Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and evaluation of abnormal user behavior based on quantum generation adversarial network

Quantum computing holds tremendous potential for processing high-dimensional data, capitalizing on the unique capabilities of superposition and parallelism within quantum states. As we navigate the noisy intermediate-scale quantum (NISQ) era, the exploration of quantum computing applications has emerged as a compelling frontier. One area of particular interest within the realm of cyberspace security is Behavior Detection and Evaluation (BDE). Notably, the detection and evaluation of internal abnormal behaviors pose significant challenges, given their infrequent occurrence or even their concealed nature amidst vast volumes of normal data. In this paper, we introduce a novel quantum behavior detection and evaluation algorithm (QBDE) tailored for internal user analysis. The QBDE algorithm comprises a Quantum Generative Adversarial Network (QGAN) in conjunction with a classical neural network for detection and evaluation tasks. The QGAN is built upon a hybrid architecture, encompassing a Quantum Generator ($G_Q$) and a Classical Discriminator ($D_C$). $G_Q$, designed as a parameterized quantum circuit (PQC), collaborates with $D_C$, a classical neural network, to collectively enhance the analysis process. To address the challenge of imbalanced positive and negative samples, $G_Q$ is employed to generate negative samples. Both $G_Q$ and $D_C$ are optimized through gradient descent techniques. Through extensive simulation tests and quantitative analyses, we substantiate the effectiveness of the QBDE algorithm in detecting and evaluating internal user abnormal behaviors. Our work not only introduces a novel approach to abnormal behavior detection and evaluation but also pioneers a new application scenario for quantum algorithms. This paradigm shift underscores the promising prospects of quantum computing in tackling complex cybersecurity challenges.
A semi-supervised Teacher-Student framework for surgical tool detection and localization

ABSTRACT Surgical tool detection in minimally invasive surgery is an essential part of computer-assisted interventions. Current approaches are mostly based on supervised methods requiring large annotated datasets. However, labelled datasets are often scarce. Semi-supervised learning (SSL) has recently emerged as a viable alternative showing promise in producing models retaining competitive performance to supervised methods. Therefore, this paper introduces an SSL framework in the surgical tool detection paradigm, which aims to mitigate training data scarcity and data imbalance problems through a knowledge distillation approach. In the proposed work, we train a model with labelled data which initialises the Teacher-Student joint learning, where the Student is trained on Teacher-generated pseudo-labels from unlabelled data. We also propose a multi-class distance with a margin-based classification loss function in the region-of-interest head of the detector to segregate the foreground-background region effectively. Our results on m2cai16-tool-locations dataset indicates the superiority of our approach on different supervised data settings (1%, 2%, 5% and 10% of annotated data) where our model achieves overall improvements of 8%, 12%, and 27% in mean average precision on 1% labelled data over the state-of-the-art SSL methods and the supervised baseline, respectively. The code is available at https://github.com/Mansoor-at/Semi-supervised-surgical-tool-detection.
Why So Inflammatory? Explainability in Automatic Detection of Inflammatory Social Media Users

Hate speech and misinformation, spread over social networking services (SNS) such as Facebook and Twitter, have inflamed ethnic and political violence in countries across the globe. We argue that there is limited research on this problem within the context of the Global South and present an approach for tackling them. Prior works have shown how machine learning models built with user-level interaction features can effectively identify users who spread inflammatory content. While this technique is beneficial in low-resource language settings where linguistic resources such as ground truth data and processing capabilities are lacking, it is still unclear how these interaction features contribute to model performance. In this work, we investigate and show significant differences in interaction features between users who spread inflammatory content and others who do not, applying explainability tools to understand our trained model. We find that features with higher interaction significance (such as account age and activity count) show higher explanatory power than features with lower interaction significance (such as name length and if the user has a location on their bio). Our work extends research directions that aim to understand the nature of inflammatory content in low-resource, high-risk contexts as the growth of social media use in the Global South outstrips moderation efforts.
Generalized image matching by the method of differences

