Miracle-dz/UniFAR-FTUs huggingface dataset:
Harnessing Social Media to Identify Homeless Youth At-Risk of Substance Use

Homeless youth are a highly vulnerable population and report highly elevated rates of substance use. Prior work on mitigating substance use among homeless youth has primarily relied on survey data to get information about substance use among homeless youth, which can then be used to inform the design of targeted intervention programs. However, such survey data is often onerous to collect, is limited by its reliance on self-reports and retrospective recall, and quickly becomes dated. The advent of social media has provided us with an important data source for understanding the health behaviors of homeless youth. In this paper, we target this specific population and demonstrate how to detect substance use based on texts from social media. We collect ~135K Facebook posts and comments together with survey responses from a group of homeless youth and use this data to build novel substance use detection systems with machine learning and natural language processing techniques. Experimental results show that our proposed methods achieve ROC-AUC scores of ~0.77 on identifying certain kinds of substance use among homeless youth using Facebook conversations only, and ROC-AUC scores of ~0.83 when combined with answers to four survey questions that are not about their demographic characteristics or substance use. Furthermore, we investigate connections between the characteristics of people's Facebook posts and substance use and provide insights about the problem.
Iterative Learning Control for Switched Systems with Sensor Saturation Constraints

To solve trajectory tracking problem of switched system with sensor saturation, an iterative learning control algorithm is proposed. The method uses actual measurement error to modify the control variable of system on the premise that switched rule does not change along iteration axis, but it randomly changes along time axis. Moreover, by dealing with the saturation via diagonal matrix method, the convergence of the algorithm is strictly proved in the sense of λ-norm, and the convergence condition is derived. The algorithm can achieve complete tracking of desired trajectory in the finite time interval under the random switched rule, as iterations increase. The simulation example verifies the validity of the proposed algorithm.
A Fixed-Time Hierarchical Formation Control Strategy for Multiquadrotors

This paper deals with the problem of multiquadrotor collaborative control by developing and analyzing a new type of fixed-time formation control algorithm. The control strategy proposes a hierarchical control framework, which consists of two layers: a coordinating control layer and a tracking control layer. On the coordinating control layer, according to the fixed-time consistency theory, the virtual position and virtual velocity of each quadrotor are calculated and acquired to form a virtual formation, and the virtual velocity reaches consistency. On the tracking control layer, the real position and the real velocity track the virtual position and the virtual velocity, respectively. Thus, multiquadrotor can achieve the required formation shape and velocity consensus. Finally, the comparative simulations are carried out to illustrate the feasibility and superiority of the proposed fixed-time hierarchical formation control method for multiquadrotor collaborative control.
Evaluation framework for BIM-based VR applications in design phase

The integration of building information modeling (BIM) and virtual reality (VR) has attracted increasing attention in the architecture engineering and construction (AEC) industry. Despite the rapid emergence of BIM-based VR applications, no evaluation framework specialized for the technologies exists in the AEC industry. After extensively reviewing existing studies and interviewing experts, the research team proposed an evaluation framework for BIM-based VR applications, which consists of 3 stages, 5 areas, 14 criteria, and 29 metrics and focuses on the design phase of the projects. To assess the usefulness of the framework, the team applied it to five BIM-based VR applications using a BIM-based design project for an educational building in Hong Kong. The team also interviewed experts to discuss the comprehensiveness of the framework. The results show that the framework provides consistent results for comprehensive evaluation criteria and metrics in a quantitative and flexible manner. Further research that considers additional criteria and/or metrics related to other phases (e.g. construction phase) is required to extend the framework to the whole lifecycle.
Fast Monocular Visual-Inertial Initialization with an Improved Iterative Strategy

The initialization process has a great effect on the performance of the monocular visual inertial simultaneous localization and mapping (VI-SLAM) system. The initial estimation is usually solved by least squares such as the Gauss-Newton (G-N) algorithm, but the large iteration increment might lead to the slow convergence or even divergence. In order to solve this problem, an improved iterative strategy for initial estimation is proposed. The methodology of our initialization can be divided into four steps: Firstly, the pure visual ORB-SLAM model is utilized to make all variables observable. Secondly, the IMU preintegration technology is adopted for IMU-camera frequency alignment at the same time with key frame generation. Thirdly, an improved iterative strategy which is based on the trust region is introduced for the gyroscope bias estimation as well as the gravity direction is refined. Finally, the accelerometer bias and visual scale are estimated on the basis of previous estimations. Experimental results on the public datasets show that the estimation of initial values can be converged faster, as well as the velocity and pose of sensor suite can be estimated more accurately than the original method.
A Universal 2-state n-action Adaptive Management Solver

