Miracle-dz/UniFAR-FTUs huggingface dataset:
Using statistical encoding to achieve tree succinctness never seen before

We propose a new succinct representation of labeled trees which represents a tree T using |T|H_k(T) number of bits (plus some smaller order terms), where |T|H_k(T) denotes the k-th order (tree label) entropy, as defined by Ferragina at al. 2005. Our representation employs a new, simple method of partitioning the tree, which preserves both tree shape and node degrees. Previously, the only representation that used |T|H_k(T) bits was based on XBWT, a transformation that linearizes tree labels into a single string, combined with compression boosting. The proposed representation is much simpler than the one based on XBWT, which used additional linear space (bounded by 0.01n) hidden in the "smaller order terms" notion, as an artifact of using zeroth order entropy coder; our representation uses sublinear additional space (for reasonable values of k and size of the label alphabet {\sigma}). The proposed representation can be naturally extended to a succinct data structure for trees, which uses |T|H_k(T) plus additional O(|T|k log_{\sigma}/ log_{\sigma} |T| + |T| log log_{\sigma} |T|/ log_{\sigma} |T|) bits and supports all the usual navigational queries in constant time. At the cost of increasing the query time to O(log log |T|/ log |T|) we can further reduce the space redundancy to O(|T| log log |T|/ log_{\sigma} |T|) bits, assuming k <= log_{\sigma} |T|. This is a major improvement over representation based on XBWT: even though XBWT-based representation uses |T|H_k(T) bits, the space needed for structure supporting navigational queries is much larger: (.)
Assessing fish abundance from underwater video using deep neural networks

Uses of underwater videos to assess diversity and abundance of fish are being rapidly adopted by marine biologists. Manual processing of videos for quantification by human analysts is time and labour intensive. Automatic processing of videos can be employed to achieve the objectives in a cost and time-efficient way. The aim is to build an accurate and reliable fish detection and recognition system, which is important for an autonomous robotic platform. However, there are many challenges involved in this task (e.g. complex background, deformation, low resolution and light propagation). Recent advancement in the deep neural network has led to the development of object detection and recognition in real time scenarios. An end-to-end deep learningbased architecture is introduced which outperformed the state of the art methods and first of its kind on fish assessment task. A Region Proposal Network (RPN) introduced by an object detector termed as Faster R-CNN was combined with three classification networks for detection and recognition of fish species obtained from Remote Underwater Video Stations (RUVS). An accuracy of 82.4% (mAP) obtained from the experiments are much higher than previously proposed methods.
Active Vibration Suppression of Flexible Spacecraft During Attitude Maneuver With Actuator Dynamics

A novel active vibration suppression approach based on a flywheel actuator for flexible spacecraft during attitude maneuvering is proposed in this paper. The flywheel is regarded as the only actuator. The rotation of the flywheel, the motion of the rigid body, and the vibration of the modes are coupled in this paper. The system model is obtained using the Euler-Lagrange methodology and the Assumed-mode Method, in which the dynamics of the flywheel is considered. The stability of the flywheel transfer function from input voltage to output torque is analyzed using the Lyapunov method. Effects on system caused by flywheel are analyzed. Based on component synthesis vibration suppression theory, a typical vibration suppress law during attitude maneuver is derived. The input voltage of the flywheel actuator is designed according to this law. The presented novel approach is easily applied and practical without changing the spacecraft’s structure properties. The simulation results and experiments on an air bearing spacecraft simulator are presented to verify the efficacy of the proposed approach.
Do We Need New Strategies for Testing Systems-of-Systems?

This paper overviews the main Systems-of-Systems (SoS) characteristics that can impact on their verification, validation and testing (VV&T). Furthermore, it addresses technical, conceptual, social and organizational challenges, discusses which existing approaches of VV&T can be used for SoS, and points out future research in the field.
Compressed-Sensing-Aided Space-Time Frequency Index Modulation

In space-time shift keying (STSK), the information is conveyed by both the spatial and time dimensions, which can be used to strike a tradeoff between the diversity and multiplexing gains. On the other hand, orthogonal frequency-division multiplexing (OFDM) relying on index modulation (IM) conveys information not only by the conventional signal constellations as in classical OFDM, but also by the indices of the subcarriers. In this paper, we combine the benefits of STSK and IM in order to strike a flexible tradeoff between the throughput and bit-error performance by transmitting extra information bits in each subcarrier block, while increasing the diversity order. In order to further improve this tradeoff, as well as to decrease the complexity of the detector, compressed sensing is combined with the proposed STSK-aided IM system. We first present the maximum likelihood (ML) detection, which forms the best-case bound on the proposed system's performance. Then, we propose a pair of reduced-complexity detection algorithms capable of approaching the ML detector's performance. Furthermore, in order to attain a near capacity performance, we propose a soft-input soft-output decoder that is capable of exchanging soft information with a channel decoder through iterative decoding.
A Project on Software Defect Prevention at Commit-Time: A Success Story of University-Industry Research Collaboration

In this talk, we describe a research collaboration project between Concordia University and Ubisoft. The project consists of investigating techniques for defect prevention at commit-time for increased software quality. The outcome of this project is a tool called CLEVER (Combining Levels of Bug Prevention and Resolution techniques) that uses machine learning to automatically detect coding defects as programmers write code. The main novelty of CLEVER is that it relies on code matching techniques to detect coding mistakes based on a database of historical code defects found in multiple related projects. The tool also proposes fixes based on known patterns.
Multi-Modality Imaging: A Software Fusion and Image-Guided Therapy Perspective

With the introduction of computers in medical imaging, which were popularized with the presentation of Hounsfield’s ground-breaking work in 1971, numerical image reconstruction and analysis of medical images became a vital part of medical imaging research. While mathematical aspects of reconstruction dominated research in the beginning, a growing body of literature attests to the progress made over the past 30 years in image fusion. This article describes the historical development of non-deformable software-based image co-registration and it’s role in the context of hybrid imaging and provides an outlook on future developments.
Genome-Wide Signatures of Selection Reveal Genes Associated With Performance in American Quarter Horse Subpopulations

Selective breeding for athletic performance in various disciplines has resulted in population stratification within the American Quarter Horse (QH) breed. The goals of this study were to utilize high density genotype data to: (1) identify genomic regions undergoing positive selection within and among QH subpopulations; (2) investigate haplotype structure within each QH subpopulation; and (3) identify candidate genes within genomic regions of interest (ROI), as well as biological pathways, predicted to play a role in elite performance in each group. For that, 65K SNP genotyping data on 143 elite individuals from 6 QH subpopulations (cutting, halter, racing, reining, western pleasure, and working cow) were imputed to 2M SNPs. Signatures of selection were identified using FST-based (di) and haplotype-based (hapFLK) analyses, accompanied by identification of local haplotype structure and sharing within subpopulations (hapQTL). Regions undergoing positive selection were identified on all 31 autosomes, and ROI on 2 chromosomes were identified by all 3 methods combined. Genes within each ROI were retrieved and used to identify pathways and genes that might contribute to performance in each subpopulation. These included, among others, candidate genes associated with skeletal muscle development, metabolism, and central nervous system development. This work improves our understanding of equine breed development, and provides breeders with a better understanding of how selective breeding impacts the performance of QH populations.
Corridor Learning Using Individual Trajectories

The rapid development and commercialization of location acquisition technologies generates large trajectory datasets, that trace moving objects' trips. In this work, we propose a new trajectory mining algorithm, for discovering paths that are frequently followed by the given trajectories, named as corridors. We claim that the moving objects follow common paths-corridors. Detecting corridors from a collection of trajectories is extremely challenging due to the nature of the data (low sampling rates, different speeds, noisy measurements etc.). In this work we propose and evaluate a pipelined algorithm that abstracts from trajectories their underlying frequent paths.
A Synthetic Disaccharide Derivative of Diphyllin, TAARD, Activates Human Natural Killer Cells to Secrete Interferon-Gamma via Toll-Like Receptor-Mediated NF-κB and STAT3 Signaling Pathways

Natural products and their derivatives have long been used as pharmacological agents in the fight against cancer. Human natural killer (NK) cells are critical in our immune system in that they are capable of destroying tumor cells directly. However, there are few reports that elucidate the role of natural products in activating NK cells. In this study, we discovered that a synthetic disaccharide derivative of diphyllin, 4-O-{[2′′,3′′,4′′-tri-O-acetyl-α-D-arabinopyranosyl-(1′′→4′)]-2′,3′-di-O-acetyl-α-L-rhamnopyranosyl}diphyllin (TAARD), can alone stimulate interferon (IFN)-γ secretion in primary human NK cells and the NKL cell line. Additionally, it had an additive effect with IL-12 or IL-15 on IFN-γ production, but little adverse effects on NK cells. Mechanistically, TAARD induced the phosphorylation of NF-κB and STAT3, resulting in their binding on the IFNG promoter, which was dependent on TLR1 and TLR3 signaling, respectively. STAT3 and NF-κB knockdown with lentivirus shRNA as well as the NF-κB-specific inhibitor, N-tosyl-l-phenylalaninechloromethyl ketone, significantly suppressed TAARD-induced IFN-γ generation in primary NK cells. Blockade of TLR1 and TLR3 with neutralizing antibodies considerably decreased TAARD-induced activation of NF-κB and STAT3, respectively, as well as IFN-γ generation in NK cells. Collectively, our data suggest that TAARD can induce NK cell IFN-γ production through TLR1-NF-κB and TLR3-STAT3 signaling pathways, rendering its potential use as an agent for cancer prevention or treatment.
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence

User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust. In our work, we explore the use of regular equivalence applied to a trust network to generate a similarity matrix that is used to select the k-nearest neighbors for recommending items. We evaluate our approach on Epinions and we find that we can outperform related methods for tackling cold-start users in terms of recommendation accuracy.
Extracellular Matrix Membrane Induces Cementoblastic/Osteogenic Properties of Human Periodontal Ligament Stem Cells

Objective: Periodontitis affects nearly 90% of adults over the age of 70, resulting to periodontal tissue infection, destruction, and ultimately tooth loss. Guided tissue regeneration (GTR) is a method widely used to treat severe periodontal disease, and involves placement of an occlusive barrier to facilitate regeneration of the damaged area by periodontal ligament stem cells (PDLSCs). In this study, we evaluate natural extracellular matrix (ECM) as a scaffold material to provide a suitable microenvironment to support the proliferation, differentiation, and tissue-regenerating properties of PDLSCs. Design: The viability, proliferation, apoptosis, and migration of PDLSCs cultured on ECM membrane, that was isolated from porcine urinary bladders, were compared with those cultured on type I collagen membrane, a commonly used scaffold in GTR. To evaluate the effects of ECM vs. type I collagen on the tissue-regenerating properties of PDLSCs, the bio-attachment and cementoblastic/osteogenic differentiation of PDLSCs were evaluated. Results: Incubation of PDLSCs with ECM resulted in increased viability, proliferation, and reduced apoptosis, compared with type I collagen treated PDLSCs. Co-culture with ECM membrane also increased the migration and bio-attachment of PDLSCs. Incubation of PDLSCs with ECM membrane increased expression of the cementoblastic/osteogenic differentiation markers BSP, RUNX2, ALP, OPN, OCN, and periostin. Conclusion: ECM membrane enhances the proliferation and regenerative properties of PDLSCs, indicating that ECM membrane can serve as a suitable scaffold in the application of GTR to treat periodontal disease.
Eliminating the VGlut2-Dependent Glutamatergic Transmission of Parvalbumin-Expressing Neurons Leads to Deficits in Locomotion and Vocalization, Decreased Pain Sensitivity, and Increased Dominance

The calcium-binding protein parvalbumin (PV) is a recognized marker of short-axon GABA-ergic neurons in the cortex and the hippocampus. However in addition, PV is expressed by excitatory, glutamatergic neurons in various areas of the brain and spinal cord. Depending on the location of these neurons, loading of their synaptic vesicles with glutamate is mediated by either of three vesicular glutamate transporters (VGlut): VGlut1, VGlut2, or VGlut3. Driven by our interest in one of these glutamatergic/PV-expressing cell clusters-the lateral hypothalamic parvafox nucleus-we investigated the functions of this population of neurons by the selective deletion of VGlut2 expression in PV-expressing cells according to the Cre/Lox-approach. PV-Cre;VGlut2-Lox mutant mice are phenotypically characterized by deficits in locomotion and vocalization, by a decreased thermal nociception, and by an increased social dominance. We conducted a search of the Allen Brain Atlas for regions that might co-express the genes encoding PV and VGlut2, and that might thus contribute to the manifestation of the observed phenotypes. Our survey revealed several structures that could contribute to the deficits in locomotion and vocalization, such as the red, the subthalamic and the deep cerebellar nuclei. It also disclosed that a shift in the balance of afferental glutamatergic neurotransmission to the periaqueductal gray matter might be accountable for the decrease in sensitivity to pain and for the increase in social dominance. As a whole, this study broadens the state of knowledge about PV-expressing excitatory neurons.
Association and diagnostic utility of diastolic dysfunction and myocardial fibrosis in patients with Fabry disease

