Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Risk Indices for Autonomous Vehicle Training Systems

The development of Autonomous Vehicles (AV) presents an opportunity to save and improve lives. However, achieving SAE Level 5 (full) autonomy will require overcoming many technical challenges. There is a gap in the literature regarding the measurement of safety for self-driving systems. Measuring safety and risk is paramount for the generation of useful simulation scenarios for training and validation of autonomous systems. The limitation of current approaches is the dependence on near-crash data. Although near-miss data can substantially increase scarce available accident data, the definition of a near-miss or near-crash is arbitrary. A promising alternative is the introduction of the Responsibility-Sensitive Safety (RSS) model by Shalev-Shwartz et al., which defines safe lateral and longitudinal distances that can guarantee impossibility of collision under reasonable assumptions for vehicle dynamics. We present a framework that extends the RSS model for cases when reasonable assumptions or safe distances are violated. The proposed framework introduces risk indices that quantify the likelihood of a collision by using vehicle dynamics and driver's risk aversion. The present study concludes with proposed experiments for tuning the parameters of the formulated risk indices.
Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniques

This survey provides an overview of the evolution of visually grounded models of spoken language over the last 20 years. Such models are inspired by the observation that when children pick up a language, they rely on a wide range of indirect and noisy clues, crucially including signals from the visual modality co-occurring with spoken utterances. Several fields have made important contributions to this approach to modeling or mimicking the process of learning language: Machine Learning, Natural Language and Speech Processing, Computer Vision and Cognitive Science. The current paper brings together these contributions in order to provide a useful introduction and overview for practitioners in all these areas. We discuss the central research questions addressed, the timeline of developments, and the datasets which enabled much of this work. We then summarize the main modeling architectures and offer an exhaustive overview of the evaluation metrics and analysis techniques.
Using the Structural Kinome to Systematize Kinase Drug Discovery

Kinase-targeted drug design is challenging. It requires designing inhibitors that can bind to specific kinases, when all kinase catalytic domains share a common folding scaffold that binds ATP. Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery. This begins with deciphering the kinase-ligand characteristics, analyzing the structure–activity relationships and prioritizing the desired drug molecules across the whole kinome. Currently, there are more than 300 kinases with released PDB structures, which provides a substantial structural basis to gain these necessary insights. Here, we review in silico structure-based methods – notably, a function-site interaction fingerprint approach used in exploring the complete human kinome. In silico methods can be explored synergistically with multiple cell-based or protein-based assay platforms such as KINOMEscan. We conclude with new drug discovery opportunities associated with kinase signaling networks and using machine/deep learning techniques broadly referred to as structural biomedical data science.
Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention

We advance a novel explanation of similarity-based interference effects in subject-verb and reflexive pronoun agreement processing, grounded in surprisal values computed from a pretrained large-scale Transformer model, GPT-2. Specifically, we show that surprisal of the verb or reflexive pronoun predicts facilitatory interference effects in ungrammatical sentences, where a distractor noun that matches in number with the verb or pronouns leads to faster reading times, despite the distractor not participating in the agreement relation. We review the human empirical evidence for such effects, including recent meta-analyses and large-scale studies. We also show that attention patterns (indexed by entropy and other measures) in the Transformer show patterns of diffuse attention in the presence of similar distractors, consistent with cue-based retrieval models of parsing. But in contrast to these models, the attentional cues and memory representations are learned entirely from the simple self-supervised task of predicting the next word.
Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation

Graph Neural Networks (GNNs) learn low dimensional representations of nodes by aggregating information from their neighborhood in graphs. However, traditional GNNs suffer from two fundamental shortcomings due to their local (l-hop neighborhood) aggregation scheme. First, not all nodes in the neighborhood carry relevant information for the target node. Since GNNs do not exclude noisy nodes in their neighborhood, irrelevant information gets aggregated, which reduces the quality of the representation. Second, traditional GNNs also fail to capture long-range non-local dependencies between nodes. To address these limitations, we exploit mutual information (MI) to define two types of neighborhood, 1) Local Neighborhood where nodes are densely connected within a community and each node would share higher MI with its neighbors, and 2) Non-Local Neighborhood where MI-based node clustering is introduced to assemble informative but graphically distant nodes in the same cluster. To generate node presentations, we combine the embeddings generated by bi-level aggregation - local aggregation to aggregate features from local neighborhoods to avoid noisy information and non-local aggregation to aggregate features from non-local neighborhoods. Furthermore, we leverage self-supervision learning to estimate MI with few labeled data. Finally, we show that our model significantly outperforms the state-of-the-art methods in a wide range of assortative and disassortative graphs11Source Code at: https://github.com/forkkr/LnL-GNN.
Stability of solar atmospheric structures harboring standing slow waves

