Search

We investigate the energy-energy correlator (EEC) of hadrons produced on the same side in $e^+e^-$ annihilation or in leading jets in $pp$ collisions. We observe a remarkable universality of the correlator. Using a non-perturbative transverse momentum dependent (TMD) fragmentation function to model the transition from the ``free-hadron" region to the perturbative collinear region, we are able to describe the near-side shapes and peaks over a wide range of energy for both the $e^+e^-$ annihilation and the $pp$ jet substructure measurements in terms of just two parameters. We present further predictions for the ratio of the projected three-point energy correlator to the EEC. The excellent agreement between our calculations and the experimental data may provide new insights into the role of non-perturbative physics for EECs, and suggests the possibility of exploring non-perturbative TMDs using theoretical tools developed for the energy correlators., Comment: 6+4 pages, 5+7 figures

Laser pulses in the near-infrared spectral range with a high repetition rate and high pulse energy offer the possibility to perform strong-field experiments such as high-order harmonic generation in gases or strong-field photoemission from the surfaces of nanostructures. Here, we present a compact laser system that delivers ultrashort pulses with a duration of less than 20 fs in the spectral range around 2 $\mu$m and with a pulse energy of up to 30 $\mu$J. The 200-kHz system consists of a commercial turnkey pump laser followed by three home-built frequency conversion/parametric amplification stages. We find an excellent pulse energy stability of 0.19 % measured over 2000 consecutive pulses averaged over 5 minutes for the output pulses of the conceptually simple and passively carrier-envelope phase stable system. An outlook on strong-field experiments performed with the laser system is given., Comment: 13 pages, 10 figures

Generative LLMs, such as GPT, have the potential to revolutionize Requirements Engineering (RE) by automating tasks in new ways. This column explores the novelties and introduces the importance of precise prompts for effective interactions. Human evaluation and prompt engineering are essential in leveraging LLM capabilities., Comment: Published in IEEE Software 41 (5)

We present a next-to-next-to-leading order (NNLO) global QCD analysis of the proton's helicity parton distribution functions (PDFs), the first of its kind. To obtain the distributions, we use data for longitudinal spin asymmetries in inclusive and semi-inclusive lepton-nucleon scattering as well as in weak-boson and hadron or jet production in proton-proton scattering. We analyze the data using QCD perturbation theory at NNLO accuracy, employing approximations provided by the threshold resummation formalism in cases where full NNLO results for partonic hard-scattering functions are not readily available. Our numerical results suggest a remarkable perturbative stability of the extracted helicity PDFs., Comment: 5 pages, 5 figures

Traceability, the ability to trace relevant software artifacts to support reasoning about the quality of the software and its development process, plays a crucial role in requirements and software engineering, particularly for safety-critical systems. In this chapter, we provide a comprehensive overview of the representative tasks in requirement traceability for which natural language processing (NLP) and related techniques have made considerable progress in the past decade. We first present the definition of traceability in the context of requirements and the overall engineering process, as well as other important concepts related to traceability tasks. Then, we discuss two tasks in detail, including trace link recovery and trace link maintenance. We also introduce two other related tasks concerning when trace links are used in practical contexts. For each task, we explain the characteristics of the task, how it can be approached through NLP techniques, and how to design and conduct the experiment to demonstrate the performance of the NLP techniques. We further discuss practical considerations on how to effectively apply NLP techniques and assess their effectiveness regarding the data set collection, the metrics selection, and the role of humans when evaluating the NLP approaches. Overall, this chapter prepares the readers with the fundamental knowledge of designing automated traceability solutions enabled by NLP in practice., Comment: Book Chapter in the Handbook of Natural Language Processing for Requirements Engineering

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software., Comment: Accepted at the 32nd IEEE International Requirements Engineering 2024 (RE) conference

We study the single-inclusive production of prompt photons in electron proton collisions, $ep \to \gamma X $, for kinematics relevant at the Electron-Ion Collider (EIC). We perform a perturbative calculation of the differential cross section to next-to-leading order in QCD and to lowest order in QED. We consider unpolarized collisions as well as scattering of longitudinally polarized incident electrons and protons. We show that the cross sections are sensitive to the parton distribution functions of photons inside the proton, which we find to generate the dominant contributions in certain kinematical regions at the EIC. We also investigate the effects of photon isolation on the unpolarized and polarized cross sections., Comment: 19 pages, 8 figures; references added, final version published in Phys. Rev. D

