Miracle-dz/UniFAR-FTUs huggingface dataset:
X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Network

Controller Area Network (CAN) is an essential networking protocol that connects multiple electronic control units (ECUs) in a vehicle. However, CAN-based in-vehicle networks (IVNs) face security risks owing to the CAN mechanisms. An adversary can sabotage a vehicle by leveraging the security risks if they can access the CAN bus. Thus, recent actions and cybersecurity regulations (e.g., UNR 155) require carmakers to implement intrusion detection systems (IDSs) in their vehicles. The IDS should detect cyberattacks and provide additional information to analyze conducted attacks. Although many IDSs have been proposed, considerations regarding their feasibility and explainability remain lacking. This study proposes X-CANIDS, which is a novel IDS for CAN-based IVNs. X-CANIDS dissects the payloads in CAN messages into human-understandable signals using a CAN database. The signals improve the intrusion detection performance compared with the use of bit representations of raw payloads. These signals also enable an understanding of which signal or ECU is under attack. X-CANIDS can detect zero-day attacks because it does not require any labeled dataset in the training phase. We confirmed the feasibility of the proposed method through a benchmark test on an automotive-grade embedded device with a GPU. The results of this work will be valuable to carmakers and researchers considering the installation of in-vehicle IDSs for their vehicles.
Dynamic Partial Order Reduction for Checking Correctness against Transaction Isolation Levels

Modern applications, such as social networking systems and e-commerce platforms are centered around using large-scale databases for storing and retrieving data. Accesses to the database are typically enclosed in transactions that allow computations on shared data to be isolated from other concurrent computations and resilient to failures. Modern databases trade isolation for performance. The weaker the isolation level is, the more behaviors a database is allowed to exhibit and it is up to the developer to ensure that their application can tolerate those behaviors. In this work, we propose stateless model checking algorithms for studying correctness of such applications that rely on dynamic partial order reduction. These algorithms work for a number of widely-used weak isolation levels, including Read Committed, Causal Consistency, Snapshot Isolation and Serializability. We show that they are complete, sound and optimal, and run with polynomial memory consumption in all cases. We report on an implementation of these algorithms in the context of Java Pathfinder applied to a number of challenging applications drawn from the literature of distributed systems and databases.
Autonomous Robotic Drilling System for Mice Cranial Window Creation: An Evaluation with an Egg Model

Robotic assistance for experimental manipulation in the life sciences is expected to enable precise manipulation of valuable samples, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability and deformation, and therefore require autonomous robotic control. As an example, we are studying the installation of a cranial window in a mouse. This operation requires the removal of the skull, which is approximately 300 um thick, to cut it into a circular shape 8 mm in diameter, but the shape of the mouse skull varies depending on the strain of mouse, sex and week of age. The thickness of the skull is not uniform, with some areas being thin and others thicker. It is also difficult to ensure that the skulls of the mice are kept in the same position for each operation. It is not realistically possible to measure all these features and pre-program a robotic trajectory for individual mice. The paper therefore proposes an autonomous robotic drilling method. The proposed method consists of drilling trajectory planning and image-based task completion level recognition. The trajectory planning adjusts the z-position of the drill according to the task completion level at each discrete point, and forms the 3D drilling path via constrained cubic spline interpolation while avoiding overshoot. The task completion level recognition uses a DSSD-inspired deep learning model to estimate the task completion level of each discrete point. Since an egg has similar characteristics to a mouse skull in terms of shape, thickness and mechanical properties, removing the egg shell without damaging the membrane underneath was chosen as the simulation task. The proposed method was evaluated using a 6-DOF robotic arm holding a drill and achieved a success rate of 80% out of 20 trials.
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering

Community detection is a critical challenge in analysing real graphs, including social, transportation, citation, cybersecurity, and many other networks. This article proposes three new, general, hierarchical frameworks to deal with this task. The introduced approach supports various linkage-based clustering algorithms, vertex proximity matrices, and graph representation learning models. We compare over a hundred module combinations on the Stochastic Block Model graphs and real-life datasets. We observe that our best pipelines (Wasserman-Faust and the mutual information-based PPMI proximity, as well as the deep learning-based DNGR representations) perform competitively to the state-of-the-art Leiden and Louvain algorithms. At the same time, unlike the latter, they remain hierarchical. Thus, they output a series of nested partitions of all possible cardinalities which are compatible with each other. This feature is crucial when the number of correct partitions is unknown in advance.
Mock Seifert matrices and unoriented algebraic concordance