Context. In the context of the solar coronal heating problem, one possible explanation for the high coronal temperature is the release of energy by magnetohydrodynamic (MHD) waves. The energy transfer is believed to be possible, among others, by the development of the Kelvin-Helmholtz instability (KHI) in coronal loops. Aims. Our aim is to determine if standing slow waves in solar atmospheric structures such as coronal loops, and also prominence threads, sunspots, and pores, can trigger the KHI due to the oscillating shear flow at the structure’s boundary. Methods. We used linearized nonstationary MHD to work out an analytical model in a cartesian reference frame. The model describes a compressible plasma near a discontinuous interface separating two regions of homogeneous plasma, each harboring an oscillating velocity field with a constant amplitude which is parallel to the background magnetic field and aligned with the interface. The obtained analytical results were then used to determine the stability of said interface, both in coronal and photospheric conditions. Results. We find that the stability of the interface is determined by a Mathieu equation. In function of the parameters of this equation, the interface can either be stable or unstable. For coronal as well as photospheric conditions, we find that the interface is stable with respect to the KHI. Theoretically, it can, however, be unstable with respect to a parametric resonance instability, although it seems physically unlikely. We conclude that, in this simplified setup, a standing slow wave does not trigger the KHI without the involvement of additional physical processes.
Fisheye Lens Camera based Autonomous Valet Parking System

This paper proposes an efficient autonomous valet parking system utilizing only cameras which are the most widely used sensor. To capture more information instantaneously and respond rapidly to changes in the surrounding environment, fisheye cameras which have a wider angle of view compared to pinhole cameras are used. Accordingly, visual simultaneous localization and mapping is used to identify the layout of the parking lot and track the location of the vehicle. In addition, the input image frames are converted into around view monitor images to resolve the distortion of fisheye lens because the algorithm to detect edges are supposed to be applied to images taken with pinhole cameras. The proposed system adopts a look up table for real time operation by minimizing the computational complexity encountered when processing AVM images. The detection rate of each process and the success rate of autonomous parking were measured to evaluate performance. The experimental results confirm that autonomous parking can be achieved using only visual sensors.
On the behavior of nodal lines near the boundary for Laplace eigenfunctions on the square

We are interested in the effect of Dirichlet boundary conditions on the nodal length of Laplace eigenfunctions. We study random Gaussian Laplace eigenfunctions on the two dimensional square and find a two terms asymptotic expansion for the expectation of the nodal length in any square of side larger than the Planck scale, along a denisty one sequence of energy levels. The proof relies on a new study of lattice points in small arcs, and shows that the said expectation is independent of the position of the square, giving the same asymptotic expansion both near and far from the boundaries.
Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News

In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications. We experiment with two methods: (1) an extractive method based on Biased TextRank – a resource-effective unsupervised graph-based algorithm for content extraction; and (2) an abstractive method based on the GPT-2 language model. We perform comparative evaluations on two misinformation datasets in the political and health news domains, and find that the extractive method shows the most promise.
Bioavailability of ibuprofen from orally and rectally administered hydroxypropyl methyl cellulose capsules compared to corresponding gelatine capsules

The aim of the present study was to determine whether there are any differences in the bioavailability of ibuprofen from hydroxypropyl methyl cellulose (HPMC) capsules (a new type of hard two-piece capsules) compared to hard gelatine capsules when they are administered orally and rectally. Two groups of eight healthy volunteers participated in randomised, cross-over, single-dose studies. One group administered capsules orally (HPMC and gelatine capsules) and the other rectally, There were no statistically significant differences between the calculated pharmacokinetic parameters (C max , t max , t log , AUC, MRT, C max /AUC, t 1/2 ) after the oral administration of the HPMC and gelatine capsules. Following rectal administration, the t log and C max /AUC values for the HPMC capsules were significantly higher (p < 0.05) than those for the gelatine capsules. The orally administered HPMC and gelatine capsules were bioequivalent, but the rectally administered capsules could not be proven as bioequivalent, probably because of the greater variation in the pharmacokinetics. The study shows that HPMC capsules are comparable to gelatine capsules and thus a noteworthy alternative to gelatine capsules. However, attention should be paid to the time lag at the onset of absorption following rectal administration.
Multi-view Deep One-class Classification: A Systematic Exploration

One-class classification (OCC), which models one single positive class and distinguishes it from the negative class, has been a long-standing topic with pivotal application to realms like anomaly detection. As modern society often deals with massive high-dimensional complex data spawned by multiple sources, it is natural to consider OCC from the perspective of multi-view deep learning. However, it has not been discussed by the literature and remains an unexplored topic. Motivated by this blank, this paper makes four-fold contributions: First, to our best knowledge, this is the first work that formally identifies and formulates the multi-view deep OCC problem. Second, we take recent advances in relevant areas into account and systematically devise eleven different baseline solutions for multi-view deep OCC, which lays the foundation for research on multi-view deep OCC. Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC. Finally, by comprehensively evaluating the devised solutions on benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines, and hopefully provide other researchers with beneficial guidance and insight to multi-view deep OCC. Our data and codes are opened at https://github.com/liujiyuan13/MvDOCC-datasets and https://github.com/liujiyuan13/MvDOCC-code respectively to facilitate future research.
Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoders

We present a deep learning based approach to containerized application runtime stability analysis, and an intelligent publishing algorithm that can dynamically adjust the depth of process-level forensics published to a backend incident analysis repository. The approach applies variational autoencoders (VAEs) to learn the stable runtime patterns of container images, and then instantiates these container-specific VAEs to implement stability detection and adaptive forensics publishing. In performance comparisons using a 50-instance container workload, a VAE-optimized service versus a conventional eBPF-based forensic publisher demonstrates 2 orders of magnitude (OM) CPU performance improvement, a 3 OM reduction in network transport volume, and a 4 OM reduction in Elasticsearch storage costs. We evaluate the VAE-based stability detection technique against two attacks, CPUMiner and HTTP-flood attack, finding that it is effective in isolating both anomalies. We believe this technique provides a novel approach to integrating fine-grained process monitoring and digital-forensic services into large container ecosystems that today simply cannot be monitored by conventional techniques
Provenance-based Data Skipping (TechReport)

Database systems analyze queries to determine upfront which data is needed for answering them and use indexes and other physical design techniques to speed-up access to that data. However, for important classes of queries, e.g., HAVING and top-k queries, it is impossible to determine up-front what data is relevant. To overcome this limitation, we develop provenance-based data skipping (PBDS), a novel approach that generates provenance sketches to concisely encode what data is relevant for a query. Once a provenance sketch has been captured it is used to speed up subsequent queries. PBDS can exploit physical design artifacts such as indexes and zone maps. Our approach significantly improves performance for both disk-based and main-memory database systems.
secml-malware: A Python Library for Adversarial Robustness Evaluation of Windows Malware Classifiers

Machine learning has been increasingly used as a ﬁrst line of defense for Windows malware detection. Recent work has however shown that learning-based malware detectors can be evaded by well-crafted, adversarial manipulations of input malware, highlighting the need for tools that can ease and automate the adversarial robustness evaluation of such detectors. To this end, we present secml-malware , the ﬁrst Python library for computing adversarial attacks on Windows malware detectors. secml-malware implements state-of-the-art white-box and black-box attacks on Windows malware classiﬁers, by leveraging a set of functionality-preserving manipulations that can be applied to Windows programs without corrupting their functionality. The library can be used to assess the adversarial robustness of Windows malware detectors, and it can be easily extended to include novel attack strategies. It is available at https://github.com/zangobot/secml_malware .
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation

Self-attention mechanism recently achieves impressive advancement in Natural Language Processing (NLP) and Image Processing domains. And its permutation invariance property makes it ideally suitable for point cloud processing. Inspired by this remarkable success, we propose an end-to-end architecture, dubbed Cross-Level Cross-Scale Cross-Attention Network (CLCSCANet), for point cloud representation learning. First, a point-wise feature pyramid module is introduced to hierarchically extract features from different scales or resolutions. Then a cross-level cross-attention is designed to model long-range inter-level and intra-level dependencies. Finally, we develop a cross-scale cross-attention module to capture interactions between-and-within scales for representation enhancement. Compared with state-of-the-art approaches, our network can obtain competitive performance on challenging 3D object classification, point cloud segmentation tasks via comprehensive experimental evaluation.
Navigation of a Self-Driving Vehicle Using One Fiducial Marker

Navigation using only one marker, which contains four artificial features, is a challenging task since camera pose estimation using only four coplanar points suffers from the rotational ambiguity problem in a real-world application. This paper presents a framework of vision-based navigation for a self-driving vehicle equipped with multiple cameras and a wheel odometer. A multiple camera setup is presented for the camera cluster which has 360° vision such that our framework solely requires one planar marker. A Kalman-Filter-based fusion method is introduced for the multiple-camera and wheel odometry. Furthermore, an algorithm is proposed to resolve the rotational ambiguity problem using the prediction of the Kalman Filter as additional information. Finally, the lateral and longitudinal controllers are provided. Experiments are conducted to illustrate the effectiveness of the theory.
In Search of Optimal Data Placement for Eliminating Write Amplification in Log-Structured Storage

