Large Background Research Articles

Intelligence-assisted college English teaching: The application of artificial intelligence technology in personalized learning path design

English learning is an integrated learning process in which listening, speaking, reading, writing and other parts are interrelated. Because of this feature of English learning, higher requirements are put forward for the design of English learning assistance systems. On the other hand, the wide application of advanced science and technology such as automation and cloud computing in all walks of life has promoted the development of society into the era of artificial intelligence. Under such a large development background, how to build a multifunctional integrated English learning system to realize the personalized learning of learners with different levels and different needs has become a focus of current English teaching research. With the increasing abundance of English learning materials, it has become very important to help users find suitable English learning resources from the massive materials, which directly affects the cost of learning time and interest of users. Based on this, this paper introduces a multi-similarity ranking model to achieve personalized design, and then uses artificial intelligence technology to design and implement a personalized intelligent assistant system for college English teaching.

Analysis of shared variants between cancer biospecimens.

Mutational data from multiple solid and liquid biospecimens of a single patient is often integrated to track cancer evolution. However, there is no accepted framework to resolve if individual samples from the same individual share variants due to common identity versus coincidence. Utilizing 8,000 patient tumors from The Cancer Genome Atlas (TCGA) across 33 cancer types, we estimated background rates of co-occurrence rates of mutations between discrete pairs of samples across cancers and by cancer type. We developed a mutational profile similarity score (MPS) that uses a large background database to produce confidence estimates that two tumors share a unique, related molecular profile. The MPS algorithm was applied to randomly paired tumor profiles, including patients who underwent repeat solid tumor biopsies sequenced with MSK-IMPACT (n=53,113). We also evaluated the MPS in sample pairs from single patients with multiple cancers (n=2,012), as well as patients with plasma and solid-tumor variant profiles (n=884 patients). In unrelated tumors, nucleotide-specific variants are shared in 1.3% (cancer-type agnostic) and in 10-13% (cancer-type specific) of cases. The mutational profile similarity (MPS) method contextualized shared variants to specify whether patients had a single cancer versus multiple distinct cancers. When multiple tumors were compared from the same patient, and an initial clinicopathologic diagnosis was discordant with molecular findings, the MPS anticipated future diagnosis changes in 28% of examined cases. Use of a novel shared variant framework can provide information to clarify the molecular relationship between compared biospecimens with minimal required input.

Protocol for Therapeutic Drug Monitoring Within the Clinical Range Using Mid-infrared Spectroscopy.

Therapeutic drug monitoring (TDM), which involves measuring drug levels in patients' body fluids, is an important procedure in clinical practice. However, the analysis technique currently used, i.e. liquid chromatography-tandem mass spectrometry (LC-MS/MS), is laboratory-based, so does not offer the short response time that is often required by clinicians. We suggest that techniques based on Fourier transform infrared spectroscopy (FTIR) offer a promising alternative for TDM. FTIR is rapid, highly specific and can be miniaturized for near-patient applications. The challenge, however, is that FTIR for TDM is limited by the strong mid-IR absorption of endogenous serum constituents. Here, we address this issue and introduce a versatile approach for removing the background of serum lipids, proteins and small water-soluble substances. Using phenytoin, an antiepileptic drug, as an example, we show that our approach enables FTIR to precisely quantify drug molecules in human serum at clinically relevant levels (10 μg/mL), providing an efficient analysis method for TDM. Beyond mid-IR spectroscopy, our study is applicable to other drug sensing techniques that suffer from the large background of serum samples.