In poor data and urgent decision-making applications, managers need to make decisions without complete knowledge of the system dynamics. In biodiversity conservation, adaptive management (AM) is the principal tool for decision-making under uncertainty. AM can be solved using simplified Mixed Observable Markov Decision Processes called hidden model MDPs (hmMDPs) when the unknown dynamics are assumed stationary. hmMDPs provide optimal policies to AM problems by augmenting the MDP state space with an unobservable state variable representing a finite set of predefined models. A drawback in formalising an AM problem is that experts are often solicited to provide this predefined set of models by specifying the transition matrices. Expert elicitation is a challenging and time-consuming process that is prone to biases, and a key assumption of hmMDPs is that the true transition matrix will be included in the candidate model set. We propose an original approach to build a hmMDP with a universal set of predefined models that is capable of solving any 2-state n-action AM problem. Our approach uses properties of the transition matrices to build the model set and is independent of expert input, removing the potential for expert error in the optimal solution. We provide analytical formulations to derive the minimum set of models to include into an hmMDP to solve any AM problems with 2 states and n actions. We assess our universal AM algorithm on two species conservation case studies from Australia and randomly generated problems.
Is Active Learning Always Beneficial? (Student Abstract)

This study highlights the limitations of automated curriculum learning, which may not be a viable strategy for tasks in which the benefits of the chosen curriculum are not apparent until much later. Using a simple convolutional network and a two-task training regime, we show that in some cases a network is not able to derive an optimal learning strategy using only the data available during a single training run.
Bison Hacks the Yard: Assisting Underrepresented Students Overcome Impostor Syndrome with Augmented Reality and Artificial Intelligence

The prevalence of impostor syndrome in computer science students from underrepresented backgrounds contributes to low retention rates. Bison Hacks the Yard is an augmented reality game that aims to reduce impostor syndrome in underrepresented students by presenting a novel way to strengthen their knowledge of fundamental data structures and providing specialized videos of Historically Black College or University alumni, sharing their struggles with impostor syndrome.
Semi-Supervised Log-Based Anomaly Detection via Probabilistic Label Estimation

With the growth of software systems, logs have become an important data to aid system maintenance. Log-based anomaly detection is one of the most important methods for such purpose, which aims to automatically detect system anomalies via log analysis. However, existing log-based anomaly detection approaches still suffer from practical issues due to either depending on a large amount of manually labeled training data (supervised approaches) or unsatisfactory performance without learning the knowledge on historical anomalies (unsupervised and semi-supervised approaches). In this paper, we propose a novel practical log-based anomaly detection approach, PLELog, which is semi-supervised to get rid of time-consuming manual labeling and incorporates the knowledge on historical anomalies via probabilistic label estimation to bring supervised approaches' superiority into play. In addition, PLELog is able to stay immune to unstable log data via semantic embedding and detect anomalies efficiently and effectively by designing an attention-based GRU neural network. We evaluated PLELog on two most widely-used public datasets, and the results demonstrate the effectiveness of PLELog, significantly outperforming the compared approaches with an average of 181.6% improvement in terms of F1-score. In particular, PLELog has been applied to two real-world systems from our university and a large corporation, further demonstrating its practicability
Neograd: Near-Ideal Gradient Descent

The purpose of this paper is to improve upon existing variants of gradient descent by solving two problems: (1) removing (or reducing) the plateau that occurs while minimizing the cost function, (2) continually adjusting the learning rate to an"ideal"value. The approach taken is to approximately solve for the learning rate as a function of a trust metric. When this technique is hybridized with momentum, it creates an especially effective gradient descent variant, called NeogradM. It is shown to outperform Adam on several test problems, and can easily reach cost function values that are smaller by a factor of $10^8$, for example.
Learning Stochastic Optimal Policies via Gradient Descent

We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies. We propose a derivation of adjoint sensitivity results for stochastic differential equations through direct application of variational calculus. Then, given an objective function for a predetermined task specifying the desiderata for the controller, we optimize their parameters via iterative gradient descent methods. In doing so, we extend the range of applicability of classical SOC techniques, often requiring strict assumptions on the functional form of system and control. We verify the performance of the proposed approach on a continuous–time, finite horizon portfolio optimization with proportional transaction costs.
Peripheral Blood BRCA1 Methylation Positively Correlates with Major Alzheimer’s Disease Risk Factors