As SRAM-based caches are hitting a scaling wall, manufacturers are integrating DRAM-based caches into system designs to continue increasing cache sizes. While DRAM caches can improve the performance of memory systems, existing DRAM cache designs suffer from high miss penalties, wasted data movement, and interference between misses and demand requests. In this paper, we propose TDRAM, a novel DRAM microarchitecture tailored for caching. TDRAM enhances HBM3 by adding a set of small low-latency mats to store tags and metadata on the same die as the data mats. These mats enable fast parallel tag and data access, on-DRAM-die tag comparison, and conditional data response based on comparison result (reducing wasted data transfers) akin to SRAM caches mechanism. TDRAM further optimizes the hit and miss latencies by performing opportunistic early tag probing. Moreover, TDRAM introduces a flush buffer to store conflicting dirty data on write misses, eliminating turnaround delays on data bus. We evaluate TDRAM using a full-system simulator and a set of HPC workloads with large memory footprints showing TDRAM provides at least 2.6$\times$ faster tag check, 1.2$\times$ speedup, and 21% less energy consumption, compared to the state-of-the-art commercial and research designs.

We calculate the perturbative $T$-odd contributions to the lepton angular distribution in the Drell-Yan process. Using collinear factorization, we work at the first order in QCD perturbation theory where these contributions appear, ${\cal O}(\alpha_s^2)$, and address both $W^\pm$ and $\gamma/Z^0$ boson exchange. A major focus of our calculation is on the regime where the boson's transverse momentum $Q_T$ is much smaller than its mass $Q$. We carefully expand our results up to next-to-next-to-leading power in $Q_T/Q$. Our calculation provides a benchmark for studies of $T$-odd contributions that employ transverse-momentum dependent parton distribution functions. In the neutral-current case we compare our results for the $T$-odd structure functions to available ATLAS data., Comment: 34 pages, 10 figures

Large Language Models (LLMs) are the cornerstone in automating Requirements Engineering (RE) tasks, underpinning recent advancements in the field. Their pre-trained comprehension of natural language is pivotal for effectively tailoring them to specific RE tasks. However, selecting an appropriate LLM from a myriad of existing architectures and fine-tuning it to address the intricacies of a given task poses a significant challenge for researchers and practitioners in the RE domain. Utilizing LLMs effectively for NLP problems in RE necessitates a dual understanding: firstly, of the inner workings of LLMs, and secondly, of a systematic approach to selecting and adapting LLMs for NLP4RE tasks. This chapter aims to furnish readers with essential knowledge about LLMs in its initial segment. Subsequently, it provides a comprehensive guideline tailored for students, researchers, and practitioners on harnessing LLMs to address their specific objectives. By offering insights into the workings of LLMs and furnishing a practical guide, this chapter contributes towards improving future research and applications leveraging LLMs for solving RE challenges.

Explainability is crucial for complex systems like pervasive smart environments, as they collect and analyze data from various sensors, follow multiple rules, and control different devices resulting in behavior that is not trivial and, thus, should be explained to the users. The current approaches, however, offer flat, static, and algorithm-focused explanations. User-centric explanations, on the other hand, consider the recipient and context, providing personalized and context-aware explanations. To address this gap, we propose an approach to incorporate user-centric explanations into smart environments. We introduce a conceptual model and a reference architecture for characterizing and generating such explanations. Our work is the first technical solution for generating context-aware and granular explanations in smart environments. Our architecture implementation demonstrates the feasibility of our approach through various scenarios., Comment: 22nd International Conference on Pervasive Computing and Communications (PerCom 2024)

Human explanations are often contrastive, meaning that they do not answer the indeterminate "Why?" question, but instead "Why P, rather than Q?". Automatically generating contrastive explanations is challenging because the contrastive event (Q) represents the expectation of a user in contrast to what happened. We present an approach that predicts a potential contrastive event in situations where a user asks for an explanation in the context of rule-based systems. Our approach analyzes a situation that needs to be explained and then selects the most likely rule a user may have expected instead of what the user has observed. This contrastive event is then used to create a contrastive explanation that is presented to the user. We have implemented the approach as a plugin for a home automation system and demonstrate its feasibility in four test scenarios., Comment: 2024 Workshop on Explainability Engineering (ExEn '24 )

We show that the negative polarized gluon distribution $\Delta g$ found in a recent global next-to-leading order QCD analysis of the nucleon helicity structure is incompatible with the fundamental requirement that physical cross-sections must not be negative. Specifically, we show that the fact that this polarized gluon strongly violates the positivity condition $|\Delta g|\leq g$ in terms of the unpolarized gluon distribution $g$ leads to negative cross-sections for Higgs boson production at RHIC as a physical process, implying that this negative $\Delta g$ is unphysical., Comment: 8 pages, 2 figures, final published version