Large-Kernel Central Block Masked Convolution and Channel Attention-Based Reconstruction Network for Anomaly Detection of High-Resolution Hyperspectral Imagery

In recent years, the rapid advancement of drone technology has led to an increasing use of drones equipped with hyperspectral sensors for ground imaging. Hyperspectral data captured via drones offer significantly higher spatial resolution, but this also introduces more complex background details and larger target scales in high-resolution hyperspectral imagery (HRHSI), posing substantial challenges for hyperspectral anomaly detection (HAD). Mainstream reconstruction-based deep learning methods predominantly emphasize spatial local information in hyperspectral images (HSIs), relying on small spatial neighborhoods for reconstruction. As a result, large anomalous targets and background details are often well reconstructed, leading to poor anomaly detection performance, as these targets are not sufficiently distinguished from the background. To address these limitations, we propose a novel HAD network for HRHSI based on large-kernel central block masked convolution and channel attention, termed LKCMCA. Specifically, we first employ the pixel-shuffle technique to reduce the size of anomalous targets without losing image information. Next, we design a large-kernel central block masked convolution to make the network pay more attention to the surrounding background information, enabling better fusion of the information between adjacent bands. This, coupled with an efficient channel attention mechanism, allows the network to capture deeper spectral features, enhancing the reconstruction of the background while suppressing anomalous targets. Furthermore, we introduce an adaptive loss function by down-weighting anomalous pixels based on the mean absolute error. This loss function is specifically designed to suppress the reconstruction of potentially anomalous pixels during network training, allowing our model to be considered an excellent background reconstruction network. By leveraging reconstruction error, the model effectively highlights anomalous targets. Meanwhile, we produced four benchmark datasets specifically for HAD tasks using existing HRHSI data, addressing the current shortage of HRHSI datasets in the HAD field. Extensive experiments demonstrate that our LKCMCA method achieves superior detection performance, outperforming ten state-of-the-art HAD methods on all datasets.

Irregular adaptive refinement network for semantic segmentation of high-resolution remote sensing images

The semantic segmentation of high-resolution remote sensing images has broad application prospects in land cover classification, road extraction, urban planning and other fields. To alleviate the influence of the large data volume and complex background of high-resolution remote sensing images, the usual approach is to downsample them or cut them into small pieces for separate processing. Even if combining the two methods can improve the segmentation efficiency, it ignores the differences between the middle and the edge regions. Therefore, we consider the characteristics of large and irregular region in high-resolution remote sensing images, and then propose an irregular adaptive refinement network to locate the irregular edge region, which will be refined adaptively. Specifically, on the basis of effectively preserving the global and local information, the prediction confidence is calculated to locate pixel points that are poorly segmented, so as to form irregular regions requiring further refinement, avoiding to ‘over-refine’ intermediate region with good segmentation. At the same time, considering the difference in the refinement degree of different pixels, we propose to adaptively integrate the local segmentation results to refine the coarse segmentation results. In addition, in order to bridge the gap between the two extreme ends of the scale space, we introduce a multi-scale framework. Finally, we conducted experiments on the Deepglobe dataset showing that the proposed method performed 0.37% to 0.87% better than the previous state-of-the-art methods in terms of mean Intersection over Union (mIoU).

On the trapped magnetic moment in type-II superconductors

Abstract Measurements of the trapped (remanent) magnetic moment, M trap ( H ) , when a small magnetic field H is turned off after cooling below the superconducting transition temperature, Tc , or ramping a magnetic field up and down after cooling in a zero magnetic field, offer substantial advantages in difficult cases of small samples and large field-dependent backgrounds, which is relevant for hydrogen-based ultra-high-T c superconductors (UHTS). Until recently, there was no need for a separate paper on the trapped magnetic flux for well-known critical-state models due to the simplicity of the physics involved. However, recent publications showed the need for such an analysis. This note summarizes the expectations for the Bean model with constant critical current density and the Kim model with field-dependent critical currents. It is shown that if the trapped moment is fitted to the power law, M trap ∝ H α , the fixed exponent α = 2 is exact for the Bean model, while Kim models show a wide interval of possible values, 2 ⩽ α ⩽ 4 . Furthermore, accounting for reversible magnetization expands the range of possible exponents to 1 ⩽ α ⩽ 4 . In addition, demagnetizing factors are essential and make the trapped moment orientation dependent even in isotropic materials. As a concrete application, it is shown that flux trapping experiments performed on H3S UHTS compounds can be well described using this generalized approach, lending further support to the type-II superconducting nature of H3S under ultra-high pressure.