A mock Seifert matrix is an integral square matrix representing the Gordon-Litherland form of a pair $(K,F)$, where $K$ is a knot in a thickened surface and $F$ is an unoriented spanning surface for $K$. Using these matrices, we introduce a new notion of unoriented algebraic concordance, as well as a new group denoted $\mathcal{m} \mathcal{G}^{\mathbb Z}$ and called the unoriented algebraic concordance group. This group is abelian and infinitely generated. There is a surjection $\lambda \colon \mathcal{v} \mathcal{C} \to \mathcal{m} \mathcal{G}^{\mathbb Z}$, where $\mathcal{v} \mathcal{C} $ denotes the virtual knot concordance group. Mock Seifert matrices can also be used to define new invariants, such as the mock Alexander polynomial and mock Levine-Tristram signatures. These invariants are applied to questions about virtual knot concordance, crosscap numbers, and Seifert genus for knots in thickened surfaces. For example, we show that $\mathcal{m} \mathcal{G}^{\mathbb Z}$ contains a copy of ${\mathbb Z}^\infty \oplus ({\mathbb Z}/2)^\infty \oplus({\mathbb Z}/4)^\infty. $
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering

Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A typical approach collects nodes relevant to the QA pair from a KG to form a Working Graph (WG) followed by reasoning using Graph Neural Networks (GNNs). This faces two major challenges: (i) it is difficult to capture all the information from the QA in the WG, and (ii) the WG contains some irrelevant nodes from the KG. To address these, we propose GrapeQA with two simple improvements on the WG: (i) Prominent Entities for Graph Augmentation identifies relevant text chunks from the QA pair and augments the WG with corresponding latent representations from the LM, and (ii) Context-Aware Node Pruning removes nodes that are less relevant to the QA pair. We evaluate our results on OpenBookQA, CommonsenseQA and MedQA-USMLE and see that GrapeQA shows consistent improvements over its LM + KG predecessor (QA-GNN in particular) and large improvements on OpenBookQA.
A multi-functional simulation platform for on-demand ride service operations

On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been developed to help ride-sourcing platforms design operational strategies with higher efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will be very important to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models or algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. In addition, it can be used to test how well the theoretical models approximate the simulated outcomes. Evaluated on real-world data based experiments, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions

Deep learning has seen rapid growth in recent years and achieved state-of-the-art performance in a wide range of applications. However, training models typically requires expensive and time-consuming collection of large quantities of labeled data. This is particularly true within the scope of medical imaging analysis (MIA), where data are limited and labels are expensive to be acquired. Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data. In this survey, we extensively investigated over 300 recent papers to provide a comprehensive overview of recent progress on label-efficient learning strategies in MIA. We first present the background of label-efficient learning and categorize the approaches into different schemes. Next, we examine the current state-of-the-art methods in detail through each scheme. Specifically, we provide an in-depth investigation, covering not only canonical semi-supervised, self-supervised, and multi-instance learning schemes, but also recently emerged active and annotation-efficient learning strategies. Moreover, as a comprehensive contribution to the field, this survey not only elucidates the commonalities and unique features of the surveyed methods but also presents a detailed analysis of the current challenges in the field and suggests potential avenues for future research.
TULLY-FISHER DISTANCES AND MOTIONS IN THE NORTHERN SKY