Generalized Reed-Solomon (RS) codes are a common choice for efficient, reliable error correction in memory and communications systems. These codes add $2t$ extra parity symbols to a block of memory, and can efficiently and reliably correct up to $t$ symbol errors in that block. Decoding is possible beyond this bound, but it is imperfectly reliable and often computationally expensive. Beyond-bound decoding is an important problem to solve for error-correcting Dynamic Random Access Memory (DRAM). These memories are often designed so that each access touches two extra memory devices, so that a failure in any one device can be corrected. But system architectures increasingly require DRAM to store metadata in addition to user data. When the metadata replaces parity data, a single-device failure is then beyond-bound. An error-correction system can either protect each access with a single RS code, or divide it into several segments protected with a shorter code, usually in an Interleaved Reed-Solomon (IRS) configuration. The full-block RS approach is more reliable, both at correcting errors and at preventing silent data corruption (SDC). The IRS option is faster, and is especially efficient at beyond-bound correction of single- or double-device failures. Here we describe a new family of "unraveling" Reed-Solomon codes that bridges the gap between these options. Our codes are full-block generalized RS codes, but they can also be decoded using an IRS decoder. As a result, they combine the speed and beyond-bound correction capabilities of interleaved codes with the robustness of full-block codes, including the ability of the latter to reliably correct failures across multiple devices. We show that unraveling codes are an especially good fit for high-reliability DRAM error correction., Comment: Changes vs first arxiv version: wordsmithing, typo corrections and citation fixes

Electrons photoemitted by extreme ultraviolet attosecond pulses derive spatially from the first few atomic surface layers and energetically from the valence band and highest atomic orbitals. As a result, it is possible to probe the emission dynamics from a narrow two-dimensional region in the presence of optical fields as well as obtain elemental specific information. However, combining this with spatially-resolved imaging is a long-standing challenge because of the large inherent spectral width of attosecond pulses as well as the difficulty of making them at high repetition rates. Here we demonstrate an attosecond interferometry experiment on a zinc oxide (ZnO) surface using spatially and energetically resolved photoelectrons. We combine photoemission electron microscopy with near-infrared pump - extreme ultraviolet probe laser spectroscopy and resolve the instantaneous phase of an infrared field with high spatial resolution. Our results show how the core level states with low binding energy of ZnO are well suited to perform spatially resolved attosecond interferometry experiments. We observe a distinct phase shift of the attosecond beat signal across the laser focus which we attribute to wavefront differences between the pump and the probe fields at the surface. Our work demonstrates a clear pathway for attosecond interferometry with high spatial resolution at atomic scale surface regions opening up for a detailed understanding of nanometric light-matter interaction.

This paper studies a cluster robust variance estimator proposed by Chiang, Hansen and Sasaki (2024) for linear panels. First, we show algebraically that this variance estimator (CHS estimator, hereafter) is a linear combination of three common variance estimators: the one-way unit cluster estimator, the "HAC of averages" estimator, and the "average of HACs" estimator. Based on this finding, we obtain a fixed-$b$ asymptotic result for the CHS estimator and corresponding test statistics as the cross-section and time sample sizes jointly go to infinity. Furthermore, we propose two simple bias-corrected versions of the variance estimator and derive the fixed-$b$ limits. In a simulation study, we find that the two bias-corrected variance estimators along with fixed-$b$ critical values provide improvements in finite sample coverage probabilities. We illustrate the impact of bias-correction and use of the fixed-$b$ critical values on inference in an empirical example on the relationship between industry profitability and market concentration.

We calculate the one-loop quark box diagrams relevant to polarized and unpolarized Deeply Virtual Compton Scattering by introducing an off-forward momentum $l^\mu$ as an infrared regulator. This regularization approach allows us to reveal the poles associated with the chiral anomaly in the polarized scenario, as well as the trace anomaly in the unpolarized case. We provide an interpretation of our findings in the context of pertinent Generalized Parton Distributions (GPDs). Furthermore, we discuss the implications of these poles on the QCD factorization pertaining to Compton amplitudes., Comment: 7 pages, 1 figure; contribution to DIS2023: XXX International Workshop on Deep-Inelastic Scattering and Related Subjects, Michigan State University, USA, 27-31 March 2023