Log-structured storage has been widely deployed in various do-mainsofstoragesystemsforhigh performance.However,itsgarbage collection (GC) incurs severe write amplification (WA) due to the frequent rewrites of live data. This motivates many research studies, particularly on data placement strategies, that mitigate WA in log-structured storage. We show how to design an optimal data placement scheme that leads to the minimum WA with the future knowledge of block invalidation time (BIT) of each written block. Guided by this observation, we propose SepBIT , a novel data placement algorithm that aims to minimize WA in log-structured storage. Its core idea is to infer the BITs of written blocks from the underlying storage workloads, so as to place the blocks with similar estimated BITs into the same group in a fine-grained manner. We show via both mathematical and trace analyses that SepBIT can infer the BITs by leveraging the write skewness property in real-world storage workloads. Evaluation on block-level I/O traces from real-world cloud block storage workloads shows that SepBIT achieves the lowest WA compared to eight state-of-the-art data placement schemes.
Clinical Outcomes in Iraqi Patients with Rheumatoid Arthritis Following Earlier or Later Treatment with Etanercept

Purpose The development of evidence-based guidelines on early pharmacotherapeutic treatment of rheumatoid arthritis (RA) could be useful in Middle Eastern nations striving to improve outcomes in patients with this chronic, debilitating disease. Evidence obtained from local populations should inform such guidelines and therefore our aim was to use real-world data to evaluate the clinical responses of Iraqi patients with RA who received earlier or later treatment with the TNF inhibitor etanercept. Patients and Methods Data from patients registered in the Iraq National Center of Rheumatology database from May 2012 to December 2018, inclusive, were analyzed retrospectively. Inclusion criteria were age ≥18 years, meeting the ACR/EULAR 2010 criteria for RA, referral for etanercept treatment, and ≥1 year of follow-up after etanercept initiation. Patients were excluded if they had received another biologic for RA. Included patients were categorized according to two separate stratifications: whether duration of RA symptoms prior to etanercept initiation was ≤10 or >10 years (10 years represented the mean duration for the entire analysis population); and according to whether duration of RA symptoms prior to etanercept initiation was ≤1, >1 to ≤4, >4 to ≤10, >10 to ≤20, or >20 years. The evaluated outcomes were mean change from baseline in Clinical Disease Activity Index (CDAI) and 28-joint Disease Activity Score (DAS28) after 1 year of etanercept treatment. Results A total of 979 patients were included. CDAI and DAS28 were significantly reduced (p<0.001 for both) after 1 year of etanercept treatment irrespective of whether duration of RA symptoms prior to treatment was ≤10 or >10 years. Patients with RA symptoms for ≤1 year prior to etanercept initiation showed a significant reduction in CDAI after 1 year of treatment (p=0.01). Conclusion Iraqi patients with RA who received earlier treatment with etanercept had superior outcomes compared with those who received later treatment.
Noncoding mutation in RPGRIP1 contributes to inherited retinal degenerations

Purpose Despite the extensive use of next-generation sequencing (NGS) technology to identify disease-causing genomic variations, a major gap in our understanding of Mendelian diseases is the unidentified molecular lesion in a significant portion of patients. For inherited retinal degenerations (IRDs), although currently close to 300 disease-associated genes have been identified, the mutations in approximately one-third of patients remain unknown. With mounting evidence that noncoding mutations might contribute significantly to disease burden, we aimed to systematically investigate the contributions of noncoding regions in the genome to IRDs. Methods In this study, we focused on RPGRIP1, which has been linked to various IRD phenotypes, including Leber congenital amaurosis (LCA), retinitis pigmentosa (RP), and macular dystrophy (MD). As several noncoding mutant alleles have been reported in RPGRIP1, and we observed that the mutation carrier frequency of RPGRIP1 is higher in patient cohorts with unsolved IRDs, we hypothesized that mutations in the noncoding regions of RPGRIP1 might be a significant contributor to pathogenicity. To test this hypothesis, we performed whole-genome sequencing (WGS) for 25 patients with unassigned IRD who carry a single mutation in RPGRIP1. Results Three noncoding variants in RPGRIP1, including a 2,890 bp deletion and two deep-intronic variants (c.2710+233G>A and c.1468–263G>C), were identified as putative second hits of RPGRIP1 in three patients with LCA. The mutant alleles were validated with direct sequencing or in vitro assays. Conclusions The results highlight the significance of the contribution of noncoding pathogenic variants to unsolved IRD cases.