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Mining for Lags in Updating Critical Security Threats: A Case Study of Log4j Library
No full textThe Log4j-Core vulnerability, known as Log4Shell, exposed significant challenges to dependency management in software ecosystems. When a critical vulnerability is disclosed, it is imperative that dependent packages quickly adopt patched versions to mitigate risks. However, delays in applying these updates can leave client systems exposed to exploitation. Previous research has primarily focused on NPM, but there is a need for similar analysis in other ecosystems, such as Maven. Leveraging the 2025 mining challenge dataset of Java dependencies, we identify factors influencing update lags and categorize them based on version classification (major, minor, patch release cycles). Results show that lags exist, but projects with higher release cycle rates tend to address severe security issues more swiftly. In addition, over half of vulnerability fixes are implemented through patch updates, highlighting the critical role of incremental changes in maintaining software security. Our findings confirm that these lags also appear in the Maven ecosystem, even when migrating away from severe threats.conference pape
Pre-Instruction for Pedestrians Interacting Autonomous Vehicles With eHMI: Effects on Their Psychology and Walking Behavior
Get PDFExternal human-machine interface (eHMI) is considered as a new explicit communication method for pedestrian-AV interactions, particularly in encounter scenarios. Pedestrians without prior negotiation experience with eHMI may misinterpret the driving intentions of the AV, leading to confusion and unpredictable behavior. To address this issue, this study suggests providing pre-instruction on eHMI to enhance comprehension. To compare pedestrians’ subjective feelings and walking behavior changes with and without the use of eHMI, as well as before and after receiving pre-instructions, a road crossing experiment using a within-subject design was conducted. In the experiment, the participants were challenged to recognize situations and experienced uncertainty when encountering AVs lacking eHMI, in contrast to manual driving vehicles. After the pre-instruction, participants could understand the driving intention of an AV with eHMI and predict its driving behavior more easily. Furthermore, participants’ subjective feelings and hesitation to make decisions improved to align with the same criteria as encountered with a manual driving vehicle. Additionally, this study found that the information guidance effect of using eHMI made participants’ walking speeds more consistent over multiple trials, as they gain a comprehensive understanding of eHMI principles through the pre-instruction.journal articl
Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition
No full textBackground: Androgen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named entity recognition (NER) system might be used to extract AEs recorded in pharmaceutical care records generated by community pharmacists. Objective: This study aimed to evaluate whether an NER system can effectively and systematically identify AEs in outpatients undergoing ARAT therapy by reviewing pharmaceutical care records generated by community pharmacists, focusing on assessment notes, which often contain detailed records of AEs. Additionally, the study sought to determine whether outpatient pharmacotherapy monitoring can be enhanced by using NER to systematically collect AEs from pharmaceutical care records. Methods: We used an NER system based on the widely used Japanese medical term extraction system MedNER-CR-JA, which uses Bidirectional Encoder Representations from Transformers (BERT). To evaluate its performance for pharmaceutical care records by community pharmacists, the NER system was first applied to 1008 assessment notes in records related to anticancer drug prescriptions. Three pharmaceutically proficient researchers compared the results with the annotated notes assigned symptom tags according to annotation guidelines and evaluated the performance of the NER system on the assessment notes in the pharmaceutical care records. The system was then applied to 2193 assessment notes for patients prescribed ARATs. Results: The F1-score for exact matches of all symptom tags between the NER system and annotators was 0.72, confirming the NER system has sufficient performance for application to pharmaceutical care records. The NER system automatically assigned 1900 symptom tags for the 2193 assessment notes from patients prescribed ARATs; 623 tags (32.8{\%}) were positive symptom tags (symptoms present), while 1067 tags (56.2{\%}) were negative symptom tags (symptoms absent). Positive symptom tags included ARAT-related AEs such as ``pain,'' ``skin disorders,'' ``fatigue,'' and ``gastrointestinal symptoms.'' Many other symptoms were classified as serious AEs. Furthermore, differences in symptom tag profiles reflecting pharmacists' AE monitoring were observed between androgen synthesis inhibition and androgen receptor signaling inhibition. Conclusions: The NER system successfully extracted AEs from pharmaceutical care records of patients prescribed ARATs, demonstrating its potential to systematically track the presence and absence of AEs in outpatients. Based on the analysis of a large volume of pharmaceutical medical records using the NER system, community pharmacists not only detect potential AEs but also actively monitor the absence of severe AEs, offering valuable insights for the continuous improvement of patient safety management.journal articl
Pre-Equalized MIMO Transmission Over Analog RoF Downlink in the Pres-Ence of Gain Imbalance
No full textIn this article, we propose a pre-equalization method in a wired-wireless multiple-input and multiple-output (MIMO) transmission over an analog radio-on-fiber (A-RoF) network. The MIMO transmission based on A-RoF suffers from un-flat and unbalanced response in the wired part, re-sulting in decreased channel capacity. A partial pre-equalization technique for wired part is compared to the one for the joint channels of wired-and-wireless by using 2 x 2 transmission of orthogonal frequency division multiplexing (OFDM) in a 5-GHz band testbed.journal articl
Contextualized Messages Boost Graph Representations