User Experience (UX) professionals need to be able to analyze large amounts of usage data on their own to make evidence-based design decisions. However, the design process for In-Vehicle Information Systems (IVIS) lacks data-driven support and effective tools for visualizing and analyzing user interaction data. Therefore, we propose ICEBOAT, an interactive visualization tool tailored to the needs of automotive UX experts to effectively and efficiently evaluate driver interactions with IVISs. ICEBOAT visualizes telematics data collected from production line vehicles, allowing UX experts to perform task-specific analyses. Following a mixed methods User-centered design (UCD) approach, we conducted an interview study (N=4) to extract the domain specific information and interaction needs of automotive UX experts and used a co-design approach (N=4) to develop an interactive analysis tool. Our evaluation (N=12) shows that ICEBOAT enables UX experts to efficiently generate knowledge that facilitates data-driven design decisions., Comment: to be published at the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '23), September 18--22, 2023, Ingolstadt, Germany

Explainability, i.e. the ability of a system to explain its behavior to users, has become an important quality of software-intensive systems. Recent work has focused on methods for generating explanations for various algorithmic paradigms (e.g., machine learning, self-adaptive systems). There is relatively little work on what situations and types of behavior should be explained. There is also a lack of support for eliciting explainability requirements. In this work, we explore the need for explanation expressed by users in app reviews. We manually coded a set of 1,730 app reviews from 8 apps and derived a taxonomy of Explanation Needs. We also explore several approaches to automatically identify Explanation Needs in app reviews. Our best classifier identifies Explanation Needs in 486 unseen reviews of 4 different apps with a weighted F-score of 86%. Our work contributes to a better understanding of users' Explanation Needs. Automated tools can help engineers focus on these needs and ultimately elicit valid Explanation Needs.

Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large touchscreens provide the driver with numerous options for entertainment, information, or communication, and are a potential source of distraction. However, little is known about how driving automation affects how drivers interact with the center stack touchscreen, i.e., how drivers self-regulate their behavior in response to different levels of driving automation. To investigate this, we apply multilevel models to a real-world driving dataset consisting of 31,378 sequences. Our results show significant differences in drivers' interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. Specifically, at higher levels of driving automation (level 2), the mean glance duration toward the center stack touchscreen increases by 36% and the mean number of interactions per sequence increases by 17% compared to manual driving. Furthermore, partially automated driving has a strong impact on the use of more complex UI elements (e.g., maps) and touch gestures (e.g., multitouch). We also show that the effect of driving automation on drivers' self-regulation is greater than that of vehicle speed and road curvature. The derived knowledge can inform the design and evaluation of touch-based infotainment systems and the development of context-aware driver monitoring systems., Comment: Accepted for publication in the "International Journal of Human-Computer Interaction". arXiv admin note: substantial text overlap with arXiv:2207.04284

We outline the physics opportunities provided by the Electron Ion Collider (EIC). These include the study of the parton structure of the nucleon and nuclei, the onset of gluon saturation, the production of jets and heavy flavor, hadron spectroscopy and tests of fundamental symmetries. We review the present status and future challenges in EIC theory that have to be addressed in order to realize this ambitious and impactful physics program, including how to engage a diverse and inclusive workforce. In order to address these many-fold challenges, we propose a coordinated effort involving theory groups with differing expertise is needed. We discuss the scientific goals and scope of such an EIC Theory Alliance., Comment: 44 pages, ReVTeX, White Paper on EIC Theory Alliance

We extend the discussion of the recently discovered 'anomaly poles' in QCD Compton scattering. We perform the complete one-loop calculation of the Compton amplitude using momentum transfer $t$ as the regulator of collinear divergences. In the gluon channel, we confirm the presence of poles $1/t$ in both the real and imaginary parts of the amplitude. In the quark channel, we find unexpected infrared single $1/\epsilon$ and double $1/\epsilon^2$ poles. We then perform the one-loop calculation of the leading-twist quark generalized parton distributions (GPDs) for quark and gluon external states with the same regulators and find that all these singular terms can be systematically absorbed into the GPDs, showing that QCD factorization is restored to this order. Having established this, we discuss the fate of the $1/t$ poles. We argue that they become the nonperturbative building blocks of GPDs that encode the chiral and trace anomalies of QCD, in a way consistent with the known constraints these anomalies impose on the nucleon axial and gravitational form factors. The scope of research on GPDs can therefore be expanded to address the manifestation and implications of quantum anomalies in high-energy exclusive processes., Comment: 22 pages, 2 figures, Finite terms in the one-loop calculation of GPDs in the ERBL region included, version to appear in PRD