Image matching refers to aligning two similar images related by a transformation such as a translation, rotation, etc. In its general form image matching is a problem of estimating the parameters that determine that transformation. These parameters may be a few global parameters or a field of parameters describing local transformations. This thesis explores in theory and by experiment image matching by the method of differences. The method uses intensity differences between the images together with the spatial intensity gradient to obtain from each image point a linear constraint on the match parameters; combining constraints from many points yields a parameter estimate. The method is particularly suitable where an initial estimate of the match parameters is available. In such cases it eliminates search which can be costly, particularly in multi-dimensional parameter spaces. Essential to the technique are smoothing, which increases the range of validity of the constraint provided by the gradient, and iteration, because the parameter estimate is an approximation. Smoothing increases the range of convergence but it decreases accuracy, so a coarse-fine approach is needed. A theoretical analysis supports these claims and provides a means for predicting the algorithm's behavior. The first application considered here, optical navigation, requires matching two images to determine the relative camera positions. Here the match parameters are the position parameters, because they determine the image transformation. In many cases, such as robot guidance, the required parameter estimate is available. Using information from points near edges minimizes error due to noise, specularity, etc. The relationship between the three-space geometry of the reference points and the stability of the algorithm is investigated. Optical navigation experiments using both real and synthetic images are presented. They support the claims of the theoretical analysis, and demonstrate a range of convergence and accuracy adequate for many tasks. The second application, stereo vision, is a problem of determining a field of local parameters, namely the distance values. Constraints from the neighborhood of each point contribute to its distance estimate. Experiments on both real and synthetic data provide encouraging results.
Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is being deployed in real-world applications, however many state-of-the-art solutions still struggle in many common scenarios. A key necessity in progressing SLAM research is the availability of high-quality datasets and fair and transparent benchmarking. To this end, we have created the Hilti-Oxford Dataset, to push state-of-the-art SLAM systems to their limits. The dataset has a variety of challenges ranging from sparse and regular construction sites to a 17th century neoclassical building with fine details and curved surfaces. To encourage multi-modal SLAM approaches, we designed a data collection platform featuring a lidar, five cameras, and an IMU (Inertial Measurement Unit). With the goal of benchmarking SLAM algorithms for tasks where accuracy and robustness are paramount, we implemented a novel ground truth collection method that enables our dataset to accurately measure SLAM pose errors with millimeter accuracy. To further ensure accuracy, the extrinsics of our platform were verified with a micrometer-accurate scanner, and temporal calibration was managed online using hardware time synchronization. The multi-modality and diversity of our dataset attracted a large field of academic and industrial researchers to enter the second edition of the Hilti SLAM challenge, which concluded in June 2022. The results of the challenge show that while the top three teams could achieve an accuracy of 2 cm or better for some sequences, the performance dropped off in more difficult sequences.
Coordinating charging request allocation between self-interested navigation service platforms

Current electric vehicle market trends indicate an increasing adoption rate across several countries. To meet the expected growing charging demand, it is necessary to scale up the current charging infrastructure and to mitigate current reliability deficiencies, e.g., due to broken connectors or misreported charging station availability status. However, even within a properly dimensioned charging infrastructure, a risk for local bottlenecks remains if several drivers cannot coordinate their charging station visit decisions. Here, navigation service platforms can optimally balance charging demand over available stations to reduce possible station visit conflicts and increase user satisfaction. While such fleet-optimized charging station visit recommendations may alleviate local bottlenecks, they can also harm the system if self-interested navigation service platforms seek to maximize their own customers' satisfaction. To study these dynamics, we model fleet-optimized charging station allocation as a resource allocation game in which navigation platforms constitute players and assign potentially free charging stations to drivers. We show that no pure Nash equilibrium guarantee exists for this game, which motivates us to study VCG mechanisms both in offline and online settings, to coordinate players' strategies toward a better social outcome. Extensive numerical studies for the city of Berlin show that when coordinating players through VCG mechanisms, the social cost decreases on average by 42 % in the online setting and by 52 % in the offline setting.
Nonlinear interaction of torsional and longitudinal guided waves in hyperelastic cylinders

