Miracle-dz/UniFAR-FTUs huggingface dataset:
Derivation of Generalized Equations for the Predictive Value of Sequential Screening Tests

Using Bayes' Theorem, we derive generalized equations to determine the positive and negative predictive value of screening tests undertaken sequentially. Where a is the sensitivity, b is the specificity, $\phi$ is the pre-test probability, the combined positive predictive value, $\rho(\phi)$, of $n$ serial positive tests, is described by: $\rho(\phi) = \frac{\phi\displaystyle\prod_{i=1}^{n}a_n}{\phi\displaystyle\prod_{i=1}^{n}a_n+(1-\phi)\displaystyle\prod_{i=1}^{n}(1-b_n)}$ If the positive serial iteration is interrupted at term position $n_i-k$ by a conflicting negative result, then the resulting negative predictive value is given by: $\psi(\phi) = \frac{[(1-\phi)b_{n-}]\displaystyle\prod_{i=b_{1+}}^{b_{(n-1)+}}(1-b_{n+})}{[\phi(1-a_{n-})]\displaystyle\prod_{i=a_{1+}}^{a_{(n-1)+}}a_{n+}+[(1-\phi)b_{n-}]\displaystyle\prod_{i=b_{1+}}^{b_{(n-1)+}}(1-b_{n+})}$ Finally, if the negative serial iteration is interrupted at term position $n_i-k$ by a conflicting positive result, then the resulting positive predictive value is given by: $\lambda(\phi)= \frac{\phi a_{n+}\displaystyle\prod_{i=a_{1-}}^{a_{(n-1)-}}(1-a_{n-})}{\phi a_{n+}\displaystyle\prod_{i=a_{1-}}^{a_{(n-1)-}}(1-a_{n-})+[(1-\phi)(1-b_{n+})]\displaystyle\prod_{i=b_{1-}}^{b_{(n-1)-}}b_{n-}}$ The aforementioned equations provide a measure of the predictive value in different possible scenarios in which serial testing is undertaken. Their clinical utility is best observed in conditions with low pre-test probability where single tests are insufficient to achieve clinically significant predictive values and likewise, in clinical scenarios with a high pre-test probability where confirmation of disease status is critical.
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks

Human collaborators can effectively communicate with their partners to finish a common task by inferring each other’s mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrepancy among collaborators’ mental states, and is crucial to the success in human ad-hoc teaming. We believe that robots collaborating with human users should demonstrate similar pedagogic behavior. Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user’s mental state. To evaluate our framework, we conduct a user study on a real-time human-robot cooking task. Experimental results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot. Code and video demos are available on our project website: https://xfgao.github. io/xCookingWeb/.
A Two-Locus Global DNA Barcode for Land Plants: The Coding rbcL Gene Complements the Non-Coding trnH-psbA Spacer Region

Background A useful DNA barcode requires sufficient sequence variation to distinguish between species and ease of application across a broad range of taxa. Discovery of a DNA barcode for land plants has been limited by intrinsically lower rates of sequence evolution in plant genomes than that observed in animals. This low rate has complicated the trade-off in finding a locus that is universal and readily sequenced and has sufficiently high sequence divergence at the species-level. Methodology/Principal Findings Here, a global plant DNA barcode system is evaluated by comparing universal application and degree of sequence divergence for nine putative barcode loci, including coding and non-coding regions, singly and in pairs across a phylogenetically diverse set of 48 genera (two species per genus). No single locus could discriminate among species in a pair in more than 79% of genera, whereas discrimination increased to nearly 88% when the non-coding trnH-psbA spacer was paired with one of three coding loci, including rbcL. In silico trials were conducted in which DNA sequences from GenBank were used to further evaluate the discriminatory power of a subset of these loci. These trials supported the earlier observation that trnH-psbA coupled with rbcL can correctly identify and discriminate among related species. Conclusions/Significance A combination of the non-coding trnH-psbA spacer region and a portion of the coding rbcL gene is recommended as a two-locus global land plant barcode that provides the necessary universality and species discrimination.
The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis

The COVID-19 coronavirus pandemic has affected virtually every region of the globe. At the time of conducting this study, the number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods. Analyzing these trends using data mining methods might provide useful insights and observations regarding the COVID-19 outbreak. The objective of this study was to consider the predictive ability of different search terms (i.e., bars and restaurants) with regards to the increase of daily cases in the US. We considered the causation of two different search query trends, namely restaurant and bars, on daily positive cases in top-10 states/territories of the United States with the highest and lowest daily new positive cases. In addition, to measure the linear relation of different trends, we used Pearson correlation. Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average. California, for example, had most searches for restaurants on June 7th, 2020, which affected the number of new cases within two weeks after the peak with the P-value of . 004 for Granger's causality test. Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases for regions with higher numbers of daily new cases in the United States. We showed that such influential search trends could be used as additional information for prediction tasks in new cases of each region. This prediction can help healthcare leaders manage and control the impact of COVID-19 outbreaks on society and be prepared for the outcomes.
FedEmail: Performance Measurement of Privacy-friendly Phishing Detection Enabled by Federated Learning

Artificial intelligence (AI) has been applied in phishing email detection. Typically, it requires rich email data from a collection of sources, and the data usually contains private information that needs to be preserved. So far, AI techniques are solely focusing on centralized data training that eventually accesses sensitive raw email data from the collected data repository. Thus, a privacy-friendly AI technique such as federated learning (FL) is a desideratum. FL enables learning over distributed email datasets to protect their privacy without the requirement of accessing them during the learning in a distributed computing framework. This work, to the best of our knowledge, is the first to investigate the applicability of training email anti-phishing model via FL. Building upon the Recurrent Convolutional Neural Network for phishing email detection, we comprehensively measure and evaluate the FL-entangled learning performance under various settings, including balanced and imbalanced data distribution among clients, scalability, communication overhead, and transfer learning. Our results positively corroborate comparable performance statistics of FL in phishing email detection to centralized learning. As a trade-off to privacy and distributed learning, FL has a communication overhead of 0.179 GB per global epoch per its clients. Our measurement-based results find that FL is suitable for practical scenarios, where data size variation, including the ratio of phishing to legitimate email samples, among the clients, are present. In all these scenarios, FL shows a similar performance of testing accuracy of around 98%. Besides, we demonstrate the integration of the newly joined clients with time in FL via transfer learning to improve the client-level performance. The transfer learning-enabled training results in the improvement of the testing accuracy by up to 2.6% and fast convergence.
Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation

Planar pushing remains a challenging research topic, where building the dynamic model of the interaction is the core issue. Even an accurate analytical dynamic model is inherently unstable because physics parameters such as inertia and friction can only be approximated. Data-driven models usually rely on large amounts of training data, but data collection is time consuming when working with real robots.In this paper, we collect all training data in a physics simulator and build an LSTM-based model to fit the pushing dynamics. Domain Randomization is applied to capture the pushing trajectories of a generalized class of objects. When executed on the real robot, the trained recursive model adapts to the tracked object’s real dynamics within a few steps. We propose the algorithm Recurrent Model Predictive Path Integral (RMPPI) as a variation of the original MPPI approach, employing state-dependent recurrent models. As a comparison, we also train a Deep Deterministic Policy Gradient (DDPG) network as a model-free baseline, which is also used as the action generator in the data collection phase. During policy training, Hindsight Experience Replay is used to improve exploration efficiency. Pushing experiments on our UR5 platform demonstrate the model’s adaptability and the effectiveness of the proposed framework.
Light-Field DVR on GPU for Streaming Time-Varying Data

Direct Volume Rendering (DVR) of volume data can be a memory intensive task in terms of footprint and cache-coherency. Ray-guided methods may not be the best option to interactively render to light-ﬁelds due to feedback loops and sporadic sampling, and pre-computation can rule out time-varying data. We present a pipelined approach to schedule the rendering of sub-regions of streaming time-varying volume data while minimising intermediate sub-buffers needed, sharing the work load between CPU and GPU. We show there is signiﬁcant advantage to using such an approach.
Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey

Joint radar and communication (JRC) has recently attracted substantial attention. The first reason is that JRC allows individual radar and communication systems to share spectrum bands and thus improves the spectrum utilization. The second reason is that JRC enables a single hardware platform, e.g., an autonomous vehicle or a UAV, to simultaneously perform the communication function and the radar function. As a result, JRC is able to improve the efficiency of resources, i.e., spectrum and energy, reduce the system size, and minimize the system cost. However, there are several challenges to be solved for the JRC design. In particular, sharing the spectrum imposes the interference caused by the systems, and sharing the hardware platform and energy resource complicates the design of the JRC transmitter and compromises the performance of each function. To address the challenges, several resource management approaches have been recently proposed, and this paper presents a comprehensive literature review on resource management for JRC. First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems. Then, we review and analyze resource management approaches, i.e., spectrum sharing, power allocation, and interference management, for JRC. In addition, we present security issues to JRC and provide a discussion of countermeasures to the security issues. Finally, we highlight important challenges in the JRC design and discuss future research directions related to JRC.
Detection and Performance Analysis for Non-Coherent DF Relay Networks with Optimized Generalized Differential Modulation

This paper studies the detection and performance analysis problems for a relay network with $N$ parallel decode-and-forward (DF) relays. Due to the distributed nature of this network, it is practically very challenging to fulfill the requirement of instantaneous channel state information for coherent detection. To bypass this requirement, we consider the use of non-coherent DF relaying based on a generalized differential modulation (GDM) scheme, in which transmission power allocation over the $M$-ary phase shift keying symbols is exploited when performing differential encoding. In this paper, a novel detector at the destination of such a non-coherent DF relay network is proposed. It is an accurate approximation of the state-of-the-art detector, called the almost maximum likelihood detector (AMLD), but the detection complexity is considerably reduced from $\mathcal{O}(M^2N)$ to $\mathcal{O}(MN)$. By characterizing the dominant error terms, we derive an accurate approximate symbol error rate (SER) expression. An optimized power allocation scheme for GDM is further designed based on this SER expression. Our simulation demonstrates that the proposed non-coherent scheme can perform close to the coherent counterpart as the block length increases. Additionally, we prove that the diversity order of both the proposed detector and the AMLD is exactly $\lceil N/2 \rceil + 1$. Extensive simulation results further verify the accuracy of our results in various scenarios.
Fluctuations and correlations of reactive scalars near chemical equilibrium in incompressible turbulence

The statistical properties of species undergoing chemical reactions in a turbulent environment are studied. We focus on the case of reversible multi-component reactions of second and higher orders, in a condition close to chemical equilibrium sustained by random large-scale reactant sources, while the turbulent flow is highly developed. In such a state a competition exists between the chemical reaction that tends to dump reactant concentration fluctuations and enhance their correlation intensity and the turbulent mixing that on the contrary increases fluctuations and remove relative correlations. We show that a unique control parameter, the Damkh\"{o}ler number ($Da_\theta$) that can be constructed from the scalar Taylor micro-scale, the reactant diffusivity and the reaction rate characterises the functional dependence of fluctuations and correlations in a variety of conditions, i.e., at changing the reaction order, the Reynolds and the Schmidt numbers. The larger is such a Damkh\"{o}ler number the more depleted are the scalar fluctuations as compared to the fluctuations of a passive scalar field in the same conditions, and vice-versa the more intense are the correlations. A saturation in this behaviour is observed beyond $Da_\theta \simeq \mathcal{O}(10)$. We provide an analytical prediction for this phenomenon which is in excellent agreement with direct numerical simulation results.
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks

Bovine Respiratory Disease Complex (BRDC) is a complex respiratory disease in cattle with multiple etiologies, including bacterial and viral. It is estimated that mortality, morbidity, therapy, and quarantine resulting from BRDC account for significant losses in the cattle industry. Early detection and management of BRDC are crucial in mitigating economic losses. Current animal disease diagnostics is based on traditional tests such as bacterial culture, serolog, and Polymerase Chain Reaction (PCR) tests. Even though these tests are validated for several diseases, their main challenge is their limited ability to detect the presence of multiple pathogens simultaneously. Advancements of data analytics and machine learning and applications over metagenome sequencing are setting trends on several applications. In this work, we demonstrate a machine learning approach to identify pathogen signatures present in bovine metagenome sequences using k-mer-based network embedding followed by a deep learning-based classification task. With experiments conducted on two different simulated datasets, we show that networks-based machine learning approaches can detect pathogen signature with up to 89.7% accuracy. We will make the data available publicly upon request to tackle this important problem in a difficult domain.
ORIBATID MITES AND ORGANIC CARBON CONTENT OF SOIL