[Context and motivation] Requirements assessment by means of the Kano model is common practice. As suggested by the original authors, these assessments are done by interviewing stakeholders and asking them about the level of satisfaction if a certain feature is well implemented and the level of dissatisfaction if a feature is not or not well implemented. [Question/problem] Assessments via interviews are time-consuming, expensive, and can only capture the opinion of a limited set of stakeholders. [Principal ideas/results] We investigate the possibility to extract Kano model factors (basic needs, performance factors, and delighters) from a large set of user feedback (i.e., app reviews). We implemented, trained, and tested several classifiers on a set of 2,592 reviews. In a 10-fold cross-validation, a BERT-based classifier performed best with an accuracy of 0.928. To assess the classifiers' generalization, we additionally tested them on another independent set of 1,622 app reviews. The accuracy of the best classifier dropped to 0.725. We also show that misclassifications correlate with human disagreement on the labels. [Contribution] Our approach is a lightweight and automated alternative for identifying Kano model factors from a large set of user feedback. The limited accuracy of the approach is an inherent problem of missing information about the context in app reviews compared to comprehensive interviews, which also makes it hard for humans to extract the factors correctly.

This White Paper presents the community inputs and scientific conclusions from the Hot and Cold QCD Town Meeting that took place September 23-25, 2022 at MIT, as part of the Nuclear Science Advisory Committee (NSAC) 2023 Long Range Planning process. A total of 424 physicists registered for the meeting. The meeting highlighted progress in Quantum Chromodynamics (QCD) nuclear physics since the 2015 LRP (LRP15) and identified key questions and plausible paths to obtaining answers to those questions, defining priorities for our research over the coming decade. In defining the priority of outstanding physics opportunities for the future, both prospects for the short (~ 5 years) and longer term (5-10 years and beyond) are identified together with the facilities, personnel and other resources needed to maximize the discovery potential and maintain United States leadership in QCD physics worldwide. This White Paper is organized as follows: In the Executive Summary, we detail the Recommendations and Initiatives that were presented and discussed at the Town Meeting, and their supporting rationales. Section 2 highlights major progress and accomplishments of the past seven years. It is followed, in Section 3, by an overview of the physics opportunities for the immediate future, and in relation with the next QCD frontier: the EIC. Section 4 provides an overview of the physics motivations and goals associated with the EIC. Section 5 is devoted to the workforce development and support of diversity, equity and inclusion. This is followed by a dedicated section on computing in Section 6. Section 7 describes the national need for nuclear data science and the relevance to QCD research., Comment: QCD Town Meeting White Paper, as submitted to 2023 NSAC LRP committee on Feb. 28, 2023

We show that inclusive single-spin asymmetries (SSAs) with transversely polarized protons or electrons at a future electron ion collider (EIC) are sensitive to new physics contributions to electroweak dipole operators of electrons and quarks. We use the Standard Model Effective Field Theory (SMEFT) to parameterize possible heavy new physics contributions to these couplings. We show that new physics scales at or beyond the TeV-scale can be probed assuming realistic EIC run parameters, and that the transverse spin asymmetries are sensitive to different combinations of the dipole couplings than other measurements such as anomalous magnetic or electric dipole moments. We also study the physics potential of SSAs at a possible future upgrade of the EIC to collide muons and protons. Measurements at such an upgrade could probe the same SMEFT parameters that explain the current anomaly in the muon anomalous magnetic moment, and could also improve current bounds on the muon electric dipole moment., Comment: 12 pages, 8 figures

With modern infotainment systems, drivers are increasingly tempted to engage in secondary tasks while driving. Since distracted driving is already one of the main causes of fatal accidents, in-vehicle touchscreen Human-Machine Interfaces (HMIs) must be as little distracting as possible. To ensure that these systems are safe to use, they undergo elaborate and expensive empirical testing, requiring fully functional prototypes. Thus, early-stage methods informing designers about the implication their design may have on driver distraction are of great value. This paper presents a machine learning method that, based on anticipated usage scenarios, predicts the visual demand of in-vehicle touchscreen interactions and provides local and global explanations of the factors influencing drivers' visual attention allocation. The approach is based on large-scale natural driving data continuously collected from production line vehicles and employs the SHapley Additive exPlanation (SHAP) method to provide explanations leveraging informed design decisions. Our approach is more accurate than related work and identifies interactions during which long glances occur with 68 % accuracy and predicts the total glance duration with a mean error of 2.4 s. Our explanations replicate the results of various recent studies and provide fast and easily accessible insights into the effect of UI elements, driving automation, and vehicle speed on driver distraction. The system can not only help designers to evaluate current designs but also help them to better anticipate and understand the implications their design decisions might have on future designs., Comment: Accepted for publication in Accident Analysis and Prevention

Inspired by recent work by Tarasov and Venugopalan, we calculate the one-loop quark box diagrams relevant to polarized and unpolarized Deep Inelastic Scattering (DIS) by introducing off-forward momentum $l^\mu$ as an infrared regulator. In the polarized case, we rederive the pole $1/l^2$ related to the axial (chiral) anomaly. In addition, we obtain the usual logarithmic term and the DIS coefficient function. We interpret the result in terms of the generalized parton distributions (GPDs) $\tilde{H}$ and $\tilde{E}$ and discuss the possible violation of QCD factorization for the Compton scattering amplitude. Remarkably, we also find poles in the unpolarized case which are remnants of the trace anomaly. We argue that these poles are cancelled by the would-be massless glueball poles in the GPDs $H$ and $E$ as well as in their moments, the nucleon gravitational form factors $A,B$ and $D$. This mechanism sheds light on the connection between the gravitational form factors and the gluon condensate operator $F^{\mu\nu}F_{\mu\nu}$., Comment: 14 pages, 1 figure, minor additions to text, updated references, version to appear in PRD

In this work, we present ICEBOAT an interactive tool that enables automotive UX experts to explore how users interact with In-vehicle Information Systems. Based on large naturalistic driving data continuously collected from production line vehicles, ICEBOAT visualizes drivers' interactions and driving behavior on different levels of detail. Hence, it allows to easily compare different user flows based on performance- and safety-related metrics., Comment: The Adjunct Publication of the 35th Annual ACM Symposium on User Interface Software and Technology

We study the power corrections for electroweak boson production that are implied by threshold resummation, which we have extended to massive particles produced at measured transverse momentum, $p_T$, and rapidity. Power corrections in the resulting expressions arise from ambiguities in the low-scale behavior of the perturbative running coupling. Arguing for the relevance of the eikonal approximation, we show that such power corrections begin at order $1/p_T^2$ in full QCD, consistent with fixed-order, massive-gluon analysis. For large-$N$ Mellin moments, the leading behavior is $N^2/p_T^2$, which exponentiates along with the logarithms of threshold resummation., Comment: 35 pages

Nuclear parton distribution functions (nPDFs) can be determined in a global QCD analysis using a wide range of experimental data. In addition to older fixed-target deep inelastic scattering and Drell-Yan (DY) dilepton production data, several analyses from p+Pb collisions at the LHC provide further constraints and extend the kinematic reach of applicable data. Here we present an update of our previous TUJU19 analysis where we now include also electroweak-boson production data recently measured by ATLAS and CMS. For the first time, LHC data are included in a nPDF analysis performed at next-to-next-to-leading order (NNLO) in perturbative QCD. As before, our setup is based on the open-source analysis framework xFitter and we fit our own proton baseline, ensuring a fully consistent setup. We find good agreement with the applied data and that the resulting $\chi^2/N_{\mathrm{df}}$ is significantly smaller in case of the NNLO analysis (0.84) compared to our NLO analysis (0.94). Also, we present comparisons between our NNLO calculations and electroweak-boson production data in Pb+Pb collisions from ATLAS and CMS and DY data recently measured by CMS where NNLO corrections are found significant., Comment: 5 pages, presented at DIS2022: XXIX International Workshop on Deep-Inelastic Scattering and Related Subjects, Santiago de Compostela, Spain, May 2-6 2022

With ever-improving driver assistance systems and large touchscreens becoming the main in-vehicle interface, drivers are more tempted than ever to engage in distracting non-driving-related tasks. However, little research exists on how driving automation affects drivers' self-regulation when interacting with center stack touchscreens. To investigate this, we employ multilevel models on a real-world driving dataset consisting of 10,139 sequences. Our results show significant differences in drivers' interaction and glance behavior in response to varying levels of driving automation, vehicle speed, and road curvature. During partially automated driving, drivers are not only more likely to engage in secondary touchscreen tasks, but their mean glance duration toward the touchscreen also increases by 12% (Level 1) and 20% (Level 2) compared to manual driving. We further show that the effect of driving automation on drivers' self-regulation is larger than that of vehicle speed and road curvature. The derived knowledge can facilitate the safety evaluation of infotainment systems and the development of context-aware driver monitoring systems., Comment: 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications