K. S. Lee

Four different combinations of 96 44 Ru and 96 40 Zr nuclei, both as projectile and target, were investigated at the same bombarding energy of 400A MeV using a 4π detector.The degree of isospin mixing between projectile and target nucleons is mapped across a large portion of the phase space using two different isospin-tracer observables, the number of measured protons and the t/ 3 He yield ratio.The experimental results show that the global equilibrium is not reached even in the most central collisions.Quantitative measures of stopping and mixing are extracted from the data.They are found to exhibit a quite strong sensitivity to the in-medium (n,n) cross section used in microscopic transport calculations.

The ratio of K- to K+ meson yields has been measured in the systems RuRu at 1.69 A GeV, Ru+Zr at 1.69 A GeV, and Ni+Ni at 1.93 A GeV incident beam kinetic energy. The yield ratio is observed to vary across the measured phase space. Relativistic transport-model calculations indicate that the data are best understood if in-medium modifications of the kaons are taken into account.

The detector built at KEK to search for T-violating transverse muon polarization in K+ --> pi0 mu+ nu (Kmu3) decay of stopped kaons is described. Sensitivity to the transverse polarization component is obtained from reconstruction of the decay plane by tracking the mu+ through a toroidal spectrometer and detecting the pi0 in a segmented CsI(Tl) photon calorimeter. The muon polarization was obtained from the decay positron asymmetry of muons stopped in a polarimeter. The detector included features which minimized systematic errors while maintaining high acceptance.

To combat false information, social media sites have heavily relied on content moderation, mostly performed by human workers. However, human content moderation entails multiple problems, including huge labor costs, ineffectiveness, and ethical issues. To overcome these concerns, social media companies are aggressively investing in the development of artificial intelligence-powered false information detection systems. Extant efforts, however, have failed to understand the nature of human argumentation, delegating the process of making inferences of the truth to the black box of neural networks. They fail to attend to important aspects of how humans make judgments on the veracity of an argument, creating important challenges. To this end, based on Toulmin’s model of argumentation, we propose a computational framework that helps machine learning for false information identification understand the connection between a claim (whose veracity needs to be verified) and evidence (which contains information to support or refute the claim). The two experiments for testing model performance and explainability reveal that our framework shows stronger performance and better explainability, outperforming cutting-edge machine learning methods and presenting positive effects on human task performance, trust in algorithms, and confidence in decision making. Our results shed new light on the growing field of automated false information identification.

The CMS muon system includes in both the barrel and endcap region Resistive Plate Chambers (RPC). They mainly serve as trigger detectors and also improve the reconstruction of muon parameters. Over the years, the instantaneous luminosity of the Large Hadron Collider gradually increases. During the LHC Phase 1 (~first 10 years of operation) an ultimate luminosity is expected above its design value of 10^34/cm^2/s at 14 TeV. To prepare the machine and also the experiments for this, two long shutdown periods are scheduled for 2013-2014 and 2018-2019. The CMS Collaboration is planning several detector upgrades during these long shutdowns. In particular, the muon detection system should be able to maintain a low-pT threshold for an efficient Level-1 Muon Trigger at high particle rates. One of the measures to ensure this, is to extend the present RPC system with the addition of a 4th layer in both endcap regions. During the first long shutdown, these two new stations will be equipped in the region |eta|<1.6 with 144 High Pressure Laminate (HPL) double-gap RPCs operating in avalanche mode, with a similar design as the existing CMS endcap chambers. Here, we present the upgrade plans for the CMS RPC system for the fist long shutdown, including trigger simulation studies for the extended system, and details on the new HPL production, the chamber assembly and the quality control procedures.

Abstract A new generation of resistive plate chambers, capable of withstanding high particle fluxes (up to 2000 Hz · cm -2 ) and instrumented with precise timing readout electronics is proposed to equip two of the four high pseudorapidity stations of the CMS muon system. Double-gap RPC detectors, with each gap made of two 1.4 mm High Pressure Laminate electrodes and separated by a gas gap of the same thickness, are proposed. The new layout reduces the amount of the avalanche charge produced by the passage of a charged particle through the detector. This improves the RPC rate capability by reducing the needed time to collect this charge. To keep the RPC efficiency high, a sensitive, low-noise and high time resolution front-end electronics is needed to cope with the lower charge signal of the new RPC. An ASIC called PETIROC that has all these characteristics has been selected to read out the strips of new chambers. Thin (0.6 mm) printed circuit board, 160 cm long, equipped with pickup strips of 0.75 cm average pitch, will be inserted between the two new RPC's gaps. The strips will be read out from both ends, and the arrival time difference of the two ends will be used to determine the hit position along the strip. Results from the improved RPC equipped with the new readout system and exposed to cosmic muons in the high irradiation environment at CERN GIF++ facility are presented in this work.

