bluuebunny/crossref_metadata_2025_split huggingface dataset:
Managing Hi-Railer Set-on and Set-Off in a Train Control System Using Axle Counters
Authors: Diemunsch Kenneth , Walker Alyssa 
Published: June 2024

Abstract
This paper presents a new comprehensive method for managing hi-rail vehicle set-on and set-off in a Communications-Based Train Control (CBTC) system using axle counters as a secondary train detection system.
Due to its high performance in terms of headway combined with low required maintenance [1], CBTC is now the preferred technology for metropolitan rapid transit systems around the world. Metropolitan transit lines such as New York City Transit and Toronto Transit Commission are often underground or elevated on a structure and/or are relatively short so maintenance vehicles access work areas only via rail from the yard or a spur track used as storage.
In the past few years, CBTC has started to be deployed on what the industry refers to as commuter rail lines. Commuter rail lines have short distances between stations within the city centers and long distances between stations in suburban areas using at grade tracks running along highways. Therefore, to facilitate maintenance activities, operators of commuter rail lines use hi-rail vehicles that drive on the roads and enter the rail network at specific set-on areas. Examples of CBTC projects on commuter rail lines include Bay Area Rapid Transit, Paris Réseau Express Régional, and Montreal Réseau Express Métropolitain.
The majority of new CBTC systems for commuter rail lines are using axle counters as a secondary means of train detection. The CBTC and axle counter systems have a complex functional interface. Using hi-railers in a CBTC system that relies on axle counters generates challenges for both the CBTC system and axle counters. The method to handle those challenges is the result of collaborative design effort over several years by a transit agency, a CBTC contractor, an axle counter vendor, and numerous consultants.
This work starts by introducing relevant CBTC technology functions. Principles of axle counters, including the need for a reset of axle counter blocks and the different reset methods are described. The challenges resulting from hi-rail set-on and set-off are explained, showing the impact on both the axle counter system and the CBTC train movements. Then, the preferred method for managing the challenges introduced by hi-rail vehicles set-on and set-off are presented. There are two variations depending on whether events arise during revenue service or at night when revenue service is stopped. Other disregarded methods are briefly explained with the reasons why they are not suitable. Finally, the authors provide thoughts on this method and describe the possible improvements.
AI-Enabled Vibration Sensing System for Early Detection of Trains at Active Highway-Rail Grade Crossings
Authors: Amjadian Mohsen , Rahman Md Masnun , Tarawneh Constantine , Villarreal Valik , Rocha Dylan 
Published: June 2024

Abstract
Highway-rail grade crossings (HRGCs) play an essential role in ensuring the secure traversal of road users across railway tracks. However, despite their significance, they present safety challenges, particularly when trains go undetected, heightening the risk of potential collisions between the road user and train. This paper aims to explore the viability of employing vibration sensors for detection and characterization of an approaching train’s speed at HRGCs. The methodology involves analyzing rail vibrations and developing a time series predictive machine learning (ML) model. To accomplish this, a Finite Element (FE) model of a ballasted track railway is created in SAP2000, consisting of essential track components such as rails, sleepers, rail pads, ballast, and subgrade. The acceleration time history of the rails, induced by a moving single car, is recorded, and employed as input data for training and testing a Long Short-Term Memory (LSTM) network. The LSTM model predicts crucial parameters of the approaching train, including its location and speed. Successful prediction of these parameters is anticipated to enable the determination of the precise moment when the train reaches the HRGC, affording road users sufficient reaction time to safely traverse the railway track.
Research on Wheel Wear Coefficient Considering Wheel Profile and Operating Route Based on Multi-Rigid-Body Method
Authors: Song Ruiyang , Sun Lixia , Zhang Jimin 
Published: June 2024