Objectives Current guidelines highlight important therapy implications of cardiac fibrosis in patients with Fabry disease (FD). However, association between morphological and functional impairments with cardiac fibrosis in hereditary cardiomyopathies remains elusive. We investigated the association between echocardiography-determined cardiac dysfunction and cardiac MRI (cMRI)-detected myocardial fibrosis (late gadolinium enhancement, LE) in patients with FD with preserved left ventricular ejection fraction (≥50%). Methods 146 patients with FD (aged 39±14 years, 57 men) were analysed, all receiving echocardiography and cMRI within a 1 week interval. Longitudinal systolic strain (LS_sys), strain rate (LSr_sys) and diastolic strain rate (LSr_E/LSr_A) were assessed using speckle-tracking imaging. Receiver operating characteristic (ROC) analysis was performed to identify the diagnostic performance of various markers for LE. Results LE was detected in 57 (39%) patients with FD. LV wall thickness, left atrial volume, septal E/e′, diastolic dysfunction grade, global LS_sys and E/LSr_E, mid-lateral LS_sys and LSr_E, as well as N-terminal pro-brain natriuretic peptide were all associated with LE independent of age, sex, body mass index, New York Heart Association functional class and kidney function. In ROC curve analysis, septal E/e′ performed best (area under the curve=0.86, 95% CI=0.79 to 0.92). Septal E/e′>14.8 was strongly associated with LE (specificity=97.8% and sensitivity=49.1%). In 9% of patients, localised LE was present even though no other cardiac or kidney abnormalities were detected. Conclusions Echocardiography-derived diastolic dysfunction is closely linked to LE in FD. Septal E/e′ ratio is the best echocardiographic marker suggestive of LE. Diastolic dysfunction is not a prerequisite for LE in FD, since LE can be detected in the absence of measurable cardiac functional impairments. Trial registration number ClinicalTrials.gov Identifier (NCT03362164).
Test case prioritization for acceptance testing of cyber physical systems: a multi-objective search-based approach

Acceptance testing validates that a system meets its requirements and determines whether it can be sufficiently trusted and put into operation. For cyber physical systems (CPS), acceptance testing is a hardware-in-the-loop process conducted in a (near-)operational environment. Acceptance testing of a CPS often necessitates that the test cases be prioritized, as there are usually too many scenarios to consider given time constraints. CPS acceptance testing is further complicated by the uncertainty in the environment and the impact of testing on hardware. We propose an automated test case prioritization approach for CPS acceptance testing, accounting for time budget constraints, uncertainty, and hardware damage risks. Our approach is based on multi-objective search, combined with a test case minimization algorithm that eliminates redundant operations from an ordered sequence of test cases. We evaluate our approach on a representative case study from the satellite domain. The results indicate that, compared to test cases that are prioritized manually by satellite engineers, our automated approach more than doubles the number of test cases that fit into a given time frame, while reducing to less than one third the number of operations that entail the risk of damage to key hardware components.
FMCW Radar for Data Acquisition on High-Density Polyethylene Guides

Current labeling techniques have several disadvantages, such as label durability on outdoor conditions, that hinder the growth of automated data acquisition systems. We present a millimeter-wave radar-based alternative that can solve some of these problems by reading prerecorded binary air gaps in a low-cost plastic guide made from high-density polyethylene. In this article, bit detection in a plastic guide is analyzed and validated with a 300-GHz radar prototype transmitting 27GHz of bandwidth in a laboratory environment.
Interpretable Latent Spaces for Learning from Demonstration

Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained within instructions) in a corresponding high-dimensional sensory input stream from the world. Models such as deep neural networks, with high capacity through their large parameter spaces, can be used to compress the high-dimensional sensory data to lower dimensional representations. These low-dimensional representations facilitate symbol grounding, but may not guarantee that the representation would be human-interpretable. We propose a method which utilises the grouping of user-defined symbols and their corresponding sensory observations in order to align the learnt compressed latent representation with the semantic notions contained in the abstract labels. We demonstrate this through experiments with both simulated and real-world object data, showing that such alignment can be achieved in a process of physical symbol grounding.
Efficient Genome Editing of Magnetospirillum magneticum AMB-1 by CRISPR-Cas9 System for Analyzing Magnetotactic Behavior

Magnetotactic bacteria are a diverse group of microorganisms with the ability to use geomagnetic fields for direction sensing. This magnetotactic behavior can help microorganisms move towards favorable habitats for optimal growth and reproduction. Highly efficient genomic editing is very useful for a comprehensive understanding of the magnetotactic mechanism at the molecular level. In this study, we adapted an engineered CRISPR-cas9 system for efficient inactivation of gene in a widely used magnetotactic bacteria model strain, Magnetospirillum magneticum AMB-1. By combining an engineered nuclease-deficient Cas9 and single-guide RNA, a CRISPR interference system was successfully developed to silence amb0994 expression. More importantly, we succeeded in the construction of a single amb0994 gene deletion mutant using CRISPR-Cas9 with approximate 60-fold high efficiency compared to classical homology double-crossing replacement procedure. This mutant synthesized normally the magnetosomes, but reacted quicker and with less time than the wild-type strain to abrupt magnetic field reversals. A dynamics simulation by modeling M. magneticum AMB-1 cell as an ellipsoid showed that the difference of the motions between wild and Δamb0994 is due to flagellar influence. The behavior observation being consistent with dynamics simulation indicated that Amb0994 is involved in the cellular response to magnetic torque change via controlling flagella. Besides the contribution to a better understanding of the magnetotaxis mechanism, this study demonstrates the CRISPR system as a useful genetic toolbox for high-efficiency genome editing in magnetotactic bacteria.