This dissertation consists of a study of large-scale velocity deviations in a uniformly expanding universe. At the onset of this work, galaxy motions in the northern hemisphere, contrary to the impressive sky coverage in the south, were still very poorly mapped. To improve on this situation, we identified a well-defined sample of 380 northern Sb-Sc UGC spirals with a magnitude cutoff of mB=15.5. Distances are estimated using the Tully-Fisher relation but with rotation velocities measured from long-slit spectra in lieu of 21 cm delta-V's. Surface-brightness profiles and total magnitudes are extracted for 820 r-band images, and (H-alpha +[N II]) rotation curves, fluxes, and rotation profiles are measured from 450 red spectra. The galaxy magnitudes are accurate internally to ±0.022 mag and the delta-V's agree internally to ± 12 km s-1. A tight linear correlation of optical versus radio velocity widths with a scatter of 24 km s-1 is also derived. The redshift distribution peaks at 5400 km s-1, with a tail extending out to 15,000 km s-1. We find evidence for large-scale motions near the concentration of Perseus-Pisces (PP), but identify a vast region of unperturbed, quiet Hubble flow (QHF) perpendicular to PP. The peculiar velocity (PV) data are corrected for uniform-density Malmquist bias when they are analyzed in estimated distance space; an empirical correction for selection effects mainly due to magnitude cutoff is derived from the QHF region and applied to the PV's in redshift space. Both analyses are consistent in showing the PP supercluster as streaming in bulk towards the Local Group at 392 ± 79 km s-1 (with respect to the microwave background rest frame). The cause for such motion remains undetermined, but it is most certainly not due to one single object. Gravitational collapse within PP is detected, but the statistical error is large. While we have not calculated a formal bulk motion for the whole volume of space bounded by the Great Attractor and PP, it appears, from simple considerations, that a reasonable streaming solution will have a net amplitude of 450 ± 100 km s-1 in the general direction of l=310 degrees, b=+20 degrees. We show that the Rubin-Ford effect (using Schechter's non-diameter-corrected solution) is perfectly consistent with our estimated rough bulk streaming. However, comparison of our measured PV's with radial-velocity reconstructions using IRAS galaxies shows conflicting results at PP which remain, at present, unresolved. An emerging picture of large-scale velocity fields shows the main infall regions to be correlated with the observed clumpiness of luminous matter. This provides support in favor of models based on the gravitational instability picture. Our data, combined with published PV surveys, provide strong support for the existence of low-amplitude perturbations in the universe on scales not yet probed by current samples (> 120 h-1 Mpc). The implications of cosmic bulk motions with such large coherence lengths pose a serious challenge for models of generation of structure in the universe.
Phase regeneration of QPSK signals based on Kerr soliton combs in a highly nonlinear optical fiber

We demonstrate an all-optical phase regeneration technique based on Kerr soliton combs, which can realize degraded quaternary phase shift keying (QPSK) signal regeneration through phase-sensitive amplification. A Kerr soliton comb is generated at the receiver side of optical communication systems based on a carrier recovery scheme and is used as coherent dual pumps to achieve phase regeneration. Our study will enhance the relay and reception performance of all-optical communication systems.
Transposable Element Interactions Shape the Ecology of the Deer Mouse Genome

Abstract The genomic landscape of transposable elements (TEs) varies dramatically across species, with some TEs demonstrating greater success in colonizing particular lineages than others. In mammals, long interspersed nuclear element (LINE) retrotransposons are typically more common than any other TE. Here, we report an unusual genomic landscape of TEs in the deer mouse, Peromyscus maniculatus. In contrast to other previously examined mammals, long terminal repeat elements occupy more of the deer mouse genome than LINEs (11% and 10%, respectively). This pattern reflects a combination of relatively low LINE activity and a massive invasion of lineage-specific endogenous retroviruses (ERVs). Deer mouse ERVs exhibit diverse origins spanning the retroviral phylogeny suggesting they have been host to a wide range of exogenous retroviruses. Notably, we trace the origin of one ERV lineage, which arose ∼5–18 million years ago, to a close relative of feline leukemia virus, revealing inter-ordinal horizontal transmission. Several lineage-specific ERV subfamilies have very high copy numbers, with the top five most abundant accounting for ∼2% of the genome. We also observe a massive amplification of Kruppel-associated box domain-containing zinc finger genes, which likely control ERV activity and whose expansion may have been facilitated by ectopic recombination between ERVs. Finally, we find evidence that ERVs directly impacted the evolutionary trajectory of LINEs by outcompeting them for genomic sites and frequently disrupting autonomous LINE copies. Together, our results illuminate the genomic ecology that shaped the unique deer mouse TE landscape, shedding light on the evolutionary processes that give rise to variation in mammalian genome structure.
Probing helium nano-bubble formation in tungsten with grazing incidence small angle x-ray scattering

Helium nano-bubble formation in plasma facing materials has emerged as a major concern for the next-step fusion experiment ITER, where helium plasmas will be used during the tokamak's start-up phase. Here, we demonstrate that grazing incidence small-angle x-ray scattering is a powerful technique for the analysis of helium nano-bubble formation in tungsten. We measured helium bubbles with sizes between 1.5–2.5 nm in tungsten exposed to helium plasma at 700 °C, where a smaller number of larger bubbles were also observed. Depth distributions can be estimated by taking successive measurements across a range of x-ray incidence angles. Compared with traditional approaches in the field, such as transmission electron microscopy, this technique provides information across a much larger volume with high statistical precision, whilst also being non-destructive.
Online veterinary anatomy education during Covid‐19 pandemic in Iran: Challenges and opportunities