Abstract
Wheel wear has always been a problem in the railway industry, and wear prediction simulation is an important approach to address this problem. However, the use of simulation requires the use of wear coefficients. The existing wear coefficients are based on laboratory rolling test rigs, which have different operating environments and materials compared to actual wheel-rail systems. Directly applying these coefficients to wear calculations can result in significant errors, so they need to be calibrated accordingly.
In this paper, a wheel wear prediction model that considers wheel profile and operating route is established. The model consists of a vehicle dynamics model and a wear rate calculation model, and the prediction method takes into account the track roughness, curve ratio, and different wheel profiles. In the dynamics model, the Hertz-Fastsim wheel-rail contact model is used to calculate the physical quantities such as normal force and tangential force in the contact patch, which are then inputted into the wear rate calculation model to obtain the wear amount. During the calculation process, the influence of various curve radii and the proportion of straight tracks is considered. Based on the proportion of different curve radii and straight tracks in the actual operating track, the wear amount is weighted to obtain wear prediction results that are consistent with the actual track conditions.
The wear model adopts the Archard local model, which has four wear regions (i.e., seizure mild I, severe and mild II). Simulation and experimental results show that when the two endpoint values in the Jendel wear coefficient diagram are selected, there is a significant prediction error. For the study in this paper, the initial values are set to the median values of each region in the Jendel wear coefficient graph, that is k1 = 350 × 10−4, k2 = k4 = 5 × 10−4, k3 = 35 × 10−4. The wear of the wheels is predicted based on the dynamic model and wear rate calculation model. This prediction is then compared with actual measured data at different operating distances. The differences in the comparison results are used to narrow down the range of values for each region in the Jendel wear coefficient graph. In the end, the wheel-rail wear coefficient was obtained.
H3N2 canine influenza virus-like particle vaccine with great protection in beagle dogs
Authors: Ge Fei-fei , Shen Li-pin , Yang De-quan , Yang Xian-cao , Li Xin , Shen Hai-xiao , Wang Jian , Huang Shixin 
Published: June 2024

ABSTRACT

In 2016, a distinct branch of H3N2 canine influenza virus (CIV) emerged, which has mutations related to mammalian adaptation and has replaced previously prevalent strains. This branch poses a risk of zoonotic infection. To prevent and control H3N2 CIV, an H3N2 virus-like particle (VLP) vaccine based on the insect cell baculovirus expression system has been developed in the study. The H3N2 VLP vaccine induced high titers of hemagglutination inhibition (HI) antibodies in nasal and muscular immunized beagle dogs. Meanwhile, the VLP vaccine provided effective protection against homologous virus challenge comparable to inactivated H3N2 canine influenza virus. In addition, the intranasal H3N2 VLP vaccine induced significantly higher Th1, Th2, and Th17 immune responses, respectively (p,0.05). Importantly, intramuscular injection of VLP and inactivated H3N2 virus has complete protective effects against homologous H3N2 virus attacks. Nasal immunization with H3N2 VLP can partially protect beagles from H3N2 influenza.

IMPORTANCE
A new antigenically and genetically distinct canine influenza virus (CIV) H3N2 clade possessing mutations associated with mammalian adaptation emerged in 2016 and substituted previously circulating strains. This clade poses a risk for zoonotic infection. In our study, intramuscular injection of the H3N2 virus-like particle (VLP) vaccine and inactivated H3N2 CIV confer completely sterilizing protection against homologous H3N2 canine influenza virus challenge. Our results provide further support for the possibility of developing VLP vaccines that can reliably induce immunity in animal species.
Changes in the prevalence of mental health problems during the first year of the pandemic: a systematic review and dose-response meta-analysis
Authors: Salanti Georgia , Peter Natalie Luise , Tonia Thomy , Holloway Alexander , Darwish Leila , Kessler Ronald C , White Ian , Vigod Simone N. , Egger Matthias , Haas Andreas D , Fazel Seena , Herrman Helen , Kieling Christian , Patel Vikram , Li Tianjing , Cuijpers Pim , Cipriani Andrea , Furukawa Toshi A , Leucht Stefan 
Published: June 2024