Reactor neutrino liquid xenon coherent elastic scattering experiment

Coherent elastic neutrino-nucleus scattering (CEνNS) provides a unique probe for neutrino properties beyond the Standard Model physics. The reactor neutrino liquid xenon coherent scattering experiment (RELICS), a proposed reactor neutrino program using liquid xenon time projection chamber technology, aims to investigate the CEνNS process of antineutrinos off xenon atomic nuclei. In this work, the design of the experiment is studied and optimized based on Monte Carlo simulations. To achieve a sufficiently low-energy threshold for CEνNS detection, an ionization-only analysis channel is adopted for RELICS. A high emission rate of delayed electrons after a big ionization signal is the major background, leading to an analysis threshold of 120 photoelectrons in the CEνNS search. The second largest background, nuclear recoils induced by cosmic-ray neutrons, is suppressed via a passive water shield. The physics potential of RELICS is explored with a 30 kg·yr exposure at a baseline of 25 m from a reactor core with a 3 GW thermal power. In an energy range of 120–300 photoelectrons, corresponding to an average nuclear recoil from 0.63 to 1.36 keV considering the liquid xenon response and detector-related effect, we expect 4639.7 CEνNS and 1687.8 background events. The sensitivity of RELICS to the weak mixing angle is investigated at a low momentum transfer. Our study shows that RELICS can further improve the constraints on the nonstandard neutrino interaction compared to the current best results. Published by the American Physical Society 2024

Boosted four-top production at the LHC: A window to Randall-Sundrum or extended color symmetry

Scenarios seeking to address the issue of electroweak symmetry breaking often have heavy colored gauge bosons coupling preferentially to the top quark. Considering the bulk Randall-Sundrum as a typical example, we consider the prospects of the first Kaluza-Klein mode (G(1)) of the gluon being produced at the LHC in association with a tt¯ pair. The enhanced coupling not only dictates that the dominant decay mode would be to a tt¯ pair, but also to a very large G(1) width, necessitating the use of a renormalized G(1) propagator. This, along with the presence of large backgrounds (specially tt¯jj), renders a conventional cut-based analysis ineffective, yielding only marginal significances of only around 2σ. The use of machine learning (ML) techniques alleviates this problem to a great extent. In particular, the use of artificial neural networks helps us identify the most discriminating observables, thereby allowing a significance in excess of 4σ for G(1) masses of ∼4 TeV. Published by the American Physical Society 2024

YOLO-Faster: An efficient remote sensing object detection method based on AMFFN.

As a pivotal task within computer vision, object detection finds application across a diverse spectrum of industrial scenarios. The advent of deep learning technologies has significantly elevated the accuracy of object detectors designed for general-purpose applications. Nevertheless, in contrast to conventional terrestrial environments, remote sensing object detection scenarios pose formidable challenges, including intricate and diverse backgrounds, fluctuating object scales, and pronounced interference from background noise, rendering remote sensing object detection an enduringly demanding task. In addition, despite the superior detection performance of deep learning-based object detection networks compared to traditional counterparts, their substantial parameter and computational demands curtail their feasibility for deployment on mobile devices equipped with low-power processors. In response to the aforementioned challenges, this paper introduces an enhanced lightweight remote sensing object detection network, denoted as YOLO-Faster, built upon the foundation of YOLOv5. Firstly, the lightweight design and inference speed of the object detection network is augmented by incorporating the lightweight network as the foundational network within YOLOv5, satisfying the demand for real-time detection on mobile devices. Moreover, to tackle the issue of detecting objects of different scales in large and complex backgrounds, an adaptive multiscale feature fusion network is introduced, which dynamically adjusts the large receptive field to capture dependencies among objects of different scales, enabling better modeling of object detection scenarios in remote sensing scenes. At last, the robustness of the object detection network under background noise is enhanced through incorporating a decoupled detection head that separates the classification and regression processes of the detection network. The results obtained from the public remote sensing object detection dataset DOTA show that the proposed method has a mean average precision of 71.4% and a detection speed of 38 frames per second.

13C-Formate as an indirect low-temperature 1H lineshape polarimeter

1H polarization quantification is important for dissolution-dynamic nuclear polarization (dDNP) but can be cumbersome due to the requirement of acquiring thermal equilibrium signals and measurements that are complicated by large background signals. 1H nuclear magnetic resonance (NMR) spectra can also be deleteriously influenced by line distortions linked with radiation damping from 1H DNP and cannot be used for accurate calculation of 1H polarization. Determining 1H polarization via immediate 13C lineshape analysis of a simple molecule removes such complications. We present 13C-sodium formate as a straightforward system for indirect 1H polarimetry. The 13C NMR spectra acquired under dDNP conditions have distinct features that are readily reproduced with 13C lineshape simulations. 1H polarizations built-up during 1H DNP were indirectly inferred by fitting simulations to 13C lineshapes. We provide the MATLAB scripts used for 13C lineshape analysis in order that the method can be readily implemented in other laboratories.

AID-YOLO: An Efficient and Lightweight Network Method for Small Target Detector in Aerial Images

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the mAP@0.5 metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the mAP@0.5 metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection.

Smart prediction of rock crack opening displacement from noisy data recorded by distributed fiber optic sensing

The commonly used method for estimating crack opening displacement (COD) is based on analytical models derived from strain transferring. However, when large background noise exists in distributed fiber optic sensing (DFOS) data, estimating COD through an analytical model is very difficult even if the DFOS data have been denoised. To address this challenge, this study proposes a machine learning (ML)-based methodology to complete rock’s COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models. The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models. To ensure that the best hyper-parameters will not be missing, the configuration space in Hyperopt is specified by probability distribution. The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness. The proposed models are compared each other in terms of goodness of fit and mean squared error. The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.

Numerical Study of Skipping Motion of Blended-Wing–Body Aircraft Ditching on Calm/Wavy Water

The blended-wing–body (BWB) is hoped to be the main configuration of next-generation transport aircraft. However, its large flat belly may cause a violent skipping motion during the ditching on water. In this paper, the skipping motions of a BWB aircraft ditching on calm and wavy water are numerically investigated. The finite volume method with the volume-of-fluid approach is employed to solve the unsteady Reynolds-averaged Navier–Stokes equations. A coupled method between global moving mesh and overset mesh is proposed to avoid a large background domain and improve the prediction accuracy of the free surface, and its accuracy is well validated by predicting the motions of a skipping stone, the hydrodynamic load and pressure on a flat plate in high-speed ditching, and the fifth-order Stokes wave in a dynamic wave tank. At the beginning of every surfing stage, the flat belly of BWB aircraft impacts heavily on the water surface at a smaller pitch angle and larger speed, resulting in the local vertical overload peak, which is much larger than the peak in the porpoising motion. The wavy water surface intensifies the heave and pitch motions and increases the risk of the cockpit diving into the water.

Comparing the complexity of written and molecular symbolic systems

Symbolic systems (SSs) are uniquely products of living systems, such that symbolism and life may be inextricably intertwined phenomena. Within a given SS, there is a range of symbol complexity over which signaling is functionally optimized. This range exists relative to a complex and potentially infinitely large background of latent, unused symbol space. Understanding how symbol sets sample this latent space is relevant to diverse fields including biochemistry and linguistics.We quantitatively explored the graphic complexity of two biosemiotic systems: genetically encoded amino acids (GEAAs) and written language. Molecular and graphical notions of complexity are highly correlated for GEAAs and written language. Symbol sets are generally neither minimally nor maximally complex relative to their latent spaces, but exist across an objectively definable distribution, with the GEAAs having especially low complexity. The selection pressures guiding these disparate systems are explicable by symbol production and disambiguation efficiency. These selection pressures may be universal, offer a quantifiable metric for comparison, and suggest that all life in the Universe may discover optimal symbol set complexity distributions with respect to their latent spaces. If so, the “complexity” of individual components of SSs may not be as strong a biomarker as symbol set complexity distribution.

An extraction method for early fault features of rolling bearings based on resonance sparse signal decomposition and non-convex second-order total variation denoising

A primary challenge in the field of fault diagnosis is extracting weak fault characteristics of bearings under large background noise and non-stationary conditions. Due to significant noise interference in the signals of rolling bearings, resonance sparse signal decomposition (RSSD) cannot efficiently extract the transient impact components in the early failure stage and the total variation denoising (TVD) method distorts signal waveforms. In this study, a combined extraction method for early fault features of rolling bearings based on RSSD and non-convex second-order total variation denoising (NCSOTVD) is proposed. Firstly, a non-convex function is introduced to define the regularisation term in the second-order TVD method, and the regularisation parameter and the convexity parameter in the NCSOTVD method are respectively screened using the noise standard deviation and the permutation entropy value to enhance the impact characteristics of signals and induce signal sparsity. The NCSOTVD model is solved using the optimisation-minimisation algorithm, so as to achieve noise reduction and feature enhancement of the vibration signals. Then, the low-resonance components of RSSD are denoised using the NCSOTVD method to highlight periodic pulse signals and extract the fault features of the rolling bearings. The simulation results and experimental data show that the method largely suppresses the noise interference, highlights the fault characteristics and reduces the problems of waveform distortion and poor sparsity of the TVD method in the denoising process.

Measurements of jet quenching using semi-inclusive hadron+jet distributions in pp and central Pb-Pb collisions at sNN=5.02 TeV

The ALICE Collaboration reports measurements of the semi-inclusive distribution of charged-particle jets recoiling from a high transverse momentum (high pT) charged hadron, in pp and central Pb-Pb collisions at center-of-mass energy per nucleon–nucleon collision sNN=5.02 TeV. The large uncorrelated background in central Pb-Pb collisions is corrected using a data-driven statistical approach which enables precise measurement of recoil jet distributions over a broad range in pT,chjet and jet resolution parameter R. Recoil jet yields are reported for R=0.2, 0.4, and 0.5 in the range 7<pT,chjet<140 GeV/c and π/2<Δφ<π, where Δφ is the azimuthal angular separation between hadron trigger and recoil jet. The low-pT,chjet reach of the measurement explores unique phase space for studying jet quenching, the interaction of jets with the quark–gluon plasma generated in high-energy nuclear collisions. Comparison of pT,chjet distributions from pp and central Pb-Pb collisions probes medium-induced jet energy loss and intra-jet broadening, while comparison of their acoplanarity distributions explores in-medium jet scattering and medium response. The measurements are compared to theoretical calculations incorporating jet quenching. ©2024 CERN, for the ALICE Collaboration 2024 CERN

Observation of Medium-Induced Yield Enhancement and Acoplanarity Broadening of Low-p_{T} Jets from Measurements in pp and Central Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV.

The ALICE Collaboration reports the measurement of semi-inclusive distributions of charged-particle jets recoiling from a high transverse momentum (high p_{T}) hadron trigger in proton-proton and central Pb-Pb collisions at sqrt[s_{NN}]=5.02 TeV. A data-driven statistical method is used to mitigate the large uncorrelated background in central Pb-Pb collisions. Recoil jet distributions are reported for jet resolution parameter R=0.2, 0.4, and 0.5 in the range 7<p_{T,jet}<140 GeV/c and trigger-recoil jet azimuthal separation π/2<Δφ<π. The measurements exhibit a marked medium-induced jet yield enhancement at low p_{T} and at large azimuthal deviation from Δφ∼π. The enhancement is characterized by its dependence on Δφ, which has a slope that differs from zero by 4.7σ. Comparisons to model calculations incorporating different formulations of jet quenching are reported. These comparisons indicate that the observed yield enhancement arises from the response of the QGP medium to jet propagation.