The nonlinear forcing terms for the wave equation in general curvilinear coordinates are derived based on a hyperelastic material. The expressions for the nonlinear part of the first Piola-Kirchhoff stress are specialized for axisymmetric torsional and longitudinal fundamental wave fields in a cylinder. The matrix characteristics of the nonlinear forcing terms and secondary mode wavestructures are manipulated to analyze the higher harmonic generation due to the guided wave mode self interactions and mutual interactions. It is proven that both torsional and longitudinal mode secondary fields can be cumulative by specific type of guided wave mode interactions. A method for the selection of preferred fundamental excitations that generate strongly cumulative higher harmonics is formulated, and described in detail for second harmonic generation. Hyperelastic finite element models are built to simulate second harmonic generation by T(0,3) and L(0,4) modes. A linear increase of the modal amplitude ratio A2/A1^2 over the propagation distance is observed for both cases, which indicates mode L(0,5) is effectively generated as cumulative second harmonics.
Persisting Concussion Symptoms from Bodychecking: Unrecognized Toll in Boys’ Ice Hockey

ABSTRACT: Objectives: Youth hockey is a high-impact sport and can cause concussions with lasting effects. We hypothesized that important injury prevention information would accrue from longitudinal tracking of concussed players with persisting concussion symptoms (PCS). Methods: This case series comprised 87 consecutive concussed ice hockey players aged 10–18 including 66 males and 21 females referred to our Concussion Clinic from 1997 to 2017 and followed longitudinally by clinic visits and questionnaires. Results: PCS occurred in 70 (80.4%) of 87 concussed players and lasted 1–168 months in males and 3–26 months in females. Bodychecking was the most common concussion mechanism in 34 (39.1%) players and caused PCS in 24 (70.6%) with symptom duration 4.00 [2.75, 14.50] months (median [IQR]). The remaining 53 players had other concussion mechanisms with PCS in 86.8% (p = 0.113) with similar duration (p = 0.848). Conclusions: This is the first longitudinal study of concussion with PCS in youth hockey and showed that symptoms can last for several years. Bodychecking was the commonest mechanism of prolonged disability from concussion in boys and girls’ hockey with average PCS duration of 12.3 months but several years in some players. The injury prevention message is to raise the age of permitted bodychecking to 18 in boys’ hockey from age 13 to 14 where it is currently. In this case series, this change could have prevented the majority of the bodycheck concussions and several years of suffering from PCS and is strong evidence for raising the permitted age for bodychecking in boys’ ice hockey to age 18.
Effects of combined nitrification inhibitors on soil nitrification, maize yield and nitrogen use efficiency in three agricultural soils

Application of nitrification inhibitors (NIs) with nitrogen (N) fertilizer is one of the most efficient ways to improve nitrogen use efficiency (NUE). To fully understand the efficiency of NIs with N fertilizer on soil nitrification, yield and NUE of maize (Zea mays L.), an outdoor pot experiment with different NIs in three soils with different pH was conducted. Five treatments were established: no fertilizer (Control); ammonium sulfate (AS); ammonium sulfate + 3, 4-dimethyl-pyrazolate phosphate (DMPP) (AD); ammonium sulfate + nitrogen protectant (N-GD) (AN); ammonium sulfate + 3, 4-dimethyl-pyrazolate phosphate + nitrogen protectant (ADN). The results showed that NIs treatments (AD, AN and ADN) significantly reduced soil nitrification in the brown and red soil, especially in AD and ADN, which decreased apparent nitrification rate by 28% - 44% (P < 0.05). All NIs treatments significantly increased yield and NUE of maize in three soils, especially ADN in the cinnamon soil and AD in the red soil were more efficiency, which significantly increased maize yield and apparent nitrogen recovery by 5.07 and 6.81 times, 4.39 and 8.16 times, respectively. No significant difference on maize yield was found in the brown soil, but AN significantly increased apparent nitrogen recovery by 70%. Given that the effect of NIs on both soil nitrification and NUE of maize, DMPP+N-GD was more efficient in the cinnamon soil, while N-GD and DMPP was the most efficiency in the brown and red soil, respectively. In addition, soil pH and soil organic matter play important role in the efficiency of NIs.
Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.
Co-workers commitment to safety: A link between safety climate and safety behaviors in the construction industry

