost mitigation mechanism incurs 1.80% higher average performance overhead than ABACuS, while ABACuS requires 2.50 smaller chip area to implement. At a future RowHammer threshold of 125, ABACuS performs very similarly to (within 0.38% of the performance of) the best prior performance- and energy-efficient RowHammer mitigation mechanism while requiring 22.72 smaller chip area. We show that ABACuS's performance scales well with the number of DRAM banks. At the RowHammer threshold of 125, ABACuS incurs 1.58%, 1.50%, and 2.60% performance overheads for 16-, 32-, and 64-bank systems across all single-core workloads, respectively. ABACuS is freely and openly available at -SAFARI/ABACuS.

Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth potential security risks. CAVs' driving decisions rely on remote untrusted data, making them susceptible to attacks carried out by malicious participants in the collaborative perception system. However, security analysis and countermeasures for such threats are absent. To understand the impact of the vulnerability, we break the ground by proposing various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results, leading to hard brakes or increased collision risks. Our attacks demonstrate a high success rate of over 86% on high-fidelity simulated scenarios and are realizable in real-world experiments. To mitigate the vulnerability, we present a systematic anomaly detection approach that enables benign vehicles to jointly reveal malicious fabrication. It detects 91.5% of attacks with a false positive rate of 3% in simulated scenarios and significantly mitigates attack impacts in real-world scenarios.

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