The Resistive Plate Chambers (RPCs) are employed in the CMS Experiment at the LHC as dedicated trigger system both in the barrel and in the endcap. This article presents results of the radiation background measurements performed with the 2011 and 2012 proton-proton collision data collected by CMS. Emphasis is given to the measurements of the background distribution inside the RPCs. The expected background rates during the future running of the LHC are estimated both from extrapolated measurements and from simulation.

The HL-LHC phase is designed to increase by an order of magnitude the amount of data to be collected by the LHC experiments. To achieve this goal in a reasonable time scale the instantaneous luminosity would also increase by an order of magnitude up to 6 · 1034 cm−2s−1. The region of the forward muon spectrometer (|η| > 1.6) is not equipped with RPC stations. The increase of the expected particles flux up to 2 kHz/cm2 (including a safety factor 3) motivates the installation of RPC chambers to guarantee redundancy with the CSC chambers already present. The current CMS RPC technology cannot sustain the expected background level. The new technology that will be chosen should have a high rate capability and provide a good spatial and timing resolution. A new generation of Glass-RPC (GRPC) using low-resistivity glass is proposed to equip at least the two most far away of the four high η muon stations of CMS. First the design of small size prototypes and studies of their performance in high-rate particles flux are presented. Then the proposed designs for large size chambers and their fast-timing electronic readout are examined and preliminary results are provided.

We report on a systematic study of double-gap and four-gap phenolic resistive plate chambers (RPCs) for the Phase-2 upgrade of the CMS muon system at high η. In the present study, we constructed real-sized double-gap and four-gap RPCs with gap thicknesses of 1.6 and 0.8 mm, respectively, with 2-mm-thick phenolic high-pressure-laminated (HPL) plates. We examined the prototype RPCs with cosmic rays and with 100-GeV muons provided by the SPS H4 beam line at CERN. To examine the rate capability of the prototype RPCs both at Korea University and at the CERN GIF++ facility, the chambers were irradiated with 137Cs sources providing maximum gamma rates of about 1.5 kHz cm−2. For the 1.6-mm-thick double-gap RPCs, we found the relatively high threshold on the produced detector charge was conducive to effectively suppressing the rapid increase of strip cluster sizes of muon hits with high voltage, especially when measuring the narrow-pitch strips. The gamma-induced currents drawn in the four-gap RPC were about one-fourth of those drawn in the double-gap RPC. The rate capabilities of both RPC types, proven through the present testing using gamma-ray sources, far exceeded the maximum rate expected in the new high-η endcap RPCs planned for future phase-II runs of the Large Hadron Collider (LHC).

The CMS experiment recorded 177.75 /fb of proton-proton collision data during the RUN-1 and RUN-2 data taking period. Successful data taking at increasing instantaneous luminosities with the evolving detector configuration was a big achievement of the collaboration. The CMS RPC system provided redundant information for the robust muon triggering, reconstruction, and identification. To ensure stable data taking, the CMS RPC collaboration has performed detector operation, calibration, and performance studies. Various software and related tools are developed and maintained accordingly. In this paper, the overall performance of the CMS RPC system and experiences of the data taking during the RUN-2 period are summarised.