BACKGROUND: There is growing empirical evidence that as safety climate improves work site safety practice improve. Safety climate is often measured by asking workers about their perceptions of management commitment to safety. However, it is less common to include perceptions of their co-workers commitment to safety. While the involvement of management in safety is essential, working with co-workers who value and prioritize safety may be just as important. OBJECTIVE: To evaluate a concept of safety climate that focuses on top management, supervisors and co-workers commitment to safety, which is relatively new and untested in the United States construction industry. METHODS: Survey data was collected from a cohort of 300 unionized construction workers in the United States. The significance of direct and indirect (mediation) effects among safety climate and safety behavior factors were evaluated via structural equation modeling. RESULTS: Results indicated that safety climate was associated with safety behaviors on the job. More specifically, perceptions of co-workers commitment to safety was a mediator between both management commitment to safety climate factors and safety behaviors. CONCLUSIONS: These results support workplace health and safety interventions that build and sustain safety climate and a commitment to safety amongst work teams.
The cell biology of charophytes: Exploring the past and models for the future

Abstract Charophytes (Streptophyta) represent a diverse assemblage of extant green algae that are the sister lineage to land plants. About 500–600+ million years ago, a charophyte progenitor successfully colonized land and subsequently gave rise to land plants. Charophytes have diverse but relatively simple body plans that make them highly attractive organisms for many areas of biological research. At the cellular level, many charophytes have been used for deciphering cytoskeletal networks and their dynamics, membrane trafficking, extracellular matrix secretion, and cell division mechanisms. Some charophytes live in challenging habitats and have become excellent models for elucidating the cellular and molecular effects of various abiotic stressors on plant cells. Recent sequencing of several charophyte genomes has also opened doors for the dissection of biosynthetic and signaling pathways. While we are only in an infancy stage of elucidating the cell biology of charophytes, the future application of novel analytical methodologies in charophyte studies that include a broader survey of inclusive taxa will enhance our understanding of plant evolution and cell dynamics.
“I Felt Better When I Moved Into My Own Place”: Needs and Experiences of Intimate Partner Violence Survivors in Rapid Rehousing

Accessing stable housing is a basic need for intimate partner violence (IPV) survivors, and rapid rehousing programs are a critical way to address homelessness. However, little is known about survivor experiences, needs, and outcomes in rapid rehousing services within IPV agencies. This study uses an exploratory approach to understand the needs and experiences of 31 survivors using vouchers facilitated by an IPV program in the U.S. Southwest. Thematic analysis of structured interviews resulted in four summary themes: getting to housing, managing multiple needs, accessing support, and facing barriers. Practice and evaluation implications are discussed.
Association between Preoperative Urine Culture and Urinary Tract Infection after Spinal Surgery

Study Design This is a retrospective study. Purpose This study assessed risk factors accounting for urinary tract infections (UTIs) to determine whether preoperative asymptomatic UTI (aUTI) could be used to predict UTIs in patients after spinal surgery. Overview of Literature UTI is a spinal surgery complication that increases the incidence of surgical site infections. However, the risk factors for UTIs after spinal surgery remain unclear. Methods This study included 509 (mean age, 54.5 years; 239 males and 270 females) patients who underwent posterior spine surgery at the department of the current study. First, clean catch urine was collected, after which a urine culture was performed for all patients before surgery. Preoperative detection of the aUTI (>105 colony-forming units/mL) rate was then determined. Subsequently, risk factors for postoperative UTI were evaluated using logistic regression analysis with the following as independent variables: age, sex, obesity, diabetes, spinal cord tumor, the preoperative Japanese Orthopedic Association (JOA) score, JOA-bladder function, preoperative urine culture positivity, aUTI, preoperative Escherichia coli detection, the postoperative catheter placement period, instrumentation, number of surgical levels, surgery duration, and blood loss. Results The preoperative aUTI and postoperative UTI incidences were 8.1% and 4.1%, respectively. Furthermore, multivariate logistic analysis showed that the risk factor for postoperative UTI was preoperative aUTI (odds ratio, 4.234; 95% confidence interval, 1.532–11.702; p=0.005). Conclusions Preoperative aUTI is a risk factor for UTI in patients after spinal surgery.