Abstract Background The covid‐19 pandemic changed veterinary anatomy in‐person training to inexperienced virtual training rapidly. Objectives This survey was designed to assess the perceptions of first‐ and second‐year Iranian veterinary students on learning outcomes during the online transition. Methods The opinions of 684 students were examined using questionnaire. Results The data showed that 23.8% of students were interested in learning anatomy online and 24.8% were satisfied with it. Only 13.5% of the students in the survey generally agree with replacing remote teaching with ordinary training. Less than 1% of students considered online education suitable for the practical part of anatomy. Most of the students' sources for theoretical part included professor's booklets, narrated PowerPoint lectures, in‐class notes, and anatomy books. Professors' dissection videos, YouTube videos and also anatomy atlases were the most used sources for practical part. Many students (69.3%) were comfortable using technology. Few numbers of the students (36.1%) were satisfied with the result of the online anatomy exam. Students commented missing dissection, lack of interaction and technology challenges as main negative‐points of E‐learning. While time management and reviewing the recorded lessons was described as one of the most important benefits. Conclusions Online education cannot replace face‐to‐face education in anatomy and it can be used along ordinary training as an additional educational tool. However, group online learning activities, using dissection videos and 3‐D software are suggested for online learning. The data of this study, collected for the first time in Iran, can be used for future decisions in veterinary anatomy.
A 51x51 Multiplier Design Based on Signed 18x18 and Unsigned 17x17 Multipliers

This report provides a brief overview of the two popular schemes of performing large operand multiplication, sign extension and Baugh-Wolley’s algorithm, and provides the implementation and performance analysis for an unsigned 17x17 and a signed 18x18 multiplier. Furthermore, an efficient scheme for combining the unsigned 17x17 or signed 18x18 multipliers is used to implement two different versions of a signed 51x51 multiplier and comparative analysis between the two methods is included, where the area, speed, and power consumption of the two schemes are compared. VHDL and synopsis are used to implement the logic blocks and layout of the 51x51 signed multiplier, respectively.
VSOP-2 project

VSOP-2 project is a next generation Space-VLBI project approved in Institute of Space and Astronautical Science (ISAS) of Japan Aerospace Exploration Agency (JAXA). The nominal launch of the VSOP-2 spacecraft, ASTRO-G, is planed in FY2012 by a HIIA rocket as the 7th astronomy satellite in ISAS/JAXA. ASTRO-G satellite will employ a 9.23-m off-axis deployable reflector antenna (LDR) with a surface accuracy of 0.4 mm rms and cryogenically cooled receivers at 22, and 43 GHz and passively cooled receivers at 8 GHz in dual-polarization. A major axis of the orbit is 35,000 km, which will yield an angular resolution of 38 micro-arcseconds achievable at 43 GHz. Then VSOP-2 can observe innermost regions of AGN jets and astronomical masers at 10 times higher angular resolution compared to the previous Space-VLBI, VSOP(-1). These are ideal laboratories to experience physical phenomena in extreme conditions, which cannot be made in any laboratories on the earth. A phase-referencing capability of ASTRO-G satellite makes dramatic improvement of sensitivity and astrometric measurements possible. Basic design for the mechanical and thermal parts of ASTRO-G itself is on going. The radio telescope including the LDR is in EM phase. We would present the overview of VSOP-2 project.
Silicon nanoparticles encapsulated in Si3N4/carbon sheaths as an anode material for lithium-ion batteries

Novel composite materials comprising of silicon nanoparticles (SiNPs) encapsulated with thin layers of silicon nitride and reduced graphene oxide shells (Si@Si3N4@rGO) are prepared using a simple and scalable method. The composite exhibits significantly improved cycling stability and rate capability compared to bare SiNPs. The presence of inactive α and β phases of Si3N4 increases the mechanical endurance of SiNPs. Amorphous SiN x , which is possibly present with Si3N4, also contributes to high capacity and Li-ion migration. The rGO sheath enhances the electronic conduction and improves the rate capability. 15-Si@Si3N4@rGO, which is prepared by sintering SiNPs for 15 min at 1300 °C, spontaneous-coating GO on Si@Si3N4, and reducing GO to rGO, delivers the highest specific capacity of 1396 mAh g−1 after 100 cycles at a current density of 0.5 A g−1. The improved electrochemical performance of 15-Si@Si3N4@rGO is attributed to the unique combination of positive effects by Si3N4 and rGO shells, in which Si3N4 mitigates the issue of large volume changes of Si during charge/discharge, and rGO provides efficient electron conduction pathways. Si@Si3N4@rGO composites are likely to have great potential for a high-performance anode in lithium-ion batteries.