AimTo describe the pattern of the prevalence of mental health problems during the first year of the COVID-19 pandemic and examine the impact of containment measures on these trends.MethodsWe identified articles published until 30 August 2021 that reported the prevalence of mental health problems in the general population at two or more time points. A crowd of 114 reviewers extracted data on prevalence, study and participant characteristics. We collected information on the number of days since the first SARS-CoV-2 infection in the study country, the stringency of containment measures and the number of cases and deaths. We synthesised changes in prevalence during the pandemic using a random-effects model. We used dose-response meta-analysis to evaluate the trajectory of the changes in mental health problems.ResultsWe included 41 studies for 7 mental health conditions. The average odds of symptoms increased during the pandemic (mean OR ranging from 1.23 to 2.08). Heterogeneity was very large and could not be explained by differences in participants or study characteristics. Average odds of psychological distress, depression and anxiety increased during the first 2 months of the pandemic, with increased stringency of the measures, reported infections and deaths. The confidence in the evidence was low to very low.ConclusionsWe observed an initial increase in the average risk of psychological distress, depression-related and anxiety-related problems during the first 2 months of the pandemic. However, large heterogeneity suggests that different populations had different responses to the challenges imposed by the pandemic.
Artificial intelligence and regional anesthesiology education curriculum development: navigating the digital noise
Authors: Schroeder Kristopher M , Elkassabany Nabil 
Published: June 2024

Artificial intelligence (AI) has demonstrated a disruptive ability to enhance and transform clinical medicine. While the dexterous nature of anesthesiology work offers some protections from AI clinical assimilation, this technology will ultimately impact the practice and augment the ability to provide an enhanced level of safe and data-driven care. Whether predicting difficulties with airway management, providing perioperative or critical care risk assessments, clinical-decision enhancement, or image interpretation, the indications for AI technologies will continue to grow and are limited only by our collective imagination on how best to deploy this technology.An essential mission of academia is education, and challenges are frequently encountered when working to develop and implement comprehensive and effectively targeted curriculum appropriate for the diverse set of learners assigned to teaching faculty. Curriculum development in this context frequently requires substantial efforts to identify baseline knowledge, learning needs, content requirement, and education strategies. Large language models offer the promise of targeted and nimble curriculum and content development that can be individualized to a variety of learners at various stages of training. This technology has not yet been widely evaluated in the context of education deployment, but it is imperative that consideration be given to the role of AI in curriculum development and how best to deploy and monitor this technology to ensure optimal implementation.
NIR-sighted: A Programmable Streaming Architecture for Low-Energy Human-Centric Vision Applications
Authors: Mamish John , Alharbi Rawan , Sen Sougata , Holla Shashank , Kamath Panchami , Sangar Yaman , Alshurafa Nabil , Hester Josiah 
Published: June 2024

Human studies often rely on wearable lifelogging cameras that capture videos of individuals and their surroundings to aid in visual confirmation or recollection of daily activities like eating, drinking, and smoking. However, this may include private or sensitive information that may cause some users to refrain from using such monitoring devices. Also, short battery lifetime and large form factors reduce applicability for long-term capture of human activity. Solving this triad of interconnected problems is challenging due to wearable embedded systems’ energy, memory, and computing constraints. Inspired by this critical use case and the unique design problem, we developed NIR-sighted, an architecture for wearable video cameras that navigates this design space via three key ideas: (i) reduce storage and enhance privacy by discarding masked pixels and frames, (ii) enable programmers to generate effective masks with low computational overhead, and (iii) enable the use of small MCUs by moving masking and compression off-chip. Combined together in an end-to-end system, NIR-sighted’s masking capabilities and off-chip compression hardware shrinks systems, stores less data, and enables programmer-defined obfuscation to yield privacy enhancement. The user’s privacy is enhanced significantly as nowhere in the pipeline is any part of the image stored before it is obfuscated. We design a wearable camera called NIR-sightedCam based on this architecture; it is compact and can record IR and grayscale video at 16 and 20+ fps, respectively, for 26 hours nonstop (59 hours with IR disabled) at a fraction of comparable platforms power draw. NIR-sightedCam includes a low-power Field Programmable Gate Array that implements our mJPEG compress/obfuscate hardware, Blindspot. We additionally show the potential for privacy-enhancing function and clinical utility via an in-lab eating study, validated by a nutritionist.
Sectored DRAM: A Practical Energy-Efficient and High-Performance Fine-Grained DRAM Architecture
Authors: Olgun Ataberk , Bostanci F. Nisa , Francisco de Oliveira Junior Geraldo , Tugrul Yahya Can , Bera Rahul , Yaglikci Abdullah Giray , Hassan Hasan , Ergin Oguz , Mutlu Onur 
Published: June 2024

Modern computing systems access data in main memory at coarse granularity (e.g., at 512-bit cache block granularity). Coarse-grained access leads to wasted energy because the system does not use all individually accessed small portions (e.g.,
words
, each of which typically is 64 bits) of a cache block. In modern DRAM-based computing systems, two key coarse-grained access mechanisms lead to wasted energy: large and fixed-size (i) data transfers between DRAM and the memory controller and (ii) DRAM row activations.

We propose Sectored DRAM, a new, low-overhead DRAM substrate that reduces wasted energy by enabling fine-grained DRAM data transfer and DRAM row activation. To retrieve only useful data from DRAM, Sectored DRAM exploits the observation that many cache blocks are not fully utilized in many workloads due to poor spatial locality. Sectored DRAM predicts the words in a cache block that will likely be accessed during the cache block’s residency in cache and (i) transfers only the predicted words on the memory channel by dynamically tailoring the DRAM data transfer size for the workload and (ii) activates a smaller set of cells that contain the predicted words by carefully operating physically isolated portions of DRAM rows (i.e., mats). Activating a smaller set of cells on each access relaxes DRAM power delivery constraints and allows the memory controller to schedule DRAM accesses faster.
We evaluate Sectored DRAM using 41 workloads from widely used benchmark suites. Compared to a system with coarse-grained DRAM, Sectored DRAM reduces the DRAM energy consumption of highly memory intensive workloads by up to (on average) 33% (20%) while improving their performance by up to (on average) 36% (17%). Sectored DRAM’s DRAM energy savings, combined with its system performance improvement, allows system-wide energy savings of up to 23%. Sectored DRAM’s DRAM chip area overhead is 1.7% of the area of a modern DDR4 chip. Compared to state-of-the-art fine-grained DRAM architectures, Sectored DRAM greatly reduces DRAM energy consumption, does not reduce DRAM bandwidth, and can be implemented with low hardware cost. Sectored DRAM provides 89% of the performance benefits of, consumes 12% less DRAM energy than, and takes up 34% less DRAM chip area than a high-performance state-of-the-art fine-grained DRAM architecture (Half-DRAM). It is our hope and belief that Sectored DRAM’s ideas and results will help to enable more efficient and high-performance memory systems. To this end, we open source Sectored DRAM at https://github.com/CMU-SAFARI/Sectored-DRAM.
SAL: Optimizing the Dataflow of Spin-based Architectures for Lightweight Neural Networks
Authors: Zhao Yunping , Ma Sheng , Liu Hengzhu , Li Dongsheng 
Published: June 2024

As the Convolutional Neural Network (CNN) goes deeper and more complex, the network becomes memory-intensive and computation-intensive. To address this issue, the lightweight neural network reduces parameters and Multiplication-and-Accumulation (MAC) operations by using the Depthwise Separable Convolution (DSC) to improve speed and efficiency. Nonetheless, the energy efficiency of classical Von Neumann architectures for CNNs is limited due to the memory wall challenge. Spin-based architectures have the potential to address this challenge thanks to the integration of memory and computing with ultra-high energy efficiency. However, deploying the DSC on spin-based architectures with the traditional dataflow leads to huge activation movements and low hardware utilization. Moreover, the inter-layer data dependency of neural networks increases latency. These factors become the bottleneck of improving energy efficiency and performance.Inspired by these challenges, we propose a novel dataflow on Spin-based Architectures for Lightweight neural networks (SAL). The novel dataflow replaces convolution unrolling by selecting activations in the crossbar according to the convolution window and also realizes the inter-layer data reuse. Moreover, the novel dataflow also reduces the latency due to the data dependency between layers, realizing higher performance. To the best of our knowledge, this is the first design to use hybrid dataflow for the PIM architecture. We also optimize the structure of the spin-based crossbar and the pipeline based on the dataflow to achieve better data reuse and computational parallelism. For deploying the MobileNet V1, the novel dataflow improves the hardware utilization by 23×∼ 105× and reduces the data traffic by 1.09×∼ 18.6×. Compared with the NEBULA, a spin-based non-Von Neumann architecture, the SAL reduces the energy consumption by 4× and improves the performance by 7.3×, which are 0.32mJand 10.43GOPs-1, respectively. Moreover, the SAL improves power efficiency over 29 times more than the NEBULA. Compared with the Eyeriss, the SAL improves the energy efficiency by four orders of magnitude.
GuaNary: Efficient Buffer Overflow Detection In Virtualized Clouds Using Intel EPT-based Sub-Page Write Protection Support
Authors: Bitchebe Stella , Kone Yves , Olivier Pierre , Boukhobza Jalil , Bromberg Yérom-David , Hagimont Daniel , Tchana Alain 
Published: June 2024

Buffer overflow is a widespread memory safety violation in C/C++, reported as the top vulnerability in 2022. Secure memory allocators are generally used to protect systems against attacks that may exploit buffer overflows. Existing allocators mainly rely on two types of countermeasures to prevent or detect overflows: canaries and guard pages, each with pros and cons in terms of detection latency and memory footprint.
This paper follows the Out of Hypervisor (OoH) trend for virtualized cloud applications. It introduces GuaNary, a novel safety guard against overflows allowing synchronous detection at a low memory footprint cost. OoH is a new virtualization research axis introduced in 2022 advocating the exposure of hardware features for virtualization to the guest OS so that its processes can take advantage of them. Based on the OoH principle, GuaNary leverages Intel Sub-Page write Permission (SPP), a recent hardware virtualization feature that allows to write-protect guest memory at the granularity of 128B (namely, sub-page) instead of 4KB. We implement a software stack, LeanGuard, which promotes the utilization of SPP from inside virtual machines by new secure allocators that use GuaNary. Our evaluation shows that for the same number of protected buffers, LeanGuard consumes 8.3x less memory than SlimGuard, a state-of-the-art secure allocator. Furthermore, for the same memory consumption, LeanGuard protecting 25x more buffers than SlimGuard.
Lightweight Acquisition and Ranging of Flows in the Data Plane
Authors: Monterubbiano Andrea , Langlet Jonatan , Walzer Stefan , Antichi Gianni , Reviriego Pedro , Pontarelli Salvatore 
Published: June 2024

As networks get more complex, the ability to track almost all the flows is becoming of paramount importance. This is because we can then detect transient events impacting only a subset of the traffic. Solutions for flow monitoring exist, but it is getting very difficult to produce accurate estimations for every <flowID,counter> tuple given the memory constraints of commodity programmable switches. Indeed, as networks grow in size, more flows have to be tracked, increasing the number of tuples to be recorded. At the same time, end-host virtualization requires more specific flowIDs, enlarging the memory cost for every single entry. Finally, the available memory resources have to be shared with other important functions as well (e.g., load balancing, forwarding, ACL).
To address those issues, we present FlowLiDAR (Flow Lightweight Detection and Ranging), a new solution that is capable of tracking almost all the flows in the network while requiring only a modest amount of data plane memory, which is not dependent on the size of flowIDs. We implemented the scheme in P4, tested it using real traffic from ISPs, and compared it against four state-of-the-art solutions: FlowRadar, NZE, PR-sketch, and Elastic Sketch.