Hidden Large Aerosol‐Driven Cloud Cover Effect Over High‐Latitude Ocean

AbstractUnderstanding how aerosols affect cloud cover is critical for reducing the large uncertainty of the aerosol‐cloud interaction (ACI). The 2014 Holuhraun effusive eruption in Iceland resulted in a significant increase in cloud drop number concentration (Nd) relative to the climatological Nd observed during periods of relatively infrequent volcanic activity. Previous studies show a significant “Twomey” effect during this eruption; however, aerosol‐induced changes in cloud fraction (Cf) appeared negligible. This leads to the question of why changes in aerosols do not cause Cf changes. To address this question, prediction models were derived to predict Cf based on Nd and meteorological parameters. These validated models allow us to investigate aerosol perturbations on Cf in various Nd scenarios by controlled meteorological conditions. Here our analysis unveiled that the increase in Nd was primarily observed under polluted conditions where Nd surpassing the threshold of 60 cm−3. After this point, cloud cover stops increasing even as Nd increases. On the contrary, the cloud cover did increase by 9.0% under conditions of clean backgrounds (Nd &lt; 60 cm−3). Accordingly, the aerosol‐driven cloud adjustment is hidden behind the seemingly insignificant cloud cover effect in areas with large background Nd. These findings provide insights into the importance of considering background Nd and the saturation status of cloud covers in ACI studies.

APW: An ensemble model for efficient wheat spike counting in unmanned aerial vehicle images

The accurate estimation of wheat ear is of great significance to gain high yield for ensuring food security. The use of computer vision for wheat ear counting to improve the estimation efficiency and accuracy has been explored by many scholars. However, existing single-computer vision recognition methods have insufficient robustness and recognition of adherent wheat ears. This study mainly focused on to solve the problem of adherent wheat ears, and proposed a combined algorithm called “APW” that ensemble with alternating direction methods of multipliers (ADMM), Potts algorithm and Watershed to achieve fast recognition and counting of wheat ears. The images of wheat ears were collected under three different scenarios including growth stages, planting densities, and flight heights by using unmanned aerial vehicle (UAV), and the Potts model was used to recognise the images. The masked images of the wheat ear were converted into a greyscale image, and were layered using a greyscale gradient. Finally, the watershed algorithm was used to segment adherent wheat ears in the layered image, and the centroids were labelled and counted. The results shows that the best accuracy was obtained under low planting density scenario, with an R2 of 0.89 and a root mean square error (RMSE) of 3.72, suggesting that APW can handle scenarios with large background differences and low target adherence. These findings provides a new approach for efficient counting of wheat ears in UAV acquired images.

Discovering quirks through timing at FASER and future forward experiments at the LHC

Quirks are generic predictions of strongly-coupled dark sectors. For weak-scale masses and a broad range of confining scales in the dark sector, quirks can be discovered only at the energy frontier, but quirk-anti-quirk pairs are produced with unusual signatures at low pT, making them difficult to detect at the large LHC detectors. We determine the prospects for discovering quirks using timing information at FASER, FASER2, and an “ultimate detector” in the far-forward region at the LHC. NLO QCD corrections are incorporated in the simulation of quirk production, which can significantly increase the production rate. To accurately propagate quirk pairs from the ATLAS interaction point to the forward detectors, the ionization energy loss of charged quirks traveling through matter, the radiation of infracolor glueballs and QCD hadrons during quirk pair oscillations, and the annihilation of quirkonium are properly considered. The quirk signal is separated from the large muon background using timing information from scintillator detectors by requiring either two coincident delayed tracks, based on arrival times at the detector, or two coincident slow tracks, based on time differences between hits in the front and back scintillators. We find that simple cuts preserve much of the signal, but reduce the muon background to negligible levels. With the data already collected, FASER can discover quirks in currently unconstrained parameter space. FASER2, running at the Forward Physics Facility during the HL-LHC era, will greatly extend this reach, probing the TeV-scale quirk masses motivated by the gauge hierarchy problem for the broad range of dark-sector confining scales between 100 eV and 100 keV.