No full textGraph neural networks (GNNs) have gained significant attention in recent years for their ability to process data that may be represented as graphs. This has prompted several studies to explore their representational capability based on the graph isomorphism task. Notably, these works inherently assume a countable node feature representation, potentially limiting their applicability. Interestingly, only a few study GNNs with uncountable node feature representation. In the paper, a new perspective on the representational capability of GNNs is investigated across all levels—node-level, neighborhood-level, and graph-level— when the space of node feature representation is uncountable. Specifically, the injective and metric requirements of previous works are softly relaxed by employing a pseudometric distance on the space of input to create a soft-injective function such that distinct inputs may produce similar outputs if and only if the pseudometric deems the inputs to be sufficiently similar on some representation. As a consequence, a simple and computationally efficient soft-isomorphic relational graph convolution network (SIR-GCN) that emphasizes the contextualized transformation of neighborhood feature representations via anisotropic and dynamic message functions is proposed. Furthermore, a mathematical discussion on the relationship between SIR-GCN and key GNNs in literature is laid out to put the contribution into context, establishing SIR-GCN as a generalization of classical GNN methodologies. To close, experiments on synthetic and benchmark datasets demonstrate the relative superiority of SIR-GCN, outperforming comparable models in node and graph property prediction tasks.journal articl
Efficient Maintenance of Large-Scale Medical Dictionaries Using Large Language Models: A Case for Biomarkers
No full textDictionaries are essential in natural language processing and provide significant value across tasks; however, their construction and maintenance are expensive. Leveraging manual revision histories to suggest automatic corrections for unedited terms offers a promising solution to enhance quality while reducing costs. This study proposes a method for automatically correcting metadata in a large-scale medical dictionary containing more than 500,000 terms. By utilizing large language models that excel in zero-shot settings, the system estimates the dictionary information without task-specific configurations. This method was demonstrated through experiments on variations in gene biomarker expression, a task that requires specialized medical knowledge. The results indicate that this approach can significantly reduce the dictionary maintenance burden.conference pape
X-ray fluorescence holography under high-pressure conditions
Get PDFThis study reports the first application of X-ray fluorescence holography (XFH) under high-pressure conditions. We integrated XFH with a diamond anvil cell to investigate the local structure around Sr atoms in single-crystal SrTiO3 under high pressure. By utilizing nano-polycrystalline diamond anvils and a yttrium filter, we effectively eliminated significant background noise from both the anvils and the gasket. This optimized experimental configuration enabled the measurement of Sr Kα holograms of the SrTiO3 sample at pressures up to 13.3 GPa. The variation of lattice constants with pressure was calculated by the shifts of Kossel lines, and real-space images of the atomic structures were reconstructed from the Sr Kα holograms at different pressures. This work successfully demonstrates the feasibility of employing XFH under high-pressure conditions as a novel method for visualizing pressure-induced changes in the three-dimensional local structure around the specified element.journal articl
Effect of Response Time Distribution inWeak Lane Discipline on Linear Stability
No full textThe increase in mixed traffic with weak lane discipline (2D mixed traffic) hasattracted significant research attention. To better replicate and understand traffic withweak lane discipline, this study examined the variation in response time relative to theposition of the leading vehicle, including lateral shifts. Through experiments conductedusing a driving simulator and functional fitting, we demonstrated that changes in responsetime due to longitudinal and lateral locational shifts are well represented by linear andexponential functions, respectively. Additionally, we proposed an extended formulationof the 2D optimal velocity model (2D OVM) that incorporates variable response times,termed the 2D OVM with varying sensitivities (2D OVMVS). The stability condition wasderived using a linear approximation. A comparative analysis of the phase diagrams of the2D OVM and 2D OVMVS, along with a sensitivity analysis, revealed that the proposed2D OVMVS exhibited a larger unstable region in the phase diagram and lower stabilityin stable regions than the 2D OVM. As a result, in 2D traffic with weak lane discipline,the equilibrium formation of vehicles was more susceptible to disruption. Our findingsindicate that variable response times, as observed in this study, substantially influence thestability of no-lane traffic. Unlike fixed-response models, incorporating response timevariability accentuates unstable tendencies. This underscores the necessity of accountingfor non-uniform response time distributions in future traffic models.KeywordsTraffic flow·weak lane discipline·response time·stabilityCollective Dynamics V10, A188:1–32 (2025)Licensed underjournal articl
Variable frequency and quantitative capacitance measurements in dual bias modulation electrostatic force microscopy
No full textElectrostatic force microscopy (EFM) has been used to investigate the local electronic properties of materials, although frequency response and quantitative capacitance measurements are difficult in conventional EFM because of its narrow measurement bandwidth and insufficient quantitativity. In this paper, we introduce dual bias modulation EFM (DEFM) as a method for achieving variable frequency measurements and quantitative capacitance analysis. Dual bias modulation technique enables independent control of a frequency pair (ω1, ω2) for electrical modulation while a differential frequency, such as ω2 − ω1 and/or ω2 − 2ω1, is kept constant, and this frequency component in the electrostatic force is extracted. We confirmed the effectiveness of DEFM through experiments on Si and Cu(In,Ga)Se2 solar cell materials, where clear frequency responses were observed. Furthermore, the quantitative analysis of capacitance between a tip and sample in DEFM was validated by comparing carrier density values estimated from surface depletion capacitance with those from conventional Hall effect measurements. These results reveal that DEFM is an effective technique for analyzing local defects in semiconductors.journal articl