Recent biomarker studies demonstrated that the central nervous system (CNS) environment can be observed from peripherally-derived samples. In a previous study, we demonstrated significant hypomethylation of the BRCA1 promoter region in neuronal cells from post-mortem brains of Alzheimer’s disease patients through neuron-specific methylome analysis. Thus, we investigate the methylation changes in the BRCA1 promoter region in the blood samples. To analyze the methylation level of the BRCA1 promoter in peripheral blood from AD patients and normal controls. Genomic DNA samples from peripheral blood were obtained from the J-ADNI repository, and their biomarker data were obtained J-ADNI from the National Bioscience Database Center. Genomic DNA samples from an independent cohort for validation was obtained from Niigata University Hospital (Niigata, Japan). Amyloid positivity was defied by visual inspection of amyloid PET or a CSF Aβ42 value ≤ 333 pg/mL at the baseline. Methylation level of the BRCA1 promoter was analyzed by pyrosequencing. Compared to normal controls, methylation of the BRCA1 promoter in AD patients was not significantly changed; however, in AD patients, it showed a positive correlation with AD risk factors. Our data confirmed the importance of celltype specific methylome analysis and also suggested that environmental changes in the CNS can be detected by observing the peripheral blood, implying that the peripheral BRCA1 methylation level can be a surrogate for AD.
Measurement of residence time, air entrainment rate, and base pressure in the near wake of a cylindrical body in supersonic flow

The air entrainment rate into the near wake was calculated from the residence time. THe residence time was measured by injecting carbon dioxide (CO 2 ) into the near wake and then monitoring the time required for dissipation of the gas after the injection was shut off. The dissipation was monitored using a radiometer that measured absorption of radiation from an infrared (IR) source in the 4.3-μm CO 2 band. The test body was 2 1/2in. in diameter and 5 in. long. Residence time varied from 14.4 ms at an air velocity of 150 ft/s to 8.98 ms at 312 ft/s
A potential source of Omega-6 and Omega-9 in Cucumis melo Seed Oil from Arid Zone of Rajasthan

Omega-6 and Omega-9 fatty acids are a type of polyunsaturated fatty acids. Omega-6 is used for reducing the risk of heart disease, lowering total cholesterol levels, lowering bad cholesterol levels, raising good cholesterol levels and reducing cancer risk. The nutritional value of oleic acid (omega-9) in a balanced diet has been the subject of a number of studies with particular emphasis on the cardiovascular system. The seeds of Cucumis melo were collected from arid zone of Rajasthan, India. The physicochemical properties of Cucumis melo oil were evaluated. Results of proximate analysis show crude fat (31.2%) and crude protein (28.5%). Muskmelon seed oil had clear bright yellowish colour with offensive odour with iodine value (124.2 mg/g oil) and saponification value (196.2 mg/g oil). We concluded that the muskmelon seeds are rich in oil and protein therefore, have a reasonable economic potential which can be harnessed. On HPLC analysis, stearic acid (9.4%), palmitic acid (13.2%), linoleic acid (60.2%) and oleic acid (18.9%) were found to be present in Cucumis melo seed oil. Most predominant polyunsaturated fatty acid was linoleic acid or omega-6. Oleic acid or omega-9 was also found as major fatty acid. Heavy metals are among the major environmental contaminants and pose a severe threat to human and animal health by their long-term persistence. MP-AES technique was used for determination of heavy metals (Cd, Zn, Fe, Ni and Pb) accumulated in seed oil of Cucumis melo.
Development of Rare-Earth Elements Database for the East Kootenay Coalfield of Southeastern British Columbia (NTS 082G/10, 15) Using Field Collected Samples: Preliminary Results

Rare-earth elements (REEs) are the group of 17 elements in the periodic table that include 15 lanthanides and two chemically similar transition metals—scandium and yttrium. Using their atomic number, REEs are classified as heavy REEs (HREEs) or light REEs (LREEs), with elements from Tb to Lu and Y belonging to the former group and La to Gd and Sc belonging to the latter group (Moldoveanu and Papangelakis, 2013; Zhang et al., 2015). With the emergence of new clean energy and defence-related technologies, consumption of REEs has increased rapidly (Tse, 2011). For example, it is projected that the demand for dysprosium, one of the REEs, is expected to increase by as much as 2600% by 2025 (Standing Committee on Natural Resources, 2014). In addition, traditional rareearth ore deposits are fast depleting and supply is only expected to meet demand for next 15–20 years (Seredin and Dai, 2012). Based on the projected dwindling supply and increasing demand, REEs are classified as critical elements by the United States and the European Union due to their importance in clean energy and defence applications (U.S. Department of Energy, 2010; Deloitte Sustainability et al., 2017). The National Energy Technology Laboratory (NETL) in the United States has conducted a prospective analysis of coal deposits as a source of REEs using the U.S. Geological Survey’s (USGS) coal database, which contains the concentrations of REEs in coalfields across the United States (Bryan et al., 2015). The NETL has launched a research and development program to demonstrate the techno-economic feasibility of developing domestic technologies to separate REEs from coal and/or its byproducts. The study uses samples that contain a minimum of 300 ppm total REEs and concentrating the REEs to a level greater than or equal to 2% (by weight) in processed streams (U.S. Department of Energy, 2016). The program will focus on areas of research such as resource sampling and characterization, separation technology development, REE sensor development, process and systems modelling, and technoeconomic analyses (U.S. Department of Energy, 2016).
Sharing More and Checking Less: Leveraging Common Input Keywords to Detect Bugs in Embedded Systems