The importance of organic matter in relation to the population dynamics of micro-arthropods was suggested by STRENZKE (1952), KARPPINEN (1955) and DAVIs & MURPHY (1961). According to KEVAN (1965), species of cryptostigmata feed on organic matter. According to LooTs & RYKE (1967), Oribatei in general prefers soils with a high organic content. Seasonal variation has received increasing attention and there has been an encouraging tendency toward long term studies. It appear probable that the effect of macroflora is largely indirect as through litter development and its effect on physical factors and micro-organisms.
IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using Deep Neural Networks and Linear Baselines

Sentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with well-established methods and tools. One of these challenges is code-mixing, which means using different languages to convey thoughts in social media texts. Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict the sentiment of a given code-mixed tweet. We used different preprocessing techniques and proposed to use different methods that vary from NBSVM to more complicated deep neural network models. Our best performing method obtains an F1 score of 0.751 for the Spanish-English sub-task and 0.706 over the Hindi-English sub-task.
Graph Gamma Process Generalized Linear Dynamical Systems

We introduce graph gamma process (GGP) linear dynamical systems to model real-valued multivariate time series. For temporal pattern discovery, the latent representation under the model is used to decompose the time series into a parsimonious set of multivariate sub-sequences. In each sub-sequence, different data dimensions often share similar temporal patterns but may exhibit distinct magnitudes, and hence allowing the superposition of all sub-sequences to exhibit diverse behaviors at different data dimensions. We further generalize the proposed model by replacing the Gaussian observation layer with the negative binomial distribution to model multivariate count time series. Generated from the proposed GGP is an infinite dimensional directed sparse random graph, which is constructed by taking the logical OR operation of countably infinite binary adjacency matrices that share the same set of countably infinite nodes. Each of these adjacency matrices is associated with a weight to indicate its activation strength, and places a finite number of edges between a finite subset of nodes belonging to the same node community. We use the generated random graph, whose number of nonzero-degree nodes is finite, to define both the sparsity pattern and dimension of the latent state transition matrix of a (generalized) linear dynamical system. The activation strength of each node community relative to the overall activation strength is used to extract a multivariate sub-sequence, revealing the data pattern captured by the corresponding community. On both synthetic and real-world time series, the proposed nonparametric Bayesian dynamic models, which are initialized at random, consistently exhibit good predictive performance in comparison to a variety of baseline models, revealing interpretable latent state transition patterns and decomposing the time series into distinctly behaved sub-sequences.
Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior’s influence is annealed gradually. During deployment, the policy’s uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine-tuning. The code for this project is made publicly available at https://sites.google. com/view/mcf-nav/home
Rate-Splitting Multiple Access: Unifying NOMA and SDMA in MISO VLC Channels

The increased proliferation of connected devices requires a development of innovative technologies for the next generation of wireless systems. One of the key challenges, however, is the spectrum scarcity, owing to the unprecedented broadband penetration rate in recent years. Based on this, visible light communication (VLC) has recently emerged as an effective potential solution for enabling high-speed short-range communications. Yet, despite their undoubted advantageous features, VLC systems suffer from several limitations which constraint their capabilities. As a result, several multiple access (MA) techniques, such as space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA), have been considered in VLC networks as an effective approach, among others, to circumvent these limitations. However, despite their achievable multiplexing gain, their overall performance is still limited compared to the overall potential of this technology. Motivated by this, the presented article offers two contributions: firstly, we provide an overview of the key MA technologies used in VLC systems and then we introduce rate-splitting multiple access (RSMA), and discuss its capabilities and potentials in VLC systems. Secondly, through realistic system modeling and simulations of an RSMA-based two-user scenario, we illustrate the flexibility of RSMA as well as its superiority in terms of the achievable weighted sum rate over NOMA and SDMA in the context of VLC. Finally, we discuss technical challenges, open issues, and research directions, along with the offered results and insights that are expected to be useful towards the effective practical realization of RSMA in VLC configurations.
Methods for detecting order-by-disorder transitions: the example of the Domino model

Detecting the zero-temperature thermal order-by-disorder (ObD) transition in classical magnetic systems is notably difficult. We propose a method to probe this transition in an indirect way. The idea is to apply adequate and suitably engineered magnetic fields to transform the zero-temperature transition into a finite-temperature sharp crossover, which should be much easier to observe and characterise with usual laboratory methods. Such a crossover should constitute proof of existence of the ObD transition in the underlying unperturbed system.