While many prior studies have applied computational approaches, such as machine learning, to detect and moderate hate speech, only scant attention has been paid to the task of identifying the underlying cause of hate speech. In this study, we introduce the concept of hate instigating speech, which refers to a specific type of textual posts on online platforms that stimulate or provoke others to engage in hate speech. The identification of hate instigating speech carries substantial practical implications for effective hate speech moderation. Rather than targeting individual instances of hate speech, by focusing on their roots, i.e., hate instigating speech, it becomes possible to significantly reduce the volume of content that requires review for moderation. Additionally, targeting hate instigating speech enables early prevention of the spread and propagation of hate speech, further enhancing the effectiveness of moderation efforts. However, several challenges hinder researchers from addressing the identification of hate instigating speech. First, there is a lack of comprehensive datasets specifically annotated for hate instigation, making it difficult to train and evaluate computational models effectively. Second, the subtle and nuanced nature of hate instigating speech (e.g., seemingly non-offensive texts serve as catalysts for triggering hate speech) makes it difficult to apply off-the-shelf machine learning models to the problem. To address these challenges, in this study, we have developed and released a multilingual dataset specifically designed for the task of identifying hate instigating speech. Specifically, it encompasses both English and Korean, allowing for a comprehensive examination of hate instigating speech across different linguistic contexts. We have applied existing machine learning models to our dataset and the results demonstrate that the extant models alone are insufficient for effectively detecting hate instigating speech. This finding highlights the need for further attention from the academic community to address this specific challenge. We expect our study and dataset to inspire researchers to explore innovative methods that can enhance the accuracy of hate instigating speech detection, ultimately contributing to more effective moderation and prevention of hate speech propagation online.

Based on previous experience and attempt, a real-size mosaic Multi-gap Resistive Plate Chamber (MRPC) has been developed within the framework of the CMS muon upgrade efforts. The chamber is a 5-gap with plates made each of 6 pieces of low resistive glass. Cosmic ray test at CERN 904 shows that its efficiency can reach above 95% with a gas mixture of 90% C2H2F4, 5% i-C4H10 and 5% SF6. The chamber was also tested with CMS dry gas(95.2% C2H2F4, 4.5% i-C4H10, 0.3% SF6) at the CERN Gamma Irradiation Facility (GIF++). Efficiency results calculated by a simple tracking method show that the good performance is maintained at rates up to 10 kHz/cm2.

This is a NMR FID data of phthalides isolated from endolichenic <em>Arthrinium</em> sp. EL000127. Isolation and structural elucidation of these compounds will be reported in the article titled "Phthalides isolated from the endolichenic <em>Arthrinium </em>sp. EL000127 of <em>Arthrinium</em> sp. exhibit anti-angiogenic activity"

The film morphology of screen-printed carbon nanotube emitters was modified by using the liquid elastomer. The entangled carbon nanotube (CNT) bundles were broken up into individual free standing nanotubes to remarkably improve the field-emission characteristics over the as-deposited CNT film. In addition, the modification of the cathode film morphology of top-gated triode type structures can be easily solved by using our based surface treatment technique, which is a low-cost, simple process. This technique has showed good possibility for surface treatment of large-size field emission displays (FEDs) in the future.

The CMS experiment has 1054 RPCs in its muon system. Monitoring their currents is the first essential step towards maintaining the stability of the CMS RPC detector performance. The current depends on several parameters such as applied voltage, luminosity, environmental conditions, etc. Knowing the influence of these parameters on the RPC current is essential for the correct interpretation of its instabilities as they can be caused either by changes in external conditions or by malfunctioning of the detector in the ideal case. We propose a Machine Learning(ML) based approach to be used for monitoring the CMS RPC currents. The approach is crucial for the development of an automated monitoring system capable of warning for possible hardware problems at a very early stage, which will contribute further to the stable operation of the CMS RPC detector.

This is a NMR FID data of phthalides isolated from endolichenic <em>Arthrinium</em> sp. EL000127. Isolation and structural elucidation of these compounds will be reported in the article titled "Phthalides isolated from the endolichenic <em>Arthrinium </em>sp. EL000127 of <em>Arthrinium</em> sp. exhibit anti-angiogenic activity"

We present the production procedure and the quality assurance process of gas gaps for the forward resistive plate chambers of the CMS detector at LHC/CERN. We also present the results from an aging study performed by using a gamma ray source. No significant aging effect was observed because of intense gamma irradiation, equivalent to approximately 12 years of the CMS operation.

Resistive Plate Chambers have a very important role for muon triggering both in the barrel and in the endcap regions of the CMS experiment at the Large Hadron Collider (LHC) . In order to optimize their performance, it is of primary importance to tune the electronic threshold of the front-end boards reading the signals from these detectors. In this paper we present the results of a study aimed to evaluate the effects on the RPC efficiency, cluster size and detector intrinsic noise rate, of variations of the electronics threshold voltage.