IoT devices have brought invaluable convenience to our daily life. However, their pervasiveness also amplifies the impact of security vulnerabilities. Many popular vulnerabilities of embedded systems reside in their vulnerable web services. Unfortunately, existing vulnerability detection methods cannot effectively nor efficiently analyze such web services: they either introduce heavy execution overheads or have many false positives and false negatives. In this paper, we propose a novel static taint checking solution, SaTC, to effectively detect security vulnerabilities in web services provided by embedded devices. Our key insight is that, string literals on web interfaces are commonly shared between front-end files and back-end binaries to encode user input. We thus extract such common keywords from the frontend, and use them to locate reference points in the back-end, which indicate the input entry. Then, we apply targeted dataflow analysis to accurately detect dangerous uses of the untrusted user input. We implemented a prototype of SaTC and evaluated it on 39 embedded system firmwares from six popular vendors. SaTC discovered 33 unknown bugs, of which 30 are confirmed by CVE/CNVD/PSV. Compared to the state-ofthe-art tool KARONTE, SaTC found significantly more bugs on the test set. It shows that, SaTC is effective in discovering bugs in embedded systems.
No evidence for loss aversion disappearance and reversal in Walasek and Stewart (2015).

Loss aversion-the idea that losses loom larger than equivalent gains-is one of the most important ideas in Behavioral Economics. In an influential article published in the Journal of Experimental Psychology: General, Walasek and Stewart (2015) test an implication of decision by sampling theory: Loss aversion can disappear, and even reverse, depending on the distribution of gains and losses people have encountered. In this article, we show that the pattern of results reported in Walasek and Stewart (2015) should not be taken as evidence that loss aversion can disappear and reverse, or that decision by sampling is the origin of loss aversion. It emerges because the estimates of loss aversion are computed on different lotteries in different conditions. In other words, the experimental paradigm violates measurement invariance, and is invalid. We show that analyzing only the subset of lotteries that are common across conditions eliminates the pattern of results. We note that other recently published articles use similar experimental designs, and we discuss general implications for empirical examinations of utility functions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Tag Based Verifiable Outsourced Decryption for Reducing User Resource Complexity

: To ensure data privacy over third party data storage services (cloud storage) a common solution is to store data in encrypted form so that it will remain private, even if data servers or storage devices are not trusted or compromised. The encrypted data, however, will be shared and access controlled. For supporting data encryption along with access control we use attribute-based encryption scheme (ABE). But standard ABE scheme creates relatively large cipher text size and high decryption cost, and this problem is especially acute for resource limited devices such as mobile devices. So we introduce an ABE scheme which outsources part of decryption process to third party servers with a verification procedure for validating their decryption procedure. Finally the partial outcomes are efficiently decrypted at the user end. This reduces the user’s resource limitation complexities and computation load.
A Comprehensive Study on the Application of Structured Pruning methods in Autonomous Vehicles

Deep neural networks (DNNs) have achieved huge successes in many autonomous driving tasks. However, existing deep neural networks are computationally and memory expensive, making it difﬁcult to be deployed on embedded systems with limited processing power and low memory resources. In this work, a detailed network structured pruning study is conducted for autonomous driving tasks, including object detection, skin semantic segmentation and steering angle prediction. This study considers the network performance for different algorithms at different pruning sparsity levels. We conclude this paper by comparing and discussing the experimental results and proposing the recommended structured pruning algorithms for these tasks.
Contrastive tone and its implementation

Tone is defined as a lexically-contrastive pitch pattern (Pike 1948, Yip 2002, Gussenhoven 2004). Not all languages use pitch to create lexical contrasts, but the majority do (Fromkin 1978, Yip 2002). Tone contrasts may consist of different pitch levels (from 2 to as many as 5), or of pitch movements of varying direction, slope and shape: see Figures 1 and 2 for two examples. While pitch changes constantly, and for many reasons, across an utterance, a laboratory phonology approach to tone assumes that there are categorical elements that underlie the constantly changing F0 trace and its articulatory and perceptual correlates. Thus the laboratory phonology approach to tone can be defined as the use of experimental techniques to study the acoustic, articulatory, and perceptual correlates of tone, in order to learn about the underlying categories, and the relation between the underlying representation and its phonetic manifestations, including the co-ordination between tonal and non-tonal elements. Important questions that arise in the study of contrastive tone include: